انجمن کامپیوتر ایران

Optimal tasks allocation is one of the most important problems in multiprocessing. Optimal assignment of tasks to a multiprocessor is an NPhard problem in general cases, and precedence task graph makes it more complex. Many factors affect optimal tasks allocation. One of them is cache reload time in multiprocessor systems. These problems exist in real-time systems, too. Due to high sensitivity of ‘time’ in real-time systems, scheduling with respect to time constraints becomes very important. This paper proposes a suboptimal scheduler for hard realtime heterogeneous multiprocessor systems considering time constraints and cache reload time simultaneously, using multiobjective genetic algorithm. In addition, it tries to propose a generalized method for real-time multiobjective scheduling in multiprocessor systems using genetic algorithms.

The embedded systems usage in different applications is prevalent in recent years. These systems include a wide range of equipments from cell phones to medical instruments, which consist of hardware and software. In many examples of embedded systems, fault occurrence can lead to serious dangers in system behavior (for example in satellites). Therefore, we try to increase the fault tolerance feature in these systems. Therefore, we need some mechanisms that increase the robustness and reliability of such systems. These objects cause the on-line test to be a great concern. It is not important that these mechanisms work in which level (Hardware level, Software level or Firmware). The major concern is that how well these systems can provide debugging, test and verification features for the user regardless of their implementation levels. Background Debug Module is a real time tool for these features. In this paper we apply an innovative way to use the BDM tool for fault injection in an embedded system.

We address, in this paper, the problem of estimating the input sequence of a known, non-minimum phase, FIR system, when a large number of its roots are located near or on the unit circle. This issue cannot be solved by conventional methods known to date. Recently, algorithms based on spectral factorization are considered as possible solutions of inversing nonminimum phase systems but, these techniques cannot prohibit the instability of the systems whose roots are located on the unit circle. We propose an alternative method based on adaptive filtering resulted from a new point of view of the deconvolution problem that avoids inversing the system. The LMS adaptive filter is used to meet our objective while faster implementation than optimization-based techniques, be it gradient based or genetic, is achieved. Moreover, the technique is validated by experimental results, in simulated cases, which are mainly focused on large sequence of signals in noisy conditions.

In this paper, a fuzzy inference system by which reliability of features can be measured is designed. The reliability determines discriminative power of a feature in separating target from background. We focus our attention on design of membership functions. With a rational explanation on available information over a particle filter-base tracking process, we infer a coarse estimation of membership functions. It follows with a fine-tuning stage by using genetic algorithm. Color, edge, texture and TED are used in current work but the extension to a wider number of features is straightforward.

Particle Swarm Optimization (PSO) is an optimization method that is inspired by nature and is used frequently nowadays. In this paper we proposed a new dynamic geometric neighborhood based on Voronoi diagram in PSO. Voronoi diagram is a geometric naturalistic method to determine neighbors in a set of particles. It seems that in realistic swarm, particles take Voronoi neighbors into account. Also a comparison is made between the performance of some traditional methods for choosing neighbors and new dynamic geometric methods like Voronoi and dynamic Euclidean. In this comparison it is found that PSO with geometric neighborhood can achieve better accuracy overall especially when the optimum value is out of the initial range.

Vehicle tracking is an essential requirement of any vision based Intelligent Transportation System for extracting different traffic parameters, efficiently. Handling inter-object occlusion is the most challenging part of tracking as a process of finding and following interested objects in a sequence of video frames. In this paper we present a system, based on code-book background model for motion segmentation and Kalman filter for tracking with a new approach for occlusion. This approach separates occluded vehicles based on longest common substring of chain codes. We use this tracking system to estimate some traffic parameters. Experimental results show the efficiency of the method

The existing workflow modeling languages have some limitations and drawbacks. The aim is to introduce a new workflow modeling language based on stochastic activity networks (SANs). SANs are a powerful extension of Petri nets, which have been used in a wide range of applications. SAN-based workflow modeling language (SWML) has some high-level modeling primitives for easily modeling workflow patterns. For analysis of the modeled workflow systems with SWML, we have introduced the transformation techniques from SWML models into ordinary SANs. The transformed models can be analyzed using the existing tools for SANs, such as the Möbius modeling tool. In this paper, we present definitions of SWML modeling language and its primitives and an example of SWML models.