The complexity of the system design is increasing very rapidly as the number of transistors on Integrated Circuits (IC) doubles as per Moore’s law. There is big challenge of testing this complex VLSI circuit, in which whole system is integrated into a single chip called System on Chip (SOC). Cost of testing the SOC is also increasing with complexity. Cost modeling plays a vital role in reduction of test cost and time to market. This paper includes the cost modeling of the SOC Module testing which contains both analog and digital modules. The various test cost parameters and equations are considered from the previous work. The mathematical relations are developed for cost modeling to test the SOC further cost modeling equations are modeled in Graphical User Interface (GUI) in MATLAB, which can be used as a cost estimation tool. A case study is done to calculate the cost of the SOC testing due to Logic Built in Self Test (LBIST) and Memory Built in Self Test (MBIST). VLSI Test engineers can take the benefits of such cost estimation tools for test planning.

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.

A low power Test Pattern Generator (TPG) designed by modifying Linear Feedback Shift Register is proposed to produce low power test vectors that are deployed on Circuit under Test (CUT) to slenderize the dynamic power consumption by CUT. The technique involved in generating low power test patterns is performed by increasing the correlativity between the successive vectors; the ambiguity in increasing the similarity between consecutive vectors is resolved by reducing the number of bit flips between successive test patterns. Upon deploying the low power test patterns at the inputs of CUT, slenderizes the switching activities inside CUT that in turn reduces its dynamic power consumption. The resulted low power test vectors are deployed on CUT to obtain fault coverage. The experimental results demonstrate significant power reduction by low power TPG than compared to standard LFSR.

Data mining is the process used to analyze a large quantity of heterogeneous data to extract useful information. Meanwhile, many data mining techniques are used; clustering classified to be an important technique, used to divide data into several groups called, clusters. Those clusters contain, objects that are homogeneous in one cluster, and different from other clusters. As a reason of the dependence of many applications on clustering techniques, while there is no combined method for clustering; this study compares k-mean, Fuzzy c-mean, self-organizing map (SOM), and support vector clustering (SVC); to show how those algorithms solve clustering problems, and then; compares the new methods of clustering (SVC) with the traditional clustering methods (K-mean, fuzzy c-mean and SOM). The main findings show that SVC is better than the k-mean, fuzzy c-mean and SOM, because; it doesn’t depend on either number or shape of clusters, and it dealing with outlier and overlapping. Finally; this paper show that; the enhancement using the gradient decent, and the proximity graph, improves the support vector clustering time by decreasing its computational complexity to O(nlogn) instead of O(n2d), where; the practical total time for improvement support vector clustering (iSVC) labeling method is better than the other methods that improve SVC.

The business activities are highly incorporated with the technology development and need to be updated periodically. Business activities have made the technology to be more creative and innovative. The organizations need to integrate their business activities into a single system to achieve their aims and objectives successfully. Various studies are already conducted in integrating such business activities as Business Process Management System. In particular, various management systems are available in the market to integrate the educational business processes. The educational business processes are changing due to the various international bench markings. The educational business processes perspectives could not be met exactly to the requirements in some of the available software. As the currently available systems suits to some of the educational institutions in particular region of the world, there are needs to review them to the current requirements. This paper identifies the gaps between the current potential educational requirements and availability of educational business processes management systems. The major impact factors of the educational processes are identified and based on the findings a model has been proposed.

Electronic passports (e-passports) are to prevent the illegal entry of traveller into a specific country and limit the use of counterfeit documents by more accurate identification of an individual. The e-passport, as it is sometimes called, represents a bold initiative in the deployment of two new technologies: cryptography security and biometrics (face, fingerprints, palm prints and iris). A passport contains the important personal information of holder such as photo, name, date of birth and place, nationality, date of issue, date of expiry, authority and so on. The goal of the adoption of the electronic passport is not only to expedite processing at border crossings, but also to increase security. The paper explores the privacy and security implications of this impending worldwide experiment in biometrics authentication technology.

In this research, model reference fuzzy based control is presented as robust controls for IC engine. The objective of the study is to design controls for IC engines without the knowledge of the boundary of uncertainties and dynamic information by using fuzzy model reference PD plus mass of air while improve the robustness of the PD plus mass of air control. A PD plus mass of air provides for eliminate the mass of air and ultimate accuracy in the presence of the bounded disturbance/uncertainties, although this methods also causes some oscillation. The fuzzy PD plus mass of air is proposed as a solution to the problems crated by unstability. This method has a good performance in presence of uncertainty.

In this paper we study fuzzy soft matrix based on reference function.Firstly, we define some new operations such as fuzzy soft complement matrix and trace of fuzzy soft matrix based on reference function.Then, we introduced some related properties, and some examples are given. Lastly, we define a new fuzzy soft matrix decision method based on reference function.