Abstract

The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This work addresses the problem of transforming source data collected by sensor nodes into a sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding of the data. As the underlying semiconductor technologies are getting less and less reliable, the probability that some components of computing devices fail also increases, preventing designers from realizing the full potential benefits of on-chip exascale integration derived from near atomic scale feature dimensions. As the quest for performance confronts permanent and transient faults, device variation, and thermal issues, major breakthroughs in computing efficiency are expected to benefit from unconventional and new models of computation, such as brain inspired computing. The challenge is then high-performance and energy-efficient, but also fault-tolerant computing solutions. Dr. P. Srimanchari | Dr. G. Anandharaj"Real-Time Data Representation Control in Convolution Neural Networks Based Indoor Wi-Fi Localization for Internet of Things" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4694.pdf http://www.ijtsrd.com/computer-science/real-time-computing/4694/real-time-data-representation-control-in-convolution-neural-networks-based-indoor-wi-fi-localization-for-internet-of-things/dr-p-srimanchari

Authors and Affiliations