Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

An experimental insight of friction stir welding of dissimilar AA 6061/Mg AZ 31 B joints

Abstract: In the present scenario, aerospace and automobile industries depend on lightweight materials such as magnesium and aluminum alloys because of their great balance between mechanical properties and w...

Multifocus image fusion scheme based on discrete cosine transform and spatial frequency

Abstract: Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give good quality. Hence to obtain a good quality image, this work proposes an algorithm for fusing multifocus images using Discrete Cosine Transform and spatial frequency. The proposed algorithm works for fusing any number of images. The second step calculates the average and maximum of all the source images and reduces the source images to be processed as two. Then Discrete Cosine Transform (DCT) is applied over the two input images. Min-Max normalization is done on the DCT coefficients and fusion is done using spatial frequency. Inclusion of the second step of the proposed algorithm in some existing algorithms such as Stationary Wavelet Transform, Principal Component Analysis and spatial fusion improves the performance. The metrics used for evaluation proves that the proposed algorithm gives better results than the other algorithms using DCT and state of the art techniques.

FPGA based filters for EEG pre-processing

Abstract: The EEG pre-processing steps involve removing noise and artifacts from EEG. The noise from the main source like electro-oculogram, electrocardiogram, electromyogram and other sources should be eliminated to increase accuracy in classification. As these artifacts may be misinterpreted as originating from the brain, there is a need to minimize or remove them from recorded EEG signals. The artifacts are undesirable potentials of non-cerebral origin and eye blinking that contaminate the EEG signal. EEG artifacts originate from two sources namely, physiological and technical. Technical artifacts are mainly due to equipment malfunction; result from poor electrode contact or line interference. Offset, filter settings, or incorrect gain of the amplifier will cause distortion clipping or saturation of the recorded signals. Technical artifacts can be avoided through consistent monitoring, meticulous inspection of equipment and proper apparatus setup. Physiological artifacts arise from a variety of body activities that are either due to movements, skin resistance fluctuations or other bioelectrical potentials. Proper filters need to be designed to filter these artifacts. Moving average filter and Median filters are easy to implement and these filters acts as best pre-processing stage for noise removal. In this paper filters such as Moving average and Median filter are implemented in FPGA (Virtex-5) and compared in terms of area, power and delay. Though Moving average is fast when compared to Median filter. Median filter is the best for pre-processing since it occupies less area and power.