JOURNAL OF ADVANCES IN COMPUTER RESEARCH
Year:2012 | Volume:3 | Issue:4|Papers Count:8

In recent years, formal methods have been used as an important tool for performance evaluation and verification of a wide range of systems. In the view points of engineers and practitioners, however, there are still some major difficulties in using formal methods. In this paper, we introduce a new formal modeling language to fill the gaps between object-oriented programming languages (OOPLs) used by engineers and the formalisms used for evaluation and verification purposes. We propose the syntax and semantics of a new object-oriented modeling language for discrete-event systems called RayLang. We have designed the syntax of RayLang similar to OOPLs. In RayLang models, objects that are instantiated from classes, run concurrently and can communicate with each other by requesting services. Every object in RayLang models has some internal state variables and some service handlers for executing the requests of other objects. We have shown that Markovian RayLang models can be transformed into continuous-time Markov chains (CTMCs) and then can be solved by existing solution techniques. For modeling, discrete-event simulation and analytic solution of RayLang models, we have implemented these models in the PDETool framework.

Proper scheduling of tasks leads to optimum using of time and resources, in order to obtaining best result. One of the most important and complicated scheduling problems is open shop scheduling problem. There are jobs in open shop scheduling problem which should be processed by m machines. Purpose of scheduling open shop problem is attaining to a suitable order of processing jobs by specified machines so that make span can be minimized. Open shop scheduling problem has very large and complex solution space and so is one of NP-Problems. Till now, different algorithms have been presented for open shop scheduling problem. In this paper, we have used combined genetics algorithm as a strategy for solving scheduling open shop problem and compared proposed algorithm with DGA algorithm. Results show that the proposed algorithm has better effectiveness than DGA algorithm.

Image analysis is an image processing technique that aims to extract features or information from images. Image analysis in medicine has a special place because is a basis for disease diagnosis for physicians. Electrophoresis is a laboratory separating technique. Electrophoresis images are created during the electrophoresis process. Serum protein and hemoglobin electrophoresis test are the most common test in Laboratory. Each lane represents a patient's blood sample. Lane extraction is one of the most important steps in analyzing of electrophoresis images. This paper, proposed automatic lane extraction method based on Otsu thresholding and Savitzky-Golay smoothing. The results show the proposed method has 98% accurate.

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The suggested neural net can adjust the weights using a learning algorithm that based on the gradient descent method. The proposed method is illustrated by several examples with computer simulations.

At present, there is no conventional scientific method to evaluate the area of the burnt regions of forests and in this field, the related organizations use different methods and variables. Also, the speed in performing the processes of area computing and damage evaluation, especially in the extensive damaged forest regions is very slow, consequently, the expression of results takes more time. Now, one of the most important parameters to evaluate the forests fire damages is computing the area of the burnt forest regions. In this research, an aerial digital image from a forest region after fire occurrence is used and the primary area of the burnt region is calculated in two methods of, integration and interconnected components extraction, by using image processing techniques. Also, in order to estimate the real area, a coefficient "m" is calculated to obtain real area based an measuring the forest land surface by exerting the imaging scale on the primary area. The methods which are introduced to calculate real area of the burnt regions, are dependent on some secondary tools such as aerial camera and special plane, but in the extensive forest regions damaged by fire, they cause to decrease in the evaluation costs and faster notification of the results.

The vehicle routing problem with simultaneous pickup and delivery (VRPSPD) is a well-known combinatorial optimization problem which addresses provided service to a set of customers using a homogeneous fleet of capacitated vehicles. The objective is to minimize the distance traveled. The VRPSPD is an NP-hard combinatorial optimization problem. Therefore, practical large-scale instances of VRPSPD cannot be solved by exact solution methodologies within acceptable computational time. Our interest was therefore focused on meta-heuristic solution approaches. For this reason, a modified tabu search (PA) is proposed for solving the VRPSPD in this paper. T Computational results on several standard instances of VRPSPD show the efficiency of the PA compared with other meta-heuristic algorithms.

One of the most important items for better file system performance is efficient buffering of disk blocks in main memory. Efficient buffering helps to reduce the wide speed gap between main memory and hard disks. In this buffering system, the block replacement policy is one of the most important design decisions that determines which disk block should be replaced when the buffer is full. To overcome the problem of performance gap between hard disks and main memory, there have been lots of proposed policies. Most of these policies are the development of the Least-Recently-Used (LRU) and Least-Frequently-Used (LFU) models. WRP(Weighting Replacement Policy) is a replacement algorithm that its performance is better than the LRU and LFU. The most advantage of this model is it’s similarity to both LRU and LFU, which means it has the benefits of both. This research proposes a new block replacement Policy namely, DWRP (Developed Weighting Replacement Policy) which solves the problems of WRPalgorithm and retains its advantages.

Mobile Ad hoc networks (MANETs) are composed of mobile stations communicating through wireless links, without any fixed backbone support. In these networks, limited power energy supply, and frequent topology changes caused by node mobility, makes their routing a challenging problem. TORA is one of the routing protocols that successfully copes with the nodes’ mobility side effects, but it doesn’t take into the nodes’ energy level. To solve this problem, this paper employs Honey Bee Mating Optimization (HBMO) principles to present a new routing protocol, called HBMO-TORA algorithm. Simulation results show that HBMO-TORA outperforms TORA in terms of packet delivery ratio, network life time, system life time and end-to-end delay.