2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)最新文献

Maximizing Airtime Efficiency for Reliable Broadcast Streams in WMNs with Multi-Armed Bandits 多武装盗匪WMNs中可靠广播流的时间效率最大化

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298050

Giovanni Perin, David Nophut, L. Badia, F. Fitzek

{"title":"Maximizing Airtime Efficiency for Reliable Broadcast Streams in WMNs with Multi-Armed Bandits","authors":"Giovanni Perin, David Nophut, L. Badia, F. Fitzek","doi":"10.1109/UEMCON51285.2020.9298050","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298050","url":null,"abstract":"Wireless broadcast routing is a complex problem, shown in the literature to be NP-complete. Current protocols implement either heuristics to find solutions that are not guaranteed to be optimal or classic flooding. However, many future use cases, like automotive applications, industrial robotics, and multimedia broadcast, will require efficient yet reliable methods. In this work, we use contextual multi-armed bandits together with opportunistic routing (OR) and network coding (NC) to find approximately optimal solutions to the problem of broadcast routing in a distributed fashion. Each router independently learns its own transmission credit, i.e., the number of packets to forward for each innovative packet received, so that the airtime cost, subject to latency constraints, is minimized. Results show that the proposed solutions, particularly the deep learning based one, vastly improve the overall reliability, while performing close to MORE multicast in terms of airtime and to B.A.T.M.A.N. in latency, both being the best candidates in the respective discipline among the tested ones.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117165035","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

Three-Dimensional Down-View Imaging Based on MIMO Through-Wall-Radar 基于MIMO穿壁雷达的三维下视成像

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298034

Zihan Xu, Li Jiang, Yiduo Liang, Yong Jia, G. Cui, Longfei Tan

引用次数: 2

Backbone Neural Network Design of Single Shot Detector from RGB-D Images for Object Detection 基于RGB-D图像的单镜头目标检测骨干神经网络设计

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298175

P. Sharma, Damian Valles

引用次数: 4

Forecasting of COVID-19 in India Using ARIMA Model 基于ARIMA模型的印度COVID-19预测

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298045

Narayana Darapaneni, D. Reddy, A. Paduri, Pooja Acharya, H. S. Nithin

引用次数: 12

MINI-SSD: A Fast Object Detection Framework in Autonomous Driving MINI-SSD:自动驾驶中的快速目标检测框架

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298130

Shaimaa Ezz-ElDin, Omar Nabil, Hussam Murad, Farah Adel, Ahmed AbdEl-Jalil, K. Salah, Ayub Khan

{"title":"MINI-SSD: A Fast Object Detection Framework in Autonomous Driving","authors":"Shaimaa Ezz-ElDin, Omar Nabil, Hussam Murad, Farah Adel, Ahmed AbdEl-Jalil, K. Salah, Ayub Khan","doi":"10.1109/UEMCON51285.2020.9298130","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298130","url":null,"abstract":"In this paper, a python-implemented infrastructure of a CNN-based multi-object detector in autonomous driving using the single shot detector (SSD) is presented. The infrastructure consists of both training and inference for object detection. The main contribution of this paper is the design of the default anchor boxes tiling that reduce the amount of computations by simplifying the software implementation of the SSD object detector. This simplification is done by reducing the data path of the proposed detector. Moreover, a decrease in the inference time of the detector is the result of using tiled defaults boxes and a small number of layers in the VGG CNN. In addition, the CNN model presents an advantage in terms of high confidence boxes prediction. The proposed approach is faster due to the reduced number of layers and computations. The segmentation design of the input image anchor boxes is introduced to explain the software implementation. In addition, both the training and validation loss variations along the period of the training are illustrated.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125360727","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

Performance Evaluation of Machine Learning for Prediction of Network Traffic in a Smart Home 机器学习在智能家居网络流量预测中的性能评估

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298134

Faisal Alghayadh, D. Debnath

引用次数: 5

Conceptual Neuroadaptive Brain Computer Interface for Autonomous Control of Automobile Brakes 汽车刹车自主控制的概念神经自适应脑机接口

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298185

Devaj Parikh, K. George

引用次数: 1

Radar Pulse on Pulse Identification Parallel FFT and Power Envelope Algorithm 雷达脉冲对脉冲识别的并行FFT和功率包络算法

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298132

Jordan Juliano, Jaron Lin, Alex Erdogan, K. George

引用次数: 2

An Event Detection Platform to Detect Gender Using Deep Learning 基于深度学习的事件性别检测平台

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298104

Abdulrahman Aldhaheri, Je Lee, Khaled Almgren

引用次数: 0

Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection 基于BERT提取话题无关情感特征的社交媒体机器人检测

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298158

Maryam Heidari, James H. Jones

{"title":"Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection","authors":"Maryam Heidari, James H. Jones","doi":"10.1109/UEMCON51285.2020.9298158","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298158","url":null,"abstract":"Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event, or product. However, this use raises an important question: what percentage of the information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a \"bot\" instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. This paper introduces a new model that uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features for the social media bot detection model. Using a Natural Language Processing approach to derive topic-independent features for the new bot detection model distinguishes this work from previous bot detection models. We achieve 94% accuracy classifying the contents of Cresci data set [1] as generated by a bot or a human, where the most accurate prior work achieved an accuracy of 92%.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116274076","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: 61