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The Latest News, Articles, and Events in Signal Processing
Distributed estimation fusion is concerned with the combination of local estimates from multiple distributed sensors to produce a fused result. In this paper, we characterize local estimates as posterior probability densities, and assume that they all belong to a parametric family. Our starting point is to consider this family as a Riemannian manifold by introducing the Fisher information metric.
Linear regression models contaminated by Gaussian noise (inlier) and possibly unbounded sparse outliers are common in many signal processing applications. Sparse recovery inspired robust regression (SRIRR) techniques are shown to deliver high-quality estimation performance in such regression models. Unfortunately, most SRIRR techniques assume a priori knowledge of noise statistics like inlier noise variance or outlier statistics like number of outliers.
Recently, many sparse arrays have been proposed to increase the number of degrees of freedom for direction of arrival (DoA) estimation especially for circular signals. Though many practical signals are noncircular, still the properties of noncircularity are hardly exploited in the design of sparse linear arrays. A new array geometry for noncircular signals, which significantly increases the aperture of the virtual array, has been proposed in this paper.
Various signal processing applications can be expressed as large-scale optimization problems with a composite objective structure, where the Lipschitz constant of the smooth part gradient is either not known, or its local values may only be a fraction of the global value. The smooth part may be strongly convex as well. The algorithms capable of addressing this problem class in its entirety are black-box accelerated first-order methods, related to either Nesterov's Fast Gradient Method or the Accelerated Multistep Gradient Scheme, which were developed and analyzed using the estimate sequence mathematical framework.
Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis.
Postdoctoral (convertible to research faculty) position, with competitive salary, is available for researchers with demonstrated outstanding publications in IEEE Transactions on signal processing, wireless communications, and similar quality journals to work on signal processing and communications for IoT, mMTC, and massive MIMO problems as well as other important problems.
The authors of [1], which was published in the November 2017 issue of IEEE Signal Processing Magazine, wish to add an acknowledgment to their article. The acknowledgment is as follows: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (number 2016R1A2B2014525) and by a grant from the National Science Foundation (IIS-1116656) awarded to Alan C. Bovik.
The Advertisers Index contained in this issue is compiled as a service to our readers and advertisers: the publisher is not liable for errors or omissions although every effort is made to ensure its accuracy. Be sure to let our advertisers know you found them through IEEE Signal Processing Magazine.
The list of tables of contents (TOCs) are now available for December 2018. The TOCs are formatted to the style of its publication and offers not only links to the full issue, but also to the individual articles in IEEEXplore®.
April 16-18, 2019
Location: Montreal, QC, Canada
October 20-23, 2019
Location: New Paltz, NY, USA
Job Description
Five colleagues were honored with the appointment of Distinguished Lecturer and they are: Petros Boufounos (Mitsubishi Electric Research Laboratories), Israel Cohen (Technion - Israel Institute of Technology), Janusz Konrad (Boston University), Anna Scaglione (Arizona State University), and Rui Zhang (National University of Singapore).
In patent no. 9,477,869 a mutual-capacitance palm print identification method is provided, a mutual-capacitance palm print identification device and a mutual-capacitance palm print identification touch panel.
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. Visit the SPS Job Submission Form.
The IEEE Signal Processing Society continues to track emerging technical areas. As a result, the Society has decided to create two new SPS Technical Interest Profile (TIP) Code categories in the fields of Data Science and Autonomous Systems to keep pace with the expanding technical disciplines.
The OU Analytics tool provides IEEE volunteer access to member data. It replaces SAMIEEE which was sunset in May. The OU Analytics User Group was formed to facilitate discussions among users, identify the top issues affecting user productivity, and provide a communication path between users and IEEE staff working to improve OU Analytics.
The Signal Processing Society (SPS) has 12 Technical Committees and 3 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
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