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In this paper, a novel, low-complexity, and hardware efficient signal detection algorithm and its corresponding VLSI architecture are proposed for massive multiple-input multiple-output (MIMO) systems. This method is based on the parallel Gauss-Seidel (PGS) iterative method, and achieves comparable detection performance as the linear minimum mean-square error (MMSE) detection. It successfully avoids...
Given a corrupted low-rank matrix, robust principal component analysis performs a low-rank-plus-sparse matrix decomposition by solving a convex program. In this paper we first develop an efficient rank-revealing decomposition algorithm aided by randomization, which provides information about the singular subspaces and singular values of a given data matrix. The proposed factorization termed randomized...
With the introduction of several new coding tools and technology for depth maps in 3D-HEVC, the coding efficiency is improved at the expense of computational complexity. Meanwhile, the inter-view correlation is not considered and used well for depth maps, while it is developed maturely in texture video coding. Therefore, in this paper, we propose an early determination scheme for the best prediction...
Deep learning based speaker verification methods (SV) have achieved the state-of-the-art performance. However, SV with short voice commands (SV-SVC) is still challenging and its performance degrades significantly when noise presents. Carefully examining of SV-SVC task in real applications reveals that there are two unavoidable limitations. One is the very short utterances used (less than 1 second)...
In two recent contributions, minimization of number of adders in realization of digital finite impulse response (FIR) filter has been discussed. The proposed method extends the concept of these techniques to further reduce the requirement of adders in FIR filter for different applications. This paper is based on merging of concepts involved in vertical and horizontal common sub-expression elimination...
A discrete Fourier transform (DFT) enhanced complex least mean square (CLMS) algorithm, which utilizes the underlying time series relationship among the consecutive fundamental DFT components, is proposed to adaptively mitigate the spur pollution in multi-standard transceivers. The transient and steady-state performances of the proposed algorithm are investigated, demonstrating faster convergence...
Deconvolution of the glottal-pulse waveform from the speech signal remains an active field of research although dating back over half a century. In the main, existing approaches use classical inverse filtering frequency-domain methods to estimate both the vocal-tract and glottal-pulse waveforms. In this paper, we adopt a new approach which takes advantage of two relatively recent developments: firstly,...
Clustering techniques have gained great popularity in neuroscience data analysis especially in analysing data from complex experiment paradigm where it is hard to apply traditional model-based method. However, when employing clustering analysis, many clustering algorithms are available nowadays and even with an individual clustering algorithm, choices like parameter settings and distance metrics are...
This paper deals with the estimation of the flat fading Rayleigh channel with Jakes' Doppler spectrum (model due to R.H. Clarke in 1968) and slow fading variations. A common method in literature consists in approximating the variations of the channel using an auto-regressive model of order p (AR(p)), whose parameters are adjusted according to the “correlation matching” (CM) criterion and then estimated...
Monaural source separation is an important research area which can help to improve the performance of several real-world applications, such as speech recognition and assisted living systems. Huang et al. proposed deep recurrent neural networks (DRNNs) with discriminative criterion objective function to improve the performance of source separation. However, the penalty factor in the objective function...
Edge is the most used and important segmentation feature in most of the object based image processing applications. Primary challenging issues with all the edge detectors are their adaptability for different scenes, noise immunity and most importantly complexity of implementation which can hinder real time performance for high resolution images. In this paper, we have proposed a novel, efficient and...
In this paper, a highly adaptive swarm intelligence inspired optimally gamma corrected intensity coverage maximization approach has been proposed for quality enhancement of dark and low contrast remotely sensed images. Various image enhancement techniques have been proposed till date, but in case of dark images, most of them are suffering from saturation effects in higher intensity regions along with...
In this paper, we propose a new person re-identification algorithm based on bi-directional superpixel earth mover's distance (BD-SP-EMD). To address the viewpoint change issue, the human body segmentation is first extracted based on background modeling and saliency maps. A bi-directional scheme is then applied to obtain the forward and backward SP-EMD distances. Based on these two distances, pedestrians...
In this paper, the reconstruction of non-stationary audio signals is considered. Audio signals are approximately sparse in the joint time-frequency representation domain. The reconstruction is based on a reduced set of samples, and it is considered that the signals are sparse. The short-time Fourier transform (STFT) is considered as the representation domain where the audio signals are sparse. The...
This paper presents a new reconfigurable fast filter bank based on node-modulation. Compared with current reconfigurable filter bank, the proposed method has stronger control ability to the sub-bands. Not only the bandwidths but also the center frequencies of sub-bands can be controlled flexibility and accurately in a wide range without changing the filter bank structure. The filter bank is achieved...
Previous research has shown by using event-related potentials (ERPs) that the human brain can process and understand music at a pre-attentive level. Music-specific ERPs include the Early Right Anterior Negativity (ERAN) and a late Negativity (N5). This study aims to further investigate this issue using two types of syntactic manipulations in music: mild violations, containing no out-of-key tones and...
Environmental sound classification task (ESC) is still open and challenging. In contrast to speech, sounds of a specific acoustic event may be produced by a wide variety of sources. Thus for one class, feature spectrums of acoustic events are much more transformative than human speech. In order to learn better high-level feature representations from these transformative feature spectrums, convolution...
The impressive evolution of neural networks and deep learning techniques during the last few years has offered new incomparable routes to solve many complex problems. Moreover, the fact that neural networks are structured and supervised has made it possible to perform automatic parameter tuning that guarantees convergence to the best expressive model for the problem assessed. In this work, we investigated...
Acoustic parameters are very useful in voice screening, diagnosis and rehabilitation, and also in forensic voice comparison tasks. In this paper we present results for the acoustic analysis performed by two different voice analysis platforms and involving five sustained vowels uttered by 10 female speakers and 9 male speakers. We consider contemporaneous high-quality (HQ) and GSM voice recordings,...
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