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IEEE TSP Article

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space.

Inspired by the recent success of deep neural networks and the recent efforts to develop multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) which is optimized to address a specific regression task known as single image super-resolution. Contrary to other multi-layer dictionary models, our architecture contains L layers of analysis dictionary and soft-thresholding operators to gradually extract high-level features and a layer of synthesis dictionary which is designed to optimize the regression task at hand.

This paper is focused on simultaneous target detection and angle estimation with a multichannel phased array radar. Resorting to a linearized expression for the array steering vector around the beam pointing direction, the problem is formulated as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target directional cosines displacements with respect to the array nominal coarse pointing direction. 

We consider identification of linear dynamical systems comprising of high-dimensional signals, where the output noise components exhibit strong serial, and cross-sectional correlations. Although such settings occur in many modern applications, such dependency structure has not been fully incorporated in existing approaches in the literature. 

Channel estimation is of paramount importance in most communication systems in order to optimize the data rate/energy consumption tradeoff. In modern systems, the possibly large number of transmit/receive antennas and subcarriers makes this task difficult. Designing pilot sequences of reasonable size yielding good performance is thus critical. 

Speech dereverberation has been an important component of effective far-field voice interfaces in many applications. Algorithms based on multichannel linear prediction (MCLP) have been shown to be especially effective for blind speech dereverberation and numerous variants have been introduced in the literature. Most of these approaches can be derived from a common framework, where the MCLP problem for speech dereverberation is formulated as a weighted least squares problem that can be solved analytically.

We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-layer feedforward network, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected network (FCN) and the non-overlapping convolutional neural network (CNN).

A new technique for locating a moving source radiating a wide-band almost-cyclostationary signal is proposed. For this purpose, the signals received on two possibly moving sensors are modeled as jointly spectrally correlated, a new nonstationarity model that allows one to describe the Doppler effect accounting for a time-scale or time-stretch factor in the complex envelopes of the received signals.

In this paper, we study the problem of compressed sensing using binary measurement matrices and 1 -norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices.

This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration.

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