Time-Frequency Ridge Analysis Based on the Reassignment Vector

International audienceThis paper considers the problem of detecting and estimating AM/FM components in the time-frequency plane. It introduces a new algorithm to estimate the ridges corresponding to the instantaneous frequencies of the components, and to segment the time-frequency plane into different `basins of attraction', each basin corresponding to one mode. The technique is based on the structure of the reassignment vector, which is commonly used for sharpening time-frequency representations. Compared with previous approaches, this new method does not need extra parameters, exhibits less sensitivity to the choice of the window and shows better reconstruction performance. Its effectiveness is demonstrated on simulated and real datasets

Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

The role of the image phase in cardiac strain imaging

International audienceThis paper reviews our most recent contributions in the field of cardiac deformation imaging, which includes a motion estimation framework based on the conservation of the image phase over time and an open pipeline to benchmark algorithms for cardiac strain imaging in 2D and 3D ultrasound. The paper also shows an original evaluation of the proposed motion estimation technique based on the new benchmarking pipeline

Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio

Decentralized Clustering and Linking by Networked Agents

We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy. We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter. While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance. Simulation results illustrate the performance of the proposed method in comparison to other useful techniques

Ultrasound compressive deconvolution with lp-norm prior

International audienceIt has been recently shown that compressive sampling is an interesting perspective for fast ultrasound imaging. This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image resolution improvement. The main contribution of this work is to apply the framework of compressive deconvolution on ultrasound imaging and to propose a novel ℓp-norm (1 ≤ p ≤ 2) algorithm based on Alternating Direction Method of Multipliers. The performance of the proposed algorithm is tested on simulated data and compared with those obtained by a more intuitive sequential compressive deconvolution method

Proximal Multitask Learning over Networks with Sparsity-inducing Coregularization

In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entries. We propose a fully distributed algorithm for solving this problem. The approach relies on minimizing a global mean-square error criterion regularized by non-differentiable terms to promote cooperation among neighboring clusters. A general diffusion forward-backward splitting strategy is introduced. Then, it is specialized to the case of sparsity promoting regularizers. A closed-form expression for the proximal operator of a weighted sum of $\ell_1$-norms is derived to achieve higher efficiency. We also provide conditions on the step-sizes that ensure convergence of the algorithm in the mean and mean-square error sense. Simulations are conducted to illustrate the effectiveness of the strategy

Symmetrical EEG-FMRI Imaging by Sparse Regularization

International audienceThis work considers the problem of brain imaging using simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). To this end, we introduce a linear coupling model that links the electrical EEG signal to the hemodynamic response from the blood-oxygen level dependent (BOLD) signal. Both modalities are then symmetrically integrated, to achieve a high resolution in time and space while allowing some robustness against potential decoupling of the BOLD effect. The novelty of the approach consists in expressing the joint imaging problem as a linear inverse problem, which is addressed using sparse regularization. We consider several sparsity-enforcing penalties, which naturally reflect the fact that only few areas of the brain are activated at a certain time, and allow for a fast optimization through proximal algorithms. The significance of the method and the effectiveness of the algorithms are demonstrated through numerical investigations on a spherical head model

Applications of polynomial eigenvalue decomposition to multichannel broadband signal processing : part 2: eigenvalue decomposition

Multichannel broadband signals arise at the core of many essential military technologies such as radar, sonar and communications, and commercial applications like telecommunications, speech processing, healthcare monitoring and seismic surveillance. The success of these applications often depends on the performance of signal processing tasks such as source localization, channel coding, signal enhancement, and source separation. U n multichannel broadband arrays or convolutively mixed signals, the array signals are generally correlated in time across different sensors. Therefore, the time delays for broadband signals cannot be represented by phase shift alone but need to be explicitly modelled. The relative time shifts are captured using the polynomial space-time covariance matrix, where decorrelation over a range of time shifts can be achieved using a polynomial EVD (PEVD). This tutorial is dedicated to recent developments in PEVD for multichannel broadband signal processing applications. We believe this tutorial and resources, such as code and demo webpages, will motivate and inspire many colleagues and aspiring PhD students working on broadband multichannel signal processing to try PEVD. The applications and demonstrations covered in this proposed tutorial include direction of arrival estimation, beamforming, source identification, weak transient detection, voice activity detection, speech enhancement, source separation and subband coding