logo EDITE Khalil HACHICHA
Identité
Khalil HACHICHA
État académique
Thèse soutenue le 2005-12-02
Laboratoire: personnel permanent
Encadrement de thèses (depuis 2007)
0
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
edite:1332792378241
Intégration des modèles Markoviens aux codeurs vidéo MPEG4/H264: Développement et portage sur des systèmes embarqués à base de DSP/FPGA.
Editions universitaires europeennes 2011
978 953 307 181 7
Recent Advances on Video Coding
MJPEG2000 performances improvement by Markov models, intech 2011
edite:1332792454607
Empirical Method Based on Neural Networks for Analog Power Modeling
We introduce an empirical method for power consumption modeling of analog components at system level. The principal step of this method uses neural networks to approximate the mathematical curve of the power consumption as a function of the inputs and parameters of the analog component. For a node of a wireless sensors network, we found an average error of 1.53% with a maximum error of 3.06% between our estimation and the measured power consumption. This novel method is suitable for Platform-Based Design and has three key features for architecture exploration purposes. Firstly, the method is generic as it can be applied to any analog component in any modeling and simulation environment. Secondly, the method is suitable for the total (analog and digital) power consumption estimation of a heterogeneous system. Thirdly, the method provides an online estimation of the instantaneous power consumption of analog blocks.
Vol. 29, No. 5, pp. 839-844 2010
edite:1332792472699
Markov-MJPEG2000 pour la surveillance vidéo sur des réseaux de capteurs sans fils
Vol. 27, No. 6 2010
978 1 4244 6338 1
Temperature and Supply Voltage Aware Power Modeling of Analog Functions at System Level
Nowadays a system level estimation method of power consumption for heterogeneous systems is a major concern. In this article, we introduce an empirical method for power consumption modeling of analog components at system level. The principal step of this method uses neural networks to approximate the mathematical curve of the power consumption as a function of the inputs, supply voltage and ambient temperature of an analog component. For an amplifier, we found an average error of 4.72% between our high level estimation and PSPICE power consumption results. This novel method is suitable for IP-based design and has three key features. Firstly, the method provides an online estimation of the instantaneous power consumption of analog blocks. Secondly, the method is generic as it can be applied to any analog component in any modeling and simulation environment. Thirdly, the method is suitable for the total (analog and digital) power consumption estimation of a heterogeneous system.
DTIS: IEEE International Conference on Design & Test of Integrated Systems in Nanoscale Technology, Hammamet, Tunisia 2010
edite:1332792524961
Accelerating the multiple reference frames compensation in the H.264 video coder
Vol. 4, No. 1 2009