TSP Featured Articles

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

TSP Featured Articles

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.

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.

Classical algorithms for the multiple measurement vector (MMV) problem assume either independent columns for the solution matrix or certain models of correlation among the columns. The correlation structure in the previous MMV formulation does not capture the signals well for some applications like photoplethysmography (PPG) signal extraction where the signals are independent and linearly mixed in a certain manner. In practice, the mixtures of these signals are observed through different channels.

The focus of this paper is on detection theory for union of subspaces (UoS). To this end, generalized likelihood ratio tests (GLRTs) are presented for detection of signals conforming to the UoS model and detection of the corresponding “active” subspace. One of the main contributions of this paper is bounds on the performances of these GLRTs in terms of geometry of subspaces under various assumptions on the observation noise.

Recovery of certain piecewise continuous signals from noisy observations has been a major challenge in sciences and engineering. In this paper, in a tight-dimensional representation space, we exploit sparsity hidden in a class of possibly discontinuous signals named finite-dimensional piecewise continuous (FPC) signals. More precisely, we propose a tight-dimensional linear transformation which reveals a certain sparsity in discrete samples of the FPC signals. This transformation is designed by exploiting the fact that most of the consecutive samples are contained in special subspaces.

Pages

SPS on Twitter

  • Join SPS President Ahmed Tewfik on Wednesday, 22 September for the IEEE Signal Processing Society Town Hall in conj… https://t.co/31AOCWXvam
  • DEADLINE EXTENDED: The deadline to apply to PROGRESS at ICIP 2021 has been extended to this Thursday, 16 September!… https://t.co/8V2O4lpXr9
  • Voting is now live for the 5-Minute Video Clip Contest! Support SPS students by watching their videos on this year'… https://t.co/PTXiUzRI1u
  • Our newly-formed Synthetic Aperture Standards Committee is now recruiting new members for its initial roster! Check… https://t.co/RcMuQB86kR
  • PROGRESS returns in conjunction with ICIP 2021! Join us 17-18 September for an exciting new program and plenary spe… https://t.co/yJ9rMG73uu

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar