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Data fusion for Unsupervised Video Object Detection, Tracking and Geo-Positioning 18

Data Fusion for Unsupervised Video Object Detection, Trackin...

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18th International conference on Information fusion (fusion)

作者： Kolev, Denis Markarian, Garik Kangin, Dmitry Rinicom Ltd R&D Dept Lancaster Lancs England Univ Lancaster Sch Comp & Commun Data Sci Grp Lancaster LA1 4YW England

ISBN: (纸本)9780982443866

In this work we describe a system and propose a novel algorithm far moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements.

关键词： Bayesian filters UAV object tracking unsupervised detection rigid motion segmentation

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Object Classification in a High-Level sensor Data fusion Architecture for Advanced Driver Assistance Systems 18

Object Classification in a High-Level Sensor Data Fusion Arc...

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18th IEEE International conference on Intelligent Transportation Systems

作者： Aeberhard, Michael Bertram, Torsten BMW Grp Res & Technol Grp ConnectedDrive Munich Germany Tech Univ Dortmund Inst Control Theory & Syst Engn D-44221 Dortmund Germany

ISBN: (纸本)9781467365963

Reliable estimation of an object's type is an important aspect of advanced driver assistance systems (ADAS) and automated driving applications. A type-specific ADAS reaction or object prediction can therefore be realized, improving the performance of the system. Object detection research usually focuses strongly on the state and existence estimation of detected objects. In this paper, an approach is presented for estimating an the class type of an object within the framework of a high-level sensor data fusion architecture. A novel classification fusion approach using the Dempster-Shafer evidence theory is presented. The performance of the algorithms are evaluated using a test vehicle with 12 sensors for surround environment perception in an overtaking scenario on a closed test track and on the highway in real traffic.

关键词： image classification image fusion inference mechanisms object detection road traffic traffic engineering computing uncertainty handling Dempster-Shafer evidence theory advanced driver assistance systems automated driving applications high-level sensor data fusion architecture highway traffic object classification type-specific ADAS reaction Advanced driver assistance systems Data integration Data models Object detection Probability Training data Vehicles

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Section Based Hybrid Routing Protocol for WSN using Artificial Bee Colony 2

Section Based Hybrid Routing Protocol for WSN using Artifici...

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International conference on Advances in Computer Engineering and applications (ICACEA)

作者： Khushboo, Kumari Daniel, A. K. Madan Mohan Malaviya Univ Technol Dept Comp Sci & Engn Gorakhpur Uttar Pradesh India

ISBN: (纸本)9781467369114

Wireless sensor network is a network consists of large number of sensors and a base station. There are many novel architectures, algorithms, protocols implemented so far but one of the major issues in wireless sensor network is to develop an energy efficient routing protocol which affects the stability and overall lifetime of the network. Clustering of nodes is one of the effective techniques used to improve energy, scalability and lifetime. The proposed protocol is a Section Based Hybrid Routing Protocol for heterogeneous WSN using Artificial Bee Colony (SBHRA). There are some nodes which send data directly to base station and other uses clustering technique to transmit data to base station (BS). The hybrid technique minimizes energy consumption of sensors and increases the network lifetime and throughput of the network.

关键词： wireless sensor network heterogeneous section based clustering Artificial bee colony

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Single Anchor Node Based Localization in Mobile Underwater Wireless sensor Networks 15th

Single Anchor Node Based Localization in Mobile Underwater W...

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15th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Das, Anjana P. Thampi, Sabu M. Coll Engn Trivandrum 695016 Kerala India LBS Ctr Sci & Technol Trivandrum 695001 Kerala India Indian Inst Informat Technol & Management Kerala Trivandrum 695581 Kerala India

ISBN: (纸本)9783319271613;9783319271606

Underwater sensor Networks (UWSN) provide a promising solution for aquatic applications. Localization in Mobile Underwater sensor Networks is very challenging because of the harsh environmental characteristics and limitations of radio communication. Minimization of energy utilization is another critical issue in UWSN domain. Hence, networking protocols with least communication overhead are desirable. In this paper, we propose a single anchor node based localization scheme to minimize communication packets required for location estimation. A sensor node estimates its location using Time of Arrival and Angle of Arrival measurements. Location of mobile anchor nodes is updated periodically by adopting the mobility pattern of particles in ocean waves. We analyzed the performance of the scheme with real geographic coordinates of different locations in the Arabian ocean collected using Google Earth. Experimental results showed that the proposed method provided better performance for short range sensor nodes.

关键词： Underwater sensor networks Localization Time of arrival Angle of arrival Equirectangular projection

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Neural network fusion and inversion model for NDIR sensor measurement 16

Neural network fusion and inversion model for NDIR sensor me...

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16th conference on Optical Fibers and their applications

作者： Cieszczyk, Slawomir Komada, Pawel Lublin Univ Technol Inst Elect & Informat Technol PL-20618 Lublin Poland

ISBN: (纸本)9781510600577

This article presents the problem of the impact of environmental disturbances on the determination of information from measurements. As an example, NDIR sensor is studied, which can measure industrial or environmental gases of varying temperature. The issue of changes of influence quantities value appears in many industrial measurements. Developing of appropriate algorithms resistant to conditions changes is key problem. In the resulting mathematical model of inverse problem additional input variables appears. Due to the difficulties in the mathematical description of inverse model neural networks have been applied. They do not require initial assumptions about the structure of the created model. They provide correction of sensor non-linearity as well as correction of influence of interfering quantity. The analyzed issue requires additional measurement of disturbing quantity and its connection with measurement of primary quantity. Combining this information with the use of neural networks belongs to the class of sensor fusion algorithm.

关键词： NDIR sensors inverse problem neural network sensor fusion

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The Application of Machine Learning in Multi sensor Data fusion for Activity Recognition in Mobile Device Space 2

The Application of Machine Learning in Multi Sensor Data Fus...

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conference on Image Sensing Technologies - Materials, Devices, Systems, and applications II

作者： Marhoubi, Asmaa H. Saravi, Sara Edirisinghe, Eran A. Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781628415971

The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light sensor, Proximity sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device's behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.

关键词： Activity Recognition Mobile phone Mobile sensors Gyroscope Accelerometer Multi sensor

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A Look at Signal Analysis in Resource-constrained Medical-sensor applications 11

A Look at Signal Analysis in Resource-constrained Medical-se...

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11th IEEE Annual Biomedical Circuits and Systems conference (BioCAS)

作者： Verma, Naveen Wang, Zhuo Zhang, Jintao Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781479972340

Given the increasing emphasis on data-driven medicine, this overview paper explores how healthcare functions can be extended to on-patient sensor platforms. While the functions that will be of interest in this context are not precisely known, the approaches within data-driven medicine are becoming clear. This guides us to the algorithms that will be used for diagnosis, monitoring, and therapy. In particular, there is an emphasis on machine-learning algorithms, which enable the creation of robust models for signal analysis from data. Focusing on such algorithms, this paper looks first at digital platforms for signal analysis that provide adequate programmability while addressing computational energy. Then, it considers approaches to signal acquisition that focus on acquiring the specific information needed within algorithms for signal analysis, with the aim that such focus relaxes both the specifications for mixed-signal interfaces and the subsequent computations required. The ideas presented reference hardware prototype demonstrations.

关键词： biomedical electronics machine learning medical signal processing

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Symmetric Game for Distributed Estimation in Energy Harvesting Wireless sensor Networks with Selfish sensors 15th

Symmetric Game for Distributed Estimation in Energy Harvesti...

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15th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Liu, Guiyun Yao, Jing Chen, Hongbin Zhang, Han Tang, Dong Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Minist Educ Guilin 541004 Peoples R China S China Normal Univ Sch Phys & Telecommun Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319271613;9783319271606

The power control problem for distributed estimation in energy-harvesting wireless sensor networks (EH-WSNs) poses a unique set of problems with the uncertain availability of resources. The fusion center (FC) usually is hard to predict the energy state of sensors that use ambient energy harvesting. Concerning the problem that existing decentralized power control schemes are not suitable in EH-WSHs, we propose a novel distributed symmetric game, which takes both energy harvesting and the characteristics of distributed estimation into account. And, each sensor makes decisions autonomously and is treated fairly. Then, some conclusions of Nash equilibriums (NEs) are introduced, and that can explain the trend observed in simulations. Finally, numerical results validate the effectiveness of the proposed symmetric game.

关键词： Wireless sensor networks Energy harvesting Distributed estimation Symmetric game

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Fuzzy Gain Scheduled EKF for Model-Based Skid-Steered Mobile Robot 6

Fuzzy Gain Scheduled EKF for Model-Based Skid-Steered Mobile...

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6th International conference on Automation, Robotics and applications (ICARA)

作者： Kadmiry, Bourhane Callaghan Innovat Auckland New Zealand Sensing & Automat Grp Logan UT USA

ISBN: (纸本)9781479964666

This article describes an approach to autonomous robotic for agricultural applications. Technological setup aims at stable navigation based on estimation through Extended Kalman filtering (EKF), to enforce robust Skid-Steered Mobile Robot (SSMR) navigation. The scientific contribution is the implementation of two model-based estimators, using EKF algorithms, one on a nonlinear model, and one on a piece-wise linearized robot model. The later is a Fuzzy Gain Scheduled (FGS)-based developement. The process is taking into account tire-road modelling of friction forces in order to improve model performance. State estimation and correction using sensor data fusion (Odometry-IMU-GPS) is considered, to improve the SSMR control in critical motions, reducing inherent drifts due to skid-steer properties;for the purpose of better regulation and tracking control designs. Whilst the experimental results demonstrated the usefulness of FGS approach for optimal EKF estimation, further modelling and live testing are required to determine robot ability to cope with different scenarios in naturally varying environment.

关键词： Autonomous Farm robotics FGS EKF SSMR dynamics sensor fusion

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A Predictive Data Reliability Method for Wireless sensor Network applications 15th

A Predictive Data Reliability Method for Wireless Sensor Net...

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15th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Sheikh, Adil Amjad Lbath, Ahmed Warriach, Ehsan Ullah Felemban, Emad Umm Al Qura Univ Sci & Technol Unit Mecca Saudi Arabia Univ Grenoble Alpes LIG Dept Comp Sci Grenoble France Transportat & Crowd Management Ctr Res Excellence Mecca Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Mecca Saudi Arabia

ISBN: (纸本)9783319271613;9783319271606

Wireless sensor networks consist of a large number of heterogeneous devices that communicate to collaboratively perform various tasks for users. Heterogeneous devices are deployed to sense the context of the environment. The context information is use to actuate various devices or services to support various activities of a user in a smart environment. Therefore, data correction is vital in managing issues arising from missing or corrupt contextual data due to system internal and external influences. We would like to investigate the machine learning techniques to ensure a complete and accurate sensor dataset for smart environment applications by runtime correcting missing or corrupt data due to sensor failures. We proposed a framework to correct dynamically sensory data. Specifically, we deal with the problems of faulty data (outliers, spikes, stuck-at, and noise), and missing information. Our proposed framework is able to learn temporal correlations in collected data from smart objects using Artificial Neural Network algorithm. We utilize the learned correlations to discover faulty data patterns to recover them, and imitate missing information. We implement the proposed data correction framework and test it on two real-world datasets collected from transportation domain (parking system, and road traffic).

关键词： Data correction Missing data Prediction analysis Reliable data Wireless sensor networks Artificial neural network Timeseries data analysis

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