016 DEVELOPMENT OF A ROBUST ALGORITHM FOR IMAGING COMPLEX TISSUE ELASTICITY.

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Towards a robust BCI:Error potentials and online learningAnna Buttﬁeld,Pierre W.Ferrez and Jos´e del l´a nAbstractRecent advances in theﬁeld of Brain-Computer Interfaces(BCIs)have shown that BCIs have the potential to provide a powerful new channel of communication,completely independent of muscular and nervous systems.However,while there have been successful laboratory demonstrations,there are still issues that need to be addressed before BCIs can be used by non-experts outside the laboratory.At IDIAP we have been investigating several areas that we believe will allow us to improve the robustness,ﬂexibility and reliability of BCIs.One area is recognition of cognitive error states,that is,identifying errors through the brain’s reaction to mistakes.The production of these error potentials(ErrP)in reaction to an error made by the user is well established.We have extended this work by identifyinga similar but distinct ErrP that is generated in response to an error made by the interface,(a misinterpretation of acommand that the user has given).This ErrP can be satisfactorily identiﬁed in single trials and can be demonstrated to improve the theoretical performance of a BCI.A second area of research is online adaptation of the classiﬁer.BCI signals change over time,both between sessions and within a single session,due to a number of factors.This means that a classiﬁer trained on data from a previous session will probably not be optimal for a new session.In this paper we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase.We also discuss the future direction of this research,including the combination of these two currently separate issues to create a potentially very powerful BCI.Index TermsBrain-computer interface,Cognitive error state recognition,Online learning,Adaptive classiﬁersI.I NTRODUCTIONThe goal of a brain-computer interface(BCI)is to create a direct channel of communication between a user’s brain and a computer,completely bypassing the traditional muscle-dependent communication channels.This is still a very youngﬁeld of research and it encompasses many different approaches to a variety of problems.The BCI system that we have developed at the IDIAP Research Institute has shown good results in distinguishing between up to three mental states,including imagination of left and right hand movements,3D visualisation andThis work is supported by the European IST Programme FET Project FP6-003758and by the Swiss National Science Foundation NCCR “IM2”.This paper only reﬂects the authors’views and funding agencies are not liable for any use that may be made of the information contained herein.Authors are with the IDIAP Research Institute,CH-1920Martigny,Switzerland(e-mail:{anna.buttﬁeld,pierre.ferrez,lan}@idiap.ch).language tasks[1].We use a Gaussian mixture classiﬁer to distinguish between the given tasks,which allows us to deﬁne an“unknown”output when the probability of none of the deﬁned classes is above a deﬁned conﬁdence threshold,effectively creating an“idle”state without having to model it directly.While this technology has been demonstrated in the laboratory,it is not yet ready to be taken out of the laboratory and used in real-world situations. Our work at the moment is focussing on different ways of improving the robustness of BCIs with the goal of making BCIs a more practical and reliable technology.One avenue of research that we are investigating is the use of high-level cognitive and emotional information in BCIs.In particular we are looking at error potentials(ErrP),the neural correlates to error awareness.The appearance of error potentials in response to errors made by the user are well established.Our research has identiﬁed a new type of error potential that is generated in response to errors made by the BCI rather than the user.In addition to identifying errors and stopping the BCI from executing incorrect commands,this new type of error potential may provide us with performance feedback that could allow us to improve the performance of the classiﬁer while it is being used.Online learning is another issue that we are currently investigating.EEG signals naturally change over time,both between different sessions and within a single session.Online learning can be used to adapt the classiﬁer throughout its use and keep it tuned to drifts in the signals it is receiving in each session.Our hypothesis is that online learning used during initial training will reduce training time by facilitating mutual adaptation between the user and the BCI,allowing the user to reﬁne his or her mental strategy through rapid feedback.During ongoing use we believe we can improve the performance of the classiﬁer by constantly tuning it with alternative learning paradigms that do not require explicit knowledge of the target class.However,dynamic adaptation of the BCI is risky,since we may confuse the user by rapid and unpredictable changes,or degrade the performance of the classiﬁer through inappropriate adaptation.In this paper we will be describing the current state of the IDIAP BCI,including the hardware and statistical classiﬁer.We will also present our current research in the two areas outlined above,error potentials and online learning.II.T HE IDIAP BCIWe have recently shown that after a few days of training,subjects are able to control a miniature robot in an indoor environment with several rooms and corridors using mental commands derived from an EEG-based BCI[1]. Key aspects that make it possible are the use of an asynchronous BCI and the combination of the user’s high-level commands with advanced robotics that implement those commands efﬁciently.We are working to improve this initial demonstrator,in collaboration with several European institutions,along four lines.Theﬁrst is the development of a more powerful adaptive shared autonomy framework for the cooperation of the human user and the robot in achieving the target.The second line is the use of a technique recently developed by Grave et al.[2]that estimates the local ﬁeld potentials(LFP)in the whole human brain from scalp EEG.Recent results show signiﬁcant improvements in the classiﬁcation of bimanual motor tasks using estimated LFP with respect to scalp EEG[3].The third and fourthresearch avenues seek to improve the robustness of a BCI and will be discussed in this paper.A.Hardware and signal acquisitionEEG potentials are acquired with a portable BioSemi system using a cap with either32or64integrated electrodes arranged in the modiﬁed10/20International System[4].The EEG recordings are monopolar and taken at512Hz. The common average reference(CAR)procedure is used to suppress the background brain activity,where at each time step the average potential over all the channels is subtracted from each channel.This re-referencing procedure removes the background activity,leaving activity from local sources beneath the electrodes.B.Statistical classiﬁerThis is a short summary of the classiﬁer we use in the IDIAP BCI.For more details,see[1].We use a Gaussian classiﬁer to separate the signal into the different classes of mental task.Each class is represented by a number of Gaussian prototypes,typically less than four.That is,we assume that the class-conditional probability function of class C k is a superposition of N k Gaussian prototypes.We also assume that all classes have equal prior probability. All classes have the same number of prototypes N p,and for each class each prototype has equal weight1/N p. Thus,the activity a ikof the i th prototype of class C k for a given sample x is the value of the Gaussian with centreµi k and covariance matrixΣik.From this we calculate the posterior probability y k of the class C k.The posteriorprobability y k of the class C k is now the sum of the activities of all the prototypes of class k divided by the sum of the activities of all the prototypes of all the classes.The input vector x can be composed of either temporal or frequency features from a selection of electrodes,depending on the experiment.The classiﬁer output for input vector x is now the class with the highest probability,provided that the probability is above a given threshold,otherwise the result is“unknown”.This rejection criteria gives the BCI theﬂexibility to not make a decision at any point without explicitly modelling an idle state.Usually each prototype of each class would have an individual covariance matrixΣik,but to reduce the number of parameters the model has a single diagonal covariance matrix common to all the prototypes of the same class. During ofﬂine training of the classiﬁer,the prototype centres are initialised by a clustering algorithm,generally self-organising maps[5].This initial estimate is then improved by stochastic gradient descent to minimise the meansquare error E=12k(y k−t k)2,where t is the target vector in the form1-of-C;that is,if the second of threeclasses was the desired output,the target vector is(0,1,0).The covariance matrices are computed individually then averaged over the prototypes of each class to giveΣk.III.E RROR P OTENTIALSEEG-based BCIs are prone to errors in the recognition of the user’s intent.However,EEG signals provide us with a tool to help us overcome this problem.In addition to the command signals generated intentionally by the user,EEG signals carry high level information about the cognitive states of the user.These states include attention,fatigue and motivation as well as the one we are investigating—error recognition,where the user“reacts”to the occurrence of an error.Brain signals associated with the recognition of an error made by the user are well established[6],[7],[8].“Response ErrP”occur when the user makes an error and recognizes it immediately[6],[7].This situation can be provoked by asking a user to perform a task that requires fast reactions so that the user makes some mistakes.A few studies have addressed the recognition of this kind of ErrP in single trials as a potential tool to improve the performance of a BCI[9],[10].A second type of error potentials has also been identiﬁed.Known as“Feedback ErrP”,they occur when a user makes an error but is unaware of it until he or she is informed by feedback[8]. The brain response in this case is similar to that of response ErrP,but is a reaction to the feedback rather than the incorrect action.In both cases the timing of the brain reaction is tied to the point where the user realises his or her mistake,be that at the time of action or on receipt of negative feedback.An important aspect of the described ErrP(response ErrP and feedback ErrP)is that they always follow an error made by the user.First the user makes a selection,and then the ErrP arise either after the occurrence of an error or after a feedback indicating the error.In the context of a BCI,the central question is whether ErrP are also elicited when the interface makes an error in recognising the user’s intent.Schalk et al.[11]have previously observed that such an ErrP appears in a BCI when the cursor reaches wrong targets,an operation that requires a number of consecutive mental commands.Here we are interested in the recognition of such a kind of ErrP in single trials—i.e.,after the user delivers each mental command.A.Experimental SetupIn a previous study,we presented experimental results showing the presence of ErrP in response to an error made by the interface rather than the user[12].In the three subjects studied we were able to identify these“Interaction ErrP”on a single-trial basis,which is crucial for their integration into a real-time BCI.In these subjects were were able to identify to presence of the ErrP in the error trials with an average accuracy of79.9%and the abscence of this ErrP in correct trials with an average accuracy of82.4%.The results of this study are presented and discussed further here.The experiment simulated a real interaction with a robot,where the user gives repetitive commands to bring the robot to the left or right side of a room.Feedback is delivered by two progress bars showing the number of times each command has been delivered.In order to separate the complex usage of the BCI from the reaction to the error,the task was presented in a simpliﬁed manual form.Instead of using a BCI to deliver the commands, the user presses a key to issue the command.By removing the BCI classiﬁer from this part of the experiment and making the user task trivial,we ensure that the user performance is perfect and the only mistakes are the deliberately introduced mistakes made by the interface.So,the user repeatedly presses a key to issue his or her command(without making mistakes,due to the triviality of the task),but20%of the time the interface makes a mistake and provides the wrong feedback.Three healthy male subjects between the ages of24and42participated in this experiment.The EEG signals were recorded with a32electrode cap,but only channels Cz and Fz wereused for classiﬁcation since this combination produced the best results.This selection of electrodes makes sense because the distribution of ErrP is known to be fronto-central along the midline.The signals were ﬁltered with a 1-10Hz bandpass ﬁlter,since error potentials are known to be relatively slow cortical potentials.The classiﬁer then considers the 0.5second window starting between 150ms and 650ms after the feedback.The input to the classiﬁer is a 128element vector of the temporal features (0.5seconds at 128Hz for Cz and Fz).B.Experimental ResultsExamining the average difference error-minus-correct (Figure 1)of our experimental results reveals what seems to be a new kind of ErrP,different from “Response”and “Feedback”ErrP,which for convenience we call “Interaction ErrP”.A ﬁrst sharp negative peak occurs 270ms after the feedback and is followed by a positive peak (between 350and 450ms after the feedback)and by a later negative peak (∼550after the feedback).Since our protocol is quite similar to an oddball paradigm the question arises of whether the potentials we describe are simply oddball N200and P300.To clarify this issue we ran a second experiment with an error rate of 50%,where error trials are no less frequent than correct trials.The potentials observed in this case have the same timing as those observed in the case of 20%(Figure 2).This means that while we cannot exclude the possibly that N200and P300contribute when the error rate is 20%,the oddball N200and P300are not sufﬁcient to explain the reported potentials.Time [s]A m p l i t u d e [µV ]Fig.1.Average EEG for the difference error-minus-correct for channel Cz for the three subjects plus the grand average of them for 20%error.Feedback is delivered at time 0seconds.The negative and positive peaks show up about 270ms and between 350and 450ms after the feedback,respectively.A later negative peak shows up about 550ms after the feedback.For ErrP to be useful in a real-time BCI,we need to be able to identify error potentials in single trials,not just in grand averages.Table I gives the classiﬁcation rates of error and correct single trials in a 10-fold cross-validation study.These classiﬁcation rates can be demonstrated to improve the theoretical performance of a BCI in terms of bit rate.The bit rate is a measure of performance that represents the amount of information communicated per unitTime [s]A m p l i t u d e [µV ]Fig.2.Grand average EEG of two subjects showing the difference error-minus-correct for channel Cz when the error rate is 50%.Feedback is delivered at time 0seconds.TABLE IC LASSIFICATION RATES OF EACH SUBJECT ON ERROR AND CORRECT SINGLE TRIALS .Subject 1Subject 2Subject 3Average Error trials [%]87.3±11.374.4±12.478.1±14.879.9±6.6Correct trials [%]82.8±7.275.3±10.089.2±4.982.4±7.0time.It is generally expressed in bits per trial.Traditional bit rate measures can be adapted to model the integration of error potentials into a BCI [12].The most general method of integrating the error potentials is to stop the execution of a command when an error potential is detected.Alternatively,in the speciﬁc case of a 2-class BCI we can replace a command with the other command when we detect an error potential.Using these bit rate equations we can calculate the theoretical performance of a BCI,assuming that the accuracy of the BCI is 80%and the subject-speciﬁc ErrP recognition rates are as reported in Table I.The performance of the BCI integrating ErrP is compared with that of standard 2-class and 3-class BCIs,which have performances of 0.28and 0.66bits per trial respectively.Table II shows that we can achieve a signiﬁcant increase in the bit rate by stopping the commands identiﬁed as erroneous,with an average gain over the standard BCI of 72%in the case of a 2-class BCI and 28%in the case of a 3-class BCI.Surprisingly,the average gain from replacing the erroneous commands in a 2-class BCI is much lower,14%,and the performance of subject #2actually decreases under this paradigm.IV.O NLINE LEARNINGFor a BCI to be an effective tool it must have the ability to adapt dynamically throughout its use.The EEG signals received by the BCI will always change over time,both within a single session and between sessions,dueTABLE IIP ERFORMANCES OF THE BCI INTEGRATING E RR P FOR THE3SUBJECTS AND THE AVERAGE OF THEM.Subjects#1#2#3AverageN c=3BpT0.910.730.920.85stop Increase37.0%9.5%38.0%27.6%N c=2BpT0.530.400.520.48stop Increase90.9%41.9%85.5%71.7%N c=2BpT0.360.190.440.32replace Increase28.9%-31.5%58.9%14.2%to a number of factors.These factors include change in strategy by the user,user fatigue,and small differences in electrode position.So while at the beginning of each session the classiﬁer will be initialised with respect to previous sessions,it must adapt itself to the particular signals it is receiving in the current session.However,this adaptation must be undertaken with great care—rapid and unpredictable changes in the classiﬁer will confuse the user,and incorrect adaptation will degrade the performance of the classiﬁer.Work so far has focused on online learning during the training phase,where the user is told which command to give and so the real target is always known,making it a supervised learning task.The goal is to reduce the time a user takes to learn to use the system efﬁciently by facilitating mutual adaptation between the user and the BCI. This means that the user can reﬁne his or her mental strategies while receiving rapid feedback from the BCI.Once the user has been trained and can produce the BCI commands reliably,he or she is ready to move to unstructured, unsupervised tasks such as controlling a robot.In this situation the techniques described below could be used in short recalibration sessions.Previous preliminary work on this task[13]evaluated the advantages of using continued online learning with the basic gradient descent algorithm on the Gaussian classiﬁer.Since then we have been investigating extensions of the basic gradient descent algorithm such as stochastic meta descent[14],which accelerates training by adapting individual learning rates for each parameter of the classiﬁer.A.Stochastic Meta DescentStochastic Meta Descent(SMD)[14]is an extension of gradient descent that uses adaptive learning rates to accelerate learning.The SMD algorithm is applied to each parameter in the classiﬁer separately(the centre and covariance of each Gaussian prototype),and each parameter maintains and adapts an individual learning rate.This is in contrast to basic gradient descent,which uses a single learning rate for all parameters.The details of our implementation of SMD can be found in[15].This approach has the potential for faster adaptation,but requires more computation.It also has more parameters that need to be tuned—basic gradient descent has two parameters, learning rates for the centres and covariances of the prototypes,while SMD has four parameters,initialisation values for the learning rates as well as an additional hyper-parameter.B.Experimental resultsInitial ofﬂine experiments have been performed on data recorded while giving no feedback to the subjects and no online learning was used during recording.We tested these algorithms on a three-class problem(imagination of left and right hand movements,and vocabulary search).Each class was performed for one second before switching to a different class in random order.Data is from three subjects,each with four sessions of almost four minutes collected with a break of ten minutes between sessions.Samples are passed to the classiﬁer16times per second and the output from eight samples is averaged to give a decision every0.5seconds.The features vector used for classiﬁcation was constructed by estimating the power spectral density over the previous second in the frequency range8-30Hz at2Hz resolution for the8centro-parietal channels(C3,Cz,C4,CP1,CP2,P3,Pz,and P4),giving a96element feature vector.In this experiment we measured how well the classiﬁer tracked the changing signals by applying online learning through all the sessions and measuring the classiﬁcation performance.For each subject,the classiﬁer wasﬁrst trained ofﬂine on the data from theﬁrst session.The resulting classiﬁer was then applied to sessions two through four,with theﬁnal adapted classiﬁer from the end of the previous session used as the initial classiﬁer for the next session.In these sessions we are continually adapting the classiﬁer—each new sample comes in,is classiﬁed with the existing classiﬁer,and the classiﬁer is updated based on this sample.We compared basic gradient descent and the SMD algorithm against the static classiﬁer with no adaptation,where the classiﬁer trained on theﬁrst session is used to classify the following sessions with no further modiﬁcation.Preliminary tests of basic gradient descent over a range of learning parameter values showed that the optimal parameters vary between subjects and sessions.We selected learning parameter values(the same for all sessions of all subjects)for gradient descent.This same value also serves as the initialisation value for SMD.In these experiments both gradient descent and SMD outperform the static classiﬁer.Figure3shows the performance over time of the different classiﬁers for the three sessions of the second.It can be seen that the classiﬁcation rates of the adaptive algorithms are statistically signiﬁcantly better than the static classiﬁer,with SMD better than gradient descent,especially towards the end of each session.Similar trends are observed with the other subjects.In addition to applying the online learning algorithms throughout the session,we wanted to see whether there is a performance gain in applying the online learning algorithms for theﬁrst half of the session only,then applying the resultant classiﬁer to the second half of the session with no further learning.This is similar to a recalibration scenario,where we want to use supervised learning for only part of the session.Results from this experiment show that there is a small improvement when using the classiﬁer trained on theﬁrst half of the data over the classiﬁer with no further training,but there is no statistically signiﬁcant difference between the performance of basic gradient descent and SMD.Table III shows the classiﬁcation results for the three subjects averaged over their three sessions,and the overall average.The results are given as the percentage improvement of the online learning algorithms over the static classiﬁer.There are three parts:the improvement in theﬁrst half of the data,the improvement in the second half1234567891050100Subject 2 − Session 2Subject 2 − Session 3Fig.3.Percentage of trials correctly classiﬁed in 20second bins for the static classiﬁer,basic gradient descent and SMD.Both of the adaptive algorithms have signiﬁcantly better performance than the static classiﬁer,with SMD performing better than basic gradient descent,especially towards the end of each session.of the data when the online learning is continued,and the improvement on the second half of the data when the classiﬁer is only trained on the ﬁrst half of the data.Although there is variation between the subjects,the ﬁgures hold to the general trend as discussed above.TABLE IIIA VERAGE PERCENTAGE IMPROVEMENT OF ONLINE LEARNING ,SMD AND BASIC GRADIENT DESCENT (GD)OVER STATIC CLASSIFIER First halfSecond half Adapting Not adapting Subject 1GD 3.57.9 3.9SMD 4.99.3 5.9Subject 2GD 6.17.8 3.1SMD 9.318.6 5.2Subject 3GD18.318.77.1SMD28.033.0 2.5Average GD9.311.5 4.7SMD 14.020.3 4.5V.C ONCLUSION AND FUTURE WORKThe two issues discussed in this paper,error potentials and online learning,have shown promising results in their early stages of investigation.Work needs to be done to integrate these principles into the BCI system.Operating individually these research areas can improve the performance of the bined,they open up a new area ofresearch—continued reinforcement learning during general usage of the BCI using error potentials as feedback on the performance of the BCI.This means that for every decision the BCI makes we do not know what the correct decision should have been,but we are informed by the presence of the error potential when a wrong decision has been made,and we can use this information to adapt the classiﬁer.Another area of research is the use of estimates of the localﬁeld potentials(LFP)to improve the classiﬁer[2][3]. This technique allows us to generate a three dimensional map of the activity of from the EEG signals.Through this technique we can also gain a better understanding of the nature of the brain activity driving the BCI.R EFERENCES[1]J.del l´a n,F.Renkens,J.Mouri˜n o,and W.Gerstner,“Non-invasive brain-actuated control of a mobile robot by human EEG,”IEEETrans.Biomedical Engineering,vol.51,pp.1026–1033,2004.[2]R.Grave de Peralta,M.Murray,C.Michel,R.Martuzzi,and S.Gonzalez Andino,“Electrical neuroimaging based on biophysicalconstraints,”NeuroImage,vol.21,pp.527–539,2004.[3]R.Grave de Peralta,S.Gonzalez Andino,L.Perez,P.Ferrez,and J.del l´a n,“Non-invasive estimation of localﬁeld potentials forneuroprosthesis control,”Cognitive Processing,vol.6,pp.59–64,2005.[4] F.Sharbrough,G.Chatrian,R.Lesser,H.Lders,M.Nuwer,and W.Picton,“Am.electroencephalogr.society guidelines for standardelectrode position nomenclature,”Clin.Neurophysiol.,vol.8,pp.200–202,1991.[5]T.Kohonen,Self-Organising Maps,2nd ed.Berlin:Springer-Verlag,1997.[6]W.Gehring,M.Coles,D.Meyer,and E.Donchin,“The error-related negativity:An event-related brain potential accompanying errors,”Psychophysiology,vol.27,1990.[7]M.Falkenstein,J.Hoormann,S.Christ,and J.Hohnsbein,“ERP components on reaction errors and their functional signiﬁcance:Atutorial,”Biological Psychology,vol.51,pp.87–107,2000.[8] C.Holroyd and M.Coles,“The neural basis of human error processing:Reinforcement learning,dopamine,and the error-related negativity,”Psychological Review,vol.109,pp.679–709,2002.[9] B.Blankertz,G.Dornhege,C.Sch¨a fer,R.Krepki,J.Kohlmorgen,K.-R.M¨u ller,V.Kunzmann,F.Losch,and G.Curio,“Boosting bit ratesand error detection for the classiﬁcation of fast-paced motor commands based on single-trial EEG analysis,”IEEE Trans.Neural Systems and Rehabilitation Engineering,vol.11,pp.127–131,2003.[10]L.Parra,C.Spence,A.Gerson,and P.Sajda,“Response error correction—A demonstration of improved human-machine performanceusing real-time EEG monitoring,”IEEE Trans.Neural Systems and Rehabilitation Engineering,vol.11,pp.173–177,2003.[11]G.Schalk,J.Wolpaw, D.McFarland,and G.Pfurtscheller,“EEG-based communication:Presence of an error potential,”ClinicalNeurophysiology,vol.111,pp.2138–2144,2000.[12]P.Ferrez and J.del l´a n,“You are wrong!—Automatic detection of interaction errors from brain waves,”in Proc.19th Int.JointConf.Artiﬁcial Intelligence,2005.[13]J.del l´a n,“On the need for on-line learning in brain-computer interfaces,”in Proc.Int.Joint Conf.Neural Networks,2004.[14]N.Schraudolph,“Local gain adaptation in stochastic gradient descent,”in Proc.9th Int.Conf.Artiﬁcial Neural Networks,1999.[15] A.Buttﬁeld and J.del l´a n,“Online classiﬁer adaptation in brain computer interfaces,”IDIAP,IDIAP Research Report06-16,2006.[Online].Available:http://www.idiap.ch/publications.php。

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高二英语科技词汇单选题40题(带答案)

高二英语科技词汇单选题40题(带答案)1.The new smartphone has a large _____.A.screenB.keyboardC.mouseD.printer答案：A。

“screen”是屏幕，新智能手机有一个大屏幕，符合常理。

“keyboard”是键盘，“mouse”是鼠标，“printer”是打印机，都与智能手机不匹配。

2.We can use a _____ to take pictures.puterB.cameraC.televisionD.radio答案：B。

“camera”是相机，可以用来拍照。

“computer”是电脑，“television”是电视，“radio”是收音机，都不能用来拍照。

3.The _____ can play music and videos.ptopB.speakerC.projectorD.scanner答案：A。

“laptop”是笔记本电脑，可以播放音乐和视频。

“speaker”是扬声器，“projector”是投影仪，“scanner”是扫描仪，都不能播放音乐和视频。

4.My father bought a new _____.A.tabletB.bookC.penD.pencil答案：A。

“tablet”是平板电脑。

“book”是书，“pen”是钢笔，“pencil”是铅笔，只有平板电脑是科技设备。

5.The _____ is very useful for online meetings.A.headphoneB.microphoneC.speakerD.camera答案：D。

“camera”摄像头在在线会议中很有用。

“headphone”是耳机，“microphone”是麦克风，“speaker”是扬声器，都不如摄像头在在线会议中的作用直接。

6.We can store a lot of data in a _____.A.flash driveB.penC.pencilD.book答案：A。

简述算法设计的一般流程

简述算法设计的一般流程Algorithm design is a structured process that involves breaking down a complex problem into smaller, more manageable subproblems,and then developing a step-by-step solution to each subproblem. 算法设计是一个结构化过程，涉及将一个复杂问题分解为更小、更易管理的子问题，然后对每个子问题逐步进行解决。

This process typically startswith identifying the inputs, outputs, and constraints of the problem, as well as understanding the desired behavior of the solution. 这个过程通常从识别问题的输入、输出和约束开始，以及了解解决方案的期望行为。

Once the problem has been properly understood, the next step in algorithm design is to choose an appropriate algorithmic approachor technique to solve the problem. 一旦问题被正确理解，算法设计的下一步就是选择一个合适的算法方法或技术来解决问题。

This decision is often influenced by the specific characteristics of the problem, such as its size, complexity, and the nature of the input data. 这个决定常常受到问题的具体特征的影响，如问题的规模、复杂性和输入数据的性质。

hybrid method

hybrid methodHybrid MethodHybrid Method is a powerful technique that has been used in many areas of research and development, including optimization, simulation, control, and machine learning. A hybrid method combines the strengths of both numerical and symbolic methods to develop an efficient and accurate algorithm. In this article, we will discuss the basic concept, applications, advantages, and limitations of the Hybrid Method.Basic Concept of Hybrid MethodA hybrid method combines the numerical power of mathematical modeling and the symbolic power of algorithmic approach. It is designed to tackle difficult problems that are beyond the reach of traditional numerical and symbolic methods. Hybrid method is a methodology that integrates numerical and symbolic algorithms in a single algorithmic framework. It combines the numerical accuracy of numerical methods with the symbolic reasoning ofsymbolic methods, thus providing a powerful toolfor solving complex problems.Applications of Hybrid MethodHybrid method has wide applications in many areas. For example, in optimization problems,hybrid methods are used to combine the strengths of different optimization techniques such as linear programming, genetic algorithms, and simulated annealing. This combination of methods results in a more efficient and effective optimization method. Hybrid methods are also used in control systems, especially for modeling and control of complex systems such as power plants, chemical plants, and robotics.In machine learning, hybrid methods are used in the development of intelligent algorithms, such as support vector machines, deep learning, anddecision trees. These algorithms combine the advantages of both numerical and symbolic approaches, resulting in better accuracy and performance. Hybrid methods are also used in simulation, particularly in the simulation ofphysical systems, where mathematical models and simulations are combined to create a more accurate and realistic simulation.Advantages of Hybrid MethodThe main advantage of hybrid method is its ability to tackle complex problems that are beyond the reach of traditional methods. Hybrid methods are capable of handling problems that are mathematically complex, computationally intensive, or have high dimensionalities. The hybrid method also provides a robust and flexible framework that can handle different types of problems and data structures.Hybrid methods provide accurate and efficient solutions by combining the strengths of different approaches. They allow for a more holistic understanding of the problem by utilizing multiple perspectives. Additionally, these methods are extensible, meaning that they allow for the integration of new techniques and algorithms as they become available.Limitations of Hybrid MethodDespite the many advantages of hybrid methods, they also have some limitations. First, hybrid methods can be computationally intensive, and they may require high-performance computing resources. Additionally, these methods may be difficult to implement and may require specialized expertise. As such, they may not be accessible to less experienced users or those without access to advanced computing resources.Another limitation is that hybrid methods may not always result in the best solution. Although hybrid methods can provide accurate and efficient solutions, they may not always be the optimal solution. This is because the hybrid method relies on combining multiple methods, and the final solution can depend on the specific combination of methods used. Additionally, the hybrid method may not always be the most transparent approach. This means that it may be difficult to understand why the algorithm produced a particular result.ConclusionHybrid Method is an important and valuable technique that has been widely used in various fields of research and development. The hybrid method combines the strengths of both numerical and symbolic methods to develop an efficient and accurate algorithm. The hybrid method has been used in optimization, simulation, control, and machine learning. It provides accurate and efficient solutions by combining the strengths of different approaches. Although hybrid methods have limitations, they are still an essential tool for solving complex problems. As such, hybrid methods remain an important area of research and development, and we can expect to see continued growth and application of this technique in the future.。

sci中的长难句

sci中的长难句在科学论文中，长难句常常出现，主要是为了表达复杂的概念和关系。

以下是一些常见的长难句例子：1. "The development and implementation of a robust and scalable machine learning algorithm, combined with advanced data analytics techniques, have significantly improved the accuracy and efficiency of predicting and analyzing complex biological systems, thereby enabling researchers to gain deeper insights into the underlying mechanisms driving disease progression."“强大且可扩展的机器学习算法的开发和实施，结合先进的数据分析技术，显著提高了预测和分析复杂生物系统的准确性和效率，从而使研究人员能够更深入地了解驱动疾病进展的潜在机制。

”2. "The integration of nanomaterials with traditional construction materials, such as concrete and steel, not only enhances their mechanical properties, but also provides additional functionalities, such as self-healing, self-cleaning, and energy harvesting capabilities, contributing to the development of sustainable and smart infrastructure."“将纳米材料与混凝土和钢材等传统建筑材料相结合，不仅增强了它们的机械性能，还提供了额外的功能，如自我修复、自清洁和能量收集能力，有助于可持续和智能基础设施的发展。

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘要
在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检测技术相比，自动化的视觉检测系统更加经济、快捷、高效与安全。纹理物体在工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代化电子系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化检测提供高效而可靠的检测算法。纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体的检测也可取得不错的效果。在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列具有实际应用价值的图像。实验结果表明本文提出的纹理物体缺陷检测算法具有高效性和易于实现性。关键字: 缺陷检测；纹理；物体变形；可调控金字塔；重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

卡尺找圆英文算法

卡尺找圆英文算法Accurate and precise measurement is a fundamental requirement in various fields of science, engineering, and manufacturing. One of the most versatile and commonly used measuring instruments is the Vernier caliper. The Vernier caliper, with its ability to measure both linear and circular dimensions, plays a crucial role in numerous applications. One such application is the determination of the diameter of a circular object, which can be achieved through the implementation of a Vernier caliper finding circle algorithm.The Vernier caliper is a device that consists of a main scale, a sliding jaw, and a Vernier scale. The main scale is typically marked in millimeters or inches, while the Vernier scale is used to provide a more precise reading. The Vernier scale is divided into a number of equal parts, usually 10 or 20, and is used to measure fractions of the main scale's smallest division.To measure the diameter of a circular object using a Vernier caliper, the following steps can be followed:1. Open the jaws of the Vernier caliper wide enough to accommodate the circular object.2. Gently place the circular object between the jaws of the Vernier caliper, ensuring that the object is centered and the jaws are in full contact with the object's surface.3. Observe the main scale reading, which will indicate the whole number of the measurement.4. Examine the Vernier scale and identify the mark on the Vernier scale that aligns most closely with a mark on the main scale.5. The value indicated by the aligned mark on the Vernier scale, in relation to the main scale, will provide the fractional part of the measurement.6. Add the whole number from the main scale and the fractional part from the Vernier scale to obtain the final diameter measurement.The Vernier caliper finding circle algorithm can be further refined to enhance the accuracy and precision of the measurement. One such algorithm involves the use of multiple measurements taken at different orientations around the circular object.The steps for the Vernier caliper finding circle algorithm with multiple measurements are as follows:1. Measure the diameter of the circular object at four equidistant points around its circumference, ensuring that the measurements aretaken at 0°, 90°, 180°, and 270° orientations.2. Record the four diameter measurements obtained.3. Calculate the average of the four measurements to determine the final diameter of the circular object.The rationale behind this algorithm is that by taking measurements at multiple orientations, any irregularities or deviations in the circular object's shape can be minimized, leading to a more accurate representation of the true diameter.The advantages of using the Vernier caliper finding circle algorithm with multiple measurements include:1. Improved accuracy: By averaging the measurements taken at different orientations, the impact of any local variations or irregularities in the circular object's shape is reduced, resulting in a more accurate diameter estimation.2. Enhanced precision: The use of multiple measurements and the subsequent averaging process can help to minimize random errors, leading to a more precise final result.3. Identification of potential irregularities: If the individual measurements show significant variations, it may indicate the presence of irregularities or deformities in the circular object, which can be useful information for further analysis or quality control purposes.The Vernier caliper finding circle algorithm with multiple measurements is particularly useful in applications where high levels of accuracy and precision are required, such as in engineering, manufacturing, and scientific research. By following this algorithm, users can obtain reliable and consistent diameter measurements for circular objects, enabling them to make informed decisions and take appropriate actions based on the obtained results.In conclusion, the Vernier caliper is a versatile and essential tool for measuring both linear and circular dimensions. The Vernier caliper finding circle algorithm, with its multiple measurement approach, provides a robust and reliable method for determining the diameter of circular objects. This algorithm, combined with the inherent precision of the Vernier caliper, ensures that users can obtain accurate and precise measurements, making it a valuable asset in a wide range of applications.。

决策树算法及其英文缩写

决策树算法及其英文缩写Decision Tree Algorithm: A Comprehensive Guide.Introduction.A decision tree is a powerful machine learningalgorithm that can be used for both classification and regression tasks. It is a non-parametric supervisedlearning method, which means that it does not make any assumptions about the underlying data distribution.Decision trees are often used for exploratory data analysis, as they can help to identify important features and relationships in the data.How Decision Trees Work.Decision trees work by recursively splitting the data into smaller and smaller subsets, until each subsetcontains only one type of data point. The tree is builtfrom the top down, starting with the entire data set. Ateach node in the tree, the algorithm selects a feature to split the data on. The feature that is selected is the one that best separates the data into two distinct groups.The process of splitting the data continues until a stopping criterion is met. The stopping criterion can be based on the number of data points in each subset, the depth of the tree, or the impurity of the subsets.Impurity Measures.The impurity of a subset is a measure of how well the data points in the subset are separated. There are several different impurity measures that can be used, including:Gini impurity: The Gini impurity is calculated as the sum of the probabilities of each class occurring in the subset. The lower the Gini impurity, the more pure the subset.Entropy: Entropy is a measure of the randomness of a subset. The higher the entropy, the more random the subset.Information gain: Information gain is a measure of how much the entropy of a subset decreases when it is split on a particular feature. The higher the information gain, the more informative the feature.Choosing a Splitting Feature.The choice of splitting feature at each node in the tree is crucial to the performance of the decision tree. The goal is to choose a feature that will best separate the data into two distinct groups.There are several different methods that can be used to choose a splitting feature, including:Greedy search: The greedy search method simply chooses the feature that has the highest information gain at each node.Random search: The random search method randomly selects a subset of features at each node and then choosesthe feature that has the highest information gain within that subset.Ensemble methods: Ensemble methods, such as random forests and gradient boosting, combine multiple decision trees to improve performance. These methods can use different criteria to choose splitting features, such as the Gini impurity or the entropy.Pruning Decision Trees.Decision trees can be prone to overfitting, which occurs when the tree is too complex and fits the training data too closely. Overfitting can lead to poor performance on new data.To prevent overfitting, decision trees can be pruned. Pruning is the process of removing branches from the tree that are not necessary for making accurate predictions.There are several different pruning methods that can be used, including:Pre-pruning: Pre-pruning stops the tree from growing when it reaches a certain size or when the information gain of the new nodes falls below a certain threshold.Post-pruning: Post-pruning removes branches from the tree after it has been fully grown. The branches that are removed are the ones that do not contribute to the accuracy of the tree on a validation set.Advantages of Decision Trees.Decision trees have a number of advantages over other machine learning algorithms, including:Easy to interpret: Decision trees are easy to interpret, even for non-experts. This makes them a good choice for exploratory data analysis and for communicating the results of machine learning models to stakeholders.Robust to noise and missing data: Decision trees are robust to noise and missing data. This makes them a goodchoice for data sets that are noisy or have missing values.Can handle both categorical and continuous features: Decision trees can handle both categorical and continuous features. This makes them a versatile algorithm that can be used on a wide variety of data sets.Disadvantages of Decision Trees.Decision trees also have some disadvantages, including:Can be unstable: Decision trees can be unstable, meaning that small changes in the data can lead to large changes in the tree. This can make them difficult to usefor making accurate predictions on new data.Can be biased: Decision trees can be biased towards the majority class in the data. This can make them less effective for classifying data sets that have a skewed class distribution.Conclusion.Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to interpret, robust to noise and missing data, and can handle both categorical and continuous features. However, decision trees can be unstable and biased, and they can be prone to overfitting.。

The development and comparison of robust methods for estimating the fundamental matrix

1. Introduction
In most computer vision algorithms it is assumed that a least squares framework is su cient to deal with data corrupted by noise. However, in many applications, visual data are not only noisy, but also contain outliers, data that are in gross disagreement with a postulated model. Outliers, which are inevitably included in an initial t, can so distort a tting process that the tted parameters become arbitrary. This is particularly severe when the veridical data are themselves degenerate or near-degenerate with respect to the model, for then outliers can appear to break the degeneracy. In such circumstances, the deployment of robust estimation methods is essential. Robust methods continue to recover meaningful descriptions of a
statistical population even when the data contain outlying elements belonging to a di erent population. They are also able to perform when other assumptions underlying the estimation, say the noise model, are not wholly satis ed. Amongst the earliest to draw the value of such methods to the attention of computer vision researchers were Fischler and Bolles (1981). Figure 1 shows a table of x; y data from their paper which contains a gross outlier (Point 7). Fit 1 is the result of applying least squares, Fit 2 is the result of applying least squares after one robust method has removed the outlier, and the solid line is the result of applying their fully robust RANSAC algorithm to the data. The data set can also be used to demonstrate the failings of na ve heuristics to remove outliers. For example, discarding

XN9000血液分析仪两模块的性能评价

XN9000血液分析仪两模块的性能评价赵妍; 喻超; 倪维【期刊名称】《《中国医药导报》》【年(卷),期】2019(016)031【总页数】5页(P152-155,168)【关键词】血液分析仪; 模块; 性能评价; 白细胞分类; 网织红细胞计数【作者】赵妍; 喻超; 倪维【作者单位】湖北省中医院检验科湖北武汉430074【正文语种】中文【中图分类】R446.1全血细胞分类计数是血液学实验室最经常使用的测试方法，不断开发更新的技术使自动血液分析仪能够提供更准确的结果[1]。

模块化的血液分析系统内部的智能有机配合，能够实现大批量标本的检测[2]。

XN-9000是一款可扩展的模块化自动化血液分析系统，为了满足目前医学实验室认可的需求，保证实验室检测结果的准确性[3-5]，现对实验室配置A1（XN20）和B4（XN10）分析模块的XN9000血液分析系统性能进行验证。

1 材料与方法1.1 样本来源本研究所选取新鲜抗凝全血标本均来自湖北省中医院光谷院区门诊和住院患者，研究起止时间为2017年7～9月。

1.2 仪器与试剂XN-9000血液分析系统及原装试剂、染液、玻片、校准品和质控品（日本Sysmex 公司），LH750血液分析仪及原装试剂、校准品和质控物（美国BECKMANCOULTER 公司）、Olympus 显微镜（日本Olympus 公司）、瑞氏-吉姆萨和煌焦油兰染液（珠海BASO 公司）。

1.3 方法按仪器操作规程操作，结果均按照《WS/T 406-2012临床血液学检验常规项目分析质量要求》[6]进行判定。

1.3.1 本底及空白计数取稀释液当作样本上机检测，连测3次。

分别记录两个模块测试结果。

1.3.2 携带污染率每个项目各取1份高、低浓度样本，连续在两模块上测定3次，高浓度结果记为H1、H2、H3；低浓度检测结果为L1、L2、L3。

按公式：携带污染率=（L1-L3）/（H3-L3）×100%计算每个模块各项目的携带污染率。

人工智能在农业方面应用英语作文

人工智能在农业方面应用英语作文全文共3篇示例，供读者参考篇1The Role of AI in Modern AgricultureAs a student studying computer science and having grown up on my family's farm, I have become increasingly fascinated by the potential applications of artificial intelligence (AI) in the agricultural sector. Agriculture has been a cornerstone of human civilization for thousands of years, providing the food that sustains us all. However, with the world's population continuing to grow and the challenges posed by climate change, there is an urgent need to increase food production while simultaneously reducing our environmental impact. This is where AI can play a crucial role, offering innovative solutions to some of the most pressing challenges facing modern agriculture.One of the primary applications of AI in agriculture is in the realm of precision farming. Traditional farming methods often rely on a "one-size-fits-all" approach, treating entire fields uniformly. However, soil conditions, nutrient levels, and water requirements can vary significantly even within a single field.AI-powered systems can analyze vast amounts of data from sensors, drones, and satellite imagery to create detailed maps of these variations. This information can then be used to optimize the application of fertilizers, pesticides, and water, resulting in more efficient resource utilization and reduced environmental impact.Crop monitoring is another area where AI is making significant strides. By leveraging computer vision algorithms and machine learning models, AI systems can analyze images of crops to detect signs of disease, nutrient deficiencies, or pest infestations at an early stage. This early detection allows farmers to take prompt and targeted action, minimizing crop losses and reducing the need for broad-spectrum pesticide applications.AI is also playing a pivotal role in improving livestock management. Sophisticated algorithms can analyze data from wearable sensors on animals to monitor their health, behavior, and productivity. This information can be used to optimize feeding regimes, identify potential health issues before they become severe, and even assist in breeding programs by selecting the most suitable genetic traits.Beyond these direct applications, AI is also contributing to advancements in agricultural research and development.Machine learning models can analyze vast datasets encompassing genomic information, environmental factors, and historical yield data to identify correlations and develop new crop varieties that are more resilient to pests, drought, and other challenges. Additionally, AI-powered simulations can help researchers explore different scenarios and evaluate the potential impact of new farming practices or technologies before implementation in the field.However, the integration of AI in agriculture is not without its challenges. One significant hurdle is the need for large,high-quality datasets to train AI models effectively. Collecting and curating this data can be a time-consuming and expensive process, particularly in regions with limited technological infrastructure. Additionally, there are concerns about the potential for AI systems to perpetuate existing biases or make decisions that disproportionately impact certain communities or regions.Another challenge lies in the adoption of these technologies by farmers, many of whom may be unfamiliar or hesitant to embrace new and complex technologies. Effective training programs and user-friendly interfaces will be crucial to ensurethat the benefits of AI in agriculture are accessible to a wide range of users, regardless of their technical expertise.Despite these challenges, the potential benefits of AI in agriculture are too significant to ignore. As a student passionate about both technology and agriculture, I am excited to witness and potentially contribute to the ongoing integration of AI in this vital sector. By leveraging the power of AI, we can work towards a future where food production is more efficient, sustainable, and resilient, ensuring that we can feed a growing global population while minimizing our environmental footprint.In conclusion, the applications of AI in agriculture are vast and rapidly evolving. From precision farming and crop monitoring to livestock management and agricultural research, AI is poised to revolutionize the way we grow and produce food. While there are certainly challenges to overcome, the potential benefits are immense, offering a path towards a more sustainable and secure food future for all.篇2The Impact of AI on Modern AgricultureAs a student studying agricultural sciences, I have become increasingly fascinated by the rapid integration of artificialintelligence (AI) into various aspects of farming and food production. The potential benefits of AI in streamlining processes, optimizing resource utilization, and enhancing overall sustainability are immense. In this essay, I will explore some of the key applications of AI in modern agriculture and discuss their implications for the future of our food systems.Precision Farming and Crop ManagementOne of the most significant applications of AI in agriculture is precision farming, which involves the use of advanced technologies to monitor and optimize crop growth, soil conditions, and resource usage. AI-powered systems can analyze vast amounts of data from sensors, drones, and satellites, providing farmers with valuable insights into their fields' conditions. This information can then be used to make informed decisions about irrigation, fertilizer application, and pest management, leading to more efficient resource utilization and higher yields.For example, AI algorithms can analyze soil moisture levels, nutrient content, and weather patterns to determine the optimal timing and amount of water and fertilizer to be applied. This precision approach not only conserves valuable resources but also reduces the environmental impact of excessive fertilizerrunoff and water wastage. Furthermore, AI-powered systems can detect early signs of pest infestations or plant diseases, enabling timely intervention and minimizing crop losses.Automated Farming Machinery and RoboticsAnother exciting application of AI in agriculture is the development of automated farming machinery and robotics. These systems can perform tasks such as planting, weeding, harvesting, and sorting with remarkable precision and efficiency. AI algorithms can guide these machines and robots, enabling them to navigate fields, identify crops and weeds, and make real-time adjustments based on environmental conditions.For instance, AI-powered robotic harvesters can selectively pick ripe fruits or vegetables, reducing waste and ensuring optimal quality. Similarly, robotic weeders can accurately identify and remove unwanted plants, minimizing the need for herbicides and promoting sustainable farming practices. These automated systems not only increase productivity but also address labor shortages and reduce the physical strain on agricultural workers.Livestock Management and Animal HealthAI has also made significant inroads into livestock management and animal health monitoring. By analyzing data from sensors and cameras, AI systems can track the behavior, health, and productivity of individual animals in real-time. This information can be used to optimize feeding regimes, detect early signs of illness, and identify breeding opportunities, ultimately improving animal welfare and productivity.For example, AI-powered systems can monitor the movement patterns and feeding habits of cattle, enabling farmers to identify potential health issues or dietary deficiencies promptly. Additionally, AI algorithms can analyze milk production data, assisting dairy farmers in optimizing their herd management practices.Predictive Analytics and Supply Chain OptimizationBeyond on-farm applications, AI is also transforming the broader agricultural supply chain. Predictive analytics powered by AI can forecast crop yields, market demand, and pricing trends, enabling farmers, distributors, and retailers to make more informed decisions regarding production, storage, and distribution.By analyzing vast amounts of data on weather patterns, soil conditions, market trends, and consumer behavior, AI algorithmscan provide valuable insights into potential risks and opportunities. This information can help stakeholders across the supply chain optimize their operations, reduce waste, and better meet consumer demands.Challenges and ConsiderationsWhile the potential benefits of AI in agriculture are substantial, it is crucial to acknowledge and address the challenges and considerations associated with its implementation. One significant concern is the initial cost of investing in AI-powered technologies, which may be prohibitive for smallholder farmers or those in developing countries. Additionally, there is a need for appropriate training and skill development to ensure that farmers and agricultural workers can effectively utilize and maintain these advanced systems.Furthermore, the widespread adoption of AI in agriculture raises ethical concerns regarding data privacy, algorithm bias, and the potential displacement of human labor. It is essential to develop robust governance frameworks and guidelines to ensure the responsible and equitable deployment of AI technologies in the agricultural sector.ConclusionThe integration of artificial intelligence into modern agriculture represents a pivotal shift in how we approach food production and resource management. From precision farming and automated machinery to livestock monitoring and supply chain optimization, AI is revolutionizing various aspects of the agricultural industry. By leveraging the power of data analytics, machine learning, and automation, we can increase productivity, enhance sustainability, and better meet the growing global demand for food.However, it is crucial to address the challenges and considerations associated with AI adoption, such as cost barriers, skill development, data privacy, and potential job displacement. Through collaborative efforts involving policymakers, researchers, technology providers, and farmers, we can navigate these challenges and harness the transformative potential of AI to build a more resilient, efficient, and sustainable agricultural system for the future.篇3The Role of AI in Modern Farming PracticesAs a student studying agricultural sciences, I have become increasingly fascinated by the potential applications of artificialintelligence (AI) in the realm of farming and food production. The integration of AI technologies has already begun to revolutionize traditional agricultural methods, offering innovative solutions to address the pressing challenges of feeding a rapidly growing global population while promoting sustainability and environmental stewardship.At its core, AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. In the context of agriculture, AI has the potential to profoundly transform virtually every aspect of the farming process, from seed selection and crop management to harvesting and distribution.One of the most promising applications of AI in agriculture is precision farming. Through the use of advanced sensors, drones, and satellite imagery, farmers can gather vast amounts of data on soil conditions, weather patterns, crop health, and other key variables. AI algorithms can then analyze this data and provide highly accurate recommendations for optimizing inputs such as water, fertilizers, and pesticides, thereby maximizing yields while minimizing waste and environmental impact.For instance, AI-powered systems can detect early signs of pest infestations or plant diseases, allowing farmers to take prompt and targeted action, reducing the need for blanket applications of chemicals. Additionally, AI can assist in identifying the most suitable crop varieties for specific soil and climatic conditions, increasing the likelihood of successful harvests.Furthermore, AI plays a crucial role in the development of autonomous farming equipment, such as self-driving tractors and robots capable of performing tasks like planting, weeding, and harvesting. These advanced systems not only increase efficiency and productivity but also address labor shortages and reduce the physical strain on human workers.In the realm of animal husbandry, AI is being utilized to monitor livestock health, optimize feeding regimes, and even breed superior genetic lines. Sensor-equipped wearable devices can track various parameters, such as body temperature, activity levels, and rumination patterns, allowing for early detection of potential health issues and enabling timely interventions.Beyond on-farm applications, AI is also transforming the broader agricultural supply chain. Predictive analytics powered by AI can help streamline logistics and distribution, minimizingfood waste and ensuring that fresh produce reaches consumers in optimal condition. Additionally, AI-driven demand forecasting models can assist in better aligning production with market needs, reducing oversupply and stabilizing prices.However, the adoption of AI in agriculture is not without its challenges. One significant hurdle is the initial investment required for implementing advanced technologies, which may be prohibitive for smallholder farmers or those operating in resource-constrained environments. Additionally, concerns have been raised regarding data privacy, cybersecurity risks, and the potential for AI systems to perpetuate biases or make ethically questionable decisions.To address these challenges, collaborative efforts between researchers, policymakers, and industry stakeholders are crucial. Governments and international organizations can play a pivotal role in providing financial support, fostering public-private partnerships, and developing regulatory frameworks to ensure the responsible and ethical deployment of AI in agriculture.Moreover, educational institutions must adapt their curricula to equip the next generation of agricultural professionals with the necessary skills and knowledge to leverage AI effectively. Interdisciplinary programs that combine agricultural scienceswith computer science, data analytics, and ethics will be invaluable in shaping a workforce capable of harnessing the full potential of AI while mitigating its risks.As a student passionate about sustainable agriculture, I am excited by the prospects of AI in addressing some of the most pressing challenges facing our global food system. From enhancing crop yields and resource efficiency to improving livestock management and supply chain optimization, AI offers a wealth of opportunities to increase food security while promoting environmental stewardship.However, it is crucial to approach the integration of AI in agriculture with a critical and ethical mindset. We must remain vigilant about potential unintended consequences, such as job displacement or the exacerbation of existing inequalities. Robust governance frameworks and inclusive decision-making processes will be essential to ensure that the benefits of AI are distributed equitably and that its deployment aligns with societal values and ethical principles.As we navigate this technological revolution, it is imperative that we strike a careful balance between embracing innovation and preserving the fundamental values that underpin sustainable and responsible agricultural practices. By fosteringinterdisciplinary collaboration, promoting ethical AI development, and empowering farmers and communities, we can harness the transformative potential of AI to build a more resilient, efficient, and equitable global food system.In conclusion, the integration of AI in agriculture represents a paradigm shift with far-reaching implications for our ability to feed a growing population while safeguarding the planet's fragile ecosystems. As a future agricultural professional, I am committed to staying informed about the latest AI developments and contributing to the responsible and ethical adoption of these technologies. Together, we can shape a future where AI and human ingenuity converge to create a more sustainable and prosperous world for all.。

物流管理英文含翻译

development of efficientalgorithms.
FortheproblembyacademicresearchersandprofessionalsocietiesinOR/MS,resul
tinginanumberofpapersconcerningthedevelopmentofanumberofVehicleRouti
vehicle costand the total distance travelled by the vehicles, subjectto the
following constraints:
each vehicle has a predetermined load capacity, typically different from
suchapplications.Typically such systems serve as a central depot
fordistributing mon services in the field of logistics.The mercial application is
stored in a central serverand services are provided for each member of the
clients
(Tarantilis, Kiranoudis, & Vassiliadis, 2003, 2004).Therefore, the system was
designed in order to automatically generate vehicle routes (which vehicles
process. Whensuch a network is introduced in order to exploit amercial idea

随机森林在微生物组学中的应用

随机森林在微生物组学中的应用Random forest is a popular machine learning algorithm that has been widely used in various fields, including microbiomics. It has shown significant potential in analyzing complex microbiome data due to its ability to handle high-dimensional data and capture non-linear relationships between variables. 随机森林是一种流行的机器学习算法，在各个领域都被广泛应用，包括微生物组学。

由于其处理高维数据和捕捉变量之间非线性关系的能力，它在分析复杂微生物组数据方面表现出巨大潜力。

One of the key advantages of using random forest in microbiomics is its ability to handle missing data. In microbiome studies, missingdata is a common issue due to the nature of biological samples. Random forest can effectively deal with missing values by imputing them based on the available data, allowing researchers to make full use of the available information. 使用随机森林在微生物组学中的一个关键优势是其处理缺失数据的能力。

在微生物组研究中，由于生物样本的特性，缺失数据是一个常见问题。

算法c语言基础PPT课件

Concept of Algorithm
Step 1: 比较两个数，显示较大的数 Step 2: 算法结束
没有输入!
算法的五个特性【难点】没有输出的情形
Concept of Algorithm
Step 1: 输入一个数 Step 2: 求大于该数的最小偶数 Step 3: 算法结束
没有输出!
从计算的本质看算法* 计算：从一个已知的符号串【输入】开始，按照一定的规则【确定性】改变符号串【有效性】，经过有限步骤【有穷性】，最后得到一个满足预先规定的符号串【输出】，这种变换过程就是计算计算就是符号串的连续变换【算法的执行】
变量(内存单元)
变量(内存单元)
自然语言描述【重点】用自然语言把算法表示为有穷的步骤需要保证算法的五个特征（在一定的抽象层次上）一般形式
Representation of Algorithm
算法名称：【算法命名】输入：【算法的输入信息】输出：【算法的输出结果】 Step 1： Step 2： …
Concept of Algorithm
计算过程(e.g.,OS)
算法的五个特性【难点】有穷性破坏的情形
Concept of Algorithm
Step 1: 打印数字1 Step 2: 打印下一个自然数 Step 3: 转Step 2
死循环!
算法的五个特性【难点】确定性破坏的情形
Concept of Algorithm
伪代码（Pseudo-code）——掌握程序控制结构之后经常会使用算法的类程序语言描述形式，目的是为了使被描述的算法可以容易地以任何一种编程语言（Pascal，C，Java）实现要求结构清晰、代码简单、可读性好，并且类似自然语言，介于自然语言与编程语言之间可以用C语言的语法编写，但只强调程序控制结构

并网逆变器孤岛控制技术

第38卷第9期电力系统保护与控制Vol.38 No.9 2010年5月1日 Power System Protection and Control May 1, 2010并网逆变器孤岛控制技术曹海燕, 田悦新（石家庄经济学院信息工程学院，河北石家庄 050031）摘要：为解决传统孤岛保护控制存在孤岛检测盲区，使得孤岛保护失效的问题，提出基于频率正反馈扰动的孤岛保护控制方法，既减小了传统有源孤岛保护控制对输出电能质量的影响，又实现了无盲区的孤岛保护控制。

对于大功率应用场合，孤岛保护控制不再适用。

因此，IEEE Std. 1547-2003标准中提出孤岛运行控制的概念。

针对标准要求提出基于工作模式切换的孤岛运行控制，使得孤岛发生前后，并网逆变器输出电压均满足用户负载运行要求。

仿真结果验证了两种孤岛控制方案的正确性。

关键词: 并网逆变器；孤岛检测；孤岛保护；孤岛运行Islanding control for grid-connected invertersCAO Hai-yan, TIAN Yue-xin（Shijiazhuang University of Economics, Shijiazhuang 050031，China）Abstract: Non-detection zone disables the conventional islanding protection. In order to solve the problem, a novel control solution based on the frequency positive feedback disturbance is proposed to achieve the effective islanding protection. Considering high power applications, it is not practical to apply the islanding protection control any more. Therefore, the concept of controlled islanding is proposed in IEEE Std. 1547-200. This paper presents a novel controlled islanding based on the operation mode transfer, which maintains the voltage to keep critical loads in safe operation. The proposed islanding control solutions are verified by the simulation results.Key words: grid-connected inverters; islanding detection; islanding protection; controlled islanding中图分类号： TM615 文献标识码：A 文章编号： 1674-3415(2010)09-0072-030 引言并网逆变器（GCI）运行过程中可能出现孤岛运行状态，孤岛运行将产生严重后果[1]。

中国算法应用发展现状

中国算法应用发展现状英文文档：Title: Development Status of Algorithm Applications in ChinaChina has witnessed remarkable growth in the field of algorithm applications, particularly in recent years.The country"s commitment to technological innovation and the availability of large datasets have fueled the development of various algorithmic solutions across multiple sectors.One of the most prominent areas where algorithms are extensively used in China is e-commerce.Platforms like Alibaba and Tencent have leveraged machine learning algorithms to provide personalized shopping recommendations to their users, resulting in enhanced user experience and increased sales.In addition, China"s transportation sector has also embraced algorithm panies like Didi Chuxing have implemented sophisticated algorithms to optimize route planning and improve traffic management.This has not only reduced travel time but also alleviated congestion on the roads.Furthermore, China"s healthcare industry has made significant strides in utilizing algorithms for diagnosing diseases and predicting patient outcomes.AI-powered tools are increasingly being used to analyze medical images and assist doctors in making accurate diagnoses.Overall, the development of algorithm applications in China is proceeding at a rapid pace, driven by the country"s robust technological infrastructure and the innovative efforts of its enterprises.中文文档：标题：中国算法应用发展现状中国在算法应用领域的发展令人瞩目，特别是在近年来。

Two_Dimensional_Phase_Unwrapping_Final

Two-Dimensional Phase Unwrapping ProblemBy Dr. Munther Gdeisat and Dr. Francis LilleyPre-requisite:In order to understand this tutorial it is necessary for you to have already studied and completed the “one-dimensional phase unwrapping problem” tutorial before reading this document. There are many applications that produce wrapped phase images. Examples of these are synthetic aperture radar (SAR), magnetic resonance imaging (MRI) and fringe pattern analysis. The wrapped phase images that are produced by these applications are not usable unless they are first unwrapped so as to form a continuous phase map. This means that the development of a robust phase unwrapping algorithm is an important topic for all these applications. In this article, we will not discuss phase unwrapping only in the specific context of these applications, but we will instead explain the concept of the 2D phase unwrapping problem in general terms.1.Introduction to 2D phase unwrappingWe shall explain the 2D phase unwrapping process as follows. Suppose that we have a computer-generated continuous phase image that does not contain any phase wraps (2π jumps). This image may be displayed as a visual intensity array, as shown in Figure 1(a). The same image may also be plotted as a 3D surface, as shown in Figure 1(b). The intensities from a single row of this image (row 410) are graphically plotted in Figure 1(c). The Matlab code that is used to generate this phase image is as follows. The peaks Matlab function is used to generate the continuous phase image. Please note that we are using the term “continuous” here to refer not to an analogue signal, but to a discrete 1D phase signal, or a discrete 2D phase image, that does not contain any phase wraps.%This program is to simulate a continuous phase distribution to act as a dataset %for use in the 2D phase unwrapping problemclc; close all; clearN = 512;[x,y]=meshgrid(1:N);image1 = 2*peaks(N) + 0.1*x + 0.01*y;figure, colormap(gray(256)), imagesc(image1)title('Continuous phase image displayed as a visual intensity array')xlabel('Pixels'), ylabel('Pixels')figuresurf(image1,'FaceColor','interp', 'EdgeColor','none', 'FaceLighting','phong')view(-30,30), camlight left, axis tighttitle(' Continuous phase map image displayed as a surface plot')xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')figure, plot(image1(410,:))title('Row 410 of the continuous phase image')xlabel('Pixels'), ylabel('Phase in radians')(a) (b) (c)Figure 1: (a) A computer-generated continuous phase image displayed as a visual intensity array, (b) thesame image plotted as a surface, (c) intensities from row 410 of the phase image. Now let us wrap the computer-generated continuous phase image. The Matlab code to perform this task is as follows;%wrap the 2D imageimage1_wrapped = atan2(sin(image1), cos(image1)); figure, colormap(gray(256)), imagesc(image1_wrapped)title('Wrapped phase image displayed as a visual intensity array') xlabel('Pixels'), ylabel('Pixels')figuresurf(image1_wrapped,'FaceColor','interp', 'EdgeColor','none', 'FaceLighting','phong')view(-30,70), camlight left , axis tighttitle('Wrapped phase image plotted as a surface')xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')figure, plot(image1_wrapped(410,:))title('Row 410 of the wrapped phase image') xlabel('Pixels'), ylabel('Phase in radians')The wrapped image is shown below.PixelsP i x e l s5010015020025030035040045050050100150200250300350400450500PixelsP h a s e i n r a d i a n s(a) (b) (c)Figure 2: (a) A wrapped phase image displayed as a visual intensity array, (b) the wrapped image plotted asa surface, (c) row 410 of the wrapped phase image. Recall from the 1D phase unwrapping tutorial, that when we were dealing with lines of phase values, the phase wraps appeared as multiple 2π jumps forming a saw-tooth waveform like that shown in Figure 2(c). Note that in the 2D case, where we now have phase images in the form of a 2D array, the phase wraps appear as contour curves, as shown in Figure 2(a), which we shall refer to as wrap curves. These curves will appear in the form of either closed, or open, curves and you can see both types of curve in Figure 2(a). Note that in the latter case, if an open curve enters a wrapped phase image, it must therefore also leave it. In order to unwrap the image we can use the Itoh 2D phase unwrapper. There are two main methods by which the Itoh 2D phase unwrapper may be implemented. The first method involves unwrapping the rows in the wrapped image sequentially (one at a time). This produces an intermediate image that is only partially phase unwrapped. Next we perform a similar process, but this time unwrap all the columns within the partially unwrapped image. The resultant unwrapped phase image, as produced by this first implementation of the Itoh unwrapper, is shown in Figures 3(a) & (b). The Matlab code to perform this task is as follows.%Unwrap the image using the Itoh algorithm: the first method is performed %by first sequentially unwrapping the all rows, one at a time. image1_unwrapped = image1_wrapped; for i=1:Nimage1_unwrapped(i,:) = unwrap(image1_unwrapped(i,:)); end%Then sequentially unwrap all the columns one at a time for i=1:Nimage1_unwrapped(:,i) = unwrap(image1_unwrapped(:,i)); endfigure, colormap(gray(256)), imagesc(image1_unwrapped)title('Unwrapped phase image using the Itoh algorithm: the first method') xlabel('Pixels'), ylabel('Pixels')figuresurf(image1_unwrapped,'FaceColor','interp', 'EdgeColor','none', 'FaceLighting','phong')view(-30,30), camlight left , axis tighttitle('Unwrapped phase image using the Itoh algorithm: the first method') xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')PixelsP i x e l s5010015020025030035040045050050100150200250300350400450500PixelsP h a s e i n r a d i a n sThe second method of implementing the Itoh unwrapper simply works the other way around. In other words, it involves first unwrapping all the columns within the wrapped phase image, one at a time. And this again produces a partially phase unwrapped image. Then we sequentially unwrap all rows of the partially unwrapped image. The resultant unwrapped phase image, produced using this second implementation of the Itoh unwrapper, is shown in Figure 3(b). The Matlab code to perform this task is as follows.%Unwrap the image using the Itoh algorithm: the second method%performed by first sequentially unwrapping all the columns one at a time. image2_unwrapped = image1_wrapped; for i=1:Nimage2_unwrapped(:,i) = unwrap(image2_unwrapped(:,i)); end%Then sequentially unwrap all the a rows one at a time for i=1:Nimage2_unwrapped(i,:) = unwrap(image2_unwrapped(i,:)); endfigure, colormap(gray(256)), imagesc(image2_unwrapped)title('Unwrapped phase image using Itoh algorithm: the second method') xlabel('Pixels'), ylabel('Pixels')figuresurf(image2_unwrapped,'FaceColor','interp', 'EdgeColor','none', 'FaceLighting','phong')view(-30,30), camlight left , axis tighttitle('Unwrapped phase image using the Itoh algorithm: the second method') xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')(a)(b)(c)(d)Figure 3: Unwrapped image using the 2D Itoh algorithm; implemented using the first method in (a) & (b),and implemented using the second method in (c) & (d).PixelsP i x e l s5010015020025030035040045050050100150200250300350400450500unwrapped imagePixelsP i x e l s5010015020025030035040045050050100150200250300350400450500It is obvious from the exercise that has been performed above that both of these implementations of the Itoh phase unwrapping algorithm actually produce the same output. This is because this wrapped phase image is not a real one, but is instead an artificial dataset that does not contain any errors.The wrapped phase image that is shown in Figure 2(a) is a good example of an ideal phase image that does not contain any sources of error. We can easily process this image using any 2D phase unwrapper. As has been explained above, in this case we have processed the image using the 2D Itoh algorithm. This is a very simple phase unwrapping algorithm, which only works in cases where the phase images are virtually error free. Most real-world applications produce wrapped phase images that do contain errors. In this case, we need to use more complex 2D phase unwrappers in order to deal with these images.In 2D phase unwrapping, there are four sources of errors that complicate the phase unwrapping process. These sources of errors are as follows.1.Noise2.Under sampling3.When the continuous phase image contains sudden, abrupt phase changes4.Errors produced by the phase extraction algorithm itselfIn this tutorial we will discuss only the first three sources of errors and their effects upon the 2D phase unwrapping process. We will also explain how to successfully unwrap images in these three different situations. The fourth source of error depends on the specific algorithm that is used to extract the wrapped phase. The reader should be aware of this as another potential source of error when performing phase unwrapping, however a detailed discussion of the effects of an algorithm itself on the extracted wrapped phase is out of the scope of this tutorial and will not be covered here.2.The effect of noise on two-dimensional phase unwrappingA phase unwrapper detects the existence of a phase wrap in an image by calculating the difference between two successive samples. If this difference is larger than +π, then the phase unwrapper considers there to be a wrap at this location. This could either be a genuine phase wrap, or it could actually be a fake wrap due to the presence of noise. To study the effect of noise on 2D phase unwrapping, let us add noise to the simulated continuous phase image that was shown previously in Figure 1(a). Then we shall wrap the noisy phase image. After that, we will attempt to phase unwrap the simulated object. This process is implemented in the Matlab program that is shown below. The noise variance is set here to a value of 0.4. As we can see from Figure 4, such a low level of added noise does not adversely affect the operation of the Itoh unwrapping algorithm.%This program shows the problems encountered when unwrapping a noisy 2D phase%image by using computer simulationclc; close all; clearN = 512;[x,y]=meshgrid(1:N);noise_variance = 0.4;image1 = 2*peaks(N) + 0.1*x + 0.01*y + noise_variance*randn(N,N);figure, colormap(gray(256)), imagesc(image1)title('Noisy continuous phase image displayed as visual intensity array')xlabel('Pixels'), ylabel('Pixels')figuresurf(image1,'FaceColor','interp', 'EdgeColor','none', 'FaceLighting','phong')view(-30,30), camlight left, axis tighttitle('Noisy continuous phase image displayed as a surface plot')xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')figure, plot(image1(410,:))title('Row 410 of the original noisy continuous phase image')xlabel('Pixels'), ylabel('Phase in radians')%wrap the 2D imageimage1_wrapped = atan2(sin(image1), cos(image1));figure, colormap(gray(256)), imagesc(image1_wrapped)title('Noisy wrapped phase image displayed as visual intensity array')xlabel('Pixels'), ylabel('Pixels')figuresurf(image1_wrapped,'FaceColor','interp', 'EdgeColor','none','FaceLighting','phong')view(-30,70), camlight left, axis tighttitle('Noisy wrapped phase image plotted as a surface plot')xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')figure, plot(image1_wrapped(410,:))title('Row 410 of the wrapped noisy image')xlabel('Pixels'), ylabel('Phase in radians')%Unwrap the image using the Itoh algorithm: the first method%Unwrap the image first by sequentially unwrapping the rows one at a time.image1_unwrapped = image1_wrapped;for i=1:Nimage1_unwrapped(i,:) = unwrap(image1_unwrapped(i,:));end%Then unwrap all the columns one-by-onefor i=1:Nimage1_unwrapped(:,i) = unwrap(image1_unwrapped(:,i));endfigure, colormap(gray(256)), imagesc(image1_unwrapped)title('Unwrapped noisy phase image using the Itoh algorithm: the first method') xlabel('Pixels'), ylabel('Pixels')figuresurf(image1_unwrapped,'FaceColor','interp', 'EdgeColor','none','FaceLighting','phong')view(-30,30), camlight left, axis tighttitle('Unwrapped noisy phase image using the Itoh unwrapper: the first method') xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')%Unwrap the image using the Itoh algorithm: the second method%Unwrap the image by first sequentially unwrapping all the columns.image2_unwrapped = image1_wrapped;for i=1:Nimage2_unwrapped(:,i) = unwrap(image2_unwrapped(:,i));end%Then unwrap all the a rows one-by-onefor i=1:Nimage2_unwrapped(i,:) = unwrap(image2_unwrapped(i,:));endfigure, colormap(gray(256)), imagesc(image2_unwrapped)title('Unwrapped noisy image using the Itoh algorithm: the second method')xlabel('Pixels'), ylabel('Pixels')figuresurf(image2_unwrapped,'FaceColor','interp', 'EdgeColor','none','FaceLighting','phong')view(-30,30), camlight left, axis tighttitle('Unwrapped noisy phase image using the Itoh algorithm: the second method') xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')Figure 4: (a) & (b) A noisy computer-generated continuous phase image. (c) & (d) The noisy phase image is wrapped. (e) & (f) Phase unwrapping using the Itoh algorithm: first method. (g) & (h) Phase unwrapping using the Itoh algorithm: second method. The noise variance is set here to a value of 0.4.PixelsP i x e l s5010015020025030035040045050050100150200250300350400450500Wrapped image displayed as visual intensity arrayPixelsP i x e l s5010015020025030035040045050050100150200250300350400450500unwrapped image using the first methodPixelsP i x e l s5010015020025030035040045050050100150200250300350400450500unwrapped image using the second methodPixelsP i x e l s5010015020025030035040045050050100150200250300350400450500Figure 5: (a) & (b) A noisy computer-generated continuous phase image. (c) & (d) The noisy phase image is wrapped. (e) & (f) Phase unwrapping using the Itoh algorithm: first method. (g) & (h) Phase unwrapping using the Itoh algorithm: second method. The noise variance is set here to a higher value of 0.6.PixelsP i x e l s5010015020025030035040045050050100150200250300350400450500Wrapped image displayed as visual intensity arrayPixelsP i x e l s5010015020025030035040045050050100150200250300350400450500unwrapped image using the first methodPixelsP i x e l s5010015020025030035040045050050100150200250300350400450500unwrapped image using the second methodPixelsPi x e l s5010015020025030035040045050050100150200250300350400450500When we increase the noise variance to a value of 0.6 there are problems. In this case the Itoh phase unwrapping algorithm fails to successfully unwrap this image. Notice that there are 2π discontinuities still present in the unwrapped phase images. Also notice that this time, the first and the second methods of implementing the Itoh algorithm, now produce different results.As explained in the separate 1D phase unwrapping tutorial that you should have studied previously, error accumulation occurs during the phase unwrapping process, and this is the reason that complicates the process of unwrapping noisy 2D wrapped phase images. Figure 5(e) shows an image that has been processed using the Itoh algorithm: implemented using the first method. This algorithm unwraps the image by firstly sequentially phase unwrapping all the rows one at a time, and then when the unwrapping of all the rows is complete, it subsequently moves on to unwrap all the columns, one at a time. Close inspection of Figure 5(e) reveals some information about the error accumulation problem. For example, row 455 in Figure 5(e) contains a fake wrap. This fake wrap produces a 2π error which propagates throughout the row, from the location of the fake wrap right through until the end of the row. Similar errors also occur during processing for a number of other rows in this wrapped phase image. The Itoh phase unwrapping algorithm: implemented using the the first method, here produces 2πerrors that appear as horizontal lines in the resultant unwrapped phase image.Figure 5(g) shows an image processed using the Itoh algorithm: implemented using the second method. This algorithm changes the order of phase unwrapping the rows and columns when compared to the first implementation. In other words, it unwraps the image by firstly sequentially phase unwrapping all the columns, one at a time. Then, once all the columns are unwrapped, the algorithm moves on to sequentially unwrap all the rows, one at a time. Close inspection of Figure 5(g) reveals some information about the error accumulation problem. For example, column 360 in Figure 5(g) contains a fake wrap. This fake wrap produces a 2πerror which propagates throughout the column, from the location of the fake wrap right through until the end of the column. Similar errors also occur during processing for a number of other columns in the wrapped phase image. The Itoh phase unwrapping algorithm: implemented using the second method, here produces 2π errors that appear as vertical lines in the unwrapped phase image. Researchers have developed many phase unwrapping algorithms that attempt to prevent error propagation occurring. A number of these algorithms are explained in [2]. Also, here at the General Engineering Research Institute (GERI) at LJMU we have developed a robust 2D phase unwrapped algorithm called the 2D-SRNCP phase unwrapper [3]. Our algorithm is based on sorting by reliability, following a non-continuous path and exhibits excellent performance in coping with the noise that corrupts real wrapped phase images. Don’t worry about the detail of how it works, just regard it as a very advanced and robust unwrapping algorithm and use it as a tool. You can download the 2D-SRNCP phase unwrapper in Matlab by following the link /GERI/90225.htm.The wrapped phase image shown in Figure 5(c) is processed using the 2D-SRNCP phase unwrapper. The resultant image is displayed as a visual intensity array in Figure 6(a) and also as a 3D surface plot as shown in Figure 6(b). Comparing Figures 6(a) & (b) with Figures 5(a) & (b) respectively reveals that here our algorithm has succeeded in correctly processing the wrapped phase image and has prevented error propagation.Please note that the 2D-SRNCP phase unwrapper is written in the C programming language. This C program is callable from Matlab using the Mex ‘Matlab Executable’ dynamically linked subroutine functionality. The C code must be compiled in Matlab first, before it is called. To compile the C code in Matlab, at the Matlab prompt, type the following;mex Miguel_2D_unwrapper.cppThe Matlab code that may be used to unwrap the image is given below.%How to call the 2D-SRNCP phase unwrapper from the C language%You should have already compiled the phase unwrapper’s C code first%If you haven’t, to compile the C code: in the Matlab Command Window type % mex Miguel_2D_unwrapper.cpp%The wrapped phase that you present as an input to the compiled C function %should have the single data type (float in C) WrappedPhase = single(image1_wrapped);UnwrappedPhase = Miguel_2D_unwrapper(WrappedPhase); figure, colormap(gray(256)) imagesc(UnwrappedPhase);xlabel('Pixels'), ylabel('Pixels')title('Unwrapped phase image using the 2D-SRNCP algorithm')figuresurf(double(UnwrappedPhase),'FaceColor','interp', 'EdgeColor','none', 'FaceLighting','phong')view(-30,30), camlight left , axis tighttitle('Unwrapped phase image using the 2D-SRNCP displayed as a surface') xlabel('Pixels'), ylabel('Pixels'), zlabel('Phase in radians')(a)(b)Figure 6: (a) & (b) Unwrapped phase image using the 2D-SRNCP algorithm.Unwrapped phase map using the 2D-SRNCP algorithm50100150200250300350400450500501001502002503003504004505003. The effect of under sampling on two-dimensional phase unwrappingAs has been explained previously, a phase unwrapper detects the existence of a wrap in an image by calculating the difference between two successive samples. If this difference is larger than a value of +π or smaller than -π, then the phase unwrapper considers that there is a wrap in existence at this location. This might be a genuine phase wrap, or it might also be a fake wrap that is caused by noise, or under-sampling. Phase unwrapping of phase images that are under sampled can be difficult, or in some cases even impossible. This occurs when the difference between two successive samples is larger than +π, or is less than -π. This large difference between adjacent samples is present merely due to the fact that the phase image does not contain enough samples and is not because of the existence of a real phase wrap. Such a situation automatically generates an incorrect ‘fake wrap’.Let us first review the effect of under-sampling on the 1D phase unwrapping process. According to Nyquist sampling theory, if a function f (x ) contains no frequencies higher than B Hertz, then it may be completely determined by sampling it at the rate of 2B or greater. In the case where f (x ) is a pure sinusoidal signal, then every period in f (x ) must be sampled by at least with two samples. This principle also applies to a wrapped phase signal.Suppose that we consider the 1D continuous phase signal that is shown in Figure 7(a). This signal contains 20 samples and covers exactly one period of the cyclic waveform. This signal is phase wrapped as shown in Figure 7(b). This wrapped signal is sampled at a sufficiently high rate and it contains four genuine wraps. This wrapped signal may be phase unwrapped using the 1D Itoh algorithm and the unwrapped result is shown in Figure 7(c). Notice that in this case the relatively simple 1D Itoh algorithm correctly unwraps the wrapped phase signal. Also note here that whilst the shape of the unwrapped signal is the same as the original, that the actual phase values for each point on the graph is now different, i.e. the original signal in Figure 7(a) ranges from +6 to -6 radians, whereas the unwrapped signal in Figure 7(c) ranges from 0 to -12 radians. You should be aware that most phase unwrappers only produce such ‘relative’ rather than ‘absolute’ phase values as their output. Some advanced unwrappers will produce the same shaped relative phase output, but with different numbers in terms of the absolute phase values, every time the code is executed. You should be aware that it is possible to adopt certain measurement strategies which actually do measure absolute phase, rather than relative phase.(a) (b) (c)Figure 7: (a) A continuous phase signal that contains 20 samples. (b) The phase wrapped signal. (c) Thephase unwrapped signal.time in secondsO r i g i n a l p h a s e i n r a d i a n stime in secondsW r a p p e d p h a s e i n r a d i a n stime in secondsU n w r a p p e d p h a s e i n r a d i a n sNow let us reduce the number of samples in the same 1D phase signal that appears in Figure 7(a), halving the sampling rate so that now only 10 samples are taken for this signal, as shown in Figure 8(a). This signal is then wrapped as is shown below in Figure 8(b). This wrapped signal now contains four genuine wraps and also two fake wraps. These two fake wraps occur due to the under sampling of the signal and their positions are highlighted in Figure 8(b). The difference between the third and the fourth samples is smaller than -π. A phase unwrapper would consider this large difference to be a wrap and would add a value of 2π to the fourth sample and also to all the samples to the right of it, as shown in Figure 8(c). This would have the effect of corrupting the whole 1D phase unwrapped signal. Similarly, the difference between the eighth and the ninth samples is larger than +π and once again a phase unwrapper would consider this to be a wrap and hence would subtract a value of 2π from the ninth and tenth samples, as shown in Figure 8(c). This would also have the effect of corrupting the rest of the 1D phase unwrapped signal. Notice that the phase unwrapped signal is now completely different to the original continuous phase signal that was shown in Figure 8(a). Note that this signal has been processed here using the 1D Itoh algorithm.Figure 8: (a) A continuous phase signal that now contains only 10 samples. (b) The wrapped signal. (c) Theunwrapped signalNext we will use computer-generated under sampled phase images to explain the effects of undersampling on 2D phase unwrapping algorithms. First we will create artificial under-sampled phase images. Then we shall theoretically analyse the computer generated phase images to investigate the maximum permissible sampling rates in the x and y directions for the specific datasets, according to sampling theory. Next we will wrap these images. After that we will process these images using two different phase unwrapping algorithms: namely the Itoh algorithm and the 2D-SRNCP algorithm. Finally we will compare the images produced by these two unwrappers with the original continuous phase map.Suppose that we have the computer-generated continuous phase image f (x ,y ), which is shown as both a visual intensity array and also as a 3D surface in Figures 9(a) & (b) and is represented by the equation;f (x ,y )=20e −14(x 2+y 2)+2x +y , −3≤x ≤3,−3≤y ≤3Now we shall analyse this simulated phase image in terms of sampling theory. You should have previously completed the 1D phase unwrapping tutorial and you may wish to review the 1D discussion on under-sampling within that document, which will help you understand the discussion on under-sampling in 2D that follows.time in secondsO r i g i n a l p h a s e i n r a d i a n stime in secondsW r a p p e d p h a s e i n r a d i a n stime in secondsU n w r a p p e d p h a s e i n r a d i a n s。

Path Smoothing Robot Movement Optimization

Path Smoothing Robot Movement Optimization Path smoothing is a critical aspect of robot movement optimization, as it allows for more efficient and precise movement in various applications such as manufacturing, logistics, and even in the field of robotics. By reducing the complexity of the robot's movement path, path smoothing can lead to improved performance, reduced energy consumption, and increased productivity. However, achieving effective path smoothing requires careful consideration of various factors, including the robot's kinematics, the environment in which it operates, and the specific task it needs to perform.One of the key challenges in path smoothing is balancing the trade-off between path complexity and smoothness. A highly complex path may result in excessive energy consumption and wear and tear on the robot's components, while a path that is too smooth may not accurately capture the intricacies of the task at hand. Finding the right balance requires a deep understanding of the robot's capabilities, the physical constraints of the environment, and the specific requirements of the task.Another important consideration in path smoothing is the incorporation of real-time feedback and adaptation. While it is possible to pre-plan smooth paths for a robot, the dynamic nature of many applications requires the robot to adapt its movement in real-time based on changing conditions. This could include avoiding obstacles, adjusting for variations in the environment, or responding to unexpected events. Incorporating real-time feedback and adaptation into path smoothing algorithms is essential for ensuring that the robot can operate effectively in dynamic and unpredictable environments.Furthermore, the development of path smoothing algorithms must also consider the computational resources available to the robot. While complex algorithms may be able to generate extremely smooth paths, they may also require significant computational power, which may not be feasible for resource-constrained robots. Balancing the computational complexity of the path smoothing algorithm with the available resources is crucial for ensuring that the robot can perform its tasks efficiently and effectively.In addition to the technical considerations, it is also important to consider the human element in path smoothing. Robots are often required to operate in close proximity to humans, and their movements must be smooth and predictable to ensure the safety of human workers. Furthermore, the visual aesthetics of a robot's movement can also impact its acceptance in various settings, such as customer-facing applications in retail or hospitality. Therefore, path smoothing algorithms must also take into account human factors to ensure that the robot's movements are not only efficient but also safe and socially acceptable.Finally, the development and implementation of path smoothing algorithms must be a collaborative effort involving experts from various fields, including robotics, artificial intelligence, human factors, and industrial engineering. By bringing together diverse perspectives and expertise, it is possible to develop path smoothing algorithms that are not only technically robust but also practical and effective in real-world applications. Collaboration can also help to identify new opportunities and applications for path smoothing, leading to further advancements in robot movement optimization.In conclusion, path smoothing is a critical aspect of robot movement optimization that requires careful consideration of technical, human, and collaborative factors. By addressing the trade-off between path complexity and smoothness, incorporating real-time feedback and adaptation, balancing computational resources, considering human factors, and fostering collaboration, it is possible to develop path smoothing algorithms that enable robots to move more efficiently, safely, and effectively in a wide range of applications. As technology continues to advance, the development of path smoothing algorithms will play a crucial role in unlocking the full potential of robots in various industries.。

Robust Control and Estimation

Robust Control and Estimation Robust control and estimation play crucial roles in the field of engineering, particularly in the development of advanced technological systems. These concepts are essential for ensuring the stability, performance, and reliability of complex control systems, making them integral to a wide range of industries, including aerospace, automotive, and manufacturing. In this discussion, we will explore the significance of robust control and estimation from various perspectives,considering their practical applications, challenges, and future developments. From a practical standpoint, robust control techniques are essential foraddressing uncertainties and variations in system dynamics, which are prevalent in real-world applications. By incorporating robust control strategies, engineers can design systems that are capable of maintaining stable and predictable behavior, even in the presence of external disturbances or parameter variations. This is particularly critical in safety-critical domains such as aerospace and automotive engineering, where the reliability of control systems directly impacts human lives. Furthermore, robust estimation methods are essential for accurately inferring the state of a system based on noisy or incomplete measurements. In many real-world scenarios, obtaining precise and reliable measurements can be challenging due to environmental factors, sensor limitations, or communication noise. Robustestimation techniques enable engineers to effectively process imperfect data and extract meaningful information about the underlying system dynamics, facilitating informed decision-making and control action. Despite their significance, the implementation of robust control and estimation techniques presents several challenges. One of the primary hurdles is the computational complexity associated with designing and implementing robust control algorithms. As systems become increasingly complex, the computational resources required to execute robustcontrol strategies can become prohibitive, necessitating innovative approaches to algorithm design and optimization. Moreover, the integration of robust controland estimation techniques with emerging technologies such as artificialintelligence and machine learning presents both opportunities and challenges.While these advanced technologies have the potential to enhance the performanceand adaptability of control systems, their integration also introduces new layersof complexity and uncertainty, requiring careful consideration of how to effectively leverage their capabilities while maintaining robustness and reliability. Looking ahead, the future development of robust control and estimation is likely to be shaped by advancements in areas such as cyber-physical systems, autonomous vehicles, and smart manufacturing. As these technologies continue to evolve, the demand for robust control and estimation techniques that can effectively address the complexities and uncertainties inherent in these domains will only increase. This necessitates ongoing research and innovation in the development of advanced control strategies that can adapt to dynamic and unpredictable environments while ensuring safety and reliability. In conclusion, robust control and estimation are indispensable components of modern engineering systems, playing a critical role in ensuring stability, performance, andreliability in the face of uncertainties and variations. As technology continues to advance and systems become increasingly complex, the development and application of robust control and estimation techniques will remain a focal point of research and innovation, driven by the need to address emerging challenges and harness the potential of new technologies.。

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Ä indicates Presenter 32
016 DEVELOPMENT OF A ROBUST ALGORITHM FOR IMAGING COMPLEX TISSUE ELASTICITY.
Yong Zhang 1, Robert W. Kramer 1Ä, Dmitry B. Goldgof 2, Vasant Manohar 2.
1Youngstown State University, Meshell Hall, One University Plaza, Youngstown, OH, 44555, USA; 2University of South Florida, ENB 118, 4202 East Fowler Avenue, Tampa, FL, 33620, USA.
Background: Imaging elastic properties of soft tissue is an emerging technology that holds great promise in medical diagnosis [1]. Two steps are usually involved: (1) A dynamic or static displacement field is obtained from any one of various imaging modalities; (2) Desired parameters such as Young's modulus or Poisson's ratio are then estimated by minimizing the discrepancy between the measured data and the outputs of a forward model. This type of inverse problem is likely nonlinear and ill–posed. Iterative gradient descent methods with regularization are often employed to obtain a stable solution [2,3,4]. Aims: Gradient–based methods are sensitive to initial conditions and local minima. We present a generic algorithm that is constrained by prior knowledge to ensure a good solution from noisy data. The aim is to develop a robust recovery algorithm that can handle more complex elasticity distributions.
Methods: To link a 2D–3D finite element model to a 1D chromosome in a genetic pool, a one–to–one mapping scheme is devised to encode elasticity. In this way, a direct correspondence is established between finite elements and genes. Rank tables are used to implement a penalty term that represents prior knowledge (equivalent to the smoothness constraint in a Tikhonov functional). The rank–based approach allows less quantitative information to be incorporated into the regularization process. A Gaussian mutation operator with a relatively high mutation rate helps maintain solution diversity. A one-point crossover operator is used to generate children from parents that are selected randomly by a K-2 tournament procedure. Best solutions are kept through generations by the elitism mechanism.
Results: A finite element model is used to simulate deformation of a square–shaped soft tissue model (Figure 1). The model has a dimension of 6 by 6 cm 2 that is discretized to a 300 by 300 quadrilateral mesh. Two hard inclusions are embedded in the model with Young's modulus values that are five and ten times higher than that of background tissues, respectively. The model has a Dirichlet boundary condition at the bottom and is compressed from the top by a specified Neumann condition. Displacement data were generated by running a forward model and then was altered by 1% additive white noise. In experiments with various initial conditions, both inclusions were successfully recovered with good resolutions, indicating the high potential of the proposed algorithm (Figure 2).
Conclusions: Results suggest that, given noisy data, a constrained generic algorithm is a robust solution strategy for ill–posed elasticity imaging problems. The stochastic nature allows the algorithm to avoid local solutions and hence be more effective in dealing with complex elasticity distributions.
Acknowledgements: This work is supported in part by URC Grant 2006–2007 #16–07, Youngstown State University, OH, USA.
References:
[1] J. Ophir, E. I. Cespedes, H. Ponnekanti, Y. Yazdi, and X. Li, "Elastography: a quantitative method for imaging the elasticity of biological tissues", Ultrasonic Imaging, vol. 13, pp. 111–134, 1991.
[2] F. Kallel and M. Bertrand "Tissue elasticity reconstruction using linear perturbation method", IEEE Trans. Medical Imaging, vol. 15, pp. 299–313, 1996.
[3] M. Doyley, P. Meaney, and J. Bamber, "Evaluation of an iterative reconstruction method for quantitative elastography", Phys. Med. Biol. vol. 45, pp. 1521–1540, 2000.
[4] Y. Zhu, T. J. Hall, and J. Jiang, "A finite–element approach for Young's modulus reconstruction", IEEE Trans. Medical Imaging, vol. 22, pp. 890–901, 2003.
Figure 1: Model configuration with two hard inclusions. Figure 2: Recovered elasticity in grayscale image.。