2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)最新文献_第10页

An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene 基于改进高斯混合法的室外运动目标检测模型

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616883

Supriya Agrawal, P. Natu

{"title":"An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene","authors":"Supriya Agrawal, P. Natu","doi":"10.1109/icecct52121.2021.9616883","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616883","url":null,"abstract":"Detection of moving objects has become an essential step in video surveillance applications. Due to lack of automatic thresholding and self-adaptive updating of background model at pixel level, foreground-background separation is not correctly classified. In this research work, we have proposed a novel approach to detect moving objects (like person and vehicle) from static scene using single stationary camera. Firstly, we used statistical background model Gaussian Mixture Model (GMM) to generate the binary mask. At this stage, we have tuned GMM parameters to update the background model pixel wise. Then, blob analysis connected components labeling and morphology operations have been applied as post processing step to detect foreground efficiently. The proposed work was experimented on two benchmark datasets PET 2006 and Highway. The performance of the proposed approach is analyzed by calculating precision, recall, f1-score and accuracy. Experimental results reveal that the proposed method performs well as compared to popular Gaussian based and Non-Gaussian based methods","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129428392","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: 3

Modified inertia weight approach in PSO algorithm to enhance MMSE Equalization 改进PSO算法中的惯量权方法增强MMSE均衡性

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616720

D. Diana, S. Rani

引用次数: 3

Regional Congestion Aware Odd Even Routing with Fair Arbitration for Network on Chip 基于公平仲裁的片上网络区域拥塞感知奇偶路由

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616741

Rohith Somisetty, V. Karthik, M. R. S. Srujan, M. Vinodhini

{"title":"Regional Congestion Aware Odd Even Routing with Fair Arbitration for Network on Chip","authors":"Rohith Somisetty, V. Karthik, M. R. S. Srujan, M. Vinodhini","doi":"10.1109/icecct52121.2021.9616741","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616741","url":null,"abstract":"Advancement in current VLSI technologies increases the number of cores or Intellectual Properties (IPs) integrated in System on Chip (SoC) and this results in many-core and multi-core SoCs. Traditional interconnects used in these SoCs introduce multiple processing and traverse delays. In SoC, Network on Chip (NoC) overcomes the problem faced with traditional interconnect and provides an effective and efficient data transfer between the cores. Further in NoC, there is a growing need for routing algorithms which are flexible enough to provide minimal paths and congestion awareness. In this paper, a Regional Congestion Aware Odd Even routing algorithm with Fair Arbitration (RCAOE-FA) based on adaptive Odd Even (OE) turn model is proposed which provides deadlock freedom, on a 2D mesh topology based NoC. The proposed algorithm is implemented and analyzed using the cycle accurate simulator for four different traffic patterns namely – Random, Transpose-1, Transpose-2 and Bit-Reversal. The results of the same are compared with the predecessor algorithm under all the aforementioned traffic patterns. After implementing the algorithm it is observed that, the proposed routing algorithm shows considerable reduction in packet latency for transpose 1, transpose 2, bit reversal and random synthetic traffic patterns.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122573860","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

Automated Brain Tumor Detection Model Using Modified Intrinsic Extrema Pattern based Machine Learning Classifier 基于改进内禀极值模式的机器学习分类器自动脑肿瘤检测模型

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616764

K. Sankaran, A. S. Poyyamozhi, Shaik Siddiq Ali, Y. Jennifer

引用次数: 0

Design of Highly Nonlinear Asymmetric Bored Core Hexagonal Photonic Crystal Fiber with Large Negative Dispersion 大负色散高非线性非对称钻孔芯六方光子晶体光纤的设计

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616767

S. Biswas, Rishad Arfin, Syed Athar Bin Amir, Z. Zahir, Ashfia Binte Habib, R. Khan

{"title":"Design of Highly Nonlinear Asymmetric Bored Core Hexagonal Photonic Crystal Fiber with Large Negative Dispersion","authors":"S. Biswas, Rishad Arfin, Syed Athar Bin Amir, Z. Zahir, Ashfia Binte Habib, R. Khan","doi":"10.1109/icecct52121.2021.9616767","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616767","url":null,"abstract":"In this work, we present a novel design of hexagonal photonic crystal fiber (H-PCF) with asymmetric bored core, which simultaneously achieves large negative optical dispersion and high nonlinearity. To carry out the investigation of the optical characteristics of the proposed H-PCF numerically, electromagnetic solver based on finite element method (FEM) is employed. The modal properties of the photonic fibers such as dispersion and nonlinearity are explored over a wide spectral range from 1.35 μm to 1.6 μm by tuning the optimum geometrical parameters of the design. The numerical study shows that a significant dispersion of –2013 ps/(nm.km) with a nonlinear coefficient of 109.9 W−1km−1 is obtained at the operating wavelength of 1.55 μm. One of the advantages our proposed structure offers is the design flexibility during the fabrication process since the proposed structure comprises periodic arrangement of circular air holes only. The enhanced negative dispersion and nonlinearity of our proposed photonic structure make it a reliable candidate for high speed optical broadband communication and nonlinear optical processes.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126920635","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

Artificial (or) Fake Human Face Generator using Generative Adversarial Network (GAN) Machine Learning Model 使用生成对抗网络(GAN)机器学习模型的人工(或)假人脸生成器

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616779

Mohana, Daanish Mohammed Shariff, Abhishek H, A. D.

{"title":"Artificial (or) Fake Human Face Generator using Generative Adversarial Network (GAN) Machine Learning Model","authors":"Mohana, Daanish Mohammed Shariff, Abhishek H, A. D.","doi":"10.1109/icecct52121.2021.9616779","DOIUrl":"https://doi.org/10.1109/icecct52121.2021.9616779","url":null,"abstract":"Graphics algorithms for high quality image rendering are highly involved process, as layout, components, and light transport must be explicitly simulated. While existing algorithms excel in this task, creating and formatting virtual environments is a costly and time-consuming process. Thus, there is an opportunity for automating this labor intensive process by leveraging recent development in computer vision. Recent development in deep generative models, especially GANs, has spurred much interest in the computer vision domain for synthesizing realistic images. GANs combine backpropagation with a competitive process involving a pair networks, called Generative Network G and Discriminative Network D, in which G generate artificial images and D classifies it into real or artificial image categories. As the training proceeds, G learns to generate realistic images to confuse D [1]. In this work, a convolutional architecture based on GAN, specifically Deep Convolutional Generative Adversarial Networks (DCGAN) has been implemented to train a generative model that can produce good quality images of human faces at scale. CelebFaces Attributes Dataset (CelebA) has been used to train the DCGAN model. Structural Similarity Index (SSIM), that measures the structural and contextual similarity of two images, has been used for quantitative evaluation of the trained DCGAN model. Obtained results shows that the quality of generated images is quite similar to the high quality images of the CelebA dataset.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128110953","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: 3

Validating response of wearable prototype device on estimating gait pattern 验证可穿戴原型装置对步态模式估计的响应

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616726

R. Jayarajan, G. Nallavan

引用次数: 0

Edge Clustering Coefficient based Label Initialization for Label Propagation Algorithm to Detect Community Structures in Complex Networks 基于边缘聚类系数的标签初始化算法在复杂网络中检测社区结构

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616716

Jyothimon Chandran, V. M. Viswanatham

{"title":"Edge Clustering Coefficient based Label Initialization for Label Propagation Algorithm to Detect Community Structures in Complex Networks","authors":"Jyothimon Chandran, V. M. Viswanatham","doi":"10.1109/ICECCT52121.2021.9616716","DOIUrl":"https://doi.org/10.1109/ICECCT52121.2021.9616716","url":null,"abstract":"Identifying community structure in complex networks is a critical process that divides the entities according to their similarities in characteristic or behavior that define and control the function and organization of networks. One of the fastest and simplest community detection algorithms is the label propagation algorithm (LPA). However, the LPA produces different results in each run due to the randomness in label propagation, leading to uncertainty and instability to the detected communities. To address this problem, several algorithms have been proposed which mainly concentrates on eliminating randomness. In this paper, an improved label propagation method (ECLI-LPA) based on edge clustering coefficient-based label initialization has been proposed. In ECLI-LPA, instead of assigning unique labels to every node, the same labels are assigned to nodes whose edge clustering coefficient is above a threshold value to detect communities. The experimental results on real-world networks and synthetic networks show that the proposed method improves stability and performs better than the compared algorithms.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125247494","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

Urban Flood Susceptibility Mapping of Kochi Taluk Using Remote Sensing and GIS 基于遥感和GIS的高知塔鲁克城市洪水易感性制图

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/icecct52121.2021.9616790

Shilpa Shreekumar, Dhanya Madhu, Aswani Kumar Akella

引用次数: 2

An Effective Ensemble Model to Predict Employment Status of Graduates in Higher Educational Institutions 高校毕业生就业状况预测的有效集成模型

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2021-09-15 DOI: 10.1109/ICECCT52121.2021.9616952

N. Premalatha, S. Sujatha

{"title":"An Effective Ensemble Model to Predict Employment Status of Graduates in Higher Educational Institutions","authors":"N. Premalatha, S. Sujatha","doi":"10.1109/ICECCT52121.2021.9616952","DOIUrl":"https://doi.org/10.1109/ICECCT52121.2021.9616952","url":null,"abstract":"Several higher education institutions face the issue or difficulty of graduating more than 90% of their students who can competently satisfy and meet the industry's requirements. However, the industry is also challenged by the difficulty of locating skilled tertiary institution graduates who meet their requirements. The success or failure of any organisation is primarily determined by how its workforce is recruited and retained. As a result, one of the major and critical problems of management decision-making is the selection of an acceptable or satisfactory candidate for the job position. As a result, this work proposes a modern, accurate, and worthy machine learning classification model that can be deployed, implemented, and used to make predictions and assessments on job applicant attributes from academic performance datasets to meet the industry's selection criteria. This study took into account both supervised and unsupervised machine learning classifiers. Naive Bayes, MLP, Simple Logistic, Adaboost, Bagging and Ensemble Model are chosen for analysis. The proposed model outperforms other reported methods with an accuracy of 98.4253%.","PeriodicalId":155129,"journal":{"name":"2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123935696","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: 3