2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)最新文献_第3页

Analytical analysis of QoS for lossy network congestion control 有损网络拥塞控制的QoS分析

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249070

Aaron E. Cohen

引用次数: 1

A crowdsensing algorithm for imputing Zika outbreak location data 一种用于输入寨卡病毒爆发地点数据的群体感应算法

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249065

J. Livingston, Robert Steele

引用次数: 1

Efficient structural health monitoring with wireless sensor networks using a vibration-based frequency domain pattern matching technique 基于振动频域模式匹配技术的无线传感器网络结构健康监测

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249074

W. Contreras, S. Ziavras

{"title":"Efficient structural health monitoring with wireless sensor networks using a vibration-based frequency domain pattern matching technique","authors":"W. Contreras, S. Ziavras","doi":"10.1109/UEMCON.2017.8249074","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249074","url":null,"abstract":"Structural health monitoring (SHM) concerns the development of strategies to assess the condition of instrumented engineering structures. Most SHM techniques involve some form of physics-based modeling, which can be difficult and costly. We propose an efficient vibration-collecting method using wireless sensor networks (WSNs) that minimizes the need to perform modeling, making the SHM system easy to deploy on any structure with little to no modification. In our algorithm, we employ a two-layer stacking technique, similar to protocol stacking in networking. At the core of the algorithm is a frequency domain pattern matching technique applied to observed vibrations that is executed by each sensor node in the bottom layer of the algorithm. An energy management scheme for attaining the desired lifetime of the WSN is also proposed. The scheme is based upon a novel energy management circuit that decreases current for inactive sensor nodes, such to increase the lifetime of the nodes up to 90-fold. Upper bounds on system lifetime are determined experimentally using sensor nodes based upon the Zolertia Z1 mote. Finally, the pattern matching technique is validated on an experimental structure.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126725345","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

Analyzing the effect of DoS attacks on network performance 分析DoS攻击对网络性能的影响

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249079

Manish Kumar, Eman Abdelfattah, Johnathon Holbrooks, Alexander Pérez

引用次数: 3

A survey on the industrial readiness for Internet of Things 一项关于物联网工业准备情况的调查

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249015

Xianping Wang, Hao Qiu, Fei Xie

引用次数: 7

Residual squeeze CNDS deep learning CNN model for very large scale places image recognition 残差挤压CNDS深度学习CNN模型用于超大尺度场所图像识别

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8248975

Abhishek Verma, Hussam Qassim, David Feinzimer

{"title":"Residual squeeze CNDS deep learning CNN model for very large scale places image recognition","authors":"Abhishek Verma, Hussam Qassim, David Feinzimer","doi":"10.1109/UEMCON.2017.8248975","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248975","url":null,"abstract":"Deep convolutional neural network models have achieved great success in the recent years. However, the optimization of size and the time needed to train a deep network is a research area that needs much improvement. In this paper, we address the issue of speed and size by proposing a compressed convolutional neural network model namely Residual Squeeze CNDS. Proposed models compresses the earlier very successful Residual-CNDS network and further improves on following aspects: (1) small model size, (2) faster speed, (3) uses residual learning for faster convergence, better generalization, and solves the issue of degradation, (4) matches the recognition accuracy of the non-compressed model on the very large-scale grand challenge MIT Places 365-Standard scene dataset. In comparison to Residual-CNDS the proposed model is 87.64% smaller in size and 13.33% faster in the training time. This supports our claim that the proposed model inherits the best aspects of Residual-CNDS model and further improves upon it. Moreover, we present our attempt at a more disciplined approach to searching the design space for novel CNN architectures. In comparison to SQUEEZENET our proposed framework can be more easily adapted and fully integrated with the residual learning for compressing various other contemporary deep learning convolutional neural network models.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122409637","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

Evaluating the performance of density grid-based clustering using ABC technique for efficient routing in WSNs 基于ABC技术的密度网格聚类在无线传感器网络中高效路由的性能评估

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249057

Er. Navpreet Singh

引用次数: 0

The design of ultra low noise CMOS transimpedance amplifier for biosensing applications 生物传感用超低噪声CMOS跨阻放大器的设计

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8248971

Kavyashree Puttananjegowda, Majdi M. Ababneh, Sylvia W. Thomas

引用次数: 3

Performance of multi-level amplify-and-forward relays with multihop diversity 具有多跳分集的多级放大转发中继的性能

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249067

B. Sirkeci-Mergen, M. Roy

引用次数: 1

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249021

Khaled Slhoub, Marco M. Carvalho, Walter P. Bond

引用次数: 4