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

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

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

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

An IOT Based Nurse Calling System for Real-time Emergency Alert Using Local Wireless Network 基于物联网的护士呼叫系统，利用本地无线网络实现实时紧急警报

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/uemcon53757.2021.9666705

Omar Faruk Riyad, Ahraf Sharif, Arif-ur-Rahman Chowdhury Suhan, Mohammad Monirujjaman Khan

引用次数: 1

Buffer-Aided Collision Resolution for UHF RFID 超高频射频识别的缓冲辅助碰撞分辨率

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/uemcon53757.2021.9666570

Gan Luan, N. Beaulieu, Xianpeng Wang, Mengxing Huang

引用次数: 0

Joint Disaster Classification and Victim Detection using Multi-Task Learning 基于多任务学习的联合灾害分类与受害者检测

2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2021-12-01 DOI: 10.1109/uemcon53757.2021.9666576

Mau-Luen Tham, Y. Wong, Ban-Hoe Kwan, Y. Owada, M. Sein, Yoong Choon Chang

{"title":"Joint Disaster Classification and Victim Detection using Multi-Task Learning","authors":"Mau-Luen Tham, Y. Wong, Ban-Hoe Kwan, Y. Owada, M. Sein, Yoong Choon Chang","doi":"10.1109/uemcon53757.2021.9666576","DOIUrl":"https://doi.org/10.1109/uemcon53757.2021.9666576","url":null,"abstract":"Recent advances in deep learning and computer vision have transformed surveillance into an important application for smart disaster monitoring systems. Based on the detected number of victims and activity of disasters, emergency response unit can dispatch manpower more efficiently, which could save more lives. However, most of existing disaster detection methods fall into the class of single-task learning, which can either detect victim or classify disaster. In contrast, this paper proposes a YOLO-based multi-task model which performs the aforementioned tasks simultaneously. This is accomplished by attaching a disaster classification head model to the backbone of a victim detection model. The head model is inherited from the MobileNetv2 architecture, and we precisely select the backbone feature map layer to which the head model is attached. For the victim detection, results reveal that the solution achieves up to 0.6938 and 20.31 in terms of average precision and frame per second, respectively. Whereas for the disaster classification, the algorithm is comparable with most deep learning models that are specifically trained for single task. This shows that our solution is flexible and robust enough to handle both victim detection and disaster classification.","PeriodicalId":127072,"journal":{"name":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"26 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":"128259220","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

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