2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)最新文献_第6页

Security and Privacy for the Internet of Things 物联网的安全和隐私

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/eecsi50503.2020.9251914

B. Sikdar

{"title":"Security and Privacy for the Internet of Things","authors":"B. Sikdar","doi":"10.23919/eecsi50503.2020.9251914","DOIUrl":"https://doi.org/10.23919/eecsi50503.2020.9251914","url":null,"abstract":"The Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. The IoT is envisioned as the enabling technology for smart cities, power grids, health care, and control systems for critical installments and public infrastructure. This diversity, increased control and interaction of devices, and the fact that IoT systems use public networks to transfer large amounts of data make them a prime target for cyber attacks. In addition, IoT devices are usually small, low cost and have limited resources. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. This presentation will start by highlighting the unique security requirements of IoT devices and the inadequacy of existing security protocols and techniques of the Internet in the context to IoT systems. Next, we will focus on security solutions for the IoT, with special focus on protection against physical and side channel attacks. In particular, we will focus on mutual authentication protocols for IoT devices based on security primitives that exploit hardware level characteristics of IoT devices.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90592410","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

[Copyright notice] (版权)

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/eecsi50503.2020.9251880

引用次数: 0

Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks 基于单流空间卷积神经网络的手部运动识别

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251896

Aldi Sidik Permana, E. C. Djamal, Fikri Nugraha, Fatan Kasyidi

{"title":"Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks","authors":"Aldi Sidik Permana, E. C. Djamal, Fikri Nugraha, Fatan Kasyidi","doi":"10.23919/EECSI50503.2020.9251896","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251896","url":null,"abstract":"Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely ‘right’, ‘left’, ‘grab’, and ‘phone’. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"28 1","pages":"172-176"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78276667","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

Investigation of Structural Parameter Variation on Extended Gate TFET for Bio-Sensor Applications 用于生物传感器的扩展栅极TFET结构参数变化研究

2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI) Pub Date : 2020-10-01 DOI: 10.23919/EECSI50503.2020.9251884

S. Mukherjee, Somnath Chakraborty, D. Diwakar, A. Laha, U. Ganguly, S. Ganguly

{"title":"Investigation of Structural Parameter Variation on Extended Gate TFET for Bio-Sensor Applications","authors":"S. Mukherjee, Somnath Chakraborty, D. Diwakar, A. Laha, U. Ganguly, S. Ganguly","doi":"10.23919/EECSI50503.2020.9251884","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251884","url":null,"abstract":"Traditional Gate engineered Metal Oxide Semiconductor (MOS) technology faced serious challenges in terms of greater sensitivity for target biomolecules and to be utilized as the state-of-the-art Nano-recognition tool. Research on a tunnel field-effect transistor (TFET) started with the aim to achieve fast detection, low power consumption, and its potential for on-chip integration capability. Dielectric Modulated TFET (DMTFET) has established itself to be a primary candidate for sensing both charged and charge-neutral species with volumetric sensitivity. As extended gate DMTFET happens to be inferior to its short gate counterpart, we have devised ways to achieve superior performance only by making variations over structural electrostatics. With the incorporation of most possible ways of modulation, we present two orders of magnitude on-current increment and a considerable percentage of sensitivity improvement over the conventional one. Future scopes having noteworthy diversifications have also been analyzed with proper justification.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"106 1","pages":"187-191"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85745670","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