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02/20/2023
A DNN based Normalized Time-frequency Weighted Criterion for Robust Wideband DoA Estimation
Deep neural networks (DNNs) have greatly benefited direction of arrival ...
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10/13/2022
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
A deep neural network using rectified linear units represents a continuo...
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10/07/2022
Maximum Likelihood-based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL with Grid Refinement
We consider the parametric data model employed in applications such as l...
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11/17/2021
A Generalized Proportionate-Type Normalized Subband Adaptive Filter
We show that a new design criterion, i.e., the least squares on subband ...
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11/10/2021
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees
Models recently used in the literature proving residual networks (ResNet...
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11/05/2020
Inter-Cell Interference and Load Balancing Aware Access Point Placement in Small-Cell Networks
In this paper, we provide solutions to the access point (AP) placement p...
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06/30/2020
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection
In this paper, we present a novel neural network for MIMO symbol detecti...
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04/10/2018
Multimodal Sparse Bayesian Dictionary Learning
The purpose of this paper is to address the problem of learning dictiona...
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02/05/2018
Re-Weighted Learning for Sparsifying Deep Neural Networks
This paper addresses the topic of sparsifying deep neural networks (DNN'...
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03/30/2017
Relevance Subject Machine: A Novel Person Re-identification Framework
We propose a novel method called the Relevance Subject Machine (RSM) to ...
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03/08/2017
A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
In this paper, we present an algorithm for the sparse signal recovery pr...
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04/07/2016
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS ...
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01/22/2016
Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem
In this paper, we develop a Bayesian evidence maximization framework to ...
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04/21/2014
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless telemoni...
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11/15/2013
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities
As a lossy compression framework, compressed sensing has drawn much atte...
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05/20/2012
Sparse Signal Recovery in the Presence of Intra-Vector and Inter-Vector Correlation
This work discusses the problem of sparse signal recovery when there is ...
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05/07/2012
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. ...
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01/04/2012
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
We examine the recovery of block sparse signals and extend the framework...
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05/04/2011
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
A trend in compressed sensing (CS) is to exploit structure for improved ...
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04/28/2011
Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors
Iterative reweighted algorithms, as a class of algorithms for sparse sig...
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02/19/2011