A Suyampulingam's research while affiliated with Amrita Vishwa Vidyapeetham and other places

The growing need for automation has a significant impact on our daily lives. Automating the essentials of our society like transportation system has plenty of applications like unmanned ground vehicles in military, wheel chair for disabled, domestic robots, etc., There are driving, braking, obstacle tackling etc., to a transportation system that can be automated. This paper particularly focuses on automating the obstacle avoidance which provides intelligence to the vehicle and ensures a high degree of safety and is performed using image processing algorithms. Edge based detection, image segmentation, and Machine Learning based method are the three image processing techniques used to detect and avoid obstacles. Haar cascade classifier is the machine learning method where Haar cascade analysis is performed for better accurate results with justifying graphs and parametric values obtained. A comparison of the three image processing algorithms is also tabulated considering obstacle size, colour, familiarities and environmental lightings and the best image processing algorithm is inferred.

In the twenty-first century, there is a drastic depletion of non-renewable resources and a steady rise in greenhouse gas emission which causes global warming. In this case, it is wise to increase the use of renewable energy instead of non-renewable ones which will reduce the consumption of AC power. Sustainable energy sources like solar and wind are also used to control AC or DC loads, yet the execution is unsteady because of the ecological issues. Hence, it is essential to have a system with various input power sources which could provide power during a power-cut, grid failure and peak demand. It ensures better to maximum energy utilization and also results in a low electricity bill. This paper analyses the battery energy management which is crucial in the lives of the residential consumers by forming a system which can be operated both in grid connected and battery fed modes. To perform switching, it is mandatory to monitor both the input power sources and the loads simultaneously. When the loads are light loads and the charge of the battery is sufficient to drive the load, the loads are driven through the DC source. When the loads are heavy loads or the charge of the battery is less, the consumer cannot rely upon the DC source for driving the load. So, AC source is used to drive the loads. Depending upon the type of load, battery status and grid availability automatic switching takes place between the AC and the DC source.

Technology advancements in healthcare give us a platform to address ever-changing patient requirements and healthcare reforms. Remote Health Monitoring Systems, which work in real time, have been identified as a solution to address the issue that current healthcare facilities may not be able to handle huge crowds of people in an efficient manner. This paper discusses the design and development of a system for real-time monitoring of health and important body parameters like blood pressure, body temperature and pulse rate, using sensors connected to a system on chip. The data is stored in cloud using Internet of things, and the collected along with preliminary analysis is communicated to the user, as well as healthcare providers, and next of kin, alerting them to any possible emergencies.