Welcome to Journal of Theoretical and Applied Information Technology

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DNA Computing utilizes the properties of DNA for performing the computations. The computations include arithmetic and logical operations such as multiplication. We first show a procedure for multiplication of a pair of two binary numbers. The procedure mainly consist of bit-shift operation where the operation depends on bit position of one’s (ignoring zero’s) in the multiplicand and finally addition operations which take place simultaneously in each steps. The above method takes O(1) time in the best case which exists when each bit of multiplicand is zero. However, the time complexity of proposed algorithm is O(n) for average and worst case and the space complexity of proposed algorithm is O(n) for average case, worst case and best case. In addition the most merit of this model is simple coding and its time efficiency.

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 In a Least Developed Country (LDC) like Bangladesh where the textile is the main core of our economy; still there is a major drawback in this sector which is the defect detection of the fabric. In the manual fault detection system with highly trained inspectors, very less percentage of the defects are being detected in upon fabrics in the textile industries. But a real time automatic system can increase this percentage in a maximum number. This research implements a textile defect detector which uses computer vision methodology with the combination of multi-layer neural networks to identify the classification of textile defects and detect it with a real time configured mechanical system containing a microcontroller. The recognizer, suitable for LDC countries, specially for Bangladesh where textile exports earns the maximum for the country’s economy.

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This research paper deals with the implementation of face recognition using neural network (recognition classifier) on multi-scale features of face (such as eyes, nose, mouth and remaining portions of face). The proposed system contains three parts, preprocessing, multi scale feature extraction and face classification using neural network. The basic idea of the proposed method is to construct facial features from multi-scale image patches for different face components. The multi-scale features of face (such as Eyes, Nose, Mouth and remaining portion of face) becomes the input to neural network classifier, which uses back propagation, algorithm and radial function network to recognize familiar faces (trained) and faces with variations in expressions, illumination changes, tilt of 5 to 10 degrees and with spects. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straight forward approach towards face recognition. The proposed algorithm was tested on FERET face data base for 200 images of 40 subjects (120 faces for training 80 for testing) and results are encouraging compared to other face recognition techniques. (1, 2, 3)

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A neural network based implementation of space vector modulation of a voltage-sourced inverter has been proposed in this paper. Use of ANN based technique avoids the direct computation of trigonometric function (e. g. sine function) as in conventional space vector modulation implementation. The main disadvantages with conventional implementation are, use of any look-up table implies the need for additional memory, interpolation of non-linear functions lead to poor accuracy and thus to increased harmonics in the PWM waveforms, the use of a look-up table and interpolation demands additional computing time that limits the maximum inverter switching frequency. The proposed scheme is simple and straight forward and avoids the direct computation of non-linear functions. This scheme is evaluated under simulation for a variety of operating conditions of the drive system and comparison is made with the conventional method of implementation. Results show improved performance and robustness of the drive system.

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We have developed an agent based intelligent tutoring system for the parameter passing mechanisms in computer science (2), an introductory Java programming language, in Al-Azhar University in Gaza. The agent based intelligent tutoring system helps students better understand parameter passing mechanisms in Java using problem based technique. In this paper, we will describe the architectural design and features of the agent based intelligent tutoring system. An initial evaluation of effectiveness of the system was carried out and the result was found to be positive. The evaluation confirmed the established hypothesis that using the intelligent tutoring system would result in an improvement in the learning of the students [5]-[9].

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Dense Wavelength Division Multiplexing (DWDM) is a key component of the world’s communications infrastructure. The tremendous growth in telecommunications services is possible today in part through optical networks, where DWDM systems allow much greater bandwidth over existing optical systems. Telecommunication companies have sought out such technologies to help respond to a growing array of customer demands, including streaming video, which require large amounts of bandwidth to create transmissions in real time. Some leading service providers have reported the doubling of bandwidths about every six to nine months. DWDM allows such transmissions by virtually splitting the fibers' capabilities into more than two carriers. From both technical and economic perspectives, the ability to provide potentially unlimited transmission capacity is the most obvious advantage of DWDM technology. The current investment in fiber plant can not only be preserved, but optimized by a factor of at least 32. As demands change, more capacity can be added, either by simple equipment upgrades or by increasing the number of lambdas on the fiber, without expensive upgrades. Capacity can be obtained for the cost of the equipment, and existing fiber plant investment is retained.

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The algebraic approach to database query processing continues to be successful long after its introduction in relational database. This paper is devoted to database algebras. The purpose of this paper is to convey some idea of the general nature of database algebras in the hope of gaining some insight into why certain operations exist and of identifying common themes among these algebras.

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Rapid advances in communications technology and the proliferation of inexpensive PCs and workstations have created a wide avenue for Distributed Computing Systems to move into mainstream computing. A Distributed Computing System (DCS) consists of a number of PCs or workstations interconnected through PPP, LAN or WAN. These systems provide a higher performance, better reliability and throughput over centralized mainframe systems. Clearly, we have a set of M tasks connected in some fashion and a heterogeneous DCS composed of N computers of di erent capabilities. Tasks of a given application require certain computer resources (memory, processor and communication link). Indeed, computers and communication resources in the system are also capacitated. For these reasons, the issue is how to assign (allocate/schedule) the tasks of a given application onto the available computers of the system so as to maximize the system throughput i.e. minimize the total sum of execution and communication costs.

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Traditional classification algorithms are ideally suited to the processing of small datasets with a stationary distribution, and therefore yield significant errors when applied to real-world datasets subject to concept drift. In the current study, this problem is resolved using an incremental genetic algorithm (IGA). An assumption is made that new training data are generated at a steady rate and pass through a fixed-size window. In the initialization process, training samples are accumulated until the window is full, and a genetic algorithm (GA) is then applied to determine the set of classification rules. As new training samples arrive in the window, old instances are forgotten. Once all the original samples have been replaced by new samples, the GA is re-executed to determine the new set of best classification rules. This procedure is repeated sequentially for as long as a learning function is required. To account for concept drift, the GA utilizes a memory-based random immigrant module, in which the initial population pool of the GA applied at each stage of the incremental learning process comprises a mix of best solutions obtained in the previous stage and an appropriate number of random immigrants. The feasibility of the proposed approach is confirmed by performing a series of classification rules mining simulations using two standard datasets, namely Mushroom and Zoo. The results demonstrate that IGA achieves a comparable classification performance to that obtained using existing incremental and non-incremental methods, but incurs a significantly lower computational overhead.

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Demand for quick access to Web-based applications and real-time delivery of multimedia transmissions via an integrated network infrastructure drives implementation of ATM (Asynchronous Transfer Mode) broadband solutions. ATM is a high-performance technology that provides bandwidth on-demand for seamless transport of full-motion video, audio, data, animations, and still images in local and wider area environments. Asynchronous transfer mode (ATM) is a cell-oriented switching and multiplexing technology that utilizes fixed-length packets to carry different types of traffic and also ATM will enable carriers to capitalize on a number of revenue opportunities through multiple ATM classes of services; high-speed local-area network (LAN) interconnection; voice, video, and future multimedia applications in business markets in the short term; and in community and residential markets in the longer term. ATM reduces infrastructure costs through efficient bandwidth management, operational simplicity, and the consolidation of overlay networks. Carriers can no longer afford to go through the financial burden and time required to deploy a separate network for each new service requirement (e.g., dedicating a network for a single service such as transparent LAN or frame relay). ATM technology will allow core network stability while allowing service interfaces and other equipment to evolve rapidly.

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The paper illustrates a new fuzzy approach for optimizing routing decisions. This illustration is being done, here, using packet switched networks. It implements the approach and analyzes the new coherent formats of link-state packets and routing table accrued from the implementation of approach. In this algorithm, each node in the network maintains a database containing values corresponding to linguistic variables described for the output mixed metric formed from delay and load. Therefore, describing the complete network topology more meticulously. Because the traffic in the network is quite variable, therefore, it is combined with delay to yield more efficacy in achieving improved routing decisions. Also, provides a fuzzy measure to quantify the effect of load on delay. Thus, the whole procedure is implemented over an example of packet switched network to analyze its working comprehensively.

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Reactive power dispatch (RPD) is one of the important tasks in the operation and control of power system. This paper presents a Differential Evolution (DE) - based approach for solving optimal reactive power dispatch including voltage stability limit in power systems. The monitoring methodology for voltage stability is based on the L-index of load buses. The objective is to minimize the real power loss subjected to limits on generator real and reactive power outputs, bus voltages, transformer taps and shunt power control devices such as SVCs. The proposed algorithm has been applied to IEEE 30-bus system to find the optimal reactive power control variables while keeping the system under safe voltage stability limit and is found to be effective for this task. The optimal reactive power allocation results obtained using DE are compared with other methods. It is shown that the objective function value is less than those of other methods.

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In this paper, a new robust nonlinear observer is proposed for the reconstruction of the whole process state variables in a simulated polymerization reactor. It presents an interconnected high gain observer to perform the estimation. This observer has robust performance in the presence of measurement noise and model uncertainty. The global stability of the observer is analytically treated using the Lyapunov theory in order to show the conditions under which exponential convergence can be achieved. Finally, we present a numerical simulation to illustrate the effectiveness of the suggested approach.

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The unabated growth of the Web and the increasing expectation placed by the user on the search engine to anticipate and infer his/her information needs and provide relevant results has fostered the development of the field of Web Information Retrieval (Web IR). The recent surveys claim that 85% of internet users use search engines and search services to find specific information [1]. The same surveys, however, show that users are not satisfied with the performance of the current generation search engines. The slow retrieval speed, poor quality of retrieved results, handling a huge quantity of information, addressing subjective & time-varying search needs, finding fresh information and dealing with poor quality queries are commonly cited glitches. This paper expounds the Web Information Retrieval paradigm, a variant of classical Information Retrieval, by illustrating its basics, the components, model categories, tools, tasks and the performance measures that quantify the quality of retrieval results.