Background India has over 77 million diabetic adults the second highest globally. Diabetic Retinopathy (DR) affects ~18% of this population and is a leading cause of preventable blindness. Early screening can prevent90% of vision loss, but India has only ~1 ophthalmologist per 100,000 rural population, making mass manual screening infeasible. Existing AI solutions function as black boxes, lack clinical validation rigor, and fail with variable image quality from portable fundus cameras in field conditions. A robust, explainable, and validated screening system is essential for deployment in primary healthcare centres across rural India. Description Design a MATLAB-based retinal image analysis pipeline for automated DR screening addressing real-world deployment challenges 1. Image Quality Assessment and Enhancement Automatically evaluate fundus images for adequacy (focus, illumination, field of view). Apply adaptive enhancement (CLAHE, illumination normalization, denoising) for borderline images; reject ungradeable ones with recapture feedback. 2. Retinal Structure Segmentation Extract clinically relevant structures optic disc/fovea localization, vessel segmentation, microaneurysm detection, exudate segmentation, hemorrhage classification, and neovascularization detection. 3. DR Severity Grading Classify using the International Clinical DR severity scale (Levels 0-4, from no DR to proliferative DR) with clinically acceptable sensitivity (>90%) and specificity (>85%) for referable DR (Level 2+). 4. Explainability Module Implement Grad-CAM attention maps, lesion-level evidence correlated with clinical criteria, calibrated confidence scores, and automated annotated reports enabling ophthalmologist validation in under 30 seconds for a human-in-theloop workflow. 5. Simulink Workflow Simulation Model the telemedicine screening pipeline in Simulink image acquisition rates, bandwidth constraints, processing throughput, and review capacity to optimize resource allocation for district-level programs serving 100,000+ patients annually. This problem demands clinical validation rigor, sub-pixel microaneurysm detection, and clinically meaningful explainability Tools Image Processing Toolbox, Computer Vision Toolbox, Deep Learning Toolbox, Medical Imaging Toolbox, Simulink, Statistics and Machine Learning Toolbox Expected Solution A working prototype demonstrating: DR classification with >90% sensitivity and >85% specificity for referable DR; explainable Grad-CAM outputs rated as clinically useful; a Simulink model optimizing screening resource allocation; and validation against published benchmarks showing the integrated pipeline outperforms any single technique approach.

Development of Smart Scan Strategy for Electronic Warfare in the absence of prior reliable intelligence of emitters and their operating characteristics. Background Detection of hostile communication or radar signals starts with search / scan of a wide frequency spectrum which covers relevant emitters. Sensors with typically high sensitivity but with at least an order lower instantaneous bandwidth compared to overall bandwidth of the system are used to maintain surveillance over the entire spectrum. This requires a receiver / receivers to sweep over frequency bands. Hitherto strategies based on pre mission data / prior data (Open loop) are used. Usually the first priority is to rapidly sweep the entire band with the best speed possible. Open loop strategies focus only on this requirement and may lose time to nonthreatening emitters by not giving time to new or threatening ones. Detailed Description This problem statement focusses on development of Smart Scan Strategy for Electronic Warfare. Interception of signals is a two dimensional search problem since it involves adjusting receiver’s frequency at correct time. This includes building up figures of merit for interception performance such as probability of detection, probability of false alarm, sensitivity, Avg intercept rate, Avg Reward / cost function, percentage of correct predictions and average intercept time error. A system model for the receiver needs to be developed with measurements obtained from a simulated RF environment which has truth information on status of emitters in each band and at each time slot. The frequency spectrum for own receiver consists of many bands. The status of environment for each frequency band at each time step can be recorded as a transmission or a non-transmission. The model should enable prediction of intercept time and interception ratio of a scanning receiver against spatially scanning and frequency agile emitters. Development of a robust scheduler using machine learning to minimize intercept time and ensure a high interception rate is the primary objective of the strategy. The model should then be trained based on hits and misses. Further, approaches to intercept a periodic scan receiver optimally should be outlined. Algorithms and techniques for the same need to be developed. Expected Solution Machine learning based Electronic Support receiver scheduler software