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The Latest News, Articles, and Events in Signal Processing
The IEEE Signal Processing Society (SPS) is honored to announce the elevation of 130 of its members to the grade of IEEE Senior Member. These members have demonstrated outstanding professional performance, exhibited professional maturity through long-term experience, and established themselves as leaders in their respective IEEE-designated fields of interest.
Do you have a colleague whose signal processing knowledge and expertise might be of greater interest to the broader Signal Processing Society community? SPS is now soliciting nominations for speakers to serve for two Society programs, Distinguished Lecturer Program (DL) and Distinguished Industry Speaker Program (DIS).
Advances in nano-fabrication and MEMS devices have led to radical improvements in sensing technologies in recent years. These improvements are most visible to all of us in our Smartphones that already feature a panoply of miniaturized sensors.
Donald S. Williamson is an Associate Professor in the Department of Computer Science and Engineering at The Ohio State University, where he is also an affiliate member of the Translational Data Analytics Institute (TDAI) and the Center for Cognitive and Brain Sciences.
Dr. Monika Aggarwal is an Electrical Engineering graduate, with a doctorate in signal processing. Dr. Aggarwal is currently working as a Professor at the Indian Institute of Technology (IIT), Delhi, India, one of the premier engineering institutes of India.
IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.
The Signal Processing Society (SPS) has 12 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.
Date: 12 July 2023
Time: 9:00 AM ET (New York Time)
Speaker(s): Dr. Weijie Yuan, Dr. Zhiqiang Wei, Dr. Shuangyang Li
Date: 7 July 2023
Time: 1:30 PM ET (New York Time)
Speaker(s): Dr. Kumar Vijay Mishra
Date: 30 June 2023
Time: 9:00 AM ET (New York Time)
Speaker(s): Dr. Fan Liu, Dr. Ya-Feng Liu, Dr. Christos Masouros
Date: 15-19 July 2024
Location: Niagara Falls, ON, Canada
Paper Submission Deadline: TBD
Website: Coming soon
Date: 4-7 December 2023
Location: Nürnberg, Germany
Paper Submission Deadline: TBD
Website: Coming soon
Date: 16-20 December 2023
Location: Taipei, Taiwan
Date: 31 March 2023
Time: 1:00 PM ET (New York Time)
Speaker(s): Dr. Dean Salisbury
The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a novel online edge learning offloading (OELO) scheme for UAV-assisted MEC secure communications, which can improve the secure computation performance. Moreover, the problem of information security is further considered since the offloading information of terminal users (TUs) may be eavesdropped due to the light-of-sight characteristic of UAV transmission.
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference.
To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange.
The papers in this special section focus on distributed signal processing for edge learning (EL). EL is a new and promising technology for implementing artificial intelligence (AI) algorithms at edge devices over wireless networks.
Date: 27-29 September 2023
Location: Poitiers, France
Date: 7-9 November 2023
Location: Daegu, South Korea
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