Ashoke Sinha

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This paper illustrates the applicability of neural networks in classifying events using Space Acceleration Measurement System (SAMS) data. Computer programs have been written in the MATLAB environment for the following purposes: automatic retrieval of SAMS data from NASA CDROM disks, computation of power spectral densities for SAMS data and construction(More)
Filtered backpropagation (FBPP) is a well-known technique used in Diffraction Tomography (DT). For accurate reconstruction of a complex-valued image using FBPP, full 360◦ angular coverage is necessary. However, it has been shown that by exploiting inherent redundancies in the projection data, accurate reconstruction is possible with 270◦ coverage. This is(More)
In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from(More)
The Space Acceleration Measurement System (SAMS) has been developed by NASA to monitor the microgravity acceleration environment aboard the space shuttle. The amount of data collected by a SAMS unit during a shuttle mission is in the several gigabytes range. Adaptive Resonance Theory 2-A (ART2-A), an unsupervised neural network, has been used to cluster(More)
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