We are pleased to announce that, as of January 2019, the IEEE Transactions on Signal and Information Processing over Networks has formally been accepted for indexing by the Clarivate Analytics Web of Science.
Articles published in TSIPN as of March 2016 will be covered in the following Clarivate Analytics products:
Several Ph.D. positions with full financial assistantship are available at the Autonomous Systems Perception, Intelligence, & Navigation Laboratory (https://aspin.ucr.edu); University of California, Irvine (https://uci.edu). The research will be in the broad areas of Autonomous Systems, Communication Systems, and Signal Processing with applications to: satellites, unmanned aerial vehicles, self-driving cars, indoor robotics, and mobile devices.
Immediate opening for a postdoctoral researcher in radionavigation and wireless communication systems at the Autonomous Systems Perception, Intelligence, & Navigation Laboratory (https://aspin.ucr.edu); University of California, Irvine (https://uci.edu).
Signal reconstruction is a challenging aspect of computational imaging as it often involves solving ill-posed inverse problems. Recently, deep feed-forward neural networks have led to state-of-the-art results in solving various inverse imaging problems. However, being task specific, these networks have to be learned for each inverse problem. On the other hand, a more flexible approach would be to learn a deep generative model once and then use it as a signal prior for solving various inverse problems.
It is well-established in the compressive sensing (CS) literature that sensing matrices whose elements are drawn from independent random distributions exhibit enhanced reconstruction capabilities. In many CS applications, such as electromagnetic imaging, practical limitations on the measurement system prevent one from generating sensing matrices in this fashion.
We have 2 PhD Positions open in Distributed Statistical Learning in IoT with Adversarial Environments
Job Description
The low-rank plus sparse (L+S) decomposition model enables the reconstruction of undersampled dynamic parallel magnetic resonance imaging data. Solving for the low rank and the sparse components involves nonsmooth composite convex optimization, and algorithms for this problem can be categorized into proximal gradient methods and variable splitting methods. This paper investigates new efficient algorithms for both schemes.
https://nato.taleo.net/careersection/2/jobdetail.ftl?job=190339&lang=en#.XKyV6uNspCw.gmail
Deputy Director-190339
Primary Location
Italy-La Spezia
NATO Body
Centre for Maritime Research and Experimentation (CMRE)
Schedule
Full-time
Euro (EUR) Yearly
Ghost imaging has recently been successfully achieved in the X-ray regime. Due to the penetrating power of X-rays this immediately opens up the possibility of ghost-tomography. No research into this topic currently exists in the literature. Here, we present adaptations of conventional X-ray tomography techniques to this new ghost-imaging scheme. Several numerical implementations for tomography through X-ray ghost-imaging are considered.
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