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The Latest News, Articles, and Events in Signal Processing
The list of tables of contents (TOCs) are now available for November 2019. The TOCs are formatted to the style of its publication and offers links to the full issue on the front cover, as well as links to the individual articles in IEEEXplore®; simply mouse-over the article title and click on it.
The IEEE Signal Processing Society (SPS) announced the 2020 Class of Distinguished Lecturers and Distinguished Industry Speakers for the term of 1 January 2020 to 31 December 2021, which are noted below. The IEEE SPS Distinguished Lecturer (DL) Program provides a means for Chapters to have access to well-known educators and authors in the fields of signal processing to lecture at Chapter meetings.
Beginning this year, all eligible IEEE Signal Processing Society Members were able to cast their vote for the next IEEE Signal Processing Society President-Elect. The President-Elect will sit on the IEEE Signal Processing Society Board of Governors beginning 1 January 2020 and will serve until 31 December 2021, when they will be elevated to Society President.
The Signal Processing Society is pleased to announce the 5-Minute Video Clip Contest (5-MICC) at ICASSP 2020 in Barcelona (May 4-8). The topic chosen this year is Beamforming and the submitted video can cover any aspects of beamforming related areas.
Ph.D. positions with full financial support are now available in Dr. Ying Liu’s group in the Department of Computer Science and Engineering at Santa Clara University (SCU). Dr. Liu is looking for self-motivated PhD students to work on image/video processing, machine learning and deep learning. Visiting scholars and students are also welcome.
New enhancements to the display of Section and Chapter data are now available in the Geographic Map within OU Analytics. Boundaries have been expanded to incorporate additional Sections/Subsections not previously included.
New and improved monthly statistics reports are now available containing enriched visual analytics dashboards that can be used for monthly trending. These comprehensive dashboards improve efficiency by allowing OUs to quickly access data that is relevant to them by OU, Region, Section, Grade, Division, etc.
As a reminder for continuing chapter chairs and for incoming chapter chairs for 2019, we would like to highlight a valuable resource available to you. Launched last year was IEEE OU Analytics, a web-based business intelligence tool to deliver essential metrics on memberships or subscriptions to OUs, including Societies, Councils, Technical Communities, etc.
The title of "Memberships, Subscriptions, and More..." dashboard on the OU Analytics landing page has been renamed to add a parenthetical reference within the title to include (Societies, TCs, Affinities). There has been some confusion as to the content provided within the "Members and Affiliates" versus the "Memberships, Subscriptions, and More..." dashboard.
Please be advised that IEEE gauges the vitality of a chapter by tracking how many meetings are reported during the year through vTools. If a chapter reports "0" meetings for three consecutive years, the chapter is placed on a dissolution list that is reviewed at the November Board Meeting.
Many of us marveled in awe in March 2018 at the sight of the Ghana teacher who, using colored chalk, drew on his blackboard a snapshot of how an open window of the Microsoft Word software would look like on the screen of a computer...
This is an age of mobility. Phones, tablets, notebook computers, smart watches, and various other devices now supply people around the world with instant communication capabilities that were only dreamed of a generation ago. Mobility technologies are also transforming medicine, helping to improve the quality of care for people at all stages of life, giving both patients and healthcare providers deeper...
The success of artificial neural networks (ANNs) in carrying out various specialized cognitive tasks has brought renewed efforts to apply machine learning (ML) tools for economic, commercial, and societal aims, while also raising expectations regarding the advent of an artificial “general intelligence” [1]–[2][3]. Recent highly publicized examples of ML breakthroughs include the ANN-based algorithm AlphaGo...
The success of artificial neural networks (ANNs) in carrying out various specialized cognitive tasks has brought renewed efforts to apply machine learning (ML) tools for economic, commercial, and societal aims, while also raising expectations regarding the advent of an artificial “general intelligence” [1]–[2][3]. Recent highly publicized examples of ML breakthroughs include the ANN-based algorithm AlphaGo...
In my September editorial [1], I outlined the important components of good feature articles in response to feedback from our recent IEEE Periodicals Review and Advisory Committee (PRAC) meeting. In this issue's editorial, I discuss the process of organizing a special issue (SI) for IEEE Signal Processing Magazine (SPM).
In this paper, we consider the problem of bandwidth-constrained distributed estimation of a Gaussian vector with linear observation model. Each sensor makes a scalar noisy observation of the unknown vector, employs a multi-bit scalar quantizer to quantize its observation, and maps it to a digitally modulated symbol.
Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world.
In this paper, a multi-hypothesis distributed detection technique with non-identical local detectors is investigated. Here, for a global event, some of the sensors/detectors can observe the whole set of hypotheses, whereas the remaining sensors can either see only some aspects of the global event or infer more than one hypothesis as a single hypothesis.
Generating images via a generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects.
Sheila S. Hemami received the B.S.E.E. degree from the University of Michigan, Ann Arbor, MI, USA, in 1990 and the M.S.E.E. and Ph.D. degrees from Stanford University, Stanford, CA, USA, in 1992 and 1994, respectively and all in electrical engineering. She is presently Director of Strategic Technical Opportunities at Draper in Cambridge, MA...
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- If you're at @IEEEsps GlobalSIP tomorrow, be sure to check out the keynote address by Dennis Wei of @IBMResearch in… https://t.co/2ql9FO7aRD
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