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Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems最新文献

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Attention-based LSTM-CNNs For Time-series Classification 基于注意力的lstm - cnn时间序列分类
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275208
Qianjin Du, Weixi Gu, Lin Zhang, Shao-Lun Huang
{"title":"Attention-based LSTM-CNNs For Time-series Classification","authors":"Qianjin Du, Weixi Gu, Lin Zhang, Shao-Lun Huang","doi":"10.1145/3274783.3275208","DOIUrl":"https://doi.org/10.1145/3274783.3275208","url":null,"abstract":"Time series classification is a critical problem in the machine learning field, which spawns numerous research works on it. In this work, we propose AttLSTM-CNNs, an attention-based LSTM network and convolution network that jointly extracts the underlying pattern among the time-series for the classification. The attention-based LSTM automatically captures the long-term temporal dependency among the series, and the CNN describes the spatial sparsity and heterogeneity in the data. The extensive experiments show that the proposed model outperforms the other methods for time-series classification.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130886201","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: 18
A Hybrid Air Pollution Reconstruction by Adaptive Interpolation Method 基于自适应插值的混合空气污染重建方法
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275207
Min Wu, Jiayi Huang, Ning Liu, Rui Ma, Yue Wang, Lin Zhang
{"title":"A Hybrid Air Pollution Reconstruction by Adaptive Interpolation Method","authors":"Min Wu, Jiayi Huang, Ning Liu, Rui Ma, Yue Wang, Lin Zhang","doi":"10.1145/3274783.3275207","DOIUrl":"https://doi.org/10.1145/3274783.3275207","url":null,"abstract":"Air pollution in a city is the major environmental risk to health. Mobile sensing has become a popular solution in recent years. However, it still suffers from problems such as lack of data and high system uncertainty. This is because that the data amount and distribution vary over time. To address the problems, this paper combines two classic data driven models -- Kriging and Inverse Distance Weighting (IDW). We adopt the Random Forest Algorithm (RF) to adaptively choose the more accurate models (Kriging or IDW) according to the features we extracted. The experiment based on real world testbed shows our adaptive method achieves up to 30.6% error reduction.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123000555","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: 9
Long Range Retroreflective V2X Communication with Polarization-based Differential Reception 基于偏振差分接收的远距离反向反射V2X通信
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275193
Guojun Chen, Purui Wang, Lilei Feng, Yue Wu, Xieyang Xu, Yang Shen, Chenren Xu
{"title":"Long Range Retroreflective V2X Communication with Polarization-based Differential Reception","authors":"Guojun Chen, Purui Wang, Lilei Feng, Yue Wu, Xieyang Xu, Yang Shen, Chenren Xu","doi":"10.1145/3274783.3275193","DOIUrl":"https://doi.org/10.1145/3274783.3275193","url":null,"abstract":"Vehicle-to-anything (V2X) communications technology is an essential substrate to realize future road intelligence and autonomous driving, especially in the areas where there are no existing (radio) network infrastructure. The emerging visible light backscatter communication technique shows great potentials in enabling the massive on-road retroreflective objects to delivery dynamic information to host vehicles. In this work, we design a polarization-based differential reception scheme to suppress ambient noise and realize long range retroreflective V2X communications.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123059290","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
Bringing Full-Scale TCP to Low-Power Networks 将TCP全面引入低功耗网络
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275196
Sam Kumar, Michael P. Andersen, Hyung-Sin Kim, D. Culler
{"title":"Bringing Full-Scale TCP to Low-Power Networks","authors":"Sam Kumar, Michael P. Andersen, Hyung-Sin Kim, D. Culler","doi":"10.1145/3274783.3275196","DOIUrl":"https://doi.org/10.1145/3274783.3275196","url":null,"abstract":"Although TCP has widespread adoption in the Internet, wireless sensor networks (WSNs) generally use simpler UDP-based protocols. The few existing TCP implementations for sensor network operating systems do not support all of the features of TCP. We present a full-scale TCP implementation for sensor networks, called TCPlp, based on the TCP protocol logic of the FreeBSD Operating System. Our implementation demonstrates that full-scale TCP can run within the resource constraints of a modern WSN platform, and serves as a vehicle to explore the benefits of using a full TCP stack in the WSN setting. We showcase TCPlp via three applications of TCP: (1) reliable data collection in the context of an application, (2) an interactive configuration/debug shell, and (3) a mote-based web server.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116651504","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: 7
Toward Automated Acupressure Therapy 走向自动指压疗法
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3278480
Kun-Chan Lan, Guan-Sheng Li, Jun-Xiang Zhang
{"title":"Toward Automated Acupressure Therapy","authors":"Kun-Chan Lan, Guan-Sheng Li, Jun-Xiang Zhang","doi":"10.1145/3274783.3278480","DOIUrl":"https://doi.org/10.1145/3274783.3278480","url":null,"abstract":"An acupuncture points localization method is implemented on an Android platform. Such a system can be used to locate the relevant acupuncture point and/or drive a robot arm for the purpose of symptom relief (e.g. through acupressure).","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132838619","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
Multimodal Emotion Recognition by extracting common and modality-specific information 多模态情感识别:提取共同和特定模态信息
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275200
Wei Zhang, Weixi Gu, Fei Ma, S. Ni, Lin Zhang, Shao-Lun Huang
{"title":"Multimodal Emotion Recognition by extracting common and modality-specific information","authors":"Wei Zhang, Weixi Gu, Fei Ma, S. Ni, Lin Zhang, Shao-Lun Huang","doi":"10.1145/3274783.3275200","DOIUrl":"https://doi.org/10.1145/3274783.3275200","url":null,"abstract":"Emotion recognition technologies have been widely used in numerous areas including advertising, healthcare and online education. Previous works usually recognize the emotion from either the acoustic or the visual signal, yielding unsatisfied performances and limited applications. To improve the inference capability, we present a multimodal emotion recognition model, EMOdal. Apart from learning the audio and visual data respectively, EMOdal efficiently learns the common and modality-specific information underlying the two kinds of signals, and therefore improves the inference ability. The model has been evaluated on our large-scale emotional data set. The comprehensive evaluations demonstrate that our model outperforms traditional approaches.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133465110","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: 12
Safeguarded ZigBee via WiFi Guard Band 通过WiFi保护带保护ZigBee
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275180
Yoon Chae, S. Kim
{"title":"Safeguarded ZigBee via WiFi Guard Band","authors":"Yoon Chae, S. Kim","doi":"10.1145/3274783.3275180","DOIUrl":"https://doi.org/10.1145/3274783.3275180","url":null,"abstract":"Low power IoT suffers from performance degradation due to severe cross-technology interference (CTI) such as WiFi. In this demo, we present a novel ZigBee system that effectively maintains high reliability even under saturated WiFi traffic. This is achieved by placing a ZigBee packet on the guard band of ongoing, ambient WiFi traffic. Guard band is designed to be kept clear of interference from other WiFi, thereby safeguarding the ZigBee within. Our system effectively captures WiFi (802.11b) guard band on the fly, using physical layer information accessible on commodity ZigBee RF. We demonstrate real-time guard band detection and robust ZigBee communication, showcasing a practical pathway to operating low power IoT under excessive CTI.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134021293","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
Speech Emotion Recognition via Attention-based DNN from Multi-Task Learning 多任务学习中基于注意的深度神经网络语音情绪识别
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275184
Fei Ma, Weixi Gu, Wei Zhang, S. Ni, Shao-Lun Huang, Lin Zhang
{"title":"Speech Emotion Recognition via Attention-based DNN from Multi-Task Learning","authors":"Fei Ma, Weixi Gu, Wei Zhang, S. Ni, Shao-Lun Huang, Lin Zhang","doi":"10.1145/3274783.3275184","DOIUrl":"https://doi.org/10.1145/3274783.3275184","url":null,"abstract":"Speech unlocks the huge potentials in emotion recognition. High accurate and real-time understanding of human emotion via speech assists Human-Computer Interaction. Previous works are often limited in either coarse-grained emotion learning tasks or the low precisions on the emotion recognition. To solve these problems, we construct a real-world large-scale corpus composed of 4 common emotions (i.e., anger, happiness, neutral and sadness). We also propose a multi-task attention-based DNN model (i.e., MT-A-DNN) on the emotion learning. MT-A-DNN efficiently learns the high-order dependency and non-linear correlations underlying in the audio data. Extensive experiments show that MT-A-DNN outperforms conventional methods on the emotion recognition. It could take one step further on the real-time acoustic emotion recognition in many smart audio-devices.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"1216 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133842347","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: 11
Enabling Concurrent IoT Transmissions in Distributed C-RAN 在分布式C-RAN中实现并发物联网传输
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275197
Xiaoran Fan, Zhenzhou Qi, Zhenhua Jia, Yanyong Zhang
{"title":"Enabling Concurrent IoT Transmissions in Distributed C-RAN","authors":"Xiaoran Fan, Zhenzhou Qi, Zhenhua Jia, Yanyong Zhang","doi":"10.1145/3274783.3275197","DOIUrl":"https://doi.org/10.1145/3274783.3275197","url":null,"abstract":"As rapid expansion of the low-cost next billion devices, wireless sensor networks (WSN) undertake much denser low-end internet of things (IoT) nodes nowadays. In the meantime, the future next 5 generation (5G) radio base stations (BS) are granted more capabilities. Distributed cloud radio access network (C-RAN) is becoming available for the future massive WSN. However, real-world distributed C-RAN is less explored for low-end IoT based WSN due to its difficulties in implementation. In this paper, we built a distributed C-RAN which has tens of distributed radio frontends using USRP N210s in a 20 × 20 × 3 m3 area. By exploiting the inherent hardware properties of low-end IoT devices and the spatial diversity of distributed C-RAN system, we show the distributed C-RAN can potentially decode collided signals from low-end IoT devices with all signal processing been done on the cloud.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375953","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
Robust Detection of Motor-Produced Audio Signals 电机产生音频信号的鲁棒检测
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275209
Adeola Bannis, H. Noh, Pei Zhang
{"title":"Robust Detection of Motor-Produced Audio Signals","authors":"Adeola Bannis, H. Noh, Pei Zhang","doi":"10.1145/3274783.3275209","DOIUrl":"https://doi.org/10.1145/3274783.3275209","url":null,"abstract":"Indoor localization systems cannot rely on the same mechanisms, like GPS, that are used for outdoor or large-scale localization. Instead, autonomous or user-carried devices are often localized by measuring the time taken for an emitted signal to reach a known location; this signal can be sound, light, radio waves, or another similar sensed quantity. Autonomous mobile devices already contain motors, which produce sounds as a side effect of their operation, and so can potentially be included in a localization scheme without new hardware. In this paper, we briefly outline the challenges that need to be met for accurate detection and identification of motor-produced signals. We present a method for improving signal resolution for linear chirps that improves cross-correlation based signal detection by up to 2.8X.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117165509","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
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