2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)最新文献_第5页

Principal Message 主要的信息

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/c2i454156.2021.9689410

Toormi Mates, Jessica Cotter, Katherine C. Cotter, P. Miles

引用次数: 0

A Tutorial on Design of Datapath and Controller of an ALU using Verilog and Verification using Open Source EDA Tools 使用Verilog和使用开源EDA工具进行验证的ALU数据路径和控制器设计教程

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689339

Guru Prasad

引用次数: 0

Voice Based Deep Learning Enabled User Interface Design For Smart Home Application System 基于语音的深度学习智能家居应用系统用户界面设计

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689435

Shrey Aggarwal, Sachin Sharma

引用次数: 2

Design and Analysis of a Compact CSRR Loaded Defected Ground Plane Antenna for Ultra-wideband Applications 一种超宽带小型CSRR负载缺陷地平面天线设计与分析

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689449

P. Sharma, N. Gupta

引用次数: 0

Optimization of Laser Marking Process on Microelectronic Packages for Aerospace Applications 航空微电子封装激光打标工艺优化

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689414

Poorna Nageswara Rao M, Srinivasarao Bollu, Darukesha B H M, Kamaljeet Singh, Nirmal A V

引用次数: 0

Power Quality Analysis in Microgrid System with Bayesian Regularized Neural Network 基于贝叶斯正则化神经网络的微电网电能质量分析

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689222

Nishant Chaudhari, Chaitanya Nimbargi, Samruddhi Gupta, Aman Kumar, Chetan Khadse

引用次数: 0

Adaptive Deep Learning Model for Air Pollution Analysis Using Meteorological Big Data 基于气象大数据的大气污染分析自适应深度学习模型

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689298

S. Suganya, T. Meyyappan

{"title":"Adaptive Deep Learning Model for Air Pollution Analysis Using Meteorological Big Data","authors":"S. Suganya, T. Meyyappan","doi":"10.1109/C2I454156.2021.9689298","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689298","url":null,"abstract":"To successfully control and prevent air pollution, it is important to focus on mandatory variables of air quality. Various past choices have explored the links between air pollution and related factors. Nevertheless, the techniques now used do not deal well with the problem of multipolarity or refuse to clarify the importance of mandatory variables. Moreover, much of the current writing restricts their focus to a city or a small area and focuses on factors from a single angle. Hence, in this paper, Adaptive deep learning model for air pollution analysis using meteorological big data. The proposed deep learning model is consisting of Deep Recurrent Neural Network (DRNN) and Arithmetic Optimization Algorithm (AOA). Initially, the databases are collected from the open source system. After that, the data's send to the proposed deep learning model for prediction of air pollution. In the DRNN, the weights are selected by utilizing the AOA. The combined model of the adaptive deep learning model is utilized to identify the air pollution prediction. The proposed methodology is implemented in R and performance are evaluated by performance matrices such as accuracy, precision, recall, specificity and F_Measure. The proposed methodology is compared with the conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) respectively.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133153809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

AMD Core for Analog Signal Multiplication in 18nm FinFET Technology 18nm FinFET技术中模拟信号倍增的AMD核心

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689390

Vivek B A, Chiranjit R Patel

{"title":"AMD Core for Analog Signal Multiplication in 18nm FinFET Technology","authors":"Vivek B A, Chiranjit R Patel","doi":"10.1109/C2I454156.2021.9689390","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689390","url":null,"abstract":"This research presents an efficient method for performing analog multiplication. A system with dual voltage inputs and output as a voltage product is developed. Output voltage can be scaled by a factor, and the process has a very low latency. This study demonstrates that analog signal multiplication performed in the digital domain and then translated back to analog output signal can be accomplished with the least amount of delay. The goal of this project is to create a high-speed, low-power ADC, multiplier, and DAC for the AMD Core. Three blocks are used in the operation of an analog signal multiplier. Flash ADC design was chosen for its high performance. The multiplier's design is based on the Urdhva Tiryagbhyam Vedic Sutra, but it has been significantly optimized in terms of PPA using an inverted gate design. A high-speed 4-bit ADC with a power consumption of 1.2mW capable of 425 MSPS, a 10 GHz 4-bit multiplier with a power consumption of 50uW, and an 8-bit DAC with a period as short as 30ps and a power consumption of 21uW are all developed. The architecture of the analog signal multiplier allows for multiplication of two 425MHz signals with an average power consumption of 2.5mW at 0.8V. Cadence 18nm FinFET technology was used to de develop the proposed designs.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MuLSA - Multi Linguistic Sentimental Analyzer for Kannada and Malayalam using Deep Learning 使用深度学习的卡纳达语和马拉雅拉姆语多语言情感分析器

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689301

Merin Thomas, Latha Ca

引用次数: 0

Portable Health Monitoring Module using Machine Learning 使用机器学习的便携式健康监测模块

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689395

Aritri Debnath, Barnali Dey, S. Routray

引用次数: 0