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An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Journal of Management Analytics 2015年第2期2卷 121-137页

作者： Hairong Yan LiDa Xu Zhuming Bi Zhibo Pang Jie Zhang Yong Chen Embedded Software and System Institute Beijing University of TechnologyBeijingChina Institute of Computing Technology Chinese Academy of SciencesBeijingChinaShanghai Jiao Tong UniversityShanghaiChinaUniversity of Science and Technology of ChinaHefeiChinaOld Dominion UniversityNorfolkUSA Department of Engineering Indiana University Purdue University Fort WayneFort WayneUSA ABB Corporate Research Center Sweden Old Dominion University NorfolkUSA

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological *** collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions *** this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the *** novelty of the detected model lies in its relevance to *** of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and *** effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.

关键词： e-healthcare Internet of Things wearable wireless sensor network(WWSN) anomaly detection condition monitoring data acquisition and processing

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Building belonging

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Interactions 2014年第4期21卷 58-61页

作者： DiSalvo, Carl M., Gregg T., Lodato Digital Media Program Georgia Institute of Technologys United States Intel Science and Technology Center for Social Computing United States Intel Labs United States

Researchers are conducting investigations to demonstrate how community technology practice and the interaction design field play a key role in the society. Researchers have been conducting research into issue-oriented hackathons, events that draw together activists, citizens, entrepreneurs, and coders to address social conditions and their consequences. This research connects to broader trends in the fields of HCI and CSCW of examining informal design and development environments and practices. The researchers particularly focus is on hackathons as events, and how these events are formed by and at the same time give form to the issues they purport to engage and address. Examples of such issue-oriented hackathons include those organized around environmental conditions, such as Eco-Hack or food systems.

关键词： Human computer interaction

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Formal modeling and verification of cloudproxy 6

Formal modeling and verification of cloudproxy

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6th International Conference on Verified Software: Theories, Tool and Experiments, VSTTE 2014

作者： Tan, Wei Yang Sinha, Rohit Manferdelli, John L. Seshia, Sanjit A. University of California BerkeleyCA United States Intel Science and Technology Center for Secure Computing Berkeley United States

ISBN: (纸本)9783319121536

Services running in the cloud face threats from several parties, including malicious clients, administrators, and external attackers. CloudProxy is a recently-proposed framework for secure deployment of cloud applications. In this work, we present the first formal model of CloudProxy, including a formal specification of desired security properties. We model CloudProxy as a transition system in the UCLID modeling language, using term-level abstraction. Our formal specification includes both safety and non-interference properties. We use induction to prove these properties, employing a back-end SMT-based verification engine. Further, we structure our proof as an "assurance case", showing how we decompose the proof into various lemmas, and listing all assumptions and axioms employed. We also perform some limited model validation to gain assurance that the formal model correctly captures behaviors of the implementation. © Springer International Publishing Switzerland 2014.

关键词： Formal specification

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Stronger properties for low-latency geo-replicated storage

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Queue 2014年第3期12卷 30-45页

作者： Lloyd, Wyatt Freedman, Michael J. Kaminsky, Michael Andersen, David G. Facebook University of Southern California United States Princeton University National Science Foundation Office of Naval Research United States Intel Labs Carnegie Mellon University Intel Science and Technology Center for Cloud Computing Pittsburgh PA United States Carnegie Mellon University University of Utah. Salt Lake City UT United States

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Physical querying with multi-modal sensing

Physical querying with multi-modal sensing

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Iljoo Baek Taylor Stine Denver Dash Fanyi Xiao Yaser Sheikh Yair Movshovitz-Attias Mei Chen Martial Hebert Takeo Kanade LG Electronics Seoul South Korea The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Intel Science and Technology Center on Embedded Computing Carnegie Mellon University Pittsburgh PA USA

We present Marvin, a system that can search physical objects using a mobile or wearable device. It integrates HOG-based object recognition, SURF-based localization information, automatic speech recognition, and user feedback information with a probabilistic model to recognize the “object of interest” at high accuracy and at interactive speeds. Once the object of interest is recognized, the information that the user is querying, e.g. reviews, options, etc., is displayed on the user's mobile or wearable device. We tested this prototype in a real-world retail store during business hours, with varied degree of background noise and clutter. We show that this multi-modal approach achieves superior recognition accuracy compared to using a vision system alone, especially in cluttered scenes where a vision system would be unable to distinguish which object is of interest to the user without additional input. It is computationally able to scale to large numbers of objects by focusing compute-intensive resources on the objects most likely to be of interest, inferred from user speech and implicit localization information. We present the system architecture, the probabilistic model that integrates the multi-modal information, and empirical results showing the benefits of multi-modal integration.

关键词： Speech Object recognition Feature extraction Speech recognition Computer architecture Context Visualization

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Extending temporal logics with data variable quantifications 34

Extending temporal logics with data variable quantifications

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34th International Conference on Foundations of Software technology and Theoretical Computer science, FSTTCS 2014

作者： Song, Fu Wu, Zhilin Shanghai Key Laboratory of Trustworthy Computing and National Trusted Embedded Software Engineering Technology Research Center East China Normal University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China LIAFA Université Paris Diderot France

ISBN: (纸本)9783939897774

Although data values are available in almost every computer system, reasoning about them is a challenging task due to the huge data size or even infinite data domains. Temporal logics are the well-known specification formalisms for reactive and concurrent systems. Various extensions of temporal logics have been proposed to reason about data values, mostly in the last decade. Among them, one natural idea is to extend temporal logics with variable quantifications ranging over an infinite data domain. In this paper, we focus on the variable extensions of two widely used temporal logics, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL). Grumberg, Kupferman and Sheinvald recently investigated the extension of LTL with variable quantifications. They defined the extension as formulas in the prenex normal form, that is, all the variable quantifications precede the LTL formulas. Our goal in this paper is to do a relatively complete investigation on this topic. For this purpose, we define the extensions of LTL and CTL by allowing arbitrary nestings of variable quantifications, Boolean and temporal operators (the resulting logics are called respectively variable-LTL, in brief VLTL, and variable-CTL, in brief VCTL), and identify the decidability frontiers of both the satisfiability and model checking problem. In particular, we obtain the following results: 1) Existential variable quantifiers or one single universal quantifier in the beginning already entails undecidability for the satisfiability problem of both VLTL and VCTL, 2) If only existential path quantifiers are used in VCTL, then the satisfiability problem is decidable, no matter which variable quantifiers are available. 3) For VLTL formulas with one single universal variable quantifier in the beginning, if the occurrences of the non-parameterized atomic propositions are guarded by the positive occurrences of the quantified variable, then its satisfiability problem becomes decidable. Based on these results o

关键词： Temporal logic

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ST-HMP: Unsupervised Spatio-Temporal feature learning for tactile data

ST-HMP: Unsupervised Spatio-Temporal feature learning for ta...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Marianna Madry Liefeng Bo Danica Kragic Dieter Fox CSC KTH Royal Institute of Technology Stockholm Sweden Intel Science and Technology Center on Pervasive Computing Seattle USA Department of Computer Science & Engineering University of Washington Seattle USA

Tactile sensing plays an important role in robot grasping and object recognition. In this work, we propose a new descriptor named Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP) that captures properties of a time series of tactile sensor measurements. It is based on the concept of unsupervised hierarchical feature learning realized using sparse coding. The ST-HMP extracts rich spatio-temporal structures from raw tactile data without the need to predefine discriminative data characteristics. We apply it to two different applications: (1) grasp stability assessment and (2) object instance recognition, presenting its universal properties. An extensive evaluation on several synthetic and real datasets collected using the Schunk Dexterous, Schunk Parallel and iCub hands shows that our approach outperforms previously published results by a large margin.

关键词： Databases Matching pursuit algorithms Tactile sensors Hidden Markov models Synchronous digital hierarchy

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Unsupervised feature learning for 3D scene labeling

Unsupervised feature learning for 3D scene labeling

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kevin Lai Liefeng Bo Dieter Fox Department of Computer Science & Engineering University of Washington Seattle WA USA Amazon Inc. Intel Science and Technology Center on Pervasive Computing Seattle WA USA

This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D classifiers are trained using a synthetic dataset of virtual scenes generated using CAD models from an online database. Our scene labeling system combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every 3D point in the scene. Experiments on the RGB-D Scenes Dataset v.2 demonstrate that the proposed approach can be used to label indoor scenes containing both small tabletop objects and large furniture pieces.

关键词： Three-dimensional displays Labeling Feature extraction Videos Matching pursuit algorithms Dictionaries Solid modeling

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Hierarchical sparse coded surface models

Hierarchical sparse coded surface models

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Michael Ruhnke Liefeng Bo Dieter Fox Wolfram Burgard Autonomous Systems Lab University of Freiburg Intel Science and Technology Center for Pervasive Computing (ISTC-PC) Robotics and State Estimation Lab University of Washington

In this paper, we describe a novel approach to construct textured 3D environment models in a hierarchical fashion based on local surface patches. Compared to previous approaches, the hierarchy enables our method to represent the environment with differently sized surface patches. The reconstruction scheme starts at a coarse resolution with large patches and in an iterative fashion uses the reconstruction error to guide the decision as to whether the resolution should be refined. This leads to variable resolution models that represent areas with few variations at low resolution and areas with large variations at high resolution. In addition, we compactly describe local surface attributes via sparse coding based on an overcomplete dictionary. In this way, we additionally exploit similarities in structure and texture, which leads to compact models. We learn the dictionary directly from the input data and independently for every level in the hierarchy in an unsupervised fashion. Practical experiments with large-scale datasets demonstrate that our method compares favorably with two state-of-the-art techniques while being comparable in accuracy.

关键词： Three-dimensional displays Dictionaries Solid modeling Computational modeling Standards Accuracy Encoding

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Medical Image Classification with Convolutional Neural Network

Medical Image Classification with Convolutional Neural Netwo...

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International Conference on Control, Automation, Robotics & Vision

作者： Qing Li Weidong Cai Xiaogang Wang Yun Zhou David Dagan Feng Mei Chen Biomedical and Multimedia Information Technology (BMIT) Research Group School of IT University of Sydney Australia Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Johns Hopkins University School of Medicine United States Intel Science and Technology Center on Embedded Computing Carnegie Mellon University United States

ISBN: (纸本)9781479952007

Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

关键词： Image classification image patch lung imaging Interstitial lung disease Neural network intrinsic image Medical

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