Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
“Blu-Versity” is a prototype implementation for localizing the information gathering within a university campus. By making the information ubiquitous, blu-versity promotes its ability to support a wide range of information gathering for the students. Information availability is particularly low to the students despite of technological advancement. Students within a university campus, find it increasingly difficult to locate and access the essential information. This paper aims at providing a solution for eradicating the above problem, and make the necessary environmental modifications for the students in gathering information. A context-aware university campus is built, in combination with the widespread availability of handheld devices (like mobile devices, PDAs and smart devices) and which eliminate the difficulties present for the students in acquiring the essential information.
Software testing is one of the most important, costly and time consuming phase in software development. Anti-random testing chooses the test case where it's total distance from all previous test cases is the maximum, using the Hamming distance and Cartesian distance as measures of difference. In this paper, the authors present an anti-random technique to achieve high branch coverage in white-box testing, depending on the hypothesis that any two test values with small distance mostly discover the same errors and faults. Experimental results show that anti-random testing yields acceptable results, but the target of branch coverage is not achieved in all cases. We executed the algorithm 60 times over ten different programs, and they found that coverage achieved for eight programs runs with high performance in terms of execution time.
Mobile Ad-hoc Network (MANET) is an emerging area of research in the communication network world. MANET is a group of wireless mobile nodes dynamically establishing a short lived network without any use of network infrastructure or centralized administration. In addition to the high degree of mobility, MANET nodes are distinguished by their limited resources such as power, bandwidth, processing, and memory. In this paper, the authors modified proactive Optimized Link State Routing (OLSR) Protocol for MANET through the proposed new routing algorithm named Energy Saver Path Routing (ESPR) algorithm and the evaluation of this routing algorithm is compared with ordinary OLSR protocol through experiments and simulations. This proposed ESPR algorithm takes minimum energy to find the path between source and destination through edge node calculation. This edge node calculation is executed based on the highest potential score node selection towards destination with sufficient forward capacity. This ESPR algorithm provide better performance than ordinary OLSR and also improved packet delivery ratio, reduced end-to-end delay and reduced transmission power to transfer the packet from source to destination.
There is an increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms used for personal identification. This paper proposed a new method for extracting features from palmprints using the Competitive Coding Scheme and Robust Line Orientation Coding(RLOC) Scheme. The Competitive Coding Scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. This information is then stored in a feature vector called the Competitive Code. In Robust Line Orientation Code for palmprint verification, performance is improved by using three strategies. Firstly, a modified finite Radon transform (MFRAT) is proposed, which can extract the orientation feature of palmprint more accurately and solve the problem of sub-sampling. Secondly, the authors construct an enlarged training set to solve the problem of large rotations caused by imperfect preprocessing. Finally, a matching algorithm based on pixel-to-area comparison has been designed, which has better fault tolerant ability. The experimental results of verification on Palmprint Database show that the proposed approach has higher recognition rate and faster processing speed.
Data mining aims at extracting only the useful information from very large databases. Association Rule Mining (ARM) is a technique that tries to find the frequent itemsets or closely associated patterns among the existing items from the given database. Traditional methods of frequent itemset mining, assumes that the data is centralized and static which impose excessive communication overhead when the data is distributed, and they waste computational resources when the data is dynamic. To overcome this, Utility Pattern Mining Algorithm is proposed, in which itemsets are maintained in a tree based data structure, called as Utility Pattern Tree, which generates the itemset without examining the entire database, and has minimal communication overhead when mining with respect to distributed and dynamic databases. Hence, it provides faster execution, that is reduced time and cost.
