Solar-Wind Energy Assessment by Big Data Analysis

Big data refer to the massive datasets that are collected from a variety of data sources for business needs to reveal new insights for optimized decision-making. The solar and wind energy system is the modernization of electrical energy generation systems due to the pollution free nature and the continuous advancement of photo-voltaic and wind turbine system technologies. In the solar and wind energy surroundings, the application of big data analysis based decision-making and control are mainly in the following three aspects: data stream side management, storage side management and load side management. The objective of this research is to present a technological framework for the management of large volumes, variety, and velocity of solar system related information through big data tools such as Hadoop to support the assessment of solar and wind energy system. The framework includes a modeling of system, storage, management, monitoring and forecast based on large amounts of global and diffuse solar radiation and wind energy system. This chapter also includes market basket model, the concept of solar and wind depository and application of the Map Reduce algorithm

Optimized Reversible Logic Multiplexer Designs for Energy-Efficient Nanoscale Computing

Nano- and quantum-based low-power applications are where reversible logic really shines. By using digitally equivalent circuits with reversible logic gates, energy savings may be achieved. Reducing garbage output and ancilla inputs is a primary emphasis of this study, which aims to lower power consumption in reversible multiplexers. Multiplexers with switchable 2:1, 4:1, and 8:1 ratios may be built using the SJ gate and other simple reversible logic gates. The number of ancilla inputs has been cut in half from four to zero, and the amount of garbage output has been cut in half as well, from eight to three, making the 2:1 multiplexer an improvement over the prior design. New 4:1 multiplexer has 10' ancilla inputs, up from 2' in the previous designs. The proposed 4:1 multiplexer also cuts waste production in half from the current 5-to-6 bins per day. The 8:1 multiplexer has two ancilla inputs and nine trash outputs, while the current architecture only has one of each. The functionality of the VHDL and Xilinx 14.7-coded designs is validated by ISIM simulations

Parallel Simulated Annealing Algorithm for Standard Cell Placement in VLSI Design

Simulated Annealing (SA) is a stochastic based heuristic optimization technique based on physical process of metal crystallization. Optimization of Non-Deterministic polynomial hard (NP-hard) problems of non-trivial sizes is done using heuristic approach. Until now, simulated annealing (SA), genetic algorithm (GA) and Hopfield neural network (HNN) were individually used for solving the standard cell placement (SCP) problem. Simulated annealing established as a powerful SCP optimization tool, its drawback has always been its appetite for computational resources. In light of this, we are interested in the application of these parallel simulated annealing algorithms with respect to standard cell placement. Several generalized algorithms proposed for parallelizing simulated annealing, only a few have been applied to cell placement. Parallel moves has been the most popular strategy and in this paper we present a new implementation of this approach

Wireless Sensor Network application to Centralize the Water Tanks Filling & Monitoring System of

Abstract: Wireless sensor networks (WSNs) are increasingly important for enabling continuous monitoring in many fields including environment sciences, water resources, ecosystems, and structural health and health-care applications. In this paper we have suggested an application and design of wireless sensor network for centralization of water tank filling & monitoring process. This system will be very economical in terms of the hardware cost, power consumption and labor utilization