Kullback-Leibler Divergence-Guided Copula Statistics-Based Blind Source Separation of Dependent Signals

In this paper, we propose a blind source separation of a linear mixture of dependent sources based on copula statistics that measure the non-linear dependence between source component signals structured as copula density functions. The source signals are assumed to be stationary. The method minimizes the Kullback-Leibler divergence between the copula density functions of the estimated sources and of the dependency structure. The proposed method is applied to data obtained from the time-domain analysis of the classical 11-Bus 4-Machine system. Extensive simulation results demonstrate that the proposed method based on copula statistics converges faster and outperforms the state-of-the-art blind source separation method for dependent sources in terms of interference-to-signal ratio.Comment: Submitted to the ISGT NA 202

Parallelization Strategies for Modern Computing Platforms: Application to Illustrative Image Processing and Computer Vision Applications

RÉSUMÉ L’évolution spectaculaire des technologies dans le domaine du matériel et du logiciel a permis l’émergence des nouvelles plateformes parallèles très performantes. Ces plateformes ont marqué le début d’une nouvelle ère de la computation et il est préconisé qu’elles vont rester dans le domaine pour une bonne période de temps. Elles sont présentes déjà dans le domaine du calcul de haute performance (en anglais HPC, High Performance Computer) ainsi que dans le domaine des systèmes embarqués. Récemment, dans ces domaines le concept de calcul hétérogène a été adopté pour atteindre des performances élevées. Ainsi, plusieurs types de processeurs sont utilisés, dont les plus populaires sont les unités centrales de traitement ou CPU (de l’anglais Central Processing Unit) et les processeurs graphiques ou GPU (de l’anglais Graphics Processing Units). La programmation efficace pour ces nouvelles plateformes parallèles amène actuellement non seulement des opportunités mais aussi des défis importants pour les concepteurs. Par conséquent, l’industrie a besoin de l’appui de la communauté de recherche pour assurer le succès de ce nouveau changement de paradigme vers le calcul parallèle. Trois défis principaux présents pour les processeurs GPU massivement parallèles (ou “many-cores”) ainsi que pour les processeurs CPU multi-coeurs sont: (1) la sélection de la meilleure plateforme parallèle pour une application donnée, (2) la sélection de la meilleure stratégie de parallèlisation et (3) le réglage minutieux des performances (ou en anglais performance tuning) pour mieux exploiter les plateformes existantes. Dans ce contexte, l’objectif global de notre projet de recherche est de définir de nouvelles solutions pour aider à la programmation efficace des applications complexes sur les plateformes parallèles modernes. Les principales contributions à la recherche sont: 1. L’évaluation de l’efficacité d’accélération pour plusieurs plateformes parallèles, dans le cas des applications de calcul intensif. 2. Une analyse quantitative des stratégies de parallèlisation et implantation sur les plateformes à base de processeurs CPU multi-cœur ainsi que pour les plateformes à base de processeurs GPU massivement parallèles. 3. La définition et la mise en place d’une approche de réglage de performances (en Anglais performance tuning) pour les plateformes parallèles. Les contributions proposées ont été validées en utilisant des applications réelles illustratives et un ensemble varié de plateformes parallèles modernes.----------ABSTRACT With the technology improvement for both hardware and software, parallel platforms started a new computing era and they are here to stay. Parallel platforms may be found in High Performance Computers (HPC) or embedded computers. Recently, both HPC and embedded computers are moving toward heterogeneous computing platforms. They are employing both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) to achieve the highest performance. Programming efficiently for parallel platforms brings new opportunities but also several challenges. Therefore, industry needs help from the research community to succeed in its recent dramatic shift to parallel computing. Parallel programing presents several major challenges. These challenges are equally present whether one programs on a many-core GPU or on a multi-core CPU. Three of the main challenges are: (1) Finding the best platform providing the required acceleration (2) Select the best parallelization strategy (3) Performance tuning to efficiently leverage the parallel platforms. In this context, the overall objective of our research is to propose a new solution helping designers to efficiently program complex applications on modern parallel architectures. The contributions of this thesis are: 1. The evaluation of the efficiency of several target parallel platforms to speedup compute-intensive applications. 2. The quantitative analysis for parallelization and implementation strategies on multicore CPUs and many-core GPUs. 3. The definition and implementation of a new performance tuning framework for heterogeneous parallel platforms. The contributions were validated using real computation intensive applications and modern parallel platform based on multi-core CPU and many-core GPU

Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas

The present study investigated coastal fish assemblages, using Underwater Visual Census (UVC) transects, in Tunisia (south Mediterranean basin). The rationale behind this work is to get i) a suggestive evidence about the status of fish assemblages, and ii) baseline data at 3 locations in Tunisia where 3 MPAs will be established, before the implementation of protection measures. At each location, we used a sampling design where fish censuses were performed in two types of zone: zones that will be inside MPAs, and zones that will remain outside. On the whole, 49 taxa belonging to 19 families were censused. Data reveal clear symptoms of overfishing, especially in terms of dominance of small- and medium-sized individuals of commercially relevant species. Our analyses, moreover, did not show any significant difference in whole fish assemblage structures (considering both density and biomass), patterns of average species richness, total fish density and biomass, density and biomass of different trophic categories of fishes, size distribution of commercially relevant species, between future-protected and unprotected zones. Overall, results suggest that 1) current fish assemblages at the three studied locations are likely to be seriously impacted by fishing activities, and 2) these data could be used as reliable baselines to assess the effectiveness of protection measures within the MPAs that will be established in the future. Our study is the first in Tunisia, and in North African coasts, that assessed distribution patterns of coastal fish assemblages by means of UVC, using a formal spatially replicated sampling design for resource management

Knowledge Guided Integration of Structured and Unstructured Data in Health Decision Process

Data in the health domain is continuously increasing. It is collected from several sources, has several formats and is characterized by its sensibility (protection of personal health data). These characteristics make the management and the expert interaction with the collected data, in order to facilitate decision-making in Health Information Systems (HIS) a challenging field. In this paper, we propose a Knowledge guided integration of structured and unstructured data for health decision process. The knowledge is represented by domain ontology, which allows the integration of structured and unstructured data, stored in NoSQL format. Our motivation is to combine the confirmed advantages of ontologies and NoSQL databases both in data integration and decision aided processes. The proposed ontology has been implemented and evaluated using quality metrics. The approach was evaluated and results show response time optimization, compared with traditional approaches, and improvement of data relevance

Millimeter-Wave Massive MU-MIMO Performance Analysis for Private Underground Mine Communications

In this article, a performance analysis of millimeter wave (mmWave) massive multiuser multiple-input and multiple-output (MU-MIMO) channel within an underground mine is performed. The analysis is based on channel measurements conducted at 28 GHz using a base station of 64 virtual antenna elements serving multiple users. Channel characteristics such as large-scale path loss, time dispersion, coherence bandwidth and sum-rate capacity are reported and evaluated. The results indicate that multislope path loss model is better suited for precise prediction of path loss across various propagation segments within the mining gallery. The time dispersion analysis reveals that the underground mine channel does not cause significant time dispersion, as 90% of the root-mean-square (rms) delay spreads are below 4 ns. In addition, it was found that the rms delay spread is not dependent on the propagation distance. The study on sum-rate capacity highlights the potential of employing massive MIMO technology to improve the channel’s spectral efficiency. The analysis reveals that the capacity, with eight active users, can reach up to 33.54 bit/s/Hz. The outcomes of this article offer valuable insights into the propagation properties of underground mine environment, which is characterized by rich-scattering and irregular topology