This issue of IEEE Signal Processing Magazine is mainly focused on neurorehabilitation and assistive technologies. For a few decades, microelectronics, signal processing, robotics, and computer science have been the driver of many scientific and technological advances, with applications in many domains, including health.
Prostheses provide a means for individuals with amputations to regain some of the lost functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling prosthetic devices, play a critical role in users' experiences with prostheses. This review article provides an overview of the HMIs commonly adopted for upper-limb prosthesis control and inspects collected signals and their processing methods.
This article reviews technologies and algorithms for decoding volitional movement intent using bioelectrical signals recorded from the human body. Such signals include electromyograms, electroencephalograms, electrocorticograms, intracortical recordings, and electroneurograms. After reviewing signal features commonly used for interpreting movement intent, this article describes traditional movement decoders based on Kalman filters (KFs) and machine learning (ML).
The Signal Processing (SP) research group at the Universität Hamburg in Germany is hiring a Postdoc (E13/E14) "Machine Learning for Speech and Audio Processing".
Lecture Date: August 10, 2021 -- Virtual Lecture
Chapter: Hyderabad Chapter
Chapter Chair: Abhinav Kumar
Topic: Random Walk on a Tree for Stochastic Optimization and Learning
July, 2021-Postpone of the Workshop IEEE CAMSAP2021 and Approval of the Workshop IEEE SAM2022
by Xiangrong Wang and Wei Liu
Due to uncertainty generated by the pandemic, the Ninth IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2021) has been postponed to 2023.
As approved by the SAM TC, the Twelfth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM2022) will be held at Trondheim, Norway, on 20-23 June 2022 (the dates are subject to change).
This paper investigates an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer (SWIPT) system. Multiple IRSs deployed on unmanned aerial vehicles (UAVs) and ground building are considered in the proposed system for enhancing transmission of information and energy simultaneously. The optimization problem is formulated to maximize the average achievable rate over N time slots by jointly optimizing power splitting (PS) ratio, transmit beamforming, phase shifts and trajectories of UAVs.
Edge networks offer a promising solution for satisfying the increasing energy and computation needs of user devices with new data intensive services. A mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) has the ability to support multiple users for both data computation and wireless charging. We propose an integrated solution for wireless charging with computation offloading to satisfy the largest feasible proportion of requested wireless charging while keeping the total energy consumption at the minimum, subject to the MEC-AP transmit power and latency constraints.