Wail S. Elkilani

A new petri net modeling technique for the performance analysis of discrete event dynamic systems

An interesting modeling problem is the need to model one or more of the system modules without exposition to the other system modules. This modeling problem arises due to our interest in these modules or incomplete knowledge, or inherent complexity, of the rest of the system modules. Whenever the performance measures (one or more) of the desired modules are available through previous performance studies, data sheets, or previous experimental works, the required performance measures for the whole system can be predicted from our proposed modeling technique. The incomplete knowledge problem of the dynamic behavior of some system modules has been studied by control theory. In the control area, such systems are known as partially observed discrete event dynamic systems, or POS systems. To the best of our knowledge, the performance evaluation of the POS system has not been addressed by the Petri net theory yet. Therefore, in this paper, we propose a new modeling technique for solving this kind of problem based on using the Petri net theory (i.e. Stochastic Reward Nets (SRNs)) in conjunction with the optimal control theory. In this technique, we develop an SRN Equivalent Model (EM) for the modeled system. The SRN EM-model consists of two main nets and their interface nets. One of the main nets represents the part(s) of interest or the known part(s) of the overall POS system that allows us to model its dynamic behavior and evaluate its performance measures. The other main net represents the remaining part(s) of the overall POS system that feeds the part(s) of interest. The well-known maximum principles have been used to develop an algorithm for determining the unknown transition rates of the proposed model. Numerical simulations are given to show that the proposed approach is more effective than the conventional modeling techniques, especially when dealing with systems having a large number of states.

Arabic character recognition using statistical and geometric moment features

Feature extraction is one of the most important steps in character recognition system as each character has different features that distinguish it from other characters. Choosing good features is an important factor that affects recognition process. This paper proposed a novel and effective procedure for recognizing Arabic characters using a combination of statistical features and geometric moment features which are independent of the font and size of the character. These features are used by backpropagation neural network to classify the characters. Recognition rate of 97% is achieved using 6 different fonts.

A cost efficient location management technique for mobile users with frequently visited locations

Managing location information of mobile terminals is an important issue in mobile computing systems. Unfortunately, current standards of location management schemes, such as GSM and IS-41, behave inefficiently for mobile terminals moving between the same set of areas, or users tending to call and move in a fixed pattern. This is in turn results in a high location management cost. Several approaches have been proposed to tackle with this problem. One of the most efficient techniques is to replicate user profile according to the most frequently visited location areas. In this paper, we propose a new location management scheme based on replicated databases utilizing mobile stations to capture frequently visited location areas. We compare the performance of our scheme with that of GSM and other replicated databases location management schemes in respect of communication cost, database cost, and total cost. Simulation results show that the proposed scheme performs better than other schemes

A New Petri Net Modeling Technique for the Performance Analysis of Discrete Event Dynamic Systems

An interesting modeling problem is the need to model one or more of the system modules without exposition to the other system modules. This modeling problem arises due to our interest in these modules or incomplete knowledge, or inherent complexity, of the rest of the system modules. Whenever the performance measures (one or more) of the desired modules are available through previous performance studies, data sheets, or previous experimental works, the required performance measures for the whole system can be predicted from our proposed modeling technique. The incomplete knowledge problem of the dynamic behavior of some system modules has been studied by control theory. In the control area, such systems are known as partially observed discrete event dynamic systems, or POS systems. To the best of our knowledge, the performance evaluation of the POS system has not been addressed by the Petri net theory yet. Therefore, in this paper, we propose a new modeling technique for solving this kind of problem based on using the Petri net theory (i.e. Stochastic Reward Nets (SRNs)) in conjunction with the optimal control theory. In this technique, we develop an SRN Equivalent Model (EM) for the modeled system. The SRN EM-model consists of two main nets and their interface nets. One of the main nets represents the part(s) of interest or the known part(s) of the overall POS system that allows us to model its dynamic behavior and evaluate its performance measures. The other main net represents the remaining part(s) of the overall POS system that feeds the part(s) of interest. The well-known maximum principles have been used to develop an algorithm for determining the unknown transition rates of the proposed model. Numerical simulations are given to show that the proposed approach is more effective than the conventional modeling techniques, especially when dealing with systems having a large number of states. Keywords: Discrete event dynamic systems; stochastic reward nets; largeness problem; parameter identification; partially observed systems.

Improving TCP performance over Mobile Ad Hoc Networks using an adaptive backoff response approach

Mobile Ad Hoc Network (MANET) suffers from temporary link failures and route changes. Moreover, TCP performs poorly when most packet losses are due to congestion. Most of research performed for improving TCP performance over MANET requires feedback from lower layers. Several attempts have been proposed for a layered TCP improvement. Yet, their percentage enhancements are not satisfactory. In this paper, we explore a new approach to improve TCP performance using a TCP layered approach. The proposed methodology depends on beginning transmission as soon as a failed route is reestablished. It utilizes an adaptive back-off response strategy through which congestion window and slow start threshold values are decreased when an acknowledgement is received. The proposed technique does not require feedback from the network layer. Simulation results showed that this approach had achieved an average performance improvement of 17%.

A behavior and delay equivalent petri net model for performance evaluation of communication protocols

Performance evaluation is required at every stage in the life cycle of a network protocol. Analytical modeling is the method of choice for fast and cost effective evaluation of a network protocol. One of the most efficient high-level formalisms used for analytical modeling of network protocols is stochastic petri nets (SPNs). Yet, complexity of nowadays network protocols, which results in the state space explosion of the underlying Markov chain (MC) model, has hindered the wide use of SPNs in the analysis of these protocols. Decomposition techniques are considered one of the main methodologies used for the approximate solution of the state explosion problem. Unfortunately, most of these techniques either require special form of the model or give insufficient accuracy of the solution. Moreover, small rates of communication protocols have caused additional overhead on any proposed technique for approximate evaluation of these protocols. This paper presents a new decomposition technique called behavior and delay equivalent block (BDEB) technique. This technique overcomes most of drawbacks of other techniques proposed in the literature. It introduces a new aggregation method that depends on using a new type of transitions called intelligent transition. The proposed technique adopts the delay equivalent criteria but in a new philosophy different from other techniques using the same criteria. The new technique is explained by applying it to an illustrative example and a scalable model of a courier protocol.

A texture recognition coloring technique for natural gray images

Gray image coloring is utilized to increase the visual appeal of images such as old black and white photos, movies or scientific illustrations. Most of authors working in coloring of gray scale images have used primitive methods for coloring which are both inaccurate and limited. In this paper we propose a new technique for computer coloring gray scale images. This technique works for texture based images like natural scenes. Its based on segmenting the image into different regions according to their textures. Real colors of the image can then be obtained by classifying these textures to predefined texture classes. Recognition of these textures is performed by matching these textures with a training set stored in a special database. We validate the efficiency of our coloring system by coloring several sets of natural gray images with high quality real colors.

A new disk-based technique for solving the largeness problem of stochastic modeling formalisms

Stochastic modeling formalisms such as stochastic Petri nets, generalized stochastic Petri nets, and stochastic reward nets can be used to model and evaluate the dynamic behavior of realistic computer systems. Once we translate the stochastic system model to the underlying corresponding Markov Chain (MC), the developed MC grows wildly to several hundred thousands states. This problem is known as the largeness problem. To tolerate the largeness problem of Markov models, several iterative and direct methods have been proposed in the literature. Although the iterative methods provide a feasible solution for most realistic systems, a major problem appears when these methods fail to reach a solution. Unfortunately, the direct method represents an undesirable numerical technique for tolerating large matrices due to the fill-in problem. In order to solve such problem, in this paper, we develop a disk-based segmentation (DBS) technique based on modifying the Gauss Elimination (GE) technique. The proposed technique has the capability of solving the consequences of the fill-in problem without making, assumptions about the underlying structure of the Markov processes of the developed model. The DBS technique splits the matrix into a number of vertical segments and uses the hard disk to store these segments. Using the DBS technique, we can greatly reduce the memory required as compared to that of the GE technique. To minimize the increase in the solution time due to the disk accessing processes, the DBS utilizes a clever management technique for such processes. The effectiveness of the DBS technique has been demonstrated by applying it to a realistic model for the Kanban manufacturing system.

A robust methodology for performance evaluation of communication networks protocols

Performance evaluation is required at every stage in the life cycle of a network protocol. Analytical modeling is the method of choice for fast and cost effective evaluation of a network protocol. One of the most efficient high level formalisms used for analytical modeling of network protocols is stochastic Petri nets (SPNs). Yet, complexity of nowadays network protocols, which results in the state space explosion of the underlying Markov Chains (MC) model, has hindered the wide use of SPNs in the analysis of these protocols. Decomposition techniques are considered one of the main methodologies used for the approximate solution of the state explosion problem. Unfortunately, most of these techniques either require special form of the model or give insufficient accuracy of the solution. Moreover, small rates of communication protocols have caused additional overhead on any proposed technique for approximate evaluation of these protocols. This paper presents a novel decomposition technique called behavior and delay equivalent block (BDEB) technique. This technique overcomes most of drawbacks of other techniques proposed in the literature. It introduces a new aggregation method that depends on using a new type of transitions called intelligent transition. The proposed technique adopts delay equivalent criteria but in a new philosophy different from other techniques using the same criteria. The new technique is explained by applying it to a scalable model of the courier protocol

A New Disk-based Technique for Solving the Largeness Problem of Stochastic Modeling Formalisms

Stochastic modeling formalisms such as stochastic Petri nets, generalized stochastic Petri nets, and stochastic reward nets can be used to model and evaluate the dynamic behavior of realistic computer systems. Once we translate the stochastic system model to the underlying corresponding Markov Chain (MC), the developed MC grows wildly to several hundred thousands states. This problem is known as the largeness problem. To tolerate the largeness problem of Markov models, several iterative and direct methods have been proposed in the literature. Although the iterative methods provide a feasible solution for most realistic systems, a major problem appears when these methods fail to reach a solution. Unfortunately, the direct method represents an undesirable numerical technique for tolerating large matrices due to the fill-in problem. In order to solve such problem, in this paper, we develop a Disk-Based Segmentation (DBS) technique based on modifying the Gauss Elimination (GE) technique. The proposed technique has the capability of solving the consequences of the fill-in problem without making assumptions about the underlying structure of the Markov processes of the developed model. The DBS technique splits the matrix into a number of vertical segments and uses the hard disk to store these segments. Using the DBS technique, we can greatly reduce the memory required as compared to that of the GE technique. To minimize the increase in the solution time due to the disk accessing processes, the DBS utilizes a clever management technique for such processes. The effectiveness of the DBS technique has been demonstrated by applying it to a realistic model for the Kanban manufacturing system.