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As a new emerging technology for wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end circuits in a dense circuit board. In this paper, we apply the compressive sensing technique to reduce the required number of front-end circuits and the overall computational complexity. Unlike the commonly adopted random...
In this paper, we address the problem of spectrum estimation of frequency-hopping (FH) signals in the presence of random missing samples. The signals are analyzed within the bilinear time-frequency representation framework, where a time-frequency kernel is designed based on inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts due to missing...
The paper deals with sparsely sampled nonstationary signals in a multi-sensor array platform. We examine direction-of-arrival (DOA) estimation using sparsity-based time-frequency signal representation (TFSR). While conventional time-frequency analysis techniques suffer from noise-like artifacts due to missing data samples, high-fidelity time-frequency signatures can be obtained by applying kernelled...
In this paper, we examine the sparsity-based time-frequency signal representation (TFSR) of randomly thinned nonstationary signals in a multi-sensor platform to yield improved performance with reduced number of samples in each sensor. The property that different sensors share identical auto-term time-frequency regions renders the TFSR a group sparse reconstruction problem, which is effectively solved...
In this paper, we propose a novel structured compressive sensing algorithm based on non-parametric Bayesian framework for the reconstruction of sparse entries with a continuous structure. A paired spike-and-slab prior is first employed to impose signal sparsity. A logistic Gaussian kernel model, which involves the logistic model and location-dependent Gaussian kernel, is then proposed to encourage...
Compressive sensing (CS) has successfully been applied to reconstruct sparse signals and images from few observations. For multi-component nonstationary signals characterized by instantaneous frequency laws, the sparsity exhibits itself in the time-frequency domain as well as the ambiguity domain. In this paper, we examine CS in the context of nonstationary array processing. We show that the spatial...
A nonlinear DCT discriminant feature extraction approach for face recognition is proposed. First, we analyze the nonlinear discriminabilities of DCT frequency bands and select appropriate bands. Second, we extract nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. generalized kernel discriminative common vector (KDCV) method. The experimental...
A novel nonlinear feature extraction and recognition approach which is based on improved 2D Fisherface plus Kernel discriminant analysis is proposed. We provide an improved 2D Fisherface method that designs a new strategy to select appropriate 2D principal components and discriminant vectors, then we use 2D features to perform the Kernel discriminant analysis. The nearest neighbor classifier with...
Tongue line refers to the surface of the tongue covered with fissures or lines in deep or shallow shape and is one type of important features in clinical practice of Traditional Chinese Tongue Diagnosis (TCTD). However, it is hard to extract tongue lines completely due to the large variation of the widths of tongue lines and the strong noise caused by the rough surface of tongue and uneven illumination...
In this paper, by using support vector machine (SVM) regression, the simulation model of the influences of stakeholders on capital management in commercial banks is established. The results are tested by utilizing the samples of Chinese joint-stock commercial banks during the period of 1999-2006 and compared with results of PLS regression and BP neural network. The result is gotten that the forecasting...
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