2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)最新文献_第8页

Design and Dynamic Modelling of Quadrotor VTOL aircraft 四旋翼垂直起降飞行器设计与动力学建模

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297379

Purna Patel, J. Dave

引用次数: 3

Automated Vehicle Number Plate Recognition System using Stability Score and K-Means Clustering Algorithm 基于稳定性评分和k均值聚类算法的车牌自动识别系统

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297428

B. Dhanalakshmi, R. Ramesh, D. Raguraman, R. Menaka

{"title":"Automated Vehicle Number Plate Recognition System using Stability Score and K-Means Clustering Algorithm","authors":"B. Dhanalakshmi, R. Ramesh, D. Raguraman, R. Menaka","doi":"10.1109/ICECA49313.2020.9297428","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297428","url":null,"abstract":"Due to the increase in vehicle usage, itis a challenging task to monitor, analyze the vehicles by a human for security purposes. There is a need for an automatic vehicle recognition system since various places nowadays have checkpoints for vehicles, to track the stolen vehicles, and to monitor traffic violations. The problem exists when the vehicle number plate is encountered in different formats, different scales, and illumination to number-plates. In the case of an indeterminate situation, identifying vehicle number plates in poor lighting conditions and worse traffic situations can be analyzed using an automatic vehicle number plate recognition system. The vehicle name board edge finding techniques are used to easily identify the vehicle number in the name board. A dataset with 200 license plates has been collected as training datasets for recognition, estimation, and identification, thus improving system accuracy of recognition when compared to existing works. The training input samples include images of vehicle number plates taken from the traffic department. The automated vehicle number recognition system is improvised in terms of accuracy by estimating stability score and using the k-means clustering algorithm.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415646","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

Cluster Head and Optimal Path Slection Using K-GA and T-FA Algorithms for Wireless Sensor Networks 基于K-GA和T-FA算法的无线传感器网络簇头和最优路径选择

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297535

M. Ram, Kuda Nageswara Rao, S. J. Basha, S. S. Reddy

{"title":"Cluster Head and Optimal Path Slection Using K-GA and T-FA Algorithms for Wireless Sensor Networks","authors":"M. Ram, Kuda Nageswara Rao, S. J. Basha, S. S. Reddy","doi":"10.1109/ICECA49313.2020.9297535","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297535","url":null,"abstract":"Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127437727","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

City Brand Image Building Strategy Based on Interactive Data Mining and Visual Saliency 基于交互式数据挖掘和视觉显著性的城市品牌形象塑造策略

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297404

Sining Hua

引用次数: 0

Detection of Arrhythmia using ECG waves with Deep Convolutional Neural Networks 基于深度卷积神经网络的心电波检测心律失常

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297467

A. Gowtham, L. Anirudh, B. Sreeja, BA Aakash, S. Adittya

{"title":"Detection of Arrhythmia using ECG waves with Deep Convolutional Neural Networks","authors":"A. Gowtham, L. Anirudh, B. Sreeja, BA Aakash, S. Adittya","doi":"10.1109/ICECA49313.2020.9297467","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297467","url":null,"abstract":"If there is an availability of technological medical electronic devices to classify heart disease, it would absolutely change the future in terms of making it more economical and qualitative for all the people suffering from heart-related ailments. With the increasing medical expenses and non-affordability of the poor families, it becomes logical to design a system that can detect heart disease in particular Arrhythmia, without higher expense. Recently, the Cardiovascular systems are evaluated more reliably by using Electrocardiogram (ECG) waves. This project in particular is designed to check for any irregularities in heart beats, which is represented in the variations of an ECG wave, and then compared it with normal beats to detect Arrhythmia. The electronics behind this project is Raspberry Pi and ADS1115, an ADC, which converts the real-time, analog ECG wave signal into a digital wave with the help of heart rate sensor-AD8232, and a three-lead system. A normalized wave is fed into the deep convolutional neural network to predict the output into one of the 5 different categories. Furthermore, the ADASYN – Adaptive Synthetic Sampling - algorithm is used to effectively classify the disease in accordance with the MIT-BIH dataset.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016170","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

Design of Low Power & High Speed Comparator of SAR ADC using 180nm Technology 基于180nm技术的SAR ADC低功耗高速比较器设计

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297473

Harshita kushwah, R. Gamad, R. Gurjar

引用次数: 0

IoT based Indoor Air Quality Monitoring system using Raspberry Pi4 基于物联网的室内空气质量监测系统使用树莓Pi4

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297442

Syed Faiazuddin, M. Lakshmaiah, K. Alam, M. Ravikiran

{"title":"IoT based Indoor Air Quality Monitoring system using Raspberry Pi4","authors":"Syed Faiazuddin, M. Lakshmaiah, K. Alam, M. Ravikiran","doi":"10.1109/ICECA49313.2020.9297442","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297442","url":null,"abstract":"Poor quality is a major concern in urbanized areas. With more than 85% of people exposed to high levels of a particular matter. According to the World Health Organization, people are more cautious to look up the quality of air, their health by focusing on the spaces where they spend most of their time at home, school etc., and in their car. In this concept, a system with low power and data consumption is introduced. In this article, the air quality using a Raspberry Pi4 with Grove - Air Quality Sensor v1.3, CCS811 CO2 Air Quality Sensor, DHT 11 Temperature and Humidity Sensor were discussed. The communication between the sensor and Raspberry Pi4 will be through a serial port communication protocol and the code is implemented on the Python interface. Air pollution is a global environmental health problem many people’s are dying every year due to some of the visible and invisible parameters like small particles, gases and so on. Most of the parameters of the environment to be monitored such as volume of CO, CO2, Temp, Humidity, Gas Leakage, Smoke, temperature sensor, and etc. These parameters information can received by Rasp Pi4, Arduino Uno and process the information and transmitted to clouds where they are being continuously monitored and information will be stored in the cloud database.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116703122","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: 10

Decision Making Method ETOP for Handoff in Cognitive Radio Network 认知无线网络切换决策方法ETOP

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297548

R. Prasad, T. Jaya

引用次数: 0

Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN 基于视觉词袋和基于mlp的bp神经网络的混合计算机人脸识别系统

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297499

L. Rao, Coneri Harshitha, C. Z. Basha, Nazia Parveen

引用次数: 1

Machine Learning based Image Classification of Papaya Disease Recognition 基于机器学习的木瓜病害识别图像分类

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2020-11-05 DOI: 10.1109/ICECA49313.2020.9297570

M. Islam, Md. Shahriar Islam, M. Hossen, Minhaz Uddin Emon, Maria Sultana Keya, Ahsan Habib

{"title":"Machine Learning based Image Classification of Papaya Disease Recognition","authors":"M. Islam, Md. Shahriar Islam, M. Hossen, Minhaz Uddin Emon, Maria Sultana Keya, Ahsan Habib","doi":"10.1109/ICECA49313.2020.9297570","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297570","url":null,"abstract":"To help farmers and rural people of Bangladesh, many research works are proposed in the recent years to recognize the papaya diseases that takes a great deal of advantage in machine learning fields. This research is mainly required to support agriculture to make it highly effective and helpful particularly for papaya cultivation. The primary objective of this paper is to compare some algorithms for papaya disease recognition and identify the ailment by capturing image and classify them based on their diseases with an intelligent system. To overcome this advantage, the recognition of papaya diseases will mainly involve two challenges and those are detecting the disease and classifying the diseases based on their symptoms. The proposed system is presenting an online machine learning based papaya disease in which a person captures an image via mobile app and sends it to the system for disease detection and also compare some algorithms accuracy those are random forest, k-means clustering, SVC and CNN. The system process the images and will give feedback. This intelligent system can easily detect the diseases with a high accuracy of about 98.4% to predict the papaya diseases.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132127888","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: 25