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Infrared/Radar Data fusion and Tracking Algorithm Based on the Multi-Scale model

Infrared/Radar Data Fusion and Tracking Algorithm Based on t...

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Annual conference of the Chinese-Society-for-Optical-Engineering (CSOE) on Applied Optics and Photonics China (AOPC) - Optical Sensing and Imaging Technology and applications

作者： Sun Yongli Wang Bingjian Yi Xiang Hu Ge Xidian Univ Sch Phys & Optoelect Engn Xian 710071 Shaanxi Peoples R China

ISBN: (数字)9781510614062

ISBN: (纸本)9781510614062;9781510614055

Infrared and Radar data fusion algorithms have drawn a great deal of attention due to its implementation of complementary information, improvement of target tracking and enhancement of system viability. However, in the step of estimating the target state by multi-sensor, different sampling rates between two sensors make it difficult for data fusion. In order to solve this problem and make full use of the advantages of the data obtained by multi-sensor, an effective state estimation algorithm by combining the theory of multi-scale and converted measurement Kalman filter (CMKF) algorithm is presented in this paper. By establishing the multi-scale model, target state is estimated at the finest scale with the Interacting Multiple Model (IMM) algorithm at first. Then, at the coarse scale, appropriate observational information is selected in accordance with specific conditions. Angle information estimated by infrared sensor and the distance information obtained by radar sensor are fused to locate the target when two sensors have the same sampling time instant, otherwise, the target is located only by using the angle and distance information acquired by radar sensor. In addition, CMKF algorithm is used to estimate the target state and obtain the optimal fusion estimation. The simulation results under the environment of MATLAB show that the proposed algorithm effectively improves the precision and the instability of infrared/radar detection system.

关键词： infrared radar data fusion multi-scale theory CMKF

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ESD-WSN: An Efficient SDN-Based Wireless sensor Network Architecture for IoT applications 17th

ESD-WSN: An Efficient SDN-Based Wireless Sensor Network Arch...

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17th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Zhang, Zhiwei Zhang, Zhiyong Wang, Rui Jia, Zhiping Lei, Haijun Cai, Xiaojun Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China Guangdong Key Lab Popular High Performance Comp Shenzhen Peoples R China Shenzhen Key Lab Serv Comp & Applicat Shenzhen Peoples R China

ISBN: (纸本)9783319654829;9783319654812

Wireless sensor networks (WSNs) are considered as a key enabler for the paradigm of Internet of Things (IoT). With increasing number of devices connected to the IoT environments, traditional solutions for WSNs tend to be costly in terms of network maintenance and management. Software-Defined Networking (SDN) appears as a viable alternative network architecture since it enables new services and policies to be deployed flexibly and easily. However, SDN brings excessive control overhead which significantly degrades the network performance. To relieve this problem, in this paper, we propose an Efficient Software-Defined Wireless sensor Network (ESD-WSN) architecture to make full use of the advantages of SDN while overcoming its constraints. In the proposed architecture, the controller dynamically selects certain nodes as proxies for control traffic processing and aggregating. To this end, a Dynamic Proxy Management (DPM) strategy is presented to select the optimal subset of network nodes as proxies. Experimental results show that our scheme achieves considerable performance improvement compared with the SDN-WISE scheme [1].

关键词： Wireless sensor networks Internet of Things Software Defined Network Control overhead Proxy management

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Computational Intelligence for Smart Air Quality Monitors Calibration 17th

Computational Intelligence for Smart Air Quality Monitors Ca...

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17th International conference on Computational Science and its applications (ICCSA)

作者： Esposito, Elena De Vito, Saverio Salvato, Maria Fattoruso, Grazia Di Francia, Girolamo ENEA DTE FSN DIN CR Portici PLe Enrico Fermi 1 I-80055 Naples Italy

ISBN: (纸本)9783319623986;9783319623979

Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application.

关键词： Machine learning Air quality Chemical sensor networks sensor fusion Smart cities

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Signal Processing - algorithms, architectures, Arrangements, and applications conference Proceedings, SPA

Signal Processing - Algorithms, Architectures, Arrangements,...

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21st IEEE Signal Processing: algorithms, architectures, Arrangements, and applications, SPA 2017

ISBN: (纸本)9788362065301

The proceedings contain 70 papers. The topics discussed include: probabilistic and unsupervised machine learning for auditory data and pattern recognition;forward error correction in wireless communication systems for industrial application;test signals used in electroacoustics and speech technology;Petrovsky, design and implementation of reversible integer quaternionic paraunitary filter banks on adder-based distributed arithmetic;implementation of DIFAR processing in ASW dipping sonar;simultaneous mutual localization of transmitters and receivers from time difference of arrival measurements;comparison of selected electroencephalographic signal classification methods;influence of sEMG electrode matrix configuration on hand gesture recognition performance;localisation method for sEMG electrode array, towards hand gesture recognition HMI development;towards sensor position-invariant hand gesture recognition using a mechanomyographic interface;design of an active noise controller for reduction of tire/road interaction noise in environmentally friendly vehicles;analysis of signal of X8 unmanned aerial vehicle;a novel objective function based on a generalized Kelly criterion for deep learning;rule-based data-driven approach for computer aided diagnosis of the peripheral zone prostate cancer from multiparametric MRI: proof of concept;reconstruction of high-resolution T2W MR images of the prostate using Maximum A Posteriori Approach and Markov Random Field regularization;and the coarse and fine granular fault tolerance techniques in FPGA-based Processors.

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Discretization of SO(3) Using Recursive Tesseract Subdivision 13

Discretization of SO(3) Using Recursive Tesseract Subdivisio...

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13th IEEE International conference on Multisensor fusion and Integration for Intelligent Systems (MFI)

作者： Kurz, Gerhard Pfaff, Florian Hanebeck, Uwe D. Karlsruhe Inst Technol Inst Anthropomat & Robot Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (纸本)9781509060641

The group of rotations in three dimensions SO(3) plays a crucial role in applications ranging from robotics and aeronautics to computer graphics. Rotations have three degrees of freedom, but representing rotations is a nontrivial matter and different methods, such as Euler angles, quaternions, rotation matrices, and Rodrigues vectors are commonly used. Unfortunately, none of these representations allows easy discretization of orientations on evenly spaced grids. We present a novel discretization method that is based on a quaternion representation in conjunction with a recursive subdivision scheme of the four-dimensional hypercube, also known as the tesseract.

关键词： Quaternions Three-dimensional displays Robot sensing systems Computer graphics Hypercubes Approximation algorithms

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A NEW RESULT ON DISTRIBUTED INPUT AND STATE ESTIMATION FOR HETEROGENEOUS sensor NETWORKS

A NEW RESULT ON DISTRIBUTED INPUT AND STATE ESTIMATION FOR H...

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10th ASME Annual Dynamic Systems and Control conference

作者： Tran, Dzung Yucelen, Tansel Jagannathan, Sarangapani Univ S Florida Dept Mech Engn Tampa FL 33620 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9780791858288

An important research area in sensor networks is the design and analysis of distributed estimation algorithms for dynamic information fusion in the presence of heterogeneity resulting from (i) nonidentical information roles of nodes and (ii) nonidentical modalities of nodes. In particular;(i) implies that both active (i.e., subject to observations of a process of interest) and passive (i.e., subject to no observations) nodes can be present in the sensor network. Furthermore, (ii) implies that active nodes can observe different measurements from a process (e.g., a subset of active nodes can observe position measurements and the rest can observe velocity measurements for a target tracking problem). In this paper, we focus on heterogeneous sensor networks, sensor networks with (i) and (ii), and present a new distributed input and state estimation approach. In addition to the presented theoretical contribution including the stability and performance of the proposed estimation approach, an illustrative numerical example is also given to demonstrate its efficacy.

关键词： sensor networks State estimation

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Learning Long-Term Situation Prediction for Automated Driving 16

Learning Long-Term Situation Prediction for Automated Drivin...

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16th IEEE International conference on Machine Learning and applications (ICMLA)

作者： Hoermann, Stefan Bach, Martin Dietmayer, Klaus Ulm Univ Inst Measurement Control & Microtechnol Ulm Germany

ISBN: (纸本)9781538614174

A major challenge in autonomous driving is the prediction of complex downtown scenarios with mutiple road users. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation and machine learning as long-term predictor. Therefore, a dynamic occupancy grid map representing the static and dynamic environment around the ego-vehicle is utilized as input to a deep convolutional neural network. This yields the advantage of using data from a single timestamp for prediction, rather than an entire time series. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data containing multiple road users, e.g., pedestrians, bikes and vehicles.

关键词： Vehicle dynamics Time series analysis Laser radar Heuristic algorithms sensor fusion Roads Bayes methods

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Uncertainty Modeling in Wireless sensor Networks 2017

Uncertainty Modeling in Wireless Sensor Networks

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International conference on Big Data and Internet of Things (BDIOT)

作者： Dogan, Gulustan Brown, Ted Yildiz Tech Univ Istanbul Turkey CUNY New York NY 10021 USA

ISBN: (纸本)9781450354301

In wireless sensor networks, the certainty of the data created by a node can change due to many reasons such as drained energy, outer factors. In addition dynamic physical environment and hardware failures (broken sensor etc.) might cause sensors to produce erred or incomplete data. Therefore raw wireless sensor network data reflects an approximate observation of the monitored environment and it can be faulty or partially wrong. Wireless sensor network applications that are used in military and health systems are used in critical decision-making. To make a healthy decision and to analyze the data correctly, the end users have to know the uncertainty level of the collected data. As a result, a model that can represent the uncertainty associated with different types of sensor data in varying models is needed. There has been some efforts of modeling uncertainty in database community. We aim to develop an uncertainty-modeling framework to aid decision-making. The methodology that we will use in this work is as follows (1) developing an uncertainty model by referring to database literature (2) designing the algorithms and equations (3) modeling finite state machines to characterize the behavior of the nodes and the transitions between different uncertainty states (4) improving a distributed architecture that defines the actions taken by the network based on the uncertainty levels (such as omitting a node with a low uncertainty level and replacing it) (5) calculating the energy and memory requirements of our architecture by carrying simulations. We have done previous work on trust computation of wireless sensor networks. In this position paper, we give a general overview of the planned research.

关键词： Wireless sensor Networks Data Uncertainty Data fusion

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Energy Analysis of Two-tiered Clustered architectures for Smart World applications 5

Energy Analysis of Two-tiered Clustered Architectures for Sm...

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5th IEEE-Region-10 Humanitarian Technology conference (R10-HTC)

作者： Singh, Rajvir Sharma, Rajnish Krishna, C. Rama Vig, Renu Chitkara Univ Inst Engn & Technol Dept ECE Rajpura India Natl Inst Tech Teachers Training & Res Dept Comp Sci Chandigarh India Univ Inst Engn & Technol Dept ECE Chandigarh India

ISBN: (纸本)9781538621752

Use of two-tiered clustered architecture in deploying sensor networks for smart world applications is well-known in research community. No other single routing algorithm has spawned that many variations as the LEACH clustering algorithm. Till date researchers are interested in exploring the operation and performance of LEACH algorithm and its improved versions. Most of these works compare their performance with original/adapted LECH algorithm by using aggregate measures such as network lifetime, stability and payload delivered by the sensor network. The methods used for improving the performance include generating optimum no. of clusters, ensuring uniform cluster distribution, and using an energy-aware clustering mechanism for CH selection. To understand the utility of these different methods to improve the basic LEACH algorithm, this work presents an improved clustered architecture (ICAR) that uses an energy-aware CH selection mechanism along with uniform cluster distribution in the networked area. The energy analysis presented in this work shows that as compared to other methods, using an energy aware cluster-head selection mechanism greatly improves the stability period of a two-tiered clustered architecture.

关键词： Clustering algorithms

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Sampling Jitter Mitigation in Latency-Critical State-Estimation applications using Particle Filters 3

Sampling Jitter Mitigation in Latency-Critical State-Estimat...

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3rd SICE International Symposium on Control Systems (SICE ISCS)

作者： El Hakim, Viktorio S. Bekooij, Marco J. G. Univ Twente Dept Comp Architectures Embedded Syst Enschede Netherlands NXP Semicond Dept Embedded Software & Signal Proc Eindhoven Netherlands

ISBN: (纸本)9784907764548

State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.

关键词： Sampling jitter Particle filtering

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