Innovations in Signals and Systems
In recent years, tremendous progress has been made in fast computational resources, high speed wireless communications, and smart system operations and optimizations. Since many of these operations are being conducted on portable devices, low-power and low-energy requirements are of necessity.
In this talk, we will discuss some of these issues already available and will be even more prevalent in the near-future. In the last seventy years, theoretical advances in coding, detection, and estimation both at the single user as well as at the web-based levels can achieve near-optimum transmission and processing results.
Given a weak signal in the presence of moderately strong noisy or interference disturbances, full classical maximum-likelihood decoding and detection and estimation techniques, not to mention more modern adaptive machine-learning and non-convex optimized algorithms can be used. Computational requirements of these tools that were considered too demanding in terms of computer memories or processing speeds can be achieved with readily available pipelined/parallel processors. If a data or signal is worthy of extraction, it can be obtained with modest efforts. Data (including a large amount of data) can be moved either by latest optical fiber channels or near-future multi-hop 5G wireless systems. To achieve all these capabilities, not only do we need “smart” algorithms and processing, but also require low-power devices presently available or will be available soon.
As a student of system-theory (fifty or sixty years ago), these optimum or near-optimum decoding, detection and estimation tools were available only on main-frame computer processing running some higher-languages operating systems (using Fortran, C-‘s, and Matlabs.). Today, we can almost perform many of these operations on battery-based processors; if not today, but will be in the near-future.
Large amount of science-motivated field collected data on land or over the sea can be transmitted to a node with work-station processing capabilities interpreting the scientific efforts of these data.
In conclusion, smart algorithms and low-power and low-energy devices are revolutionizing the technical world and probably will be benefiting human lives.
Kung Yao received the B.S. (Summa Cum Laude) and Ph.D. degrees in EE from Princeton University. He was a NAS-NRC Post-Doctoral Fellow at the University of California, Berkeley. He has served as an Assistant Dean of the School of Engineering at UCLA. Presently, he is a Distinguished Professor Emeritus in the ECE Department at UCLA. His professional interests include wireless fading channel modelling, digital communication theory, acoustic beamforming, sensor array system, and simulation. He received the IEEE Signal Processing Society’s 1993 Award in VLSI Signal Processing, the 2008 IEEE Communications Society/Information Theory Society Joint Paper Award, and the 2012 JCN Journal Best Paper Award. He is a co-author of “Detection and Estimation in Communication and Radar Systems”, Cambridge Univ. Press, in 2013 and the author of “Signal Processing Algorithms for Communication and Radar Systems,” Cambridge Univ. Press, 2019. He also has extensive practical system experiences in sensor networks, satellite-wireless communications, radar system, and systolic and microphone array designs. He is a Life Fellow of the IEEE