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On the Verification by Approximation of Duration Systems

Narjes Berregeb and Riadh Robbana

LIP2 Laboratory, Tunisia 

Abstract: We consider the problem of verifying invariance properties for duration systems. Such systems are (extended) timed graphs with duration variables. They are especially suitable for describing real time schedulers. However, for this kind of systems, the verification problem of invariance properties is in general undecidable. We propose an over approximation method based on a particular extension of a given duration system, and we show that our over approximation includes all the digitization of all the real computations of the duration system. The over-approximated system can then be used to perform an interesting close analysis of invariance properties of the initial system, while other existing approaches fail. 

Keywords: Approximation, digitization, duration systems, formal verification, real-time scheduler. 

Received March 1, 2003; accepted October 30, 2003 

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Arabic Text Recognition

Ramzi Haraty and Catherine Ghaddar 

Lebanese American University, Lebanon 

Abstract: The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space. Thus, the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: There are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters. 

Keywords: Arabic text classification, artificial neural networks. 

Received May 18, 2003; accepted July 24, 2003

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Fractal Structure of the Urban Objects

Mohamed-Khireddine Kholladi

Department of Computer Science, Mentouri University of Constantine, Algeria 

Abstract: With regards to the big agglomerations and especially the cities, the constructed surface has often got an irregular shape, in spite of the efforts of the urbanists to promote more compact shapes. The internal spatial order of the big agglomerations is a matter of the fractal geometry. This article shows the interest of applying the fractal geometry in cities and in networks of communication of the urban zones. Thus, we are going to focus our attention especially on the coarse analysis of the constructions’ shape structure which concern the occupation of the soil surfaces (the fractal of surface such as the carpets of Sierpinski), and to scale permitting the analysis of the agglomerations and cities such like the city of Constantine, Algeria.

 Keywords: Geographical information system, fractal geometry, urban object, urban analysis. 

Received May 18, 2003; accepted September 9, 2003

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A Survey on Fault Injection Techniques

Haissam Ziade 1, Rafic Ayoubi 2, and Raoul Velazco 3

1 Faculty of Engineering I, Lebanese University, Lebanon

2 Faculty of Engineering, University of Balamand, Lebanon

3 IMAG Institute, TIMA Laboratory, France 

Abstract: Fault tolerant circuits are currently required in several major application sectors. Besides and in complement to other possible approaches such as proving or analytical modeling whose applicability and accuracy are significantly restricted in the case of complex fault tolerant systems, fault-injection has been recognized to be particularly attractive and valuable. Fault injection provides a method of assessing the dependability of a system under test. It involves inserting faults into a system and monitoring the system to determine its behavior in response to a fault. Several fault injection techniques have been proposed and practically experimented. They can be grouped into hardware-based fault injection, software-based fault injection, simulation-based fault injection, emulation-based fault injection and hybrid fault injection. This paper presents a survey on fault injection techniques with comparison of the different injection techniques and an overview on the different tools. 

Keywords: Fault tolerance, fault injection, fault simulation, VLSI circuits, fault injector, VHDL fault models.

 Received May 19, 2003; accepted October 13, 2003

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Potential of Computer-Supported Collaborative Learning Application Use in Malaysian Schools

Zarinah Mohd Kasirun and Siti Salwa Salim

Department of Software Engineering, University of Malaya, Malaysia 

Abstract: The evolution of courseware in schools with its various attributes, strengths and limitations are increasing. The situation in Malaysian context is not widely known. Particularly with the government effort in implementing the smart school project, the readiness among school teachers would play important role. This paper focuses on the evolution of courseware in schools in general and discusses its attributes, strengths and limitations. Then, the paper presents the details of the survey on collaborative learning carried out among school teachers. The important issues that will be investigated include. the awareness of CL activities, the awareness of using CSCL applications, the teachers involvement in CSCL application development, the CL main and CL success factors.

 Keywords: Computer-supported collaborative learning, CSCL, CSCL application. 

Received May 26, 2003; accepted August 29, 2003
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Image Retrieval System Based on Density Slicing of Colour Histogram of Images Subareas and Colour Pair Segmentation

Jehad Odeh 1, Hjah Fatimah Ahmad 2, Mohamed Othman 3, and Rozita Johari 3

 1Faculty of Information Science  and Technology, Multimedia University, Malysia

 2 Multimedia Department, University Putra Malaysia, Malysia

3 Computer Network Department, University Putra Malaysia, Malysia  

Abstract: Techniques to identify objects within an image and searching for similar objects in the database is not claiming a lot of progress, due to the limitations of the capabilities of the existing techniques and algorithms in image processing and computer vision to perform such task. In this paper, a new technique based on slicing the images to equally sub-areas, then applying the density slicing to the colour histogram of these areas combined with the colour pair technique is presented. We tried to overcome problems related to the original colour pair segmentation, as well as overcome problems related to the computational complexity in histogram localization through proposing density slicing or multiple thresholds. In this paper, new techniques proposed, new ranking formula, and a complete framework with the interface consideration. 

Key Words: Colour pair segmentation, content-based image retrieval, city block, density slicing.

 Received June 24, 2003; accepted August 25, 2003  


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An Improved Implementation of Elliptic Curve Digital Signature by Using Sparse Elements

Essam Al-Daoud

Computer Science Department, Zarka Private University, Jordan 

Abstract: This paper introduces several new techniques and algorithms to speed up the elliptic curve digital signature and reduce the size of the transited parameters. The basic idea is to use sparse elements for the curve coefficients and the first base point coordinate. The implementation analysis shows that the addition formula calculations are improved about 40 percent. The sparse elements are introduced with a compact representation, thus the digital signature calculations are speeded up about 40-60 percent, and the public key parameters are reduced about 37-48 percent. 

Keywords: Elliptic curve cryptography, projective coordinate, sparse elements, elliptic curve digital signature. 

Received July 14, 2003; accepted September 4, 2003 

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A Hierarchical Neuro-Fuzzy MRAC of a Robot in Flexible Manufacturing Environment

Kasim Al-Aubidy and Mohammed Ali

Computer Engineering Department, Philadelphia University, Jordan 

Abstract: In one hand, the Model Reference Adaptive Control (MRAC) architecture has been widely used in linear adaptive control field. The control objective is to adjust the control signal in a stable manner so that the plant’s output asymptotically tracks the reference model’s output. The performance will depend on the choice of a suitable reference model and the derivation of an appropriate learning scheme. While in the other hand, clusters analysis has been employed for many years in the field of pattern recognition and image processing. To be used in control the aim is being to find natural groupings among a set of collected data. The mean-tracking clustering algorithm is going to be used in order to extract the input-output pattern of rules from applying the suggested control scheme. These rules will be learnt later using the widely used Multi-layer perceptron neural network to gain all the benefits offered by those nets. A hierarchical neuro-fuzzy MRAC is suggested to control robots in a flexible manufacturing system. This proposed controller will be judged for different simulated cases of study to demonstrate its capability in dealing with such a system. 

Keywords: MRAC, mean-tracking clustering algorithm, MLP neural nets, computer control, real-time systems, robots, FMS.

 Received July 29, 2003; accepted March 8, 2004

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Design and Implementation of Double Cube Data Model for Geographical Information System

Mohd Shafry Mohd Rahim, Daut Daman, and Harihodin Selamat

Faculty of Computer Science and Information System, University of Technology Malaysia, Malaysia

 Abstract: In general, Geographical Information System (GIS) has four main components, namely data input, data model, data analysis and manipulation, and data presentation. These components are vital in order to make GIS fully functional. This paper is centred upon the research activities of selecting, synthesis, designing and implementing the data model components. The primary research issue is to develop a data model that encompasses the capability of storing and managing changes in geographic features. A new perspective approach on the current data modelling is proposed in order to alleviate the current issue plaguing the GIS data management.  In developing this new perspective, the feature based approach system cube method is synthesized to produce a new data model. Consequently, the combination of these approaches resulted in the design of double cube data model, which integrates temporal information of geographic features. The double cube data model has been implemented using relational database system. After extensive testing, the double cube data model performed admirably in managing the dynamic changes of geographic features. In conclusion, temporal information is the prime importance in managing geographic data. In this paper, the author has proved that temporal information can be integrated into a single GIS data model. 

Keywords: Data model, database, temporal GIS.  

Received August 3, 2003; accepted December 5, 2003 

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A New Approach for Contrast Enhancement Using Sigmoid Function

Naglaa Hassan1&2 and Norio Akamatsu1

1 Department of Information Science and Intelligent Systems, Tokushima University, Japan

2 Operation and Maintenance Center, Egypt Telecom, Egypt

Abstract: The contrast of any image is a very important characteristic by which the image can be judged as good or poor. In this paper, we introduce a simple approach for the process of image contrast enhancement using the sigmoid function in spatial domain. To achieve this simple contrast enhancement, a  novel mask based on using the input value together with the sigmoid function formula in an equation that will be used as contrast enhancer. This new contrast enhancer is passing over the target image, operates on its pixels, one by one. The new contrast enhancer is a scaled version of the input that is performed by applying a sigmoid function to the signal itself. The intensity value of each pixel in the output image is computing according to a specific formula. The parameters of the sigmoid function were determined by using three different methods. The effect of gain’s value on the contrast enhancement process was studied. The new enhancing approach has been successfully, applied in several gray scale images.  We proved that it works efficiently in different dark and bright images adjusting their contrasts. Moreover, the new enhancing approach was effective in dealing with colored images resulting in high-quality outputs. Our proposed algorithm is a simple approach that can be used successfully, in various applications suffering from different image’s contrast problems.

Keywords: Image processing, contrast enhancement, sigmoid function. 

Received September 6, 2003; accepted January 6, 2004 

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Recognition of Spoken Arabic Digits Using Neural Predictive Hidden Markov Models

Rafik Djemili, Mouldi Bedda, and Hocine Bourouba

Automatic and Signals Laboratory of Annaba, Badji Mokhtar University, Algeria


Abstract: In this study, we propose an algorithm for Arabic isolated digit recognition.  The algorithm is  based   on extracting  acoustical  features  from  the  speech  signal  and  using  them  as  input  to  multi-layer  perceptrons  neural  networks.  Each word in the vocabulary digits (0 to 9) is associated with a network. The networks are implemented as predictors for the speech samples for certain duration of time. The back-propagation algorithm is used to train the networks. The hidden markov model (HMM) is implemented to extract temporal features (states) for the speech signal. The input vector to the networks consists of twelve mel frequency cepstral coefficients, log of the energy, and five elements representing the state. Our results show that we are able to reduce the word error rate comparing with an HMM word recognition system.


Keywords: Speech recognition, hidden Markov models, artificial neural networks, hybrid HMM/MLP.


Received September 15, 2003; accepted January 19, 2004

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