This paper describes an Internet of things (IoT) based track monitoring system for increasing the comfort of rail track transportation. Usually, rail tracks are inspected only at midnight and also the inspection is carried out only once in a month or lesser. Unfortunately, any emergency issues not detected within the stipulated time leads to train derailment and other safety concerns. Hence in-service vehicle has been introduced to increase the safety of train transportation. In this paper, track irregularities are detected using bogie and car body acceleration measurements and these measurements is further optimized using particle swarm optimization algorithm (PSO). The proposed system is compared with the conventional track geometry measurement system. The monitoring and control information can be sent through IoT. Simulation results are performed for two different speed scenarios and it shows that the proposed system produces better results than the traditional track geometry measurement system and the effectiveness of the proposed method.

The Diabetic Retinopathy (DR) is playing a crucial role in clinical data analysis to diagnose abnormality in retina. Many situations the early stage of patient is not aware of any symptoms until it is too late for effective treatment. The abnormality in the blood vessels of diabetics, a way will be paved for prompt diagnosis of DR. In this work, we proposed the Dehazing method of fundus image to detect and classify the disease condition based on changes in blood vessels using thresholding segmentation technique using mean square error (MSE). Then formulate the area of extracted blood vessels in the subsequent analysis to classify accurately.