Yskandar Hamam

Assignment of program modules to processors: A simulated annealing approach

Abstract A simulated annealing approach to the assignment of program modules to processors in a distributed computer system is presented. Modules of a program require certain capacitated computer resources. They also communicate at a given rate. Processors are interconnected by a communication network constituted of various types of links: local area network (LAN), wide area network (WAN) and specialised links. The communication resources are also capacitated. The purpose is to find the assignment of modules to processors such that a measure of performance is optimised, the requirements of each module are met and the capacities of the resources are not violated. Various versions of the problem are identified and formulated. The design of the simulated annealing algorithm to solve the most general version is then described. The results of computational experience are reported.

Two phase algorithm for load balancing in heterogeneous distributed systems

A fundamental issue affecting the performance of a parallel application running on a distributed system is the distribution of the workload over the various machines in the system. This problem is known to be NP-hard in most cases and therefore untractable as soon as the number of tasks and/or computers exceeds a few units. This paper first presents a mathematical model for load balancing problem. It then proposes an optimal, memory efficient, two phase algorithm for allocating program modules (tasks) onto processors of a heterogeneous distributed system to minimize the makespan (i.e., the completion time at the maximum loaded processor). The algorithm first finds a near optimal allocation by applying simulated annealing (SA) and then finds an optimal distribution by applying branch-and-bound (BB) technique considering the solution of SA as the initial solution. The proposed algorithm overcomes the low solutions quality that may be obtained by using heuristics. It also overcomes the computational time complexity of the exact algorithms. Some experimental results are given to show the effectiveness of the proposed algorithm.

Optimal trajectory planning of manipulators with collision detection and avoidance

This article presents an optimal trajectory-planning method for robot manipulators with collision detection and avoidance. The obstacles and robot segments are represented by a set of convex polyhedra. The collision detection is performed at each discretized robot configuration by an efficient procedure devel oped with the computational geometry method, which computes a distance function of the robot segments and the obstacles. By introducing this function for specifying the collision-free con straint, the path-planning problem is formulated as an optimal control problem using the augmented Lagrangian, which may be considered as a combination of the duality, penalty and con straint relaxation methods. The problem is solved by a robust UZAWA-like algorithm, where a subgradient method is applied for the primal optimization, as the distance function is not ev erywhere differentiable. An example is given for the trajectory planning of a robot arm with three revolute joints.

Using hidden Markov models for sleep disordered breathing identification

Abstract In this work, an automatic diagnosis system based on hidden Markov models (HMMs) is proposed to help clinicians in the diagnosis of sleep apnea syndrome. Our system offers the advantage of being based on solid probabilistic principles rather than a predefined set of rules. Conventional and new simulated annealing based methods for the training of HMMs are incorporated. The inference method of this system translates parameter values into interpretations of physiological and pathophysiological states. The interpretation is extended to sequences of states in time to obtain a state-space trajectory. Some of the measurements of the respiratory activity issued by the technique of polysomnography (brain activity, respiratory activity, oxygen levels, and cardiac activity) are considered for off-line and on-line detection of the different sleep apnea syndromes: obstructive, central and hypopnea. Experimental results using respiratory clinical data and some future perspectives of our work are presented.

Simulated annealing for fuzzy controller optimization: principles and applications

In this paper we propose a method for the optimization of a fuzzy logic controller, based on simulated annealing. The main feature of this method is to provide efficient solutions independently of the choice of the controller structure. The method is applied for fuzzy control of a thermal process.

Convex Hull in Medical Simulations: A New Hybrid Approach

Nowadays, virtual reality techniques have become widely used in different fields such as medical and architecture. Since a real object does not have a deterministic shape, it is impossible to define a geometric equation to model it. Thus, alternative approaches are the convex hull algorithms to form the convex envelopes of any object and to mimic realistic environment with exact collision detection between objects in the virtual world. In this paper, a hybrid approach to generate the convex hull is developed and presented. The new algorithm is validated by performing a comparison with three conventional algorithms namely the brute force, the gift wrapping and the QuickHull algorithms. The evaluation is achieved by generating the convex envelope of 3D wrist and knee bones using the four different approaches. The results show the improvement associated with the proposed approach

P300 based brain-computer interface using Hidden Markov Models

This paper reports on preliminary work on the use of hidden Markov models (HMMs) approach for tasks classification in P300-based brain-computer interface (BCI) system. Every HMM is trained on a set of electroencephalogram (EEG) records issued from different sessions corresponding to the same task. The HMMs that has been built take into account the variability of EEGs during different sessions. Based on Bayesian inference criterion (BIC), the proposed HMM training algorithm is able to select the optimal number of states corresponding to each set of EEG training records. For every state number, each iteration is initialized by the most appropriate model using data clustering, and by the rejection of the least probable state of the previous iteration. Consequently, every training iteration begin by a more precise model. We report training procedures and validation results of the models. The obtained results give a correct and promising classification rates for all subjects which is the objective of this work.

Computer-Based Training System for Simulating Wrist Arthroscopy

The minimally invasive approach of arthroscopy means less pain and faster recovery time for patients compared to open surgery. However, it implies a high difficulty of performance. In this paper, a functional prototype of a computer-based training simulator for wrist arthroscopy is introduced. A 3-D virtual representation of the bones constituting the wrist of a patient is shown. Objects are modeled using the convex hull approaches and an algorithm to simulate real time collision detection during the training on the operation is presented. In addition, a force feedback device is used as a haptic interface with the computer simulation system. This leads in the development of a low cost system that is used by trainees with the same benefits as professional devices. In this regard, the wrist arthroscopy can be simulated and medical students can learn the basic skills required with safety, flexibility and less cost.

Collision Detection in Computer Simulations for Wrist Arthroscopic Surgery Training

Computer-Based surgical simulators are one of the most recent topics in virtual reality development. They have become the training method and the tool to acquire valuable information for many medical students and medical practitioners. The real-time interactive collision detection is an important problem that must be addressed to make such simulators more realistic. In this regard, this paper addresses the issue of precise collision detection between virtual objects and proposes a new technique. First, the convex hull of each object is constructed. Then, the problem is formulated and a linear programming solution is obtained to determine whether a collision exists or not. The algorithm is tested on a medical application. The proposed collision detection approach is compared with two conventional algorithms namely the IVRI-CD and SWIFT techniques and validated using a 3D wrist model. A Haptic feedback system is implemented for evaluation purposes. The results show that the proposed approach is efficient, accurate, fast and robust in detecting collision between virtual objects during training and experimenting surgery.

An integrated environment for hidden Markov models-a Scilab toolbox

A hidden Markov model toolbox is presented within the Scilab environment. In this toolbox popular methods for the resolution of HMM problems are incorporated. These methods cover the training and recognition phases. Models may be used with discret and continuous observations. This toolbox includes conventional methods as well as extensions.