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Home / programming language / FPGA / An Efficient Reverse Converter for the Three-Moduli Set ( 2 n +1 − 1 , 2 n, 2n − 1)
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An Efficient Reverse Converter for the Three-Moduli Set ( 2 n +1 − 1 , 2 n, 2n − 1)

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SKU: PROJ8256 Categories: 2018-2019 Projects, FPGA, VLSI Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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An Efficient Reverse Converter for the Three-Moduli Set ( 2 n +1 − 1 , 2 n, 2n − 1)

Abstract– The well-known three moduli set (2 n + 1 , 2 n, 2 n −1), where n is a positive integer, has received a considerable attention over the last three decades. Many researchers have proposed residue-to-binary (reverse) converters for this moduli set. However, the problem of designing a reverse converter for the moduli set (2 n +1 − 1, 2 n, 2 n − 1) did not receive a significant attention. In this paper, we are proposing a reverse converter for this particular moduli set. When compared with the available similar converters, considerable reductions have been achieved. Using VLSI design tools, the area, time and power reductions are in the range of: (10.6 −60.1)%, (13.8 −25.2)% and (7.7 −52.1)%, respectively.

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<strong>Abstract</strong>- While the global production of wind energy is increasing, there exists a significant gap in the academic and practice literature regarding the analysis of wind turbine accidents. This paper presents the results obtained from the analysis of 240 wind turbine accidents from around the world. The main focus of this paper is revealing the associations between several factors and deaths and injuries in wind turbine accidents. Specifically, the associations of death and injuries with the stage of the wind turbine’s life cycle (transportation, construction, operation, and maintenance) and the main cause factor categories (human, system/equipment, and nature) were studied. To this end, we conducted a detailed investigation that integrates exploratory and statistical data analysis and data mining methods. This paper presents a multitude of insights regarding the accidents and discusses implications for wind turbine manufacturers, engineering and insurance companies, and government organizations.

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