J.K. Tugnait
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View article: On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series
On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series Open
Estimation of the conditional independence graph (CIG) of high-dimensional multivariate Gaussian time series from multi-attribute data is considered. Existing methods for graph estimation for such data are based on single-attribute models …
View article: Learning Conditional Independence Differential Graphs From Time-Dependent Data
Learning Conditional Independence Differential Graphs From Time-Dependent Data Open
Estimation of differences in conditional independence graphs (CIGs) of two time series Gaussian graphical models (TSGGMs) is investigated where the two TSGGMs are known to have similar structure. The TSGGM structure is encoded in the inver…
View article: Multi-Attribute Graph Estimation With Sparse-Group Non-Convex Penalties
Multi-Attribute Graph Estimation With Sparse-Group Non-Convex Penalties Open
We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associa…
View article: Learning Multi-Attribute Differential Graphs With Non-Convex Penalties
Learning Multi-Attribute Differential Graphs With Non-Convex Penalties Open
We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with non-convex penalties. The GGM structure is en…
View article: Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data
Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data Open
We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on high-dimensional matrix graphical models assumes that independent…
View article: Learning High-Dimensional Differential Graphs From Multi-Attribute Data
Learning High-Dimensional Differential Graphs From Multi-Attribute Data Open
We consider the problem of estimating differences in two Gaussian graphical models (GGMs) which are known to have similar structure. The GGM structure is encoded in its precision (inverse covariance) matrix. In many applications one is int…
View article: Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation Open
We consider the problem of inferring the conditional independence graph (CIG) of a high-dimensional stationary multivariate Gaussian time series. In a time series graph, each component of the vector series is represented by distinct node, …
View article: Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series
Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series Open
We consider the problem of inferring the conditional independence graph (CIG) of a high-dimensional stationary multivariate Gaussian time series. A sparse-group lasso based frequency-domain formulation of the problem has been considered in…
View article: On sparse high-dimensional graphical model learning for dependent time series
On sparse high-dimensional graphical model learning for dependent time series Open
View article: On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series
On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series Open
We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional stationary multivariate Gaussian time series. A sparse-group lasso-based frequency-domain formulation of the problem based on frequ…
View article: Sparse Graph Learning Under Laplacian-Related Constraints
Sparse Graph Learning Under Laplacian-Related Constraints Open
We consider the problem of learning a sparse undirected graph underlying a given set of multivariate data. We focus on graph Laplacian-related constraints on the sparse precision matrix that encodes conditional dependence between the rando…
View article: Introducing Software Defined Radio into Undergraduate Wireless Engineering Curriculum through a Hands-on Approach
Introducing Software Defined Radio into Undergraduate Wireless Engineering Curriculum through a Hands-on Approach Open
Introducing Software Defined Radio into Undergraduate Wireless Engineering Curriculum through a Hands-on ApproachA software defined radio (SDR) is a modern radio communication system [1]. Unlike traditionalradios that implement components,…
View article: A Data-Cleaning Approach to Robust Multisensor Detection of Improper Signals
A Data-Cleaning Approach to Robust Multisensor Detection of Improper Signals Open
We consider the problem of detecting the presence of an improper complex-valued signal, common among two or more sensors (channels), in the presence of spatially independent, colored improper noise and additive outliers. A source of improp…
View article: Robust Spectrum-Based Comparison of Multivariate Complex Random Signals
Robust Spectrum-Based Comparison of Multivariate Complex Random Signals Open
We consider the problem of comparing two complex multivariate random signal realizations, possibly contaminated with additive outliers, to ascertain whether they have identical power spectral densities. For clean data (i.e., known to be ou…
View article: TCP-Drinc: Smart Congestion Control Based on Deep Reinforcement Learning
TCP-Drinc: Smart Congestion Control Based on Deep Reinforcement Learning Open
As wired/wireless networks become more and more complex, the fundamental assumptions made by many existing TCP variants may not hold true anymore. In this paper, we develop a model-free, smart congestion control algorithm based on deep rei…
View article: IEEE Wireless Communications Letters
IEEE Wireless Communications Letters Open
View article: IEEE Transactions on Signal Processing publication information
IEEE Transactions on Signal Processing publication information Open
The Signal Processing Society is an organization, within the framework of the IEEE, of members with principal professional interest in the technology of transmission, recording, reproduction, processing, and measurement of speech; other au…