2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)最新文献_第5页

Environment Classification and Deinterleaving using Siamese Networks and Few-Shot Learning 使用Siamese网络和Few-Shot学习的环境分类和去交错

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/uemcon53757.2021.9666659

Cesar Martinez Melgoza, Tyler Groom, Henry Lin, Ameya Govalkar, Kayla Lee, Acacia Codding, K. George

{"title":"Environment Classification and Deinterleaving using Siamese Networks and Few-Shot Learning","authors":"Cesar Martinez Melgoza, Tyler Groom, Henry Lin, Ameya Govalkar, Kayla Lee, Acacia Codding, K. George","doi":"10.1109/uemcon53757.2021.9666659","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666659","url":null,"abstract":"In the age of digital communications, radar receivers prove to be essential for applications involving classification such as air traffic control towers, defense systems, and navigation systems. Detecting Emitters within a Radar Environment presents hurdles to the System Designers such as accounting for interference and trying to classify multiple emitters when they are stacked. This paper presents a few-shot machine learning model that utilizes Siamese networks with classification. Given a relatively small dataset, the Siamese network's task is to find the difference between stacked pulses and normal pulse trains, as well as classify the pulse-descriptor words (PDWs), of the signals in the environment. The PDWs will characterize various aspects of the signal with help from a dynamic-thresholding deinterleaving algorithm. The data for this experiment are laboratory generated signals that are transmitted and received using MATLAB, the Zynq Ultrascale+ MPSoC ZCU104 FPGA board, and the AD-FMCOMMS2-EBZ RF module.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133046803","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

Kinematic Analysis of an 4 DOF Upper-Limb Exoskeleton 四自由度上肢外骨骼运动学分析

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666604

Deyby Huamanchahua, Jorge Sierra-Huertas, Dana Terrazas-Rodas, Alexander Janampa-Espinoza, Jorge Gonzáles, Sofia Huamán-Vizconde

引用次数: 1

Design and Implementation of an RFID Based Tactile Communication Device 基于RFID的触觉通信设备的设计与实现

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/uemcon53757.2021.9666647

Dakota Barrios, Tyler Groom, K. George

引用次数: 2

Using CNN and Tensorflow to recognise ‘Signal for Help’ Hand Gestures 使用CNN和Tensorflow来识别“求救信号”手势

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666484

Gavin Elliott, Kevin Meehan, Jennifer Hyndman

{"title":"Using CNN and Tensorflow to recognise ‘Signal for Help’ Hand Gestures","authors":"Gavin Elliott, Kevin Meehan, Jennifer Hyndman","doi":"10.1109/UEMCON53757.2021.9666484","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666484","url":null,"abstract":"Domestic violence is a prevalent crime in our society, more so with the introduction of COVID19 restrictions. For the victim, it can be a traumatic experience, so much as to not report the crime. Consequently, the ‘Signal for Help’ hand gestures were recently introduced as a discrete method to enable the victim to confidently express their need for help. This research investigates the classification of these hand gestures using a deep learning approach, which has not previously been implemented in this context. A deep learning approach is chosen due to the favourable results obtained in different contexts on hand gesture classification. Due to the unavailability of a dataset containing images of these hand gestures, a ‘Signal for Help’ dataset containing 112 images is generated as part of this study. These images are pre-processed to be of size 50x50 dimensions. Furthermore, a synthetic version of this dataset is also generated from the pre-processed images containing 2,352 images. The aims of this research are to show that using a synthetic ‘Signal for Help’ dataset improves model performance, and using deep learning is effective in ‘Signal for Help’ hand gesture classification. The results in this research show that using a synthetic ‘Signal for Help’ dataset improves model performance and is effective for ‘Signal for Help’ hand gesture classification.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124575430","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

A Lightweight and Fog-based Authentication Scheme for Internet-of-Vehicles 一种基于雾的轻量级车联网认证方案

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666603

Jamal Alotaibi, Lubna K. Alazzawi

{"title":"A Lightweight and Fog-based Authentication Scheme for Internet-of-Vehicles","authors":"Jamal Alotaibi, Lubna K. Alazzawi","doi":"10.1109/UEMCON53757.2021.9666603","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666603","url":null,"abstract":"The advancement of the Internet-of-Vehicles (IoV) innovation aids the development of intelligent transportation systems (ITS). There are several interoperability challenges in today’s IoV networks, such as security and privacy issues, information irregularity, and so on. Because vehicle data is private and sensitive, it necessitates extra caution. Authentication of communicating devices is one such technique for securing data. The information sent via public channels is secured using authentication. Many protocols have been developed; however, traditional authentication models cannot be applied directly to circumstances needing low latency in particular. Furthermore, they are ineffective for two primary reasons: first, they are unable to adapt to the growing volume of data collected, and second, they are prone to cyber-attacks. As a result, in this paper, we attempt to propose a viable solution that is fully robust and overcomes the aforementioned problems. To protect IoV devices data during communication, we designed a lightweight and fog-based authentication scheme. Our approach ensures minimal communication cost and complies with high-security standards. Finally, we assess and compare our method’s performance in terms of network parameters such as throughput, end-to-end delay, and the rate of packet loss. Results indicate that our method scale well with the increasing number of vehicles while maintaining a minimal communication cost.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134099550","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

Guide-Me: Voice authenticated indoor user guidance system Guide-Me:语音认证的室内用户引导系统

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666733

D. Dissanayake, R. Rajapaksha, U. P. Prabhashawara, S. A. D. S. P. Solanga, J. Jayakody

引用次数: 1

Review of Graph Neural Network in Text Classification 图神经网络在文本分类中的研究进展

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/uemcon53757.2021.9666633

Masoud Malekzadeh, P. Hajibabaee, Maryam Heidari, Samira Zad, Özlem Uzuner, James H. Jones

引用次数: 31

Development of an Online Based Babysitting System: Bonne 基于在线保姆系统的开发:Bonne

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666483

Md. Abu Obaidah, Faria Soroni, Mohammad Monirujjaman Khan

引用次数: 0

An Efficient and Fast-convergent Detector for 5G and Beyond Massive MIMO Systems 5G及以后大规模MIMO系统的高效快速收敛检测器

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666709

Robin Chataut, R. Akl, U. K. Dey

引用次数: 3

CNN Based COVID-19 Prediction from Chest X-ray Images 基于CNN的胸部x射线图像COVID-19预测

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/UEMCON53757.2021.9666508

Kazi Nabiul Alam, Mohammad Monirujjaman Khan

{"title":"CNN Based COVID-19 Prediction from Chest X-ray Images","authors":"Kazi Nabiul Alam, Mohammad Monirujjaman Khan","doi":"10.1109/UEMCON53757.2021.9666508","DOIUrl":"https://doi.org/10.1109/UEMCON53757.2021.9666508","url":null,"abstract":"Coronavirus disease COVID-19 is an infectious disease caused by a newly discovered coronavirus. COVID-19 virus affects the respiratory system of healthy individuals. Chest X-ray is one of the important imaging methods to identify the coronavirus. In deep learning, a convolutional neural network (CNN), is a class of deep learning models, most commonly applied for better outcomes to analyzing visual imagery. Automated covid-19 using Deep Learning techniques could, therefore, serve as an effective diagnostic aid. In this study, we used a convolutional neural network (CNN) for detecting COVID-19 from chest X-ray images. The overall project comprises various convolutional layers. The Max-pooling layers diminish the size of the picture significantly and by joining convolutional and pooling layers, the net is able to combine its features to learn more global features of the Image. Eventually, we utilize the highlights in two completely associated (Dense) layers. Dropout is a regularization strategy, where the layer arbitrarily replaces an extent of its weights to zero for each training sample. This forces the net to learn features in an appropriate way, not depending a lot on specific weight, and thus improves speculation and 'relu' is the activation function. Applying convolutional neural network which is a Deep Learning algorithm that can take in an input image, relegate significance to different perspectives in the images and have the option to separate one from the other.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493290","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