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

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