Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

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高级驾驶辅助系统传感器布置策略研究

高级驾驶辅助系统传感器布置策略研究

2021年第6期何班本文翊李瑞翩(东风汽车公司技术中心,武汉430056)【摘要】首先,介绍传感器布置策略在高级驾驶辅助系统中的重要性,提出高级驾驶辅助系统传感器种类,包含前视智能摄像头、前向和侧向毫米波雷达(77GHz /22GHz )、超声波雷达以及环视摄像头,简要阐述各传感器性能特点。

然后,以目前某量产供应商方案为例,详细介绍不同传感器性能参数,包括探测距离、探测范围和对外部布置环境的要求。

介绍不同传感器独自搭载车辆上可实现的功能和对不同驾驶辅助级别、不同功能组合下的不同传感器的融合策略。

最后,介绍如何将不同传感器合理安装到车辆上,根据需要达到的性能要求和探测范围冗余性,提出具体实施方案,并对其布置要求进行细化解析说明。

主题词:自动驾驶雷达摄像头传感器布置中图分类号:U463.6文献标识码:ADOI:10.19822/ki.1671-6329.20200240Research on Sensors ’Layout Strategy of ADASHe Banben,Wen Yi,Liu Shuai(Dongfeng Motor Corp.Technical Center,Wuhan 430056)【Abstract 】Firstly,this paper aims to introduce the importance of sensors ’placement in advanced driver assistance systems,and proposes the types of sensors for advanced driver assistance systems,including forward-looking smart cameras,forward and lateral millimeter-wave radars (77GHz and 22GHz),ultrasonic radars,and surround-view cameras.Furthermore,the main purpose of this thesis refers to briefly describe the performance characteristics of each sensor and take the current production supplier ’s program as an example to introduce the performance parameters of different sensorsin detail,including detection distance,detection range,and requirements for the external layout environment.After that the functions realized by each sensor alone,and the fusion strategies of different sensors for different driving assistance levels and different function combinations are introduced,lastly rationally installation of different sensors on the vehicle is proposed and the layout requirements are explained according to the required performance requirements,detection rangeredundancy and specific implementation schemes.Key words:Autopilot,Radar,Camera,Sensor,Layout高级驾驶辅助系统传感器布置策略研究【欢迎引用】何班本,文翊,李瑞翩.高级驾驶辅助系统传感器布置策略研究[J].汽车文摘,2021(6):50-55.【Cite this paper 】He B,Wen Y,Li R.Research on Sensors ’Layout Strategy of ADAS [J].Automotive Digest (Chinese),2021(6):50-55.缩略语ADAS Advanced Driving Assistance System AEB-C Autonomous Emergency Braking-CarLDW Lane Departure Warning LKA Lane Keeping Assist TSR Traffic sign recognitionACC Adaptive Cruise Control FCW Forward Collision Warning TJATraffic Jam AssistICAIntelligent Cruise AssistBSDBlind Spot DetectionLCWLane Change Warning RCTA Rear Cross Traffic Alert DOW Door Open WarningFCTA Front Cross Traffic Alert HWA-ML Highway Assist Multi Lane ALC Active Lane ChangeTLC Trigger Lane ChangeELK Emergency Lane Keeping ESA Emergency Steering AssistJAJunction Assistant501引言随着科技的进步、自动驾驶技术的快速发展,目前越来越多汽车配备了高级驾驶辅助系统或辅助驾驶系统,自动驾驶汽车在SAE J3016TM自动驾驶等级中被归类为五级自动驾驶[1]。

受伤及意外事故处理需要遵循的流程

受伤及意外事故处理需要遵循的流程

受伤及意外事故处理需要遵循的流程1.首先,要确保伤者的安全。

First, it is important to ensure the safety of the injured person.2.然后,要立即拨打急救电话寻求专业帮助。

Next, immediately call for emergency medical help.3.如果可能,要对伤者进行基本的急救处理,如止血或保持呼吸通畅。

If possible, administer basic first aid to the injured person, such as stopping bleeding or maintaining clear airways.4.同时,要防止现场继续发生意外,将伤者移到安全地点。

Simultaneously, prevent further accidents at the scene and move the injured person to a safe location.5.在处理意外伤者时要保持冷静,不要惊慌。

When dealing with the injured person, it is important to remain calm and avoid panicking.6.为伤者寻求医疗帮助的过程中,要尽量提供相关信息,如伤者的身体状况、意外发生地点等。

Provide as much information as possible during the process of seeking medical help for the injured person, such as their physical condition and the location of the accident.7.如果可能,尽量记录伤者的伤情,包括拍照或录像。

If possible, document the injuries of the injured person, including taking photographs or videos.8.在紧急情况下,确保周围的人员和目击者留下联系方式,以便后续调查和处理。

河南省2021届高三上学期8-9月英语试卷精选汇编:阅读理解专题

河南省2021届高三上学期8-9月英语试卷精选汇编:阅读理解专题

阅读理解专题河南省2020-2021学年上学期高中毕业班阶段性测试(一)第一节(共15小题;每小题2分,满分30分)阅读下列短文,从每题所给的四个选项(A、B、C、和D)中,选出最佳选项,并在答题卡上将该项涂黑。

AFrom helping rescued animals find permanent homes to providing care for chimpanzees(黑猩猩)that have found shelter, there’s always a volunteering site for every animal lover. Here are some amazing opportunities.Seattle Humane SocietySeattle Humane Society has been helping animals since 1897. In 2013, it adopted out a record of 6, 297 pets. It provides a wide range of programs, including dog training, a pet food bank and volunteer opportunities for animal lovers. A six-month commitment is required, and all volunteers must he at least 18 years old.Chimpanzee Shelter NorthwestChimpanzee Shelter Northwest was founded in 2003 as a shelter for chimpanzees. It lies in the foothills of the Cascade Mountains, 90 miles east of Seattle. V olunteers have a unique opportunity to see what it takes to run a shelter. It has three levels of volunteers; Level I volunteers help with meal preparation; Level II volunteers help the staff clean the shelter; level III volunteers are trained to interact directly with the chimpanzees.Hope For HorsesHope For Horses has an all-volunteer team that has helped hundreds of severely abused horses since 2001. It's also rescued a variety of other animals over the years, including donkeys, chickens, geese and a goat. Volunteers provide hands-on care for horses and other animals, including medical research, site visits and assistance with adoption. All volunteers must be 18 years old.Purrfect PalsPurrfect Pals, which was founded in 1988, is the Pacific Northwest's largest cat-only adoption shelter and organization. And it's one of the largest cat-only shelters in North America us well. It finds home for over 2, 000 cats each year. It asks for a five-month commitment and volunteers must he 18 or older.21. What’s the text intended to introduce?A. Benefits of being animal lovers.B. Permanent homes for rescued animals.C. V olunteer opportunities for animal lovers.D. Commitments of volunteers at animal shelters.22. Which of the following has the longest history?A. Seattle Humane Society.B. Chimpanzee Shelter Northwest.C. Hope For Horses.D. Purrfect Pals.23. In what way is Chimpanzee Shelter Northwest different from the other three?A. It lies within the city of Seattle.B. It sets no age limit for volunteers.C. It is completely run by volunteers.D. It is a shelter for only one kind of animals.BEmily Egan was born and raised in Essex, United Kingdom and speaks no foreign language, but hearing her speak, you could swear she's a Russian immigrant(移民)or a tourist, because of her Eastern European accent. But the funny thing is that she sometimes sounds French, Italian or even Polish, depending on how tired she is.31 -year-old Emily's life changed greatly in January, when a mysterious condition left her unable to speak for two months. She’d had headaches for two weeks before one day developing a deeper voice suddenly. Her colleagues at a children's home then noticed her speech had become slow and unclear, both signs of a stroke(中风). By the time she was rushed to the hospital, Emily had lost her ability to speak completely, but after running some tests, doctors ruled out the stroke, instead blaming her voice loss on some sort of injury to her brain.After spending three weeks in the hospital, Emily Egan was still unable to speak and relied only on basic sign language she’d picked up at work and a text-to-speech app on her phone to communicate. A doctor encouraged her to go on a vacation in Thailand she and her husband had already booked, and to try and relax as much as possible. She did just that and a few days into the vacation, she started to speak again."I was so thrilled when my voice started coming back b ut now I don’t even discern the voice that comes out of my mouth. It doesn't sound like me," Egan said. Ever since her voice came back, she's taken time off work because stress only makes her condition worse.What has happened to Emily sounds shockingly similar to that of Michelle Myers, an Arizona woman who never traveled outside of America, but woke up to speaking with many accents-British, Irish and Australian-after experiencing severe headaches.24. Why is Emily Egan considered a Russian immigrant?A. She resembles a Russian very much.B. She speaks Russian like a native speaker.C. She speaks English with a Russian accent.D. She can freely switch between English and Russian.25. What resulted in Emily’s inability to speak?A. Brain injury.B. A sudden stroke.C. Long-term tiredness.D. Serious headaches.26. The underlined word "discern" in paragraph 4 most probably means " ".A. controlB. ignoreC. transformD. recognize27. What can be inferred about Emily from the text?A. She has already returned to work.B. Her case isn't alone in the world.C. She learned sign language after her voice loss.D. Her voice returned after three weeks' treatment.CA relative of starfish doesn't have eyes, but can sill see. That information comes from scientists who studied sea creatures in the coral reefs of the Caribbean and Gulf of Mexico.The researchers reported this month that the starfish’s relative-called the red brittle star-is only the second creature known to be able to see without having eyes. This ability is call extraocular vision(眼外视觉). The other creature said to have extraocular vision is a kind of sea urchin.Brittle stars, with five arms extending from a central disk, are part of a group of sea life called echinoderms. They have a nervous system but no brain. The red brittle star measures up to about 35 centimeters wide, from the end of one arm to the other. It lives in bright and complex environments.Because of the possibility of being eaten by fish, the creature hides during daylight hours.The red brittles star possesses extraocular vision as a result of light-sensing cells, called photoreceptors. These photoreceptors cover its body and chromatophores, the cells responsible for coloring. During the daytime, the chromatophores narrow the field of light being detected, making each photoreceptors like the pixel(像素) of a computer image. When combined with other pixels, the image becomes complete.The visual system doesn't work at night. Laboratory experiments suggested that the brittle stars have very simple vision. Placed in a circular environment, for example, they moved toward walls that were white with a black bar, suggestive of al daytime hiding place.Lauren Sumner-Rooney is a research fellow at the Oxford University Museum of Natural History. She led the study. She said, "It's such a different concept for us, as very visually driven animals, to imagine how an animal might see its habitat without eyes, but now we know of two examples."28. What's researchers' new finding about the red brittle star?A. It is a relative of starfish.B. It lives in the coral reefs.C. It has a very rare ability.D. It is a sea creature.29. What’s the author’s purpose in writing paragraph 3?A. To give: reasons why the brittle star has no brain.B. To make a general introduction of the brittle star.C. To stress the role the brittle star plays in the sea.D. To explain why the brittle star keeps a unique lifestyle.30. According to the text, photoreceptors .A. spread on the surface of the red brittle starB. function normally even in the nighttimeC. control the field of light being detectedD. serve as cells responsible for coloring31. What is the best title for the text?A. How Sea Creature Finds Its Habitat UnderwaterB. How Sea Creature's Visual System WorksC. Sea Creature Finds New HabitatD. Sea Creature Sees Without EyesDA research team led by UCLA materials scientists has shown ways to make super white paint that reflects as much as 98% of incoming heat from the sun. The advance shows practical pathways for designing paints that, if used on rooftops and other parts of a building, could greatly reduce cooling costs, beyond what standard white cool-roof paints can achieve."When you wear a white T-shirt on a hot sunny day, you feel cooler than if you wear one that's darker in color- that's because the white shirt reflects more sunlight and it’s the same concept for buildings," said Aaswath Raman, a researcher of the study. "A roof painted white will be cooler inside than one in a darker shade by rejecting heat at infrared(红外线的) wavelengths, which we humans cannot see with our eyes. This super white paint could allow buildings to cool down even more by radiative(辐射的) cooling."The best performing white paints now available typically reflect around 85% of incoming sun heat. The remainder is absorbed by the chemical makeup of the paint. The researchers showed that simple changes in a paint's ingredients could offer a big jump, reflecting as much as 98% of incoming radiation. The potential cooling benefits this super white paint can achieve may be realized in the near future because the changes suggested are within the abilities of the paint and coatings industry.Many cities and governments, including the stale of California and New York City, have started to encourage cool-roof technologies for new buildings. "We hope that the work will inspire future creativity in super-white coatings for not only energy savings in buildings, but also lessening the heat island effects of cities, and perhaps even showing a practical way that, if applied globally, could affect climate change," said Raman, who has studied cooling paint technologies for several years, "This would require experts in diverse fields to work together."32. What’s the fun ction of the super white paint?A. Reflecting all the sun heat.B. Lowering building costs.C. Keeping buildings cool.D. Absorbing heat quickly.33. How does Aaswath Raman explain the working principle of the paint?A. By telling a story.B. By listing scientific data.C. By conducting a11 experiment.D. By making a similar comparison.34. What can be inferred about the super white paint from the text?A. It is completely chemical free.B. It is still unavailable in the market.C. Its ingredients still need improving.D. Is effects have been greatly improved.35. What do Raman’s words in the las t paragraph imply about the work?A. He has high expectations of it.B. He urges greater attention to it.C. He believes it can stop climate change.D. He thinks it's easy to carry out worldwide.河南省洛阳市2021届高三上学期期中考试英语试题第一节(共15小题;每小题2分,满分30分)阅读下列短文,从每题所给的A、B、C和D四个选项中,选出最佳选项。

汽车领域的介绍英文作文

汽车领域的介绍英文作文

汽车领域的介绍英文作文英文,In the realm of automobiles, there exists a world of innovation, passion, and functionality. Cars, as we commonly refer to them, have become an indispensable part of our lives, providing us with convenience, freedom, and sometimes even a sense of identity.From the sleek lines of a sports car to the ruggedness of an off-road vehicle, automobiles come in various shapes and sizes, catering to different needs and preferences. For instance, when it comes to family outings, a spacious SUV might be the ideal choice, offering ample room for both passengers and cargo. On the other hand, for those craving adrenaline-pumping experiences, a nimble sports car with powerful acceleration and precise handling would be more enticing.The automotive industry is not just about the vehicles themselves; it encompasses a wide array of components and technologies that work together to create the drivingexperience. Take, for example, the engine, often hailed as the heart of a car. Whether it's a traditional internal combustion engine or an electric motor powering the vehicle, advancements in engine technology have led to improvementsin performance, fuel efficiency, and environmental friendliness.Moreover, safety features play a pivotal role in modern automobiles, providing peace of mind to drivers and passengers alike. From anti-lock braking systems (ABS) to advanced driver-assistance systems (ADAS), these technologies help mitigate risks and prevent accidents on the road. For instance, adaptive cruise controlautomatically adjusts the vehicle's speed to maintain asafe distance from the car ahead, reducing the likelihoodof rear-end collisions.In addition to functionality, automobiles also serve as cultural symbols and status markers. A luxury sedan parkedin the driveway may convey success and prestige, while a vintage convertible cruising down the coastal highwayevokes nostalgia and charm. Brands like Ferrari,Lamborghini, and Rolls-Royce are not just manufacturers of cars; they represent a lifestyle, a dream for many enthusiasts around the world.In conclusion, the world of automobiles is afascinating blend of engineering marvels, personal expression, and societal influence. Whether it's the thrill of the open road or the comfort of a daily commute, cars continue to shape our lives in profound ways.中文,在汽车领域,存在着创新、激情和实用性的世界。

基于运动想象的脑电特征提取及特征迁移方法研究

基于运动想象的脑电特征提取及特征迁移方法研究

摘要运动想象脑-机接口技术不依赖人的外周神经和肌肉组织,直接实现人脑对外部设备的控制,它可以帮助有运动障碍的患者,更好地与外界进行信息交流,在军事、航天、医疗和虚拟现实等领域有巨大的应用价值。

脑电信号具有非平稳性,而传统运动想象技术在应用前需要标注大量的训练样本,并采用多通道采集的方式,这大大限制了其应用范围。

本文在传统脑电信号处理方法的基础上,将迁移学习的思想应用于运动想象的分类,减少训练样本和测试样本的分布差异,以提高分类准确率。

此外,针对运动想象技术对运算实时性要求高的问题,研究通道选择优化方法,在保证分类正确率损失有限的条件下,减少分析脑电信号的通道数量,以提高运动想象脑-机接口技术的实时性。

本文具体研究工作如下。

基于运动想象生理基础,研究运动想象脑电信号预处理方法。

利用AR模型对运动想象脑电信号频谱分析,得出信号有效的频带范围8-30Hz,为滤波器通带频率的选择提供分析依据;并分析公共平均参考法(CAR)空间滤波增加不同思维脑电信号空间分布差异的优势,为获得高信噪比的脑电信号奠定基础。

研究基于小波包变换的特征提取方法,选择小波包分解后特定子节点的小波系数,并提取能量特征,利用支持向量机,识别两种类型的运动想象任务,得出平均分类正确率为79.4%。

在此基础上,研究通道选择的优化方法,基于Relief-F 算法计算通道权重,在对分类效果影响有限的条件下,减少分析脑电信号的通道数量,有助于减少计算量,提高运动想象脑-机接口实时性。

研究基于最小化MMD的迁移学习算法,并将算法应用于运动想象的分类。

结果表明,该方法有助于提高实验者一段时间内运动想象的分类正确率,且能够使一个实验者训练的分类模型更加适用于另一个实验者的测试。

证明了迁移学习算法比传统的分类方法有更好的适应性。

结合以上研究,设计基于运动想象迁移学习实验。

针对真实的脑电信号含有的伪迹问题,研究小波分析眼电伪迹滤除的方法,并探讨迁移学习在线实现方案。

IT专业英语词汇精选(T1)_计算机英语词汇

IT专业英语词汇精选(T1)_计算机英语词汇

t t reference point t基准点〖isdn〗t tab 标签,tab键t table 表t telemechanique 远动技术t telephone 电话t teletex 智能用户电报t telex 电传t tera 太,兆兆(=1012)t terminal 终端t test 测试t toolbars 工具条t track 磁道t transmission 播出t transmitter 发射机t tree 树t tril 万亿t trimmer 微调电容t true 真.t tads的源程序文件格式〖后缀〗.t reagenix代码发生器的测试仪符号表文件格式〖后缀〗.t 非压缩磁带存档文件格式〖后缀〗.t44 dbase iv的排序或索引临时文件格式〖后缀〗.t64 c64s仿真器的程序文件格式〖后缀〗t / a trouble analysis 故障分析t / f time – to – frequency coding(-er) 时间-频率编码(器)t / r transmit / receive 发 / 收,传送 / 接收t / t telegraphic transfer 电报传送t&b 贝通公司(美国,有百年历史,出品专业电器和综合布线系统,1998年进入中国)t&e rec time and events recorder 时间与事件记录t&v test and verify program 测试与校验程序t. f. true fault 实际误差,真故障ta technical assembly 技术汇编ta telegraph address 电报挂号ta terminal adapter 终端适配器ta test analyzer 测试分析器ta total annihilation 《横扫千军》〖游戏名〗ta transmission adapter 传输适配器tab tabletop game 桌面类游戏tab tape automated bonding 磁带自动粘接tab trunk test adaptation board 中继线测试适配板tab 制表符,标签,tab键tabsim tabulating equipment simulator 制表设备模拟器tabsol tabular systems oriented language 面向制表系统的语言(美国通用电气公司研制)tac tactical advanced computer 战术高级计算机tac technical assistance center 技术援助中心tac telenet access controller 远程网访问控制器tac telephone association of canada 加拿大电话协会tac terminal access controller 终端访问控制器tac test access control 测试接入控制tac tokyo university automated computer 东京大学自动计算机tac total access communication 完全接入通信tac transformer analog computer 变压器模拟计算机tac transistored analog computer 晶体管模拟计算机tac transistorized automatic computer 晶体管化自动计算机tac transistorized automatic control 晶体管化的自动控制器tac translator – assembler compiler 翻译机汇编程序编译器tac tree access and control 树形网络的访问与控制taca terminal access control agent 终端访问控制代理tacacs terminal access controller access control system 终端访问控制器访问控制系统(美国系统公司研制)tacden tactical data entry unit 战术数据登录器(美国陆军)tac / jw tactical advanced computer – joint workstation 战术高级计算机 / 联合工作站tacacs terminal access control(ler) access control system 终端访问控制(器)接入控制系统tacl teaching – and – course writing computer language 教学与教案编写计算机语言tacom tactical communication 战术通信tacomsat tactical communications satellite 战术通信卫星tacos tactical communications system 战术通信系统tacos tool for automatic conversion of operational software 操作软件自动转换工具tacs total access communication system 全部访问访问通信系统tacsat tactical satellite 战术卫星tact telephone access control technology 电话接入控制技术tad target acquisition data 目标捕获数据tad telemetry analog to digital information converter 遥测数字模拟信息转换器tad telephone answering device 电话应答设备tad terminal address designator 终端地址命名符tadars tropo automated data analysis recorder system 对流层自动数据分析记录系统tad / p terminal area distribution processing 终端区域分布处理tadco timing and data collection 时间测定与数据收集tadi telemetry acquisition data interface 遥测捕获数据接口tadic telemetry analogue digital information converter 遥测数据相似数字信息转换器tads tacking and display system 盯死与显示系统tads teletypewriter automatic dispatch system 电传打字机自动发送系统tads transportable automatic digital switch 便携式自动数字开关tae test access equipment 测试接入设备taec toshiba america electronic components 东芝美国电子组件公司(出品多媒体处理器芯片)taed telex automatic emitting device 电传自动发射器tag technical advisory group 技术咨询组tag telecomputer application group 远程计算机应用组tag time automated grid 时间的自动化网格.tag dataflex的查询标记名称文件格式〖后缀〗tagc temperature automatic gain control 温度自动增益控制.tah borland c++的turbo汇编程序帮助文件格式〖后缀〗tai technical application index 技术应用软件索引tais toshiba america information systems inc. 东芝美国信息系统公司(出品笔记本电脑、桌面系统等)tal transaction application language 事物处理应用语言.tal typealign的文本插图文件格式〖后缀〗talk unix操作系统的实时文字“交谈”的实用程序〖因特网〗tam telecommunication access method 远程通信存取法tam terminal access method 终端存取法tammis theater army medical management information system 战区野战军医疗管理信息系统tamos terminal automatic monitoring system 终端自动监视系统tandem 天腾公司(美国,出品容错型计算机)〖厂标〗tanet 中国台湾学术网路〖网站〗tanstaafl there ain’t no such things as a free lunch哪里有吃饭不要钱的好事!〖网语〗tao totally automated office 全自动化办公室tao track – at – once 立即寻道〖光驱〗tap terrain analysis package 地形分析软件包tap test assistance program 测试辅助程序tap testing access port 测试使用端口tap time – sharing accounting package 时分会计包,时间分享账户处理软件包tap time – sharing assembly program 时分汇编程序,时间分享汇编程序tap trunk adapter 中继线适配器tapac tape automatic positioning and control 磁带自动定位控制器tapi telephone application programming interface 电话应用编程接口(微软和英特尔公司研制)tapi telephony application programming interface 电话应用编程接口taps telemetry automatic processing system 遥测自动处理系统tar tape archival and retrieval 磁带存档和检索格式tar temporary address register 临时地址寄存器tar terminal address register 自动地址寄存器tar transfer address register 传送地址寄存器tar transparent adaptive routing 透明自适应路由选择.tar unix用tape archiver程序生成的无压缩磁带档案文件格式〖后缀〗.tar 由tar (pax2exe.zoo) 生成的压缩存档文件格式〖后缀〗.tar.z unix的压缩文档文件格式〖后缀〗tare telegraph automatic relay equipment 电报自动中继设备tare telemetry automatic reduction equipment 遥测自动缩影设备tare transistor analysis recording equipment 晶体管分析记录设备targa 德国电脑及外设的头号品牌tarp test and repair processor 测试与修理处理器tas telecom analysis simulator 电信分析模拟器tas telecom analysis system 电信分析系统tas telecommunication authority of singapore 新加坡电信局tas telephone access server 电话访问服务器tas telephone answering service 电话应答服务tas teleprogrammer assembly system 远程程序员汇编系统tas terminal address selector 终端地址选择器tas test access selector 测试接入选择器tas time address signal 时间地址信号ats tracking antenna system 跟踪天线系统tas trouble analysis system 故障分析系统tasa time address signaling adapter 时间地址信令适配器tasc tabular sequence control 制表顺序控制tasc telecommunications alarm surveillance and control 电信警报的监视与控制tasc terminal area sequence and control 终端区域的顺序与控制tascon television automatic sequence control 电视自动顺序控制tasi time – assignment speech interpolation 时间分配的话音内插技术(用于模拟语音传输)tasm turbo assembler “涡轮”汇编程序(博兰德公司研制)tasp telemetry analysis and simulation program 遥测分析和模拟程序tat transatlantic telephone cable 横跨大西洋的电话电缆tav the advanced visualizes 高级直观化tawcs tactical air weapons control system 战术航空武器控制系统taxi transparent asynchronous exchange interface 透明异步交换器接口taxi transparent asynchronous transceiver / receiver interface 透明异步交换器接口taxir taxonomic information retrieval 分类信息检索系统(美国科罗纳多大学研制).taz 由tar和compress (.tar.z) 生成的美国标准码压缩存档文件格式〖后缀〗tb telephone booth 电话亭(美国)tb terabyte 特字节,万亿字节(1tb=1000gb)tb terminal block 终端模块tb time base 时基tb toy benchmark 简单基准程序〖测试〗tb transmission buffer 传输缓冲器tb transparent bridge 透明网桥tb trunk block 中继线数据块tb twin bridge “双桥”多语言平台系统(美国pc express 公司研制).tb1 borland turbo c的字体文件格式〖后缀〗.tb2 borland turbo c的字体文件格式〖后缀〗tba television broadcaster association 电视广播工作者协会tbb telecommunications bonding backbone 电信搭接主干网tbc testing bus controller 测试总线控制器tbc time base corrector 时间基准校正器,时基校正器tbdb task number definition block 任务编号定义块tbe time base error 时间基准错误tbem terminal – based electronic mail 基于终端的电子邮政.tbf imavox turbofax的传真文件格式〖后缀〗tbfdb transparent bridge forwarding database 透明网桥前向数据库tbg time base generator 时间基准发生器tbj telecommunications bonding jumper 电信跳线连接器.tbk 临时数据库文件〖后缀〗.tbk 多媒体编辑软件toolbook的文件格式〖后缀〗.tbk dbase iv和foxpro的备忘录备份文件格式〖后缀〗tbl tim berners – lee 蒂姆·伯纳斯–李(欧洲原子物理研究所的英国科学家,1989年发明万维网).tbl pagemaker tableeditor的图形文件格式〖后缀〗.tbl os/2的数值表文件格式〖后缀〗tbm terabit memory 太位存储器,兆兆位存储器tbp three – beam projector 三束投影仪tbp telephone bill payment 电话账单支付tbr table base register 表格基址寄存器tbr tag boundary router 标记边界路由器tbs telephone bank system 电话银行系统tbs terminal business system 终端商务系统tbs turner broadcasting service 特纳广播公司(美国,1996年被时代华纳公司并购)tbs turtle beach systems “海龟沙滩”系统公司(美国,出品声卡).tbs ms word的文本单元文件格式〖后缀〗tbsc testing bus standard committee 测试总线标准委员会tbti thunderbyte tct international corp. “雷霆字节”tct国际公司(美国,出品反病毒工具)tbu transfer buffer unit.tbx project scheduler 4的表格文件格式〖后缀〗tc task control 任务控制tc team computing 协同计算功能tc telecommunication committeetc teleconference 远程会议,电信会议tc telegram in code 密码电报tc temperature coefficient 温度系数tc tera cycle 兆兆周(1012周)tc terminal computer 终端计算机tc terminal control 终端控制tc terminal controller 终端控制器tc texture compression 纹理压缩技术〖芯片〗tc thin client 瘦客户机tc time code 时间码tc timing channel 计时通道tc toll center 长途电话中心tc tracking camera 跟踪摄影机tc transmission control(ler) 传输控制(器)〖sna〗tc transmission convergence sublayer 传输集中子层tc transport connection 传送连接tc transport control 传输控制tc trunk card 中继卡tc trunk circuit 中继线路tc turks & caicos islands 特克斯和凯科斯群岛(域名)tc twinhead corp. 伦飞公司(中国台湾,出品笔记本电脑等).tc turbo c和borland c++的配置文件格式〖后缀〗tca telecommunications association 电信协会tca terminal control area 终端控制区tca transfer – cluster – allowed 批准簇传送tca transport connection accept 同意传送连接tca twin – cache architecture 双缓存架构〖3d加速芯片〗tcam telecommunication access method 远程通信存取法tcas t carrier administration system t载波管理系统tcb task control block 任务控制块tcb teletypewriter channel buffer 电传机信道缓冲器tcb tightly closed boundary 封锁边界tcb timing control block 定时控制块tcb total cache buffers 全部高速缓冲存储器缓冲区tcb transmission control block 传输控制块tcbh time – consistent busy hour 始终繁忙时段tcc 3com corp. 3com网络设备制造公司(美国,1979年由鲍勃·梅特卡尔夫创立,出品网络设备,快速以太网适配器等)tcc technology control center 技术控制中心tcc television control center 电视控制中心tcc terminal call control 终端呼叫控制tcc thomas – conrad corp. 托马斯-康拉德公司(美国,出品快速以太网适配器)tcc time compression coding 时间压缩编码tcc traffic control center 传输量控制中心,话务管理中心tcc transaction control code 事务处理控制码tcc transmission control characters 传输控制字符tcc transport connection clear 传送连接清除tccb internet configuration control board 因特网配置控制板tcco temperature compensated crystal oscillator 温度补偿晶体振荡器tce total composite error 总合成错误tce transmission control element 传输控制元件tce transmission control equipment 传输控制设备tcf technical control facility 技术控制设备tcfpa telephone consumer fraud protection act 电话消费者欺诈保护法案tcg telephone communication group 电话通信用户组tcg teleport communication group 远程端口通信组tcg test call generator 测试调用发生器tcg time – code generator 时间码发生器tcg time controlled gain 时间控制增益tcg tune – controlled gain 调谐控制增益tch terrestrial channel 陆地信道tch test channel 测试信道.tch borland c++的turbo c帮助文件格式〖后缀〗tcg traffic channel 通信交往信道tci tangent computer inc. “正切”计算机公司(美国,出品电脑主机)tci technocraft inc. 特科能有限公司(日本,出品网上翻译词典)tci terminal control interface 终端控制接口tcic transit center identification code 中转中心识别码tcims telecommunication integrated management system 电信综合管理系统tck testing clock 测试时钟tcl terminal command language 终端命令语言tcl terminal control language 终端控制语言tcl test control language 测试控制语言tcl tool command language 工具命令语言tcl transistor – coupled logic 晶体管耦合逻辑tcl troposcatter communications link 对流层散射通信链路.tcl swat的工具命令语言源码文件格式〖后缀〗tclk transmit clock 传送时钟tcl / tk tool command language / tool kit 工具命令语言 / 工具包系统tcm telemetry code modulation 遥测编码调制tcm termianl call monitor 终端调用监视器tcm terminal – to – computer multiplexer 从终端到计算机的多路复用器tcm time compression multiplex 时间压缩多路复用tcm trellis coded modulation 格式结构编码调制,框架编码调制tcms telecommunications management systemtco total cost of ownership 拥有电脑的总开销,总体拥有成本tco trunk cut – off 干线切断tcon time converter 时间转换器tcos trunk class of service 服务的中继线等级tcp tape carrier package 带状媒介封装,薄膜封装〖芯片〗〖笔记本电脑〗tcp terminal communication processor 终端通信处理器tcp terminal communication protocol 终端通信协议tcp terminal control program 终端控制程序tcp termination connection point 终止连接点tcp test and check –up program 测试检验程序tcp test coordination procedure 测试协调规程tcp transfer – cluster – prohibited 禁止簇传送tcp transmission control protocol 传输控制协议〖因特网〗tcp / ip transmission control protocol / internet protocol 传输控制协议/ 互联网协议(美国国防部高级研究计划署开发的网络通信接口规格)tcp / ip transport control protocol / interface program 传送控制协议 / 接口程序tcps transmission control protocol segments 传输控制协议数据段tcr tape cassette recorder 盒式磁带录音机tcrd terminal control read signal 终端控制读出信号tcs telecommunication system 远程通信系统tcs telecommunication control system 远程通信控制系统tcs teleconference service 远程会议服务tcs telemetry and command system 遥测与指挥系统tcs television camera system 电视摄像机系统tcs terminal call service 终端调用服务tcs terminal communications subsystem 终端通信子系统tcs terminal control system 终端控制系统tcs traffic control subsystem 通信交往控制子系统tcs transportation control system 交通车辆控制系统tcs two channel switch 双信道开关tcse trunk call sampling equipment 中继呼叫采样设备tcsi telecommunications computer services interface 远程通信计算机服务接口tcsl transistor current steering logic 晶体管电流引导逻辑tct terminal control table 终端控制表tct time code translator 时间编码转换器tctl transistor – coupled transistor logic 晶体管耦合晶体管逻辑tcu terminal control unit 终端控制器tcu test control unit 测试控制器tcu tight close – up 近特写镜头tcu transmission control unit 传输控制器tcu trunk coupling unit 干线耦合单元tcva terminal control valid signal 终端控制有效信号tcw time code word 时间代码字.tcw turbocad for windows的绘图文件格式〖后缀〗tcwg telecommunication working group 电信工作组tcwi tribe computer works inc. “部落”电脑作品公司(美国,出品远程访问服务器)tcwr terminal control write signal 终端控制写信号td chad 乍得(域名)td tape drive 磁带机td technical data 技术数据td time delay 时间延迟,时间滞后td track data 跟踪数据td tracking devices 跟踪装置td transmit data 传输数据td transmitted data 已传送数据td transmitter distributor 发送器分配器td tunnel diode 隧道二极管.td turbo debugger for dos的配置文件格式〖后缀〗.td0 teledisk的磁盘图像文件格式〖后缀〗.td2 turbo debugger for win32的配置文件格式〖后缀〗tda tunnel diode amplifier 隧道二极管放大器tdac time domain alias cancellation 时间域别名注销tdana time domain automatic network analyzer 时域自动网络分析器tdat teleprocessing diagnostic analyzer tester 远程处理诊断分析器测试装置tdb task database 任务数据库tdb terminology database 术语库.tdb tact的数据库文件格式〖后缀〗tdc telex destination code 电传目的地编码,电传宿端码tdc time division connector 时分连接器tdc time domain coding 时域编码tdcm transistor driven core memory 晶体管驱动磁心存储器tdcs time division circuit switching 时间分开线路交换tdcu target data control unit 目标数据控制装置tdd tabbed document division 标记文档分割tdd telemetry data digitizer 遥测数据数字化仪tdd time division duplex 时间分开双工操作tdd timing data distributor 计时数据分配器tddl time – division data link 时间分开的数据连接,时分数据链路tdec telephone line digital error checkingtdf transnational data flows 跨国数据流tdf two degrees of freedom 双自由度.tdf thedraw的字体文件格式〖后缀〗.tdf speedo的字体定义文件格式〖后缀〗tdfh time division frequency hopping 时分跳频tdfw three dimensional four wire 四线三度重合(磁芯存储器)tdgl test data generating language 测试数据生成语言.tdh turbo debugger的帮助文件格式〖后缀〗tdi technical data interchange 技术数据交换tdi telecommunication data interface 远程通信数据接口tdi testing data in 测试数据输入tdi tigerdirect inc. “虎司令”公司(美国,出品微处理器)tdi transfer driver interface 传送驱动器接口tdi transport driver interface 传送驱动器接口tdi two – way direct interface 双向直接接口tdic target data input computer 目标数据输入计算机tdj transfer delay jitter 传送延时抖动tdk 创宏主板〖品牌〗.tdk turbo debugger的击键记录文件格式〖后缀〗tdl terminal display language 终端显示语言tdl test and diagnose language 测试和诊断语言tdl transformation definition language 转换定义语言tdl transistor diode logic 晶体管二极管逻辑(电路)tdm telecommunication data link monitor 电信数据链路监视器tdm telecommunication distribution method 远程通信分布方法tdm telemetric data monitor 遥测数据监视器tdm time division modulation 时分调制tdm time division multiplexing 时间分开多路复用,时分多工法tdm time division modulation 时间分开的调制,时分调制tdm time – division multiplexing 时间分开多路复用tdm time driver monitor 时间驱动器监视器tdm transparent data migration 透明数据移动tdm two dimensional memory 二度(重合)磁芯存储器tdm / pcm time –division multiplex using pulse –code modulation 使用脉冲编码调制的分时多路传输tdma time division multiple address 时间分开多路地址,时分多址tdma time division multiplexing access 时间分开多路复用存取技术tdma time domain multiple access technique 时域多路存取技术tdma / tdd time division multiplexing access / time division duplex 时间分开多路复用的访问 / 时间分开的双工操作tdmc time division multiplexed channel 时分多路转换信道tdmd time division multiplex device 时分多路复用设备tdmg telegraph and data message generator 电报与数据信息发生器tdms time – sharing data management system 时间分享数据管理系统(美国系统开发公司研制)tdn temporary directory number 临时名录编号tdn time division network 时间分开的网络tdnw time division network 时分网络tdo tabular data object 制表数据对象tdo testing data out 测试数据输出tdof three degrees of freedom 三自由度tdos tape disk operating system 磁带磁盘操作系统tdp tag dispatch protocol 标记调度协议tdp tag distribution protocol 标记发布协议tdp technical data package 技术数据包tdp technical development plan 技术发展计划tdp tele data processing 远程数据处理tdp tracking data processor 跟踪数据处理器tdp traffic data processor 通信交往数据处理器tdpl top – down parsing language 自上而下的剖析语言tdpr trace directed program restructuring 痕迹导向的程序重构tdr time delay receiver 延时接收机tdr time domain reflectometry 时域反射tdr time - domain reflectometer 时域反射器tdr time domain reflector 时间域反射仪tdr time of day routing 日期路由选择tdr tone dial receiver 双音频拨号接收机tdr transfer data ready 传送数据就绪tdr transfer date record 传送数据记录tdr transmit data register 传送数据寄存器tdrb transmit descriptor ring base 传送描述符环基准tdrs text data retrieval system 文本数据检索系统tdrs tracking and data relay satellite 跟踪域数据中继卫星tdrss tracking and data relay satellite system 跟踪与数据中继卫星系统tds tabular data stream 制表数据流tds technical data system 技术数据系统tds time division switch 时间分割开关,时分开关tds time division switching 时分转接tds track data simulator 跟踪数据模拟器tds track data storage 跟踪数据存储tds transaction distribution system 事务处理分布系统tds transaction driven system 事务处理驱动系统tds transistor display and data handling system 晶体管显示器与数据处理系统tds tri – digital software 三维数字软件公司(美国,出品多媒体创作工具).tds turbo debugger的符号表文件格式〖后缀〗tdsa telegraph and data signal analyzer 电报与数据信号分析器tdsound three dimensional sound 三维音响tdsr transmitter data service request 发送器数据服务请求tdss technique decision support system 技术决策支持系统tdss traditional decision support system 传统决策支持系统tdt thermal dye transfer 热升华转换技术〖打印机〗tdtl transistor – diode – transistor logic 晶体管-二极管-晶体管逻辑tdtl tunnel – diode transistor logic 隧道二极管晶体管逻辑(电路)tdtw three dimensional three wire 三线三度重合(磁芯存储器).tdw turbo debugger for windows的配置文件格式〖后缀〗tdx time division exchange 时间分开的交换机,时分交换机tdy task dictionary 任务索引表te terminal equipment 终端设备te terminal exchange 终端交换机,终端局te terminal extender 终端扩展器te test equipment 测试设备te thin ethernet 细缆以太网te track error 寻轨误差〖光驱〗te trunk equipment 中继设备tea technical excellence award 技术卓越奖(美国《个人电脑》杂志所设)tead terminating extended area descriptor 终结扩展区描述符team terminology evaluation and acquisition method 术语鉴定与取得方法tebol terminal business – oriented language 面向终端事物的语言tec tokyo electric co. 东京电气公司techsat technology satellite 技术卫星tecnet tokyo experiment computer network 东京实验计算机网ted tiny editor 小型编辑器,普通编辑器ted translation error detector 翻译错误检测器ted trite enterprise desktop 陈旧的企业桌面系统ted trunk encryption device 中继线加密设备.tef relisys tefax的传真文件格式〖后缀〗tei terminal end – point identifier 终端端点标识符tekram 建邦公司(中国台湾,出品主板),建邦主板tektronix 泰克公司〖厂标〗,见:titel task execution language 任务执行语言tel telephone 电话tel terminal executive language 终端执行语言.tel telnet的主机文件格式〖后缀〗telecom telecommunications 远程通信teleconf teleconference 电话会议teledac telemetric data converter 遥测数据变换器teledyne 特利丹公司(美国,电子)telemux telegraph multiplexer 电报多路复用器telenet telecommunications network 远程通信网络,远程网,泰勒网(美国远程通信公司1977年开始兴建)teleran television radar navigation system 电视雷达导航系统telesat telecommunication satellite 通信卫星telewest (英国的著名通信公司)〖厂标〗telex telegraph exchange 电传打字机telnet (telnet protocol) 远程登录协议(服务提供商),远端签入协议(中国台湾用语)〖因特网〗telops telemetry on line processing system 遥测在线处理系统telt teletext 图文电视tem transcend enterprise manager 超常的企业管理程序tem true execute mode 真执行模式.tem borland c++的turbo editor宏语言脚本文件格式〖后缀〗.tem iconauthor的输入模板文件格式〖后缀〗ten trunk equipment number 中继设备编号tep terminal error program 终端错误程序ter magnetic tape release 磁带释放ter tag edge router 标记边缘路由器ter time and event recorder 时间与事件记录器terms terminal management system 终端管理系统terps terminal enquiry / response programming system 终端询问与应答编程系统(英国研制)tes tape error statistics 磁带错误统计tes telemetering evaluation station 遥测结果计算站tes telephone earth station 电话地面站tes terminal emulator service 终端仿真服务tes text editing system 文本编辑系统tes transportable earth station 可运输的地面站tesdi testing and diagnosis aid 测试与诊断助理tes / ies telephone earth station / isdn earth station 电话地面站 / 综合服务数字网地面站系统〖因特网〗tet text enhanced technology 文本增强技术〖扫描仪〗tet text equipment tester 文本设备检测器tet text equipment tool 文本设备工具tews tactical electronic warfare system 战术电子战系统tex teleprinter exchange 电传打字机报文交换机.tex scientific word的文本文件格式〖后缀〗.tex idealist数据表文件格式〖后缀〗tf french southern territories 法属南半球领地(域名)tf texture farm 纹理加工厂公司(美国,出品多媒体创作工具)tf throughput with fax 带传真的吞吐量tf transparency film 透明胶片〖打印机介质〗tf trusted functionality 可靠泛函性,可信功能tf tween frame 过渡帧.tf turbo profiler的配置文件格式〖后缀〗tfa telex file adapter 电传文件适配器tfa transfer function analyzer 传送函数分析器tfa transfer – allowed 允许传送.tfa turbo profiler的区域文件格式〖后缀〗tfc telegraph facility control 电报设备控制tfc thin film circuit 薄膜电路tfc transfer – controlled 受控传送tfc transfer function computer 移动函数计算机tfc transmission fault control 传输故障控制tfc trigonometric function computer 三角函数计算机.tfc tobi's floppy cataloguer的编目文件格式〖后缀〗tfd transaction flow diagram 事务处理流程图tfdn transferred directory number 传送的名录编号tfel thin – film electroluminescent 薄膜电致发光(显示器)tfen transferred equipment number 传送的设备编号tfh thin film head 薄膜磁头.tfh turbo profiler的帮助文件格式〖后缀〗tfi thin film inductive 薄膜感应技术(1960 – 1996)〖硬盘〗tflops trillion floating – point operations 每秒万亿次浮点运算.tfm intellifont的公制标记字体文件格式〖后缀〗.tfm tex的公制字体文件格式〖后缀〗tfm time quantized frequency modulation 时间量化频率调制tfms trunk and facilities maintenance system 中继线设备维护系统tfr terrain – following radar 地形跟踪雷达tfr transfer restricted 受限传送tfs texture filtering speed 纹理过滤速度〖显卡测试〗tfs traffic forecasting system 通信量预测系统tfs translucent file service 半透明文件服务(sun公司的).tfs turbo profiler的统计信息文件格式〖后缀〗tfsm 24 / 7 media 七·二四媒介公司(美国,网络广告公司)tfs / trs trunk forecasting system and traffic routing system 中继线预测系统 / 通信交往路由选择系统tft thin film technology 薄膜技术tft thin film transistor 薄膜晶体管tft-lcd thin film transistor – liquid crystal display 薄膜晶体管液晶显示器tftp trivial file transfer protocol 琐碎文件传送协议,日常文件传送协议,普通文件传输协议〖因特网〗tg terminal guidance 终端制导tg test generator 测试码发生器tg tracking and guidance 跟踪与制导tg togo 多哥(域名)tg tone generator 双音频发生器tg transmission gate 传输门tg trunk group 中继群.tg1 on target的项目文件格式〖后缀〗.tga targa图像文件,美国truevision 公司开发的位图图形文件格式〖后缀〗tgb trunk group busy 中继群忙tgcs transportable ground communications station 可运输的地面通信站tgdk transparent gateway developers kit 透明的网关开发者工具箱tgid transmission group identifier 传输组标识符tgid trunk group identification 中继群识别tgn trunk group number 中继群编号tgs telemetry ground station 地面遥测站.tgz 由tar和gnuzip (.tar.gz) 生成的压缩存档文件格式〖后缀〗th talking head 特写头像th ted hoff 特德·霍夫(微处理器之父)th terminal handler 终端处理程序th thailand 泰国(域名)th towers of hanoi 汉诺塔测试th trap handler routine 陷阱处理程序例程thc terminal – to – host connection 终端-主机连接thd total harmonic distortion 总谐波失真.thd 线程文件格式〖后缀〗theme hospital 《主题医院》〖游戏名〗thor tape – handling option routines 带处理选项例程(霍尼韦尔公司研制)thp terminal handling processor 终端处理器threconf (three way conference facility) 三方会议设备.ths wordperfect for win的词库文件格式〖后缀〗thunderbird “雷鸟”——“阿斯龙”系列cpu在2000年的开发代号之一(amd公司的)thus “c盘杀手”〖病毒〗ti target identification 目标识别ti target indicating 目标显示,目标指示ti technical inspection 技术检查ti tektronix inc. 泰克公司(美国,出品打印机及相关软件)ti terminal interface 终端接口ti texas instruments inc. 德州仪器公司,得克萨斯仪器公司(美国电子行业巨头,出品笔记本电脑、外围硬件等)ti thermal imager 热成像ti time interval 时间间隔ti transform implement 变换实现tia telecommunications industries association 远程通信行业协会tia telecommunication information administration 远程通信信息管理部门tia telematic interworking application 远程信息处理协助应用tia telephone industry association 电信工业协会tia thanks in advance 先谢了,提前致谢(网语)tia the travel industry association of america 美国移动工业协会(ansi认可)tia trigger internet adapter 切换开关的因特网衔接器tic target intercept computer 目标截击计算机tic task interrupt control 任务中断控制tic technical information center 技术信息中心(ibm的)tic time compression 时间压缩tic transfer in channel 信道传送tica technical information center administration 技术情报中心管理局(美国)ticoss time compressed single sideband system 时间压缩单边带系统tics telecommunication information control system 远程通信信息控制系统tics terminal interface control system 终端接口控制系统tid tag information database 标记信息数据库tid technical information division 技术情报部(美国原子能委员会属下)tid terminal identification 终端识别tid terminal identifier 终端标识符tid technical information exchange 技术信息交换tid thread id 线程标识符tid touch input device 接触式输入设备tie technical information exchange 技术情报交换处(美国国家标准局属下)tie terminal interface equipment 终端接口设备ties technical information exchange system 技术信息交换系统ties transmission and information exchange system 传输与信息交换系统tif technical information file 技术信息文件tif telephone interference factor 电话机干扰因素.tif aldus 开发的位图图形文件格式〖后缀〗.tif pagemaker和coreldraw的标签图像文件格式〖后缀〗tiff tagged image file format 标记图像文件格式(由aldus和微软联合开发)。

脑科学的黑科技 英语

脑科学的黑科技 英语

脑科学的黑科技英语Brain Science: The Dark TechnologyThe field of brain science has been rapidly evolving, unlocking secrets and unveiling remarkable advancements that were once thought to be the realm of science fiction. From the ability to read and manipulate human thoughts to the development of brain-computer interfaces, the breakthroughs in this domain have been both awe-inspiring and unsettling. As we delve deeper into the mysteries of the human mind, we find ourselves confronted with a double-edged sword – the immense potential for good, and the equally daunting potential for abuse.One of the most captivating developments in brain science is the ability to read and interpret human thoughts. Through the use of advanced neuroimaging techniques and machine learning algorithms, researchers have demonstrated the feasibility of decoding the neural patterns associated with specific thoughts and mental states. This technology has profound implications for fields such as mental health, cognitive enhancement, and even lie detection. Imagine a world where the inner workings of the mind are no longer hidden, where our thoughts and emotions can be accessedand analyzed with unprecedented precision.While this level of insight into the human mind holds immense potential for improving our understanding of the brain and developing more effective therapies, it also raises significant ethical concerns. The prospect of having our most private thoughts and memories accessible to others, without our consent, is a chilling thought. The implications of such technology in the hands of governments, corporations, or malicious actors are far-reaching and potentially devastating. Imagine the implications of a totalitarian regime that can monitor and manipulate the thoughts of its citizens, or a corporation that can exploit the neural patterns of its employees to maximize productivity and profit.Another remarkable development in brain science is the advancement of brain-computer interfaces (BCIs). These technologies aim to create a direct communication pathway between the human brain and external devices, allowing for the control of various electronic systems through the power of thought alone. From prosthetic limbs that can be controlled by the mind to gaming experiences that are entirely driven by neural activity, the potential applications of BCIs are truly staggering.However, the development of these technologies has also raised concerns about the ethical implications of merging the human mindwith machines. Questions of personal autonomy, privacy, and the blurring of the line between human and machine become increasingly complex. Imagine a world where our thoughts and actions are no longer solely our own, but are influenced or even controlled by external devices or software. The potential for manipulation, addiction, and the erosion of individual agency becomes a pressing concern.Moreover, the advancement of brain science has also led to the exploration of neural enhancement technologies. From the development of drugs and devices that can improve cognitive function to the prospect of direct brain-to-brain communication, the ability to augment and expand the capabilities of the human mind is a tantalizing prospect. Yet, this too comes with its own set of ethical quandaries.The prospect of creating a class of "enhanced" individuals raises concerns about fairness, equity, and the potential for societal stratification. If access to these technologies is limited or unevenly distributed, it could lead to the creation of a divide between those who can afford the enhancements and those who cannot. This could exacerbate existing social and economic inequalities, further marginalizing already disadvantaged groups.Furthermore, the long-term effects of neural enhancementtechnologies on the human brain and psyche are largely unknown. The potential for unintended consequences, such as cognitive impairments, personality changes, or the disruption of natural cognitive development, must be carefully considered before widespread adoption.As we continue to delve deeper into the realm of brain science, it is crucial that we approach these advancements with a keen sense of ethical responsibility. The power to read, manipulate, and enhance the human mind is a double-edged sword, and we must ensure that the pursuit of scientific progress is balanced with a deep consideration of the moral and societal implications.Policymakers, researchers, and the public must engage in robust and ongoing dialogues to establish robust ethical frameworks and regulatory mechanisms that can guide the development and deployment of these technologies. Only through a collaborative and thoughtful approach can we harness the immense potential of brain science while mitigating the risks and preserving the fundamental rights and dignity of the human individual.The future of brain science is both exciting and daunting. As we unravel the mysteries of the mind, we must remain vigilant and committed to ensuring that these advancements serve the greatergood of humanity, rather than becoming the tools of oppression, exploitation, or the erosion of our shared humanity.。

Advances in Brain-Computer Interfaces

Advances in Brain-Computer Interfaces

Advances in Brain-Computer Interfaces The advances in Brain-Computer Interfaces (BCIs) have been a topic of interest in the field of neuroscience and technology for a long time. BCIs are a means of communication between the brain and an external device, which can be used to control various applications such as prosthetic limbs, computers, and even vehicles. The technology has come a long way since its inception, and it has the potential to revolutionize the way we interact with machines. In this essay, we will explore the advances in BCIs and their implications from multiple perspectives.From a medical perspective, BCIs have the potential to help people with disabilities and neurological disorders. For instance, BCIs can be used to control prosthetic limbs, which can be very useful for amputees. This can improve the quality of life for people with disabilities and help them perform tasks that were previously impossible. BCIs can also be used to monitor brain activity and detect abnormalities, which can help in the diagnosis and treatment of neurological disorders such as epilepsy, Parkinson's disease, and Alzheimer's disease. This can lead to more personalized and effective treatment plans, which can improve patient outcomes.From a technological perspective, BCIs have the potential to revolutionize the way we interact with machines. BCIs can be used to control computers, smartphones, and other devices using only our thoughts. This can lead to more efficient and intuitive interfaces, which can improve productivity and user experience. BCIs can also be used to control vehicles, such as drones and self-driving cars, which can improve safety and reduce accidents. This technology can also be used in gaming and entertainment, which can enhance the overall experience for users.From an ethical perspective, BCIs raise several concerns regarding privacy and security. BCIs can be used to monitor our thoughts and emotions, which can be a violation of our privacy. This technology can also be used for surveillance and control, which can be a threat to our freedom and autonomy. BCIs can also be hacked, which can lead to the theft of sensitive information and even the control of our thoughts. These concerns must be addressed before BCIs become widely adopted.From a societal perspective, BCIs have the potential to create new opportunities and challenges. BCIs can create new jobs in the field of neuroscience and technology, which can stimulate economic growth. BCIs can also lead to new forms of entertainment and social interaction, which can enhance our social lives. However, BCIs can also widen the gap between the rich and the poor, as the technology may only be accessible to those who can afford it. This can lead to social inequality and discrimination, which must be addressed.From a personal perspective, BCIs can have a profound impact on our lives. BCIs can improve our physical abilities and help us perform tasks that were previously impossible. BCIs can also improve our cognitive abilities, such as memory and attention, which can enhance our overall quality of life. However, BCIs can also change the way we perceive ourselves and our relationship with technology. BCIs can blur the line between our thoughts and external devices, which can raise questions about our identity and autonomy.In conclusion, the advances in BCIs have the potential to revolutionize the way we interact with machines. BCIs can improve the quality of life for people with disabilities and neurological disorders, create new opportunities for economic growth, and enhance our social lives. However, BCIs also raise concerns regarding privacy, security, social inequality, and personal identity. These concerns must be addressed before BCIs become widely adopted. Overall, BCIs are a fascinating technology that has the potential to change our lives in many ways.。

人工智能不会让大脑变懒英语作文

人工智能不会让大脑变懒英语作文

全文分为作者个人简介和正文两个部分:作者个人简介:Hello everyone, I am an author dedicated to creating and sharing high-quality document templates. In this era of information overload, accurate and efficient communication has become especially important. I firmly believe that good communication can build bridges between people, playing an indispensable role in academia, career, and daily life. Therefore, I decided to invest my knowledge and skills into creating valuable documents to help people find inspiration and direction when needed.正文:人工智能不会让大脑变懒英语作文全文共3篇示例,供读者参考篇1AI Won't Make Our Brains LazyWith the rapid advancement of artificial intelligence (AI) technology, concerns have been raised about the potential impact it could have on our cognitive abilities. Some peopleworry that relying too heavily on AI tools and assistance might cause our brains to become lazy or complacent, leading to a decline in critical thinking and problem-solving skills. However, I believe these concerns are unfounded and stem from a misunderstanding of how AI works and its role in our lives.First and foremost, it's important to recognize that AI is a tool, much like any other technology we've embraced throughout history. Just as calculators didn't make us lazy mathematicians and word processors didn't make us lazy writers, AI won't make us lazy thinkers. Instead, these technologies have enabled us to focus our mental energy on higher-order tasks and more complex problems.Take language translation as an example. In the past, we relied on human translators or cumbersome dictionaries to communicate across linguistic barriers. Today, AI-powered translation tools allow us to quickly and accurately translate text, enabling seamless communication and collaboration on a global scale. This not only saves time and effort but also opens up new opportunities for learning and cultural exchange. Far from making our brains lazy, it frees up cognitive resources that can be devoted to other pursuits.Similarly, AI-assisted writing tools can help streamline the writing process by suggesting improvements in grammar, style, and organization. However, the actual act of crafting ideas, structuring arguments, and expressing thoughts still requires human input and creativity. AI cannot (yet) replicate the nuanced thinking and emotional resonance that characterizes great writing. Instead, it serves as a supportive aid, allowing us to focus on the higher-order cognitive tasks involved in the writing process.In the realm of education, AI tutoring systems and personalized learning environments have the potential to revolutionize how we acquire knowledge. By tailoring content and pacing to individual needs and learning styles, AI can help students better grasp and retain information. This doesn't make their brains lazy; rather, it empowers them to learn more efficiently and effectively, fostering a love for learning and intellectual curiosity.Moreover, as AI systems become more advanced, they will increasingly be employed in complex problem-solving tasks across various domains, from scientific research to business strategy. Far from rendering our brains obsolete, this will require humans to develop even stronger critical thinking and analyticalskills to interpret and apply the insights generated by AI. We will need to ask the right questions, evaluate multiple perspectives, and make informed decisions based on the information provided by AI systems.It's also worth noting that the human brain is remarkably adaptable and has consistently risen to the challenges posed by technological advancements throughout history. Just as the advent of calculators didn't diminish our ability to perform mental arithmetic, the integration of AI into our lives will likely stimulate new cognitive pathways and ways of thinking. We may need to develop new skills and strategies for working alongside AI systems, but this will only serve to enhance our cognitive capabilities rather than diminish them.Ultimately, the fear that AI will make our brains lazy stems from a misunderstanding of the complementary relationship between human intelligence and artificial intelligence. AI is not a replacement for human cognition but rather a tool to augment and enhance our abilities. By offloading certain tasks to AI systems, we free up cognitive resources to focus on higher-order thinking, creativity, and problem-solving.Of course, as with any technology, it's essential to strike a balance and use AI judiciously. Overreliance on AI couldpotentially lead to complacency or a diminished capacity for certain skills if we neglect to exercise them. However, this is a matter of responsible use and moderation, not an inherent flaw in the technology itself.As students and lifelong learners, we must embrace AI as a tool to aid our intellectual growth and development. By leveraging the power of AI to streamline certain tasks and provide insights, we can devote more mental energy to the pursuit of knowledge, the exploration of new ideas, and the cultivation of critical thinking skills that will serve us well in an increasingly complex and technologically advanced world.In conclusion, the notion that AI will make our brains lazy is a misconception rooted in a fear of change and a lack of understanding of how AI truly works. Rather than rendering our cognitive abilities obsolete, AI has the potential to enhance and augment them, freeing us to focus on higher-order thinking and problem-solving. As long as we approach AI with a balanced and responsible mindset, recognizing its role as a tool to aid and empower us, we can harness its potential while continuing to develop and strengthen our intellectual capabilities.篇2Artificial Intelligence Will Not Make Our Brains LazyWith the rapid advancement of artificial intelligence (AI) technologies, many people are concerned that we are becoming overly reliant on AI systems, leading to a potential decline in our cognitive abilities. The fear is that as AI takes over more and more tasks, our brains will become "lazy" and lose their sharpness. However, I firmly believe that this notion is misguided and that AI can actually be a powerful tool to enhance our intellectual capabilities if used wisely.First and foremost, it's essential to understand that AI is not a replacement for human intelligence; rather, it is a technology designed to augment and complement our abilities. AI systems are incredibly adept at processing vast amounts of data, identifying patterns, and performing complex calculations at lightning speeds. However, they lack the creative thinking, emotional intelligence, and contextual understanding that humans possess.By leveraging AI's strengths in data processing and computation, we can offload tedious and repetitive tasks, freeing up our cognitive resources to focus on higher-order thinking, problem-solving, and creative endeavors. For example, in the field of medicine, AI algorithms can rapidly analyze vast amountsof patient data, medical literature, and imaging scans, enabling doctors to make more informed decisions and develop personalized treatment plans. This not only enhances the quality of care but also allows physicians to spend more time interacting with patients and exercising their diagnostic andproblem-solving skills.Similarly, in the realm of education, AI-powered adaptive learning systems can personalize the learning experience for each student, identifying their strengths and weaknesses, and tailoring content and pace accordingly. This not only makes the learning process more efficient but also encourages students to actively engage with the material, fostering critical thinking and problem-solving abilities.Moreover, AI can serve as a powerful research tool, enabling scientists and scholars to analyze vast datasets, simulate complex phenomena, and uncover patterns and insights that would be nearly impossible for human minds to discern alone. Far from making our brains lazy, AI empowers us to ask more profound questions, explore new frontiers of knowledge, and push the boundaries of human understanding.It's important to recognize that the human brain is a remarkable organ, capable of adapting and evolving in responseto new challenges and stimuli. Just as the rise of calculators and computers did not lead to a decline in our mathematical abilities, the advent of AI will not inherently make our brains lazy. Instead, it will present us with new opportunities to exercise our cognitive faculties in novel and creative ways.For instance, as AI automates more routine tasks, we will be challenged to develop new skills and expertise in areas that require human ingenuity, such as creative problem-solving, strategic thinking, and interpersonal communication. These higher-order cognitive abilities are not easily replicable by AI systems and will become increasingly valuable in theAI-augmented workforce of the future.Furthermore, the development and deployment of AI systems require a deep understanding of ethics, societal implications, and responsible implementation. As AI becomes more pervasive, we will need to engage in robust public discourse, critical analysis, and ethical deliberation to ensure that these technologies are aligned with human values and serve the greater good. This intellectual exercise will demand our full cognitive engagement and prevent any potential "laziness" of the mind.Of course, it's crucial to acknowledge that the widespread adoption of AI technologies does carry certain risks, such as job displacement, privacy concerns, and the potential for AI systems to be misused or manipulated. However, these challenges should not deter us from embracing AI but rather motivate us to develop a deeper understanding of these technologies, their implications, and how to mitigate potential negative impacts.In conclusion, the notion that artificial intelligence will make our brains lazy is a misconception born out of fear and a lack of understanding of the true nature and potential of AI. Instead of perceiving AI as a threat, we should embrace it as a powerful tool to augment our cognitive abilities, freeing us from mundane tasks and enabling us to focus on higher-order thinking, creative problem-solving, and the pursuit of knowledge.By harnessing the power of AI while cultivating our uniquely human traits, such as emotional intelligence, creativity, and ethical reasoning, we can unlock new frontiers of discovery and innovation. The future belongs to those who can seamlessly integrate AI into their workflows and leverage its capabilities to enhance their cognitive prowess. Ultimately, AI will not make our brains lazy; it will challenge us to become sharper, more adaptable, and more intellectually curious than ever before.篇3Artificial Intelligence Won't Make Our Brains LazyWith the rapid advancement of artificial intelligence (AI) technology, there has been a growing concern that our reliance on these intelligent systems might lead to a decline in our cognitive abilities, making our brains "lazy." However, as a student navigating the ever-evolving landscape of education and technology, I firmly believe that AI will not make our brains lazy; instead, it has the potential to enhance our learning experiences and intellectual growth.First and foremost, it's crucial to understand that AI is not a replacement for human intelligence; rather, it is a tool designed to augment and complement our cognitive capabilities. Just as calculators have not made us less adept at mental arithmetic, AI will not diminish our ability to think critically and problem-solve. Instead, it will free us from mundane and repetitive tasks, allowing us to focus our mental energies on higher-order thinking and creativity.One of the primary ways AI can benefit students is through personalized learning. Traditional classroom settings often struggle to cater to the diverse learning needs of each individualstudent. AI-powered adaptive learning systems, however, can analyze a student's strengths, weaknesses, and learning styles, and then tailor the content and pace of instruction accordingly. This personalized approach ensures that students receive the support they need, while also challenging them to continuously grow and develop their skills.Moreover, AI can enhance our research andinformation-gathering capabilities. With the vast amount of data available online, it can be overwhelming and time-consuming to sift through and find relevant information. AI-powered search engines and research assistants can help us navigate this information overload, providing us with curated and reliable sources, saving us valuable time and mental energy that can be redirected towards analyzing and synthesizing the information.Another area where AI can prove invaluable is in fostering collaborative learning environments. AI-powered virtual assistants and chatbots can facilitate group discussions, provide real-time feedback, and offer personalized guidance to students working on collaborative projects. This not only enhances teamwork and communication skills but also encourages deeper engagement with the subject matter, further stimulating our cognitive processes.Furthermore, AI can revolutionize the way we approach complex problem-solving. Through machine learning algorithms and advanced simulations, AI can model and analyze intricate systems, offering insights and solutions that might be difficult or impossible for the human mind to conceive alone. By leveraging these AI-powered tools, we can push the boundaries of our understanding and tackle challenges that were once considered insurmountable.It's important to note, however, that while AI can be a powerful ally in our learning journey, it should not be treated as an infallible source of knowledge. As students, we must cultivate critical thinking skills and maintain a healthy skepticism towards the information and recommendations provided by AI systems. We should strive to understand the underlying principles and limitations of these technologies, and use them as a means to supplement and enhance our own reasoning abilities, not replace them entirely.Additionally, as we embrace AI in education, we must be mindful of potential ethical concerns and work towards developing responsible and transparent AI systems. Issues such as data privacy, algorithmic bias, and the impact of AI on employment and society at large must be addressed to ensurethat these technologies are deployed in a manner that benefits humanity as a whole.In conclusion, the notion that AI will make our brains lazy is a misconception rooted in fear and a lack of understanding. As students, we should embrace the opportunities that AI presents while remaining vigilant and critical in our approach. By harnessing the power of AI as a tool to augment our cognitive abilities, we can unlock new frontiers of learning, foster creativity, and tackle complex challenges more effectively. In the end, it is our willingness to adapt, learn, and integrate AI into our educational experiences that will determine whether our brains remain sharp and engaged or succumb to complacency.。

智能机器会让人的大脑变懒吗英语作文

智能机器会让人的大脑变懒吗英语作文

智能机器会让人的大脑变懒吗英语作文全文共3篇示例,供读者参考篇1Will AI Make Our Brains Lazy?The rapid advancement of artificial intelligence (AI) technology has sparked concerns about its potential impact on the human brain. As AI systems become increasingly capable of performing tasks that once required human intelligence, some fear that our reliance on these technologies could lead to a decline in cognitive abilities, making our brains "lazy." In this essay, I will explore both sides of this argument, drawing upon research and examples to determine whether AI truly poses a threat to our mental faculties.On one hand, the integration of AI into our daily lives could indeed lead to a certain level of mental complacency. WithAI-powered digital assistants handling a wide range of tasks, from scheduling appointments to answering factual queries, we may become overly reliant on these technologies and less inclined to exercise our own cognitive skills. For instance, instead of mentally calculating a tip or memorizing a phone number, wemay instinctively turn to our AI-enabled devices for assistance. This habitual offloading of mental tasks to AI could, over time, lead to a diminished capacity for critical thinking,problem-solving, and memory retention.Moreover, the widespread use of AI in education could potentially foster a passive learning environment, where students become accustomed to having information readily provided by AI tutors or learning algorithms, rather than actively engaging in the process of knowledge acquisition and synthesis. This could hinder the development of important cognitive skills, such as analytical reasoning, creative thinking, and self-directed learning.Additionally, the increasing automation of various professions by AI systems could potentially lead to a decline in the cognitive demands placed on workers. As AI takes over routine tasks that once required human intelligence, there may be a reduced need for professionals to exercise their mental faculties to the same extent, potentially leading to cognitive stagnation or even regression.On the other hand, proponents of AI argue that these technologies can actually enhance and augment human cognitive abilities, rather than diminish them. By offloadingmundane or repetitive tasks to AI systems, humans can free up mental resources to focus on more complex, higher-order thinking and creative endeavors. For example, an AI writing assistant could handle the tedious aspects of drafting and editing, allowing the human writer to concentrate on developing ideas, structuring arguments, and crafting compelling narratives.Furthermore, AI can serve as a powerful tool for learning and knowledge acquisition. AI-powered educational platforms can adapt to individual learning styles, providing personalized instruction and feedback tailored to each student's needs and pace. This could foster a more engaging and effective learning experience, ultimately strengthening cognitive skills rather than diminishing them.Additionally, the integration of AI into various fields, such as healthcare, scientific research, and data analysis, could amplify human cognitive capabilities by providing powerful computational tools and insights that would be impossible for the human mind alone to achieve. For instance, AI systems can process vast amounts of data, identify patterns, and generate hypotheses, augmenting human researchers' ability to make groundbreaking discoveries and advance our understanding of complex phenomena.Ultimately, the impact of AI on the human brain will likely depend on how we choose to integrate and utilize these technologies. If we allow AI to replace or diminish cognitive activities altogether, there is a risk of mental complacency and a potential decline in certain cognitive abilities. However, if we approach AI as a complement to human intelligence, leveraging it as a tool to enhance our cognitive capacities and free up mental resources for more complex tasks, AI could actually serve to sharpen and augment our mental faculties.In my opinion, the key to mitigating the potential negative effects of AI on the human brain lies in striking a balance between offloading certain tasks to AI and actively engaging in cognitive activities that challenge and exercise our minds. We must remain vigilant in preserving and cultivating essential cognitive skills, such as critical thinking, problem-solving, and creativity, while simultaneously embracing AI as a powerful tool to augment and extend our intellectual capabilities.Furthermore, it is crucial to foster a culture of lifelong learning and intellectual curiosity, where individuals are encouraged to continuously expand their knowledge and exercise their cognitive faculties, rather than becoming complacent or overly reliant on AI. Educational institutions, inparticular, should prioritize the development of metacognitive skills and self-directed learning, equipping students with the tools and mindset necessary to navigate an AI-driven world while maintaining cognitive agility and resilience.In conclusion, while the rise of AI does present potential challenges to the preservation of human cognitive abilities, it also offers remarkable opportunities for cognitive enhancement and augmentation. By adopting a balanced and thoughtful approach to the integration of AI into our lives, and by prioritizing the cultivation of essential cognitive skills and lifelong learning, we can harness the power of AI to unlock new realms of human potential, rather than allowing it to make our brains lazy.篇2Will Intelligent Machines Make Our Brains Lazy?In today's rapidly advancing technological landscape, the development of intelligent machines has sparked intense debate and speculation about their potential impact on human cognition and intellectual capabilities. As students, we find ourselves grappling with the question: Will the increasing presence of artificial intelligence (AI) and sophisticatedalgorithms in our daily lives lead to a deterioration of our mental faculties, rendering our brains lazy and overly reliant on these technological aids?To begin exploring this complex issue, we must first understand the nature of intelligent machines and their current applications. AI systems, powered by intricate algorithms and vast amounts of data, are designed to mimic human intelligence and perform tasks that typically require human cognition, such as problem-solving, decision-making, and pattern recognition. From virtual assistants like Siri and Alexa to self-driving cars and advanced medical diagnostics, intelligent machines are already deeply embedded in our modern lives.Proponents of AI argue that these technologies will augment and enhance our cognitive abilities, freeing us from mundane and repetitive tasks, and allowing us to focus our mental energies on more complex and creative endeavors. They posit that by offloading routine cognitive processes to machines, we can conserve our mental resources and channel them towards higher-order thinking, innovation, and intellectual pursuits that truly challenge and stimulate our minds.On the other hand, critics warn that our increasing reliance on intelligent machines could lead to a gradual atrophy of ourcognitive skills. They argue that by outsourcing tasks to AI systems, we may become complacent and fail to exercise our own problem-solving abilities, leading to a decline in critical thinking, reasoning, and mental agility. Furthermore, the convenience and accessibility of these technologies could foster a culture of intellectual laziness, where we become overly dependent on them and neglect to develop and nurture our inherent cognitive capacities.As students, we stand at the intersection of this debate, and our experiences offer valuable insights into the potential impact of intelligent machines on our mental development. On one hand, the integration of AI-powered educational tools, such as adaptive learning platforms and intelligent tutoring systems, has undoubtedly enhanced our learning experiences. These technologies can tailor instruction to our individual needs, providing personalized feedback and adjusting the pace and content based on our strengths and weaknesses. This customized approach can foster more efficient learning and help us overcome specific cognitive barriers, ultimately strengthening our understanding and retention.Moreover, AI-driven research tools and information retrieval systems have revolutionized the way we access and processknowledge. With vast databases and powerful search algorithms at our fingertips, we can quickly locate and synthesize information from a multitude of sources, facilitating more comprehensive and well-informed research endeavors. This exposure to diverse perspectives and vast pools of knowledge could potentially stimulate our intellectual curiosity and encourage us to engage in more rigorous critical analysis and synthesis.However, we must also consider the potential pitfalls of relying too heavily on these technologies. As AI systems become increasingly sophisticated, there is a risk that we may become overly dependent on them for problem-solving anddecision-making, leading to a gradual erosion of our ability to think critically and independently. If we habitually defer to the solutions provided by intelligent machines without questioning or understanding the underlying reasoning, we may inadvertently stunt the development of our own analytical and reasoning skills.Furthermore, the abundance of information and instant gratification offered by AI-powered search engines and virtual assistants could cultivate a culture of intellectual passivity. Rather than deeply engaging with complex ideas and concepts, we maysuccumb to the temptation of seeking quick, ready-made answers, neglecting the mental effort required for genuine understanding and knowledge acquisition.As students navigating this rapidly evolving landscape, it is crucial for us to strike a balance between leveraging the power of intelligent machines and actively nurturing our own cognitive abilities. We must approach these technologies with a critical mindset, using them as tools to enhance our learning and understanding, but not as substitutes for our own mental efforts.One way to achieve this balance is by actively engaging with the processes and reasoning behind AI-driven solutions. Rather than blindly accepting the outputs of these systems, we should strive to understand the underlying algorithms, data models, and decision-making processes. By developing a deeper comprehension of how these technologies work, we can better evaluate their strengths and limitations, and apply our own critical thinking skills to assess and validate their outputs.Additionally, we must consciously prioritize activities and practices that challenge and exercise our cognitive abilities. This could involve actively participating in classroom discussions, engaging in collaborative problem-solving exercises, and pursuing extracurricular activities that require critical thinking,creativity, and intellectual rigor. By consistently pushing ourselves to think deeply, analyze information from multiple perspectives, and synthesize complex ideas, we can counteract the potential for intellectual laziness and maintain the vigor of our mental faculties.Furthermore, it is essential for educators and educational institutions to adapt their curricula and pedagogical approaches to this evolving landscape. While integrating AI-powered tools and resources can enhance the learning experience, it is equally important to emphasize the development of critical thinking, problem-solving, and independent reasoning skills. This can be achieved through project-based learning, inquiry-driven assignments, and opportunities for students to grapple with open-ended problems and formulate their own solutions.In conclusion, the advent of intelligent machines presents both opportunities and challenges for our cognitive development as students. While these technologies undoubtedly offer powerful tools for augmenting our learning and expanding our access to knowledge, we must be vigilant against the potential for intellectual complacency and over-reliance. By actively engaging with these technologies, prioritizing activities that challenge our cognitive abilities, and fostering a culture ofcritical thinking and intellectual curiosity, we can harness the power of intelligent machines while simultaneously nurturing and strengthening our own mental faculties. Only by striking this delicate balance can we truly unlock the full potential of our minds and ensure that our brains remain agile, inquisitive, and primed for lifelong learning and intellectual growth.篇3Will Intelligent Machines Make Our Brains Lazy?Ever since the dawn of new technologies like artificial intelligence (AI) and advanced robotics, there has been an ongoing debate about the potential impact these innovations could have on the human mind. As a student witnessing the rapid integration of intelligent machines into various aspects of our lives, I can't help but ponder this burning question: Will these remarkable advancements lead to a decline in our cognitive abilities, rendering our brains lethargic and complacent?To delve deeper into this inquiry, we must first understand the nature of intelligent machines and their evolving capabilities. AI systems, for instance, are designed to mimic and potentially surpass human intelligence by processing vast amounts of data, identifying patterns, and making decisions based on complexalgorithms. From digital assistants that can answer our queries to self-driving cars that navigate through intricate traffic conditions, the applications of AI are truly mind-boggling.At first glance, the advent of such advanced technologies might seem like a blessing, promising to alleviate the mental strain imposed by arduous tasks and intricate problem-solving. With machines taking over tedious computations, data analysis, and even creative endeavors, one could argue that our cognitive resources could be redirected towards more profound and intellectually stimulating pursuits.However, this rosy picture raises some valid concerns. Excessive reliance on intelligent machines could potentially lead to a phenomenon known as "cognitive offloading," where our brains become increasingly dependent on external tools and devices, gradually losing the ability to perform certain mental functions independently. This dependency could result in a gradual erosion of our critical thinking, problem-solving, and memory skills, much like how the advent of calculators has diminished our ability to perform mental arithmetic.Moreover, the constant exposure to AI-powered systems that provide instantaneous solutions and curated information might condition our minds to expect immediate gratification,hampering our capacity for patience, perseverance, and deep contemplation. The risk of intellectual laziness looms large, as we become accustomed to having machines do the "heavy lifting" for us, both figuratively and literally.On the flip side, proponents of intelligent machines argue that these technologies can serve as powerful cognitive enhancers, augmenting our mental capabilities rather than diminishing them. By offloading routine tasks to machines, we can free up our cognitive resources to focus on higher-order thinking, creativity, and intellectual pursuits that truly exemplify the uniqueness of the human mind.Additionally, the integration of AI into educational settings holds the potential to revolutionize the learning experience. Personalized learning algorithms could adapt to individual learning styles and pace, ensuring that students receive tailored instruction and support. Interactive simulations and immersive virtual environments could make abstract concepts more tangible and engaging, fostering a deeper understanding and retention of knowledge.Ultimately, the impact of intelligent machines on our cognitive abilities will largely depend on how we choose to embrace and integrate these technologies into our lives. Withthe right mindset and a balanced approach, we can harness the power of these innovations to augment our intellectual capacities while simultaneously nurturing and exercising our inherent cognitive abilities.One potential solution lies in cultivating a symbiotic relationship between human and machine intelligence, where we leverage the strengths of both to achieve remarkable feats. By recognizing the unique capabilities of each entity, we can strike a harmonious balance, utilizing machines for tasks they excel at while reserving the more abstract, creative, and emotionally intelligent endeavors for the human mind.Moreover, educational curricula and lifelong learning initiatives should emphasize the development of critical thinking, problem-solving, and adaptability skills. These cognitive competencies will not only empower us to effectively navigate the ever-changing technological landscape but also equip us with the mental resilience to resist the potential pitfalls ofover-reliance on intelligent machines.In essence, the relationship between intelligent machines and human cognition is a delicate dance, one that requires careful choreography and a deep understanding of the intricate interplay between technology and the human mind. Byembracing these advancements with a judicious and mindful approach, we can harness their potential to enhance our intellectual capabilities while safeguarding against the risks of cognitive complacency.As a student poised to navigate a world increasingly intertwined with intelligent machines, I remain cautiously optimistic about the future. While the concerns surrounding cognitive laziness are valid, I believe that with the right strategies and a commitment to nurturing our inherent mental faculties, we can leverage the power of these technologies to propel ourselves towards new heights of intellectual achievement.The onus lies on us, as individuals and as a society, to strike the delicate balance between embracing innovation and preserving the essence of human cognition. Only then can we truly unlock the synergistic potential of intelligent machines and the remarkable capabilities of the human mind, paving the way for a future where technology serves as a catalyst for intellectual growth rather than a catalyst for cognitive stagnation.。

我想做一名帮助残疾人的志愿者英语作文

我想做一名帮助残疾人的志愿者英语作文

我想做一名帮助残疾人的志愿者英语作文I Want to Help People with DisabilitiesEver since I was a little kid, I've always wanted to help people. My parents taught me that we should be kind to others and lend a hand whenever we can. They said that's what makes the world a better place.Last year, my class went on a field trip to a center for people with disabilities. That's when I realized how much I really want to volunteer and assist those with special needs. Seeing the challenges they face every day made me appreciate how fortunate I am. It also inspired me to do something to make their lives a little bit easier.At the center, we met some really nice people who have disabilities like being in a wheelchair, being blind, or having trouble speaking. Our teacher told us these are called physical and intellectual disabilities. The staff explained that their brains or bodies don't work typically, which can make everyday tasks very difficult. But with some assistance, encouragement, and adaptive equipment, they can live fulfilling lives just like anyone else.I felt bad seeing how hard simple things are for them, like getting dressed, eating, reading, or just getting around. They have to work twice as hard as me for things I take for granted. One man named David showed us how he uses a computer with special software that reads out loud since he can't see the screen.A woman named Emily had childhood brain trauma, so she needs someone to remind her about daily routines and errands. Their lives require a lot of support and help from others.What impacted me most was their positive attitudes despite the obstacles they overcome each day. David joked about his guide dog getting him lost sometimes. Emily proudly showed off her painting skills, explaining art therapy is very rewarding for her. Hearing their stories of perseverance and optimism was truly inspiring. It made me appreciate the amazing capabilities of the human body and spirit.After our visit, I couldn't stop thinking about the people we met and how I could get involved to lend a hand. I did some research online about volunteer opportunities in my community for young people to assist the disabled population. There are so many ways to help!For example, volunteers are needed to read books onto audio recordings for the blind and visually impaired. Or you canbe a friend and pen pal to someone with an intellectual disability through letter writing. If you're an adult, you can work at food pantries ensuring accessibility or even get trained to be a service dog trainer. Kids my age can also collect toys, art supplies, and recreational items to donate.One opportunity that really caught my eye was getting to play games, do arts and crafts, or just hang out with disabled children and adults at a local center a few hours per week or month. The idea of being a buddy to someone in need of friendship and support really resonated with me. I have such a wonderful circle of friends and family members who let me know I'm cared for. Everyone deserves to feel that acceptance, compassion and sense of belonging.I decided that when I'm old enough, I absolutely want to volunteer in this capacity. Just being a friend who treats someone with a disability as an equal sounds so simple, yet can make such a positive difference in their life. They may not have the same abilities as me, but they have the same hopes, feelings, and desire for companionship that all people do.I have such an appreciation now for all the challenges those with disabilities face, yet also their incredible perseverance and zest for life. They haven't let their condition stop them fromliving live to the fullest! Their optimism and resilience is something we could all learn from.I know that dedicating my time to assist and befriend people with special needs will end up helping me as much as it helps them. Interacting with this population will teach me so much about acceptance, empathy, and not taking things for granted. It will help me become a more patient, understanding, and compassionate person overall.Mostly though, I'll get the rewarding feeling of making a positive impact and bringing some sunshine into someone's day who may not get as much social interaction as they need. I can be the person who makes them feel valued, respected, and important. That's the most meaningful gift I could give - the gift of friendship, kindness, and acceptance.I really look up to volunteers who generously devote themselves to selflessly helping others in need. They don't get paid and may never receive recognition, but they make such a difference. Just knowing they brightened someone's day and made their load a tiny bit lighter is what motivates them. True helpers have huge hearts and compassion for their fellow human beings.That's the kind of person I hope to become - someone who makes it their mission to use their abilities to assist those who face more difficulties and challenges than I do. I don't want to take my privileged life for granted. This world needs more people combating loneliness, isolation, and injustice for the disabled community through the simple acts of volunteering time and companionship.I may be young now, but I have a deep desire to be that person who goes out of their way to ensure no one feels alone or devalued just because their brain or body works differently. Everyone should feel accepted for who they are and have equal opportunities to live a fulfilling life and chase their hopes and dreams just like I can.That's my goal when I'm finally old enough to volunteer regularly - to be a positive force and reminder that people care about their disabled neighbors, friends, and loved ones. With some extra assistance and the compassion of volunteers, the playing field can be leveled. I want to be one of those helpers making that possible. Knowing I'll make someone's day brighter is all the motivation I need to give back and show disabilities don't have to disable a person's quality of life.Sure, I could spend my free time playing video games or watching TV. But using those hours to lend a hand to those who need it most will be so much more worthwhile and rewarding. It's a way to make a real difference, not just for them, but for building a more inclusive, accepting world for all. I can't wait to start my volunteering journey and be that positive force for change!。

关于用手机查作业的英语作文

关于用手机查作业的英语作文

The Evolution of Homework Assistance: TheRole of SmartphonesIn the fast-paced world of technology, smartphones have revolutionized almost every aspect of our daily lives. One such area where their impact has been profound is in the realm of education, particularly in the way students approach their homework. The traditional methods of seeking help from teachers, peers, or reference books have been supplemented by the convenient and instantaneous access to information provided by mobile devices.The integration of smartphones into homework assistance has brought about several significant advantages. Firstly, it provides students with instant access to a vast trove of knowledge. With just a few taps, they can search for answers to their homework questions, access online tutorials, or even connect with experts in their field of study. This level of accessibility not only saves time but also allows students to work more efficiently, without having to wait for physical resources or limited help from others.Moreover, smartphones enable personalized learning experiences. Unlike traditional methods, where students might have to rely on a one-size-fits-all approach, mobile devices allow individuals to tailor their learning based on their unique needs and abilities. Whether it's through adaptive learning apps that adjust to the student's pace and comprehension or through interactive tools that engage them visually and aurally, smartphones cater to a diverse range of learning styles and preferences.However, the rise of smartphones in homework assistance also presents some challenges. One such challenge is the potential for distraction. With so many apps and notifications constantly beckoning for attention, it can be difficult for students to maintain focus on their studies. Additionally, the ease with which information can be accessed might foster a sense of laziness or entitlement among some students, leading them to rely excessively on others' work without developing their own understanding or problem-solving skills.Despite these challenges, the role of smartphones in homework assistance is undeniably positive. They haveopened up new avenues for learning, making the process more convenient, efficient, and personalized. While it's crucial to strike a balance between technology use and traditional methods of learning, smartphones undoubtedly play a pivotal role in the modern education landscape.**手机查作业的革命性影响**在科技飞速发展的时代,智能手机几乎改变了我们日常生活的每一个方面。

关于智能辅助驾驶的英语作文

关于智能辅助驾驶的英语作文

关于智能辅助驾驶的英语作文英文回答:Intelligent Advance Driver Assistance Systems (ADAS)are rapidly evolving and becoming increasingly sophisticated. They have the potential to make our roads safer and more efficient.ADAS use a variety of sensors, including cameras, radar, and lidar, to monitor the vehicle's surroundings. This data is then processed by a computer, which makes decisionsabout how to control the vehicle.Some of the most common ADAS features include:Adaptive cruise control: This system maintains a safe distance between your vehicle and the vehicle in front of you.Lane keeping assist: This system helps you stay inyour lane, even if you are distracted.Automatic emergency braking: This system can automatically brake your vehicle if it detects an imminent collision.ADAS can be a valuable tool for drivers, but it is important to remember that they are not a substitute for human attention. Drivers should always be aware of their surroundings and be prepared to take control of the vehicle if necessary.中文回答:智能辅助驾驶系统 (ADAS) 正在迅速发展并变得日益复杂。

发明高科技汽车作文英语

发明高科技汽车作文英语

发明高科技汽车作文英语题目,The Invention of High-Tech Cars。

In the realm of automotive innovation, the emergence of high-tech cars represents a remarkable advancement that has revolutionized the way we perceive and interact with vehicles. With the rapid integration of cutting-edge technologies such as artificial intelligence, electrification, and connectivity, high-tech cars have transcended traditional boundaries, offering unparalleled levels of safety, efficiency, and convenience to drivers and passengers alike.One of the most striking features of high-tech cars is their utilization of artificial intelligence (AI) systems to enhance driving experiences. Through the integration of advanced sensors and cameras, these vehicles possess the capability to analyze and interpret their surroundings in real-time, enabling them to make split-second decisions to avoid potential hazards and accidents. Whether it'sdetecting pedestrians, cyclists, or other vehicles on the road, AI-powered high-tech cars are equipped with sophisticated algorithms that continuously adapt to changing conditions, ensuring optimal safety for all road users.Furthermore, the advent of electrification has heralded a new era of sustainability and eco-friendliness in the automotive industry. High-tech cars are at the forefront of this revolution, with an increasing number of manufacturers shifting towards electric and hybrid powertrains to reduce carbon emissions and combat climate change. By harnessing the power of electricity, these vehicles offer a cleaner and more efficient alternative to traditional internal combustion engines, thereby mitigating the environmental impact associated with transportation while simultaneously delivering impressive performance and range.In addition to their advanced safety and environmental features, high-tech cars are also characterized by their seamless connectivity capabilities, which have transformed the driving experience in numerous ways. Through integratedinfotainment systems and smartphone connectivity, drivers and passengers can access a plethora of entertainment, navigation, and communication services with the touch of a button or a simple voice command. Whether it's streaming music, receiving real-time traffic updates, or staying connected to social media, high-tech cars enable occupants to stay connected and entertained while on the move, enhancing overall comfort and convenience.Moreover, high-tech cars are paving the way for the realization of autonomous driving technology, which promises to revolutionize the concept of mobility as we know it. By leveraging AI, sensors, and advanced mapping systems, these vehicles are capable of navigating roads and traffic conditions autonomously, without the need for human intervention. While fully autonomous vehicles are still in the development stage, the advent of semi-autonomous features such as adaptive cruise control and lane-keeping assistance has already begun to transform the way we approach transportation, offering a glimpse into a future where commuting is safer, more efficient, and less stressful.In conclusion, the invention of high-tech cars represents a monumental leap forward in automotive technology, offering a glimpse into a future where drivingis safer, cleaner, and more connected than ever before.With their advanced artificial intelligence systems, electrified powertrains, and seamless connectivity features, high-tech cars are not only reshaping the way we perceive transportation but also paving the way for a more sustainable and efficient future. As we continue to embrace and innovate upon these technologies, the possibilities for the future of mobility are truly limitless.。

脑机交互技术在医疗辅助中的应用与研究

脑机交互技术在医疗辅助中的应用与研究

脑机交互技术在医疗辅助中的应用与研究一、介绍脑机交互技术(Brain-Computer Interface, BCI)是指通过人体大脑与计算机之间的直接连接,实现对人脑活动的控制与反馈的一种技术。

近年来,随着科技水平的提升和应用场景的丰富,脑机交互技术在医疗辅助中的应用越来越受到关注。

本文将通过对脑机交互技术的介绍,重点探讨其在医疗辅助中的应用与研究。

二、脑机交互技术的基础现代脑机交互技术起源于20世纪70年代。

经过数十年的发展,目前已经实现了多种不同形式的脑机交互系统。

脑机交互技术的基础是人脑活动的测量和分析。

人脑的活动可以通过多种方式进行测量,例如电生理、功能磁共振成像(fMRI)、磁通断层成像(MRS)、近红外光谱(NIRS)等。

其中,电生理是目前最为常用的一种方法。

通过在头皮上安装多个电极,并记录脑电信号,可以获取脑部不同区域的神经信息。

此外,脑机交互技术还需要对人脑活动进行信号处理和模式识别。

信号处理是指对脑电信号进行滤波和降噪等处理,以提取出人脑活动的有效信息;模式识别则是针对特定的任务,通过机器学习等算法,训练计算机识别脑电信号的模式,在人脑活动发生时对其进行分类和判断。

三、脑机交互技术在医疗辅助中的应用1、脑机交互技术在康复医学中的应用脑机交互技术可以用于康复医学中的很多方面,例如中风后的功能恢复、脊髓损伤康复等。

通过脑机交互技术,患者可以通过意念控制机器人等设备进行日常生活活动,实现自主生活。

2、脑机交互技术在神经疾病治疗中的应用脑机交互技术可以用于神经疾病的治疗,例如癫痫、帕金森病等。

通过记录并分析人脑电信号,可以定位病变区域,进而针对性地进行治疗。

3、脑机交互技术在疼痛管理中的应用脑机交互技术可以用于疼痛管理。

通过记录脑电信号,可以实现对疼痛刺激的及时感知和处理,从而实现对疼痛的控制。

四、脑机交互技术在医疗辅助中的研究1、信号处理和模式识别算法的研究信号处理和模式识别算法是脑机交互技术研究中的重要方向。

脑机接口技术的自适应学习算法

脑机接口技术的自适应学习算法

脑机接口技术的自适应学习算法The adaptive learning algorithm in brain-computer interface technology represents a significant advancement in the field of neuroscience and technology integration. This algorithm is designed to dynamically adjust and optimize the interaction between the brain and computer systems, enabling more efficient and personalized communication.脑机接口技术的自适应学习算法是神经科学和技术融合领域的一大重要进展。

该算法旨在动态调整和优化大脑与计算机系统之间的交互,以实现更高效、更个性化的通信。

By continuously monitoring and analyzing brain signals, the adaptive learning algorithm is able to identify patterns and variations in neural activity. This information is then used to fine-tune the interface's parameters, ensuring a smoother and more responsive experience for the user.通过持续监测和分析大脑信号,自适应学习算法能够识别神经活动的模式和变化。

然后,这些信息被用来微调接口的参数,以确保用户获得更流畅、更灵敏的体验。

One of the key advantages of this algorithm is its ability to adapt to the unique characteristics of individual brains. As each person's brain functions differently, a one-size-fits-all approach is often inadequate. The adaptive learning algorithm, however, tailors theinterface to the specific needs and preferences of each user, maximizing its effectiveness.该算法的一个关键优势在于其能够适应不同个体大脑的独特特征。

脑机接口技术的自适应学习算法

脑机接口技术的自适应学习算法

脑机接口技术的自适应学习算法The adaptive learning algorithm in brain-computer interface technology represents a significant advancement in the field of human-machine interaction. This algorithm is designed to dynamically adjust its parameters based on the user's cognitive abilities and neural patterns, enabling a more personalized and efficient interface experience.脑机接口技术的自适应学习算法是人类与机器交互领域的一项重大进展。

这种算法旨在根据用户的认知能力和神经模式动态调整其参数,从而提供更加个性化和高效的接口体验。

The core of this algorithm lies in its ability to continuously learn and adapt. As the user interacts with the brain-computer interface, the algorithm collects data on neural activities, such as brainwaves and neural signals. Through sophisticated analysis and machine learning techniques, it identifies patterns and correlations in this data, allowing it to infer the user's intentions and preferences.该算法的核心在于其不断学习和适应的能力。

脑机接口技术的伦理问题探讨

脑机接口技术的伦理问题探讨

脑机接口技术的伦理问题探讨The ethical issues surrounding brain-computer interface technology are intricate and multifaceted. As this field of technology rapidly advances, it raises concerns about privacy, autonomy, and the potential misuse of personal data.脑机接口技术所涉及的伦理问题既复杂又多方面。

随着这一技术领域的迅速发展,它引发了关于隐私、自主性和个人数据可能被滥用的担忧。

One significant ethical concern is the potential infringement of privacy. Brain-computer interfaces involve capturing and analyzing brain signals, which could contain sensitive and personal information. If this data is not properly secured, it could be accessed by unauthorized individuals, leading to privacy violations.一个主要的伦理问题是可能侵犯隐私。

脑机接口涉及捕捉和分析脑信号,其中可能包含敏感和个人信息。

如果这些数据没有得到妥善保护,可能会被未经授权的人员获取,导致隐私泄露。

Autonomy is another ethical consideration. Brain-computer interfaces have the potential to enhance or even override human decision-making processes. This raises questions about whether individuals are truly making their own choices or if the technology is influencing their decisions. The balance between human autonomy and technological assistance needs to be carefully examined.自主性是另一个需要考虑的伦理问题。

电脑用途英语作文

电脑用途英语作文

电脑用途英语作文Title: The Diverse Applications of Computers。

In today's digital age, computers have become an indispensable part of our lives, permeating various aspects of society and revolutionizing how we work, communicate, and entertain ourselves. From the confines of our homes to the vast expanse of outer space, the utility of computers knows no bounds. Let's explore the diverse applications of computers that have transformed our world.1. Communication:Computers serve as the backbone of modern communication networks, facilitating instant messaging, email correspondence, video calls, and social media interactions across the globe. Through the internet, people can connect with others regardless of geographical barriers, fostering collaboration and sharing of ideas on an unprecedented scale.2. Education:In the realm of education, computers haverevolutionized the learning process. They provide access to vast repositories of knowledge through online libraries, educational websites, and digital textbooks. Furthermore, educational software and multimedia tools enhance classroom instruction, making learning more interactive and engaging for students of all ages.3. Business and Commerce:The business world relies heavily on computers for various operations, including data analysis, financial transactions, inventory management, and customer relationship management. E-commerce platforms leverage computer technology to facilitate online shopping, enabling consumers to browse, purchase, and receive goods with unparalleled convenience.4. Healthcare:Computers play a crucial role in modern healthcare systems, from managing patient records and scheduling appointments to assisting in medical diagnoses and treatment planning. Medical imaging technologies, such as MRI and CT scans, produce detailed images that aid healthcare professionals in diagnosing ailments and formulating treatment strategies.5. Entertainment:Entertainment industry thrives on computer technology, with video games, streaming services, and digital media platforms offering a plethora of content to audiences worldwide. Advanced graphics rendering and virtual reality technologies immerse users in captivating virtual worlds, while algorithms recommend personalized content based on individual preferences.6. Scientific Research:In the realm of scientific research, computersfacilitate complex simulations, data analysis, and modeling across various disciplines, including physics, biology, chemistry, and astronomy. High-performance computing clusters enable researchers to tackle grand challenges, such as climate modeling, drug discovery, and space exploration.7. Transportation:Computers play a vital role in modern transportation systems, optimizing traffic flow, managing logistics, and enhancing vehicle safety. Advanced driver-assistance systems (ADAS) leverage sensors and algorithms to assist drivers in navigation, collision avoidance, and adaptive cruise control, paving the way for autonomous vehicles.8. Aerospace and Defense:In the aerospace and defense sectors, computers power aircraft navigation systems, missile guidance systems, and reconnaissance satellites, ensuring precision, reliability, and security in military operations and space explorationendeavors. Supercomputers crunch massive amounts of datafor intelligence analysis and strategic planning.9. Agriculture:Even in agriculture, computers are making significant strides, with precision farming techniques utilizing sensors, drones, and automated machinery to optimize crop yields, monitor soil conditions, and conserve resources such as water and fertilizer. Agricultural management software assists farmers in decision-making, from planting schedules to pest control strategies.10. Personal Productivity:On a personal level, computers enhance productivity and creativity through office productivity suites, project management tools, graphic design software, and digital art applications. From drafting documents to composing music, individuals leverage computers to express themselves and accomplish tasks efficiently.In conclusion, the versatility and ubiquity of computers have transformed virtually every aspect of human endeavor, shaping the way we live, work, and interact with the world around us. As technology continues to evolve, the applications of computers will only expand, driving innovation and progress across all sectors of society.。

英语作文如何写键盘

英语作文如何写键盘

英语作文如何写键盘Title: The Evolution of Keyboards: A Comprehensive Overview。

Introduction:Keyboards have become an integral part of our daily lives, facilitating communication, work, and entertainment in the digital age. From the early typewriters to the modern ergonomic designs, the evolution of keyboards reflects advancements in technology and human-computer interaction. This essay delves into the history, design principles, and future trends of keyboards, exploring their impact on society and user experience.Historical Development:The journey of keyboards traces back to the invention of typewriters in the late 19th century. Christopher Latham Sholes, along with Carlos Glidden and Samuel W. Soule,developed the first commercial typewriter in 1873,featuring a QWERTY layout that remains prevalent to this day. Over the years, typewriters evolved with improved mechanisms and layouts, laying the foundation for computer keyboards.With the advent of computers in the mid-20th century, keyboards underwent significant transformations. The emergence of electronic keyboards replaced mechanical typewriters, offering enhanced functionality and efficiency. The introduction of the ASCII encoding standardstandardized keyboard layouts across different computer systems, fostering compatibility and ease of use.Design Principles:Keyboards are designed with careful consideration of ergonomic principles and user preferences. Traditional keyboards feature a QWERTY layout, optimized for typing efficiency and familiarity. However, alternative layouts such as Dvorak and Colemak have gained attention for their purported ergonomic benefits and typing speed improvements.In addition to layout, keyboard design encompasses factors such as key switch mechanisms, keycap materials, and form factor. Mechanical keyboards, characterized by individual switches beneath each key, offer tactile feedback and durability, catering to enthusiasts and professionals alike. Membrane keyboards, on the other hand, utilize a rubber dome or membrane layer for key actuation, providing a quieter typing experience at a lower cost.Moreover, ergonomic keyboards address ergonomic concerns by incorporating split designs, adjustable angles, and wrist rests to alleviate strain and promote comfort during extended use. These ergonomic features are particularly beneficial for individuals prone to repetitive strain injuries (RSI) or carpal tunnel syndrome (CTS).Impact on Society:Keyboards have revolutionized communication, enabling rapid exchange of information across digital platforms. The ubiquity of keyboards in computers, smartphones, tablets,and other devices has democratized access to information and empowered individuals to express themselves through written text.Furthermore, keyboards play a crucial role in various industries, including journalism, programming, and data entry. The efficiency and accuracy of typing directly influence productivity and workflow efficiency in these professions. As such, the design and functionality of keyboards directly impact the performance and well-being of users in both professional and personal settings.Future Trends:The future of keyboards is shaped by advancements in technology, user preferences, and emerging trends. With the rise of touchscreens and voice recognition technology, keyboards face competition from alternative input methods that offer intuitive and hands-free interaction. However, keyboards continue to evolve with innovative features such as customizable keycaps, RGB lighting, and wireless connectivity, catering to diverse user needs andpreferences.Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into keyboards enables predictive text input, autocorrection, and adaptive typing assistance. These intelligent features enhance typing speed and accuracy while adapting to individual typing styles and preferences.Conclusion:In conclusion, keyboards have come a long way sincetheir inception, evolving from mechanical typewriters to sophisticated input devices in the digital age. The design, functionality, and impact of keyboards continue to shape human-computer interaction and influence various aspects of society. As technology advances and user preferences evolve, the future of keyboards holds promise for innovation and enhanced user experiences.。

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Digital Object Identifier 10.1109/MCI.2015.2501550Date of publication: 13 January 2016Sareh Saeedi, Ricardo Chavarriaga, Robert Leeb, and José del R. MillánCenter for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SWITZERLANDAdaptive Assistance for Brain-Computer Interfaces by Online Prediction ofCommand ReliabilityCorresponding authors: Sareh Saeedi (e-mail: sareh.saeedi@epfl.ch), José del R. Millán (e-mail: lan@epfl.ch).32 IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2016 1556-603x/16©2016IEEEAbstract —One of the challenges of usingbrain-computer interfaces (BCIs) over extendedperiods of time is the variation of the users’ performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a ‘short’ or ‘long’ command delivery time, CDT). Using a short time window (<1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analy-sis classifier, we could reliably differentiate between short and long CDT (Area under the sen-sitivity-specificity curve, AUC . 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game by com-paring two conditions: (i) allowing a ‘fixed timeout’ to deliver each command or (ii) providing ‘adaptive assistance’ by giving more time if the performance estimator detects a long CDT . The results revealed that providing adaptive assistance increases the ratio of correct commands signifi-cantly ..p 001<^h Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance ..p 001<^h Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.Online resources —Supplementary materials can be found in http://infoscience.epfl.ch/record/212925/files/IEEECIM_supplementary .pdf. This document pro-vides a description of the ITR derivation (Section 2.1.3) as well as asecond set of experiments where we use the results obtainedin this article in a robotic navigation scenario.1. Introductionbrain-computer interfaces (BCIs) aim at offeringan interaction modality for people with severemotor disabilities. A common approach relies onthe decoding of sensorimotor rhythms (SMRs)measured using electroencephalography (EEG). Despitepromising advances, BCIs are still confronted with multi-ple challenges in determining user’s intentions reliably,mainly due to high performance variations among andwithin subjects [1].FEbruary 2016 | IEEE ComputatIonal IntEllIgEnCE magazInE 33Several studies have addressed the issue of performance variations in SMR-based BCIs. Most of these studies focus on inter-subject variability from a physiological [2–6], anatomical [7, 8], or psychological [9, 10] perspectives. Although precise distinction between user-related and system-related causes of performance variations may not be sim-ple [11], these studies provide a better understanding of these causes. T o tackle the system-related issues and to boost reliability , some studies have used adap-tive machine learning techniques [12], while others proposed methods for removing signal contamination (e.g. due to muscular or ocular artifacts) [13].Other studies have investigated the issue of intra-subject performance vari-ability . In a longitudinal study , motivational factors were shown to be correlated with BCI performance on participants with amyotrophic lateral sclerosis (ALS) [14]. Other studies have focused on neurophys-iological markers. However, most of these studies are limited to the analysis of a sin-gle session using offline recordings (where no feedback was provided to the subject). For instance, it is suggested that classifica-tion certainty for each subject is positively correlated with the power of EEG oscilla-tions in the gamma band (55–85 Hz) [3, 6]. Similarly , trial-by-trial classification per-formance was found to correlate with high-frequency gamma oscillations (70–80 Hz) prior to the beginning of each trial [15], while others found correlations with a weighted combination of the theta (3–8 Hz), alpha (8–13 Hz), and beta (16–24 Hz) oscillations in frontal, pariatel and central areas of the brain, respectively [16]. Another study made a step forward by conducting both offline and onlinerecordings (where subjects received feed-back on their performance) in a single ses-sion. In this study , pre-cue SMRs in subject-specific electrodes and frequency bands were found to be positively correlated with sin-gle-sample classification performance [17].The mentioned studies provide a good insight into the cor-relates of intra-subject performance variability . However, they do not account for performance changes over extended peri-ods of time (i.e., over several sessions/days), neither do they exploit this information online. Thus, there are still several open research questions around this issue. First, the main goal of BCIs is to provide online decoding of users’ intentions. It©/shivendu Jauhari34 IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2016follows that it is essential to evaluate performance changes inonline sessions, as distribution of data normally differs between offline and online sessions because of the feedback subjects receive in the latter. Second, one of the challenges for a BCI is to cope with performance variations over extended periods of time (e.g., over different sessions/days). Hence, a method capa-ble of evaluating these variations over different sessions would improve usability and reliability . Third, it is crucial to predict performance on a short-time basis so as to compensate for the user’s varying capabilities while using a brain-controlled device.BCIs based on motor imagery (MI-BCIs) typically combine the outcome of classification for samples in a given time win-dow in order to improve reliability. Some implementations make a decision after a fixed amount of time [2]. Others accu-mulate evidence over time by integrating the classifier outputs until it reaches a certain threshold, at which point the command is delivered [18]. The latter approach results in different com-mand delivery time (CDT) across trials [19, 20]. Figure 1 illus-trates such variations in a BCI session for two subjects. Since the integrated probability should reach a threshold for a command to be delivered, long CDT should be due to lower average clas-sifier output for samples of the trial. Indeed, after separating the trials into short and long ones based on the median delivery time (MDT), we observe that the average classifier output across samples is higher for short trials compared to long ones (.p 0001< in Wilcoxon rank sum test, Figure 1).Having long CDT is usually frustrating for subjects and may reduce their engagement in performing the mental task. In addi-tion, it can increase the workload or affect the performance of the system if the BCI application has to meet temporal constraints. For example, consider the task of controlling a brain-actuated robot [19]. In this case, the user delivers right or left commands, while the robot moves forward if the subject voluntarily decides not to deliver any command. T o make the robot cross a doorway on the right side, the user needs to be fast enough to deliver the corre-sponding command at the proper moment. Otherwise, the robot will miss the doorway and the user will need to deliver additional commands to bring it back. In such cases, predicting that a com-mand is going to take a longer time to be delivered would be extremely helpful, as it would enable providing adequate assistance to the user. For instance, the speed of the robot can be reduced so that the user has enough time to make it cross the doorway .As a result, adding a performance estimator to the BCI system may allow to compensate for performance variations by adapting the interaction to the user’s current capabilities (e.g., through the use of shared control) [21]. Figure 2 shows such a system that works in parallel with the BCI and modulates action generation based on an estimation of the performance. The goal of this study is to pro-pose a method capable of making a trial-by-trial prediction of the performance in an MI-BCI in terms of the time it takes to deliver a command. This predictor estimates the reliability of a command (i.e., having ‘short’ or ‘long’ CDT) based on the input features to theBCI. For this approach to be useful, the estimation has to be performed at the very beginning of a mentaltask execution. Here, we show that the information within 1.5 s fromthe beginning of the MI execution is sufficient to make such a predic-tion. Importantly , the experiments were done in an online setting last-ing several sessions (2–3 dependingon the subject) so as to account for the effect of feedback on the mod-ulation of brain signals. Finally , the feasibility of using this method for online adaptation of assistance hasFigure 1 Distribution of command delivery time (CDT) for two subjects (sb1 and sb2) in an MI online protocol. The comparison of short and long trials (with respect to the median delivery time, MDT) reveals significant differences in the average classifier output ..p 00011^h Figure 2 Common block diagram of a BCI in the dashed box. The goal is to design a performance estimator, which works in parallel with the BCI and can be used to provide online adaptive assistancefor the user. In this study, performance estimation is achieved by a trial-by-trial prediction of commanddelivery time (CDT).been studied in a BCI game.In the Supplementary Mate-rials, we also investigate arobot navigation scenario.2. Materials andMethods2.1. ExperimentalProtocolFigure 3 illustrates the struc-ture of the experimentalprotocol, as well as the num-ber of sessions for each con-dition. First, in the MI train-ing phase, we built the MIclassifier based on an offlinesession and then tested it inan online session. Second,the subjects played an MI-BCI game. One to two ses-sions were required to tunethe parameters and to re-train the classifier in case a drop of performance was observed (Calibration).Third, 2 to 3 sessions of the MI-BCI game were conducted with the same classifier and fixed parameters (Evalua-tion), which were used to build the performance estimator (short vs. long classifier). Finally, a single session of the MI-BCI game was performed in order to apply online adaptive assistance in the game based on performance estimation.The number of subjects N sb^h participating in each phase is displayed on the left side of this figure.2.1.1. mI trainingNine healthy subjects (sb1-sb9; five females, age ..26542! years old) participated in a synchronous MI-BCI experiment. All participants gave written informed consent and the proto-cols were approved by the local ethics committee. T wo of the subjects (sb2, sb5) had previous experience with the MI-BCI. EEG was recorded using 16 electrodes over the sensorimotor cortex at 512 Hz and band-pass filtered between 0.1 Hz and 100 Hz. Laplacian spatial filtering was applied to the signal.MI training was performed as described in [18]. First, offline calibration recordings were conducted in order to train an MI classifier to discriminate between two mental tasks (e.g., imagina-tion of left or right hand movements).Then, the participants per-formed one online MI session (i.e., experiments performed on a day), comprising 4 to 6 runs in order to get familiar with the task. Each run consisted of 15 trials per mental task.The timing of one trial is depicted in Figure 4(a).The user was required to perform a mental task following the cue while receiving visual feedback from the classifier outputs (movement of the grey bar).The command was delivered as soon as the integrated classifier output surpassed a subject-specific threshold .th d^h Feedback was then given to the user showing that the trial has ended, which was followed by a brief rest period (random between 2 and 3 s). A key point in this experiment was that the subjects had unlimited amount of time during a trial to deliver a command.This can be observed in the distributions of CDT shown in Figure 1.The threshold th d was set based on previous experience so that more than 75% accuracy was achieved in the command delivery of both classes (right and left) in each run while having the majority of the CDT lower than 8 s.2.1.2. mI-bCI gameAn MI-BCI game was designed for this study to provide a more engaging environment for users (Figure 4(b)). In this game, sub-jects were asked to rescue a parachutist by moving a platformFigure 3 Experimental protocol: MI training was performed to calibrate the MI classifier and to allow the sub-jects to practice the mental tasks. Then, subjects played an MI-BCI game for several online sessions. Finally, subjects carried out another session of the game where they received online adaptive assistance.(a)Figure 4 Timing of an online trial in the MI-BCI experiments: (a) MIonline recordings: First, a cross is shown on the screen so that the userprepares for the task. Then the cue appears and the subject needs toexecute the relevant mental task. The duration of the task execution isdifferent across trials, as it depends on the integrated classifier output.Feedback is given to the user at the end of the trial and a rest periodfollows. (b) MI-BCI game: The subject controls the platform performingthe two selected mental tasks so that the parachutist lands on it. Theparachutist lands at second 8 as default, but it can have differentspeeds depending on the level of assistance. If the platform reaches thetarget on time, it changes color to green and otherwise to red.FEbruary 2016 | IEEE ComputatIonal IntEllIgEnCE magazInE 35right or left (defined by the cue).There was the same number of trials for the two classes (right and left) in each run.The speed of the parachutist was set so that it lands at second 8. Depending on the performance, there were three types of command delivery: ‘hit’ (when the platform reaches the correct side before the para-chutist touches the ground),‘miss’ (when the platform reaches the wrong side), and ‘timeout’ (when the platform does not reach either side on time). In the first case (i.e., hit), the platform color changes to green; otherwise to red (i.e., miss or timeout). More-over, in order to increase the quality of feedback to the users, as recommended in [22], their progress (number of correct com-mands and average CDT) was displayed at the end of each run.Participants initially performed 1 to 2 sessions of the BCI game (Calibration in Figure 3) in order to tune subject-specific parameters of the BCI classifier. The threshold, ,th d was set using the same criteria as in the MI training. Also, the MI clas-sifier was re-trained if the performance dropped below 75%. Once the optimal parameters and classifier were found, 2 to 3 additional sessions (8 runs each) were conducted (Evaluation in Figure 3). No re-training or parameter update was performed either in this phase, or in the next phase (online adaptive assis-tance). The data of these Evaluation sessions was used to train the performance estimator. Finally, the performance estimator was applied in order to provide adaptive assistance during the game as being described in the next section.2.1.3. online adaptive assistance in the bCI gameFive subjects (sb1, sb3, sb4, sb7, sb8) participated in this part of the experiment (1 recording session; blue block in Figure 3). In order to assess the benefit of adaptive assistance based on the proposed performance estimator, we compared two conditions: (i) Fixed timeout: The parachutist reaches the ground at afixed timeout. That is, the user always has a constant time (fixed timeout) to deliver a command. This fixed timeout t sl^h was set to the value of the threshold used to separate trials into short and long groups (detailed in Section 2.2.2). (ii) Adaptive assistance: In this condition if the performance esti-mator classifies a command as short, the speed of the para-chutist is set so that it lands at .t sl Otherwise, the parachutist slows down and reaches the ground at 8 s.That is, the user has by default t sl to deliver a command unless the command is predicted as long, in which case she/he has 8 s to do it.The timeout of 8 s in case of long CDT was selected in order to be consistent with the previous MI-BCI game, where 8 s was sufficient for all subjects to deliver a command.Subjects carried out 3 runs per condition, each run having 15 trials per class (right and left). The two conditions were per-formed sequentially, starting from the ‘fixed timeout’ for two subjects (sb1 and sb3) and from ‘adaptive assistance’ for the other three. In order to measure the perceived workload, par-ticipants filled in the NASA-TLX (task load index) question-naire [23] at the end of each condition.Apart from this subjective measure, the two conditions were compared based on the success rate (the ratio of correct command delivery) and information transfer rate (ITR) measures. For com-puting the latter, we assume that the ‘timeout’ commands are equivalent to ‘no decision’; cf., Supplementary Materials.Through-out this paper, we have implemented the Wilcoxon paired signed-rank test to investigate statistical difference of the results in different conditions, as the data exhibit non-Gaussian distributions.2.2. EEG Decoders2.2.1. Classification of motor ImageryThe procedure for decoding of users’ intention from EEG is detailed in [18]. Power spectral densities (PSDs) were used as features for classification of different motor imagery tasks. Fea-tures were ranked based on their relevance to the mental tasks using Canonical V ariate Analysis (CVA) [24]. Considering this ranking and the neurophysiological evidence on the cortical areas/frequency bands contributing to a specific mental task, 5 to 7 features (in the alpha and beta range) were selected manu-ally for each subject. These features were the input to a Gauss-ian classifier with four prototypes per class. In the online ses-sions, the single sample classification was integrated over time. 2.2.2. Single-trial prediction of Short vs. long CDtThe performance estimator (whether the CDT of a trial will be short or long) uses the same selected PSD features as the MI classifier. W e hypothesise that the characteristics of these control features in the beginning of task execution reflect the reliability of the command and, hence, the CDT.T o design the performance estimator, we separated the trials into short and long ones based on a subject-specific temporal threshold .t sl^h In order to select ,t sl we compared short vs. long classification performance over the Evaluation sessions for differ-ent percentiles of the CDT.That is, if the threshold is chosen as 35th percentile of CDT, then 35% of the trials are considered as short and the remaining 65% as long ones.The accuracy of the designed performance estimator was assessed using 10-fold cross validation for different percentiles (from 35 to 65 with a step size of 5), so as to find the optimal threshold for each subject.36 IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2016FEbruary 2016 | IEEE ComputatIonal IntEllIgEnCE magazInE 37A short window of samples, ,W in the beginning of a trial was considered for predicting the CDT (the green window in Figure 4). The length of W was the shortest possible below the threshold t sl that yielded a reliable prediction. The median delivery time (MDT) and the length of this window is shown in the ‘Results’ section in Table 1 for all subjects.Features: For estimating the trial performance given the feature vectors x ^h extracted from EEG within ,W we define a distance measure for class i and prototype j as:,D st N x i 1ij w iktk ijk k N t N 112fw n R=-==^h // (1)where x is the feature vector with N f selected features (i.e. EEG channels and frequency bands), ij n is the center of the th j prototype of class ,i ik R is the variance of feature k for class i , and N w is the number of feature vectors within the window . The defined distance measure is similar to the one used for cal-culating the posterior probability of the Gaussian classifier. However, we consider that some prototypes may be more influential than others for discriminating between short and long trials. Given that we have N p (equal to 4) prototypes per class, the feature vector f sl for estimating the performance is composed of the distance of sample x t to every prototype ,:,:.f Dist i N j N 11sl ij c p ===^h Finally , since these features are not independent, CVA was used to select the features in f sl that better discriminate between short and long trials.Classification: As the characteristics of the commands weredifferent for the two different mental tasks (right and left)1, we built a separate short vs. long classifier for each of the two classes per subject. The threshold t sl was chosen based on the classifica-tion performance (Area under the ROC curve, AUC . 0.8) in the Evaluation phase (10-fold cross validation with a linear dis-criminant analysis, LDA, classifier). Moreover, a single t sl was chosen for both classes (right and left), which may result in a different percentile of CDT for the two classes. The output of the resulting classifier was used for providing adaptive assistance.3. Results3.1. Performance in MI-BCIT able 1 shows the success rate and the MDT for all subjects over the Evaluation sessions of the game. All subjects had a rather good accuracy , higher than 0.7 (a value often considered sufficient for BCI operation [25]).3.2. Single-Trial Prediction of Short vs. Long CDTFigure 5 shows the average AUC for 10-fold cross validation of short vs. long classification for all subjects. These results were obtained offline using the data from the MI-BCI game Evaluation sessions. As mentioned before, different percentiles of CDT have1Most of the subjects are usually more efficient in performing one task than the other. Thus, they may generate more consistent brain patterns across trials while doing that task.Figure 5 auC of the performance estimator for all subjects and for the two classes, C1 (right, red) and C2 (left, blue), using different percen-tiles of CDT. The figure shows the time threshold corresponding to each percentile.38 IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2016been used to separate the trials into short and long groups. The values of t sl (percentile of CDT) are displayed in the x-axis of each plot, as well as the average AUC for both classes: C1 (right com-mand, in red) and C2 (left command, in blue). As the figure illus-trates, a reliable classification result can be achieved in most cases.The AUC tends to decrease as higher thresholds are consid-ered. Based on the short vs. long classification performance for each subject, optimal ,t sl equal for the two classes, wasindividually chosen to be used in the adaptive assistance phase. Table 1 reports the selected t sl for all subjects.3.3. Online Adaptive Assistance in the MI-BCI GameAs depicted in Figure 6, providing adaptive assistance leads to a significantly higher success rate with respect to having a fixed timeout equal to t sl ..p 001<^h The former resulted in an average success rate of ..076004! and the latter an average success rate of ..053005! across the 5 subjects (underlined subject IDs in T able 1). However, considering timeout trials as no decision, the two cases have a comparable ITR with no sig-nificant difference.In addition, based on the users’ evaluations, there is a signifi-cant reduction in the NASA TLX scores when implementing the adaptive assistance .p 001<^h as depicted in Figure 7. The main contributing factors were the temporal demand .,p 001<^h frustration .,p 005<^h and performance ..p 005<^h 4. DiscussionOne of the challenges in using BCI systems over extended peri-ods of time is the performance variation across different sessions for the same subject. One way of tackling this issue is to adapt the level of assistance the system provides based on the users’ needs.For instance, adaptive assistance based on EEG has been shown to improve perceived performance (NASA TLX) in an air traffic control task [26]. Here, we addressed the issue of performancevariation in an MI-BCI by designing a performance estimator whose output is used to provide online adaptive assistance to the user. This estimator predicts CDT on a trial-by-trial basis. The results on 9 healthy subjects reveal that a subject-specific estimatorcan be found to reliably classify single-trials into short and longones (according to the distribution of CDT) within a short time window at the beginning of each trial. This classification was done separately for the two classes (right and left) due to differentcharacteristics of the mental tasks.The proposed performance estimator takes into considerationsome critical issues in studying intra-subject variabilities. Firstly , it isdesigned based on the recorded data in online sessions, where the users received feedback on their performance and could adapt their strategies accordingly . Secondly , its design takes into account perfor-mance variations across several ses-sions (2 to 3) and not only across trialsin a single session. Thirdly , it is capable of detecting performance variationson a single-trial basis based on a shorttime window . Thus, it can be benefi-cial for monitoring the performance of the subjects who exhibit variations on a short time interval (e.g., acrosstrials within the same run).In addition, the feasibility of implementing this performanceestimator for providing adaptive assistance was investigated for 5Figure 6 Comparison between fixed timeout and adaptive assis-tance conditions for the five subjects who participated in this phase:(a) Success rate .,p 001))1^h (b) ITr assuming that timeout trialsare equivalent to no decision.Figure 7 The NaSa TLX: the main contributing factors to the users’ perceived workload were thetemporal demand, frustration, and performance .,..p p 001005)))11^h For all factors (includingperformance), a lower score is better.subjects in an MI-BCI game. The results show that adaptive assistance (regulating the timeout for delivering a command in the MI-BCI game) improves the success rate significantly, com-pared to a fixed timeout. Although the bit rate for a fixed time-out might be comparable to the one in the adaptive assistance case, the latter showed significantly higher user acceptance as indicated by the NASA TLX score.Intra-subject variabilities in BCI systems have not been extensively studied so far. Existing studies have some limitations. For instance, they focus on a single experimental session and do not take into consideration the variability across sessions, which is usually more critical. Besides, a performance measure is usu-ally defined based on offline recordings, which might not be an ideal measure, as the subjects did not receive any feedback on their performance. Moreover, most of them focus on finding correlates of high or low performances. Thus, they are not capa-ble of making decisions on a trial-by-trial basis. A study that investigated the feasibility of performance estimation in a short-time basis revealed that the proposed method can be beneficial for subjects who show performance variations on a time scale of several minutes [15], different from our approach that works on a time scale of seconds. Finally, to the best of our knowledge, none of these studies explored the provision of adaptive assis-tance based on performance estimation.In order for subjects to be able to use an SMR-based BCI in real applications, e.g., driving a wheelchair, several training sessions are required [18]. At this stage, BCI performance tends to fluctuate substantially due to variations in the SMR corre-lates, which hinders training and requires offline recalibration of the BCI classifier. Thus, implementing a performance esti-mator for regulating the level of assistance according to the users’ performance may largely improve training.The MI task in this study was designed as a game in order to make it more engaging. Based on the data collected during the game, mainly CDT, we have simulated a navigation task. Descrip-tion of the task and results are detailed in the Supplementary Materials. Our simulation results show that providing adaptive assistance (adjusting the speed of the robot according to the CDT) reduces the time to finish the task as compared to delivering BCI commands after a fixed period. Our approach is also superior in dealing with large variations of BCI performance across sessions.Several improvements to the proposed framework can be developed in future work. First, the proposed performance esti-mator relies on the data at the beginning of a trial, which may not be compatible with asynchronous BCIs (where no cue is used and the subject can start the mental task whenever she/he intends). Extensions of this approach where the performance is estimated on the ongoing patterns (i.e., inferring the probability of delivering a command in the near future) should be devel-oped and validated. Second, due to different characteristics of the two classes (right and left), two separate performance estimators were built. Therefore, the online use of the performance estima-tor requires class labels, which are rarely available. Semi-super-vised approaches, using environmental information [27, 28] or error potentials [29] could be used to infer this information.AcknowledgmentThis work is supported by the Swiss National Center of Compe-tence in Research (NCCR) Robotics and the Hasler Foundation. References[1] M. Grosse-Wentrup and B. 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