D
Dimitris A. Pados
Researcher at Florida Atlantic University
Publications - 223
Citations - 3864
Dimitris A. Pados is an academic researcher from Florida Atlantic University. The author has contributed to research in topics: Spread spectrum & Principal component analysis. The author has an hindex of 31, co-authored 211 publications receiving 3526 citations. Previous affiliations of Dimitris A. Pados include State University of New York System & University at Buffalo.
Papers
More filters
Journal ArticleDOI
An iterative algorithm for the computation of the MVDR filter
TL;DR: It is the early, nonasymptotic elements of the generated sequence of estimators that offer favorable bias covariance balance and are seen to outperform in mean-square estimation error, constraint-LMS, RLS-type, orthogonal multistage decomposition, as well as plain and diagonally loaded SMI estimates.
Proceedings ArticleDOI
Increasing indoor spectrum sharing capacity using smart reflect-array
TL;DR: In this article, the authors proposed a new spectrum sharing solution for indoor environments based on the usage of a reconfigurable reflect-array in the middle of the wireless channel, which optimally controlled the phase shift of each element on the reflectarray, the useful signals for each transmission pair can be enhanced while the interferences can be canceled.
Journal ArticleDOI
New bounds on the total squared correlation and optimum design of DS-CDMA binary signature sets
TL;DR: New bounds on the TSC of binary signature sets are derived for any number of signatures K and any signature length L and for almost all K, L in {1,2,...,256}, and the design procedure is based on simple transformations of Hadamard matrices.
Journal ArticleDOI
Joint space-time auxiliary-vector filtering for DS/CDMA systems with antenna arrays
TL;DR: The studies show that the induced BER can be improved by orders of magnitude, while at the same time significantly lower computational optimization complexity is required in comparison with joint S-T minimum-variance distortionless response or equivalent minimum mean-square-error conventional filtering means.
Journal ArticleDOI
On overfitting, generalization, and randomly expanded training sets
TL;DR: An algorithmic procedure is developed for the random expansion of a given training set to combat overfitting and improve the generalization ability of backpropagation trained multilayer perceptrons (MLPs).