Tensor-based adaptive filtering algorithms
Tensor-based signal processing methods are usually employed when dealing with
multidimensional data and/or systems with a large parameter space. In this paper, we …
multidimensional data and/or systems with a large parameter space. In this paper, we …
Noise reduction and speech enhancement using wiener filter
Digital data transmission rate may reach over 2.5 Tb/s using the orthogonal frequency
division multiplexing (OFDM). Digital speech enhancement is crucial during the pandemic …
division multiplexing (OFDM). Digital speech enhancement is crucial during the pandemic …
Low-complexity recursive least-squares adaptive algorithm based on tensorial forms
Modern solutions for system identification problems employ multilinear forms, which are
based on multiple-order tensor decomposition (of rank one). Recently, such a solution was …
based on multiple-order tensor decomposition (of rank one). Recently, such a solution was …
Efficient functional link adaptive filters based on nearest Kronecker product decomposition
Functional link adaptive filters (FLAFs) utilize expansion blocks to nonlinearly augment the
input signal to a higher dimensional space, after which an adaptive weight algorithm is …
input signal to a higher dimensional space, after which an adaptive weight algorithm is …
LMS algorithms for multilinear forms
Solving a high-dimension system identification problem could involve significant challenges
in terms of complexity and accuracy of the solution. Due to the large parameter space, a …
in terms of complexity and accuracy of the solution. Due to the large parameter space, a …
A Kalman filter for multilinear forms and its connection with tensorial adaptive filters
The Kalman filter represents a very popular signal processing tool, with a wide range of
applications within many fields. Following a Bayesian framework, the Kalman filter …
applications within many fields. Following a Bayesian framework, the Kalman filter …
Tensor-based recursive least-squares adaptive algorithms with low-complexity and high robustness features
The recently proposed tensor-based recursive least-squares dichotomous coordinate
descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear …
descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear …
[PDF][PDF] Intelligent Fine-Grained Daily Living Locomotion Prediction Based on Skeleton Modeling and CNN.
Activities of daily living are important for human locomotion prediction. Humans perform
several activities while moving from one place to another. Some of these activities are …
several activities while moving from one place to another. Some of these activities are …
Identification of Multilinear Systems: A Brief Overview
Nonlinear systems have been studied for a long time and have applications in numerous
research fields. However, there is currently no global solution for nonlinear system …
research fields. However, there is currently no global solution for nonlinear system …
Improving the Accuracy of Systems for Measuring the Angular Position of Moving Objects with an Adaptive Wiener Filter
D Dichev, I Zhelezarov, B Georgiev… - 2024 XXXIV …, 2024 - ieeexplore.ieee.org
This paper presents a method for increasing the accuracy of systems for measuring the
angular position of moving objects by using an adaptive Wiener filter. In the context of …
angular position of moving objects by using an adaptive Wiener filter. In the context of …