2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)最新文献_第4页

CoSMiC - Common SMart iot Connectiv oneM2M Common Service Platform for Intelligent Transportation System CoSMiC -通用智能物联网连接oneM2M智能交通系统通用服务平台

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689372

Benoygopal E. B, Satheesh G, Prakash Rosayyan, Hemant Jeevan Magadum

引用次数: 0

Deep Learning Based Classification of CP-OFDM RAT-Dependent Signals 基于深度学习的CP-OFDM rat依赖信号分类

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689434

Anxo Tato, P.C. Gonzalez, J. Escudero-Garzás, Luis Pérez-Roca

引用次数: 2

A Reconfigurable Gaussian Base Normalization Deep Neural Network Design for an Energy-Efficient Voice Activity Detector 一种节能语音活动检测器的可重构高斯基归一化深度神经网络设计

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689307

Anu Samanta, I. Hatai, A. K. Mal

{"title":"A Reconfigurable Gaussian Base Normalization Deep Neural Network Design for an Energy-Efficient Voice Activity Detector","authors":"Anu Samanta, I. Hatai, A. K. Mal","doi":"10.1109/C2I454156.2021.9689307","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689307","url":null,"abstract":"Deep neural networks and approximation computation are offered in this research report to construct an energy-efficient voice activity detector. A potential energy-efficient adaptive GNDNN accelerator architecture is proposed for voice activity detection (VAD).The VAD method is divided into two sections: feature extraction and voice/noise classification using a deep neural network with Gaussian basis normalisation (GNDNN).Initial pre-processing of input data: The high-frequency components of the digitalized voice signal are pre-emphasized, and you try to put it together with a little later in the signal processing, you'll be less sensitive to finite precision effects. The feature extraction module extracts the input voice signals feature value using MFCC, or Time-Frequency Non-negative Matrix Factorization (TFNMF). The GNDNN speech prediction phase classifies the TFNMF, MFCC output from feature extraction, determining if the signal is a voice or noise. The proposed method can be adjusted dynamically to satisfy various computation accuracy requirements. The most interesting accuracy result of our proposed approach is 98.75 percent. Similar to CNN, which reach accuracy of 97.25 percent.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"10 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":"130631437","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

Pipelined Distributive Arithmetic-based FIR Filter Using Carry Save and Ripple Carry Adder 基于进位保存和纹波进位加法器的流水线分配算法FIR滤波器

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689396

M. Saritha, C. Radhika, M. N. Reddy, M. Lavanya, A. Karthik, V. Vijay, Rajeev Ratna Vallabhuni

{"title":"Pipelined Distributive Arithmetic-based FIR Filter Using Carry Save and Ripple Carry Adder","authors":"M. Saritha, C. Radhika, M. N. Reddy, M. Lavanya, A. Karthik, V. Vijay, Rajeev Ratna Vallabhuni","doi":"10.1109/C2I454156.2021.9689396","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689396","url":null,"abstract":"FIR filter bank play a vital role in any signal processing systems. The FIR filter is used to implement any frequency response digitally. Usually, the FIR filters contain multipliers, adders and many delays. As we know, the number of MAC operations (multiply and accumulate) makes the FIR filter less efficient and increases hardware complexity. So in this project, we included DA (Distributed Arithmetic) architecture in designing the efficient adaptive FIR filter. The pipelined Distributive Arithmetic architecture provides less power, less area and high values of throughput in creating an adaptive FIR filter. These are used in Echo cancellations, Radio channel equalizers and speech coding. DA architecture also helps in improving the processing speed of the FIR filter by eliminating the number of multipliers. A reconfigurable filter design can be achieved with the help of this Distributive Arithmetic architecture. Here the sum of the partial products which is pipelined is passed as the input values, and it is stored in the LUT (lookup tables) of the distributive architecture. By replacing the adder of the shift accumulation unit with a carry-save adder, the area of the proposed design is reduced. By placing the ripple carry adder, the size and delay are reduced to a further extent. Here we will use the Xilinx ISE tool to verify the simulation result, area required and time consumed.","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":"131660766","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: 2

Diamond Price Prediction using Machine Learning 利用机器学习预测钻石价格

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689412

Harshvadan Mihir, Manish I. Patel, Soham Jani, Ruchi Gajjar

{"title":"Diamond Price Prediction using Machine Learning","authors":"Harshvadan Mihir, Manish I. Patel, Soham Jani, Ruchi Gajjar","doi":"10.1109/C2I454156.2021.9689412","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689412","url":null,"abstract":"Diamond is one of the strongest and the most valuable substances produced naturally as a form of carbon. However, unlike gold and silver, determining the price of a diamond is very complex because many features are to be considered for determining its price. This paper aims to come up with the most efficient algorithm for the price prediction of diamonds. The algorithms such as Linear regression, Support Vector regression, Decision trees, Random Forest regression, K-Neighbors regression, CatBoost regression, Huber regression, Extra tree regression, Passive Aggressive regression, Bayesian Regression and XGBoost Regression are used to train the particular machine learning models on the diamond dataset for the prediction of diamond prices based on various attributes. The comparative analysis of various Machine Learning Regression models is done for the price prediction of any diamond. From the performance parameter values and analysis, it was found that the CatBoost Regression algorithm proved to be the most optimal algorithm having an R2 score of 0.9872 and formidable training and testing accuracies of 98.74% and 98.72% respectively. Hence, the CatBoost algorithm has been implemented for the price prediction of a diamond specimen with the help of the values of attributes extracted from an image of a diamond certificate.","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":"131380739","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: 4

Devanagari Handwritten Compound Character Recognition Using Various Machine Learning Algorithms Devanagari手写复合字符识别使用各种机器学习算法

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689425

S. Deore

{"title":"Devanagari Handwritten Compound Character Recognition Using Various Machine Learning Algorithms","authors":"S. Deore","doi":"10.1109/C2I454156.2021.9689425","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689425","url":null,"abstract":"Handwriting is unique for each person. The formation of letters, compound characters, spaces and numbers by each person is so unique that handwriting is considered as legal evidence in forgery cases. Hence accurate recognition of handwritten characters holds huge significance. Especially compound characters are differing in writing ways and there are some characters which can be written in many ways so it is complex to recognize. Segmentation is another issue occurs due to structure of the character is very complex. This paper proposes a method to recognize compound handwritten Devanagari characters using several machine learning algorithms like K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM). Techniques used for extracting features from the images are playing a significant role in the identification of compound characters. In this paper, the Zoning feature extraction technique is used combined with other features like eccentricity, equivalent diameter and angle. We created a new dataset of 45 compound characters. Using 10 cross-validation we achieved the highest 96.62% accuracy by SVM classifier with training time 1.56 seconds.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"66 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":"116281627","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

Twitter Sentiment Analysis using Machine Learning and Deep Learning Techniques 使用机器学习和深度学习技术的Twitter情感分析

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689241

Vedant Pandya, Anuja Somthankar, Shivanshu S Shrivastava, Megharani Patil

引用次数: 6

Thermal Aware Floorplanner for Multi-Layer ICs with Fixed-Outline Constraints 具有固定轮廓约束的多层集成电路的热感知地板规划器

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689353

J. Shanthi, D. Rani, S. Rajaram

{"title":"Thermal Aware Floorplanner for Multi-Layer ICs with Fixed-Outline Constraints","authors":"J. Shanthi, D. Rani, S. Rajaram","doi":"10.1109/C2I454156.2021.9689353","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689353","url":null,"abstract":"In the deep sub-micron technology, the chip size is increasing exponentially with the increasing number of functional modules that need to be placed on the single layer. Also, the wirelength and chip temperature are increasing which degrades the performance of the chip. The Three Dimensional Integrated Circuit (3D IC) structure is found as an alternative for conventional Two-Dimensional IC (2D IC). The 3D IC has attractive features like vertical stacking of layers, and Through-Silicon-Via (TSV) interconnect which helps to reduce chip area and wirelength. But, there are few bottleneck challenges in the design phase, such as increased number of TSVs that occupies more chip area and chip temperature due to heterogeneous integration. In order to address these challenges we aim to present a three-phase multi-objective optimization algorithm based on the Genetic Algorithm (GA) and Simulated Annealing (SA) during the floorplanning stage. The three phases of the proposed algorithm are (i). Layer Assignment, (ii). Intra-layer floorplanning, (iii). Inter-layer floorplanning. The modern design metrics such as chip area, wirelength, number of vias, and chip temperature are considered for the optimization. We attained the best results in the state-of-the-art GSRC benchmark circuits, giving a maximum of 8.19% reduction in TSV count and 23.85% reduction in the chip peak temperature.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"1084 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":"116034605","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

IoRT Based Denoising Video Streams Through Employing Special Filtering Systems in Health Care Region 基于IoRT的医疗区域视频流降噪研究

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689334

Mani Deepak Choudhry, B. Devi

{"title":"IoRT Based Denoising Video Streams Through Employing Special Filtering Systems in Health Care Region","authors":"Mani Deepak Choudhry, B. Devi","doi":"10.1109/C2I454156.2021.9689334","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689334","url":null,"abstract":"The IoT is an edition on the Web which doesn't need man interaction. The IoT is a worldwide community of vast amounts of cognitive as well as a wired “things” that link virtual and physical entities to grow the planet's borders. Each and every day, these common wise issues create huge quantities of information, necessitating immediate information processing on an assortment of sensible products. Interconnecting nodes when they're divided of big topographical places that occur in Internet of Remote Things has been already highlighted as a significant struggle for Internet of Remote Things (IoRT). It might be expected to predominate within remotely overseeing individuals inside cases in which connectivity infrastructure which allows videos transmissions from ICU is crucial. Sound, which is typical in low light factors or perhaps against restricted functionality digital cameras, is a tremendous reason for compression as well as quality wreckage. Denoising was utilized no matter the code for post-processing or pre-processing in previous investigations that attempted to incorporate a denoise algorithm along with a video recording encoder. Denoising is an action within the pre processing method which gets better the picture quality and also causes it to be much more uplifting. The SNR ratio is rather high, thus to eradicate it a non linear air filter strategy known as Median Filter is utilized. A lot of Median filter systems can be found but 3D air filtering allows for more effective steps within protecting the tips of video clip frames. For benchmarking collective functionality PSNR, SSI and UIQI are widely-used as quality evaluation metrics to make a high performance racket clear video clip. The simulation results reveal that the three dimensional median versions outshine additional picture interference air filtering approaches within a selection of metrics, leading to a powerful racket filtration system for IoT devices.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"3 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":"126468312","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

Radio Mean Labeled Graphs to Generate Keys in Cryptography 在密码学中生成密钥的无线电平均标记图

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689270

M. Saraswathi, K. Meera

引用次数: 2