Fast Multidimensional Entropy Estimation by k-d Partitioning

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An Artificial Immune System Strategy for Robust Chemical Spectra Classification via Distributed Heterogeneous Sensors

The timely detection and classification of chemical and biological agents in a wartime environment is a critical component of force protection in hostile areas. Moreover, the possibility of toxic agent use in heavily populated civilian areas has risen dramatically in recent months. This thesis effort proposes a strategy for identifying such agents vis distributed sensors in an Artificial Immune System (AIS) network. The system may be used to complement electronic nose ( E-nose ) research being conducted in part by the Air Force Research Laboratory Sensors Directorate. In addition, the proposed strategy may facilitate fulfillment of a recent mandate by the President of the United States to the Office of Homeland Defense for the provision of a system that protects civilian populations from chemical and biological agents. The proposed system is composed of networked sensors and nodes, communicating via wireless or wired connections. Measurements are continually taken via dispersed, redundant, and heterogeneous sensors strategically placed in high threat areas. These sensors continually measure and classify air or liquid samples, alerting personnel when toxic agents are detected. Detection is based upon the Biological Immune System (BIS) model of antigens and antibodies, and alerts are generated when a measured sample is determined to be a valid toxic agent (antigen). Agent signatures (antibodies) are continually distributed throughout the system to adapt to changes in the environment or to new antigens. Antibody features are determined via data mining techniques in order to improve system performance and classification capabilities. Genetic algorithms (GAs) are critical part of the process, namely in antibody generation and feature subset selection calculations. Demonstrated results validate the utility of the proposed distributed AIS model for robust chemical spectra recognition

The impact of cache misses on the performance of matrix product algorithms on multicore platforms

The multicore revolution is underway, bringing new chips introducing more complex memory architectures. Classical algorithms must be revisited in order to take the hierarchical memory layout into account. In this paper, we aim at designing cache-aware algorithms that minimize the number of cache misses paid during the execution of the matrix product kernel on a multicore processor. We analytically show how to achieve the best possible tradeoff between shared and distributed caches. We implement and evaluate several algorithms on two multicore platforms, one equipped with one Xeon quadcore, and the second one enriched with a GPU. It turns out that the impact of cache misses is very different across both platforms, and we identify what are the main design parameters that lead to peak performance for each target hardware configuration.La révolution multi-coeur est en cours, qui voit l'arrivée de processeurs dotées d'une architecture mémoire complexe. Les algorithmes les plus classiques doivent être revisités pour prendre en compte la disposition hiérarchique de la mémoire. Dans ce rapport, nous étudions des algorithmes prenant en compte les caches de données qui minimisent le nombre de défauts de cache pendant l'exécution d'un produit de matrices sur un processeur multi-coeur. Nous montrons analytiquement comment obtenir le meilleur compromis entre les caches partagés et distribués. Nous proposons une implémentation pour évaluer ces algorithmes sur deux plates-formes multi-coeur, l'une équipé d'un processeur Xeon quadri-coeur, l'autre dotée d'un GPU. Il apparaît que l'impact des défauts de cache est très différent sur ces deux plates-formes, et nous identifions quels sont les principaux paramètres de conception qui conduisent aux performances maximales pour chacune de ces configurations matérielles

Real Time Parallel Implementation of a Particle Filter Based Visual Tracking

8ppWe describe the implementation of a 3D visual tracking al- gorithm on a cluster architecture.Parallelisation of the algorithm makes it possible to obtain real-time execution (more than 20 FPS) even with large state vectors, which has been proven difficult on sequential architecture. Thanks to a user-friendly software development environment, this large gain in performance is not obtained at the price of programmability

Computing the exact worst-case End-to-end delays in a Spacewire network using Timed Automata

National audienceSpacewire is a real-time communication network for use onboard satellites. It has been designed to transmit both payload and control/command data. To guarantee that communications respect the real-time constraints, designers use tools to compute the worst-case end-to-end delays. Among these tools, recursive flow analysis and Network Calculus approaches have been studied. This paper proposes to use the model-checking approach based on timed automata to compute the exact worstcase end-to-end delays and two case studies are presented

Query Optimization Techniques for OLAP Applications: An ORACLE versus MS-SQL Server Comparative Study

Query optimization in OLAP applications is a novel problem. A lot of research was introduced in the area of optimizing query performance, however great deal of research focused on OLTP applications rather than OLAP. In order to reach the output results OLAP queries extensively asks the database, inefficient processing of those queries will have its negative impact on the performance and may make the results useless. Techniques for optimizing queries include memory caching, indexing, hardware solutions, and physical database storage. Oracle and MS SQL Server both offer OLAP optimization techniques, the paper will review both packages’ approaches and then proposes a query optimization strategy for OLAP applications. The proposed strategy is based on use of the following four ingredients: 1- intermediate queries; 2- indexes both BTrees and Bitmaps; 3- memory cache (for the syntax of the query) and secondary storage cache (for the result data set); and 4- the physical database storage (i.e. binary storage model) accompanied by its hardware solution

Slack-Time Computation for Temporal Robustness in Embedded Systems

International audienceWe propose to handle execution duration overruns (temporal faults) in real-time embedded systems. When a temporal fault occurs, the slack time can be dynamically determined and assigned to the faulty task in order to complete its treatment. This mechanism improves the temporal robustness of real-time systems. We demonstrate that an approximate slack stealer algorithm like the MASS algorithm is a good solution for real-time embedded systems. We validate the feasibility of this approach by an implementation on the Lego Mindstorm NXT platform