Session RF Photonics for CATV and HFC Systems (28-30 July) Reduction of 3 rd Order Intermod
sensors and actuators b-chemical模板 -回复
sensors and actuators b-chemical模板-回复Sensors and Actuators: Introduction to Chemical Sensors and ActuatorsIntroduction:In the world of technology, sensors and actuators play a pivotal role in many applications. These devices are designed to monitor and control various processes, often providing important feedback to a control system. In this article, we will delve into the fascinating realm of chemical sensors and actuators, exploring their function, types, and applications. So let's begin our journey into the world of chemical sensors and actuators.Understanding Chemical Sensors:Chemical sensors are devices that are specifically designed to detect and measure the concentration of chemical species in a given environment. These sensors are widely used in industries such as healthcare, environmental monitoring, agriculture, and food processing. The primary goal of chemical sensors is to convert a chemical signal into an electrical signal, which can be easily measured and interpreted.Functioning of Chemical Sensors:Chemical sensors work on the principle of selectivity and sensitivity. Selectivity refers to the ability of a sensor to respond only to a specific target chemical, while sensitivity refers to the ability of a sensor to detect and measure small changes in the concentration of the target chemical. Chemical sensors typically consist of a sensing element, transducer, and signal processing system.Types of Chemical Sensors:There are various types of chemical sensors, each designed to detect specific types of chemicals. Some of the most commonly used chemical sensors include gas sensors, pH sensors, biosensors, and electrochemical sensors.1. Gas Sensors:Gas sensors are used to detect and measure the concentration of specific gases in the atmosphere. These sensors are widely used in industries such as oil and gas, automotive, and environmental monitoring. Gas sensors can detect gases such as carbon dioxide, carbon monoxide, methane, and ozone.2. pH Sensors:pH sensors are used to measure the acidity or alkalinity of a solution. These sensors are extensively used in industries such as pharmaceuticals, agriculture, and water treatment. pH sensors are based on the principle of ion-selective electrodes, which generate an electrical signal proportional to the hydrogen ion concentration in the solution.3. Biosensors:Biosensors are specifically designed to detect and measure the concentration of biological molecules such as proteins, enzymes, and antibodies. These sensors find applications in medical diagnostics, food safety, and environmental monitoring. Biosensors typically consist of a bioreceptor, transducer, and signal processing system.4. Electrochemical Sensors:Electrochemical sensors are widely used to measure the concentration of ions in a solution. These sensors are based on the principle of electrochemical reactions, where the analyte reacts with the electrode surface, generating an electrical signal. Electrochemical sensors are used in applications such as waterquality monitoring, chemical analysis, and medical diagnostics.Working of Chemical Actuators:Actuators, on the other hand, are devices that are used to control or manipulate physical systems based on the feedback received from sensors. Chemical actuators, specifically, are devices that convert chemical energy into mechanical motion. These devices are extensively used in robotics, industrial automation, and microfluidics.Applications of Chemical Sensors and Actuators:Chemical sensors and actuators find applications in a wide range of industries and fields. Some of the key applications include environmental monitoring, healthcare diagnostics, industrial process control, and food safety.In conclusion, chemical sensors and actuators play a crucial role in various industries by detecting and measuring the concentration of chemical species and controlling physical systems. These devices enable us to monitor and control processes, ensuring safety, efficiency, and accuracy. With advancements in technology,the field of chemical sensors and actuators is continuously evolving, opening up new avenues for research and innovation.。
生物质气化技术研究现状与发展
燃气气源与加工利用生物质气化技术研究现状与发展陈冠益, 高文学, 颜蓓蓓, 贾佳妮(天津大学环境科学与工程学院,天津300072)摘 要: 综述了生物质气化技术的分类、气化炉特点、气化性能影响因素及评价指标。
介绍了生物质气化技术在国内外的发展现状,阐明了生物质气化技术需要解决的问题,提出了我国生物质气化技术的发展方向。
关键词: 生物质气化; 气化炉; 气化性能; 影响因素; 评价指标; 发展方向中图分类号:TU996 文献标识码:A 文章编号:1000-4416(2006)07-0020-07Present R esearch Status and D evelop m ent of B i o massG asificati on Technol ogiesCHEN Guan y,i GAO W en xue, YAN Be i be,i JI A Jia n i (S chool of Environm ent Science&Technology,T ianjin University,T ianjin300072,China)Abst ract: The c lassification of b i o m ass gasificati o n techno log ies,the characteristics o f gasifiers, the infl u encing factors of gasification perfo r m ance and the evaluati n g i n d icator are rev ie w ed.The presen t deve l o p m ent status o f bio m ass gasificati o n technolog ies at ho m e and abroad is i n tr oduced,the proble m s that should be so l v ed for bio m ass gasification technolog ies are descri b ed,and the deve l o pm ent d irecti o n o f b io m ass gasification technolog ies in Ch i n a is put for w ard.K ey w ords: bio m ass gasification; gasifier; gasificati o n perfor m ance; i n fluenc i n g factor; e va l u ating i n d icator; developm ent d irecti o n1 生物质气化原理与工艺1.1 生物质气化原理生物质气化是指生物质原料(薪柴、锯末、麦秸、稻草等)压制成型或经简单的破碎加工处理后,在欠氧条件下,送入气化炉中进行气化裂解,得到可燃气体并进行净化处理而获得产品气的过程。
中科院自动化所的中英文新闻语料库
中科院自动化所的中英文新闻语料库中科院自动化所(Institute of Automation, Chinese Academy of Sciences)是中国科学院下属的一家研究机构,致力于开展自动化科学及其应用的研究。
该所的研究涵盖了从理论基础到技术创新的广泛领域,包括人工智能、机器人技术、自动控制、模式识别等。
下面将分别从中文和英文角度介绍该所的相关新闻语料。
[中文新闻语料]1. 中国科学院自动化所在人脸识别领域取得重大突破中国科学院自动化所的研究团队在人脸识别技术方面取得了重大突破。
通过深度学习算法和大规模数据集的训练,该研究团队成功地提高了人脸识别的准确性和稳定性,使其在安防、金融等领域得到广泛应用。
2. 中科院自动化所发布最新研究成果:基于机器学习的智能交通系统中科院自动化所发布了一项基于机器学习的智能交通系统研究成果。
通过对交通数据的收集和分析,研究团队开发了智能交通控制算法,能够优化交通流量,减少交通拥堵和时间浪费,提高交通效率。
3. 中国科学院自动化所举办国际学术研讨会中国科学院自动化所举办了一场国际学术研讨会,邀请了来自不同国家的自动化领域专家参加。
研讨会涵盖了人工智能、机器人技术、自动化控制等多个研究方向,旨在促进国际间的学术交流和合作。
4. 中科院自动化所签署合作协议,推动机器人技术的产业化发展中科院自动化所与一家著名机器人企业签署了合作协议,共同推动机器人技术的产业化发展。
合作内容包括技术研发、人才培养、市场推广等方面,旨在加强学界与工业界的合作,加速机器人技术的应用和推广。
5. 中国科学院自动化所获得国家科技进步一等奖中国科学院自动化所凭借在人工智能领域的重要研究成果荣获国家科技进步一等奖。
该研究成果在自动驾驶、物联网等领域具有重要应用价值,并对相关行业的创新和发展起到了积极推动作用。
[英文新闻语料]1. Institute of Automation, Chinese Academy of Sciences achievesa major breakthrough in face recognitionThe research team at the Institute of Automation, Chinese Academy of Sciences has made a major breakthrough in face recognition technology. Through training with deep learning algorithms and large-scale datasets, the research team has successfully improved the accuracy and stability of face recognition, which has been widely applied in areas such as security and finance.2. Institute of Automation, Chinese Academy of Sciences releases latest research on machine learning-based intelligent transportationsystemThe Institute of Automation, Chinese Academy of Sciences has released a research paper on a machine learning-based intelligent transportation system. By collecting and analyzing traffic data, the research team has developed intelligent traffic control algorithms that optimize traffic flow, reduce congestion, and minimize time wastage, thereby enhancing overall traffic efficiency.3. Institute of Automation, Chinese Academy of Sciences hosts international academic symposiumThe Institute of Automation, Chinese Academy of Sciences recently held an international academic symposium, inviting automation experts from different countries to participate. The symposium covered various research areas, including artificial intelligence, robotics, and automatic control, aiming to facilitate academic exchanges and collaborations on an international level.4. Institute of Automation, Chinese Academy of Sciences signs cooperation agreement to promote the industrialization of robotics technologyThe Institute of Automation, Chinese Academy of Sciences has signed a cooperation agreement with a renowned robotics company to jointly promote the industrialization of robotics technology. The cooperation includes areas such as technology research and development, talent cultivation, and market promotion, aiming to strengthen the collaboration between academia and industry and accelerate the application and popularization of robotics technology.5. Institute of Automation, Chinese Academy of Sciences receivesNational Science and Technology Progress Award (First Class) The Institute of Automation, Chinese Academy of Sciences has been awarded the National Science and Technology Progress Award (First Class) for its important research achievements in the field of artificial intelligence. The research outcomes have significant application value in areas such as autonomous driving and the Internet of Things, playing a proactive role in promoting innovation and development in related industries.。
氯化石蜡的环境分析方法研究进展
引用格式:周婷婷, 杨倩玲, 翁冀远, 等. 氯化石蜡的环境分析方法研究进展[J]. 中国测试,2024, 50(4): 1-15. ZHOU Tingting,YANG Qianling, WENG Jiyuan, et al. A review on analysis of chlorinated paraffins in environment[J]. China Measurement & Test,2024, 50(4): 1-15. DOI: 10.11857/j.issn.1674-5124.2022080162氯化石蜡的环境分析方法研究进展周婷婷1,2,3, 杨倩玲1,2,3, 翁冀远1,3, 乔 林1, 高丽荣1,2,3, 郑明辉1,2,3(1. 中国科学院生态环境研究中心,环境化学与生态毒理学国家重点实验室,北京 100085;2. 国科大杭州高等研究院 环境学院,浙江 杭州 310000; 3. 中国科学院大学,北京 100049)摘 要: 短链氯化石蜡(SCCPs )具有持久性、生物毒性和生物累积性等,是一种持久性有机污染物,被列入《斯德哥尔摩公约》附件A 中,中链氯化石蜡具有SCCPs 相似的性质也备受关注。
氯化石蜡(chlorinated paraffins ,CPs )拥有成千上万种同系物、异构体、对映体,加之环境基质中存在其他有机卤素化合物,CPs 分离分析困难,目前我国仍没有环境样品中CPs 分析的标准方法。
该文对近年来不同环境基质中的CPs 分析所采用的样品前处理技术和仪器分析方法两个方面进行综述,提供CPs 分析方法最新发展动态,为相关人员对开展此方面的研究提供参考。
关键词: 短链氯化石蜡; 中链氯化石蜡; 分析方法; 前处理方法中图分类号: TB9; X-1文献标志码: A文章编号: 1674–5124(2024)04–0001–15A review on analysis of chlorinated paraffins in environmentZHOU Tingting 1,2,3, YANG Qianling 1,2,3, WENG Jiyuan 1,3, QIAO Lin 1, GAO Lirong 1,2,3, ZHENG Minghui 1,2,3(1. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; 2. School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China; 3. University of ChineseAcademy of Sciences, Beijing 100049, China)Abstract : Short-chain chlorinated paraffins (SCCPs) are persistent organic pollutants with persistence, toxicity,bioaccumulation, and were listed in Annex A of the Stockholm Convention. Medium-chain chlorinated paraffins (MCCPs) have also gained attention due to its similar properties to SCCPs. Chlorinated paraffins (CPs) have thousands of congeners, isomers and enantiomers and the presence of other organohalogen compounds in the environmental matrices makes the separation and analyze of CPs difficult. At present, there is still no standard analytical method to analysis CPs in the different environmental matrices. This article will review the sample preparation and instrumental analysis of CPs in different environmental matrices in recent years and provide an update on the latest developments in the analytical methods for CPs.Keywords : short-chain chlorinated paraffins; medium-chain chlorinated paraffins; analytical methods;preparation methods收稿日期: 2022-08-27;收到修改稿日期: 2022-10-04基金项目: 国家环境保护环境监测质量控制重点实验室开放基金(KF202203)作者简介: 周婷婷(1997-),女,福建漳州市人,硕士研究生,专业方向为新污染物的分析方法与环境行为的研究。
IPCC政府间气候变化委员会第2次评估报告
UNEP PNUEWMO OMM INTERGOVERNMENTAL PANEL ON CLIMATE CHANGEIPCC Second AssessmentClimate Change 1995A REPORT OF THEINTERGOVERNMENTAL PANEL ON CLIMATE CHANGEUNEPPNUEWMO OMM INTERGOVERNMENTAL PANEL ON CLIMATE CHANGEIPCC Second AssessmentClimate Change 1995A REPORT OF THEINTERGOVERNMENTAL PANEL ON CLIMATE CHANGEiiiivG.O.P. ObasiSecretary-GeneralWorld Meterological Organization Ms E. DowdeswellExecutive DirectorUnited Nations Environment Programmeviii N. SundararamanSecretary of the IPCC B. Bolin Chairman of the IPCC34563.1This section provides scientific and technical informationthat can be used, inter alia , in evaluating whether the projectedrange of plausible impacts constitutes “dangerous anthropogenicinterference with the climate system”, as referred to in Article 2, andin evaluating adaptation options. However, it is not yet possible tolink particular impacts with specific atmospheric concentrations ofgreenhouse gases.3.2Human health, terrestrial and aquatic ecological systems,and socio-economic systems (e.g., agriculture, forestry, fisheries andwater resources) are all vital to human development and well-beingand are all sensitive to both the magnitude and the rate of climatechange. Whereas many regions are likely to experience the adverseeffects of climate change — some of which are potentiallyirreversible — some effects of climate change are likely to bebeneficial. Hence, different segments of society can expect toconfront a variety of changes and the need to adapt to them.3.3Human-induced climate change represents an importantadditional stress, particularly to the many ecological and socio-economic systems already affected by pollution, increasing resourcedemands, and non-sustainable management practices. Thevulnerability of human health and socio-economic systems — and,to a lesser extent, ecological systems — depends upon economiccircumstances and institutional infrastructure. This implies thatsystems typically are more vulnerable in developing countries whereeconomic and institutional circumstances are less favourable.3.4Although our knowledge has increased significantlyduring the last decade and qualitative estimates can be developed,quantitative projections of the impacts of climate change on anyparticular system at any particular location are difficult becauseregional-scale climate change projections are uncertain; our currentunderstanding of many critical processes is limited; systems aresubject to multiple climatic and non-climatic stresses, the interac-tions of which are not always linear or additive; and very few studies have considered dynamic responses to steadily increasing concen-trations of greenhouse gases or the consequences of increases beyond a doubling of equivalent atmospheric CO 2concentrations.3.5Unambiguous detection of climate-induced changes in most ecological and social systems will prove extremely difficult in the coming decades. This is because of the complexity of these systems, their many non-linear feedbacks, and their sensitivity to a large number of climatic and non-climatic factors, all of which are expected to continue to change simultaneously. As future climate extends beyond the boundaries of empirical knowledge (i.e., the documented impacts of climate variation in the past), it becomes more likely that actual outcomes will include surprises and unanticipated rapid changes.Sensitivity of systems Terrestrial and aquatic ecosystems 3.6Ecosystems contain the Earth’s entire reservoir of genetic and species diversity and provide many goods and services including:(i) providing food, fibre, medicines and energy; (ii) processing and storing carbon and other nutrients; (iii) assimilating wastes, purifying water, regulating water runoff, and controlling floods, soil degrada-tion and beach erosion; and (iv) providing opportunities for recreation and tourism. The composition and geographic distribu-tion of many ecosystems (e.g., forests, rangelands, deserts, mountain systems, lakes, wetlands and oceans) will shift as individual species respond to changes in climate; there will likely be reductions in biological diversity and in the goods and services that ecosystems provide society. Some ecological systems may not reach a new equi-librium for several centuries after the climate achieves a new balance.78910Figure 1 (a). Carbon dioxide concentration profiles leading to stabilization at 450, 550, 650 and 750 ppmv following the pathways defined in IPCC (1994) (solid curves) and for pathways that allow emissions to follow IS92a until at least the year 2000 (dashed curves). A single profile that stabilizes at a carbon dioxide concen-tration of 1000 ppmv and follows IS92a emissions until at least the year 2000 has also been defined. Stabilization at concentrations of 450, 650 and 1000 ppmv would lead to equilibrium temperature increases relative to 199014due to carbon dioxide alone (i.e., not including effects of other greenhouse gases (GHGs) and aerosols) of about 1°C (range: 0.5 to 1.5°C), 2°C (range: 1.5 to 4°C) and 3.5°C (range: 2 to 7°C), respectively. A doubling of the pre-industrial carbon dioxide concentration of 280 ppmv would lead to a concen-tration of 560 ppmv and doubling of the current concentration of 358 ppmv would lead to a concentration of about 720 ppmv.Figure 1 (b). Carbon dioxide emissions leading to stabilization at concentrations of 450, 550, 650, 750 and 1000 ppmv following the profiles shown in (a) from a mid-range carbon cycle model. Results from other models could differ from those presented here by up to approximately ±15%. For comparison, the carbon dioxide emis-sions for IS92a and current emissions (fine solid line) are alsoshown.11121314157.1Economic development, social development and environ-mental protection are interdependent and mutually reinforcingcomponents of sustainable development, which is the frameworkfor our efforts to achieve a higher quality of life for all people. TheUNFCCC notes that responses to climate change should be coordi-nated with social and economic development in an integratedmanner with a view to avoiding adverse impacts on the latter,taking into full account the legitimate priority needs of developingcountries for the achievement of sustainable development and theeradication of poverty. The Convention also notes the common butdifferentiated responsibilities and respective capabilities of allParties to protect the climate system. This section reviews brieflywhat is known about the costs and benefits of mitigation and adap-tation measures as they relate, inter alia , to the sustainability ofeconomic development and environment.Social costs of climate change7.2Net climate change damages include both market and non-market impacts as far as they can be quantified at present and, in somecases, adaptation costs. Damages are expressed in net terms to accountfor the fact that there are some beneficial impacts of climate changeas well, which are, however, dominated by the damage costs. Non-market impacts, such as human health, risk of human mortality anddamage to ecosystems, form an important component of available esti-mates of the social costs of climate change. The estimates ofnon-market damages, however, are highly speculative and notcomprehensive and are thus a source of major uncertainty in assess-ing the implications of global climate change for human welfare.7.3The assessed literature quantifying total damages from 2 to 3°C warming provides a wide range of point estimates for damages given the presumed change in atmospheric greenhouse gas concen-trations. The aggregate estimates tend to be a few per cent of world GDP, with, in general, considerably higher estimates of damage to developing countries as a share of their GDP. The aggregate estimates are subject to considerable uncertainty, but the range of uncertainty cannot be gauged from the literature. The range of estimates cannot be interpreted as a confidence interval given the widely differing assumptions and methodologies in the studies. Aggregation is likely to mask even greater uncertainties about damage components.Regional or sectoral approaches to estimating the consequences of climate change include a much wider range of estimates of the net economic effects. For some areas, damages are estimated to be signif-icantly greater and could negatively affect economic development.For others, climate change is estimated to increase economic produc-tion and present opportunities for economic development.Equalizing the value of a statistical life at the level typical of that in developed countries would increase monetized damages several times, and would further increase the share of the developing coun-tries in the total damage estimate. Small islands and low-lying coastal areas are particularly vulnerable. Damages from possible large-scale catastrophes, such as major changes in ocean circulation, are not reflected in these estimates. Benefits of limiting climate change 7.4The benefits of limiting greenhouse gas emissions and enhancing sinks are: (a ) the climate change damages and167.12Cost estimates for a number of specific approaches to miti-gating emissions or enhancing sinks of greenhouse gases vary widely and depend on site-specific characteristics. This is true for renewable energy technologies, for example, as well as carbon sequestration options. The latter could offset as much as 15-30% of 1990 global energy-related emissions each year in forests for the next 50 years. The costs of carbon sequestration, which are compet-itive with source control options, differ among regions of the world.7.13Control of emissions of other greenhouse gases, especially methane and nitrous oxide, can provide significant cost-effective oppor-tunities in some countries. About 10% of anthropogenic methane emissions could be reduced at negative or low cost using available miti-gation options for such methane sources as natural gas systems, waste management and agriculture. Costs differ between countries and regions for some of these options.Subsidies, market imperfections and barriers7.14The world economy and indeed some individual national economies suffer from a number of price distortions which increase greenhouse gas emissions, such as some agricultural and fuel sub-sidies and distortions in transport pricing. A number of studies of this issue indicate that global emissions reductions of 4-18 % together with increases in real incomes are possible from phasing out fuel subsidies.7.15Progress has been made in a number of countries in cost-effectively reducing imperfections and institutional barriers in markets through policy instruments based on voluntary agreements, energy efficiency incentives, product efficiency standards and energy efficiency procurement programmes involving manufacturers and utility regulatory reforms. Where empirical evaluations have been made, many have found that the benefit-cost ratio of increasing energy efficiency was favourable, suggesting the practical feasibility of realizing “no regrets” potentials at negative net cost.Value of better information and research7.16The value of better information about the processes, impacts of and responses to climate change is likely to be great. Analysis of economic and social issues related to climate change, especially in developing countries, is a high priority for research. Further analysis is required concerning effects of response options on employment, inflation, trade, competitiveness and other public issues.1718Tundra, Taiga-Tundra, Ice BorealForestsTemperateForestsTropicalForestsSavannas,Dry Forests,WoodlandsGrasslands,Shrublands,Deserts313233regional scales, even at mid-latitudes. There may be increased risk of hunger and famine in some locations; many of the world’s poorest people — particularly those living in subtropical and tropical areas and dependent on isolated agricultural systems in semi-arid and arid regions — are most at risk of increased hunger. Many of these at-risk populations are found in sub-Saharan Africa; south, east andsoutheast Asia; and tropical areas of Latin America, as well as some Pacific island nations.Adaptation — such as changes in crops and crop varieties, improved water-management and irrigation systems, and changes in planting schedules and tillage practices — will be important in limiting34353637383940。
IPCC_Special_Report_on_Carbon_Dioxide_Capture_and_Storage
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nature photonics 模板 -回复
nature photonics 模板-回复Nature Photonics: A Template for Advancements in Photonic TechnologiesIntroduction:Photonics, a field that deals with the generation, manipulation, and detection of light, has emerged as a revolutionary technology impacting various sectors such as telecommunications, energy, healthcare, and information processing. Nature Photonics, a prestigious interdisciplinary scientific journal, has been at the forefront of showcasing breakthrough research and cutting-edge technologies in this field. This article aims to explore the contributions of Nature Photonics and discuss how it has shaped the advancements in photonic technologies.Section 1: Establishing a Premier Platform for Photonics Research Nature Photonics, since its inception in 2007, has played a critical role in providing a platform for researchers to publish theirhigh-impact studies. The journal follows a rigorous peer-review process, ensuring that only significant and groundbreaking work gets published. This approach has fostered a culture of excellence and has attracted researchers worldwide to submit their studies toNature Photonics.Section 2: Key Discoveries and InnovationsOver the years, Nature Photonics has featured numerous studies that have pushed the boundaries of photonic technologies. From ultrafast lasers to quantum optics to optical materials, the journal has showcased a diverse range of topics. One significant breakthrough that deserves attention is the development of photonic integrated circuits (PICs). These circuits, which combine multiple photonic components on a single chip, have revolutionized optical communications, enabling high-speed data transfer and increased bandwidth.Another groundbreaking area that Nature Photonics has extensively covered is biophotonics. Researchers have explored the use of light for medical diagnostics, imaging, and therapy. For instance, novel imaging techniques such as multiphoton microscopy and optical coherence tomography have allowed for high-resolution imaging of biological tissues, leading to advancements in disease diagnosis and monitoring. Additionally, photonic-based therapies, like photodynamic therapy and optogenetics, have shown promising results in cancer treatmentand neurobiology, respectively.Section 3: Bridging the Gap between Academia and Industry Nature Photonics recognizes the importance of translating research findings into practical applications. The journal has actively encouraged authors to highlight the potential commercial implications of their work. This emphasis on industry relevance has fostered collaborations between academia and industry, facilitating the development of photonic technologies for real-world applications. Several startups and spin-offs have emerged based on research features in Nature Photonics, further validating the impact of the journal.Section 4: Perspective and Future DirectionsNature Photonics has been instrumental in propelling the field of photonics to new heights. However, challenges remain, and future research directions need to address them. One area that warrants attention is the development of compact and efficient light sources for integrated photonics. Compact, low-power lasers are crucial for applications such as on-chip optical interconnects and quantum computing. Additionally, innovative and cost-effective manufacturing techniques for PICs can enhance their scalabilityand decrease production costs.Furthermore, efforts should continue to bridge the gap between fundamental research and industry applications.Industry-academia collaborations need to be fostered, and policies that incentivize commercialization of photonic technologies should be implemented. Finally, increased emphasis on sustainability should be incorporated into photonic research, promoting the development of energy-efficient photonic devices and materials.Conclusion:Nature Photonics has undeniably played a pivotal role in advancing the field of photonics. Through its commitment to excellence, the journal has provided a platform for groundbreaking research, fostering collaborations between academia and industry. Looking ahead, Nature Photonics will continue to shape the trajectory of photonic technologies, pushing boundaries, and enabling novel applications for the betterment of society.。
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sc
Julia:A Generic Static Analyser for the Java BytecodeFausto SpotoDipartimento di Informatica,Verona,Italyfausto.spoto@univr.itAbstract.We describe our software tool Julia for the static analysisof full Java bytecode,for optimisation as well as verification.This toolis generic since abstract domains(analyses)are not part of Julia butrather external classes that specialise its behaviour.Static analysis isperformed through a denotational or constraint-basedfixpoint calcula-tion,focused on some program points called watchpoints.These pointsspecify where the result of the analysis is useful,and can be automati-cally placed by the abstract domain or manually provided by the user.Julia can be instructed to include a given set of library Java classes inthe analysis,in order to improve its precision.Moreover,it gives abstractdomains the opportunity to approximate control and data-flow arisingfrom exceptions and subroutines.1IntroductionThis paper describes the Julia software tool that we have developed in order to apply the abstract interpretation technique[10]to the static analysis of Java bytecode[16].The motivation underlying our effort is to provide a software support for optimising,verifying and reasoning upon Java bytecode applications before they are run,and when their source code is not available or does not even exist.Forseeing the behaviour of programs,before their actual execution, becomes more and more relevant as such programs increase in complexity and get used in critical situations such as medical operations,flight control or banking cards.Being able to prove,in an automatic way,that programs do adhere to their functional specifications is a basic factor to their success.This is particularly true for applications written in Java bytecode,distributed on the Internet or used inside a smart card,and hence potentially harmful to the client.In this perspective,analyses for security are attracting more and more interest[20].But the information inferred by a static analysis can also be used for optimisation, documentation and debugging.Abstract interpretation[10]has served as a primary framework for the formal derivation of static analyses from the property of interest.It features the ability to express correctness as well as optimality of a static analysis.It consists in executing the program over a description(the abstract domain)of the actual run-time data.By saturating all possible program execution paths,we get adomain description743841775101610302659539Fig.1.The abstract domains currently implemented inside Julia.Their size is given in number of Java source code lines,comments included.provably correct picture of its run-time behaviour,which is more or less precise, depending on how much the chosen description approximates the actual data.The goal of Julia was to fulfill the following criteria:–the analyser is generic i.e.,it does not embed any specific abstract domain but allows instead the addition of new abstract domains as external classes;–the analyser allows one to specify the set of classes which must be analysed, called the application classes.They must not change from analysis-time to run-time(through dynamic loading);this would otherwise break the correct-ness of the analysis;–the abstract domain developer has his work simplified as much as possible.Namely,he must be able to apply the formal framework of abstract interpre-tation to define its abstract domain,even for the most complex bytecodes and in the presence of all the intricacies of the Java bytecode.All he needs to do is to provide implementations of the abstract operations corresponding to the concrete bytecodes,together with a bottom element and a least upper bound operator;–the analysis is localised i.e.,its cost is proportional to the number of program points where the abstract information must be computed(the watchpoints);–the analyser does not impose any constraint on the precision of the abstract ly,it allows a given abstract domain to exploit theflow of control due to exceptions and subroutines to get a more precise analysis,yet allowing another domain to disregard the sameflows and get a less precise analysis.Precision remains a domain-related issue[10];–the analyser uses efficient techniques for computing thefixpoint needed for the static analysis[10].These techniques are domain-independent,so that the abstract domain developer does not need to care about how thefixpoint is computed for its abstract domain.Julia is free software[23].It currently includes seven abstract domains, which are described in Figure1.Class analysis is used to transform some virtual calls into static calls,whenever their target is statically determined[25].Escape analysis determines which creation instructions can safely allocate objects in the activation stack instead of the heap,since those objects will never outlive themethod which creates them[7,15].Information-flow analysis approximates the flow of data in a program,permitting one to spot violations of non-interference conditions in the analysis of security[20].Static initialisation analysis determines the set of classes which are definitely initialised in a given program point,so that references to such classes do not induce a call to their static initialiser.We discuss it in Section9as a simple example of abstract domain.The paper is organised as follows.Section2describes related work.Sections3 to8show how each of the previous criteria have been attained with Julia. Section9shows an example of abstract domain which can be plugged inside Julia.Section10discusses the application of Julia to multi-threaded programs. Section11presents the cost in time for the analysis of some non-trivial Java bytecode applications.Section12concludes.2Related WorkBecause of the actual complexity of the Java bytecode,static analysers for full Java bytecode have not been developed intensively yet.A decompilation tool,such as Soot[26],is often used as the front-end of a static analyser for Java bytecode.Currently,class analyses similar to rapid type analysis are implemented inside Soot.Decompilation is problematic when the bytecode is not the result of the compilation of Java,and maybe contains some exotic features of the Java bytecode that have no direct counterpart in Java, such as overlapping or recursive exception handlers(i.e.,catching exceptions thrown by themselves),or recursive Java bytecode subroutines(which cannot be decompiled into finally clauses).The Indus tool[1]is an analyser based on Soot.It currently includes someflavour of class analysis,escape analysis and analyses targetted for concurrent programs,to be coupled with a model-checker.In[17],a set of tools and components for building language runtimes is shown.The bytecode they consider includes the Java bytecode as a special case. Such tools have been used to implement some static analyses as well.Their code preprocessing is much lighter than ours and consequently much faster.A generic analyser for a subset of Java(rather than Java bytecode)is de-scribed in[19].It has only be applied to small programs.It allows completely relational,flow and context-sensitive static analyses.In this sense it is quite close to our work.Variousflavours of rapid class analyses have been implemented in[25].The tool is not available and genericity is not mentioned.Some benchmarks are similar to ours and their analysis seems faster than ours.Escape analysis has been implemented through specialised analysers for Java source code only,rather than Java bytecode.For instance,the analysis in[7] works inside a commercial Java compiler.The construction is specific to escape analysis,and it cannot be immediately applied to other analyses.A more complex escape analysis has been implemented in[9]and it seems to perform like ours. However,it is not a generic nor localised tool.A generic analyser for the Java Card bytecode has been defined in [8].The approach is fascinating,since it is based on the automatic derivation of a correct static analyser from its same proof of correctness.However,the Java Card byte-code is simpler than the Java bytecode.Moreover,exceptions are not considered.No examples of analysis are shown.Hence,actual analysis times are unknown.jDF A [2]performs constant propagation and liveness analysis for variables,but none of the analyses we show in Figure 9.JNUKE [4]performs dynamic rather than static analysis.No sensible comparison is hence possible with Julia 3A Generic AnalyserBeing generic is a useful feature of a modern static analyser.Current program-ming languages,such as the Java bytecode,are so complex that the development of a new static analysis is hard and error-prone.However,different static analyses do share a lot.The preprocessing phase (Section 5)and the fixpoint computa-tion (Section 8)are the same for every abstract domain.And they represent by themselves most of the development effort of a static analysis.It is hence convenient to develop and debug them once and for all,and to see new abstract domains as plug-in’s which are added to the static analyser in order to specialise its behaviour.Genericity requires however to provide an interface between the code prepro-cessor and the analyser.We solved this problem by specifying all bytecodes as state transformers.For each state transformer the abstract domain provides an approximation (see for instance Section9).bytecode results Julia Fig.2.The structure of Julia .To fulfill this requirement,westructured Julia as in Figure 2.Acode preprocessor,called Romeo ,feeds the preprocessed code into a generic fixpoint engine calledJuliet .The latter uses an exter-nal module,the abstract domain,to abstract every single bytecode,but uses its own fixpoint strategies,independent from the abstract do-main.The Bcel library [12]is alow-level interface to .class files.Figure 1shows that genericityleads to small abstract domain implementations,and hence faster and simpler development.4Application ClassesThe Java Virtual Machine loads classes dynamically as they are needed during the execution of a program.Hence,we have no guarantee that the classes that will be loaded at run-time will correspond to those that were present in the systemduring the static analysis.We might think to analyse a class without assuming anything about its surrounding environment.Any reference to an external class is treated through a worst-case assumption[11]claiming that nothing is known about its outcome.This is definitely correct,but often useless in an object-oriented language,where classes are tightly coupled through virtual method invocations,field accesses and constructor chaining.This approach results in static analyses of very little precision.Instead,we follow here the solution to this problem used in the decompilation tool Soot[26],which allows one to make explicit assumptions about which classes(called application classes)are not allowed to change from analysis-time to run-time.As a consequence,we can inspect them during the analysis and gather abstract information which improves the precision of the analysis.Application classes are typically those of the application we are analysing. Libraries are not considered application classes,usually.Hence,any reference to a library class is resolved through the worst-case assumption.However,stronger hypotheses than the worst-case assumption can be made.For instance,in[25], the set of application classes is assumed to be closed wrt.subclassing.This improves the precision of the analysis.We assume that every abstract domain plugged inside Julia decides how to deal with references to non-application classes.It can use a worst-case assump-tion or other,stronger hypotheses.This must be clearly stated in its definition, so that the user of Julia can judge whether such hypotheses are realistic or not for his own analyses.For instance,our abstract domain rt for rapid type analysis assumes that application classes are downward closed,as in[25],while our domain er for escape analysis assumes that non-application classes have the same method andfield signatures as in the system used for the analysis;their implementation can however change.5Bytecode Simplification(Preprocessing)The application of abstract interpretation to a complex language such as the Java bytecode is a real challenge.This is because abstract interpretation allows us to derive a static analysis from a specification of the concrete semantics of a program given as an(operational or denotational)input/ouput map.But some Java bytecodes cannot be immediately seen as input/output maps.Examples are the control-related bytecodes such as goto or lookupswitch.Other bytecodes are input/ouput maps,but they are so complex that the application of abstract interpretation is very hard and error-prone.Examples are the four invoke byte-codes.Moreover,exceptions break the input/output behaviour of a bytecode, since for some input state there is no output state,but rather an exceptional state.We want to spare as much as possible the abstract domain developer from knowing the intricacies of the bytecode,and allow him to define correct(and potentially optimal)operations on the abstract domain corresponding to the concrete bytecodes.To this goal,we apply a light preprocessing to the Java bytecode,in the sense that most of the bytecodes,those which are already input/output maps,are not transformed.The result is a graph of basic blocks[3]of a simplified Java byte-code,which we call Juliet bytecode.Edges between basic blocks model control. Conditional jumps use newfilter bytecodes,which play exactly the same role as the assume statements used in[6].Thesefilter bytecodes can be used to improve the precision of a static analysis,as[6]shows.An example is in Section9.They can also be conservatively abstracted as no-ops.Figure3shows a bytecode and its translation into a graph of basic blocks,where the goon newfilter bytecodes select the execution path on the basis of the outcome of the ifnewκbytecode be-haves like the old newκbytecode,but it does not check for initialisation.All bytecodes in Figure4are now in-put/output maps.This compilation ofthe original new bytecode simplifies thesubsequent static analysis.An example is in Section9.In a similar way,an invoke instruction is compiled into a Juliet code which explicitly resolves the class,then resolves the method,then looks for the target method of the call (through a compiled lookup procedure),then creates the activation frame for the method,then calls the selected method andfinally moves the return value of the called method into the operand stack of the caller.The domain devel-oper does not need to know how a method is resolved and looked up by the Java Virtual Machine[16].He does not need to know about visibility modifiers, nor about the exceptions which might be thrown during the method call.Ev-erything has been compiled,he just has to abstract the resulting code.Also exception handlers are compiled into the code.xκyFig.4.The compilation of a new bytecode.Since Juliet bytecode is derived by splitting complex Java bytecodes,it is morefine-grained than Java bytecode.Hence all properties of the Java bytecode can be expressed as properties of Juliet bytecode.In particular,we claim that the resulting Juliet bytecode has the same concrete semantics as the original Java bytecode.We are confident in this result since most of the Java bytecodes are not changed during the translation.The most complex bytecodes are trans-lated by following their operational semantics in the Java Virtual Machine official documentation[16].Since a graph of basic blocks of bytecode is used,we canfit all the complex features of the Java bytecode into that formalism(see Section2).Namely,edges connecting the blocks of code let us represent exception handlers of any shape and recursive subroutines.6LocalisationThe information computed by a static analysis is typically useful in some special program points only,called watchpoints.The number and position of the watch-points depends on the way the abstract information is used to reason about the program.For instance,in the case of class analysis we want to know which virtual calls are actually determined i.e.,always lead to the same target method[25]. Hence a watchpoint must be put before the virtual calls of the program,so that we can use the abstract information collected there to spot determinism.In the case of escape analysis,we bracket,between an entry and an exit watchpoint, the methods containing a new bytecode.This allows us to spot the new byte-codes creating objects that never escape their creating method.Those objects can hence be allocated in the activation stack instead of the heap[7,15].Since,in general,watchpoints are internal program points,the denotation computed by a static analyser cannot be just an input/ouput map.A richer structure is needed.Moreover,it is desirable that the cost of the analysis scale with the number of watchpoints,in which case we say that the static analysis is focused or localised.This is important because it allows us to concentrate the typically little computational resources of time and memory on the watchpoints only,instead of the whole program.Hence larger programs can be analysed.A general framework for focused static analyses was developed in[22]for a simple high-level language.In[21]we show how it can be applied to the Java bytecode,by exploiting the same simplification of the bytecode highlighted in Section5.We have then implemented this localised analysis inside Juliet, thefixpoint engine of Julia.Our experiments confirm that the resulting static analyses outperform their unfocused versions[21].A positive property of our focused analyses is that the abstract domain de-veloper is not aware of how the focusing technique works[22,21].He develops his abstract domain as for a simple input/ouput analysis.Abstract domains for Julia put watchpoints automatically,since they are aware of the goal of the analysis they implement.We report an example in Section9.But the user can put watchpoints explicitly if he wants.Julia runs also unfocused,constraint-based analyses.Although they often perform worse than their focused versions,such analyses are often simpler to de-velop.For instance,we have developed both class and escape analysis in focused (denotational)and unfocused(constraint-based)way,which resulted in the fo-cused ps and er and in the unfocused cps and cer domains(Figure1).This does not mean that cps and cer are useless.Constraint-based static analyses can be made(completely or partially)flow-insensitive by merging(all or some) variable approximations.The same is much harder to achieve with denotational abstract domains.Hence,ifflow-sensitivity is not important,as experiments show for class and escape analysis,then constraint-based analyses provide fast static analyses.In other cases,such as static initialisation analysis and control-flow analysis,flow-sensitivity is very important,so denotational,localised static analyses should be preferred.7No Constraints on PrecisionAbstract interpretation[10]entails that the precision of a static analysis is domain-related.The precision of different domains can be formally compared without considering their implementation in the analysis.It is desirable that this situation be maintained in practice.Hence the analyser should not limit the precision of the analysis because of spurious constraints due to the way the analysis is implemented.Many static analyses compile the source program into a constraint whose solution is an approximation of the abstract behaviour of the program.This has the drawback that a given variable of the constraint is used to represent the approximation of a program variable throughout its whole existence.But a program variable can hold different values in different program points(flow sensitiveness).Hence,this technique merges all those approximations in the same variable,thus imposing a limit to the precision of the analysis.This situation improves by using variable splitting or variable indexing in or-der to multiply the variables used in the constraint to represent a given program variable.This means that the domain developer(who writes the compilation of the source program into a constraint)must be aware of the problem.Moreover,if a given method is called from different contexts,the same approximation is still used for all such calls.Method cloning[27]can be used here,which further complicates the analysis.Consequently,to the best of our knowledge,it has never been implemented for the Java bytecode.We prefer instead to stick to the traditional definition of abstract interpre-tation[10],so that static analysis works by computing a denotationalfixpoint over data-flow equations derived from the structure of the program.This results in aflow and context sensitive analysis.The abstract domain might decide not to exploit this opportunity of precision,but no constraint is imposed by the analyser itself.Similarly,the preprocessing of the Java bytecode performed by Julia(Sec-tion5)exposes theflows of control arising from the lookup procedures for virtual method invocations,from exceptions and from subroutines.Again,it is an ab-stract domain matter to decide whether thoseflows of control must be selectively chosen,in order to get a more precise analysis,or rather they must be considered as non-deterministic choices without any preference,thus getting a less precise analysis.Often,it is just a matter of trade-offbetween precision and cost of the analysis.For instance,the rt domain for rapid type analysis chooses between thoseflows non-deterministically,while the abstract domain ps for class analysis selects them in order to drive the analysis and collect more precise information. 8Fixpoint EngineComputing a globalfixpoint over data-flow equations can be computationally expensive or even prohibitive.We have used some techniques to tame this com-plexity issue.Thefirst consists in building the maximal strongly connected components of the call graph of basic blocks and methods.These components are then sorted topologically and used to build the analysis of the whole program through local fixpoints.There is a localfixpoint for each recursive component.For instance, Figure3contains three components.The static analyser works byfirst computing the analysis for component3(which does not require anyfixpoint)then the analysis for component2(which does require a localfixpoint)and,finally,the analysis for component1(without anyfixpoint).A second technique was originally developed for the static analysis of logic programs and is known as abstract compilation[14].During the abstract inter-pretation process,a given bytecode is repeatedly abstracted because of loops and recursion.It becomes hence convenient to abstract it once and for all,and com-pute thefixpoint over an abstract program i.e.,a program where each bytecode has been substituted with its abstraction into the abstract domain.For instance, the code shown in Figure3first gets abstracted into the chosen abstract domain, as Figure5shows.Then,thefixpoint mechanism is applied as before.Abstract compilation can be applied repeatedly.If the analysis a of a piece of code c is stable i.e.,it will not change anymore during the analysis,then c can be substituted with a.For instance,during the computation of the localfixpointFig.5.The abstract compilation of the program in Figure3.for component2in Figure5we compute repeatedly the sequential composition of a4and of a 6.It is hence convenient to compute these compositions once and for all,before thefixpoint mechanism starts.It must be noted that the abstract domain designer is not aware of the use of strongly connected components and abstract compilation,which are domain-independent techniques.Domain-specificfixpoint acceleration techniques will be added in the future to Julia,through widening operators[10].They are essential for using abstract domains with infinite ascending chains,such as polyhedra.9Writing Abstract Domains for JuliaNew abstract domains can be developed and plugged inside Julia.The domain developer must define the abstract counterparts of the concrete bytecodes,a bottom element,a least upper bound operator and how the watchpoints are put in the source code to perform the analysis implemented by the domain.Ab-stract domains for Julia are Java classes that extend juliet.BottomUpDomain, if localisation is used(Section6),or juliet.ConstraintDomain,otherwise.We describe here an abstract domain of thefirst kind,which is used for static initialisation analysis.This analysis collects the set of classes which are defi-nitely initialised in each given program point.This information is useful before a goon ifnotinitialised bytecode(Figure4),which might be found to be redundant.If that is the case,they can be safely re-moved from the code,so that subsequent static analyses(class,escape analysis, etc.)will run on a simplified program,and be potentially faster and more pre-cise.The set of initialised classes enlarges only when an initialiseκor a gooninitialised.From this idea,we implemented the abstract domain for static ini-tialisation shown in Figures6,7and8.In Figure6,we see that an abstract element contains the set initialised of definitely initialised classes.The init method prepares the domain for the static analysis.In our case,it puts a watchpoint in front of each goonifnotinitialised bytecodes,since they are always coupled with a goonifinitialised tests.There is also a method that computes the bottom element of the abstract domain,another that clones an abstract domain element and another that computes the least upper bound(lub)of two abstract domain elements.This last operation consists in the intersection of the two sets of initialised classes,since a class is definitely initialised after a conditional if it is definitely initialised at the end of both its branches.The compose method com-putes the sequential composition of two abstract domain elements.It computes the union of the classes which are definitely initialised in both.The analyse method computes the abstraction of a bytecode in occurring in a basic block cb (Section5).Normally,it returns an abstract domain element whose set of ini-tialised classes is empty,except for the initialiseκand goonifinitialisedκbytecode,and checks whether the set of initialised classes there includesκ.If this is the case, the corresponding test for initialisation is useless since it will always succeed.Appropriate import statements must be put at the beginning of Figure6. The actual code is in thefilestatic is needed since a keyword cannot be a package name in Java).However,the code in Figure6, 7and8is perfectly working,and able to analyse all valid Java bytecode.For instance,you can apply it to itself(i.e.,its compiled code)in library mode i.e., by assuming that all its public methods may be called from outside.The result is that11out of a total of36static initialisation tests are found to be redundant.In Figures6,7and8,they are ly,the recursive static calls inside putWatchpoints do not need any initialisation of the Static class,since it has already been initialised by thefirst static call.The StringBuffer creation inside toString does not need any initialisation of StringBuffer since it has already been initialised by the creation of a StringBuffer object in the previous line.public class Static extends BottomUpDomain{private HashSet initialised=new HashSet();//the classes initialised private static HashSet all;//all classes checked for initialisation private static int useless,total;//useless/total initialisation tests private Static(HashSet initialised){this.initialised=initialised;} public void init(Loader loader){//we put the watchpoints in each methodfor(int i=0;i<loader.program.methods.length;i++)putWatchpoints(loader.program.methods[i]);}private static void putWatchpoints(MethodCode mc){//we add the watchpoints in the instructionsfor(int i=0;i<mc.blocks.length;i++)mc.blocks[i].ins=putWatchpoints(mc,((SEQ)ins).left);((SEQ)ins).right=putWatchpoints。
Geographic_Information_Systems_and_Science_Today_and_Tomorrow
A technology of dynamics
• Real-time, continuous monitoring • The state of the world at all times
– the state of the transportation network – the state of human health – the state of the environment
• Theories of representation
– discrete objects and continuous fields – object fields, metamaps – unification
• Models of uncertainty
– error propagation – downscaling
– goods from production to retail display to sale – construction materials – pets, livestock, children, parolees
NYC Office of Emergency Management and NY Office of Cyber Security and Critical Infrastructure Coordination
• The science behind the systems • The fundamental issues raised by the technologies • The principles implemented in the technologies
Major discoveries in GIScience
一种新体制的高频地波雷达设计与实现
雷达科学与技术Radar Science and Technology第1期2021年2月Vol. 19 No. 1February 2021DOI : 10. 3969/j. issn. 1672-2337. 2021. 01. 006一种新体制的高频地波雷达设计与实现杨钊,吴雄斌,张兰(武汉大学电子信息学院,湖北武汉430072)摘要:传统高频地波雷达接收机与天线阵列由长电缆连接,存在成本高、架设难、不易维护等问题。
本文提出了 一种新体制的高频地波雷达系统,该系统将多通道接收机分为多个装配在接收机天线附近的独立的单通道接收单元,接收单元与天线之间采用短电缆连接模式,各个接收单元之间通过GPS/北斗进行 时钟同步,通过无线方式进行参数配置和数据传输。
在完成单通道接收单元设计与实现后,通过闭环实验和海边现场实验对整个新系统进行了检测,得到了稳定的海洋回波,证明了新体制雷达系统的可行性。
关键词:地波雷达;无线传输;新体制;单通道接收单元中图分类号:TN95& 93文献标志码:A 文章编号:1672-2337(2021)01-0035-05Design and Implementation of a New HF Ground Wave RadarYANG Zhao, WU Xiongbin, ZHANG Lan{School of Electronic Information ?Wuhan University j Wuhan 430072, China)Abstract : The receiver of the traditional high frequency (HF) surface wave radar (SWR) was usually con nected with the receiving array by long cables, which may increase the cost and difficulty of the installation and maintenance for the radar system. A novel HF SWR system is introduced in this paper. The receiving module of this system composes of several independent single-channel receiving units mounted near the receiving antennas, and a short cable connection mode is used between the receiving unit and the antenna. The clock synchronization between each receiving unit is realized through GPS/BDS )and parameter configuration, and data transition for the radar system are achieved through wireless transmission. The new radar system has been checked through the closed-loop experiments and field experiments and has received stable sea echoes 5 which demonstrates the feasi bility of the proposed radar system.Key words : ground wave radar ; wireless transmission ; new system ; single channel receiving unit0引言高频地波雷达可以实现对视距外海洋状态和海上目标的大范围、高精度和全天候的实时监 测m ,因此,高频地波雷达在海洋监测和国防等领 域具有独特的应用前景和优势,成为了立体化海洋信息监测的重要工具之一。
Fluorescence spectroscopy and multi-way techniques PARAFA
Received 12th July 2013 Accepted 9th September 2013 DOI: 10.1039/c3ay41160e /methods
Introduction
a
University of New South Wales, Water Research Centre, Sydney, Australia. E-mail: krm@.au; Fax: +61 2 9313 8624; Tel: +61 2 9385 4601 Technical University of Denmark, National Institute for Aquatic Resources, Charlottenlund, Denmark. E-mail: cost@aqua.dtu.dk
PARAllel FACtor analysis (PARAFAC) is used in the chemical sciences to decompose trilinear multi-way data arrays and facilitate the identication and quantication of independent underlying signals, termed ‘components’. In 2011–2012, 334 Scopus-indexed journal and conference papers were published with keywords “PARAFAC” or “parallel factor analysis”. In the subset of papers where PARAFAC was used primarily as a tool for data interpretation (n ¼ 238, thus excluding 96 papers concerned primarily with developing or comparing algorithms, tools or statistical methodologies), PARAFAC was applied across research elds (medical, pharmaceutical, food, environmental, social, and information science) and to a wide range of data
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图像处理领域公认的重要英文期刊和会议分级
人工智能和图像处理方面的各种会议的评级2010年8月31日忙菇发表评论阅读评论人工智能和图像处理方面的各种会议的评级澳大利亚政府和澳大利亚研究理事会做的,有一定参考价值会议名称会议缩写评级ACM SIG International Conference on Computer Graphics and Interactive Techniques SIGGRAPH AACM Virtual Reality Software and Technology VRST AACM/SPIE Multimedia Computing and Networking MMCN AACM-SIGRAPH Interactive 3D Graphics I3DG AAdvances in Neural Information Processing Systems NIPS AAnnual Conference of the Cognitive Science Society CogSci AAnnual Conference of the International Speech Communication Association (was Eurospeech) Interspeech AAnnual Conference on Computational Learning Theory COLT AArtificial Intelligence in Medicine AIIM AArtificial Intelligence in Medicine in Europe AIME AAssociation of Computational Linguistics ACL ACognitive Science Society Annual Conference CSSAC AComputer Animation CANIM AConference in Uncertainty in Artificial Intelligence UAI AConference on Natural Language Learning CoNLL AEmpirical Methods in Natural Language Processing EMNLP AEuropean Association of Computational Linguistics EACL AEuropean Conference on Artificial Intelligence ECAI AEuropean Conference on Computer Vision ECCV AEuropean Conference on Machine Learning ECML AEuropean Conference on Speech Communication and Technology (now Interspeech) EuroSpeech AEuropean Graphics Conference EUROGRAPH AFoundations of Genetic Algorithms FOGA AIEEE Conference on Computer Vision and Pattern Recognition CVPR AIEEE Congress on Evolutionary Computation IEEE CEC AIEEE Information Visualization Conference IEEE InfoVis AIEEE International Conference on Computer Vision ICCV AIEEE International Conference on Fuzzy Systems FUZZ-IEEE AIEEE International Joint Conference on Neural Networks IJCNN AIEEE International Symposium on Artificial Life IEEE Alife AIEEE Visualization IEEE VIS AIEEE Workshop on Applications of Computer Vision WACV AIEEE/ACM International Conference on Computer-Aided Design ICCAD AIEEE/ACM International Symposium on Mixed and Augmented Reality ISMAR A International Conference on Automated Deduction CADE AInternational Conference on Autonomous Agents and Multiagent Systems AAMAS A International Conference on Computational Linguistics COLING AInternational Conference on Computer Graphics Theory and Application GRAPP A International Conference on Intelligent Tutoring Systems ITS AInternational Conference on Machine Learning ICML AInternational Conference on Neural Information Processing ICONIP AInternational Conference on the Principles of Knowledge Representation and Reasoning KR A International Conference on the Simulation and Synthesis of Living Systems ALIFE A International Joint Conference on Artificial Intelligence IJCAI AInternational Joint Conference on Automated Reasoning IJCAR AInternational Joint Conference on Qualitative and Quantitative Practical Reasoning ESQARU A Medical Image Computing and Computer-Assisted Intervention MICCAI ANational Conference of the American Association for Artificial Intelligence AAAI ANorth American Association for Computational Linguistics NAACL APacific Conference on Computer Graphics and Applications PG AParallel Problem Solving from Nature PPSN AACM SIGGRAPH/Eurographics Symposium on Computer Animation SCA BAdvanced Concepts for Intelligent Vision Systems ACIVS BAdvanced Visual Interfaces AVI BAgent-Oriented Information Systems Workshop AOIS BAnnual International Workshop on Presence PRESENCE BArtificial Neural Networks in Engineering Conference ANNIE BAsian Conference on Computer Vision ACCV BAsia-Pacific Conference on Simulated Evolution and Learning SEAL BAustralasian Conference on Robotics and Automation ACRA BAustralasian Joint Conference on Artificial Intelligence AI BAustralasian Speech Science and Technology S ST BAustralian Conference for Knowledge Management and Intelligent Decision Support A CKMIDS B Australian Conference on Artificial Life ACAL BAustralian Symposium on Information Visualisation ASIV BBritish Machine Vision Conference B MVC BCanadian Artificial Intelligence Conference CAAI BComputer Graphics International CGI BConference of the Association for Machine Translation in the Americas AMTA B Conference of the European Association for Machine Translation EAMT BConference of the Pacific Association for Computational Linguistics PACLING BConference on Artificial Intelligence for Applications CAIA BCongress of the Italian Assoc for AI AI*IA BDeutsche Arbeitsgemeinschaft für Mustererkennung DAGM e.V DAGM BDigital Image Computing Techniques and Applications DICTA BEurographics Symposium on Parallel Graphics and Visualization EGPGV BEurographics/IEEE Symposium on Visualization EuroVis BEuropean Conference on Artificial Life ECAL BEuropean Conference on Genetic Programming EUROGP BEuropean Simulation Symposium ESS BEuropean Symposium on Artificial Neural Networks ESANN BFrench Conference on Knowledge Acquisition and Machine Learning FCKAML BGerman Conference on Multi-Agent system Technologies MATES BGraphics Interface GI BIEEE International Conference on Image Processing ICIP BIEEE International Conference on Multimedia and Expo ICME BIEEE International Conference on Neural Networks ICNN BIEEE International Workshop on Visualizing Software for Understanding and Analysis VISSOFT BIEEE Pacific Visualization Symposium (was APVIS) PacificVis BIEEE Symposium on 3D User Interfaces 3DUI BIEEE Virtual Reality Conference VR BIFSA World Congress IFSA BImage and Vision Computing Conference IVCNZ BInnovative Applications in AI IAAI BIntegration of Software Engineering and Agent Technology ISEAT BIntelligent Virtual Agents IVA BInternational Cognitive Robotics Conference COGROBO BInternational Conference on Advances in Intelligent Systems: Theory and Applications AISTABInternational Conference on Artificial Intelligence and Statistics AISTATS BInternational Conference on Artificial Neural Networks ICANN BInternational Conference on Artificial Reality and Telexistence ICAT BInternational Conference on Computer Analysis of Images and Patterns CAIP BInternational Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia S IGGRAPH ASIA BInternational Conference on Database and Expert Systems Applications DEXA B International Conference on Frontiers of Handwriting Recognition ICFHR BInternational Conference on Genetic Algorithms ICGA BInternational Conference on Image Analysis and Processing ICIAP BInternational Conference on Implementation and Application of Automata CIAA B International Conference on Information Visualisation IV BInternational Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems CPAIOR B International Conference on Intelligent Systems and Knowledge Engineering ISKE B International Conference on Intelligent Text Processing and Computational Linguistics CICLING BInternational Conference on Knowledge Science, Engineering and Management KSEM B International Conference on Modelling Decisions for Artificial Intelligence MDAI B International Conference on Multiagent Systems ICMS BInternational Conference on Pattern Recognition ICPR BInternational Conference on Software Engineering and Knowledge Engineering SEKE B International Conference on Theoretical and Methodological Issues in machine Translation TMI BInternational Conference on Tools with Artificial Intelligence ICTAI BInternational Conference on Ubiquitous and Intelligence Computing UIC BInternational Conference on User Modelling (now UMAP) UM BInternational Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision WSCG BInternational Fuzzy Logic and Intelligent technologies in Nuclear Science Conference F LINS B International Joint Conference on Natural Language Processing IJCNLP BInternational Meeting on DNA Computing and Molecular Programming DNA BInternational Natural Language Generation Conference INLG BInternational Symposium on Artificial Intelligence and Maths ISAIM BInternational Symposium on Computational Life Science CompLife BInternational Symposium on Mathematical Morphology ISMM BInternational Work-Conference on Artificial and Natural Neural Networks IWANN B International Workshop on Agents and Data Mining Interaction ADMI BInternational Workshop on Ant Colony ANTS BInternational Workshop on Paraphrasing IWP BInternational Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises WETICE BJoint workshop on Multimodal Interaction and Related Machine Learning Algorithms (nowICMI-MLMI) MLMI BLogic and Engineering of Natural Language Semantics LENLS BMachine Translation Summit MT SUMMIT BPacific Asia Conference on Language, Information and Computation PACLIC BPacific Asian Conference on Expert Systems PACES BPacific Rim International Conference on Artificial Intelligence PRICAI BPacific Rim International Workshop on Multi-Agents PRIMA BPacific-Rim Symposium on Image and Video Technology PSIVT BPortuguese Conference on Artificial Intelligence EPIA BRobot Soccer World Cup RoboCup BScandinavian Conference on Artificial Intelligence S CAI BSingapore International Conference on Intelligent Systems SPICIS BSPIE International Conference on Visual Communications and Image Processing VCIP B Summer Computer Simulation Conference SCSC BSymposium on Logical Formalizations of Commonsense Reasoning COMMONSENSE B The Theory and Application of Diagrams DIAGRAMS BWinter Simulation Conference WSC BWorld Congress on Expert Systems WCES BWorld Congress on Neural Networks WCNN B3-D Digital Imaging and Modelling 3DIM CACM Workshop on Secure Web Services SWS CAdvanced Course on Artificial Intelligence ACAI CAdvances in Intelligent Systems AIS CAgent-Oriented Software Engineering Workshop AOSE CAmbient Intelligence Developments Aml.d CAnnual Conference on Evolutionary Programming EP CApplications of Information Visualization IV-App CApplied Perception in Graphics and Visualization APGV CArgentine Symposium on Artificial Intelligence ASAI CArtificial Intelligence in Knowledge Management AIKM CAsia-Pacific Conference on Complex Systems C omplex CAsia-Pacific Symposium on Visualisation APVIS CAustralasian Cognitive Science Society Conference AuCSS CAustralia-Japan Joint Workshop on Intelligent and Evolutionary Systems AJWIES C Australian Conference on Neural Networks ACNN CAustralian Knowledge Acquisition Workshop AKAW CAustralian MADYMO Users Meeting MADYMO CBioinformatics Visualization BioViz CBrazilian Symposium on Computer Graphics and Image Processing SIBGRAPI C Canadian Conference on Computer and Robot Vision CRV CComplex Objects Visualization Workshop COV CComputer Animation, Information Visualisation, and Digital Effects CAivDE C Conference of the International Society for Decision Support Systems I SDSS C Conference on Artificial Neural Networks and Expert systems ANNES CConference on Visualization and Data Analysis VDA CCooperative Design, Visualization, and Engineering CDVE CCoordinated and Multiple Views in Exploratory Visualization CMV CCultural Heritage Knowledge Visualisation CHKV CDesign and Aesthetics in Visualisation DAViz CDiscourse Anaphora and Anaphor Resolution Colloquium DAARC CENVI and IDL Data Analysis and Visualization Symposium VISualize CEuro Virtual Reality Euro VR CEuropean Conference on Ambient Intelligence AmI CEuropean Conference on Computational Learning Theory (Now in COLT) EuroCOLT C European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty ECSQARU CEuropean Congress on Intelligent Techniques and Soft Computing EUFIT CEuropean Workshop on Modelling Autonomous Agents in a Multi-Agent World MAAMAW C European Workshop on Multi-Agent Systems EUMAS CFinite Differences-Finite Elements-Finite Volumes-Boundary Elements F-and-B CFlexible Query-Answering Systems FQAS CFlorida Artificial Intelligence Research Society Conference FlAIRS CFrench Speaking Conference on the Extraction and Management of Knowledge EGC C GeoVisualization and Information Visualization GeoViz CGerman Conference on Artificial Intelligence K I CHellenic Conference on Artificial Intelligence S ETN CHungarian National Conference on Agent Based Computation HUNABC CIberian Conference on Pattern Recognition and Image Analysis IBPRIA CIberoAmerican Congress on Pattern Recognition CIARP CIEEE Automatic Speech Recognition and Understanding Workshop ASRU CIEEE International Conference on Adaptive and Intelligent Systems ICAIS CIEEE International Conference on Automatic Face and Gesture Recognition FG CIEEE International Conference on Cognitive Informatics ICCI CIEEE International Conference on Computational Cybernetics ICCC CIEEE International Conference on Computational Intelligence for Measurement Systems and Applications CIMSA CIEEE International Conference on Cybernetics and Intelligent Systems CIS CIEEE International Conference on Granular Computing GrC CIEEE International Conference on Information and Automation IEEE ICIA CIEEE International Conference on Intelligence for Homeland Security and Personal Safety CIHSPS CIEEE International Conference on Intelligent Computer Communication and Processing ICCP C IEEE International Conference on Intelligent Systems IEEE IS CIEEE International Geoscience and Remote Sensing Symposium IGARSS CIEEE International Symposium on Multimedia ISM CIEEE International Workshop on Cellular Nanoscale Networks and Applications CNNA CIEEE International Workshop on Neural Networks for Signal Processing NNSP CIEEE Swarm Intelligence Symposium IEEE SIS CIEEE Symposium on Computational Intelligence and Data Mining IEEE CIDM CIEEE Symposium on Computational Intelligence and Games CIG CIEEE Symposium on Computational Intelligence for Financial Engineering IEEE CIFEr C IEEE Symposium on Computational intelligence for Image Processing IEEE CIIP CIEEE Symposium on Computational intelligence for Multimedia Signal and Vision Processing IEEE CIMSVP CIEEE Symposium on Computational Intelligence for Security and Defence Applications IEEE CISDA CIEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology IEEE CIBCB CIEEE Symposium on Computational Intelligence in Control and Automation IEEE CICA C IEEE Symposium on Computational Intelligence in Cyber Security IEEE CICS CIEEE Symposium on Computational Intelligence in Image and Signal Processing CIISP C IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making IEEE MCDM CIEEE Symposium on Computational Intelligence in Scheduling IEEE CI-Sched CIEEE Symposium on Intelligent Agents IEEE IA CIEEE Workshop on Computational Intelligence for Visual Intelligence IEEE CIVI CIEEE Workshop on Computational Intelligence in Aerospace Applications IEEE CIAA CIEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications IEEE CIB CIEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems IEEE CIWS CIEEE Workshop on Computational Intelligence in Virtual Environments IEEE CIVE CIEEE Workshop on Evolvable and Adaptive Hardware IEEE WEAH CIEEE Workshop on Evolving and Self-Developing Intelligent Systems IEEE ESDIS CIEEE Workshop on Hybrid Intelligent Models and Applications IEEE HIMA CIEEE Workshop on Memetic Algorithms IEEE WOMA CIEEE Workshop on Organic Computing IEEE OC CIEEE Workshop on Robotic Intelligence in Informationally Structured Space IEEE RiiSS C IEEE Workshop on Speech Coding SCW CIEEE/WIC/ACM International Conference on Intelligent Agent Technology IAT CIEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology WI-IAT CIFIP Conference on Biologically Inspired Collaborative Computing BICC CInformation Visualisation Theory and Practice InfVis CInformation Visualization Evaluation IVE CInformation Visualization in Biomedical Informatics IVBI CIntelligence Tools, Data Mining, Visualization IDV CIntelligent Multimedia, Video and Speech Processing Symposium MVSP C International Atlantic Web Intelligence Conference AWIC CInternational Colloquium on Data Sciences, Knowledge Discovery and Business Intelligence DSKDB CInternational Conference Computer Graphics, Imaging and Visualization CGIV CInternational Conference Formal Concept Analysis Conference ICFCA CInternational Conference Imaging Science, Systems and Technology CISST CInternational Conference on 3G Mobile Communication Technologies 3G CInternational Conference on Adaptive and Natural Computing Algorithms ICANNGA C International Conference on Advances in Pattern Recognition and Digital Techniques ICAPRDT CInternational Conference on Affective Computing and Intelligent A CII CInternational Conference on Agents and Artificial Intelligence ICAART CInternational Conference on Artificial Intelligence I C-AI CInternational Conference on Artificial Intelligence and Law ICAIL CInternational Conference on Artificial Intelligence and Pattern Recognition A IPR CInternational Conference on Artificial Intelligence and Soft Computing ICAISC C International Conference on Artificial Intelligence in Science and Technology AISAT C International Conference on Arts and Technology ArtsIT CInternational Conference on Case-Based Reasoning Research and Development ICCBR C International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems ICCCI CInternational Conference on Computational Intelligence and Multimedia ICCIMA C International Conference on Computational Intelligence and Software Engineering CISE C International Conference on Computational Intelligence for Modelling, Control and Automation CIMCA CInternational Conference on Computational Intelligence, Robotics and Autonomous Systems CIRAS CInternational Conference on Computational Semiotics for Games and New Media Cosign C International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa AFRIGRAPH CInternational Conference on Computer Theory and Applications ICCTA CInternational Conference on Computer Vision Systems I CVS CInternational Conference on Cybercrime Forensics Education and Training CFET CInternational Conference on Engineering Applications of Neural Networks EANN C International Conference on Evolutionary Computation ICEC CInternational Conference on Fuzzy Systems and Knowledge FSKD CInternational Conference on Hybrid Artificial Intelligence Systems HAIS CInternational Conference on Hybrid Intelligent Systems HIS CInternational Conference on Image and Graphics ICIG CInternational Conference on Image and Signal Processing ICISP CInternational Conference on Immersive Telecommunications IMMERSCOM CInternational Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE CInternational Conference on Information and Knowledge Engineering I KE CInternational Conference on Intelligent Systems ICIL CInternational Conference on Intelligent Systems Designs and Applications ISDA CInternational Conference on Knowledge Engineering and Ontology KEOD CInternational Conference on Knowledge-based Intelligent Electronic Systems KIES CInternational Conference on Machine Learning and Applications ICMLA CInternational Conference on Machine Learning and Cybernetics ICMLC CInternational Conference on Machine Vision ICMV CInternational Conference on Medical Information Visualisation MediVis CInternational Conference on Modelling, Simulation and Optimisation ICMSO CInternational Conference on Natural Computation ICNC CInternational Conference on Neural, Parallel and Scientific Computations NPSC C International Conference on Principles of Practice in Multi-Agent Systems PRIMA C International Conference on Recent Advances in Natural Language Processing RANLP C International Conference on Rough Sets and Current Trends in Computing RSCTC C International Conference on Spoken Language Processing ICSLP CInternational Conference on the Foundations of Digital Games FDG CInternational Conference on Vision Theory and Applications VISAPP CInternational Conference on Visual Information Systems VISUAL CInternational Conference on Web-based Modelling and Simulation WebSim CInternational Congress on Modelling and Simulation MODSIM CInternational ICSC Congress on Intelligent Systems and Applications IICISA CInternational KES Symposium on Agents and Multiagent systems – Technologies and Applications KES AMSTA CInternational Machine Vision and Image Processing Conference IMVIP CInternational Symposium on 3D Data Processing Visualization and Transmission 3DPVT C International Symposium on Applied Computational Intelligence and Informatics SACI C International Symposium on Applied Machine Intelligence and Informatics SAMI C International Symposium on Artificial Life and Robotics AROB CInternational Symposium on Audio, Video, Image Processing and Intelligent Applications ISAVIIA CInternational Symposium on Foundations of Intelligent Systems ISMIS CInternational Symposium on Innovations in Intelligent Systems and Applications INISTA C International Symposium on Neural Networks ISNN CInternational Symposium on Visual Computing ISVC CInternational Visualization in Transportation Symposium and Workshop TRB Viz C International Workshop on Combinations of Intelligent Methods and Applications CIMA C International Workshop on Genetic and Evolutionary Fuzzy Systems GEFS CInternational Workshop on Human Aspects in Ambient Intelligence: Agent Technology, Human-Oriented Knowledge and Applications HAI CInternational Workshop on Image Analysis and Information Fusion IAIF CInternational Workshop on Intelligent Agents IWIA CInternational Workshop on Knowledge Discovery from Data Streams IWKDDS CInternational Workshop on MultiAgent Based Simulation MABS CInternational Workshop on Nonmonotonic Reasoning, Action and Change NRAC C International Workshop on Soft Computing Applications SOFA CInternational Workshop on Ubiquitous Virtual Reality IWUVR CINTUITION International Conference INTUITION CISCA Tutorial and Research Workshop Automatic Speech Recognition ASR CJoint Australia and New Zealand Biennial Conference on Digital Image and Vision Computing DIVC CJoint Conference on New Methods in Language Processing and Computational Natural Language Learning NeMLaP CKES International Symposium on Intelligent Decision Technologies KES IDT CKnowledge Domain Visualisation KDViz CKnowledge Visualization and Visual Thinking KV CMachine Vision Applications MVA CNAISO Congress on Autonomous Intelligent Systems NAISO CNatural Language Processing and Knowledge Engineering IEEE NLP-KE CNorth American Fuzzy Information Processing Society Conference NAFIPS CPacific-Rim Conference on Multimedia PCM CPan-Sydney Area Workshop on Visual Information Processing VIP CPractical Application of Intelligent Agents and Multi-Agent Technology Conference PAAM C Program Visualization Workshop PVW CSemantic Web Visualisation VSW CSGAI International Conference on Artificial Intelligence SGAI CSimulation Technology and Training Conference SimTecT CSoft Computing in Computer Graphics, Imaging, and Vision SCCGIV CSpring Conference on Computer Graphics SCCG CThe Conference on visualization of information SEE CVision Interface VI CVisMasters Design Modelling and Visualization Conference DMVC CVisual Analytics VA CVisual Information Communications International VINCI CVisualisation in Built Environment BuiltViz CVisualization In Science and Education VISE CVisualization in Software Engineering SEViz CVisualization in Software Product Lines Workshop VisPLE CWeb Visualization WebViz CWorkshop on Hybrid Intelligent Systems WHIS C。
Advanced Photonics and Optoelectronics
Advanced Photonics and Optoelectronics : Bringing the Future CloserPhotonics and optoelectronics are important fields in science and technology that have a significant impact on various industries. These two areas focus on the study of light and its properties, including its generation, manipulation, and detection. The advancements in photonics and optoelectronics have enabled the development of various modern technologies, from communications and sensing to medical devices and entertainment.The Technology Behind PhotonicsPhotonics involves the study of the behavior of light and the manipulation of light to achieve specific goals. The technology behind photonics has been continuously evolving, and its applications are diverse. One of the key areas that rely on photonics is communications. The technology facilitates fast and efficient data transmission through fiber-optic cables. Unlike electrical signals, light signals are immune to interference, making it ideal for long-distance transmissions.Advances in photonics technology have led to the development of optical amplifiers that enable signals to travel over extended distances without the need for repeaters. In recent times, photonics has enabled the development of photonic integrated circuits, which enable the integration of multiple functions on a single chip. These circuits play a critical role in mediating the conversion between electrical and optical signals, and their potential applications range from telecommunications to quantum computing.The Science Behind OptoelectronicsOptoelectronics is another essential field that relies on the study of light and its interaction with matter. The field encompasses the design and fabrication of electronic devices that can emit, manipulate, or detect light. Optoelectronics is an interdisciplinary field that combines various technologies, including physics, chemistry, and materials science.One of the main applications of optoelectronics is in the design of LEDs or light-emitting diodes. These devices can convert electricity into light, making them an essential component in lighting and electronics. Advances in optoelectronics have led to the development of organic LEDs that offer improved efficiency and color accuracy. Optoelectronics technology is also used in the design of solar cells, which can convert sunlight into electrical power.Emerging Trends inAdvanced photonics and optoelectronics technology are continually evolving to meet new demands and applications. One of the key emerging trends in photonics technology is the use of nanophotonics, which involves the manipulation of light using nanoscale components. The technology enables the design and fabrication of photonic devices that are smaller, more efficient, and cheaper to produce.Another emerging trend is in the development of integrated quantum photonics. Integrated quantum photonics combines photonic integrated circuits with quantum technologies to create a new category of devices that could transform various applications. The technology was first used in photonic quantum computing, where it demonstrated improved efficiency and accuracy in quantum operations.In optoelectronics, the use of advanced materials is an emerging trend that promises new possibilities. One of the materials that have attracted significant attention is perovskites, a class of materials that have unique electronic and optical properties. These materials have demonstrated promising results in solar cells, LEDs, and other optoelectronics devices.ConclusionAdvanced photonics and optoelectronics are two areas of technology that have revolutionized various industries. The technology has enabled the design and development of modern devices that are more efficient, compact, and reliable. As research in these fields continue to advance, they present new possibilities andopportunities for innovation and transformation. The emerging trends in photonics and optoelectronics promise to bring the future much closer than many expected.。
遥感图像场景分类综述
人工智能及识别技术本栏目责任编辑:唐一东遥感图像场景分类综述钱园园,刘进锋*(宁夏大学信息工程学院,宁夏银川750021)摘要:随着科技的进步,遥感图像场景的应用需求逐渐增大,广泛应用于城市监管、资源的勘探以及自然灾害检测等领域中。
作为一种备受关注的基础图像处理手段,近年来众多学者提出各种方法对遥感图像的场景进行分类。
根据遥感场景分类时有无标签参与,本文从监督分类、无监督分类以及半监督分类这三个方面对近年来的研究方法进行介绍。
然后结合遥感图像的特征,分析这三种方法的优缺点,对比它们之间的差异及其在数据集上的性能表现。
最后,对遥感图像场景分类方法面临的问题和挑战进行总结和展望。
关键词:遥感图像场景分类;监督分类;无监督分类;半监督分类中图分类号:TP391文献标识码:A文章编号:1009-3044(2021)15-0187-00开放科学(资源服务)标识码(OSID ):Summary of Remote Sensing Image Scene Classification QIAN Yuan-yuan ,LIU Jin-feng *(School of Information Engineering,Ningxia University,Yinchuan 750021,China)Abstract:With the progress of science and technology,the application demand of remote sensing image scene increases gradually,which is widely used in urban supervision,resource exploration,natural disaster detection and other fields.As a basic image pro⁃cessing method,many scholars have proposed various methods to classify the scene of remote sensing image in recent years.This pa⁃per introduces the research methods in recent years from the three aspects of supervised classification,unsupervised classification and semi-supervised classification.Then,combined with the features of remote sensing images,the advantages and disadvantages of these three methods are analyzed,and the differences between them and their performance performance in the data set are com⁃pared.Finally,the problems and challenges of remote sensing image scene classification are summarized and prospected.Key words:remote sensing image scene classification;Unsupervised classification;Supervise classification;Semi-supervised clas⁃sification遥感图像场景分类,就是通过某种算法对输入的遥感场景图像进行分类,并且判断某幅图像属于哪种类别。
肖特基结
Tunable Graphene−Silicon Heterojunctions for Ultrasensitive PhotodetectionXiaohong An,*,†Fangze Liu,†Yung Joon Jung,‡and Swastik Kar*,††Department of Physics and‡Mechanical and Industrial Engineering,Northeastern University,Boston,Massachusetts02115,United States*Supporting Informationand scalable broadband(400<λ<900nm)photodetectors,spectroscopic and imaging devices,and further,and are architecturallyequivalent power,specific detectivityanoscale materials,due to their diverse electronic and optical properties,and with a range of architectures,are constantly being explored for an array of low-cost,sensitive,and scalable photodetection technologies.1−4For example,nano-wires of conventional semiconductor materials such as Si,Ge, GaN,GaAs,InP,and so forth provide a versatile platform for photodetection,affording direct structural and functional compatibility with existing photonic and optoelectronic circuitry.1In contrast,low-cost solution-processable quantum dots are highly appealing due to their potentials for large-area andflexible-electronic applications.Their photoconductive response characterizes high quantum gains resulting in ultrahigh responses(∼103A/W)and specific detectivities (∼1013Jones).2Nanoscale junctions of quantum dots with metals have also been reported to have ultrafast responses of the order of GHz.3Similarly,carbon nanotubes,4with their extremely narrow diameters and chirality-dependent band-gaps, can be potentially utilized for spectrally selective photo-detectors of ultrasmall dimensions.In this context,graphene-based photon-sensing and photo-switching devices have recently attracted enormous attention for their ultrafast and broadband response.5−15Although these devices are highly appealing for ultrafast optical communica-tions,they suffer limitations for weak signal detection,imaging, and spectroscopic applications due to their low responsivity values.Within the visible to telecommunications-friendly wavelength range(i.e.,400nm≤λ≤1550nm),using both photovoltaic5,10and photothermoelectric or hot-carrier ef-fects9,11,14along with enhancement techniques including asymmetric metal-contacts,6plasmonic architectures,7,8and microcavity confinements,12,13the photocurrent responsivity (R I=I ph/P)has at best remained limited within1−2×10−2A/ W.7,13These low responses have been primarily attributed to the intrinsically low optical absorption(≈2.3%)of graphene16 along with the absence of any gain mechanisms.By using graphene as the carrier collector and multiplier,an effective gain mechanism(with R I>107A/W)was recently reported in graphene/quantum-dot hybrid devices.15Despite their appeal for ultraweak signal detection,the responsivity of these devices above P≈10−13W fall as R I∼1/P,implying a rapid photocurrent saturation above these incident light powers.With considerably large dark currents that render them ineffective as photoswitches(ON/OFF ratio≪1)and large dark-power consumption,they are impractical for many large-scale applications(such as pixels in imaging devices that require large arrays of photodetectors).For many applications,photovoltage(instead of photo-current)measurements are preferred as a sensitive method for photodetection without any Joule-heating associated power consumption.Past works reveal that metal−graphene interfacesReceived:October3,2012Revised:January22,2013Published:January25,2013can generate photovoltages of ∼1V/W,5which can be enhanced to ∼5V/W using plasmonic focusing and appropriate gate voltages.7It appears that the limits of photovoltage response for low dark-current graphene-based devices,especially under extremely weak signals (where the high responsivities are more meaningful),have not been critically investigated.Further,most of the above-mentioned devices used mechanically exfoliated graphene,17which possess high carrier mobility,but are unsuitable for large-scale deployment.For realistic applications,high-performance devices using large-area chemical vapor deposition (CVD)-grown graphene 18without complex enhancement architec-tures 6−8,12,13are highly desirable.However,so far,a simple approach for obtaining tunable high-responsivity graphene-based devices with low dark currents,low-power detection limits,and high operational dynamic ranges,using simple,scalable,and potentially low-cost techniques remains undem-onstrated.We show that planar 2D heterojunctions of CVD-grown graphene and Si in a conventional Schottky-diode-like con figuration can e ffectively address these issues,providing a platform for a variety of optoelectronic devices.In these junctions,the photoexcitation resides in Si,while graphene is the carrier collector.In recent times,a number of works have explored the unique properties of graphene/Si heterojunctions to develop diodes,19solar cells,20,21and the so-called “barristor ”22 a variable-barrier switch.However,so far,these junctions have not been examined for ultrasensitive photo-detection for applications such as weak-signal imaging or spectroscopy.Further,in these junctions,low reverse-biases can very e ffectively manipulate the Fermi-levels of graphene (unlike larger voltages that are required in capacitively coupled gates).The ability to tune the dark Fermi level (Ef (Gr))of graphene and,more importantly,its relative position with respect to the quasi-Fermi level for holes in silicon (E ′f,h (Si),the modi fied Fermi level due to the generation of photoexcited holes in Si)is a key mechanism that enables a high degree of tunability and e fficient capture of photoexcited carriers,resulting in high photocurrent responsivity values whose performances can be dramatically improved by layer-thickening and simple doping approaches.The tunable photocurrent responsivity is an attractive feature for adjustment to variable-brightness imaging applications.At the same time,these junctions also possess exceptionally high photovoltage response,which increases with decreasing incident power,making it highly suitable as weak-signal detectors in the photovoltage mode.In this work,weFigure 1.(a)Schematic and (b)a digital photograph of a monolayer graphene (1LG)/Si heterojunction device,with the polarity in part (a)shown for forward bias.(c)Thermal equilibrium energy band diagram of the heterojunction in darkness,with the band pro file of n-Si pinned to the charge neutrality levelofitsown surface states (see text).The dark Fermi level of graphene E f (Gr)is also shown.(d)Current −voltage (I −V )curves ofdevice A (area =25mm 2)under darkness and weak illumination (P =1.23μW,λ=488nm)showing a conventional photodiode-like behavior.(e)Deviation of the I −V curves from a conventional photodiode response as the incident light power is increased up to P =6.5mW.The expected ideal photodiode behavior at P =6.5mW is plotted with a red dashed line.(f)Schematic showing the application of a forward bias (V fbias )that lowers E f (Gr)and reduces the number of accessible states for the injection of photoexcited holes from Si,resulting in the strongly suppressed photocurrent in forward bias seen in part e.The red surface on the Dirac cone of graphene denotes the holes injected from Si and is a measure of the maximum photocurrent when the quasi Fermi level of graphene,E ′f (Gr),aligns with the quasi Fermi level for holes in Si,E ′f,h (Si).(g)Application of a reversebias (V r bias )raises E f (Gr)and opens up a large number of accessible states that can be occupied by photoexcited holes injected from Si under illumination.This results in the unsuppressed large photocurrents under reverse bias as seen in part e.The external bias controls the position of the Fermi level and hence the number of photoexcited carriers that can inject from Si (i.e.,the photocurrent).critically investigate the various important parameters of such applications,such as responsivity,detection limit,switching speed,ON/OFF ratio,spectral bandwidth,contrast sensitivity,and dynamic range in monolayer and few-layered graphene/Si heterojunctions,operating both in photocurrent and photo-voltage modes.The photoresponse behaviors were first tested in monolayer graphene (1LG)/Si devices,and their intrinsic parameters were found to be largely independent of size.We present results from the largest (device A)and the smallest (device B)devices with junction areas =25mm 2and 5000μm 2,respectively.We used lightly n-doped Si (ρ=1−10Ωcm),and the details of device fabrication and characterization can be found in the Supporting Information.Figure 1a shows a schematic of a typical monolayer graphene 1LG/Si device,and part b shows a digital photograph of device A.The energy band diagram,showing the Fermi levels of graphene (E f (Gr))and lightly n-doped Si (E f (Si))at thermal equilibrium (in a dark condition)is shown schematically in Figure 1c.From detailed measure-ments of the Schottky barrier heights (as discussed later on),we found that in our devices,E f (Si)was pinned to the charge-neutrality level of its own surface states,with a Schottky barrier height ϕbn ∼0.8V.Figure 1d shows the dark and low-power (P =1.23μW,λ=488nm)current −voltage (I −V )curves in device A,which follow a conventional rectifying and photo-diode-like behavior,respectively.Incident photons generate e −h pairs in Si,and these photoexcited carriers thermalize rapidly to form quasi Fermi levels (separately for holes and electrons near the valence and conduction band edges (VBE and CBE)of Si,respectively).The built-in electric field at the graphene/Si junction causes holes to inject out from Si (from the small energy-band between the VBE and quasi Fermi level for holes in Si)into graphene,which causes the appearance of a quasi Fermi level in graphene,E ′f (Gr).The position of the quasi Fermi level in graphene depends on (a)the position its bias-dependent E f (Gr)and (b)the number of injected holes from Si.At low incident powers,E ′f (Gr)lies between E f (Gr)and E ′f,h (Si),and the photoexcited holes can all find accessible states in graphene to inject into,resulting in the conventional photodiode-like response.Figure 1e shows the I −V curves under increasing incident light powers (up to P =6.5mW).At higher incident powers,there is a signi ficant deviation of the I −V curves from the conventional photodiode-like response,with a strong suppression of photocurrents close to V =0,and a sharp rise and rapid saturation of photocurrents at low reverse biases.This highly tunable photocurrent response is a result of the unique electronic structure of graphene near its Fermi level.Figure 1f schematically represents the situation under a low forward bias,V f bias ,which lowers the Fermi level from its “unbiased ”position.As seen in this figure,the lowering of the Fermi level brings it closer to the quasi Fermi level for holes in Si,greatly diminishing the number of accessible states for the photoexcited carriers to inject into from Si.Hence,under a forward bias,with increasing incident power and rate of hole-injection,E ′f (Gr)lowers and quickly aligns with the quasi Fermi level for holes in Si,E ′f,h (Si)(E ′f (Gr)=E ′f,h (Si),FigureFigure 2.(a)Variation of the voltage responsivity obtained from the open-circuit voltage,V OC ,and as a function of incident power,P ,in device B.At the lowest powers,the voltage responsivity exceeds 107V/W.(b)Variation of the dynamic photovoltage responsivity (or,the contrast sensitivity d VOC /d P )as a function of P in both devices A and B.In device B,the contrast sensitivity exceeds 106V/W at P ≈10nW,and the ∼P −1dependence is identical in both devices.The voltage response to (c)turning ON and (d)turning OFF of incident light in device B,showing exponential rise and fall behaviors with millisecond time scales.In all cases,the incident light wavelength was 488nm.1f).Consequently,only a relatively small photocurrent (denoted by the small red part of the surface of the Dirac cone),limited by the small number of photoexcited holes that can inject into graphene,is possible under forward bias.Increasing the incident light power beyond this point will not allow any more photoexcited holes to inject into graphene since E ′f (Gr)cannot lie below E ′f,h (Si).However,an applied reverse-bias can lift E f (Gr)to higher values,as shown in Figure 1g,opening up a large number of accessible states for the holes to inject into and allowing a complete collection of the injected holes.As a result,the photocurrent,which is signi ficantly suppressed near V ≈0,can completely recover under small reverse biases,as seen in Figure 1e.(These deviations from a conventional photodiode behavior are explained with additional schematics in the Supporting Information document).The photocurrent saturates for a given incident power at higher reverse biases (Figure 1e)when all photoexcited holes can inject into graphene.The photocurrent saturates for a given bias at higher incident powers (see later,Figure 3a)when the quasi-Fermi level in graphene,E ′f (Gr),reaches the quasi-Fermi levelfor holes inSi,E ′f,h (Si).The voltage-induced tunability of therelative positions of the Fermi levels that enables a highphotocurrent responsivity (seelater),along with the low dark-current density (≪1μA/cm 2),results in a tunable photo-current ON/OFF ratio exceeding 104at V =−2V and at a light intensity of 260pW/μm 2making them highly suitable for low-power switches in micrometer-scale optoelectronic circuitry.Figure 2a shows the photovoltage responsivity in device B as a function of incident power.At the lowest incident power,the absolute device responsivity RV (=V OC /P ,VOC is the opencircuit voltage)exceeds 107V/W,which is signi ficantly largerthan that of previously reported graphene-based devices,7Figure 3.(a)The (dark-currentsubtracted)photocurrent (in device A)as a function of incident power for di fferent applied voltages,showing strongvoltage dependence at higher powers.The inset shows the photoresponse of both devices A and B.A device-independent responsivity of 225mA/W was obtained at V =−2V that remains constant over the entire range of powers we were able to test (∼6orders of magnitude).(b)IPCE map of device A,demonstrating the high photon-to-electron conversion e fficiency of ∼57%that can be tuned to remain constant over a large range of incident powers under reverse-bias operation.(c)Variation of IPCE as a function of the incident power at representative operational voltages in device A.The dashed lines are guides to the eye.The IPCE remains nearly unchanged atV =−2V and goes →0at V =0.2V.This small voltagerange (−2to 0.2V)can be used as a switch to turn the photocurrent on and o ffwith a high switching ratio (>104,see text).(d)Transient photocurrent response in device B,showing that the devices were capable of switching within a few milliseconds.In all cases,the incident light wavelength was 488nm.rendering it a highly sensitive device for weak signal detection/ switching/photometry.For applications such as weak-signal imaging,video-recording,or analytical chemistry,sensitivity to extremely small changes in incident power is another important parameter.To quantify this,we define the dynamic photo-voltage responsivity or contrast sensitivity as d V OC/d P.Figure 2b shows the contrast sensitivity in both devices A and B, measured over a broad range of incident powers.Wefind that the contrast sensitivity is remarkably independent of the device areas,exceeding106V/W at low light intensities.In addition, these devices show sharp rise in both the absolute and dynamic responsivity as the incident power decreases,which is a rather convenient feature appropriate for weak-signal detection.For any photodetector,the detection limit is specified by the noise-equivalent-power(NEP),23which is the incident power at which the signal is equal to the RMS dark noise density(S V), measured within a specified bandwidth(commonly1Hz),that is,NEP=S V/R V.To obtain S V,a large sequence of voltage fluctuations(V noise)was measured using a voltmeter set to0.5s integration time(which corresponds to a bandwidth of1Hz),24 while keeping the device in darkness.The RMS noise density was then calculated as S V=(⟨V2noise⟩/1Hz)1/2.For the device B,we obtained S V=1.66×10−5V/Hz1/2.From the lowest measured power of10nW,R V≈1.8×107V/W,and hence NEP=9.2×10−13W/Hz1/2(implying that in the photovoltage mode,P∼picoWatt incidences can be detected above the noise level,when integrated over0.5s),and specific detectivity D*=(device area)1/2/NEP=7.69×109Jones(cm Hz1/2/W). Further,at10nW incidence,S V/(d V OC/d P)≈5pW/Hz1/2, indicating that these detectors are capable of distinguishing materials with transmittance,T=0.9995(to compare, transmittance of monolayer graphene is about0.977)within a 0.5s integration time,making it extremely useful for absorption spectroscopy applications of ultradilute or ultrathin materials. We have also examined the transient-response time scale of these detectors,to ascertain how quickly they“switch”when an incident light is turned“on”or“off”.To do this,an optical chopper was placed in front of a laser source,and the photovoltage was recorded as a function of time using an oscilloscope triggered by the same chopper.Figure2c and d show the photovoltage rise and fall response times obtained using a50ms timed optical chopper(which took about∼1.7 ms to completely chop the beam).In both cases,the response could befitted to an exponential function as shown,with time scales of a few milliseconds(with the zero on the time axis corresponding to the point of opening and closing of the chopper).When tested at higher chopping speeds no change was found in the time-scale of the transient response,indicating that the response-time of a few milliseconds was intrinsic to the devices.We also note that the oscilloscope used had an input-impedance rated at1MΩin parallel with a20pF capacitance. The effective time constant of the oscilloscope,RC,=20×10−6seconds,is∼3orders of magnitude smaller than the rise/ fall time of the devices tested and hence did not affect detector transient response in any significant way.In addition,the long-term response to a periodically switching light was found to be extremely stable,with a variation of the OFF and ON state photovoltages well within±2.5%and±5%,respectively,over 1000switching cycles,and with absolutely no sign of drift or aging effects even after10days(see SI).The stable,millisecond level response is quite appealing for applications such as high-speed photography,videography,and rapid optical analysis of chemical reactions that require tens of milliseconds of response time.We now turn to the photocurrent response.Figure3a shows the photocurrent I ph as a function of incident powers for various biases in device A.In the inset,the response for V=−2 V has been plotted for both devices.Wefind that the response not only remains independent of device size but scales in an absolutely linear manner over six decades of incident power. The photocurrent responsivity of∼225mA/W is1−2orders-of-magnitude higher than those of previously reported graphene-based photodetectors5−14and a variety of normal-incidence(i.e.,not waveguide coupled)Ge/Si photodetec-tors,25making it an extremely sensitive linear photodetector and photometer with a large dynamic range.The responsivity can be almost doubled atλ=850nm,as we show later.Also, the presented power range is only limited by our instrumental capabilities,and with higher reverse-biases,the linear response can potentially extend much beyond the experimentally tested range.The range-independent photocurrent responsivity and the d V OC/d P∼1/P dependence suggests that the underlying mechanism in our devices is photovoltaic5−8,10and not hot-carrier-induced or photothermoelectric.9,11,14Figure3b is a3D incident photon conversion efficiency(IPCE(V,P)=(I ph(V,P)/ P)×(hc/eλ))map of device A.By applying a low reverse bias, the device can operate with an IPCE max∼57%over four orders-of-magnitude incident power.As shown in Figure3c,by applying different biases,it is also possible to almost completely tune the IPCE between0<IPCE<IPCE max,which is extremely useful for brightness adjustment in imaging devices. Figure3d shows the typical rise and fall times in response to a chopper.In this case,the responses could not befitted to exponential functions.Nevertheless,it is clear that even in the case of photocurrent detection,the response is rapid enough (within a few milliseconds)for many imaging and analytical applications.The dark noise power spectral density(obtained in a manner similar to the one described earlier for S V)was approximately S I=11pA/Hz1/2.For the undoped1LG/Si device at488nm,this gave a NEP=S I/R I=50pW/Hz1/2, which corresponds to a specific detectivity of1.4×108Jones (cm Hz1/2/W),making it quite a sensitive photodetector even in the photocurrent mode.Moreover,the sensitive behavior of these detectors remain intact over the entire visible range of incident wavelengths,as seen from the spectral dependence of NEP and D*in Figure4,which is an important criterion for broadband imaging applications.The photocurrent responsivity and hence conversion efficiencies could be further improved by increasing the graphene layer-thickness,and yer-thickening pro-vides more states for the holes to inject into,and was achieved by multiple stacking of monolayer sheets of graphene.Doping the graphene sheets can be expected to increase their sheet conductance,and has been utilized in the past to enhance the performance of graphene/Si Solar cells.21In our devices,p-type doping of the graphene sheets was obtained by drop-casting1-pyrenecarboxylic acid(PCA),on the graphene sheets.In the past,we had reported how theπ-stacking interaction between the pyrene part of PCA and graphene can lead to a stable but noncovalent attachment of these molecules to graphene and had performed extensive structural,electronic,optical,and electrochemical characterizations of PCA-functionalized gra-phene.26−28In particular,we have found that attachment of PCA does not seem to have a very significant effect on the thickness of graphene layers26but increases the surfaceroughness of graphene by about 0.2nm (see SI).In addition,Raman spectra of PCA-doped graphene shows an increase in the D-band (see SI)that is most likely due to the presence of large number of edges in the graphene-like crystalline structure of pyrene.Figure 5a shows the resulting p-type doping e ffect of PCA on a separately prepared 3-terminal 1LG transistor.The drain current minimum of pristine graphene devices is at a positive voltage,indicating that the “pristine ”graphene is already p-doped,either due to environment 29or contaminant 30e ffects.The application of PCA shifts the drain current minimum to higher gate voltage values,indicating an additional p-doping e ffect.Figure 5b and c compares the spectral dependence of IPCE and photocurrent responsivity in a three-layer graphene (3LG)/Si device (with and without doping)vis-a -vis the 1LG/Si device A,all of which had the same junction area.The IPCE of 3-layer graphene (3LG)/Si device improves over that of the 1LG/Si device,remaining at ∼60%over a larger window of visible wavelengths.The corresponding responsivity grows to higher values (up to ∼0.4A/W at λ=885nm)in the 3LG/Si device,providing a greater operational bandwidth compared to the 1LG/Si device.After PCA doping,the IPCE and responsivity values increase further over a large window of wavelengths,with maximum IPCE ≈65%between 550and 800nm;and R I ≈435mA/W for 850nm <λ<900nm,making it highly appealing for on-chip applications that could bene fit from the use of energy-e fficient 850nm VCSELs.31We note that,as in the case of the 1LG/Si device,these improved responsivity/IPCE values could be seamlessly extended to high-power applications using low reverse biases (not shown).Finally,we discuss the nature of the interface in these junctions.We have performed extensive measurements of the Schottky barrier height of these junctions,using graphene,doped graphene,and even control devices of Ti/Au with Si (to obtain the “metallic ”side of the junction with a range of work-function values).Since p-doping lowers the Fermi level ofgraphene with respect to its Dirac point,one expects a largerSchottky barrier height,32−35for the p-doped graphene samples.Surprisingly,we found that the Schottky barrier ϕbn =0.79±0.05eV was nearly independent of the “metal ”being used,a fact that can be traced to the nature of the graphene/Si junction.In an ideal Si Schottky junction,the interfaces between the metal and Si is expected to be atomically clean to prevent the formation of any surface states on Si,resulting in the formation of an “unpinned ”Schottky barrier junction,36whose barrier height ϕbn (=ϕm −χSi )is dependent on the work-function of themetal,ϕm .In thermal equilibrium,the Fermi levels on both sides of the junction get aligned,and under illumination,behaves as conventional photodiodes with a reverse-bias independent photocurrent.In contrast,we believe that in our devices,the inadvertent formation of natural oxide on the Si surface,allows the energy bands in Si to naturally “pin ”itself to its own surface states.This results in a Schottky barrier which is still rectifying but with a barrier-heightwhich is pinned to its Bardeen limit of ϕbn ∼0.8eV,36independent of the work function of the metal.With the Fermi level of Si pinned to its own charge-neutrality level,the thermal equilibrium position of the Fermi level of graphene at zero bias is determined by its own intrinsic doping level.UV-emission spectroscopy 37has shown that CVD grown graphene can have work functions as high as 5.2eV (due to substrate-induced e ffects),implyingthat Figure 4.Spectral dependence of the noise-equivalent-power (NEP)and speci fic detectivity (D *)of device B in the photocurrent mode.The minimum NEP and the maximum D *were found to be 33pW/Hz 1/2and 2.1×108Jones,respectively,at λ=730nm.Figure 5.(a)Variation of drain −current as a function of gate voltage in a monolayer graphene 3-terminal transistor without and with PCA doping.The shift of the minima toward higher gate voltages is indicative of p-type doping due to PCA.(b)Spectral dependence of IPCE (200nm <λ<1100nm)of deviceA (1LG/Si)versus a 3LG/Sidevicebeforeand after dopingwith PCA.(c)Spectral responsivity for the same devices within thesame wavelength window.The improved bandwidth and e fficiency/response is clearly visible with increased layer thickness and doping.The doped 3LG/Si device has the best IPCE exceeding 60%over a broad range and with a maximum IPCE exceeding 65%.The responsivity peaks at ≈435mA/W for 850nm <λ<900nm.the Fermi level can lie very close to the valence band edge of Si, effectively preventing the injection of photoinduced carriers into graphene under zero applied bias.We believe that this causes the suppressed photocurrent at zero-bias seen in our devices.This turns out to be an attractive feature,as it allows for an additional tunability of the photocurrent that results in the voltage-controllable responsivity discussed before. Hence,graphene/Si heterojunctions can be used for a variety of tunable optoelectronic devices with high responsivities over a broad spectral bandwidth in the visible region.Their high responses and low dark-currents render them with a high switching ratio and low dark-power consumption.The picoWatt-level detection capability in both photovoltage and photocurrent modes along with linear operation demonstrated up to milliwatts of incident powers reflects a significantly large dynamic operational range.This,in addition to their milli-second-responses makes them versatile and highly sensitive photodetectors for a variety of imaging,metrology,and analytical applications over a broad range of input powers. The voltage-tunability allows brightness control for variable light conditions and enables linear operation over a large dynamic range.The responsivity peaking at850nm is ideal for coupling with VCSELs operating at these wavelengths31for low-power integrated optoelectronic circuitry.Built using simple,low-cost,and scalable methods,additional improve-ments of CVD-graphene quality,38integration with as wave-guides,25and plasmonic7,8or microcavity11,12enhancements could lead to greater performances.Moreover,graphene junctions with other semiconductors such as Ge,GaAs,and so forth can provide furtherflexibility for controlling the peak-responsivity,spectral bandwidth,and high-speed operations.■ASSOCIATED CONTENT*Supporting InformationExperimental details about the synthesis of graphene and device fabrication;characterization of graphene samples;responsivity measurement as function of power and wavelength;time-dependent switching response;AFM and Raman character-ization of PCA-doped graphene.This material is available freeof charge via the Internet at .■AUTHOR INFORMATIONCorresponding Author*E-mail:x.an@(X.A.);s.kar@(S.K.).NotesThe authors declare no competingfinancial interest.■ACKNOWLEDGMENTSWe acknowledge thefinancial support provided by the Northeastern University start-up and internal seed grants provided to S.K.and Y.J.J.and partial support from an NSFaward:ECCS-1202376.■REFERENCES(1)Yan,R.;Gargas,D.;Yang,P.Nanowire Photonics.Nat.Photonics 2009,3,569−576.(2)Konstantatos,G.;Howard,I.;Fischer,A.;Hoogland,S.;Clifford, J.;Klem,E.;Levina,L.;Sargent,E.H.Ultrasensitive solution-cast quantum dot photodetectors.Nature2006,442,180−183.(3)Prins,F.;Buscema,M.;Seldenthuis,J.S.;Etaki,S.;Buchs,G.; Barkelid,M.;Zwiller,V.;Gao,Y.;Houtepen,A.J.;Siebbeles,L.D.A.; Zant,H.S.J.Fast and Efficient Photodetection in Nanoscale Quantum-Dot Junctions.Nano Lett.2012,12,5740−5743.(4)Avouris,P.;Freitag,M.;Perebeinos,V.Carbon-nanotubePhotonics and Optoelectronics.Nat.Photonics2008,2,341−350. 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Festo PhotoBionicCell 自动生物胶原质生物肽生长系统说明书
PhotoBionicCell Automated cultivation of biomass01PhotoBionicCellAutomated cultivation of biomassOur world is changing at an unprecedented rate. The global popula-tion is growing rapidly and the impacts of climate change are marked. We can only maintain a future worth living if people, the animal kingdom and the plant world live together in harmony. This is why we at Festo believe the bio-economy will be the economic system of the future. We aspire to make a decisive contribution to improving the quality of life of present and future generations – by cultivating biomass on a large scale using our automation tech-nology.Sustainability thanks to a circular economyIf we can live in a circular manner, innovative spaces pop up from which both people and the environment benefit at the same time.A circular economy is defined by producing in a carbon-neutral way while using as few resources as possible. The idea behind this is to cultivate living matter as a biological basis in an energy-efficient way so that raw materials can be extracted from it and processed into products. Ultimately, these will be returned to the natural cycle.At the Festo learning company, we have considered biology to be a source of inspiration and even a teacher for decades. Over the years, our bionics experts have developed a multitude of tech-nological innovations. The PhotoBionicCell research project demonstrates a possible approach for the industrial biologisation of tomorrow.Efficient photosynthesis in a high-tech bioreactorUsing the bioreactor, algae can be cultivated automatically and their growth controlled. For this purpose, the algae liquid is pumped upwards into the surface collectors, where it is distributed evenly before flowing back into the cultivator. During this circula -tion loop, the algae’s chloroplast cells photosynthesise to convert sunlight, carbon dioxide and water into oxygen and chemical energy sources – or valuable organic matter. As such, the biomass is cultivated in a closed circuit in a highly efficient and resource-saving way. Compared to systems commonly used today, such as open basins and foil bioreactors, over ten times more biomass can be produced with PhotoBionicCell.2Festo SE & Co. KG02:S tructure of the algae cell: extracting PHB for bioplasticsBiological recyclable materials for climate-neutral end products Depending on the nutrients supplied to the algal biomass, fatty acids, colour pigments and surfactants are formed as products of their metabolic processes. These serve as base materials for the production of medicines, foodstuffs, plastics, cosmetics and fuels. Unlike petroleum-based products, bio-based end products usually biodegrade and – in keeping with an overall circular economy – can always be recycled in a climate-neutral manner.As part of their work on PhotoBionicCell, our researchers focused on cultivating the blue-green algae Synechocystis, which produces colour pigments, omega-3 fatty acids and polyhydroxybutyrate (PHB). The PHB that is extracted can be processed into a filament for 3D printing by adding other substances. Thanks to this modern production technology, complex shapes of sustainable plastic com-ponents or packaging can be produced in a short time. As part of PhotoBionicCell, certain grooved mounting pins can be manufactu-red from this bioplastic. Intelligent control technologyTo create the best possible conditions for the micro-organisms, proven control technology is combined with the latest automation component. A holistic gassing concept ensures that the carbon dioxide extracted from the air is evenly distributed in the circulating biofluid.Innovative quantum sensor technologyA significant challenge relating to bioreactors is precisely deter-mining the quantity of biomass. To do so, our developers rely on a quantum-technology sensor manufactured by the start-up Q.ANT. This sensor provides precise, real-time information about the or-ganisms’ growth. The algae are fed to it automatically and con-tinuously using microfluidics from Festo. The quantum sensor is able to optically detect individual cells so that the amount of bio-mass can be determined exactly. Moreover, the sensor can investi-gate the cell vitality. Only then is it possible to react to process events with foresight and to intervene in a regulatory manner.01: A utomated bioreactor: photo-synthesis of algae in a closed circuit3PhotoBionicCell: Automated cultivation of biomassLaboratory software with cloud connection Live images refreshed every 30 secondsMonitoringTime and sample collection of:• interior temperature • surrounding temperatureRemote control of parameters such as:• pH value • temperature • light intensity• pH value • air supply• air supply • recirculation • CO 2 contentBioreactors that work with algae cells as miniature factories offer considerable potential for a climate-neutral circular economy. Algae living in the water are already extremely efficient in their natural photosynthesis outdoors: they absorb ten times more carbon dioxide (CO 2) than rooted plants. When combined with the right sensor technology, closed-loop control technology and automation, the efficiency of the algae can be increased to one hundred times that of rooted plants. Moreover, they require sig-nificantly less space and less water.VEMD proportional flow control valve precisely dosed fumigation thanks to piezo technologyCPX-E automation systemcontrols all processes in the container and communication to the cloudConnecting pins3D-printed from PHB bioplasticPeristaltic pumps3 pieces to supply nutrient solution, base and acid to regulate the pH value in the containerCultivatoracrylic glass container for an algae volume of up to 15 litresSail-shaped surface collectors 3 pieces for optimised light absorption and to regulate heat balanceValve sensor units3 pieces for sequence control of the circulation in the surface collectors PhotoBionicCellEfficient photosynthesisCO 2 absorberconverting ambient air into com-pressed carbon dioxideQuantum sensoroptical real-time determination of biomassCPX-AP-I-EC-M12 bus interfaceto communicate with the valve sensor unitsCirculating pumps3 pieces to continuously mix the liquid in the container3 pieces to transport the liquid to the collectorsCeramic gassing elementsgas supplied in the smallest possible bubbles for optimal absorption in the liquidLighting elementsfor optimum light intensity at all timesMulti-sensorfor measuring ambient temperature, light intensity and light incidence angleTransparent acrylic tubeswith circulating algae fluid for optimised light absorption and heat exchangePhotoBionicCellAutomated cultivation of biomassSoftware solutions for a digitalised laboratoryIn laboratories, many analyses have been done manually up to now. This not only takes a great deal of time and effort, but can also lead to errors. By automating such laboratory facilities, all necessary data could be read directly and in real time in the future, thereby allowing researchers to better concentrate on their key tasks.PhotoBionicCell is complemented by specially developed software. The dashboard allows multiple photo-bioreactors to be mapped with current data and live recordings. This means that manual par-ameter changes and subsequent evaluations can be made around the clock, even remotely. This allows users to react to changes in the bioreactor at any time and start harvesting products at the most suitable time, for example. The digitalised laboratory is enhanced by an augmented-reality application. Using a tablet, reality can be extended to visualise technical processes, process parameters and information about processes within the bioreactor.Artificial intelligence and digital twinsOur developers also use artificial intelligence (AI) methods to evalu-ate data. This allows the bioreactor to be optimised either to propa-gate the algae cultures or to maintain predefined growth param-eters with minimal energy input. It could also be used to predict the durability of valves and other components.The use of digital twins created with the help of AI would also be conceivable. In future, they could be used to simulate and virtually map complete life cycles for bioreactors. The expected cell growth of a wide variety of microorganisms could then also be accurately estimated, even before a real system is physically constructed.Further optimisation through artificial photosynthesisIn addition to optimising laboratory facilities through automation and digitalisation, artificial photosynthesis offers another prom-ising perspective to cultivate biomass even more efficiently.016Festo SE & Co. KGAutomated dispensing as a basisWith our project partner, the Max Planck Institute for Terrestrial Microbiology, we have developed an automatic dispenser to improve individual photosynthesis enzymes. To do so, thousands of variants of an enzyme have to be tested. Compared to manual pipetting, the newly developed automatic dispenser does this much faster and without errors. In addition, the automatic machine can be adapted to new tasks in seconds.Synthetic biology for maximum efficiencyBut not only can individual photosynthesis modules be optimised. Scientists are now working on digitally optimising entire metabolic pathways. This approach is known as synthetic biology. A metabolic pathway optimised on the computer is packaged in synthetically produced cells, also known as droplets. These have a diameter of around 90 micrometres and contain all the necessary enzymes and biocatalysts. This enables them – just like their biological models – to lock in carbon dioxide by means of light energy. Fundamental research meets automationEven though we are still in the middle of the development process,we can already see future potential: if expertise in automation and fundamental research is combined, it will be possible to achieve industrial-scale carbon-neutral production more quickly.Sustainability in the futureTo cultivate the desired amounts of biomass with controlled cell growth in the future, systems such as PhotoBionicCell would haveto be scaled up significantly. If chemical processes were replaced by biological processes, there would be no need for high tempera-tures, aggressive chemicals or fossil raw materials. Production will become both energy efficient and sustainable – benefiting peopleand the environment at the same time.We are making a significant contribution to this change towards a climate-neutral circular economy through innovative technologiesand by continually learning from nature.0501: A utomated bioreactor: best possible conditions for algae growth 02: Optimum process stability: perma-nent monitoring of multiple bioreac-tors from anywhere03: Artificial photosynthesis: cultivatingthe droplets in a second bioreactor04: S pecially developed automatic dis-penser: thousands of tests toimprove enzymes040205: On an industrial scale: a solution forthe climate-neutral circular economyof tomorrow7 PhotoBionicCell: Automated cultivation of biomassTechnical data• Overall height: ................................................................... 3.0 m • Surface collectors: ............................................................ 5.0 m 2 • Collector radius: .......................................................... 1.6-2.7 m Cultivator:• Height: ........................................................................... 57.0 cm • Diameter: ....................................................................... 25.0 cm • Capacity: ........................................................................... 15.0 l • Algae thickness: .............................................................. 5.5 cm Materials:• Cultivator: ............................................................... Acrylic glass • Connecting pins: .............................. Polyhydroxybutyrate (PHB)• Nodes: ................................ Quickgen 500 (3D-printed material)• Connecting rods: ................... Acrylic glass (glass-bead-blasted)• Distribution elements: .................. e-Clear (3D-printed material)Integrated components:• CPX-E automation system: ........................................................ 1• VEMD proportional flow control valves: .................................... 2• CMMT-ST motor controllers: ..................................................... 3• CPX-AP-I-EC-M12 bus interface: ................................................ 1• CPX-AP-I-4DI4DO-M12-5P digital input/output modules: ......... 3• VYKB media-separated solenoid valves: ................................... 6• CPX electric terminal: ................................................................ 1• Sensors in the cultivator: ....................................................... 14 Capacitive sensors for collectors: ............................................. 6 Capacitive sensors for fill level in the cultivator: ...................... 2 Flow sensors: ........................................................................... 2 Sensors for temperature, pH value and CO 2 content: ...... 1 each Quantum sensor: ..................................................................... 1• Multi-sensor for collectors: ....................................................... 1• Total number of pumps: . (11)5/2022Project participants Project initiator:Dr Wilfried Stoll, Managing Director Festo Holding GmbH Project management:Karoline von Häfen, Dr Elias Knubben, Festo SE & Co. KG Project team:Sebastian Schrof, Michael Jakob, Timo Schwarzer, Nenja Rieskamp, Dominic Micha, Esmeralda Kramer, Philipp Steck, Ralf Kapfhamer, Ferdinand Glass, Dr Nina Gaißert, Charlotte Tesch, Francis Goh, Duc Thang Vu, Florian Zieker, Christian Stich, Vanessa Bader, Alex -ander Müller, Philipp Eberl, Festo SE & Co. KG Prof. Dr Tobias Erb, Pascal Pfister, Maren Nattermann,Max Planck Institute for Terrestrial Microbiology, Marburg Dr Michael Förtsch, Dr Helge Hattermann,Q.ANT GmbH, Stuttgart Caspar Jacob,Steinbeis Embedded Systems Technologies GmbH, Esslingen Image 05, page 7: University of Hohenheim, photograph ManfredZentschFesto SE & Co. KG Ruiter Strasse 8273734 Esslingen GermanyPhone +49 711 347-0Fax+49 711 347-21 55cc @/bionics。
关于印发《东南大学博士研究生申请博士学位时科研成果考核标准(修订)》的通知
东南大学文件校发〔2013〕61号关于印发《东南大学博士研究生申请博士学位时科研成果考核标准(修订)》的通知各院、系、所,各处、室、直属单位,各学术业务单位:为了保证和进一步提高我校博士研究生的学位论文质量,激发博士研究生的科研创新能力,根据东南大学学位评定委员会第十三届第五次、第六次会议精神,对《东南大学博士研究生申请博士学位时科研成果考核标准(暂行)》进行了修订,现予印发,请遵照执行。
东南大学2013年12月9日东南大学博士研究生申请博士学位时科研成果考核标准(修订)基本要求一、博士研究生的科研成果是指博士研究生入学后发表的与其学位论文研究内容相关的学术论文、科研奖励、发明专利(授权),以及校学位委员会认定的其它科研成果。
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被EI、MI、SCI(SCIE)、CPCI、SSCI、A&HCI、CSSCI收录论文的所在期刊可不限《目录》范围。
2.在《目录》内由东南大学主办的期刊上发表的论文只计一篇。
3.进行交叉学科研究的博士研究生,在非本专业类的《目录》期刊上发表的与学位论文相关的学术论文,经所在学位评定分委员会认可,与在本专业类的《目录》期刊上发表论文同等对待。
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成果考核方案一、哲学、法学类学科的博士研究生申请学位时,需至少在CSSCI核心源刊上发表学术论文三篇。
在SSCI源刊或A&HCI 源刊上发表论文一篇等同于两篇CSSCI核心源刊的论文。
通信感知一体化混合波束赋形技术
doi:10.3969/j.issn.1003-3114.2023.04.013引用格式:朱承浩.通信感知一体化混合波束赋形技术[J].无线电通信技术,2023,49(4):689-695.[ZHU Chenghao.Hybrid Beamforming for Integrated Sensing and Communication [J].Radio Communications Technology,2023,49(4):689-695.]通信感知一体化混合波束赋形技术朱承浩(东南大学吴健雄学院,江苏南京210096)摘㊀要:为解决无线通信与感知的性能日益强大而带来的频谱资源紧缺的问题,通信感知一体化(Integrated Sens-ing and Communication,ISAC)技术逐渐开始受到重视㊂在目前最有发展潜力的毫米波(millimeter Wave,mmWave)多输入输出(Multiple Input Multiple Output,MIMO)混合波束赋形系统基础上,提出了一种通信感知一体化的波束赋形算法㊂使用均方误差(Mean Square Error,MSE)衡量该系统的通信和雷达的性能,通过引入权重因子将通信与雷达的性能指标综合考虑,得到通感一体化波束赋形系统的最优解㊂针对求解过程中的非凸优化问题,提出了基于坐标迭代的交替优化算法对问题进行求解㊂针对不同权重因子,对通信的频谱效率和雷达的波束方向图进行了仿真,仿真结果验证了所提方案可以实现通信感知一体化系统下通信与感知性能的折中㊂关键词:通感一体化;毫米波;多输入输出;混合波束赋形;坐标迭代优化法中图分类号:TN929.5㊀㊀㊀文献标志码:A㊀㊀㊀开放科学(资源服务)标识码(OSID):文章编号:1003-3114(2023)04-0689-07Hybrid Beamforming for Integrated Sensing and CommunicationZHU Chenghao(Chien-Shiung Wu College,Southeast University,Nanjing 210096,China)Abstract :In order to solve the shortage of spectrum resources caused by the increasingly powerful performance of wireless commu-nication and sensing,the technology of Integrated Sensing and Communication (ISAC)has gradually begun to receive attention.On thebasis of hybrid beamforming system for the millimeter Wave (mmWave)Multiple Input Multiple Output (MIMO)technology which hasthe most development potential at present,a beamforming algorithm of ISAC in this system is proposed.The Mean Square Error (MSE)is used to measure the performance of communication and radar in this system,and the weight factor is introduced to comprehensively consider the performance of communication and radar,to achieve the optimal solution of the beamforming system for integrated sensingand communication.To overcome the non-convex optimization problem in the process of solving,an alternative optimization algorithm based on coordinate iterative method is proposed.The spectrum efficiency of communication and the beam pattern of radar with differentweight factors are simulated.Through the simulation results,it is verified that the scheme can achieve the compromise between commu-nication and sensing performance in the integrated sensing and communication system.Keywords :ISAC;mmWave;MIMO;hybrid beamforming;coordinate iterative optimization收稿日期:2023-03-250 引言车联网㊁人机交互等应用场景对无线通信和雷达感知均提出了很高的要求[1]㊂随着无线通信技术和雷达感知技术的不断发展,通信与雷达感知这两个原本较为独立的领域展现出越来越多的联系和共同性㊂未来移动通信关键技术之一通信感知一体化(Integrated Sensing and Communication,ISAC)技术,即将无线通信和雷达感知在同一系统中进行联合设计与优化,从而提升资源利用率,降低硬件成本,实现高性能通信和高精度感知[2]㊂通信感知一体化在实现通信传输的同时,还能通过分析无线点的反射㊁散射等特性,对目标信息进行定位和识别[3]㊂通信与感知的融合可以让二者实现技术共享,在满足高性能通信的同时满足复杂多样的感知需求[4]㊂该技术具有超越传统移动通信网络连接的潜力,可以开辟民用无人机㊁智慧交通等全新业务,因此受到了学界的广泛关注[5]㊂波束赋形技术是通信感知一体化的关键技术之一㊂文献[6]提出了通信感知一体化的波束赋形方案,在通信目标信噪比的约束下以目标估计误差为优化目标㊂文献[7]在相同的约束条件下使发射矩阵接近理想的雷达方向图来提高雷达感知的性能㊂为了解决射频资源紧缺的问题,5G将毫米波(millimeter Wave,mmWave)频段写入标准,用于提升传输速率㊂毫米波通信一般使用大规模多输入多输出(Multiple Input Multiple Output,MIMO)技术来增大信号强度[8]㊂随着天线阵列规模的增加,传统的全数字结构成本已经难以承担,因此毫米波通信使用将数字与模拟波束赋形结合起来的混合波束赋形技术㊂基于毫米波频段的通感一体化波束赋形技术也得到了广泛的关注㊂文献[9]提出了通信感知一体化系统的混合波束赋形方案,在满足雷达方向图的条件下,使混合波束赋形矩阵接近理想通信矩阵㊂该方案具有较低的复杂度,且雷达性能较高㊂但是在此方案下通信性能受到抑制,无法实现通信和感知性能的权衡㊂文献[10]采用正交匹配追踪算法得到最优波束赋形矩阵,该算法迭代速度较快,然而在大数据量情况下复杂度较高,且迭代过程中会产生累积误差并影响最终结果㊂现有的研究大多将优化算法的目标设计为使混合波束赋形矩阵逼近全数字波束赋形矩阵,并且通常会在约束通信或感知一者的前提下优化另一者的性能㊂这样做的缺点在于性能上会有所损失,最终求出的结果也不会是最优的㊂因此,针对毫米波MIMO下通感一体化的性能要求,本文提出了基于最小均方误差(Mean Square Error,MSE)准则设计的混合波束赋形算法㊂通过在均方误差指标中引入因子使得优化算法与信道噪声能量相关联,使设计更加准确,同时简化了求解过程㊂通过引入辅助酉矩阵使理想雷达发射矩阵与一体化下的雷达发射矩阵维度相同,可以直接进行均方误差的计算㊂优化的目标函数含有多个待优化变量,难以直接求解,因此本文提出了交替迭代优化算法㊂在假设其他优化目标为最优解的情况下单独优化一个目标,通过不断交替循环实现系统的最优解㊂在求解模拟波束赋形矩阵时,相移器阵列受恒模约束的影响,该问题是非凸优化问题㊂针对该问题,本文提出了坐标迭代优化法来求出该优化问题的最优解㊂仿真结果表明该算法较好地兼顾了通信与感知的性能,实现了二者的融合㊂1㊀毫米波通信感知一体化系统理论基础1.1㊀毫米波大规模MIMO技术5G及今后技术的发展离不开通信速率的不断提高㊂由奈奎斯特第一准则可知,通信速率与信号的带宽成正比㊂4G技术所使用的频段较低,缺乏足够的频带资源继续拓展带宽㊂因此,为了继续提高通信速率,需要利用更高频段的毫米波段㊂相比于中低频段,毫米波段拥有数十倍以上的广阔频段,可以解决带宽资源的紧张问题[11],在毫米波段下的通信与感知性能也能得到极大的提高[12]㊂然而,毫米波也有着不容忽视的缺点,根据弗里斯传输公式[13],接收功率与波长成正比,毫米波更短的波长意味着更大的传输损耗㊂为了弥补这种损耗,在应用中多采用大规模MIMO技术对其进行补偿㊂以一维均匀排布的天线阵列为例,其天线间隔应大于半波长㊂毫米波的波长极短,因此天线间隔在毫米波段下极小,可以实现大规模MIMO传输㊂1.2㊀混合波束赋形系统在传统的全数字波束赋形系统下,每根天线都必须配备一条可以任意改变信号幅度和相位的射频链路㊂然而在大规模MIMO系统中,天线的数量激增,已无法负担为每根天线加装射频链路的巨大成本[14]㊂因此,有研究者提出了使用混合波束赋形技术㊂从图1可以看出,混合波束赋形系统的特点在于使用数个相移器构成模拟波束赋形矩阵[15],减少了数字波束赋形矩阵中射频链路的数量,在很大程度上降低了建设成本㊂其中,传输信号维度为N s,使用了N RF条射频链路,发送天线数量为N t,满足关系N sɤN RF≪N t㊂图1㊀毫米波MIMO系统混合波束赋形方案Fig.1㊀Hybrid beamforming scheme formmWave MIMO system1.3㊀通信感知一体化波束赋形技术在通信感知一体化系统中,同一种波形被同时运用于通信传输和雷达感知,这二者的功能都能通过MIMO混合波束赋形系统实现㊂因此,在求解相应的波束赋形矩阵时,可以做到同时优化通信和感知的性能,这实现了通信与感知性能的兼顾与折中,与一体化的思想一致㊂2㊀基于最小MSE准则的一体化波束赋形设计2.1㊀通信模型在混合波束赋形系统中,用户接收到的信号yɪC N sˑ1可以表示为:y=W H HF RF F BB s+W H n,(1)式中:sɪC N sˑ1为发送的数据信号向量,满足关系E(ss H)=I N s,F BBɪC N RFˑN s为数字波束赋形矩阵, F RFɪC N tˑN RF为模拟波束赋形矩阵,该矩阵仅提供相位变化,因此所有元素的模为1㊂HɪC N rˑN t为信道矩阵,N r为接收端的天线数量,WɪC N rˑN s为接收端的全数字波束赋形矩阵,nɪC N rˑ1为信道噪声矢量,服从均值为0㊁方差为σ2的复高斯分布㊂对于均匀线阵,其阵列响应矢量为:a(θ)=1㊀N[1,e j kd sin(θ),e j2kd sin(θ), ,e j(N-1)kd sin(θ)]T,(2)式中:k=2πλ,d为阵元间隔,通常取d=λ/2,N为天线数,θ为到达角或离开角㊂在毫米波频段下,信道矩阵为Saleh-Valenzuela 模型[16],可以表示为:H=㊀NtN rLðL l=1αl a r(θr,l)a H t(θt,l),(3)式中:L为多径数,αl为第l条传输路径的信道增益,服从标准复高斯分布,θr,l为第l条传输路径的到达角,θt,l为第l条传输路径的离开角㊂2.2㊀感知模型MIMO的雷达发射波束方向图为[17]:P(θ)=a H t(θ)R s a t(θ),(4)式中:R sɪC N tˑN t为发射信号的协方差矩阵,可以表示为:Rs=E(F RF F BB ss H F H BB F H RF)=F RF F BB E(ss H)F H BB F H RF=F RF F BB F H BB F H RF㊂(5)假设雷达感知的目标数量为K,相对于基站的离开角为{θt,1,θt,2, ,θt,K}㊂由式(3)可知,信道矩阵表示为L个不同离开角和到达角的散射路径的求和㊂信道的前K个散射路径即为雷达感知K个目标的路径㊂因此信道前K个路径的离开角应为雷达感知的离开角,即为{θt,1,θt,2, ,θt,K},剩下的L-K个离开角和L个到达角均服从[-π/2,π/2]的均匀分布㊂2.3㊀通信感知一体化的最小MSE模型在一体化系统的设计过程中,衡量通信系统性能的主要标准为误比特率(Bit Error Ratio,BER)等㊂在传统波束赋形设计中,通常通过降低MSE来达到降低误比特率的目的㊂本文将这一指标运用到一体化混合波束赋形的应用范围内,目的也是通过降低通信和雷达感知的均方误差来优化通信和感知的各项性能㊂通信性能的MSE定义为接收信号与原始信号的均方误差:MSE c=E( β-1y-s 2F)=E( β-1(W H HF RF F BB s+W H n)-s 2F)= tr(β-2W H HF RF F BB F H BB F H RF H H W-β-1W H HF RF F BB-β-1F H BB F H RF H H W+σ2β-2W H W+I Ns)(6)式中:引入的β因子可以将之后在功率约束下的优化求解问题大大简化,变成以β为优化目标的子问题㊂由雷达感知的波束图公式可知,雷达的波束设计等价于设计雷达的协方差矩阵㊂理想的全数字雷达发射矩阵F radɪC N tˑK为:F rad =[a t (θt ,1),a t (θt ,2), ,a t (θt ,K )]㊂(7)然而,混合波束赋形系统中的雷达发射矩阵为F RF F BB ɪCN t ˑN s,与理想的发射矩阵维度不一致,因此二者不能直接进行MSE 的计算㊂为使二者维度一致,可以引入一个辅助酉矩阵F u ɪC K ˑN s,其满足关系F u F H u =I K ,这样,理想雷达的发射矩阵可以表示为F r =F rad F u ɪCN t ˑN s㊂可以看到,引入辅助酉矩阵后,理想雷达的发射矩阵与混合波束赋形中的发射矩阵维度一致,并且原来理想雷达的方向没有改变,维持了原始的性能㊂辅助酉矩阵可以通过以下的优化问题解出:min F uF c -F rad F u 2Fs.t.㊀F u F Hu=I K{,(8)式中:F c 为理想的通信全数字波束赋形矩阵㊂对信道矩阵进行奇异值分解:H =U V H ㊂(9)取V 的前N s 列即为通信全数字波束赋形矩阵F c ㊂该优化问题表明构造辅助酉矩阵应尽可能减小全数字波束赋形下通信与雷达感知的差异,提高一体化的性能㊂该问题类似于正交普鲁克问题,可以求得F u的闭式解为[18]:F u =U 1CV H 1,(10)式中:U 1和V 1来自于F H rad F c 的奇异值分解F Hrad F c=U 11V H 1,C =[I K ,O K ˑ(N s -K )]㊂由此,雷达感知的MSE 可以定义为:MSE r = F RF F BB -F r 2F =tr(F RF F BB F H BB F H RF -F RF F BB F H r -F r F H BB F H RF +F r F Hr )㊂(11)在一体化的混合波束赋形设计中,需要同时以通信和雷达的性能作为优化对象,因此优化问题的目标函数应同时包含二者的均方误差㊂通感一体化下的混合波束赋形优化问题可以表示为:min W ,F RF ,F BB ,βρMSE c +(1-ρ)MSE r s.t.㊀(F RF )ij =1,∀i ,j F RF F BB 2FɤP ìîíïïïï,(12)式中:ρɪ[0,1]为一权重因子,代表通信性能在优化中所占的比重㊂该优化问题需要考虑模拟波束赋形矩阵的恒模约束和混合波束赋形矩阵的功率约束㊂3㊀基于交替迭代优化算法求解波束赋形设计3.1㊀基于坐标迭代的交替优化上文中通感一体化下的混合波束赋形优化问题涉及到4个待优化变量,难以直接求解㊂因此,可以每次在固定其他变量的条件下交替优化一个变量,通过多轮这样的迭代优化使目标函数最终落入目标区间内㊂①关于W 的子问题可以表示为:min W MSE c ㊂(13)将目标函数MSE c 对W 求偏导并使结果等于零可以得到W 的闭式解为:W =(HF RF F BB F H BB F H RF H H +σ2β-2I N r)-1ˑβ-1HF RF F BB ㊂(14)②关于β的子问题,由于存在发射功率的限制,只有在发射功率达到最大时β才能达到最优值㊂令F bb =β-1F BB 以简化表达,可以得到在发射功率最大时的β值为:β=P -12(tr(F RF F bb F HbbF H RF))-12㊂(15)从求解过程可以看出,若按照未引入β因子的传统MSE 标准来优化,则需要引入拉格朗日乘子将功率约束条件利用起来再进行复杂的求解,但在引入β因子后,就可以将功率约束分解为β的子问题求得闭式解,这无疑大大简化了算法流程㊂③关于F BB 的子问题可以表示为:min F BBρMSE c +(1-ρ)MSE r ㊂(16)将目标函数对F BB 求偏导并使结果等于零可以得到F BB 的闭式解为:F BB =(ρβ-2F H RF H H WW H HF RF +(1-ρ)F HRF F RF )-1ˑ(ρβ-1F H RF H H W +(1-ρ)F HRF F r )㊂(17)④关于F RF 的子问题可以表示为:min FRFρMSE c +(1-ρ)MSE rs.t.㊀(F RF )ij =1,∀i ,j{㊂(18)约束条件(F RF )ij =1,∀i ,j 使得上述优化问题是非凸的,这使得问题的理论求解十分困难㊂本文针对该问题提出坐标迭代优化法对其进行求解㊂F RF 的优化问题可以表示为:J (F RF )=ρMSE c +(1-ρ)MSE r =ρtr(β-2W H HF RF F BB F H BB F H RF H H W -β-1W HHF RF F BB -β-1F H BB F H RF H H W +σ2β-2W H W +I N s)+(1-ρ)tr(F RF F BB F H BB F H RF -F RF F BB F H r -F r F H BB F HRF +F r F H r )=ρtr(A l )+(1-ρ)tr(B l )+ρtr(β-2W H HV RF V BB V H BB V H RF H H W -2β-1W H HV RF V BB )+(1-ρ)tr(V RF V BB V H BB V H RF -2V RF V BB F H r ),(19)式中:A l =β-2W H HF -l RF F -l BB (F -l BB )H (F -l RF )H H H W -β-1W H HF -l RF F -l BB-β-1(F -l BB )H (F -l RF )H H H W +σ2β-2W H W +I Ns,(20)B l =F -l RF F -l BB (F -l BB )H (F -l RF )H -F -l RF F -l BB F Hr -F r (F -l BB )H (F -l RF )H +F r F H r,(21)式中:F -l RF 为矩阵F RF 移除第l 列后的子矩阵,F -l BB 为矩阵F BB 移除第l 行后的子矩阵,V RF 为矩阵F RF第l 列的矢量,V BB 为矩阵F BB 第l 行的矢量㊂固定矩阵F RF 其他列不变,将第l 列的矢量V RF单独作为变量优化,原优化问题可以转化为:min F RFρtr(β-2W H HV RF V BB V H BB V H RF H H W -2β-1W HHV RF V BB )+(1-ρ)tr(V RF V BB V HBBV H RF-2V RF V BB F H r)s.t.㊀(V RF )n =1,∀n ㊂(22)该优化问题同样可以用类似方法处理,每次固定V RF ,其他元素不变,将第n 个元素V RF (n )作为变量求最优解㊂令H w =W H H ,F v =V BB F H r ,由于模拟波束赋形矩阵仅有相移的功能,可令V RF (n )=e j θn ,则目标函数中与V RF (n )有关的项为:J (θn )=ρðN sm =1[β-2H w (m ,n )V BB (m )2ej2θn-2β-1H w (m ,n )ˑV BB (m )e j θn]+(1-ρ)ðN s m =1V BB (m )2e j2θn-2(1-ρ)F v (n )e j θn ㊂(23)令:X n =ðN sm =1H w (m ,n )V BB (m )2,(24)Y n =ðN s m =1H w (m ,n )V BB (m )㊂(25)求J (θn )关于θn 的偏导,使其等于零,可以求得V RF (n )的最优解为:V RF (n )=ej θn=ρβ-1Y n +(1-ρ)F v (n )ρβ-2X n +(1-ρ) V BB 2F㊂(26)对F RF 中的每个元素依次使用上述算法,即可求得当前条件下F RF 的最优解㊂基于坐标迭代的交替优化算法的详细步骤如算法1所示㊂算法1㊀交替优化算法输入:输入:H ,N s ,N RF ,N t ,N r ,P ,σ2,ρ,I max ,S min输出:F BB ,F RF ,W ,β1.㊀在约束条件(F RF )ij =1,∀i ,j 下随机初始化矩阵F RF2.根据式(9)得到通信全数字波束赋形矩阵F c ,初始化F BB =F -1RF F c3.初始化β=P -1/2(tr(F RF F BB F H BB F H RF ))-1/24.for i =1,2, ,I max do5.㊀㊀根据式(15)更新β6.㊀㊀根据式(14)更新W7.㊀㊀根据式(26)用坐标迭代优化法更新F RF 8.㊀㊀根据式(17)更新F BB9.㊀㊀根据式(6)和式(11)计算MSE c 和MSE r10.㊀㊀if ρMSE c +(1-ρ)MSE r <S min then11.㊀㊀㊀结束循环12.㊀㊀end if13.end for3.2㊀仿真分析本节通过仿真结果来分析使用基于坐标迭代的交替优化算法求解的一体化混合波束赋形系统的性能㊂仿真中,发射天线数N t =64,接收天线数N r =8,N RF =N s =4,将每条射频链路使用的发射功率归一化为1,则总系统的归一化发射功率P =4,毫米波信道多径数L =10[19],雷达检测目标K =3,离开角分别为[-45ʎ,0ʎ,45ʎ],信道中其余离开角和到达角均服从[-π/2,π/2]的均匀分布㊂图2为不同权重因子ρ下频谱效率随信噪比变化的曲线㊂可以看出,随着通信性能权重ρ的增大,混合波束赋形的频谱效率也在增大,且越来越接近全数字波束赋形下的频谱效率㊂当ρ=1时,混合波束赋形系统只考虑通信的性能,此时的频谱效率与全数字状态非常接近㊂因此可以看出,权重因子ρ的大小在优化过程中会影响一体化系统的通信性能㊂图2㊀不同权重下频谱效率随信噪比的变化曲线Fig.2㊀Curve of spectral efficiency versus signal-to-noise ratio with different weights图3为不同权重下雷达波束图与理想全数字雷达波束图的比较㊂由于ρ值越小代表雷达性能在优化中占比越大,可以看到,随着ρ值的不断下降,一体化系统下的雷达波束图与全数字下的波束图越来越接近㊂在ρ=0.7时,雷达波束存在较大的旁瓣,这会较大地干扰正确的检测目标;ρ=0.5时,旁瓣干扰仍然存在,但此时主瓣强度明显高于旁瓣,可以进行有效的检测;ρ=0.3时,旁瓣强度被显著抑制,这时的旁瓣干扰很小,主瓣方向的波束容易分辨,雷达感知的精度较高,能够准确地识别目标方位㊂由上述分析可知,本文提出的基于坐标迭代的交替优化算法在保障通信性能的同时可以实现较高的雷达感知精度,且可以通过改变权重ρ值灵活地调整通信与感知性能的占比,实现二者的权衡,达到通感一体化的效果㊂图3㊀不同权重时的雷达波束方向图Fig.3㊀Radar beam patterns with different weight factors4 结论本文使用了毫米波信道下的混合波束赋形技术实现通信感知一体化㊂通过引入因子β导出基于最小均方误差准则的通信性能优化问题,并引入辅助酉矩阵,让理想雷达发射矩阵与混合波束赋形矩阵保持维度相同,得到了基于雷达感知性能的优化问题㊂接着利用权重因子ρ结合两方面性能提出了通感一体化下的混合波束赋形优化问题㊂针对非凸优化问题提出了基于坐标迭代的交替优化算法,完成了对波束赋形优化问题的求解㊂仿真结果表明,该算法能够很好地实现通信与感知性能的折中,即在不同权重下通信与感知的性能都能有所保证,实现了通信感知一体化的效果㊂参考文献[1]㊀LIU F,CUI Y,MASOUROS C,et al.Integrated Sensingand Communications:Towards Dual-functional WirelessNetworks for 6G and Beyond[J].IEEE Journal on Select-ed Areas in Communications,2022,40(6):1728-1767.[2]㊀吴晓文,焦侦丰,刘冰,等.面向6G 的卫星通感一体化[J].移动通信,2022,46(10):2-11.[3]㊀LIU Y J,LIAO G S,XU J W,et al.Adaptive OFDM Inte-grated Radar and 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Summer Topicals ’99CONTRIBUTED PAPER FORMPaper Title:Reduction of 3rd Order Intermodulation of a Semiconductor Laser by an Adaptive Low-Cost Predistortion Circuit at 1.8 GHzAbstract: A low-cost adaptive predistortion circuit to reduce 3rd order intermodulation of semiconductor lasers has been designed. Using a microcontroller to adjust thecontrol voltages the intermodulation has been reduced by 20 dB over thefrequency range of 1750 to 1870 MHz.Author: Guido SteinerAffiliation: Swiss Federal Institute of Technology Zurich, Laboratory for Electromagnetic Fields and Microwave ElectronicsAddress:Gloriastrasse 35City:ZurichZip:CH-8092Country:SwitzerlandPhone:+41 1 632 66 81Fax:+41 1 632 11 98E-mail:steiner@ifh.ee.ethz.chSession: RF Photonics for CATV and HFC Systems (28-30 July)Reduction of 3rd Order Intermodulation of a Semiconductor Laser by an Adaptive Low-Cost Predistortion Circuit at 1.8 GHzGuido Steiner, Stephan Hunziker* and Werner BaechtoldSwiss Federal Institute of Technology (ETH) ZurichLaboratory for Electromagnetic Fields and Microwave ElectronicsCH-8092 Zurich, Switzerland, e-mail: steiner@ifh.ee.ethz.ch* Contraves Space, CH-8052 Zurich, Switzerland, e-mail: czhs@mail.wee.ocag.chAbstract- A low-cost adaptive predistortion circuit to reduce 3rd order intermodulation of semi-conductor lasers has been designed. Using a microcontroller to adjust the control voltages the intermodulation has been reduced by 20 dB over the frequency range of 1750 to 1870 MHz.I. INTRODUCTIONAnalog fiber communications systems require high linearity. In fiber optic links the laser diodes (LDs) are the most important sources of nonlinearities [1]. By decreasing the 3rd order intermodulation (IM3) the spurious free dynamic range (SFDR) of the overall link improves. There are two major methods to reduce intermodulation: feedforward compen-sation [2] and predistortion [1], [3]. Feedforward compensation also reduces the laser intensity noise, but requires a greater number of components such as an additional LD, a photodiode and an optical coupler resulting in higher costs for the total system.A simpler approach is the predistortion technique. Predistortion can reduce the IM3 of the LD by generating an IM3 with the same amplitude but inverse phase. Unlike a feedforward system, the predistortion technique does not reduce noise. A common structure of a predistortion circuit is reported in [3]. The circuit consists of two paths: A linear path and a 3rd order distortion path with a cubic law device, an adjustable phase shifter and an adjustable attenuator.Using such a predistortion circuit a LD IM3-reduction of 23 dB over 25 MHz has already been reported [1]. The bandwidth and the IM3-reduction of the predistortion circuit described in [1] is limited due to the amplitude and phase deviations between the linear and the 3rd order distortion path. In order to reduce IM3 by 20 dB the amplitude deviation must be less than 60.5 dB and the phase deviation must be less than 658. Mainly because of the amplitude deviation of the phase shifter theserequirements are hard to meet.II. NEW TOPOLOGY AND EXPERIMENTALRESULTSAdding a phase shifter and an attenuator to the linearpath improves the symmetry of the two paths withrespect to time delay, amplitude and phase deviation.This leads to an increase of bandwidth and IM3-reduction. An additional improvement can beachieved by placing the phase shifter and theattenuator in front of the cubic law device. Thisrelaxes the bandwidth requirement of the phaseshifter and the attenuator to one third, because theIM at 2f16f2 and 2f26f1 (f1, f2: input frequencies) is generated after this devices. In such a configurationit must be guaranteed that the phase shifters and theattenuators generate much less IM3 than the cubiclaw device. A schematic diagram of the predistortioncircuit is shown in Fig. 1.Fig. 1:Schematic diagram of the predistortioncircuit.The proposed predistortion circuit has been designedin microstrip technology. A 1808-hybrid (Fig. 2, a) delivers the input signal for the linear and the nonlinear path. The signal in the linear path passes through a phase shifter (b) which is kept at a fix bias voltage where the amplitude deviation shows aminimum over the desired frequency range. The phase shifter in the nonlinear path (c), providing a phase shift of more than 3608, ensures an exact phase difference of 1808 between the two paths. In the ideal case, this phase shifter would be biased at the same voltage as the phase shifter in the linear path, resulting in equal amplitude transfer functions. An adjustable attenuator (d) sets the correct amplitude of the predistorted signal. Placing an identical attenuator (biased at a fix voltage) also in the linear path (e) maintains high symmetry. An amplifier (f) ensures that the cubic law device (g), consisting of a 1808-hybrid and two antiparallel diodes [1], operates in its nonlinear region. Completing the linear path with an amplifier (h) and a delay line (j) keeps the symmetry at a high level. The delay line has been designed to have the same electrical length as the cubic law device. AThe output signal of the predistortion circuit is fed to a 1.3 µm Fabry-Perot MQW-LD (Hitachi HL 1326CF). Fig. 3 shows the measured output signals without (top left) and with predistortion circuit (left), where the control voltages were set manually. The input signals at 1790 and 1830 MHz generate IM3 at 1750 and 1870 MHz, respectively. Setting the appropriate phase and amplitude of the nonlinear path reduces the IM3 by more than 25 dB over a bandwidth of 120 MHz. The suppression of IM3 at higher power levels is limited by the occurrence ofthe 5th order intermodulation, which cannot be reduced by the presented predistortion circuit.III. ADAPTIVE PREDISTORTIONUsing the predistortion circuit for real world optical transmission links, the control voltages for the phase shifter and the attenuator have to be readjusted due to laser aging effects [1]. Downconverting and lowpass-filtering of the IM3 generates a mixing signal proportional to the IM3 in the transmission band. After the A/D-conversion, a cheap micro-controller tracks the phase and the amplitude for maximum IM3-reduction. This adaptive process reduces the intermodulation by 20 dB over a frequency range of 120 MHz (Fig. 3, top right) which is - to our knowledge - the largest bandwidth for an adaptive predistortion circuit operating atFig. 3: Measurements ofIM3 without (top left),adaptive (top right) andmanually adjustedpredistortion circuit(left).REFERENCES[1]S. Hunziker, Analysis and Optimization of fiber opticSCM-Links, Ph.D. thesis (in German) ETH Zurich, Nr.12604, 1998.[2]L. Fock et. al., "Reduction of semiconductor laserintensity noise by feedforward compensation:experiment and theory, J. Lightwave Technol., vol. 10,no. 12, pp. 1919-1925, Dec. 1992.[3]J. Namiki, "An Automatically Controlled Predistorterfor Multilevel Quadrature Amplitude Modulation,IEEE Trans Comm., vol. 31, no. 5, pp. 707-712, May1983.。