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Developing a system that can automatically detect health changes using transfer times of older adults

BMC MEDICAL RESEARCH METHODOLOGY 2016年第1期16卷 1-17页

作者： Baldewijns, Greet Luca, Stijn Vanrumste, Bart Croonenborghs, Tom KU Leuven Technol Campus Geel AdvISe Kleinhoefstr 4 Geel Belgium Katholieke Univ Leuven ESAT STADIUS Leuven Belgium iMinds Med Informat Technol Dept Leuven Belgium Katholieke Univ Leuven Dept Comp Sci DTAI Leuven Belgium Harvard Univ Sch Med Program Translat NeuroPsychiat Genom Brigham & Womens HospBroad Inst Massachusetts In Cambridge MA USA

Background: As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. Methods: This paper presents a system that employs statistical process control techniques (SPC) to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM) chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA) chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. Results: The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. Conclusions: The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.

关键词： Assisted living Gerontechnology change detection algorithms Statistical process control Log logistic distributions Gait speed

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Aerial video surveillance system for small-scale UAV environment monitoring

Aerial video surveillance system for small-scale UAV environ...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Danilo Avola Gian Luca Foresti Niki Martinel Christian Micheloni Daniele Pannone Claudio Piciarelli Department of Mathematics Computer Science and Physics University of Udine Italy Department of Computer Science Sapienza University Italy

change detection algorithms are commonly used to detect novelties for surveillance purposes in public and private places equipped by static or Pan-Tilt-Zoom (PTZ) cameras. Often, these techniques are also used as prerequisite to support more complex algorithms, including event recognition, object classification, person re-identification, and many others. With regard to small-scale Unmanned Aerial Vehicles (UAVs) at low-altitude, the change detection techniques require further investigation. In fact, most of the works currently available in the literature process video sequences acquired at very high-altitude for large-scale operations, such as vegetation monitoring, mapping of buildings, and so on. In a wide range of application contexts that require, for example, frequent monitoring or high spatial resolution for detecting small objects, video sequences acquired at high-altitude are not suitable. This paper presents a change detection system based on histogram equalization and RGB-Local Binary Pattern (RGB-LBP) operator for monitoring of wide areas by small-scale UAVs at low-altitude. Extensive experimental results show the robustness of the proposed pipeline. These latter were performed by using challenging video sequences of the public UAV Mosaicking and change detection (UMCD) dataset and measured a set of well-known statistical metrics. Finally, a performance analysis of the proposed algorithm is also provided.

关键词： Pipelines Histograms Monitoring change detection algorithms detection algorithms Buildings Vegetation mapping

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A cognitive data stream mining technique for context-aware IoT systems

A cognitive data stream mining technique for context-aware I...

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Annual Conference of Industrial Electronics Society

作者： Dinithi Nallaperuma Daswin De Silva Damminda Alahakoon Xinghuo Yu Research Centre for Data Analytics and Cognition La Trobe University Victoria Australia School of Engineering RMIT University Victoria Australia

IoT systems deployed in industrial and smart factory settings generate large volumes of data at high velocity. Context awareness is mandatory for knowledge discovery and actionable insights from such high-velocity, high-volume IoT data streams. changes to the context of a data stream are represented in the underlying data distribution. Research in concept drift aims to detect and adapt to such changes in a data distribution. Concept drift detection can be extended to suit ad hoc Big Data streams generated by IoT systems, by introducing the cognitive principles of learning. This paper proposes an unsupervised incremental learning algorithm for detection and adaption of concept drift based on the cognitive principles of learning. It executes in automated time windows, detects concept drift using movement in space and determines the type of concept drift using movement in time. The algorithm was applied to a Big Data set representing an IoT system for urban vehicular movement and traffic. Results confirm that the proposed algorithm generates context-awareness by detection and adaptation to concept drift in high-volume, high-velocity IoT systems.

关键词： Clustering algorithms Unsupervised learning Monitoring change detection algorithms Data mining Cognition

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Statistical detection and Isolation of Cyber-Physical Attacks on SCADA Systems

Statistical Detection and Isolation of Cyber-Physical Attack...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Van Long Do Viettel Research and Development Institute Viettel Group Thach That - Hanoi Vietnam

This paper addresses the problem of jointly detecting and isolating cyber-physical attacks on Supervisory Control And Data Acquisition (SCADA) systems. The SCADA systems are described as a discrete-time state space model in the presence of unknown system states and Gaussian noises. The cyber-physical attacks are modeled as additive signals on both system equations, where different attack scenarios will produce particular changes (referred as attack profiles) in system dynamics. To solve the problem, we employ the classical approach in fault detection and isolation (FDI) community which includes two steps: residual generation and residual evaluation. The so-called residuals are first generated by the parity space method. They are then evaluated by using statistical change-point detection and isolation algorithms. The proposed algorithm is applied to the detection and isolation of several attack scenarios on a simple SCADA water distribution network. Its statistical performance is also compared with several conventional quickest change detection and isolation algorithms by using the transient change detection and isolation criterion. This novel-proposed criterion involves the minimization of the probability of missed detection subject to given values on the probability of false alarm and false isolation.

关键词： Transient analysis SCADA systems Mathematical model Minimization Security Additives change detection algorithms

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In-Band Wormhole detection in Wireless Ad Hoc Networks Using change Point detection Method

In-Band Wormhole Detection in Wireless Ad Hoc Networks Using...

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Ad-Hoc and Sensor Networks Symposium

作者： Maggie X. Cheng Yi Ling Wei Biao Wu Missouri University of Science and Technology The University of Chicago

ISBN: (纸本)9781479966653

This paper addresses detecting in-band wormholes in wireless ad hoc networks. The detection scheme requires collecting the end-to-end delay of packets at the receiver and then applying a sequential change point detection algorithm to detect abrupt changes in the delay time series. A new change point detection algorithm, named SW-CLT, is proposed. The algorithm is based on the Central Limit Theorem (CLT) and does not involve using a preset detecting threshold. The algorithm is compared with the non-parametric cumulative sum (NP-CUSUM) because the non-parametric version is believed to be more robust to highly dynamic data than the parametric version. SW-CLT has the ability to adjust its detection threshold with the variance of the data, and therefore is more robust than NP-CUSUM, which uses a preset threshold. Simulation results from ns3 verified the advantage of SW-CLT over NP-CUSUM in all simulated scenarios.

关键词： Mobile ad hoc networks wireless change detection algorithms Central limit theorem wormhole detection algorithms preset inspection methods Packet detection limit

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Detecting changes in streaming data with information-theoretic windowing

Detecting changes in streaming data with information-theoret...

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IEEE International Conference on Big Data

作者： Ryoya Kaneko Kohei Miyaguchi Kenji Yamanishi Graduate School of Information Science and Technology The University of Tokyo Japan

This study addresses the problem of detecting change points in streaming data. Herein, we focus on detecting changes with low complexity and high accuracy to manage large-volume, high-velocity data. To this end, we propose a novel method of detecting changes via data compression, by introducing minimum description length (MDL) change statistics to an adaptive windowing regime. The time complexity of the resulting algorithm, Sequential Compression with Adaptive Windowing (SCAW), is optimal except for a logarithmic factor, and is theoretically justified through exponential upper bounds on error probabilities. Moreover, SCAW can be used to detect arbitrary types of changes by choosing an appropriate compressor. We also introduce the notion of asymptotic reliability as a criterion of change point detection algorithms, and determine SCAW's parameter using this criterion. Finally, we demonstrate the effectiveness of the proposed method in experiments using synthetic and real-world data from markets and industrial machinery.

关键词： Reliability Error probability change detection algorithms Big Data detection algorithms Data compression

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Information theoretic multivariate change detection for multisensory information processing in Internet of Things

Information theoretic multivariate change detection for mult...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lev Faivishevsky Intel

ISBN: (纸本)9781479999897

Internet of Things (IoT) is one of the main technological trends in the recent years. It allows machine-to-machine communication over the internet. Almost each device may transmit information from its sensors over the web to enable centralized insights derivation in an appropriate cloud architecture. In this paper we review analytical aspects of the sensory information processing. We emphasize the importance of multisensory approach, in which the joint distribution of all sensors values of a device is used to derive insights out of the stream of sensory data. We introduce a novel information theoretic multivariate change detection method based on k-nearest neighbor (kNN) estimation. The algorithm is designed and implemented to satisfy the requirements of IoT for fast online parallel multisensory information processing. We provide a numerical evidence of the validity of the proposed method on simulated and real world data.

关键词： Cloud computing change detection algorithms Sensor phenomena and characterization Information processing Estimation Internet of things

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A Scalable Probabilistic change detection Algorithm for Very High Resolution (VHR) Satellite Imagery

A Scalable Probabilistic Change Detection Algorithm for Very...

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IEEE International Conference on Services Economics (SE)

作者： Seokyong Hong Ranga Raju Vatsavai Department of Computer Science North Carolina State University Raleigh NC USA Oak Ridge National Laboratory Oak Ridge TN USA

Detecting landscape changes using very high-resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based change detection approaches, however, have several drawbacks, which render them ineffective for VHR imagery analysis. A recent probabilistic change detection framework provides more accurate assessment of changes than traditional approaches by analyzing image patches than pixels. However, this patch (grid)-based approach produces coarse-resolution (patch size) changes. In this work we present a sliding window based approach that produces changes at the native image resolution. The increased computational demand of the sliding window based approach is addressed through thread-level parallelization on shared memory architectures. Our experimental evaluation showed a 91% performance improvement compared to its sequential counterpart on a sq. KM aerial image with varying window sizes on a 16-core (32 virtual threads) Intel Xeon processor.

关键词： Probabilistic logic change detection algorithms Image resolution detection algorithms Clustering algorithms Satellites Image registration

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Some results on change detection based on advanced signal processing paradigm

Some results on change detection based on advanced signal pr...

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International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Anisia Culea-Florescu Dorel Aiordachioaie Electronics and Telecommunication Department Dunarea de Jos University of Galati Galati Romania

This work considers the problem of change detection of working regimes from industrial processes, e.g. electric machines with rotation elements, and which generates mechanical vibrations. Two approaches are considered: (i) based on signal processing and pattern recognition methods; (ii) based on sparse methods. The objective of the paper is to evaluate the preliminary results obtained by the above approaches and to promote methods based on sparse representations and computations for change detection problems, as alternative to classical methods based on transform or pattern recognition. The results are encouraging and suggest that more studies on the method of sparse computation as an optimal candidate for change detection from time detection point of view is needed.

关键词： Pattern recognition Signal to noise ratio Estimation change detection algorithms Frequency-domain analysis Sensitivity

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Context-sensitive similarity based supervised image change detection

Context-sensitive similarity based supervised image change d...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chao Li Leifu Gao College of Science Liaoning Technical University Fuxin China

ISBN: (纸本)9781509040940

To deal with the problem that traditional satellite remote sensing image change detection methods overestimate changed areas, a context-sensitive similarity based supervised satellite image change detection method was proposed. Both context-sensitive magnitude and direction of change in the vicinity of each pixel by means of local intercept and slope were exploited, and then SVM (support vector machine) with local intercept and slope was used in satellite image change detection. In the experiment for change detection of high resolution bi-temporal multispectral earthquake satellite images including building damage, the results showed that compared to standard SVM, the accuracy of satellite image change detection had been obviously improved, and overestimation of changed areas had been effectively reduced.

关键词： Support vector machines Satellites Remote sensing Buildings change detection algorithms Earthquakes Standards

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