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Chicoutimi, Canada

Eddine Khodja D.,British Petroleum | Simard S.,UQAC | Beguenan R.,Royal Military College of Canada
Control Engineering and Applied Informatics | Year: 2015

In this paper, an approximation of sigmoid function in polynomial form has been proposed, then this function is optimized in order to implement that on FPGA using the Xilinx library. This implementation aim is to contribute in the hardware integration solutions in the areas such as monitoring, diagnosis, maintenance and control of power system as well as industrial processes. Since the Simulink library provided by Xilinx, has all the blocks that are necessary for the design of Artificial Neural Networks except a few functions such as sigmoid function. Tests results for control and diagnosis with FPGA Device are satisfactory.

Quilliot A.,CNRS Laboratory of Informatics, Modeling and Optimization of Systems | Rebaine D.,UQAC
2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014 | Year: 2014

We present here new results and algorithms for the Linear Arrangement Problem (LAP). We first propose a new lower bound, which links LAP with the Max Cut Problem, and derive a LIP model as well as a branch/bound algorithm for the general case. Then we focus on the case of interval graphs: we first show that our lower bound is tight for unit interval graphs, and derive an efficient polynomial time approximation algorithm for general interval graphs. © 2014 Polish Information Processing Society.

Moutacalli M.T.,UQAC | Bouzouane A.,UQAC | Bouchard B.,UQAC
8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Proceedings | Year: 2015

Activity recognition is the most challenging stage of technological assistance which offers automatic support, when needed, to elderly and disabled people such as Alzheimer's patients living in smart homes. Many approaches and techniques were proposed for activity recognition while other technological assistance stages were barely explored. In this paper, after presenting our activity recognition approach and explaining how the artificial agent will use it to decide when to intervene offering help, we use time series forecasting in order to better choose the intervention time. © 2015 ACM.

Moutacalli M.T.,UQAC | Bouzouane A.,UQAC | Bouchard B.,UQAC
Journal of Ambient Intelligence and Humanized Computing | Year: 2015

The many disadvantages of traditional assistance available to elderly and persons with cognitive dysfunction such as patients with Alzheimer’s disease have motivated the research of Technological assistance. The artificial agent, who will support the caregiver, is equipped with hardware and software resources that enable it to observe, analyze, infer and support, when needed, the assisted person. In this paper, we present the various stages of Technological assistance and propose a new algorithm for the step of activities models detection. We also explore an activity prediction step using time series. The experiments were conducted on real data recorded at LIARA smart home and the results are satisfactory. © 2015, Springer-Verlag Berlin Heidelberg.

Moutacalli M.T.,UQAC | Bouzouane A.,UQAC | Bouchard B.,UQAC
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings | Year: 2015

When extracting frequent patterns, usually, the events order is either ignored or handled with a simple precedence relation between instants. In this paper we propose an algorithm applicable when perfect order, between events, must be respected. Not only it estimates delay between two adjacent events, but its first part allows non temporal algorithms to work on temporal databases and reduces the complexity of dealing with temporal data for the others. The algorithm has been implemented to address the problem of activities models creation, the first step in activity recognition process, from sensors history log recorded in a smart home. Experiments, on synthetic data and on real smart home sensors log, have proven the algorithm effectiveness in detecting all frequent activities in an efficient time. © 2014 IEEE.

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