Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Photovoltaic based water pumping system

Abstract: In this paper a stand-alone Photovoltaic (PV) systems is presented for water pumping. Solar PV water pumping systems are used for irrigation and drinking water. PV based pumping systems without battery can provide a cost-effective use of solar energy. For the purpose of improving efficiency of the system perturb and observe (P&O) algorithm based Maximum Power Point Tracker (MPPT) is connected to this system. The aim of this paper is to show how to achieve an effective photovoltaic pumping system without battery storage. Results are presented based on different cases of irrigation pumping application and availability of solar irradiance. Simulation results using MATLAB/SIMULINK show that the performance of the controllers both in transient as well as in steady state is quite satisfactory.

Envisioned speech recognition using EEG sensors

Abstract: Recent advances in EEG technology makes brain-computer-interface (BCI) an exciting field of research BCI is primarily used to adopt with the paralyzed human body parts However, BCI in envisioned speech recognition using electroencephalogram (EEG) signals has not been studied in details Therefore, developing robust speech recognition system using EEG signals was proposed In this paper, we propose a coarse-to-fine-level envisioned speech recognition framework with the help of EEG signals that can be thought of as a serious contribution in this field of research Coarse-level classification is used to differentiate/categorize text and non-text classes using random forest (RF) classifier Next, a finer-level imagined speech recognition of each class has been carried out EEG data of 30 text and not-text classes including characters, digits, and object images have been imagined by 23 participants in this study A recognition accuracy of 8520 and 6703% has been recorded at coarse- and fine-level classifications, respectively The proposed framework outperforms the existing research work in terms of accuracy We also show the robustness in envisioned speech recognition