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Articles by P. Manimegalai
Total Records ( 2 ) for P. Manimegalai
  M. Arun and P. Manimegalai
  Wireless Sensor Networks (WSNs) have the prospect to become the most crucial technology of the future. Based on the applications, there is a need to locate the physical location of sensor node to improve the performance. This is known as localization problem. Some traditional localization algorithms are used but still convergence problem exists. So, to solve the above problems and obtain an efficient location identification, a system has been designed using machine learning and swarm intelligence. In this research, a Relevance Vector Machine (RVM) with Glow-worm Swarm behaviour based optimization Algorithm (GSA) is proposed for efficient localization. Here, the trilateration, triangulation and Maximum Likelihood (ML) based location discovery process is focused. For high accurate localization, the proposed system considers the node density factor. In this process, the node is in the overlapping region of circles considered as trilateration problem and it is solved by RVM. The RVM is mainly used for splitting the anchor and overlapping region node and similarly to find the weight for those nodes, so that, the processing time is reduced. After finding the innermost intersection of a point, the GSA is used to update the archive based on the distance and geometric topology constraints. The evaluation of proposed RVM-GSA localization is compared with Average Weight Based Centroid Localization (AWBCL) algorithm with the help of MATLAB tool. The obtained result shows that the proposed RVM-GSA algorithm is a promising scheme that can minimize the localization problem.
  R. Suresh Kumar and P. Manimegalai
  Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real time and recorded signals in multisensory instruments contains different and huge amount of noise and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique in VLSI to evaluate their parameters such as area utilization, power dissipation and computation time.
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