2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献_第4页

Performance Characteristics of NTRU and ECC Cryptosystem in context of IoT Environment 物联网环境下NTRU和ECC密码系统的性能特征

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278074

Ipsita Upasana, Nanditha Nandanavanam, Ankitha Nandanavanam, Nadiya Naaz

{"title":"Performance Characteristics of NTRU and ECC Cryptosystem in context of IoT Environment","authors":"Ipsita Upasana, Nanditha Nandanavanam, Ankitha Nandanavanam, Nadiya Naaz","doi":"10.1109/DISCOVER50404.2020.9278074","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278074","url":null,"abstract":"With the increased use of Internet-of- Things (IoT) devices for daily activities, the necessity of improving the speed and performance of such devices also arise. The devices produce sensitive data at a massive and rapid rate. Since the infrastructure available with such small-scale devices are constrained, an algorithm which provides better security performance and is faster within the constraints is necessitated. This paper discusses two asymmetric algorithms NTRU (Nth Degree Truncated Polynomial) and Elliptic Curve Cryptography (ECC), which are widely known to be secure. ECC is a popular algorithm and widely used because of its low-key size. However, it can be easily broken by quantum computers. NTRU is one of the most recognized algorithms that can resist even quantum attacks. It has less computational complexity due to simple polynomial operations. The need is for an algorithm to provide ample security while taking the time of the computational complexities into consideration. The performance of ECC and NTRU have been evaluated based on key generation time, encryption time, and decryption time. Text data provided by the small-scale IoT devices has been considered for analysis. The analysis gives a deeper view of both algorithms.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134028883","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

Speech Signal-Based Modelling of Basic Emotions to Analyse Compound Emotion: Anxiety 基于语音信号的基本情绪建模分析复合情绪:焦虑

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278094

Rathi Adarshi Rammohan, Jeevan Medikonda, Dan Isaac Pothiyil

引用次数: 4

A Heuristic Algorithm to Find a Path to be Blocked by Minimizing Traffic Disruption 一种以最小化交通干扰为目标的启发式路径阻塞算法

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278058

M. Das, S. Ambati, K. Chandrasekaran

引用次数: 1

Single Channel based Demarcation of Yogic and Non-Yogic Sleep Patterns using Observational Sleep EEG 基于单通道的瑜伽和非瑜伽睡眠模式的睡眠脑电图划分

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278081

B. Hiremath, N. Sriraam, B. Purnima, V. Babu

引用次数: 0

Automated Crop Field Surveillance Using Computer Vision 使用计算机视觉的自动农田监控

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278072

Tejas Khare, A. Phadke

引用次数: 2

Arduino based smart and intelligent helmet system for two-wheelers 基于Arduino的两轮车智能头盔系统

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278032

Mahesh Gour, Druva Kumar S, Pradeep Kumara, M. S, S. K., Chetan H

引用次数: 6

Performance Analysis of Z - Source Inverter Topologies for Renewable Energy Sources and Fuel Cell Applications 用于可再生能源和燃料电池的Z源逆变器拓扑性能分析

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278046

S. N. Rao, Veerabhadra, P. V.

引用次数: 3

Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos 无人机航拍视频中消除相同车辆的高效车辆计数

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278095

Ashutosh Holla B, M. M, Ujjwal Verma, R. Pai

{"title":"Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos","authors":"Ashutosh Holla B, M. M, Ujjwal Verma, R. Pai","doi":"10.1109/DISCOVER50404.2020.9278095","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278095","url":null,"abstract":"Traffic surveillance using Unmanned Aerial Vehicles (UAV‘s) has gained a lot of attraction in civilian applications and remote sensing tasks. Thanks to its high mobility and large field of view and ability to cover regions at different altitudes UAVs have made a mark in recent years for surveillance. The primary purpose of UAV in traffic surveillance is to monitor the daily activities in the busy traffic areas and report the abnormal activities which may take place. In recent years, many gated campuses such as educational institutions, organizations, shopping malls, etc. have taken steps to keep a track of vehicles trespassing within its vicinity. Vehicle counting is one of the monitoring tasks performed in surveillance to estimate the density of vehicles in an event or areas where traffic congestion is common. In this paper, a vehicle counting framework is proposed to eliminate the problem of redundant vehicle information count when a vehicle has appeared in successive frames of UAV videos. This work demonstrates that the comparison of concatenated three features vectors (Histogram of Oriented Gradients, Local Binary Pattern, and mean RGB value) can be used to recognize identical vehicles in UAV aerial videos.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023139","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: 8

Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks 基于深度卷积神经网络的高效叶片病害检测技术的设计与开发

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278067

Meeradevi, Ranjana V, Monica R. Mundada, Soumya P. Sawkar, Rithika S Bellad, P. S. Keerthi

{"title":"Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks","authors":"Meeradevi, Ranjana V, Monica R. Mundada, Soumya P. Sawkar, Rithika S Bellad, P. S. Keerthi","doi":"10.1109/DISCOVER50404.2020.9278067","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278067","url":null,"abstract":"With the increase in the spread of crop diseases, there is a need to prevent and control its contamination so as to increase productivity and yield for the farmers. Plant Diseases have a detrimental effect on plants and animals and impact on market access and agricultural production. The proposed work use tomato leaf images for disease classification as tomato is one of the most important vegetable plants in the world and hence early detection of tomato leaf disease is required. Diseases of tomato plant include Bacterial leaf Spot, Yellow Curved, Late Blight, Tomato Mosaic and Septorial Leaf Spot. The dataset is taken online from plant village project. The idea of this paper is to take a dataset of the tomato leaf images with different leaf diseases and train it on a best model Convolutional Neural Network (CNN) and then use the obtained weights from the CNN for testing new tomato leaf images. The hybrid approach VGG16 with attention model is taken to achieve the best weights possible for testing and validation in the proposed model. The model showed the accuracy of 95.90 percent with hybrid approach. Performance analysis is done to identify the best model with good accuracy and also overcome the problem of overfitting.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115062910","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

Electronic System Design of a Formula Student Electric Car

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278091

Anirudh Sivakumar, P. Mohanty

引用次数: 1