In conjunction with the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA),the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst invi...In conjunction with the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA),the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst invites applications for a tenure-track position in Integrative Systems Engineering(ISE) at the Assistant Professor level to begin September 2009.展开更多
In recent years,human-cyber-physical systems(HCPSs)have become increasingly complex due to the widespread adoption of environmental sensing and behavioral adaption.Apart from the tight coupling between application log...In recent years,human-cyber-physical systems(HCPSs)have become increasingly complex due to the widespread adoption of environmental sensing and behavioral adaption.Apart from the tight coupling between application logic and sensing-adaptation modules,such applications are mainly constrained by erroneous sensing and abnormal adaptation issues,often resulting in misjudgment of scenarios or adaptation behaviors that deviate from intended goals.Reliability in constructing and maintaining such application systems faces significant challenges,especially as human-cyber-physical scenarios exhibit dynamic uncertainties and evolving requirements,further exacerbating the development difficulty.To address these challenges,we design and implement SEPAL,a consistency-driven programming framework and runtime support for HCPSs with reliable environmental sensing and dynamic adaptation.SEPAL simplifies the design of environmental sensing and behavioral adaption in HCPSs through a unified programming framework,and transparently manages the reliability of sensing and the unbiasedness of adaptation through its two built-in consistency-based services.SEPAL also provides a flexible browser-based management interface and a customizable interface design language for ease of usage.Case studies and evaluations demonstrate SEPAL’s facilitation of reliable support for various HCPSs,as well as the effectiveness and efficiency of environmental sensing and behavioral adaption capabilities.展开更多
We discuss the nature of complex number and its effect on complex-valued neural networks(CVNNs).After we review some examples of CVNN applications,we look back at the mathematical history to elucidate the features of ...We discuss the nature of complex number and its effect on complex-valued neural networks(CVNNs).After we review some examples of CVNN applications,we look back at the mathematical history to elucidate the features of complex number,in particular to confirm the importance of the phaseand-amplitude viewpoint for designing and constructing CVNNs to enhance the features.This viewpoint is essential in general to deal with waves such as electromagnetic wave and lightwave.Then,we point out that,although we represent a complex number as an ordered pair of real numbers for example,we can reduce ineffective degree of freedom in learning or self-organization in CVNNs to achieve better generalization characteristics.This merit is significantly useful not only for waverelated signal processing but also for general processing with frequency-domain treatment through Fourier transform.展开更多
文摘In conjunction with the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA),the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst invites applications for a tenure-track position in Integrative Systems Engineering(ISE) at the Assistant Professor level to begin September 2009.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFB4501801the National Natural Science Foundation of China under Grant Nos.62302209 and 62472210+1 种基金the Leading-Edge Technology Program of Jiangsu Natural Science Foundation under Grant No.BK20202001support from the Collaborative Innovation Center of Novel Software Technology and Industrialization,Jiangsu,China.
文摘In recent years,human-cyber-physical systems(HCPSs)have become increasingly complex due to the widespread adoption of environmental sensing and behavioral adaption.Apart from the tight coupling between application logic and sensing-adaptation modules,such applications are mainly constrained by erroneous sensing and abnormal adaptation issues,often resulting in misjudgment of scenarios or adaptation behaviors that deviate from intended goals.Reliability in constructing and maintaining such application systems faces significant challenges,especially as human-cyber-physical scenarios exhibit dynamic uncertainties and evolving requirements,further exacerbating the development difficulty.To address these challenges,we design and implement SEPAL,a consistency-driven programming framework and runtime support for HCPSs with reliable environmental sensing and dynamic adaptation.SEPAL simplifies the design of environmental sensing and behavioral adaption in HCPSs through a unified programming framework,and transparently manages the reliability of sensing and the unbiasedness of adaptation through its two built-in consistency-based services.SEPAL also provides a flexible browser-based management interface and a customizable interface design language for ease of usage.Case studies and evaluations demonstrate SEPAL’s facilitation of reliable support for various HCPSs,as well as the effectiveness and efficiency of environmental sensing and behavioral adaption capabilities.
基金supported by the Assistance Grant of the Hoso Bunka Foundation.
文摘We discuss the nature of complex number and its effect on complex-valued neural networks(CVNNs).After we review some examples of CVNN applications,we look back at the mathematical history to elucidate the features of complex number,in particular to confirm the importance of the phaseand-amplitude viewpoint for designing and constructing CVNNs to enhance the features.This viewpoint is essential in general to deal with waves such as electromagnetic wave and lightwave.Then,we point out that,although we represent a complex number as an ordered pair of real numbers for example,we can reduce ineffective degree of freedom in learning or self-organization in CVNNs to achieve better generalization characteristics.This merit is significantly useful not only for waverelated signal processing but also for general processing with frequency-domain treatment through Fourier transform.
