SAURAV BISWAS

With the enhancement of wireless communication and their higher data demand, telecom network operators are continuously deploying the cellular base stations (BSs). This enormous growth of cellular BSs receiving a huge amount of energy and creating immense pressure on the fossil fuel reservation by releasing the greenhouse gas (GHG) emissions. The main objective of this work to build a cost-effective and environment-friendly cellular network powered by the locally available renewable energy sources such as solar photovoltaic (PV), wind turbine (WT), and biomass generator (BG). This article addresses the key challenges of developing a green mobile communication to minimize the net present cost and GHG by maximum utilization of renewable energy. For ensuring the guaranteed continuity of power supply, an adequate battery bank is connected with the hybrid supply system. The technical criteria, optimal component size, and energy issues of the hybrid solar PV/WT/BG powered cellular BSs are...

Abstract--- Android is a Linux-based, open-source operating system(OS) designed to use on cell phones, e-readers, tablet PCs, and other mobile devices, capable of being used as an alternative for computers having bigger form factors. Android provides a very cheap and easy platform to be the brain for controlling a robot, with arduino being an interface to connect to the H/W and controlling it. In the present paper the authors have designed and implemented the controlling of a robot’s movements using distinct colored objects as recognized by the Android Application. This idea may be further extended to control a robot from a remote place also.

lead investigator. Shannon Colton also provided input to technical work reported on in this scientific notebook. All entries in this scientific notebook were made by Saurav Biswas unless otherwise indicated in this text. This notebook document procedures, data, and modeling results used in evaluating magnetic anomalies under 20.06002.01.352- SUPPORT PRELICENSING TRANSITION TO LICENSE APPLICATION REVIEW-MSOP. This text and supporting files are provided herein to meet the CNWRA requirements of QAP-001. CNWRA data contained in this report meet quality assurance requirements described in the CNWRA Quality Assurance Manual. Data used to support conclusions in this report taken from documents published by the U.S. Department of Energy (DOE) contractors and supporting organizations were generated according to the quality assurance program developed by DOE for the Yucca Mountain Project. Maps and anomaly models were generated and plotted by the software Oasis

the lead investigator. All entries in this scientific notebook were made by Saurav Biswas unless otherwise indicated in this text. This notebook document procedures, data, and modeling results used in evaluating regional scale geologic cross-sections under 20.06002.01. 292 —

In recent years, deep neural networks have led to considerable advances in the performance of neural network architectures. However, deep architectures tend to have a large numbers of parameters, leading to long training times and the need for huge amounts of training data and regularization. In addition, biological neural networks make extensive use of recurrent and feedback connections, which are absent for most commonly used deep architectures. In this paper, we investigate the use of recurrent neural networks as an alternative to deep architectures. The approach replaces depth with recurrent computations through time. It can also be seen as a deep architecture with parameter tying. We show that for a comparable numbers of parameters or complexity, replacing depth with recurrency can result in improved performance.