The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), especially since 2010 or so, yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation, and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long standing problem domains (e.g., speech, vision), as well as providing the ability to construct new
Recent years have witnessed significant progress in entropy estimation, in particular in the large alphabet regime. Concretely, there exist efficiently computable information theoretically optimal estimators whose performance with n samples is essentially that of the maximum likelihood estimator with n log(n) samples, a phenomenon termed ``effective sample size boosting''. Generalizations to processes with memory (estimation of the entropy rate) and continuous distributions (estimation of the differential entropy) have remained largely open.
Soft biomaterials such as human skin have very different mechanical properties from conventional electronics, requiring unusual materials and geometries to match the behavior of the skin. One of the biggest challenges in stretchable electronics is the transfer of power and data signals, with physical wiring easily pulled out or damaged. In my talk, I will be discussing all aspects of creating inductors and power circuits for wireless power transfer to stretchable systems. I will focus on the use of room temperature liquid metals and stretchable magnetic materials to maximize power trans
This talk will describe several approaches to reducing energy consumption in internet-of-things applications and applications of data analytics to neuro-psychiatric disorders. Machine learning and information analytics are important components in all these things. Almost all things should have embedded classifiers to make decisions on data. Thus, reducing energy consumption of features and classifiers is important. First part of the talk will present energy reduction approaches from feature selection, classification and incremental multi-stage classification perspectives.
We are witnessing an unprecedented growth in the amount of data that is being collected and made available for data mining. While the availability of large-scale datasets presents exciting opportunities for advancing sciences, healthcare, understanding of human behavior etc., mining the data set for useful information becomes a computationally challenging task. We are in an era where the volume of data is growing faster than the rate at which available computing power is growing, thereby creating a dire need for computationally efficient algorithms for data mining.