Welcome to Journal of Theoretical and Applied Information Technology

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The levels of quality, maintainability, testability, and stability of software can be improved and measured through the use of automated testing tools throughout the software development process. Automated testing tools assist software engineers to gauge the quality of software by automating the mechanical aspects of the software-testing task. Automated testing tools vary in their underlying approach, quality, and ease-of-use, among other characteristics In Software testing; Software Metrics provide information to support a quantitative managerial decision-making for the test managers. Among the various metrics, Code coverage metric is considered as the most important metric often used in analysis of software projects in the industries for testing, Code coverage analysis also helps in the testing process by finding the areas of a program not exercised by a set of test cases, creating additional test cases to increase coverage, and determine the quantitative measure of the code, which is an indirect measure of quality. The test manager needs coverage metric in making decisions while selecting test cases for regression testing. In literature there are a large number of automated tools to find the coverage of test cases in Java. Choosing an appropriate tool for the application to be tested may be a complicated process for the test Manager. To ease the job of the Test manager in selecting an appropriate tool, we propose a suite of objective metrics for measuring tool characteristics as an aid in systematically evaluating and selecting automated testing tools.

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This paper presents the design & simulation of an Optical Fiber Bragg Grating (OFBG) sensor for stress, strain measurement and also demonstrates the methodology to arrive at the optimal grating pitch dimensions for a given interrogating wavelength. The wavelength chosen for interrogation of the Fiber Bragg Grating sensor is from the third window so as to minimize attenuation of the light signal in the communication link from Fiber Bragg Grating sensor to the electronic instrumentation. Before actually inscribing the grating in the fiber, simulation tools provide valuable help in optimizing the design parameters. The simulation results presented show the effectiveness of the developed method, which can be further implemented in real time for various industrial applications.

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Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification and at the same time, the difficulty in effectively representing such details in an image. Iris recognition illustrates work in computer vision, pattern recognition, and the man-machine interface. The purpose is real-time, high confidence recognition of a person's identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. Iris is a protected internal organ whose random texture is stable throughout life, it can serve as a kind of living password that one need not remember but one always carries along. Because the randomness of iris patterns has very high dimensionality, recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases. Iris recognition has shown to be very accurate for human identification. This paper proposes a technique for iris pattern extraction utilizing the least significant bit-plane through binary morphology applied to the bit-plane and by evaluating the standard deviation of the image intensity along the vertical and horizontal axis, the pupillary boundary of the iris is determined. The limbic boundary is identified by adaptive thresholding method. Because the extraction approach restricts localization techniques to evaluating only bit-planes and standard deviations, iris pattern extraction is dependent on circular edge detection. The iris normalization was invariant for translation, rotation and scale after mapping into polar coordinates. Experiment and results show that the proposed method has an encouraging performance, shows 98.7% localization and normalization success and reduces the system operation time. The proposed method involves Bit plane slicing, Standard deviation windows, Adaptive thresholding, Normalization modules.

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We are proceeding towards a broadband age both for the communication as well as remote sensing. Recently, CALM standard has already been formulated by ITU with objectives of collision avoidance between car to car as well as car to roadside communication and safety by utilization of Sensor-friendly vehicle. Millimetre wave radar is mandatory in every car to locate accurate position. Lots of works can be thought of relating to the radar. In radar, a target is characterized by its radar cross section (RCS) function. This paper presents an overview of recent progress of radar for the image detection and characterization of targets. Radar Cross section measurement capability of full scale targets of interest is an utmost requirement for country like INDIA, considering the nature of threat it has from its neighboring countries. The full scale facility helps in identifying the radar signature of various operational configurations of different class of targets present in our country as well as with our neighbors. This will help in fine tuning and judging the survivability/ striking capability of our weapons. The system is tested to find out RCS of a simple as well as complex object and its imaging by hardware and software .The results are very much useful in quantifying the target different parameters. Finally, the paper addresses the current questions regarding the integration of radar in practical wireless systems and standards

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Association Rules are discovered by identifying relationships among sets of items in a transaction database with two measures which quantify the support and confidence of the rule. Finding frequent itemsets is computationally the most expensive step in Association Rule discovery and therefore, it has attracted significant research attention. This paper reviews Apriori related and Eclat algorithms with detailed discussion about various data structures. Computation are made for our own surveyed data sets and compared. The analysis ends with various research issues like types of rules, execution time and space complexity of algorithms.

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Meteorological forecasting is applicable for versatile applications. Accurate weather prediction saves lives, money and time in both local and global area. Forecasting accuracy is still not accurate because of the uncertain (fuzzy) data of nature, due to several reasons including: incomplete data, hand writing error, device error, precision of measurements and discreet description of connective phenomena Inherent part reflecting our understanding of things. On the other hand in global area with large amount of data to process whole the data is time consuming, thus, to improve the quality of data and execution time, we need to manage the uncertainty of data and extract desired data. Therefore the uncertainty management and process the data demand intelligent methods with knowledge based approaches. This paper reviews challenges in this field and compares advantages and drawbacks of the existing methods that essentially are only applicable for local area. Finally we proposed a hybrid technique for new research based on fuzzy c-mean clustering technique and type-2 fuzzy logic that is useable in both local and global area. Finally we show our experiments and prove that hybrid technique performs better than existing weather prediction methods in low error rate.

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Congestion control is one of the fundamental issues in computer networks. In transport control protocol (TCP) the performance of protocol is being measured based on fairness and efficiency. The fairness on round trip time (RTT) can be measured using the algorithm based on two flows or more than that. Congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. The traffic load in a network will effect the performance of a network and the fairness of algorithm. Congestion control is an introduced effect that adapts the performance. AIMD (Additive Increase Multiplicative Decrease) is an established algorithm in a set of liner algorithms that it reflects good efficiency as well as good fairness. In this paper we propose an evaluation method of fairness for New AIMD congestion control algorithm. The evaluation of fairness has been done by using multiple flows start at the same time and also by considering each flow start at a different time in other way.

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The premature ventricular contraction (PVC) and the premature atrial contraction (PAC) are cardiac arrhythmias which are widely encountered in the cardiologic field. They can be detected using the electrocardiogram signal parameters. We use in this work a Neuro-fuzzy approach to identify these abnormal beats. To achieve this objective we have developed a Neuro-Fuzzy Classifier (NFCL), its performances were evaluated by computing the percentages of sensitivity (Se), specificity (Sp) and correct classification (CC). This classifier allows extraction of rules (knowledge base) to clarify the results obtained. We use the medical database (MIT-BIH) to validate our results.

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Motivated by emerging battery operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits, by operating the devices at low currents and low voltages[1,2,3,4].It is known that MOS devices and circuits especially CMOS circuits consume relatively low power[5,6] . But there seems to be a need to reduce this power further to prolong the life of battery. One solution to achieve the ultra-power requirement is to operate in sub-threshold region [7]. Over the last 10 years, digital sub-threshold logic circuits have been developed for applications in the ultra-low power design domain, where performance is not the priority. Sub-threshold logic transistors, that is the power supply voltage is below the threshold voltage. In this paper, analysis was done both CMOS and Pseudo-NMOS logic families operating in sub-threshold region. The results were compared with CMOS in normal strong inversion region.

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In this paper, a mitigation method for minimizing the distortion caused by frequency selective fast fading is illustrated. The proposed system combines between two types of diversity schemes; frequency diversity represented by the orthogonal frequency division multiplexing (OFDM) scheme and antenna diversity represented by space time block coding (STBC) scheme these are for mitigating the distortion caused by frequency selective fading. The multiplexing technique proposed here is the code division multiple access (CDMA) scheme which is considered the solution for eliminating the distortion caused by fast fading. The main problem of the OFDM system is the high peak-to-average power ratio (PAR). In the proposed system, three techniques used for reducing the PAR are applied together. And in order to reduce the bit error rate caused by intersymbol interference (ISI) or multiple access interference (MAI) channel coding is applied which can make error detection and correction at the receiver and simulation results have shown that by applying any of channel coding techniques, the bit error rate (BER) has reached a satisfying level at a low signal-to-noise ratio (SNR).

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This paper presents an algorithm for solving Security Constrained Economic Dispatch (SCED) problem through the application of Tabu Search (TS). The SCED problem is formulated with base case and contingency case line flow constraints, which are important for practical implementation. Two representative systems namely 66-bus [14] and 191-bus [15] Indian utility systems are taken for investigations. The SCED results obtained using TS are compared with those obtained using Genetic Algorithm (GA) and Evolutionary Programming (EP). The investigations reveal that the proposed TS algorithm is relatively simple, reliable and efficient and suitable for practical applications.

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Over the past few years, there has been an increased trend toward processing of the electrocardiogram (ECG) using microcomputer. The system based on microcomputer can perform the needed medical services in extremely efficient manner. In fact, many systems have already been implemented to perform signal processing task such as 12-lead ECG analysis. All these applications require an accurate detection of QRS complex of ECG. Thus QRS complex detection is an important part of many ECG signal processing system. This paper presents application of Fuzzy C-Means algorithm (FCM) for detection of QRS complex in ECG signal. The performance of the algorithm is validated using original 12-lead ECG recording from the standard ECG data base. Significant detection rate is achieved. The onset and offset of the QRS complexes are found to be within tolerance limit given by CSE library.

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One of the most common problems faced by designers of wireless communication systems is the spatio-temporal variations in the wireless channel which arise mainly due to the phenomenon of multipath fading which is inevitable in scattering environments that are subject to changes over time. A wide variety of pre/post processing techniques have been used to mitigate the degrading effects of such channels, but with limited improvements in performance. This paper provides an evaluation of the concept of Spatial Diversity (SD), applied to multi-antenna systems that demand modifications at the physical level - the use of multiple antennas at the transmitter and/or the receiver. The performance of SISO, SIMO, MISO and MIMO systems have been evaluated and compared in AWGN and fading channels.

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The Multi Carrier Code Division Multiple Access (MC-CDMA) system with time division duplex mode, adopting unbalanced slot allocation between uplink and downlink, is a good solution for asymmetric multimedia traffic. A centralized common slot allocation to minimize inter-cell interference is not feasible as neighboring cells may carry different rates of traffic load. In this paper, we investigate and discuss the effect of asymmetric slot management strategy employing adaptive resource allocation in a MC-CDMA system, in which each cell has its own slot allocation policy according to the level of traffic load. The Bit Error Rate (BER) and Signal to Noise Ratio (SNR) of MC-CDMA considering four cases of uplink and downlink between cells is simulated separately in presence of Additive White Gaussian Noise (AWGN) and Rayleigh channel. The simulation result shows that the distance ratio and orthogonality factor are equally important parameter in BER performance, and also an SNR of around 9dB is enough to bring BER below 10^-3.

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Existing internet frameworks, like grids, wireless grids, P2P networks are now provide effective channels for gathering and processing widespread information, using the available resources within reasonable cost. Agent technology emerges as a suitable solution to combine with these frameworks for covering the existing challenges, like discovery, load balancing and resource management. Although different models introduced, practical solutions remain elusive and tend to exhibit underlying conflicts between different paradigms. This paper, aimed to revisit and discuss the characteristics on one of these system architectures based on agents. The base model is introduced, then our re-experiment is described and the results are compared, such that the challenges on this agent base model is highlighted and new ideas can be applied.

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Evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization problems, which typically require consideration of conflicting and competing design issues. A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. Penalty function is generic and applicable to any type of constraint (linear or nonlinear). Nonetheless, the most difficult aspect of the penalty function approach is to find appropriate penalty parameters. In this paper, a method combining the new Non-dominated Ranked Genetic Algorithm (NRGA), with a parameterless penalty approach are exploited to devise the search to find Pareto optimal set of solutions, alleviate the above difficulties. The parameterless penalty approach that does not require any penalty parameter where penalty parameters assignment among feasible and infeasible solutions are made with a view to provide a search direction towards the feasible region. The new Parameterless Penalty and the Non-dominated Ranked Genetic Algorithm (PP-NRGA) continuously find better Pareto optimal set of solutions. This new algorithm have been evaluated by solving five test problems, reported in the multi-objective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented.