2017 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献

{"title":"I Am the UAV: A Wearable Approach for Manipulation of Unmanned Aerial Vehicle","authors":"Yijia Lu, Fei Han, Lei Xie, Yafeng Yin, Congcong Shi, Sanglu Lu","doi":"10.1109/SMARTCOMP.2017.7947014","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947014","url":null,"abstract":"Nowadays, Unmanned Aerial Vehicles(UAVs) have been widely applied in our life. However, the existing approach of interacting with UAVs, i.e., using a remote controller with control sticks, is not a natural and intuitive way. In this paper, we present a novel approach for users to interact with personal UAVs using wearable devices. The basic idea of our approach is to manipulate UAVs based on human activity sensing, including motion recognition and pedestrian dead- reckoning. We have implemented the proposed approach on a DJI drone, and evaluated its performance in the real- world environment. Realistic experiment results show that our solution can replace the remote controller to manipulate the UAV.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124548","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}

{"title":"Identity Theft Detection in Mobile Social Networks Using Behavioral Semantics","authors":"Cheng Wang, Bo Yang, Jing Luo","doi":"10.1109/SMARTCOMP.2017.7947016","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947016","url":null,"abstract":"User behavioral analysis is expected to be a key technique for identity theft detection in the Internet, especially in mobile social networks (MSNs). While traditional methods prefer to use explicit behaviors, a series of behaviors implicit in user's texts can probably provide much more accurate identity. And these implicit behaviors can be digged from texts by LDA. Besides the latent feature in texts, a behavior also include other features (e.g., spatial and temporal features). A joint feature including these features can be a better evidence for identity theft detection. In this paper, we use a probabilistic generative model to detect identity theft in MSNs. We are going to conduct experiments on two real-life datasets: Foursquare and Yelp. A early experiment shows that semantic features achieve better performance than spatial features and we are conducting our main experiment to see a better performance with joint behavioral feature.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124117813","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}

{"title":"Identifying Carotid Plaque Composition in MRI with Convolutional Neural Networks","authors":"Yuxi Dong, Yuchao Pan, Xihai Zhao, Rui Li, C. Yuan, W. Xu","doi":"10.1109/SMARTCOMP.2017.7947015","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947015","url":null,"abstract":"Carotid plaques may cause strokes. The composition of the plaque helps assessing the risk. Magnetic resonance imaging (MRI) is a powerful technology for analyzing the composition. It is both tedious and error-prone for a human radiologist to review such images. Traditional computer-aided diagnosis tools use manually crafted features that lack both generality and accuracy. We propose a novel approach using Deep convolutional neural networks (CNN) to classify these plaque tissues. In order to accommodate the multi-contrast MRI images, we modify stateof-the-art CNN models to support different number of input channels, and also adapt the models to do pixel- wise predictions. On a dataset with 1,098 human subjects, we show that we achieve significantly better accuracy than previous models. Our result also indicates interesting relations between contrast weightings and tissue types","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128219070","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}

{"title":"A Fast Algorithm for Detecting Hidden Objects by Smart Mobile Robots","authors":"T. Cheng, C. T. Ng, E. Levner, B. Kriheli","doi":"10.1109/SMARTCOMP.2017.7946978","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7946978","url":null,"abstract":"The problem of searching for hidden or lost objects (called targets) by autonomous smart robots in an unknown environment arises in many applications, e.g., searching for and rescuing lost people during incidents in high-rise buildings, searching for fire sources and hazardous materials, searching for safe paths through the rubble during an emergency evacuation etc. Until the target is found, it may cause loss or damage whose extent depends on the location of the target and the search duration. The problem is to efficiently search for and detect the target as soon as possible with the help of a smart mobile robot. The autonomous mobile robot has no operator on board, as it is guided and controlled by on-board sensors and computer programs. In this paper we construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robot used during an emergency evacuation.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690551","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}

{"title":"LiReT: An Fine-Grained Self-Adaption Device-Free Localization with Little Human Effort","authors":"Juan He, Yue Hu, Xinyan Liu, Chen Liu, Yao Peng, Xianjia Meng","doi":"10.1109/SMARTCOMP.2017.7947020","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947020","url":null,"abstract":"Wireless localization technology is a vital component in many long-term monitoring applications, such as activity monitoring and real-time tracking. Most existing localization methods however require the target to carry communicationcapable devices to send or receive messages, which may not hold for wildlife monitoring or intrusion detection. Prior proposals are based on device-free localization techniques, such as Channel State Information (CSI). However, they cost huge human effort in fingerprint collection when locate the target in different scenarios with different area size. This paper proposes a robust and accurate at low-cost devicefree localization system named LiReT. To reduce the time cost and human effort in fingerprint collection when the monitoring environment changed, we represent a LiReT algorithm based on a multivariable linear regression model to transfer the CSI measurements (fingerprint) at distance L to L'. Thus, LiReT can locate the target accurately at low-cost. Result from experiments demonstrate that our system can improve the localization accuracy by up to 51.68%, which is competitive with existing solutions.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117160960","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}

{"title":"Urban Heartbeat: From Modelling to Applications","authors":"R. Jafari, A. Hasani","doi":"10.1109/SMARTCOMP.2017.7947058","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947058","url":null,"abstract":"Sensors and actuators are finding their way into our lives and our surroundings at a very fast pace. These heterogeneous sensors deployed in the environment can prove to be useful in providing insights into the behavior and trends of the environment. In this work, we capture a part of that knowledge and propose a novel concept called Urban Heartbeat using data captured by various sensors that essentially identify periodic activities in the environment. The Urban Heartbeat can be leveraged to identify when an unexpected event has occurred or is about to occur to more effectively prepare the citizens. We first develop techniques to find couplings between sensors using multiple operators, in cases when direct measurement of a parameter is not possible. Next, we define an algorithm that can be used to find quasi-periodic patterns from time series data that has spatiotemporal deviations. We then introduce the notion of Urban Heartbeat, which leverages data from heterogeneous sensors to identify the normal heartbeat of the environment. The Urban Heartbeat can be used not only to differentiate between normal and abnormal trends thereby giving us the ability to detect anomalies but also in making predictions about the user or the environment behavior. We also show how we build heartbeat for a lab environment, learn useful information about the users and offer predictions about their behavior in the lab.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132189648","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}

{"title":"Transmit Only: An Ultra Low Overhead MAC Protocol for Dense Wireless Systems","authors":"Yanyong Zhang, Bernhard Firner, R. Howard, R. Martin, N. Mandayam, J. Fukuyama, Chenren Xu","doi":"10.1109/SMARTCOMP.2017.7947055","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947055","url":null,"abstract":"The number of small wireless devices is rapidly increasing, making the radio channel efficiency in limited geographic areas (individual rooms or buildings) an important metric for MAC protocols. Many of these emerging devices have use-cases that are difficult to satisfy with current hardware solutions and channel access methods; for instance device mobility, small energy reserves, and requirements for low cost and small form factors. However, for most of these applications, such as health care monitoring or sensing, feedback to the radio device is unnecessary and unidirectional communication techniques are not only sufficient, but can also be advantageous. We propose an efficient, reliable technique for unidirectional communication, called Transmit Only (TO), that satisfies these requirements while maintaining packet throughput guarantees and reducing energy consumption. In this paper we will demonstrate the feasibility and performance of this kind of highly asymmetric, transmit-only protocol through theoretical, simulated, and experimental results.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134073222","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}

{"title":"Adaptive Video Streaming in Hybrid Landslide Detection System with D-S Theory","authors":"Zhi Liu, Kenji Kanai, Masaru Takeuchi, T. Tsuda, Hiroshi Watanabe","doi":"10.1109/SMARTCOMP.2017.7946984","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7946984","url":null,"abstract":"Disaster detection is an important research topic and draws great attentions from both industry and academia. In this paper, we study the hybrid landslide detection system, which utilizes the video surveillance camera and multiple kinds of sensors and can detect the landslide automatically. Edge processing is adopted in this hybrid system to fuse the sensor data based on the Dempster-Shafer (D-S) theory, i.e. utilizing multiple sensors' information to calculate the possibility of the landslide. Edge processing can make faster decisions than the control center, the results of the edge processing are then used to schedule the sensor's transmission frequency and video transmission under the network constraints in this system. The simulation results show that the proposed scheme outperforms the competing schemes in typical network scenarios.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114460304","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}

{"title":"ECG-Based Driver Distraction Identification Using Wavelet Packet Transform and Discriminative Kernel-Based Features","authors":"Shantanu V. Deshmukh, O. Dehzangi","doi":"10.1109/SMARTCOMP.2017.7947003","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947003","url":null,"abstract":"Driver Distraction is one of the main reasons behind the increasing number of fatalities on the road. In order to minimize the potential road disasters, it is essential to monitor and track the pre-requisites of driver distraction. While driving, the driver might get distracted in variety of ways such as talking on the cell phone, texting, or having a conversation with a passenger. In the recent years, extensive investigations are directed towards the problem of characterizing the impact of secondary tasks while driving, predominantly using camera-based systems. However, camera-based systems incur major challenges such as privacy or latency in detection. Using physiological signals to identify distraction such as Electroencephalography (EEG) has been shown to accomplish more reliable detection. However, EEG- based detection systems necessitate intrusive implementation and complex signal processing. On the other hand, Electrocardiogram (ECG) is a reliable signal which can characterize the physiological changes consistently, with minimal intrusiveness, and at low cost. In this paper, we focus on ECG signal processing aspect with the aim of predicting driver distraction. Eight subjects aged 24 ± 45, actively participated in the naturalistic driving experiment where distraction was induced by: 1) phone conversation and 2) engaging an active conversation between the driver and the passenger. We present an effective frequency subBand analysis using Wavelet Packet Transform (WPT) to localize the impact of distracting elements. Due to high dimensionality of the WPT generated space, we then applied Linear Discriminant Analysis (LDA) for feature space dimensionality reduction; preserving discriminative capability of the predictive model. In order to further enhance the prediction ability of the system, we used kernel transformation in order to take into account non-linear interactions of the input feature space. Based on our results, WPT transform in combination with Linear Discriminant dimensionality reduction demonstrated high potentials to detect normal vs. distracted driving scenarios. Using kernel transformation further increased feature space discrimination compared to the baseline features and let to an increase from 44.10% to 88.45% average prediction accuracy over all subjects.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130804331","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}

{"title":"Fog Computing for the Internet of Mobile Things: Issues and Challenges","authors":"C. Puliafito, E. Mingozzi, G. Anastasi","doi":"10.1109/SMARTCOMP.2017.7947010","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2017.7947010","url":null,"abstract":"The Internet of Things (IoT) conceives a world where everyday objects are able to join the Internet and exchange data as well as process, store, collect them from the surrounding environment, and actively intervene on it. An unprecedented number of services may be envisioned by exploiting the Internet of Things. Fog Computing, which is also known as Edge Computing, was proposed in 2012 as the ideal paradigm to support the resource-constrained IoT devices in data processing and information delivery. Indeed, the Fog, which does not replace the centralized Cloud but cooperates with it, distributes Cloud Computing technologies and principles anywhere along the Cloud-to-Things continuum and particularly at the network edge. The Fog proximity to the IoT devices allows for several advantages that must be continuously guaranteed, also when end devices move from one place to another. In this paper, we aim at examining in depth what it means to provide mobility support in a Fog environment and at investigating what are the main challenges to be faced. Besides, in order to highlight the importance of this topic in everyday life, we provide the reader with three scenarios where there is an integration between the IoT and Fog Computing, and in which mobility support is essential. We finally point out the main research directions in the field.","PeriodicalId":193593,"journal":{"name":"2017 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126718482","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}