Product Data Management aims to provide ‘Systems’ contributing in industries by electronically maintaining organizational data, improving data repository system, facilitating with easy access to CAD and providing additional information engineering and management modules to access, store, integrate, secure, recover and manage information. Targeting one of the unresolved issues i.e., provision of natural language based processor for the implementation of an intelligent record search mechanism, an approach is proposed and discussed in detail in this manuscript. Designing an intelligent application capable of reading and analyzing user’s structured and unstructured natural language based text requests and then extracting desired concrete and optimized results from knowledge base is still a challenging task for the designers because it is still very difficult to completely extract Meta data out of raw data. Residing within the limited scope of current research and development; we present an approach capable of reading user’s natural language based input text, understanding the semantic and extracting results from repositories. To evaluate the effectiveness of implemented prototyped version of proposed approach, it is compared with some existing PDM Systems, in the end the discussion is concluded with an abstract presentation of resultant comparison amongst implemented prototype and some existing PDM Systems.
Towards Increase in Quality by Preprocessed Source Code and Measurement Analysis of Software Applications
In this article two intensive problems to the software raised by the software industry .i.e., identification of fault proneness and increase in rate of variability’s in traditional and product line applications are discussed. To contribute in the field of software product development and to mitigate the aforementioned hurdles, a measurement analysis based approach is proposed. The proposed solution is based on the concepts of analyzing preprocesses source code characteristics, identification of the level of complexity by several procedural measurements and object oriented source metrics and visualize the results in two and three dimensional diagrams. Furthermore, the capabilities, features, potential and effectiveness of implemented solution are validated by means of an experiment.
A Performance Comparison of Adaptive Channel Estimation Algorithms using Cyclic Prefix in OFDM Systems
Cyclic prefix (CP) based adaptive channel estimation algorithms using conventional one-tap zero forcing (ZF) and minimum-mean-square-error (MMSE) equalization have been proposed for orthogonal frequency division multiplexing (OFDM) systems. In this paper, we compare performance of these schemes. Simulation results show that, at low signal-to-noise ratio (SNR), adaptive channel estimator based on one-tap MMSE equalization performs better than the adaptive channel estimator based on one-tap ZF equalization. On the other hand, at intermediate and high values of SNR, the two schemes exhibit nearly equal performance. It therefore turns out that, adaptive channel estimator based on one-tap MMSE equalization is appealing at low SNR. Conversely, the adaptive channel estimator based on one-tap ZF equalization is a simple and compatible alternative at intermediate to high SNRs.