The IEEE Signal Processing Society is dedicated to supporting the professional growth and career advancement of its members in the dynamic field of signal processing. Learn More
As part of the IEEE Signal Processing Society (SPS), the Speech and Language Technical Committee (SLTC) promotes research and development activities for technologies that are used to process speech and natural language.
The Bio-Imaging and Signal Processing Technical Committee (BISP-TC) of the IEEE Signal Processing Society (SPS) promotes activities in the broad technical areas of computerized image and signal processing with a clear focus on applications in biology and medicine.
Extracting information from a signal exhibiting damped resonances is a challenging task in many practical cases due to the presence of noise and high attenuation. The interpretation of the signal relies on a model whose order (i.e., the number of resonances) is in general unknown.
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure.
We generalize the 1-bit matrix completion problem to higher order tensors. Consider a rank- r order- d tensorT in RN ×⋯×RN with bounded entries. We show that when r=O(1) , such a tensor can be estimated efficiently from only m=Or (Nd) binary measurements. This shows that the sample complexity of recovering a low-rank tensor from 1-bit measurements of a subset of its entries is roughly the same as recovering it from unquantized measurements—a result that had been known only in the matrix case, i.e., when d=2.
In this paper, four iterative algorithms for learning analysis operators are presented. They are built upon the same optimization principle underlying both Analysis K-SVD and Analysis SimCO. The forward and sequential analysis operator learning (AOL) algorithms are based on projected gradient descent with optimally chosen step size. The implicit AOL algorithm is inspired by the implicit Euler scheme for solving ordinary differential equations and does not require to choose a step size.
The School of Informatics at the University of Edinburgh are currently recruiting faculty at the Lecturer/Senior Lecturer/Reader level (similar to Assistant/Associate Professor) in several areas, including speech technology.
Job Description
Strong Mathematical background
Strong analytic and problem solving skills
Expertise in Digital signal processing and Algorithm Development
Very Good Experience in MATLAB Coding
Fairly Good Knowledge in statistical signal processing concepts
Added Advantage: Experience in design and implementation of Filters or related algorithms
Added Advantage: Fairly Good Knowledge in orientation concepts (quaternion/Euler/angular rates/rotat
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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.
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The IEEE Signal Processing Society is dedicated to supporting the professional growth and career advancement of its members in the dynamic field of signal processing. Learn More
Signal processing education and professional development program for all career levels. Learn more.