- Все статьи 584
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 584
A Low Power BIST TPG for High Fault Coverage
Статья научная
A low hardware overhead scan based BIST test pattern generator (TPG) that reduces switching activities in circuit under test (CUTs) and also achieve very high fault coverage with reasonable length of test sequence is proposed. When the proposed TPG used to generate test patterns for test-per-scan BIST, it decreases the number transitions that occur during scan shifting and hence reduces the switching activity in the CUT. The proposed TPG does not require modifying the function logic and does not degrade system performance. The proposed BIST comprised of three TPGs: Low transition random TPG (LT-RTPG), 3-weight weighted random BIST (3-weight ERBIST) and Dual-speed LFSR (DS-LFSR). Test patterns generated by the LT-RTPG detect the easy-to-detect faults and remain the undetected faults can be detected by the WRBIST. The 3-weight WRBIST is used to reduce the test sequence lengths by improving detection probabilities of random pattern resistant faults (RPRF). The DS-LFSR consists of two LFSR's, slow LFSR and normal–speed LFSR. The DS-LFSR lowers the transition density at their circuit inputs.
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A Meta Level Data Mining Approach to Predict Software Reusability
Статья научная
Software repositories contain wealth of information about software code, designs, execution history, code and design changes, bug database, software release and software evolution. To meet increased pressure of releasing updated or new versions of software systems due to changing requirements of stakeholder, software are rarely built from scratch. Software reusability is a primary attribute of software quality which aims to create new software systems with a likelihood of using existing software components to add, modify or delete functionalities in order to adapt to new requirements imposed by stakeholders. Software reuse using software components or modules provide a vehicle for planning and re-using already built software components efficiently. In this paper, we propose a framework for our approach to predict software reusable components from existing software repository on the basis of (1) stakeholders intention (requirement) match and (2) similarity index count for better reuse prediction. To effectively manage storage and retrieval of relevant information we use concept of situational method engineering to match and analyze the information for reuse. We use Genetic algorithm, Rabin Karp algorithm for feature selection and classification and k-means clustering methods of data mining to refine our results of prediction in order to better manage and produce high quality software systems within estimated time and cost.
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A Method for Building a Mosaic with UAV Images
Статья научная
At present, satellite and aerial remote sensing are common ways to collect data for territorial resources monitoring in most countries, but they are not effective or rapid enough. Compared with traditional ways of obtaining images, the UAV based platform for photogrammetry and remote sensing is a more flexible and easy way to provide high-resolution images with lower cost. So building UAV based platforms is becoming a hot field throughout the whole world. However, there are also some problems with UAV images, e.g. the views of UAV images from UAV are smaller than those of traditional aerial images, so these images with small views should be pasted together in order to increase the visual field. Therefore, mosaicking UAV images is a critical task. The homographies between sequence images will be affected by the accumulated errors, which will lead to drifts of the position of each image in the mosaic. In this paper, we introduce a two-step optimization method for mosaicking UAV sequence images which can correct the homographies and improve the position of each image in the mosaic. Experimental results will also be presented.
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A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases
Статья научная
In our present environment, heart diseases are very rampart and they describe the various types of diseases that affect the heart. They account for the leading cause of death word-wide especially, in Africa. It is therefore very important for individuals to have adequate knowledge about their heart health in order to avoid the risk of decreased life expectancy. The high mortality rate of heart (cardiovascular) diseases is attributed to the unequal ratio of patients to scarcity of medical experts who can provide medical care, also patients are not always warn to waiting long hours on queue in the hospital, especially in cases of emergency. This paper designed and implemented a Mobile Neuro-fuzzy System that uses the combination of the intelligence technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for cardiovascular diseases through interactivity with user. It employs programs like MySQL, PHP, JAVA (Android) and XML (Android Studio) while tools like XAMPP, PhpStorm and Android O/S were used to integrate these techniques together. The system, proved to be of enormous advantage in diagnosing heart diseases, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.
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A Model Statistical during Covid-19 Future E-Commerce Revenue for Indonesia Aviation
Статья научная
Until today, Information Technology (IT) has been felt by aviation industry showed by positive growth of operating revenue before Covid-19 pandemic. The pandemic of Covid-19 changes the world especially the aviation industry by slowing down the business transaction. This study presents statistical model on recent e-commerce revenue of aviation, the number of passengers and the IT investments then predicts future of e-commerce revenue, the number of passengers and the IT spending using Neural Networks. This method is useful to predict the future because it follows the time being. The chosen variables are intended whether IT has an impact during the pandemic for passenger generation year by year. The results show that for the next few years, the revenue, the number of passengers and the IT spending are significantly increasing, while there are problems faced in aviation industry because of Covid-19. This model also can be applied for other industry.
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A Model for Information Integration Using Service Oriented Architecture
Статья научная
Business agility remains to be the keyword that drives the business into different directions and enabling a 360 degree shift in the business process. To achieve agility the organization should work on real time information and data. The need to have instant access to information appears to be ever shine requirement of all organizations or enterprise. Access to information does not come directly with a single query but a complex process termed Information integration. Information integration has been in existence for the past two decades and has been progressive up to now. The challenges and issues keep on persisting as information integration problem evolves by itself. This paper addresses the issues in the approaches, techniques and models pertaining to information integration and identifies the problem for a need for a complete model. As SOA is the architectural style that is changing the business patterns today, this paper proposes a service oriented model for information integration. The model mainly focuses on giving a complete structure for information integration that is adaptable to any environment and open in nature. Here information is converted into service and then the information services are integrated through service oriented integration to provide the integrated information also as service.
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A Multi-objective Binary Cuckoo Search for Bi-criteria Knapsack Problem
Статья научная
Cuckoo Search (CS) is one of the most recent population-based metaheuristics. CS algorithm is based on the cuckoo’s behavior and the mechanism of Lévy flights. The Binary Cuckoo Search algorithm (BCS) is new discrete version used to solve binary optimization problem based on sigmoid function. In this paper, we propose a new cuckoo search for binary multiobjective optimization. Pareto dominance is used to find optimal pareto solutions. Computational results on some bi-criteria knapsack instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.
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Статья научная
Estimation of Distribution Algorithm (EDA) is a new kinds of colony evolution algorithm, through counting excellent information of individuals of the present colony EDA construct probability distribution model, then sample the model produces newt generation. To solve the NP-Hard question as EDA searching optimum network structure a new Maximum Entropy Distribution Algorithm (MEEDA) is provided. The algorithm takes Jaynes principle as the basis, makes use of the maximum entropy of random variables to estimate the minimum bias probability distribution of random variables, and then regard it as the evolution model of the algorithm, which produces the optimal/near optimal solution. Then this paper presents a rough programming model for job shop scheduling under uncertain information problem. The method overcomes the defects of traditional methods which need pre-set authorized characteristics or amount described attributes, designs multi-objective optimization mechanism and expands the application space of a rough set in the issue of job shop scheduling under uncertain information environment. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. We use MEEDA in order to enable a definition of a solution within a reasonable amount of time. We assume that machine flexibility in processing operations to decrease the complexity of the proposed model. Muth and Thompson’s benchmark problems tests are used to verify and validate the proposed rough programming model and its algorithm. The computational results obtained by MEEDA are compared with GA. The compared results prove the effectiveness of MEEDA in the job shop scheduling problem under uncertain information environment.
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A New Optimization Approach Using Smoothed Images Based on ACO for Medical Image Registration
Статья научная
This paper studies on image registration using Ant Colony Optimization technique of the medical imag-es. Ant Colony Optimization algorithm has ability of global optimization and facilitates quick search of opti-mal parameters for image registration. In this paper, a modified Ant Colony Optimization algorithm on prepro-cessed images is proposed to improve the accuracy in terms of PSNR (peak signal to noise ratio), Entropy and convergence speed. Preprocessing of images is adopted to remove noise so that extracted features provide more accurate and precise information about the image and results are more suitable for further analysis. The experi-mental results demonstrate the performance of proposed methodology as compared with traditional approaches as very promising.
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A New Parameter Estimation Algorithm for Actual COSPAS-SARSAT Beacons
Статья научная
According to the high precision demand of estimating frequency of arrival (FOA) and time of arrival (TOA) in Galileo search and rescue (SAR) system, and considering the uncertainty of message bit width in actual received COSPAS-SARSAT beacons, a new FOA and TOA estimation algorithm which combines the Multiple Dimensions Joint Maximum Likelihood Estimation (MJMLE) algorithm and Barycenter calculation algorithm is proposed. The principle of the algorithm is derived and the concrete realization of the algorithm is given. Both Monte Carlo simulation and experimental results show that when CNR equals to the threshold of 34.8dBHz, root-mean-square errors (rmse) of FOA and TOA estimation in this algorithm are respectively within 0.03Hz and 9.5us, which are less than the system requirements of 0.05Hz and 11us. This algorithm has been applied to the Galileo Medium-altitude Earth Orbit Local User Terminal (MEOLUT station).
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A New Partial Product Reduction Algorithm using Modified Counter and Optimized Hybrid Network
Статья научная
In this paper, a new multiplier is presented which uses modified fourteen transistor adder and optimized hybrid counter for partial product reduction step. Conventional adder is modified to improve Wallace tree functionality. Reducing critical path in counter structure can reduce VLSI area in whole multiplier structure. This paper uses a new structure in partial product reduction step to increase speed. Four to two compressors are used in modified Wallace structure to minimize the critical path. In final addition step of algorithm a new carry lookahead network is presented which adds two final operands efficiently. It uses dynamic CMOS in transistor level to reduce power consumption. Proposed multiplier reduces critical path, increases speed and decreases wiring problems in compare with previous algorithms efficiently. A new Booth encoder is presented in radix 16 circuitry. It decreases number of partial products while hardware overhead is minimized.
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A Non-Parametric Statistical Debugging Technique with the Aid of Program Slicing (NPSS)
Статья научная
A method is introduced in this paper, which promotes automated bug localization. It is based on the combination of two bug localization techniques, Non-Parametric Statistical Debugging and Backward Slicing. The proposed method, computes some vectors (called execution vectors) based on the status of each basic-block’s execution in running of test-cases. According to the behavior of each basic-block in failed test-cases and passed ones, two likelihoods are computed and regards to them, basic-blocks become prioritized. At last static slice of program and dynamic backward slice for one failed test-case are computed. While seeking for faulty statement in ranked basic-blocks, the method either returns the basic-block’s statements in the static backward slice or the part of it presented in the computed dynamic backward slice. NPSS has been applied on the Siemens test suite, space, grep and gzip. Our experimental study shows the accuracy and effectiveness of the method in accurate bug localization.
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A Note on Determinant of Square Fuzzy Matrix
Статья научная
In this article, we would like to study the determinant theory of fuzzy matrices. The purpose of this article is to present a new way of expanding the determinant of fuzzy matrices and thereafter some properties of determinant are considered. Most of the properties are found to be analogus to the properties of determinant of matrices in crisp cases.
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A Novel Approach for Video Inpainting Using Autoencoders
Статья научная
Inpainting is a task undertaken to fill in damaged or missing parts of an image or video frame, with believable content. The aim of this operation is to realistically complete images or frames of videos for a variety of applications such as conservation and restoration of art, editing images and videos for aesthetic purposes, but might cause malpractices such as evidence tampering. From the image and video editing perspective, inpainting is used mainly in the context of generating content to fill the gaps left after removing a particular object from the image or the video. Video Inpainting, an extension of Image Inpainting, is a much more challenging task due to the constraint added by the time dimension. Several techniques do exist that achieve the task of removing an object from a given video, but they are still in a nascent stage. The major objective of this paper is to study the available approaches of inpainting and propose a solution to the limitations of existing inpainting techniques. After studying existing inpainting techniques, we realized that most of them make use of a ground truth frame to generate plausible results. A 'ground truth' frame is an image without the target object or in other words, an image that provides maximum information about the background, which is then used to fill spaces after object removal. In this paper, we propose an approach where there is no requirement of a 'ground truth' frame, provided that the video has enough contexts available about the background that is to be recreated. We would be using frames from the video in hand, to gather context for the background. As the position of the target object to be removed will vary from one frame to the next, each subsequent frame will reveal the region that was initially behind the object, and provide more information about the background as a whole. Later, we have also discussed the potential limitations of our approach and some workarounds for the same, while showing the direction for further research.
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A Novel Approach in Determining Areas to Lockdown during a Pandemic: COVID-19 as a Case Study
Статья научная
In December 2019, the Novel Coronavirus became a global epidemic. Because of COVID-19, all ongoing plans had been postponed. Lockdowns were imposed in areas where there was an excessive number of patients. Constantly locking down areas had a significant negative influence on the economy, particularly on developing and underdeveloped countries. But the majority of countries were locking down their areas without making any assumptions where some were successful and some were failures. In this situation, this paper presents a novel approach for determining which parts of a country should be immediately placed under lockdown during any pandemic situation while considering the lockdown history at the time of COVID-19. This work makes use of a self-established dataset containing data from several countries of the world and uses the successful presence of lockdown in that area as the target attribute for machine learning algorithms to determine the areas to keep under lockdown in the future. Here, the Random Forest algorithm has provided the highest accuracy of 92.387% indicating that this model can identify the areas with an impressive level of accuracy to retain under lockdown.
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A Novel Approach to Customer Segmentation for Optimal Clustering Accuracy
Статья научная
Customer segmentation is not only limited to the identification of user groups but searching and determining the attitude of individual customer groups toward a particular product or service aside helping organization in developing better marketing strategies. Many studies have proposed different techniques for customer segmentation, but some of these studies failed to examine individual customer’s needs in the cluster. In a customer segmentation, when customers are grouped into various cluster based on their common needs, there may be customers that have other needs that differ from the general needs of the group. In a situation where the needs of individual were not captured, organizations may find it difficult to control the rendering of their services. The aim of this study is to extract the individual customer’ needs to enhance organizations’ services that meet the needs of customers, as well as increase organization profits. This study, therefore, proposes the use of an associative rules mining algorithm augmented with assignment optimization to properly examine the needs of individual customers in the group. This approach enhances the cross-segmentation of customers for better marketing strategies and the assignment technique also improved the segmentation processing speed. The degree of accuracy of the system developed was tested with about 9,500 customers’ dataset that was obtained from goggle multi category online store dataset. Both customer transaction history dataset and customer purchasing behavior dataset were obtained for segmentation which achieved 94.5% customer segmentation accuracy. The implementation was done using Python programming language.
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Статья научная
This paper presents a novel and unreported approach developed to filter T2-weighetd Magnetic Resonance Imaging (MRI). The MRI data is fitted with a parametric bivariate cubic Lagrange polynomial, which is used as the model function to build the continuum into the discrete samples of the two-dimensional MRI images. On the basis of the aforementioned model function, the Classic-Curvature (CC) and the Signal Resilient to Interpolation (SRI) images are calculated and they are used as filter masks to convolve the two-dimensional MRI images of the pathological human brain. The pathologies are human brain tumors. The result of the convolution provides with filtered T2-weighted MRI images. It is found that filtering with the CC and the SRI provides with reliable and faithful reproduction of the human brain tumors. The validity of filtering the T2-weighted MRI for the quest of supplemental information about the tumors is also found positive
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A Novel Fixed-Point Simulation Library for the Design of FPGAs
Статья научная
Fixed-point simulation is extremely important in the design of fixed-point FPGAs. Float-point simulation is used to verify the arithmetic, while the fixed-point simulation is adopted to evaluate the performance and verify the implementation. The design of high-precision system based on FPGAs must focus on the fixed-point simulation to reduce the error acceptable even to zero. Basing on the analysis of fixed-point simulation approaches, especially the approach with MATLAB Fixed-Point Toolbox, we propose a novel fixed-point simulation library consisting of four modules. The library works under VC environment and its basic definition module imitates operating principle of MATLAB Fixed-Point Toolbox. The aim of library is to assit setting up fixed-point simulation conveniently, easily and quickly. Finally, simulation shows effect of the library.
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A Novel GRASP Algorithm for Solving the Bin Packing Problem
Статья научная
The Bin Packing Problem (BPP) is one of the most known combinatorial optimization problems. This problem consists in packing a set of items into a minimum number of bins. There are several variants of this problem; the most basic problem is the one-dimensional bin packing problem (1-BPP). In this paper, we present a new approach based on the GRASP procedure to deal with the1-BPP problem. The first GRASP phase is based on a new random heuristics based on hybridization between First Fit and Best Fit heuristics. The second GRASP phase is based on Tabu search algorithm used for the enhancement of the solutions found in the first phase. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed approach.
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A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems
Статья научная
This paper proposes a novel and efficient hybrid algorithm based on combining particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques, called PSO-GSA. The core of this algorithm is to combine the ability of social thinking in PSO with the local search capability of GSA. Many practical constraints of generators, such as power loss, ramp rate limits, prohibited operating zones and valve point effect, are considered. The new algorithm is implemented to the non-convex economic dispatch (ED) problem so as to minimize the total generation cost when considering the linear and non linear constraints. In order to validate of the proposed algorithm, it is applied to two cases with six and thirteen generators, respectively. The results show that the proposed algorithms indeed produce more optimal solution in both cases when compared results of other optimization algorithms reported in literature.
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