Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Floating solar photovoltaic systems: An overview and their feasibility at Kota in Rajasthan

Abstract: Floating solar power plant is an innovative approach of using photovoltaic modules on water infrastructures to conserve the land along with increase in efficiency of the module. Additionally, the water is also conserved due to reduction in evaporation of water from the water body. The plant can be installed on a pond, lake, reservoir, or on any other water body. This paper focuses on the floating PV technology, describing the types of floating PV plant along with studies carried out on some floating solar plants. India, with huge energy demand and scarcity of waste land for solar photovoltaic plant in cities, can harness solar energy through floating PV plant technology for sustainable energy production. In this paper, some of the floating PV plants installed in India are reviewed. Feasibility of installing 1 MW floating PV plant each at Kota barrage and Kishore Sagar lake in Kota, Rajasthan are also presented. Energy that could be produced by the two plants along with amount of water saved from evaporation and reduction in CO 2 emissions are also calculated in this paper. 1 MW floating plant at Kota barrage could produce 18,38,519 kWh energy per year and could save 37 million litres of water and can reduce about 1,714 tonnes of CO 2 emissions annually. 1 MW floating plant at Kishore Sagar lake could produce 18,58,959 kWh electrical energy per year and could save 37 million litres of water and can reduce about 1,733 tonnes of CO 2 emissions annually.

Event-Triggered Composite Control of a Two Time Scale System

Abstract: This brief proposes an event-triggered composite control of a two time scale system. A periodic sampling requirement is relaxed and both slow and fast states of the system decide independently when transmitting their current measurements based on a time-dependent triggering rule. The distinct feature of this scheme is that it does not require synchronized measurement updates of its slow and fast dynamics. Further, the theory of singular perturbation is used to decouple the system into slow and fast subsystems, and stability of the system is established. The proposed control strategy guarantees convergence of system states to an adjustable region around origin excluding the Zeno behavior. Simulation results manifest the effectiveness of the proposed approach.

Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features

Abstract: The brain tumor is a disease that affects or harms the brain with unwanted tissues. This is very difficult to detect brain tumor tissue from whole brain. Early detection of tumor is very important to save patient's life. Detection or segmentation techniques are used to detect and segment the brain-tumor region from the MRI images of brain and it is very useful method in recent days. In medical, magnetic-resonance-imaging is a tough field in image processing because accuracy percentage must be very high so doctors could get proper idea about diseases to save patient's life. Some MRI images have been taken as inputs data. The brain-tumor segmentation process is performed for separating brain-tumor tissues from brain MRI images, The MRI images should be filtering such as with the median filtering technique and skull stripping should be done in pre-processing, the thresholding process is being done on the given MRI images with using the watershed segmentation method. Then at last the segmented tumor region is obtained. And then in other phase features extracted by GLCM methods using MATLAB software. Then, the some images have been classified using support vector machine (SVM), this system obtained with the average accuracy of 93.05%. Which is quite better than other conventional models.