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The Latest News, Articles, and Events in Signal Processing

The IEEE Signal Processing Society (SPS) invites nominations for the position of Editor-in-Chief for the following journals: IEEE Signal Processing Letters for a 3-year term starting 1 January 2023.

Saarland University

Saarland University is a campus university that is internationally recognized for its strong research programmes. Fostering young academic talent and creating ideal conditions for teaching and research are a core part of the university’s mission. As part of the University of the Greater Region, Saarland University enables students and staff to share and exchange knowledge and ideas between disciplines, between universities and across borders.

IEEE Journal of Selected Topics in Signal Processing
A receiver architecture is proposed to cognitively extract navigation observables from fifth generation (5G) new radio (NR) signals of opportunity. Unlike conventional opportunistic receivers which require knowledge of the signal structure, particularly the reference signals (RSs), the proposed cognitive opportunistic navigation (CON) receiver requires knowledge of only the frame duration and carrier frequency of the signal. In 5G NR, some of these RSs are only transmitted on demand, which limits the existing opportunistic...
IEEE Journal of Selected Topics in Signal Processing
This paper investigates the problem of secret key generation from correlated Gaussian random variables in the short blocklength regime. Short blocklengths are commonly employed in massively connected IoT sensor networks in 5G and beyond wireless systems. Polar codes have previously been shown to be applicable to the secret key generation problem, and are known to perform well for short blocklengths in the channel coding context. Inspired by these findings, we propose an explicit protocol based on polar codes for generating secret keys in the short blocklength regime.
IEEE Journal of Selected Topics in Signal Processing
A private fifth generation (5G) network is a dedicated 5G network with enhanced communication characteristics, unified connectivity, optimized services, and customized security within a specific area. By subsuming the advantages of both public and non-public 5G networks, private 5G networks have found their applications across industry, business, utilities, and the public sector.
IEEE Journal of Selected Topics in Signal Processing
The papers in this special section focus on advanced signal procesing for local and private 5G mobile communication netwworks. The papers describe the latest advances in emerging private 5G networks from the perspective of signal processing to advance its theoretical underpinnings and practical applications. Some enterprises, factories and other potential users have ultra-stringent communications performance requirements in terms of throughput, latency, reliability, availability, and device density, which cannot be met by 4G long term evolution (LTE) radio features.

University of Maryland, Baltimore County

Postdoctoral position in neuroimaging data analysis and fusion

Start date: Summer 2022


Duration: One to four years (initial appointment is for one year)
 

Machine Learning for Signal Processing Laboratory (http://mlsp.umbc.edu) at University of Maryland Baltimore County, Baltimore, MD

IEEE Transactions on Mobile Computing seeks applicants for the position of Editor-in-Chief. The EIC term is three years, nonrenewable. The Steering Committee of the Transactions on Mobile Computing invites applications, which should be sent to Cathy Martin by 1 March 2022.

Lecture Date: March 23, 2022 -- Virtual Lecture
Chapter: North New Jersey Chapter
Chapter Chair: Alfredo Tan
Topic: Policy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization

University of Stellenbosch

Post-doctoral research position:

Extremely-low-resource radio browsing for humanitarian monitoring

Stellenbosch University, South Africa

INRIA Bordeaux

Title: Glottal source inverse filtering for the analysis and classification of pathological speech

Keywords: Pathological speech processing, Glottal source estimation, Inverse filtering, Machine learning, Parkinsonian disorders, Respiratory diseases

Contact and Supervisor: Khalid Daoudi (khalid.daoudi@inria.fr)

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have become very popular for sensing and wireless communications beyond 5G. While the abundant spectrum available at the mmWave frequency bands enables higher cellular data rates and precise positioning, links at mmWave frequencies are very sensitive to blockages and have significantly higher path loss.

Many important application domains generate distributed collections of heterogeneous local datasets. These local datasets are related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets. Networked federated learning aims at learning a tailored local model for each local dataset. 

Johns Hopkins University

A postdoctoral research position is available at Johns Hopkins University in the laboratory of Dr. Mounya Elhilali to investigate representation of complex sounds in biological and artificial networks. The position is available immediately for two years, with possibility of renewal.

The ideal applicant will have a doctoral degree in computer science, electrical engineering, applied mathematics, neuroscience, psychology, hearing or brain sciences, with strong quantitative skills.

Annually, the IEEE Signal Processing Society supports numerous Member Driven Initiatives from our Chapters.  Presented here are the highlights from the IEEE SPS Gujarat Chapter international webinar on "Women Leading Technology".

The use of wireless frequencies above 100 GHz has attracted considerable interest for both massive bandwidth communication links and very high resolution RADAR and sensing. Systems in these frequencies have unique characteristics in terms of device nonlinearities, MIMO architectures and radio propagation that in turn present significant design challenges.

Dr. Oriol Vinyals is a Principal Scientist at DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. 

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