Zoubir Hamici

The subject of this study was to analyze the Frequency Shift Keying caller ID FSK transmission protocol used by Jordan Telecom. and implement a call alert computer application. An FSK decoder is presented and a decoding algorithm based on asynchronous data communication is discussed. Data is captured through the RS232 serial port and a 32-bit software is presented as a call alert application using a small mail transfer protocol through a TCP/IP Internet connection.

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A mobile ad hoc network (MANET) is an autonomous system consisting of mobile hosts connected by wireless links. Every host can move in any direction at any speed and time. This leads to a dynamic topology as hosts move constantly. MANET broadcast messages to each hosts, the transmission of one host can be heard by all hosts in its communication range. If two hosts are not located in each other’s transmission range, intermediate relay hosts must be employed as bridges to build communication paths. This is the multihop characteristic of the mobile ad hoc network, for which routing decisions must be made for far-away hosts to communicate. Node mobility causes links between nodes to break frequently, thus terminating the lifetime of the routes containing those links. An alternative route has to be discovered once a link is detected as broken, incurring extra route discovery overhead and packet latency. Traditionally route discovery has been done using flooding based approaches, which sometimes leads to broadcast storm problem. In this paper, we study the problem of how to construct a route with the longest lifetime for any given one-to-one communication request as a solution to link breakage in MANET. An algorithm is proposed with time complexity of , where n is the number of the nodes and m is the number of the links. The proposed algorithm complexity is similar to that of the Dijkstra algorithm implemented using Fibonacci heap.

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A Hybrid Heuristic Algorithm for Solving the P-Median Problem

One of the most popular location allocation models is the P-Median. Most of the algorithmic research on these models has been devoted to developing heuristic solution procedures. The major drawback of heuristic methods is that the time required for finding solutions can become unmanageable- while not getting the right exact solution. In this study, we propose a new two-stage hybrid heuristic algorithm (henceforth will be termed HH) to solve the P-Median problem. In the first stage, the K-Means clustering algorithm is used. The Nearest Neighbor (NN) algorithm is then used in the second stage. The HH algorithm generates solutions quickly, making the algorithm a good choice for large applications, which require quick solutions. Different data sets have been tested and showed that the proposed algorithm achieved less accurate results, which were obtained by other researchers. The HH showed that it is faster than the exchange algorithm in execution time by 42 to 222 times. This speed up is increased as the data size increases.

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Speaker Recognition Using Spectral Cross-correlation: A Fast Algorithm

This study presents an original algorithm for computing the cross-correlation function applied for speech recognition A spectral correlation estimation algorithm based on the comparing the magnitude spectrum of the two signals is presented. The number of samples is reduced by a factor of two, after eliminating the image spectrum. A moving average filter is used to smooth the magnitude spectrum and a re-sampling is performed in the frequency domain, which reduces the spectrum size, by a factor of 8. The algorithm shows good results in recognizing the voice of a specific person, hence its application in speaker identification

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Neurocomputing Device for Image Processing and Classification

This article is concerned with problems of construction of specialized computing device for image processing and classification. The device may be used for classification of objects images in conditions of various image scales of the same object. The device may be realized by neurochips technology.

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A Proposed Optical Music Mark Recognition System

The optical music symbol recognition system still has many challenges. Most of issues focused on the recognition of the individual marks in the input concert documents. In this study a new and efficient model for optical music recognition (OMR) was proposed. The music marks was recognized and played by the computer OMR system automatically. A stored file of music documents has been treated sequentially as inputs to the off-line recognition system. A new algorithm for staff-lines removing was proposed also. This new technique is applied on the thinned image (document) without affecting the marks. Features have been extracted from isolated marks and stored as a training data. A wave files has been stored for the same marks played by a guitarist as an example. Minimum distance classifier is used as a recognition approach. Proposed model improves the music information technology (MIT) and develops music academies courses, music distance learning. That leads to enhance the business process of developing music and music scientists.

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A New Model for Multi-agent Case-base Reasoning Systems

Abdel Badeea Salem, aldahoud Ali and Ibrahiem M. M. El Emary

Multi-Agent Systems (MASs) and Case-Based Reasoning (CBR) are two recent and hot paradigms in artificial intelligence field. CBR is a reasoning methodology based on old experience reasoning or similarity-based reasoning while MAS is a new paradigm to organize AI applications. CBR offers multi-agent systems the capability of autonomously learning from experience. This study examines the integration of CBR, MASs and Expert Systems (ESs). In addition, it presents a knowledge-based model of multi-agent CBR systems from both a logical and a knowledge-based viewpoint.

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Genetic Algorithm For Multimedia Data Security

Multimedia data security is important for multimedia commerce. Previous cryptography studies have focused on text data. The encryption algorithms developed to secure text data may not be suitable to multimedia applications because of large data sizes and real time constraint. For multimedia applications, light weight encryption algorithms are attractive. This study developed an encryption method to encrypt the data of the sound files (sound file of the extension “.wav” in this work). This method focus on using the powerful features (Crossover and Mutation operations) of Genetic Algorithm (GA) to produce a good encryption for the data of the sound files. The encryption method, in this study, has been tested on some sound files and the results are recorded.

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Fault-tolerant Distributed Systems with Diagnostics Algorithms

Oleg Viktorov and Afif Mghawish

To provide consistent actions in distributed systems with faulty nodes the Byzantine agreement protocol (algorithm) is widely used. In case of using message exchange scheme without authentication the Byzantine agreement algorithm leads to agreement if the number of nodes doesn’t exceed 1/3 of the total number. The proposed algorithms based on diagnostics procedures are used to reach an agreement in distributed models with 2n+ 2 nodes and fewer than k failed nodes. The hierarchical diagnostic procedures give the possibility to vary the complexity of hardware and software overhead according to required level of fault-tolerance

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Neisser’s Cycle of Perception: Formal Representation and Practical Implementation

This article presents results of a theoretical investigation of the application of the object-oriented Unified Modelling Language to a formal description of Neisser’s cycle of perception. The article includes class diagrams, modelling the spatial structure of Neisser’s cycle of perception and demonstrates how these formal descriptions can be applied to the area of modelling the problem-independent dialogue process. The basis of such applications is the assumption that the dialogue process in relation to either of the dialogue agents is similar to the process of routine perception of the environment in accordance with Neisser’s understanding of perception

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Neural Network-based Fuzzy Inference System for Exchange Rate Prediction

Marwan N.Y. Alakhras

In this work the power of learning and parallelism of neural network is combined with the reasoning mechanism of fuzzy logic to build up a more powerful system referred in the literature as neuro-fuzzy system. So here we will take about the combination models, learning mechanism, hybrid systems architecture, then the discussion is expanded to cover ANFIS, its learning algorithm and its structure for the different types of fuzzy, i.e. Mamadani and Takagi-Sugeno types of fuzzy. The system is used to model an time series algorithm specifically prediction of currency exchange rate of Turkish Lira (TL) versus United States Dollar (USD).

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