2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)最新文献_第2页

Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network 基于腕部肌电图分析的卷积神经网络个人认证与手部动作识别

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600826

Ryohei Shioji, S. Ito, Momoyo Ito, M. Fukumi

{"title":"Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network","authors":"Ryohei Shioji, S. Ito, Momoyo Ito, M. Fukumi","doi":"10.1109/IOTAIS.2018.8600826","DOIUrl":"https://doi.org/10.1109/IOTAIS.2018.8600826","url":null,"abstract":"Recent years, EMG has attracted much attention as a tool of human interface. In hand motion recognition and personal authentication using wrist EMG, we have obtained good results. However, there has been no way to establish them at the same time. Therefore, in this paper we measure EMG by attaching dry type sensors to wrist, and carry out hand motion recognition and personal authentication. The conventional method used EMG of movement Japanese Janken. We use a multi-input and multi-output model of a Convolutional Neural Network (CNN). The average accuracy of hand motion recognition is 94.5%. The average accuracy of personal authentication is 94.6%. In the conventional method, personal authentication was classified into two classes. However, we carry out multi-class classification in the proposed method. In feature extraction, we obtain 128×8 input data from the measuring unit. Then, a filter size of the convolution layers is 3×3. CNN does not contain pooling layers in this paper. In the proposed method, the average accuracy of hand motion recognition is 94.6%. The average accuracy of personal authentication is 95.0%.","PeriodicalId":302621,"journal":{"name":"2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)","volume":"385 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731362","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: 19

Ultra-low-latency Video Coding Method for Autonomous Vehicles and Virtual Reality Devices 自动驾驶汽车和虚拟现实设备的超低延迟视频编码方法

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600851

S. Mochizuki, K. Imamura, K. Mori, Y. Matsuda, T. Matsumura

引用次数: 2

Methodology for optimizing manufacturing machines with IoT 利用物联网优化制造机器的方法

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600907

Emir Cuk, Valentina Chaparro

{"title":"Methodology for optimizing manufacturing machines with IoT","authors":"Emir Cuk, Valentina Chaparro","doi":"10.1109/IOTAIS.2018.8600907","DOIUrl":"https://doi.org/10.1109/IOTAIS.2018.8600907","url":null,"abstract":"The goal of industry 4.0 is to use all the information that can be extracted from a supply chain to continuously optimize all aspects of its operation. The data acquisition is still a big challenge and the first step of the fourth industrial revolution. Getting data from software is much easier than getting data out of hardware like manufacturing machines. Especially if the Programmable Logic Controller (PLC) data is either poorly documented or not designed for these requirements. Therefore, we created a methodology to track the most common movement of a machine, which is the linear motion. The solution is an IoT-device: a small, wireless, and low cost sensor, designed to provide data about linear motions within a machine in real-time. We designed a static generic model and a method for machine optimization with data acquisition results comparable to results in other approaches. By implementing the methodology to a real industrial scenario, the results enable us to prove our hypothesis. The IoT-device data was as good as the PLC data and even closer to real-time. Our methodology also shows a higher potential to automate the data analysis.","PeriodicalId":302621,"journal":{"name":"2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123174431","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

From Centralized Management of Robot Swarms to Decentralized Scheduling 从机器人群集中管理到分散调度

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600917

Daniel Graff, R. Karnapke

引用次数: 0

IoT Device Fingerprint using Deep Learning 使用深度学习的物联网设备指纹

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600824

Sandhya Aneja, Nagender Aneja, Md. Shohidul Islam

{"title":"IoT Device Fingerprint using Deep Learning","authors":"Sandhya Aneja, Nagender Aneja, Md. Shohidul Islam","doi":"10.1109/IOTAIS.2018.8600824","DOIUrl":"https://doi.org/10.1109/IOTAIS.2018.8600824","url":null,"abstract":"Device Fingerprinting (DFP) is the identification of a device without using its network or other assigned identities including IP address, Medium Access Control (MAC) address, or International Mobile Equipment Identity (IMEI) number. DFP identifies a device using information from the packets which the device uses to communicate over the network. Packets are received at a router and processed to extract the information. In this paper, we worked on the DFP using Inter Arrival Time (IAT). IAT is the time interval between the two consecutive packets received. This has been observed that the IAT is unique for a device because of different hardware and the software used for the device. The existing work on the DFP uses the statistical techniques to analyze the IAT and to further generate the information using which a device can be identified uniquely. This work presents a novel idea of DFP by plotting graphs of IAT for packets with each graph plotting 100 IATs and subsequently processing the resulting graphs for the identification of the device. This approach improves the efficiency to identify a device DFP due to achieved benchmark of the deep learning libraries in the image processing. We configured Raspberry Pi to work as a router and installed our packet sniffer application on the Raspberry Pi. The packet sniffer application captured the packet information from the connected devices in a log file. We connected two Apple devices iPad4 and iPhone 7 Plus to the router and created IAT graphs for these two devices. We used Convolution Neural Network (CNN) to identify the devices and observed the accuracy of 86.7%.","PeriodicalId":302621,"journal":{"name":"2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127723317","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: 63

Authentication of Aerial Input Numerals by Leap Motion and CNN Leap Motion和CNN对空中输入数字的验证

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600847

Shun Yamamoto, S. Ito, Momoyo Ito, M. Fukumi

引用次数: 1

Sentiment Analysis on the Level of Customer Satisfaction to Data Cellular Services Using the Naive Bayes Classifier Algorithm 基于朴素贝叶斯分类器的蜂窝数据服务用户满意度的情感分析

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600870

Febriyani S. Fitri, Muhammad Nasrun, C. Setianingsih

引用次数: 11

KrishiMitr (Farmer’s Friend): Using Machine Learning to Identify Diseases in Plants KrishiMitr(农民的朋友):使用机器学习来识别植物的疾病

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600898

Parul Sharma, Yash Paul Singh Berwal, Wiqas Ghai

引用次数: 13

FPGA Implementation of Modular Multiplier in Residue Number System 余数系统中模块化乘法器的FPGA实现

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600881

Yinan Kong, Md. Selim Hossain

引用次数: 6

Large Data Download Method for IoT Machines Using LPWAN and General User’s Smartphones 使用LPWAN和普通用户智能手机的物联网机器的大数据下载方法

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Pub Date : 2018-11-01 DOI: 10.1109/IOTAIS.2018.8600862

T. Ogawa, T. Yoshimura, N. Miyaho

{"title":"Large Data Download Method for IoT Machines Using LPWAN and General User’s Smartphones","authors":"T. Ogawa, T. Yoshimura, N. Miyaho","doi":"10.1109/IOTAIS.2018.8600862","DOIUrl":"https://doi.org/10.1109/IOTAIS.2018.8600862","url":null,"abstract":"By applying low power wide area network (LPWAN) to communication between a machine and the cloud, it can be anticipated that communication costs and the amount of power consumed by the machine can both be reduced. However, considering the addition of functions to the machine, it is necessary to have a technology able to transfer a large amount of data, which cannot be transferred by LPWAN, to the machine from the cloud at low cost. In this paper, a novel large data download method by Wi-Fi from cloud to machine using the terminals of general users is proposed. In the proposed method, the cloud selects the delivery terminals satisfying the data arrival rate requirement by analyzing the correlation of the movement history between the terminals. It guarantees a data arrival rate with a higher degree of accuracy than the existing DTN and CC-DTN, and at the same time minimizes the number of delivery terminals. In addition, this paper shows an authentication procedure that prevents DoS attacks on LPWAN by a spoofing terminal, which cannot be prevented by existing network authentication technology. Also, we report the effectiveness of the proposed method, which is confirmed by numerical calculation.","PeriodicalId":302621,"journal":{"name":"2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128570289","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: 1