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Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Multiple-input multiple-output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high-energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

The problem of quickest detection of a change in distribution is considered under the assumption that the pre-change distribution is known, and the post-change distribution is only known to belong to a family of distributions distinguishable from a discretized version of the pre-change distribution.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We address the downlink channel estimation problem for massive multiple-input multiple-output (MIMO) systems in this paper, where the inherit burst-sparsity structure is exploited to improve the channel estimation performance. In the literature, the commonly used burst-sparsity model assumes a uniform burst-sparse structure in which all bursts have similar sizes.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Model order selection (MOS) in linear regression models is a widely studied problem in signal processing. Penalized log likelihood techniques based on information theoretic criteria (ITC) are algorithms of choice in MOS problems. Recently, a number of model selection problems have been successfully solved with explicit finite sample guarantees using a concept called residual ratio thresholding (RRT).

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Motivated by the many applications associated with estimation of sparse multivariate models, the estimation of sparse directional connectivity between the imperfectly measured nodes of a network is studied. Node dynamics and interactions are assumed to follow a multivariate autoregressive model driven by noise, and the observations are a noisy linear combination of the underlying node activities.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We consider the problem of detecting the presence of a complex-valued, possibly improper, but unknown signal, common among two or more sensors (channels) in the presence of spatially independent, unknown, possibly improper and colored, noise. Past work on this problem is limited to signals observed in proper noise.

Issue Date: January, 2019

Section: Community & Involvement

In the last decade, a large number of techniques have been proposed to ensure integrity and authenticity of data in security-oriented applications, e.g. multime-dia forensics, biometrics, watermarking and information hiding, network intrusion detection, reputation systems, etc.... The development of these methods has re-ceived a new boost in the last few years with the advent of Deep Learning (DL) techniques and Convolutional Neural Networks (CNNs).

Section: Our Story

Aristotle Univ. of Thessaloniki Greece

The International Institute for Smart Transportation, Infrastructure, Finance and Computing (IISTIFC) affiliated with the School of Smart Cities (SSC) at South China University of Technology (SCUT), Guangzhou, China invites applicants to fill three-year term non-tenure track Research Assistant Professor positions on Guangzhou International Campus for conducting interdisciplinary engineering education and cutting-edge research.

Section: Publications & Resources

If you’ve ever been to the emergency room due to an injury, chances are you’ve had a routine encounter with what is in fact a wonder of the modern age: Magnetic Resonance Imaging, or MRI. Advancements in medical imaging allow us to study parts of the body, including the brain, to an extent unprecedented in the history of medical science.