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Annual Conference of Industrial Electronics Society

作者： C. J. Grobler C. P. Kruger B. J. Silva G. P. Hancke Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa Embedded Intelligent Systems Council for Scientific and Industrial Research Pretoria South Africa Department of Computer Science City University of Hong Kong Hong Kong

For a wide range of applications in industry, it is sometimes necessary to perform acoustic source localization. In this paper, a passive sound localization and classification system is designed and implemented. Each sensor consists of a microphone array which is used to detect the direction-of-arrival (DoA) of an acoustic signal. Multiple DoA sensors can be combined to form a wireless sensor network. The system can detect the acoustic signature of power tools and the effectiveness of the system to be used as an early warning system to detect misuse of machinery is demonstrated. It is shown that the system can detect the DoA of an acoustic signal with an overall mean estimation error of 7° and can correctly classify the signal source with a classification rate of 71.5%.

关键词： Feature extraction Array signal processing Acoustics Microphone arrays Direction-of-arrival estimation Mathematical model

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Event-triggered active-passive dynamic consensus filters

Event-triggered active-passive dynamic consensus filters

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American Control Conference

作者： J. Daniel Peterson Tansel Yucelen S. Jagannathan Eduardo Pasiliao the Mechanical and Aerospace Engineering Department and a member of the Advanced Systems Research Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Mechanical Engineering Department and the Director of the Laboratory for Autonomy Control Information and Systems at the University of South Florida Tampa FL 33620 USA the William A. Rutledge Distinguished Professor of the Electrical and Computer Engineering Department and Director of the Embedded Control Systems and Networking Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Air Force Research Laboratory Munitions Directorate Eglin Air Force Base FL 32542 USA

ISBN: (纸本)9781509045839

Multiagent systems consist of groups of agents that exchange local information to achieve given system-level goals. Traditionally, these systems assumed that all agents have some knowledge (e.g., observations of a process of interest) to contribute in order to achieve a common goal of interest, which may not be the case in many scenarios. Motivated from this standpoint, a new class of multiagent systems, active-passive multiagent systems, were recently proposed, which consists of agents subject to observations of a process of interest (i.e., active agents) and agents without any observations (i.e., passive agents). However, in this class of multiagent systems, agents need to continuously exchange information among themselves, incurring a high cost of interagent information exchange, and requiring that agents synchronize their update times. To address these shortcomings, this paper presents a new class of event-triggered active-passive dynamic consensus filters, in which agents schedule information exchange dependent on errors exceeding user-defined thresholds, significantly reducing the overall cost of interagent information exchange, and allowing agents to determine when to broadcast their information to their neighbors thus eliminating the need to synchronize their states. An illustrative numerical example is presented to demonstrate the efficacy of our approach.

关键词： consist groups agents

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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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Nature medicine 2024年第12期30卷 3779页

作者： Sebastian Moguilner Sandra Baez Hernan Hernandez Joaquín Migeot Agustina Legaz Raul Gonzalez-Gomez Francesca R Farina Pavel Prado Jhosmary Cuadros Enzo Tagliazucchi Florencia Altschuler Marcelo Adrián Maito María E Godoy Josephine Cruzat Pedro A Valdes-Sosa Francisco Lopera John Fredy Ochoa-Gómez Alfredis Gonzalez Hernandez Jasmin Bonilla-Santos Rodrigo A Gonzalez-Montealegre Renato Anghinah Luís E d'Almeida Manfrinati Sol Fittipaldi Vicente Medel Daniela Olivares Görsev G Yener Javier Escudero Claudio Babiloni Robert Whelan Bahar Güntekin Harun Yırıkoğulları Hernando Santamaria-Garcia Alberto Fernández Lucas David Huepe Gaetano Di Caterina Marcio Soto-Añari Agustina Birba Agustin Sainz-Ballesteros Carlos Coronel-Oliveros Amanuel Yigezu Eduar Herrera Daniel Abasolo Kerry Kilborn Nicolás Rubido Ruaridh A Clark Ruben Herzog Deniz Yerlikaya Kun Hu Mario A Parra Pablo Reyes Adolfo M García Diana L Matallana José Alberto Avila-Funes Andrea Slachevsky María I Behrens Nilton Custodio Juan F Cardona Pablo Barttfeld Ignacio L Brusco Martín A Bruno Ana L Sosa Ortiz Stefanie D Pina-Escudero Leonel T Takada Elisa Resende Katherine L Possin Maira Okada de Oliveira Alejandro Lopez-Valdes Brian Lawlor Ian H Robertson Kenneth S Kosik Claudia Duran-Aniotz Victor Valcour Jennifer S Yokoyama Bruce Miller Agustin Ibanez Latin American Brain Health Institute Universidad Adolfo Ibañez Santiago de Chile Chile. Cognitive Neuroscience Center Universidad de San Andrés Buenos Aires Argentina. Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA. Universidad de los Andes Bogota Colombia. Global Brain Health Institute (GBHI) University of California San Francisco CA USA. Global Brain Health Institute (GBHI) Trinity College Dublin Dublin Ireland. The University of California Santa Barbara (UCSB) Santa Barbara CA USA. Escuela de Fonoaudiología Universidad San Sebastián Santiago de Chile Chile. Grupo de Bioingeniería Decanato de Investigación Universidad Nacional Experimental del Táchira San Cristóbal Venezuela. Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile. University of Buenos Aires Buenos Aires Argentina. The Clinical Hospital of Chengdu Brain Sciences Institute University of Electronic Sciences and Technology of China Chengdu China. Technology of China Chengdu China. Cuban Neuroscience Center La Habana Cuba. Grupo de Neurociencias de Antioquia (GNA) University of Antioquia Medellín Colombia. Department of Psychology Master Program of Clinical Neuropsychology Universidad Surcolombiana Neiva Neiva Colombia. Department of Psychology Universidad Cooperativa de Colombia Arauca Colombia. Neurocognition and Psychophysiology Laboratory Universidad Surcolombiana Neiva Colombia. Reference Center of Behavioural Disturbances and Dementia School of Medicine University of Sao Paulo Sao Paulo Brazil. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center University of Sao Paulo Sao Paulo Brazil. Center for Social and Cognitive Neuroscience School of Psychology Universidad Adolfo Ibáñez Santiago Chile. Neuropsychology and Clinical Neuroscience Laboratory (LANNEC) Physiopathology Program-Institute of Biomedical Sciences (ICBM) Neuroscience and East Neuroscience Dep

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embedded multibeam sonar feature extraction for online AUV control

Embedded multibeam sonar feature extraction for online AUV c...

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OCEANS - Europe

作者： Matthieu Sion Torsten Teubler Horst Hellbrück Student of Embedded Systems Telecom & Management Sud Paris Department of Electrical Engineering and Computer Science CoSA Center of Excellence Lübeck University of Applied Sciences Department of Electrical Engineering and Computer Science CoSA Center of Excellence

ISBN: (纸本)9781467397254

For development of an intelligent unmanned autonomous underwater vehicle (AUV), sensor data needs to be processed online for navigation and mission planning. In this work, we suggest a complete workflow and a processing chain to retrieve multibeam sonar data for AUV control. Our approach is based on the well-known image processing library OpenCV which provides sophisticated image recognition algorithms. We implement a processing chain for feature extraction on multibeam sonar acoustic image which retrieves contours of objects and coordinate points of the contours. Coordinate points are discrete data which can be easily processed further with additional algorithms. E.g. size of objects can be determined with the coordinate points or an expert system can classify objects with help of the coordinate points. Our solution will be embedded in an online control of an AUV. We evaluate the performance of our feature extraction approach using pre-recorded sonar data.

关键词： Sonar Feature extraction Sonar navigation Software Data mining Acoustic pulses

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A Machine Learning Approach for Medication Adherence Monitoring Using Body-Worn Sensors

A Machine Learning Approach for Medication Adherence Monitor...

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Design, Automation & Test in Europe Conference & Exhibition

作者： Niloofar Hezarjaribi Ramin Fallahzadeh Hassan Ghasemzadeh Embedded & Pervasive Systems Lab (EPSL) School of Electrical Engineering and Computer Science Washington State University Pullman 99164-2752 USA

ISBN: (纸本)9781467392280

One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.

关键词： Machine Learning Approach Medication Adherence Monitoring Using Body-Worn Sensors most important challenges

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Fast Feature Extraction For Time Series Analysis Using Least-Squares Approximations with Orthogonal Basis Functions

Fast Feature Extraction For Time Series Analysis Using Least...

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International Symposium on Temporal Representation and Reasoning

作者： André Gensler Thiemo Gruber Bernhard Sick Intelligent Embedded Systems Group University of Kassel Department of Electrical Engineering/ Computer Science Kassel Germany

ISBN: (纸本)9781467393188

Many efforts have been made during the past decades to investigate the use of orthogonal basis functions in the field of least-squares approximation. Certain bases of orthogonal functions allow for a definition of fast update algorithms for approximations of time series in sliding or growing time windows. In fields such as technical data analytics, temporal data mining, or pattern recognition in time series, appropriate time series representations are needed to measure the similarity of time series or to segment them. This article bridges the gap between mathematical basic research and applications by making fast update techniques for standard polynomials and trigonometric polynomials accessible for time series classification or regression (e.g., forecasting), anomaly or motif detection in time series, etc. This is of utmost importance for online or big data applications. Time series or segments of time series will be represented by features derived from the orthogonal expansion coefficients of the approximating polynomials which capture the essential behavior in the time or spectral domain, i.e. trends and periodic behavior, using standard or trigonometric polynomials. Our experiments show that a reliable computation is possible at a very low runtime compared to a conventional least-squares approach. The algorithms are implemented in Java, C(++), Matlab, and Python and made publicly available.

关键词： time series least-squares approximation orthogonal functions update techniques temporal data mining

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Impact of sensor misplacement on estimating metabolic equivalent of task with wearables 12

Impact of sensor misplacement on estimating metabolic equiva...

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12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015

作者： Alinia, Parastoo Saeedi, Ramyar Mortazavi, Bobak Rokni, Ali Ghasemzadeh, Hassan Embedded and Pervasive Systems Lab School of Electrical Engineering and Computer Science Washington State University PullmanWA99164-2752 United States Center for Outcomes Research and Evaluation Yale University New HavenCT06511 United States

ISBN: (纸本)9781467372015

Metabolic equivalent of task (MET) indicates the intensity of physical activities. This measurement is used in providing physical activity intervention in many chronic illnesses such as coronary heart disease, type-2 diabetes, and cancer. Due to the small size, portability, low power consumption, and low cost, wearable motion sensors are widely used to estimate MET values. However, one major obstacle in widespread adoption of current wearable monitoring systems is that the sensors must be worn on predefined locations on the body. This imposes much discomfort for users as they are not allowed to wear the sensors on their own desired body locations. In addition, non-adherence to the predefined location of the sensors results in significant reduction in the accuracy of physical activity monitoring. In this paper, we propose a framework for sensor location-independent MET estimation. We introduce a sensor localization approach that allows users to wear the sensors on different body locations without having to adhere to a specific installation protocol. We study how such an algorithm impacts the performance of MET estimation algorithms. Using daily physical activity data, we demonstrate that an automatic sensor localization algorithm decreases the estimation error of the MET calculation by a factor of 2.3 compared to the case without sensor localization. Furthermore, our sensor localization algorithm achieves an accuracy of 90.8% in detecting on-body locations of wearable sensors. The integration of sensor localization and MET estimation achieves an accuracy of 80% in calculating the MET values of daily physical activities. © 2015 IEEE.

关键词： Location

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Decentralised variable structure observation of nonlinear interconnected systems with time delay

Decentralised variable structure observation of nonlinear in...

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作者： Yan, Xing-Gang Spurgeon, Sarah K. Shi, Peng Instrumentation Control and Embedded Systems Research Group School of Engineering and Digital Arts University of Kent Kent-CanterburyCT2 7NT United Kingdom School of Electrical and Electronics Engineering University of Adelaide SA5005 Australia College of Engineering and Science Victoria University MelbourneVIC8001 Australia

Decentralised local variable structure observers are proposed for a class of nonlinear interconnected systems in which the system states experience time delay while the inputs and outputs are delay free. The interconnections are nonlinear, unknown and time delayed, which provides particular challenge for the case of decentralised observation. It is not required that the bounds on the uncertainties in the isolated subsystems are functions of the system outputs. Unlike much of the existing work where the designed observer is an interconnected system, the observers designed in this paper are a set of decoupled dynamics and thus decentralised, which is more reliable in real implementation. A set of sufficient conditions are derived such that in the considered domain the corresponding error dynamics are asymptotically stable where the Lyapunov Razumikhin approach is employed to deal with the time delay. The delays are time varying, and there is no limitation on the rate of change of the time delays. A numerical example is presented to show the effectiveness of the results obtained. © 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Time delay

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Response Policies to Process Module Failure in Single-Arm Cluster Tools Subject to Wafer Residency Time Constraints

Response Policies to Process Module Failure in Single-Arm Cl...

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作者： Qiao, Yan Pan, Chun-Rong Wu, Nai-Qi Zhou, Mengchu Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou510006 China School of Mechatronics and Electrical Engineering Jiangxi University of Science and Technology Ganzhou341000 China Institute of Systems Engineering Macau University of Science and Technology Taipa China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102-1982 United States

In semiconductor manufacturing, wafer residency time constraints make the scheduling problem of cluster tools complicated. A process module (PM) in cluster tools is prone to failure. It is crucial to deal with any such failure in a proper and timely manner. If there are feasible periodic schedules in operating a cluster tool before and after a PM failure, it is desired to make it operate continuously when such a failure occurs. However, due to wafer residency time constraints, it is highly challenging to control a tool such that it can be correctly transferred from a feasible schedule before failure to another after it. To solve this problem, a Petri net model is developed to describe the dynamic behavior of a single-arm cluster tool and failure response policies are proposed. The proposed policies are formulated via simple control laws for their easy implementation. Examples are given to show them. © 2015 IEEE.

关键词： Scheduling

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Impact of sensor misplacement on estimating metabolic equivalent of task with wearables

Impact of sensor misplacement on estimating metabolic equiva...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Parastoo Alinia Ramyar Saeedi Bobak Mortazavi Ali Rokni Hassan Ghasemzadeh Embedded & Pervasive Systems Lab School of Electrical Engineering and Computer Science Washington State University Pullman WA Center for Outcomes Research and Evaluation Yale University CT

关键词： Accelerometers Hip Feature extraction Estimation Accuracy Mathematical model Linear regression

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