News and Updates
Jul. 2015: Check out this paper which has been accepted by IEEE Transactions on Signal Processing “A factor analysis framework for power spectra separation and multiple emitter localization,”. We consider a scenario in wireless communication, where multiple emitters exist, and the receivers wish to know their locations and their individual power spectra. This problem finds its application in dynamic spectrum access systems, e.g., cognitive radio. It may also be used for intelligent beamforming, routing, and scheduling. Existing spectrum sensing approaches mostly consider estimating the aggregate spectrum of the received signal, rather than the underlying spectral atoms, i.e., individual spectra corresponding to different sources. We consider modeling, formulating, and solving this problem; robustification against sensor failure is also considered.
Jul. 2015: The paper “Joint Tensor Factorization and Outlying Slab Suppression With Applications” has been accepted by IEEE Transactions on Signal Processing. In this work, we consider a realistic scenario where some slabs of a tensor is corrupted. Such a setup is commonly seen in speech separation, Fluorescence data analysis, and social network data mining. A simple low-rank tensor factorization algorithm is proposed to deal with this problem, and interesting interpretable results are observed.
Jun. 2015: The paper “A factor analysis framework for power spectra separation and multiple emitter localization” has been accepted by IEEE Transactions on Signal Processing. We consider a challenging problem for spectrum sensing in this paper: if there are multiple transmitters and their spectra are overlapped, is it fundamentally possible to resolve the spectra blindly, and how to do it? We formulate this problem to a low-rank tensor factorization problem so that the identifiability of the spectra is guaranteed. A robust algorithm that can deal with outlying sensors is also proposed.
Jun. 2015: We have submitted a conference paper “Joint Factor Analysis and Latent Clustering” to 2015 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE CAMSAP is an interesting conference held every two years. This time it will be at Cancun, Mexico. See details in http:inspire.rutgers.edu/camsap2015.
Feb. 2015: Our paper “Blind separation of quasi-stationary sources: Exploiting convex geometry in covariance domain” has been accepted by IEEE Transactions on Signal Processing. We revisited a classical array processing problem, i.e., blind source separation (BSS), in this work. We managed to build up a link between BSS and convex geometry analysis, and then developed some fast BSS algorithms. We also proved the identifiability of the minimum-volume enclosing simplex (MVES) criterion, which was a challenging open problem in convex geometry analysis.
May 2014: I received a Best Student Paper Award (the third prize) at ICASSP 2014, Florence, Italy. The work, titled “Blind Spectra Separation and Direction Finding for Cognitive Radio Using Temporal Correlation-domain ESPRIT,” was carried out during my overseas exchange at the University of Minnesota (Sep. 2013 - Feb. 2014), where Prof. Nikos Sidiropoulos was the host
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