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Reliable and efficient spectrum sensing through dynamic selection of a subset of spectrum sensors is studied. The problem of selecting K sensor measurements from a set of M potential sensors is considered where K
In this paper, the problem of target localization in the presence of outlying sensors is tackled. This problem is important in practice because in many real-world applications the sensors might report irrelevant data unintentionally or maliciously. The problem is formulated by applying robust statistics techniques on squared range measurements and two different approaches to solve the problem are...
In this paper, adaptive non-uniform compressive sampling (ANCS) of time-varying signals, which are sparse in a proper basis, is introduced. ANCS employs the measurements of previous time steps to distribute the sensing energy among coefficients more intelligently. To this aim, a Bayesian inference method is proposed that does not require any prior knowledge of importance levels of coefficients or...
The problem of signal detection using a flexible and general model is considered. Owing to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this study, the authors propose a new detection method based on sparse decomposition in a union of subspaces model. Their proposed detector uses a dictionary...
Many research works have been done in face recognition during the last years that indicates the importance of face recognition systems in many applications including identity authentication. In this paper we propose an approach for face recognition which is suitable for unconstrained image acquisition and has a low computational cost. Since in practical applications such as in smartphones, imaging...
In this paper, we propose a missing spectrum data recovery technique for cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It is shown that the spectrum measurements collected from secondary users (SUs) can be factorized as product of a channel gain matrix times an activation matrix. Then, an NMF method with piecewise constant activation coefficients is introduced to analyze...
Exact recovery of a sparse solution for an underdetermined system of linear equations implies full search among all possible subsets of the dictionary, which is computationally intractable, while ℓ1 minimization will do the job when a Restricted Isometry Property holds for the dictionary. Yet, practical sparse recovery algorithms may fail to recover the vector of coefficients even when the dictionary...
This paper presents a novel approach for detection and estimation of fundamental parameters of linear frequency modulation (LFM) signals, i.e., the initial frequency and Chirp rate. The proposed approach is based on sparse representation of noisy input signals over two specific dictionaries, each designed for finding a parameter of LFM signal. Moreover, an iterative framework is proposed for simultaneous...
This paper presents a novel approach for Voice Activity Detection (VAD), based on the sparse representation of an input noisy speech over a learned dictionary. For this purpose, we first generate sparse representations of the input noisy speech by Orthogonal Matching Pursuit (OMP) sparse decomposition method with an over-complete speech dictionary learned from clean speech using K-SVD. We then propose...
Dictionary learning (DL) for sparse representation has been widely investigated during the last decade. A DL algorithm uses a training data set to learn a set of basis functions over which all training signals can be sparsely represented. In practice, training signals may contain a few outlier data, whose structures differ from those of the clean training set. The presence of these unpleasant data...
Analyzing motion patterns in traffic videos can directly lead to generate some high-level descriptions of the video content. In this paper, an unsupervised method is proposed to automatically discover motion patterns occurring in traffic video scenes. For this purpose, based on optical flow features extracted from video clips, an improved Group Sparse Topical Coding (GSTC) framework is applied for...
One way of extra robustness in watermarking is to partition a logo into parts and insert them with unequal embedding strengths. In this paper, first, three major requirements for logo partitioning are introduced and then a modified version of spatial scalability as a proper partitioning method is suggested. The proposed idea is general and can be applied to any logo watermarking technique, but as...
Voice activity detection (VAD) can be considered as a binary classification problem and solved using the support vector machine (SVM). This paper presents a robust approach to improve the performance of conventional SVM based VAD methods. To this end, we first generate sparse representations by using a speech dictionary learned from clean speech, and derive some kind of audio features from the sparse...
This paper studies denoising of images contaminated with additive white Gaussian noise (AWGN). In recent years, clustering-based methods have shown promising performances. In this paper we show that low-rank subspace clustering provides a suitable clustering problem that minimizes the lower bound on the MSE of the denoising, which is optimum for Gaussian noise. Solving the corresponding clustering...
A new algorithm for learning jointly reconstructive and discriminative dictionaries for sparse representation (SR) is presented. While in a usual dictionary learning algorithm like K-SVD only the reconstructive aspect of the sparse representations is considered to learn a dictionary, in our proposed algorithm, which we call K-LDA, the discriminative aspect of the sparse representations is also addressed...
In this paper we address the problem of learning low-dimensional subspaces using a given set of training data. To this aim, we propose an algorithm that performs by sequentially fitting a number of low-dimensional subspaces to the training data. Once we found a subset of the training data that is sufficiently near a fitted subspace, we omit these signals from the set of training signals and repeat...
In this paper we propose a new algorithm for learning low-dimensional linear subspaces. Our proposed algorithm performs by sequentially finding some low-dimensional subspaces on which a set of training data lies. Each subspace is found in such a way that the number of signals lying on (or near to) it is maximized. Once we found a subset of the training data that is sufficiently close to a subspace,...
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