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The Latest News, Articles, and Events in Signal Processing
April 13-16, 2021
NOTE: Location changed to--Virtual Conference
Given the impossibility of travel during the COVID-19 crisis, Computational Imaging TC is launching an SPS Webinar Series SPACE (Signal Processing And Computational imagE formation) as a regular bi-weekly online seminar series to reach out to the global computational imaging and signal processing community.
The SAMPL Lab at the Weizmann Institute of Science offers a postdoctoral position within the project C'MON-QSENS! (Continuously Monitored Quantum Sensors: Smart Tools and Applications) funded by QuantERA EU program in Quantum Technologies.
The SAMPL Lab at the Weizmann Institute of Science offers a postdoctoral position within the project C'MON-QSENS! (Continuously Monitored Quantum Sensors: Smart Tools and Applications) funded by QuantERA EU program in Quantum Technologies.
PhD students and Postdocs in the areas of signal processing, machine learning, medical imaging, communications and radar processing:
Host Professor: Yonina Eldar, department of mathematics and computer science, Weizmann Institute
Masters students, PhD students and Postdocs interested in the areas of signal processing, machine learning, medical imaging, communications and radar processing. Contact us.
Given the impossibility of travel during the COVID-19 crisis, Computational Imaging TC is launching an SPS Webinar Series SPACE (Signal Processing And Computational imagE formation) as a regular bi-weekly online seminar series to reach out to the global computational imaging and signal processing community.
Manuscript Due: May 30, 2021
Publication Date: February 2022
CFP Document
Manuscript Due: October 15, 2020
Publication Date: May 2021
CFP Document
Classification of SAR image data continues to be a big challenge. Major difficulties include the scarcity of available data, and the difficulty of semantically interpreting the SAR backscattered signal. There are no large-scale, SAR-derived image databases for Remote Sensing image analysis and knowledge discovery.
For the full job description and more informaiton, view the job description document.
You may have been wondering what happened to the list of tables of contents (TOCs) we were offering in the SPS Newsletter each month? Well, beginning in 2020, many of the SPS journals eliminated month-based issues and moved to volume-only publications; for these, there are no month-based TOCs. In order to address this, we worked to devise a method to collect a range of articles being posted to IEEEXplore® and assemble them into a separate, yet familiar, formatted TOC.
A Multiple Input Multiple Output MIMO radar makes use of orthogonal transmit waveforms either in time, in frequency or in code, in order to exploit diversity gain (due to the larger number of degrees of freedom than their phase-array counterparts) and obtain more information from a radar scenario or target.
The range resolution of a radar system is directly propotional to its bandwidth. Applications demand more and more range resolution and thus higher high frequency bandwidths. Unfortunately, these bandwidths are limited because of several reasons like techical (hardware limitations), atmospherical windows and specific regulations.
Applying tomographic SAR inversion using compressive sensing is well established in the SAR community. In contrast to state of the art approaches applied to satellite data novel CS reconstruction approaches combining sparsity with prior information will be researched and implemented. We intend to use high resolution airborne data sets from FHR and later, from our own sensor platform. The data is superior to satellite data concerning resolution und SNR.
I am writing this column on the first official day of spring while “sheltering in place” in Northern California. In these uncertain times, we are all experiencing the anxiety that comes from an unpredictable situation that we do not control; the shock of seeing, perhaps for the first time, all of the shelves in grocery stores empty; and the stress of working, living, and sleeping in the same place.
Since the 1970s, various image and video coding techniques have been explored, and some of them have been included in the video coding standards issued by the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) Motion Pictures Expert Group (MPEG) and International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) Video Coding Experts Group (VCEG).
The current big data era routinely requires the processing of large-scale data on massive distributed computing clusters. In these applications, data sets are often so large that they cannot be housed in the memory and/or the disk of any one computer. Thus, the data and the processing are typically distributed across multiple nodes.
Batch training of machine learning models based on neural networks is well established, whereas, to date, streaming methods are largely based on linear models. To go beyond linear in the online setting, nonparametric methods are of interest due to their universality and ability to stably incorporate new information via convexity or Bayes's rule.
The field of machine learning has undergone radical transformations during the last decade. These transformations, which have been fueled by our ability to collect and generate tremendous volumes of training data and leverage massive amounts of low-cost computing power, have led to an explosion in research activity in the field by academic and industrial researchers.
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