The project is done for a Canadian construction company. The objective of the project is the analysis of single channel and two channel vibration signals from beams, measured with accelerometers.
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Nuclear magnetic resonance (NMR) imaging is done to identify the composition of chemicals by analyzing the peaks in the spectra. Each peak corresponds to each chemical. The identification becomes challenging due to the presence of false peaks, overlapped peaks, etc. The objective of the project is to separate out the peaks with the help of NMRglue and Python. Several Python functions were written to locate the peaks, separate out the regions, and make contour plots of the spectra.
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An adaptive wavelet thresholding function is employed to denoise the NMR data. The data is decomposed with Haar wavelet transform into different subbands, based on the noise content and adaptive thresholds are applied.
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NMR data are simulated, clustered into groups, and masks are generated.
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This proposal broadly aims at the adaptation of technology to improve the operation of KSRTC bus routes so that the earning per km is maximized and the reliability of operations is ensured. A pilot project was implemented for Kerala State Road Transport Corporation (KSRTC) to analyze the routes in terms of revenue and passenger traffic.
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A pilot project was implemented for Pune Mahanagar Parivahan Mahamandal Limited (PMPML) to analyze the existing 47 city bus routes in terms of revenue and passenger traffic and to suggest new routes, based on the inferences from statistical analysis
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In this project, letter data obtained from images of invoices are denoised using a modified autoencoder-based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in the encoder network and decoder network.
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