Exam seat allocation is one of the major concerns in quality education. With the increasing number of students, subjects, departments and rooms, exam seat management becomes complex. Maintaining a decent exam environment with the proper seating arrangement is one of the difficult jobs for authority. This research offers solution for exam seating arrangement problems that can be achieved through the sequential execution of three proposed algorithms. This research offers a solution for preventing some exam hall cheating by arranging seats for large students and it also finds out the best combination of rooms to be assigned for the exam to organize perfect seating based on the room orientation and size, number of students, differentiation of subjects. To do this research, we have collected data and methods from a university those are being used for their exam seating arrangement. By using university exam information we test our algorithms. It provides better seating plan then the manual system used by the university.
Lip reading, also known as visual speech processing, means recognition of spoken word based on the pattern of lip movements while speaking. Audio speech recognition systems are popular since last many decades and have achieved a great success, but recently visual speech recognition has incited the interest of researchers towards lip reading. Lip reading has an added advantage of high accuracy and noise independency. This paper presents an algorithm for automatic lip reading. The algorithm consists of two main steps: feature extraction and classification for word recognition. The lip information is extracted using lip geometric and lip appearance features. The recognition of words is done by Learning Vector Quantization neural network. The accuracy achieved by proposed approach is 97%. The proposed algorithm is applied for recognition of ten words of Hindi language and can be easily extended to include more words of other languages. The presented approach will be helpful for hearing impaired or dumb people to communicate with humans or machines. The proposed algorithm is fast as well as robust to various occlusions.
Query optimization is an invigorating task of any database system. A number of heuristics has been designed and implemented to optimize database queries. The imperishable developments in the field of Decision Support System (DSS) databases are presenting data at an exceptional rate. The massive volume of DSS data is consequential only when it is able to access and analyze. Here, an effort is made to optimize the system resources (Total Costs) required to execute a distributed DSS query. An innovative hybrid idea of Information Theory (Entropy) and restricted stochastic algorithm called Entropy based Restricted Stochastic Query Optimizer (ERSQO) has been used and implemented. The results of the proposed hybrid approach are compared with other DSS query optimizer techniques viz. Exhaustive Enumeration Query Optimizer (EAQO), Simple Genetic Query Optimizer (SGQO), Novel Genetic Query Optimizer (NGQO) and Restricted Stochastic Query Optimizer (RSQO). In addition, the effect of inter-site and intra-site parallelism on the design of query optimizer has also been examined. Finally, the results of different DSS query optimizer are statistically analyzed. The experimental study reveals that results obtained using ERSQO are 0.35%, 0.6% and 1.6% more consistent than RSQO, NGQO and SGQO respectively.
Query optimization and indexing have an immense impact on database optimization. This has been considered in many different perspectives which provide several different solutions in each case. The purpose of this paper is to primarily provide a comprehensive review and discussion of the core problems through the research on query optimization technology and indexing, based on a number of optimization techniques commonly used in the general approach of a query. A new database optimization model is designed and experiments show that this model can significantly reduce the amount of query execution time to improve the optimization efficiency. Regardless of research on the database optimization model, significant work has been done to comparatively evaluate three scenarios; non-optimized query, optimized query without following any optimization standard, and optimized query via proposed optimization model using the same experimental methodology. Further, this paper describes a benchmark that was developed specifically for the purpose of measuring the quantitative evaluations of the proposed database optimization model.
The technique of body biasing has achieved great success in modern IC design as in it, is possible to alter the threshold voltage of MOS transistor so that device leakage and timing performance can be improved. In this paper a Schmitt trigger circuit has been designed using four types of body bias techniques. The proposed circuits were simulated in SPICE 180nm CMOS technology parameters. A comparative study of all the proposed Schmitt trigger circuits in terms of lower and upper threshold voltages, noise margins and power dissipation has been done and specific applications for each circuit has been suggested.
