Mehdi Banitalebi-Dehkordi

TL;DR: A robust music genre classification method based on a sparse FFT based feature extraction method which extracted with discriminating power of spectral analysis of non-stationary audio signals, and the capability of sparse representation based classifiers is proposed.

TL;DR: The experimental results on the state-of-the-art LIVE and SJTU video datasets indicate that the proposed no-reference video quality assessment algorithm is effective and performs statistically better than several other state of theart approaches.

TL;DR: Extensive performance evaluations indicate that incorporating visual saliency information improves the performance of the quality assessment task, and results in a competitive overall performance in comparison to the state-of-the-art metrics to assess JPEG, JPEG2K, and blur distortions.

TL;DR: In this paper, the authors proposed a robust music genre classification method based on a sparse FFT based feature extraction method which extracted with discriminating power of spectral analysis of non-stationary audio signals, and the capability of sparse representation based classifiers.

TL;DR: A new modeling and analysis framework for the multipatient positioning in a wireless body area network (WBAN) which exploits the spatial sparsity of patients and a sparse fast Fourier transform (FFT)-based feature extraction mechanism for monitoring of Patients and for reporting the movement tracking to a central database server containing patient vital information is presented.