Compressed sensing (CS) is a sparse signal sampling methodology for efficiently acquiring and reconstructing a signal from relatively few measurements. Recent work shows that CS is well-suited to be applied to problems in genomics, including probe design in microarrays, RNA interference (RNAi), and taxonomic assignment in metagenomics. The principle of using different CS recovery methods in these applications has thus been established, but a comprehensive study of using a wide range of CS methods has not been done. For each of these applications, we apply three hitherto unused CS methods, namely, l1-magic, CoSaMP, and l1-homotopy, in conjunction with CS measurement matrices such as randomly generated CS m matrix, Hamming matrix, and projective geometry-based matrix. We find that, in RNAi, the l1-magic (the standard package for l1 minimization) and l1-homotopy methods show significant reduction in reconstruction error compared to the baseline. In metagenomics, we find that l1-homotopy as well as CoSaMP estimate concentration with significantly reduced time when compared to the GPSR and WGSQuikr methods.

Autism Spectrum Disorder is a developmental disorder that may manifest in a myriad of ways such as difficulties in social interaction and a tendency to engage in repetitive patterns of behaviour. Over the years, several kinds of treatment protocols have been proposed and implemented. One such area that is attracting the attention of researchers in the field is a robot-based approach in the treatment of children diagnosed with the disorder. Here we propose a viable method via the integration of apex technological methods like Artificial Intelligence, Machine Learning and Medical Robotics, coupling it with problem specific algorithms in OpenCV along with principles of Applied Behavioural Analysis to help possibly alleviate a key symptom displayed by children in terms of level of social interaction - that of eye-contact. This would be achieved via an AI-integrated Robotic Framework. The project also considers the possibility of inclusion of the growing research field of Quantum Computing to realize the process and investigates its viability as a potential source of innovation in the future.

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Abstract-The human heart is a nonlinear system because the heart rhythm is modulated by the Autonomic nervous system (ANS).The modulation of the heart rate about its mean value is called Heart rate variability (HRV). Nonlinear analysis of HRV is helpful to assess the cardiac health noninvasivly.. The analysis of HRV data of different sets of subjects is done using ApEn.The autonomic dysfunction also causes cardiac malfunction. The Subjects involved in meditation is taken as one set. The other set is depressed subjects. The third set is congestive heart failur(CHF) subjects. The fourth set is healthy subjects. Also The effect of ANS on cardiac functioning is investigated, before and after the meditation for the set of meditation subject and also for the set of depressed subjects. The cardiac health status is also investigated for the set of Congestive heart failure (CHF) subjects and and healthy subjects. It has been found that the ApEn values indicated improved cardiac health after meditation.The ApEn values of depressed subjects is lower than that of healthy subjects.The high values of ApEn for healthy subjects suggest that the functioning of heart is dynamic.The lower ApEn values of the depressed subjects observed indicate the presence of the Autonomic dysfunction ,which in turn effected cardiac function indirectly causing heart to exhibit less dynamism.. The CHF data showed the Apen values lower than healthy subjects indicating cardiac ill health.