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Work on the ECG device system.&nbsp;<ul><li>Removing noise and filter out the ECG signal.&nbsp;</li><li>Used ARM microprocessor, successive Low Pass filter, and Band PassFilter.</li></ul>

MRI Machine suite.&nbsp;<ul><li>Reduce Image acquisition time by a factor of 6 seconds.&nbsp;</li><li>Image Enhancement.&nbsp;</li><li>Noise removal from an image.</li></ul>

Precision Farming technology:&nbsp;Tea Leaf Diseases Classification: Worked on Hyperspectral images for classification. Using VGG16 CNN model classified 5 types of diseases. Later using VGG19 CNN model and achieve better classification accuracy.&nbsp;Crop Yield Prediction: Calculating NDVI, LAI,LDM for crop health monitoring using hyperspectral images. Other factors also included weather details, mineral contents &amp;, etc.&nbsp;

Container Detection and OCR recognition of Container No. - Used Computer Vision techniques on Drone video of client's ports to detect floating containers and Container No. Trained model with accuracy of about 91% with 27-30 fps and OCR recognition of Container No. - Deployed Model on AWS and created Flask APIs with scheduled instances. - Created Mobile model using .tflite (Post training dynamic range and integer quantization ) -Deployed SSD-Mobilenet tflite model on android phone using Flutter for detecting and counting the containers.

Dedicated and detail-oriented Computer Vision Engineer with 4+ years of experience in developing and testing Deep Learning algorithms for various projects. Proficiency with Python and computer vision. Strong passion for optimizing software design, researching and implementing ML/DL technology for client companies.

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