In general, the approximation of Discrete Cosine Transform (DCT) is used to decrease computational complexity without impacting its efficiency in coding. Many of the latest algorithms used in DCT approximation functions have only a smaller DCT length transform of which some are non-orthogonal. For computing DCT orthogonal approximation, a general recursive algorithm is used here, and its length is obtained using DCT pairs of length N/2 of N addition cost in input pre-processing. The recursive sparse matrix has been decomposed by using the vector symmetry from the DCT basis in order to achieve the proposed approximation algorithm that is highly scalable to enforce the highest lengths software and hardware by using a current 8-point approximation to obtain a DCT approximation with two-length power, N>8.

Predicting the Software reliability is a pertinent issue and it is a major concern of software developers and engineers in changing environment considerations. Software reliability models are developed to estimate the probability of failure free operation of the software for a long time. Many Software Reliability Growth Models (SRGM) were developed to give the latent number of faults in the software product. However none of these models performing to the expectations of the developers of the software. In this paper, A research is made using artificial neural network models to monitor the performance of the software that leads to predict the software reliability. The MLP model outperforms SVR model, and based on the results, these models can be considered to be a reasonable alternative for software quality prediction.

The role of Computerised data acquisition and energy management system i of vital importance in order to monitor ant control a large power system under real time conditions. In India the computerised load despatch centres were installed at a number of places on regional basis in early eighties. Since at that time the concept of open system and inter-connectivity among the equipment of different vendors was not common, the computerised equipment installed, was based on centralised approach and was of proprietary nature. After using the system for a period of about 10 years, it is seen that considerable equipment has became unserviceable due to obsolosence, non availability of spares and/or high expenditure on maintenance. In order to overcome such a situation in the Northerp Region of the country, a scheme for providiqg t h e real time data acquisition and energy management system has been initiated in early 1994. The scheme i s being carried out through indigenous efforts, using the hardware and software in distributed architecture and on the concept a€ open system and connectivity. The paper covers the details of the scheme, in particular the distributed architecture and the advantages derived thereof.

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In this paper, we have proposed a low complexity architecture for the Under-determined Blind Source Separation (UBSS) algorithm targeting remote healthcare applications. UBSS algorithm, departing from the typical BSS convention-equal number of the sources and sensors present, which is of tremendous interest in the field of Biomedical signal processing especially for remote health care applications. Since such applications are constrained by the on-chip area and power consumption limitation due to the battery backup, a low complexity architecture needs to be formulated. In this paper, firstly we have introduced UBSS architecture, followed by the identification of the most computationally intensive module N-point Discrete Hilbert Transform (DHT) and finally proposed a low complexity DHT architecture design to make the entire UBSS architecture suitable for such resource constrained applications. The proposed DHT architecture implementation and experimental comparison results show that the proposed design saves 50.28%, 48.40% and 46.27% on-chip area and 53.25%, 48.01% and 45.95% power consumption when compared to the state of the art method for N = 32, 64 and 128 respectively. Furthermore the proposed DHT architecture works for N = 2m point, but the state of art architecture works for N = 4m point, where m is an integer.