GitHub Repo
https://github.com/averule/Hand-Gesture-Recognition
Digital image processing helps replace several mundane activities. My project in final year was ‘Application Control using Hand Gesture Recognition From 3-Dimensional Images’. We worked extensively on processing a 3-D image to recognize the encrypted gesture, with the added 3rd dimension for more number of gestures. We started with extensive image segmentation to suppress background, handle dynamic lighting conditions and extract only portion of hand from the 3-D image. A tedious task, we researched many papers on IEEE and books on Image processing, to come-up with a complex code logic involving a combination of three different segmentation techniques involving RGB, YCbCr and HSV models. Further, with mathematical calculations involving use of Eigen values and Eigen vectors were derived based on the co-variance matrix, generated as per captured image, we managed to extract maximum information. A Euclidean distance was calculated to denote the deviation of captured image from each set of pre-captured images for all defined gestures. Hectic code optimization helped save precious execution time. The gesture corresponding to minimal Euclidean distance was the identified gesture. This project acquainted me with MATLAB and I learnt about various image enhancement and image segmentation techniques. I presented a paper at IEEE’s International Conference on Convergence of Technology (I2CT), Pune, published in the journal with ISBN “978-1-4799-3759-2”.
GitHub Repo
https://github.com/atharv6f/Feature-Subset-Selection-on-Heliophysics-Time-Series-data
Solar Energy Particles (SEPs) can be associated with solar flares and coronal mass ejections (CMEs) and offer energy spectra ranging from few KeVs to many GeVs. These events can occur without any notable indication and alter the radiation environment of the inner solar systems, which can potentially lead to precarious conditions for humans in space, affect the interior of spacecraft's sensitive electronics, and trigger radio blackouts. Identifying the most critical physical parameters of the Solar Dynamic Observatory (SDO) to detect SEPs can allow for a swift response against its adverse effects. With the profusion of high-quality time series data from the SDO, which accounts for the modulating background of magnetic activity and the inherently dynamic phenomenon of pre-flares and post-flare phases; antithetical to non-representative data with the point-in-time measurements employed earlier, selection of vital parameters for solar flare classification using machine learning algorithms appears to be a well-fitted problem in this realm. The primary issue of dealing with multivariate time series data (mvts) is the large number of physical parameters operating at a rapid frequency, making the data dimensionality very high and thus causing the learning process to curb. Moreover, manually selecting vital parameters is a tedious and costly task on which experts may not always agree on the results. In response, we examined feature subset selection using multiple algorithms on mvts data and the statistical features derived from mvts segments (vectorized data). We used the SWAN-SF (Space Weather Analytics for Solar Flares) benchmark dataset collected from May 2010 - September 2018 to conduct our experiments. The comprehensive study gives a stable scheme to recognize the critical physical parameters, which boosts the learning process and can be used as a blueprint to foretell future solar flare episodes.
GitHub Repo
https://github.com/mhowerton91/history
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