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Recent results in compressed sensing have shown that a wide variety of structured signals can be recovered from undersampled and noisy linear observations. In this paper, we show that many of these signal structures can be modeled using an union of affine subspaces, and that the fundamental number of observations needed for stable recovery is given by the number of “free” values, i.e. the dimension...
Vehicle to Infrastructure (V2I) networks can help managing different on-the-road applications and services, especially where the vehicles population is low and Internet access is needed for messaging, alerts, entertainment. Accessing a network through a proper technology requires the possibility of establishing connections with a minimum quality level; besides, a mechanism for switching from a network...
Image level fusion combines an image in different ways with its original version so that the combine image may contain more relevant information than the original one. This paper presents a novel method for face recognition by fusing original and corresponding diagonal images. Two ways of image fusion technique have been performed here. Firstly, we generate diagonal face image from original face image...
Gender classification of depth images is a challenging problem, most research work attempted to use shape information to solve this problem in the past literature. In this work, we propose a new fusion scheme for gender classification using both texture and shape features. A new ensemble scheme is advocated to combine texture and shape feature at the feature level. To evaluate the performance of our...
In text document clustering documents are represented as feature vectors where features can be either words or phrases. Documents can belong to different topics when categorized by humans; however it is noted that obtaining one to one mapping between the features and the topics is almost impossible since the same features can and will be used in documents in different topics. Such common features...
Extraction of unknown independent source signals from a noisy mixture is a fundamental problem in most signal processing applications. The existing independent component analysis (ICA) algorithms have tackled this problem for complex and real valued mixtures for both super and sub Gaussian sources. However in reality super and sub Gaussian sources exist collectively in a mix. It was observed when...
Voltage sag remains to be a serious and most common power quality problem. A definitive solution towards compensation of voltage sag with phase jump is provided by Dynamic Voltage Restorer (DVR). The conventional DVR topologies however have dc-link and two stage power conversion, which increases its size, cost and associated losses. Therefore topologies without the dc-link mitigating sag by utilizing...
We propose a solution for efficiently transforming video data over a delay tolerant network. We extract and transport only relevant features from the video frame at a minimal computational cost using low cost COTS embedded environment.
A multi-way relaying scenario is considered. Each node has to transmit an individual message and has to receive the messages of all other nodes. These multi-way communications between the multi-antenna nodes are performed via an intermediate non-regenerative multi-antenna relay station. An iterative MMSE approach is proposed to jointly design the transceive filter at the relay station and the receive...
In many practical periodic parameter estimation problems, the appropriate cost function is periodic with respect to the unknown parameter. In this paper a new class of cyclic Bayesian lower bounds on the mean cyclic error (MCE) is developed. The new class includes the cyclic version of the Bayesian Cramér-Rao bound (BCRB). The cyclic BCRB requires milder regularity conditions compared to the conventional...
This work develops a new DOA tracking technique by proposing a novel semi-parametric method of sequential sparse recovery for a dynamic sparsity model. The proposed method iteratively provides a sequence of spatial spectrum estimates. The final process of estimating direction paths from the spectrum sequence is not considered. However, the simulation results show concentration of the spectrum around...
GROUSE (Grassmannian Rank-One Update Subspace Estimation) [1] is an incremental algorithm for identifying a subspace of ℝn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis [2] has shown that GROUSE converges locally at an expected linear rate, under certain assumptions. GROUSE has a similar flavor to the incremental...
This work is concerned with solving non-convex power optimization problems by introducing the concept of “nonlinear optimization over graph”. To this end, the structure of a given nonlinear real/complex optimization with quadratic arguments is mapped into a generalized weighted graph, where each edge is associated with a weight set constructed from the known parameters of the optimization (e.g., the...
In the design of line codes for data transmission and modulation codes for data storage it is important to match the spectral properties of bipolar sequences (containing the symbols ±1) to the spectral properties of the transmission or storage medium. This paper demonstrates that spectral shaping is possible with bipolar sequences, and explores the advantage of bipolar sequences with shaped spectra...
There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications. In this work, we study the problem of wide-band spectrum sensing for cognitive radio networks using compressed sensing to exploit the underlying joint sparsity structure in a distributed setting. In particular, we use the recently proposed Approximate...
In the diffusion strategies for distributed estimation over adaptive networks, each node calculates a weighted average of the intermediate parameter estimates of its neighboring nodes. Thus, all the nodes should continuously share their intermediate estimates with their neighbors. In this paper, we consider exchanging a predetermined number of elements of each intermediate estimate vector at each...
A two-stage predictor strategy is introduced in the context of high dimensional data (large p, small n). Here the focus application is a medical one: prediction of symptomatic infection given molecular expression levels in blood. The first stage of the two-stage predictor uses the previously introduced method of Predictive Correlation Screening (PCS) to select a subset of genes that are important...
This work studies the performance of position estimation for distress beacons using time of arrival and frequency of arrival measurements. The analysis is conducted for emergency signals modeled as pulses with sigmoidal transitions. This model has shown interesting properties for Cospas-Sarsat search and rescue signals. The modified Cramér-Rao bounds of the symbol width, time of arrival, frequency...
Localization is a fundamental challenge for any wireless network of nodes, in particular when the nodes are mobile. For an anchorless network of mobile nodes, we present a relative velocity estimation algorithm based on multidimensional scaling. We propose a generalized two-way ranging model, where the time-varying pairwise distances between the nodes are expressed as a Taylor series for a small observation...
We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally efficient...
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