Improved time quantum length estimation for round robin scheduling algorithm using neural network

In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of contexts switching. In this paper we propose a RR scheduling algorithm based on Neural Network Models for predicting the optimal quantum length which lead to a minimum average turnaround time. The quantum length depends on tasks burst times available in the ready queue. Rather than conventional traditional methods using fixed quantum length, this one giving better results by minimizing the average turnaround time for almost any set of jobs in the ready queue

Priority based round robin (PBRR) CPU scheduling algorithm

This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm cannot be dedicated to real time operating system due to their large waiting time, large response time, large turnaround time and less throughput. This new algorithm improves all the drawbacks of round robin CPU scheduling algorithm. In addition, this paper presents analysis comparing proposed algorithm with existing round robin scheduling algorithm focusing on average waiting time and average turnaround time

TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot

Etude et implémentation de descripteurs de contenu AV pour les applications multimedia temps réel

Les travaux présentés dans cette thèse constituent une contribution à la conception de systèmes électroniques embarqués dédiés aux applications multimédia temps réel. Ils rentrent dans le cadre de la méthodologie de conception de nouvelles architectures matérielles et/ou logicielles dédiées à l'analyse et à la description de contenu audiovisuel. Dans cette thèse nous nous sommes intéressés, dans une première phase, à la validation et l'optimisation d'algorithmes de détection de changement de plans vidéo et à l'extraction d'informations sémantiques de haut niveau à partir de descripteurs audiovisuels de bas niveau. A la suite de cette étape, nous présentons les différentes solutions d'implémentation matérielles et/ou logicielles relatives aux détecteurs de cut et de fondu à différents niveaux d'abstraction (logique, RTL et de haut niveau basé plateforme). Dans la dernière étape de cette thèse un modèle d'architecture générique dédiée à l'analyse et à la description de contenu audiovisuel a été proposé. La transposition de ce modèle sur des systèmes embarqués est devenue possible grâce à l'évolution des FPGAs récemment commercialisés et aux nouveaux outils et méthodologies introduits dans la conception des systèmes sur puce programmable (SOPC).The works presented in this thesis contribute to the design of embedded electronic systems which are dedicated for real time multimedia applications. They fall within the framework of design methodology of the new hardware and/or software architecture used for analysis and description of audiovisual content. In this thesis we are first interested in the validation and optimization of shot boundary detection algorithms and in the extraction of high level semantic information using low level audiovisual descriptors. After that, we present the solutions of hardware and/or software implementation related to cut and dissolve detectors at different abstraction levels (logic, RTL and high level based platform). In the last part of this thesis, we propose a generic architecture template for audiovisual content analysis and description. The transposition of this template on embedded systems became possible with the evolution of recently marketed FPGA and the new tools and methodology used on system on programmable chip (SOPC).DIJON-BU Sciences Economie (212312102) / SudocSudocFranceF