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Multi-Bias Model for Power Diode Using a Very High Description Language

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Energy and Power Engineering 2010年第3期2卷 196-202页

作者： hassene mnif Montassar Najari Hekmet Samet Nouri Masmoudi 不详

This study focused on the determination and analysis of an accurate analytical model for PIN diode under different bias conditions. This approach employs analytically derived expressions including the variation of the depletion regions in the device to make the used model available over a wide range of testing conditions without remake the parameters extraction procedure. The validity of the proposed extraction procedure has been verified by the very good agreement between simulated and measured current and voltage waveforms reverse recovery at different range of the operation conditions. The model is developed and simulated with the VHDL-AMS language under Ansoft Simplorer®Environment.

关键词： Power PIN Diode VHDL-AMS Modeling High Injection Parameter Extraction

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

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The Journal of Biomedical Research 2020年第3期34卷 151-161页

作者： Itaf Ben Slimen Larbi Boubchir Zouhair Mbarki hassene Seddik Centre de Recherche et de Production Research Lab. Ecole Nationale Superieure des Ingenieurs de TunisUniversity of TunisTunis 1008Tunisia Laboratoire d^nfbrmatique Avancee de Saint-Denis Research Lab. University of Paris 8Saint-DenisCedex 93526France

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection *** paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these *** proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping *** results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

关键词： electroencephalography epileptic seizure detection feature extraction dual-tree complex wavelet transform machine learning

Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states

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The Journal of Biomedical Research 2020年第3期34卷 162-169页

作者： Itaf Ben Slimen Larbi Boubchir hassene Seddik Centre de Recherche et de Production Research Lab. Ecole Nationale Superieure des Ingenieurs de TunisUniversity of TunisTunis 1008Tunisia Laboratoire d'Informatique Avancee de Saint-Denis Research Lab. University of Paris 8Saint-DenisCedex 93526France

Epileptic seizures are known for their unpredictable ***,recent research provides that the transition to seizure event is not random but the result of evidence ***,a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic *** periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes,spikes,and the *** this study,spike rate is used as the indicator to anticipate seizures in electroencephalogram(EEG) *** detection step is used in EEG signal during interictal,preictal,and ictal periods followed by a mean filter to smooth the spike *** maximum spike rate in interictal periods is used as an indicator to predict *** the spike number in the preictal period exceeds the threshold,an alarm is *** the CHB-MIT database,the proposed approach has ensured92% accuracy in seizure prediction for all patients.

关键词： electroencephalogram epilepsy seizure prediction spikes detection

International Financial Market＇s Integration and Modelling Returns of Risky Assets

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Journal of Modern Accounting and Auditing 2012年第7期8卷 1042-1051页

作者： Ben M'Barek hassene University of Tunis Tunis Tunisia

The aim of this paper is to test the ability of conditional and unconditional capital asset pricing models （CAPMs） and to explain emerging markets returns in terms of their integration into the international market. The authors use data on five developed countries and five emerging countries as well as data on the Tunis Stock Exchange （TSE） after the reforms. The results show that the correlations between emerging markets returns and developed markets returns are very low and sometimes negative. Conditional arbitrage pricing theory （APT） as well as conditional CAPM has low predictive power for emerging markets than that for developed markets. Finally, following the financial reforms, Tunisian financial markets have became more and more integrated into the international market （excess returns and unconditional beta consistent with predictions）. However, conditional APT does not accurately explain Tunisian market returns. This study confirms the unavailability of an accurate modelling technique of the TSE structure.

关键词： conditional unconditional capital asset pricing model (CAPM) conditional arbitrage pricing theory(APT) returns

End-to-End Multiview Gesture Recognition for Autonomous Car Parking System

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Instrumentation 2019年第3期6卷 76-92页

作者： hassene Ben AMARA Fakhri KARRAY Centre for Pattern Analysis and Machine Intelligence(CPAMI) Electrical and Computer EngineeringThe University of Waterloo

The use of hand gestures can be the most intuitive human-machine interaction *** early approaches for hand gesture recognition used device-based *** methods use mechanical or optical sensors attached to a glove or markers,which hinder the natural human-machine *** the other hand,vision-based methods are less restrictive and allow for a more spontaneous communication without the need of an intermediary between human and ***,vision gesture recognition has been a popular area of research for the past thirty *** gesture recognition finds its application in many areas,particularly the automotive industry where advanced automotive human-machine interface(HMI)designers are using gesture recognition to improve driver and vehicle ***,technology advances go beyond active/passive safety and into convenience and *** this context,one of America’s big three automakers has partnered with the Centre of Pattern Analysis and Machine Intelligence(CPAMI)at the University of Waterloo to investigate expanding their product segment through machine learning to provide an increased driver convenience and comfort with the particular application of hand gesture recognition for autonomous car *** present paper leverages the state-of-the-art deep learning and optimization techniques to develop a vision-based multiview dynamic hand gesture recognizer for a self-parking *** propose a 3D-CNN gesture model architecture that we train on a publicly available hand gesture *** apply transfer learning methods to fine-tune the pre-trained gesture model on custom-made data,which significantly improves the proposed system performance in a real world *** adapt the architecture of end-to-end solution to expand the state-of-the-art video classifier from a single image as input(fed by monocular camera)to a Multiview 360 feed,offered by a six cameras ***,we optimize the proposed solution to work on a

关键词： Deep Learning Video Classification Dynamic Hand Gesture Recognition Multiview Embedded Platform Automotive Vehicle Self-Parking

Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

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Journal on Artificial Intelligence 2022年第4期4卷 201-214页

作者： Slim Ben Chaabane Rafika Harrabi Anas Bushnag hassene Seddik Computer Engineering Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk47512Saudi Arabia Department of Electrical Engineering University of TunisCEREPENSIT 5 AvTaha Hussein1008TunisTunisia

Biometrics represents the technology for measuring the characteristics of the human *** authentication currently allows for secure,easy,and fast access by recognizing a person based on facial,voice,and fingerprint *** authentication is one of the essential biometric methods for identifying a *** authentication type has become popular in research and practical *** the face and hands,the iris is an internal organ,protected and therefore less likely to be ***,the number of helpful information collected from the iris is much greater than the other biometric human *** work proposes a new iris identification model based on a multilevel thresholding technique and modified Fuzzy cmeans *** multilevel thresholding technique extracts the iris from its surroundings,such as specular reflections,eyelashes,pupils,and *** the other hand,the modified Fuzzy c-means is used to combine and classify the most useful statistical features to maximize the accuracy of the collected ***,having the most optimal iris *** proposed model results are validated using True Success Rate(TSR)and compared to other existing *** results show how effective the combination of the two stages of the proposed model is:the Otsu method and modified Fuzzy c-means for the 400 tested images representing 40 people.

关键词： Biometric authentication recognition iris recognition statistical features feature extraction fuzzy c-means TSR sensitivity classification