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The Latest News, Articles, and Events in Signal Processing
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.
Dr. Birsen Yazici received her B.S. degrees in electrical engineering and mathematics from Bogazici University, Istanbul Turkey, in 1988, and the M.S.and Ph.D.degrees in mathematics and electrical engineering from Purdue University, West Lafayette, IN, USA, in 1990 and 1994, respectively.
The ALASKA 2 Steganalysis Challenge is now open! The goal of the competition is to push the community towards image steganalysis, “into the wild” facing two main challenges: 1) The very high heterogeneity of image "sources", with widely different type of content, processing operations, sensor noises, etc. and 2) Designing machine learning method for detection with low false positive, ideally that could be guaranteed. The entry deadline is 13 July 2020 at 11:59 PM (UTC).
A new era of pervasive data generation is enabled by emerging sensing modalities and will pose new challenges to signal processing, data science, and robotics. For example, underwater robotic technology enables the development of advanced networks for underwater localization and mapping, and emerging aerial robotic technology enables the development of advanced networks for wide area localization and mapping.
COVID-19 has disrupted the day-to-day activities of governments, corporates, and people across the globe. It is unfortunate that this virus has taken humanity to a stage where most of us spend time in isolation worrying about the future.
The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques.
The IEEE Signal Processing Society would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic. We appreciate your continued patience and support as we work together to navigate these unforeseen and uncertain circumstances. We hope that you, your families, and your communities are safe!
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.
Please visit the Conferences and Events page on the IEEE Signal Processing Society website for Upcoming Lectures by Distinguished Lecturers.
The 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020) starts next week, and a fully virtual event opens even more opportunities and flexibility for you to interact and engage with your signal processing community!
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
The workshop is 22nd in the series, organized by the Multimedia Signal Processing Technical Committee of the IEEE Signal Processing Society, with the aim to bring together researchers and practitioners from academia and industry, passionate about multimedia signal processing, to share their knowledge, exchange ideas, explore future research directions and network.
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