Yannis Kopsinis

A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with

In satellites, nonlinear amplifiers used near saturation severely distort the transmitted signal and cause difficulties in its reception. Nevertheless, the nonlinearities introduced by memoryless bandpass amplifiers preserve the symmetries of the-ary quadrature amplitude modulation (-QAM) constellation. In this paper, a cluster-based sequence equalizer (CBSE) that takes advantage of these symmetries is presented. The proposed equalizer exhibits enhanced performance compared to other techniques, including the conventional linear transversal equalizer, Volterra equalizers, and RBF network equalizers. Moreover, this gain in performance is obtained at a substantially lower computational cost.

Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.

Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted

This paper presents a novel projection-based adaptive algorithm for sparse signal and system identification. The sequentially observed data are used to generate an equivalent sequence of closed convex sets, namely hyperslabs. Each hyperslab is the geometric equivalent of a cost criterion, that quantifies “data mismatch.” Sparsity is imposed by the introduction of appropriately designed weighted ℓ1 balls and the related projection operator is also derived. The algorithm develops around projections onto the sequence of the generated hyperslabs as well as the weighted ℓ1 balls. The resulting scheme exhibits linear dependence, with respect to the unknown systems order, on the number of multiplications/additions and an O(Llog2L) dependence on sorting operations, where L is the length of the system/signal to be estimated. Numerical results are also given to validate the performance of the proposed method against the Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm and two very recently developed adaptive sparse schemes that fuse arguments from the LMS/RLS adaptation mechanisms with those imposed by the lasso rational.

\ell_{1}

Empirical mode decomposition (EMD) is a relatively new, data-driven adaptive technique for analyzing multicomponent signals. Although it has many interesting features and often exhibits an ability to decompose nonlinear and nonstationary signals, it lacks a strong theoretical basis which would allow a performance analysis and hence the enhancement and optimization of the method in a systematic way. In this paper, the optimization of EMD is attempted in an alternative manner. Using specially defined multicomponent signals, the optimum outputs can be known in advance and used in the optimization of the EMD-free parameters within a genetic algorithm framework. The contributions of this paper are two-fold. First, the optimization of both the interpolation points and the piecewise interpolating polynomials for the formation of the upper and lower envelopes of the signal reveal important characteristics of the method which where previously hidden. Second, basic directions for the estimates of the optimized parameters are developed, leading to significant performance improvements.

Balls

In this paper, a sparsity promoting adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale. At each time instance and at each node of the network, a closed convex set, known as property set, is constructed based on the received measurements; this defines the region in which the solution is searched for. In this paper, the property sets take the form of hyperslabs. The goal is to find a point that belongs to the intersection of these hyperslabs. To this end, sparsity encouraging variable metric projections onto the hyperslabs have been adopted. In addition, sparsity is also imposed by employing variable metric projections onto weighted l1 balls. A combine adapt cooperation strategy is adopted. Under some mild assumptions, the scheme enjoys monotonicity, asymptotic optimality and strong convergence to a point that lies in the consensus subspace. Finally, numerical examples verify the validity of the proposed scheme compared to other algorithms, which have been developed in the context of sparse adaptive learning.

Investigation and Performance Enhancement of the Empirical Mode Decomposition Method Based on a Heuristic Search Optimization Approach

This paper presents a novel projection-based adaptive algorithm for sparse system identification. Sequentially observed data are used to generate an equivalent number of closed convex sets, namely hyperslabs, which quantify an associated cost criterion. Sparsity is exploited by the introduction of appropriately designed weighted ℓ1 balls. The algorithm uses only projections onto hyperslabs and weighted ℓ1 balls, and results into a computational complexity of order O(L) multiplications/additions and O(Llog2 L) sorting operations, where L is the length of the system to be estimated. Numerical results are also given to validate the proposed method against very recently developed sparse LMS and RLS type of algorithms, which are considered to belong to the same type of algorithmic family.

A Sparsity Promoting Adaptive Algorithm for Distributed Learning

Empirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding of EMD comes from an intuitive and experimental validation of the method. Recent research on EMD revealed improved criteria for the interpolation points selection. More specifically, it was shown that the performance of EMD can be significantly enhanced if, as interpolation points, instead of the signal extrema, the extrema of the subsignal having the higher instantaneous frequency are used. Even if the extrema of the subsignal with the higher instantaneous frequency are not known in advance, this new interpolation points criterion can be effectively exploited in doubly-iterative sifting schemes leading to improved decomposition performance. In this paper, the possibilities and limitations of the developments above are explored and the new methods are compared with the conventional EMD.

Adaptive algorithm for sparse system identification using projections onto weighted ℓ 1 balls

In this paper, techniques for time-frequency analysis and investigation of bat echolocation calls are studied. Particularly, enhanced resolution techniques are developed and/or used in this specific context for the first time. When compared to traditional time-frequency representation methods, the proposed techniques are more capable of showing previously unseen features in the structure of bat echolocation calls. It should be emphasized that although the study is focused on bat echolocation recordings, the results are more general and applicable to many other types of signal.

Improved EMD using doubly-iterative sifting and high order spline interpolation

In satellites, nonlinear amplifiers used near saturation severely distort the transmitted signal and cause difficulties in its reception. Nevertheless, the nonlinearities introduced by memoryless bandpass amplifiers preserve the symmetries of the-ary quadrature amplitude modulation (-QAM) constellation. In this paper, a cluster-based sequence equalizer (CBSE) that takes advantage of these symmetries is presented. The proposed equalizer exhibits enhanced performance compared to other techniques, including the conventional linear transversal equalizer, Volterra equalizers, and RBF network equalizers. Moreover, this gain in performance is obtained at a substantially lower computational cost.

Time-frequency and advanced frequency estimation techniques for the investigation of bat echolocation calls

This paper is based on a chapter of a new book on Machine Learning, by the first and third author, which is currently under preparation. We provide an overview of the major theoretical advances as well as the main trends in algorithmic developments in the area of sparsity-aware learning and compressed sensing. Both batch processing and online processing techniques are considered. A case study in the context of time-frequency analysis of signals is also presented. Our intent is to update this review from time to time, since this is a very hot research area with a momentum and speed that is sometimes difficult to follow up.