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The Latest News, Articles, and Events in Signal Processing
IEEE has announced the recipients of the 2021 IEEE medals. IEEE medals are the highest honor of awards presented by IEEE. The medals will be presented at the 2021 IEEE Honors Ceremony. Two Signal Processing Society members have been awarded with Ithe IEEE Jack S. Kilby Signal Processing Medal medal in 2021.
Vineeth N. Balasubramanian is an Associate Professor in the department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad (IIT-H), India, and currently also serves as the Head of the Department of Artificial Intelligence at IIT-H. His research interests include deep learning, machine learning, and computer vision.
Bayya Yegnanarayana is currently INSA Senior Scientist at the International Institute of Information Technology Hyderabad (IIIT-H). He was an Institute Professor & Microsoft Chair from 2006 to 2016 at IIIT-H. He was a Professor (1980 to 2006) and Head of the CSE Dept (1985 to 1989) at IIT Madras, a visiting Associate Professor at Carnegie-Mellon University (CMU), Pittsburgh, USA (1977 to 1980), and a member of the faculty at the Indian Institute of Science (IISc), Bangalore, (1966 to 1978).
September 14-15, 2021
Location: Edinburgh, UK
Lecture Date: March 5, 2021 -- Virtual Lecture
Chapter: Gujarat
Chapter Chair: Chirag Paunwala
Topic: Visual domain adaptation
Lecture Date: March 5, 2021 -- Virtual Lecture
Chapter: Pune
Chapter Chair: Vinayak K. Bairagi
Topic: Visual domain adaptation
Seenel Imaging is hiring its R&D Engineer or equivalent specialized in Signal Processing and C++ Software
Job applies to the following areas:
Signal Processing Theory and Methods
Computational Imaging
Bio Imaging and Signal Processing
Design and Implementation of Signal Processing Methods
Digital Signal Processing
Image, Video and Multidimensional Signal Processing
Many applications generate large data sets from which information needs to be extracted. The emerging field of structured data science extends signal processing to data science.
Adopting low-resolution analog-to-digital converters (ADCs) for receive antennas of a multiple-input multiple-output (MIMO) system can remarkably reduce the hardware cost, circuit power consumption as well as amount of data to be transferred from RF components and the baseband-processing unit.
In this paper, the particle filtering problem is investigated for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations (ROSSs). The random occurrences of the sensor saturations are characterized by a series of Bernoulli distributed stochastic variables with known probability distributions.
In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets
Recently, soft video multicasting has gained a lot of attention, especially in broadcast and mobile scenarios where the bit rate supported by the channel may differ across receivers, and may vary quickly over time. Unlike the conventional designs that force the source to use a single bit rate according to the receiver with the worst channel quality, soft video delivery schemes transmit the video such that the video quality at each receiver is commensurate with its specific instantaneous channel quality.
An automatic speech recognition (ASR) system is a key component in current speech-based systems. However, the surrounding acoustic noise can severely degrade the performance of an ASR system. An appealing solution to address this problem is to augment conventional audio-based ASR systems with visual features describing lip activity.
Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities.
Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transient, time-of-flight measurements. However, existing NLOS approaches require the imaging system to scan a large area on a visible surface, where the indirect light paths of hidden objects are sampled.
Geometry calibration is an inherent challenge in distributed acoustic sensor networks. To mitigate this problem, a passive geometry calibration approach based on distributed damped Newton optimization is proposed. Specifically, a geometric cost function incorporating direction of arrivals (DoAs) and time difference of arrivals (TDoAs) is first formulated, and then its identifiability conditions are given.
In magnetic resonance imaging (MRI), several images can be obtained using different imaging settings (e.g. T1, T2, DWI, and Flair). These images have similar anatomical structures but are with different contrasts, which provide a wealth of information for diagnosis.
As a fundamental algorithm for collaborative processing over multi-agent systems, distributed consensus algorithm has been studied for optimizing its convergence rate. Due to the close analogy between the diffusion problem and the consensus algorithm, the previous trend in the literature is to transform the diffusion system from the spatially continuous domain into the spatially discrete one.
Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neural networks. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose a novel graph pooling strategy that leverages node affinity to improve the hierarchical representation learning of graph data.
In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space.
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