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KES International conferences on Innovation in Medicine and Healthcare, KES-InMed 2019 and intelligent Interactive Multimedia Systems and Services, KES-IIMSS 2019

KES International conferences on Innovation in Medicine and ...

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KES International conferences on Innovation in Medicine and Healthcare, KES-InMed 2019 and intelligent Interactive Multimedia Systems and Services, KES-IIMSS 2019

ISBN: (纸本)9789811385650

The proceedings contain 55 papers. The special focus in this conference is on Innovation in Medicine and Healthcare. The topics include: Logistic Map and Contourlet-Based Robust Zero Watermark for Medical Images;a sensor Platform for Non-invasive Remote Monitoring of Older Adults in Real Time;Deep Learning for Detecting Breast Cancer Metastases on WSI;advanced Image processing Algorithms for Breast Cancer Decision Support and information Management System;FDCT and Perceptual Hash-Based Watermarking Algorithm for Medical Images;Comparison of CNN Models with Different Plane Images and Their Combinations for Classification of Alzheimer’s Disease Using PET Images;multimodal Behavioral Dataset of Depressive Symptoms in Chinese College Students–Preliminary Study;contour Lines to Assist Position Recognition of Slices in Transparent Stereoscopic Visualization of Medical Volume Data;fused Visualization and Feature Highlighting to Assist Depth Recognition in Transparent Stereoscopic Visualization;archiMate Business Model Patterns to e-Healthcare;A NIRS Study of Different Colour Effects on Short Memory Tasks Between Young and Elderly Subjects;biomechanical Analysis of Human Gait with Inertial sensors Using Neural networks;data Mining Electronic Health Records to Support Evidence-Based Clinical Decisions;Formalization of the Agent-Based Model for the Detection of Behavior Patterns in Older Adults Who Start Using ICT;mHealth Application for Fast Attention to People with Cerebrovascular and Cardiovascular Urgencies;ioT in Medical Context: Applications, Diagnostics, and Health Care;contributions of Machine Learning in the Health Area as Support in the Diagnosis and Care of Chronic Diseases;big Data and Predictive Health Analysis;automatic Quantification of Breast Arterial Calcification on Mammographic Images.

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Optimization of Multi-function sensor Placement Satisfying Detection Coverage 3rd

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3rd European-Alliance-for-Innovation (EAI) International conference on Industrial networks and intelligent Systems (INISCOM)

作者： Liang, Qingzhong Fan, Yuanyuan China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783319741765;9783319741758

Wireless sensor networks (WSNs) have become essential parts in Industrial Internet of Things (IIoT). However, owing to the type associated with data acquisition and the large scale of monitoring, sensors are often installed at a lot of locations and a wide variety of sensors make WSN topology more complex. To address these limitations, a complementary promising solution, known as software defined wireless sensor network (SDWSN), is proposed. SDWSN acquires desired information based on users' demands from large-scale sensor networks by dynamically customizing its function. Thanks to the SDWSN, multi-type data sensing is able to enlarge the sensing scale and reduce the cost. Existing sensor placement techniques are usually focus on simple function sensor or multi-type sensor. Witness the development of SDWSN, it is ideal to explore such abilities such that the multi-type sensing functions can be conducted in a same node. Because each area covered by different multi-function sensor nodes has different detection requirements, multi-function sensor nodes placement faces many challenges. In this paper, based on multi-objective decomposition, we study the number and function redundancy of all nodes minimization problem in multi-function sensor nodes placement. Specially, we propose an improved MOEA/D-DE algorithms based on orthogonal experiment design. Simulation and evaluations validate the efficiency of our proposal.

关键词： WSN Placement Multi-objective Optimization

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A Communication Strategy of Proactive Nodes Based on Loop Theorem in Wireless sensor networks 9

A Communication Strategy of Proactive Nodes Based on Loop Th...

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9th International conference on intelligent Control and information processing (ICICIP)

作者： Wang, Li Zhang, Chunjiong Chen, Qiang Liu, Zilu Liu, Sha Yang, Zhen Li, Hongbing Chongqing Collaborat Innovat Subctr Geol Environm Chongqing Peoples R China Chongqing Engn Res Ctr Internet Things & Intellig Chongqing Municipal Inst Higher Educ Key Lab Intelligent Informat Proc & Control Chongqing Peoples R China

ISBN: (纸本)9781538658604

In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to maintain sufficient sensing coverage and reliability. In order to address this challenge, this paper introduces a novel strategy of controllable communication based on the loop theorem. Proactive nodes utilize the perceptual effect of the loop theorem, substituting the failure nodes using the excellent communication coverage or covering the blank communication path using the shortest move path. Theoretical analysis and performance evaluation reveal that the proposed strategy has better performance than existing sensing coverage and reliability algorithms in terms of the network lifetime and coverage ratio. In addition, in a complex environment, dynamic networks usually have some nodes failing, and some nodes are redundant on some communication paths. Algorithm of this paper can provide enough reliable transmission in such applications.

关键词： Wireless sensor network loop theorem transmission reliability coverage optimization

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A New Adaptive Source Localization and Tracing Method using Smart sensor networks

A New Adaptive Source Localization and Tracing Method using ...

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International Symposium in Sensing and Instrumentation in IoT Era (ISSI)

作者： Y. J. Chu S. C. Chan Y. Zhou M. Wu State Key Laboratory of Subtropical Building Science South China University of Technology Guangzhou China The University of Hong Kong Hong Kong China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Noise and Vibration Research Institute of Acoustics China Bejing China

ISBN: (数字)9781728110226

ISBN: (纸本)9781728110233

In this research, we aim at developing a smart sensor system that localizes and traces source positions from the measured acoustic signals. The proposed method is based on a sparse representation of sensor measurements that expand an overcomplete source domain. We enforce sparsity by imposing L 1 regularization. An adaptive sparse principle component analysis (SPCA) method is used to solve the multiple time and frequency samples and the sound source can be localized and traced by adaptive audio coordination. Experiments are conducted to demonstrate the effectiveness of the method on simulated data by plots of the snapshots of the resulting sound field and the mean squares error convergence curves for a moving source. It shows that the proposed approach has a number of advantages over other source localization techniques, such as increased resolution, tracking capability, and possible frequency aliasing free effect.

关键词： acoustic signal processing adaptive signal processing audio signal processing mean square error methods principal component analysis signal representation source separation wireless sensor networks Mean square error audio signal processing adaptive signal processing signal representation Acoustic signal processing Position measurement Source separation Wireless sensor networks principal components analysis intelligent sensors Tracer techniques

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Supporting Data Stream Analytical processing in Vehicular sensor networks

Supporting Data Stream Analytical Processing in Vehicular Se...

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International conference on intelligent Transportation

作者： Wendi Nie Yaoxin Duan Victor C.S. Lee Kai Liu School of Automation Chongqing University Chongqing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education China Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science City University of Hong Kong Hong Kong

In the past decade, the Vehicular sensor Network (VSN) technology has emerged as a promising technique to support intelligent Transportation Systems (ITSs). Utilizing the information collection and communication capabilities provided by VSN, data can be firstly collected by in-vehicle sensors, and then uploaded to the infrastructure to support ITS applications. However, to get a global view of the status of the road, many ITS applications adopt a centralized approach, which requires to collect data from VSNs to a central server. In addition, to provide timely services, data has to be updated by vehicles continuously. As a result, massive amount of data could be generated by vehicles and transmitted in the network, which may exhaust the limited wireless communication bandwidth. In this work, we propose a data stream analytical processing framework for VSN named Streaming Vehdoop (SVehdoop). To reduce the bandwidth consumption, SVehdoop schedules part of data processing tasks to where the data is located. Specifically, SVehdoop utilizes the computing capability of vehicles to efficiently process the collected data over a large number of vehicles in a distributed manner. A dynamic clustering algorithm, named Streaming Vehdoop Clustering (SVC) algorithm, is tailor-designed for SVehdoop to not only consider vehicle mobility to form stable clusters, but also to take account of data aggregation and data parallelism. Comprehensive experiments have been conducted to demonstrate the efficiency of SVehdoop and the proposed SVC algorithm.

关键词： Task analysis Clustering algorithms Heuristic algorithms Servers Static VAr compensators Roads Wireless communication

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Dyfusion: Dynamic IR/RGB fusion for maritime vessel recognition 25

Dyfusion: Dynamic IR/RGB fusion for maritime vessel recognit...

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25th IEEE International conference on Image processing, ICIP 2018

作者： Santos, Cassio E. Bhanu, Bir Center for Research in Intelligent Systems University of California RiversideCA92521 United States

ISBN: (纸本)9781479970612

We propose a novel multi-sensor data fusion approach called DyFusion for maritime vessel recognition using long-wave infrared and visible images. DyFusion consists of a decision-level fusion of convolutional networks using a probabilistic model that can adapt to changes in the scene. The probabilistic model avails of contextual clues from each sensor decision pipeline to maximize accuracy and to update probabilities given to each sensor pipeline. Additional sensors are simulated by applying simple transformations on visible images. Evaluation is presented on the VAIS dataset, demonstrating the effectiveness and robustness of DyFusion with a reliable accuracy of up to 88% in hard scenarios. © 2018 IEEE.

关键词： Pipelines

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Development of Wireless Smart sensor System for Fish Image Observation 5

Development of Wireless Smart Sensor System for Fish Image O...

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IEEE International conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chou, Shih-Jie Hwang, Chi-Hung Chang, Chun-Li Wu, Wen-Hong Weng, Rui-Cian Lin, Chen-Yu NARLabs Hsinchu IEEE Instrument Technol Res Ctr Hsinchu Taiwan

ISBN: (纸本)9781538663011

The purpose of an observation platform is to send a real-time signal comprising information about oceans to an operation center. The observation platform is encased in a communication buoy. This article proposes a wireless smart sensor system for fish-image observation. The study used an image sensor to capture underwater fish images and a GPS module to receive position information. A 3G module was used for wireless transmission of the information. Moreover, an embedded system was used for power management to provide a stable power supply to maintain continual system operation. The proposed system can improve marine aquaculture and help in the monitoring of fish.

关键词： Wireless sensor networks Wireless communication Fish intelligent sensors Global Positioning System Aquaculture Cameras"

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Metrology for the factory of the future: towards a case study in condition monitoring

Metrology for the factory of the future: towards a case stud...

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IEEE Instrumentation and Measurement Technology conference

作者： Tanja Dorst Björn Ludwig Sascha Eichstädt Tizian Schneider Andreas Schütze Physikalisch-Technische Bundesanstalt Braunschweig and Berlin Germany ZeMA – Centre for Mechatronics and Automation Technology gGmbH Saarbruecken Germany Lab for Measurement Technology Department of Mechatronics Saarland University Saarbruecken Germany

The “Factory of the Future” (FoF) as a fully inter-connected production environment with an autonomous flow of information and decision-making constitutes the digital transformation of manufacturing to improve efficiency and competitiveness. Transparency, comparability and sustainable quality all require reliable measured data, processing methods and results. Hence, a metrological framework for the complete lifecycle of measured data in industrial applications is required: from calibration capabilities for individual sensors with digital pre-processed output to uncertainty quantification associated with machine learning (ML) in industrial sensor networks. This contribution presents a European metrology research project to develop such a framework. Special focus in this contribution is set on an implementation testbed where the framework will be used to demonstrate the practical applicability for condition monitoring.

关键词： Measurement uncertainty Uncertainty Calibration intelligent sensors Condition monitoring Feature extraction

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Selection of Actor Nodes in Wireless sensor and Actor networks: A Fuzzy-based Approach Considering Number of Obstacles as New Parameter 32

Selection of Actor Nodes in Wireless Sensor and Actor Networ...

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32nd IEEE International conference on Advanced information Networking and Applications (AINA)

作者： Elmazi, Donald Cuka, Miralda Bylykbashi, Kevin Spaho, Evjola Ikeda, Makoto Barolli, Leonard FIT Grad Sch Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan PUT Dept Elect & Telecommun Bul Deshmoret E KombitMother Theresa Sq 4 Tirana Albania FIT Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan

ISBN: (纸本)9781538621950

Wireless sensor and Actor Network (WSAN) is formed by the collaboration of micro-sensor and actor nodes. Whenever there is any special event i.e., fire, earthquake, flood or enemy attack in the network, sensor nodes have responsibility to sense it and send information towards an actor node. The actor node is responsible to take prompt decision and react accordingly. In this work, we consider the actor node selection problem and propose a fuzzy-based system that based on data provided by sensors and actors selects an appropriate actor node. We use 4 input parameters: Job Type (JT), Distance to Event (DE), Remaining Energy (RE) and different from our previous work we consider the Number of Obstacles (NO) parameter. The output parameter is Actor Selection Decision (ASD). Based on these parameters, the simulation results show that the proposed system makes a proper selection of actor nodes. The simulation results show that ASD is decrased 23% and 40%, by increasing NO and DE, respectively.

关键词： WSAN IoT intelligent Systems Fuzzy-logic JT DE RE NO

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3D Objects Recognition Using Artificial Neural networks 44

3D Objects Recognition Using Artificial Neural Networks

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44th Latin American Computing conference on Informatics (CLEI)

作者： Ortiz Correa, Diogo Santos Osorio, Fernando Santos Univ Sao Paulo Lab Robot Movel Sao Carlos SP Brazil

ISBN: (纸本)9781728104379

The recent advances in computational processing and the low prices of sensors able to capture three-dimensional information have contributed for the progress of computer vision researches involving 3D data and 3D images. Object recognition allows us to develop complex applications for intelligent mobile robotics, augmented reality, systems for the visually impaired, among other applications. In this context, this paper presents a method for recognizing and classifying objects which are represented in three dimensions through depth maps. The data used in this study comes from the "UW RGB-D Object Dataset" from University of Washington, which is available online and is largely used to evaluate 3D object classifiers. This object database is composed of depth maps captured by the Microsoft's Kinect sensor. The obtained results are promising and contribute positively to the computer vision area.

关键词： Object recognition Three-dimensional images Artificial neural networks Kinect

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