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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

共 2094 条记录，以下是431-440 订阅

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Hierarchical fusion Estimation for WSNs with Link Failures Based on Kalman-Consensus Filtering and Covariance Intersection

Hierarchical Fusion Estimation for WSNs with Link Failures B...

引用

Chinese Control conference (CCC)

作者： Hongxiang Xu Xingzhen Bai Peng Liu Yao Shi College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper studies hierarchical fusion estimation for wireless sensor networks (WSNs) with link failures based on Kalman-consensus filtering and covariance intersection (CI). The spatial distribution of multiple sensors are divided into several clusters, and each cluster has a certain number of sensors. First of all, the data of sensor nodes are processed by Kalman-consensus filtering algorithm. The sensors nodes can communicate with each other to maintain the consensus of local estimation. sensors nodes in the same cluster are connected to a cluster head node as local estimators. Considering link failures, these failures are modeled as a set of independent Bernoulli processes. Secondly, CI fusion algorithm is used to generate fusion estimates from the collected local estimates. In addition, the proposed hierarchical fusion estimates are suitable for real applications. The simulation results verify the effectiveness of the proposed method.

关键词： Estimation Wireless sensor networks Filtering sensor fusion Filtering algorithms Covariance matrices

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Anomaly Event Detection for sensor Networks on Apriori algorithms and Subjective Logic 19

Anomaly Event Detection for Sensor Networks on Apriori Algor...

引用

8th International conference on Software and Computer applications (ICSCA)

作者： Yuan Jinhui Zhou Hongwei Zhang Laishun Zhongyuan Univ Technol Zhengzhou Henan Peoples R China Informat Engn Univ Zhengzhou Henan Peoples R China

ISBN: (纸本)9781450365734

Aiming at the challenges that anomaly events are difficult to be characterized and heterogeneous nodes can not cooperate directly in sensor network, a novel anomaly detection method for sensor networks is proposed in this paper. We use Apriori algorithm to mine the sample data and find the mapping from the events to sensing data, which provides the rule for the detection. In the detection, the node first find the suspicious event, and the relevant nodes cooperate to validate it. To fuse the detection result of heterogeneous nodes, we first map the results to subjective logic opinions, and generate reasonable and comprehensive conclusions with its fusion rules. Our analysis shows that our solution is able to refine the range of node detection events, improve detection efficiency and generate detection results reasonably.

关键词： sensor network anomaly event detection Apriori subjective logic

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Conditional Training Based GM and GM-OPELM Data fusion Schemes in Wireless sensor Networks

Conditional Training Based GM and GM-OPELM Data Fusion Schem...

引用

IEEE Pacific Rim conference on Communications, Computers and Signal Processing (PACRIM)

作者： Yang, Lei Zhao, Qing Jing, Yindi Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

ISBN: (纸本)9781728127941

As a key infrastructure of Internet of Things (IoT), wireless sensor networks (WSN) can be utilized in a wide range of applications. The prediction based data fusion methods provide effective tools to reduce the amount of data transmissions while maintaining prediction accuracy. Recently a grey prediction model (GM) combining optimally-pruned extreme learning machine (OPELM) data fusion method has been proposed and shown to have good performance. However, the existing GM-OPELM method performs model training and broadcasting before each prediction, resulting in high complexity and energy consumption. In this paper the conditional training based GM (CT-GM) and GM-OPELM (CT-GM-OPELM) are proposed. By introducing an error threshold, the algorithms only perform model training when the prediction error is beyond the threshold. Compared with existing GM and GM-OPELM methods, the CT-GM and CT-GM-OPELM methods not only can achieve the higher rate of acceptable prediction and better time efficiency but also has significant reduction in the energy consumption on model training and transmissions.

关键词： Wireless sensor networks grey prediction model extreme learning machine data fusion

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Improved Cubature Kalman Filter for Target Tracking in Underwater Wireless sensor Networks

Improved Cubature Kalman Filter for Target Tracking in Under...

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International conference on Information fusion

作者： Junhai Luo Yanping Chen Zhiyan Wang Man Wu Yang Yang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9780578647098

ISBN: (纸本)9781728168302

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

关键词： Target tracking Kalman filters Filtering algorithms Signal processing algorithms Numerical models Approximation algorithms

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Toward the Development of a Cognitive Agent for Wide-Area Search

Toward the Development of a Cognitive Agent for Wide-Area Se...

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IEEE National Aerospace and Electronics conference (NAECON)

作者： Purman, Ben Messing, Julie Crossman, Jacob Soar Technol Inc Ann Arbor MI 48105 USA

ISBN: (纸本)9781728114163

We present the development of a cognitive agent for real-time, wide-area search applications. Wide-area search problems present specific challenges driven by limited resources, large search areas, and limited time to conduct a search or inspection. Cognitive agents present an opportunity to incorporate a range of reasoning and sensor processing approaches to more effectively focus attention and make decisions about how to interpret data. We developed a design for a cognitive agent and implemented supporting reasoning algorithms to interact with sensor data. The agent encodes knowledge about objects of interest, and how they present themselves in the environment. This allows object detection algorithms to focus on detecting single objects, keeping training data requirements manageable. The cognitive agent provides external reasoning to reduce false alarm rates and make additional inferences. In this paper, we describe the cognitive agent design, conduct feasibility studies to establish reasoning strategies, and identify areas for future agent contributions.

关键词： cognitive agent cognitive architecture computer vision sensor processing sensor fusion

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On Learning Joint Multi-biometric Representations by Deep fusion 10

On Learning Joint Multi-biometric Representations by Deep Fu...

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IEEE 10th International conference on Biometrics Theory, applications and Systems (BTAS)

作者： Damer, Naser Dimitrov, Kristiyan Braun, Andreas Kuijper, Arjan Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Math & Appl Visual Comp Darmstadt Germany

ISBN: (纸本)9781728115221

Multi-biometrics combines different biometric sources to enhance recognition, template protection, and indexing performances. One of the main challenges here is the need for joint discriminant feature representation of multi-biometric data. This is typically achieved by feature-level fusion, imposing limitations on the combinations of biometric characteristics and algorithms. Including multiple imaging sources within deep-learning networks was generally limited to multiple sources of images of the same physical object, e.g., multi-spectral object detection. Previous biometrics works were limited to use deep-learning to extract representations of single biometric characteristics. In contrast to that, our work studies creating representations of one identity by sampling different physical objects,i.e. biometric characteristics. We adapted three architectures successfully to produce and discuss jointly learned representations for different levels of correlated data, modalities, instances, and presentations. Our evaluation proved the applicability of jointly learning biometric representations, especially when the data correlation is low.

关键词： Biometrics

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Accuracy of the Orientation Estimate Obtained Using Four sensor fusion Filters Applied to Recordings of Magneto-Inertial sensors Moving at Three Rotation Rates 41

Accuracy of the Orientation Estimate Obtained Using Four Sen...

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41st Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Caruso, M. Sabatini, A. M. Knaflitz, M. Gazzoni, M. Della Croce, U. Cereatti, A. Politecn Torino Dept Elect & Telecommun Turin Italy Scuola Super Sant Anna BioRobot Inst Pisa Italy Univ Sassari Dept Biomed Sci Sassari Italy Univ Roma Foro Ital Interuniv Ctr Bioengn Human Neuromuscoloskeletal Dept Motor Human & Hlth Sci Rome Italy

ISBN: (纸本)9781538613115

Magneto-Inertial technology is a well-established alternative to optical motion capture for human motion analysis applications since it allows prolonged monitoring in free-living conditions. Magneto and Inertial Measurement Units (MIMUs) integrate a triaxial accelerometer, a triaxial gyroscope and a triaxial magnetometer in a single and lightweight device. The orientation of the body to which a MIMU is attached can be obtained by combining its sensor readings within a sensor fusion framework. Despite several sensor fusion implementations have been proposed, no well-established conclusion about the accuracy level achievable with MIMUs has been reached yet. The aim of this preliminary study was to perform a direct comparison among four popular sensor fusion algorithms applied to the recordings of MIMUs rotating at three different rotation rates, with the orientation provided by a stereophotogrammetric system used as a reference. A procedure for suboptimal determination of the parameter filter values was also proposed. The findings highlighted that all filters exhibited reasonable accuracy (rms errors < 6.4 degrees). Moreover, in accordance with previous studies, every algorithm's accuracy worsened as the rotation rate increased. At the highest rotation rate, the algorithm from Sabatini (2011) showed the best performance with errors smaller than 4.1 degrees rms.

关键词： sensor fusion Magnetometers Optical filters Magnetic recording Accelerometers Magnetic separation Gyroscopes

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Autonomous Driving architectures, Perception and Data fusion: A Review

Autonomous Driving Architectures, Perception and Data Fusion...

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IEEE International conference on Intelligent Computer Communication and Processing (ICCP)

作者： Gustavo Velasco-Hernandez De Jong Yeong John Barry Joseph Walsh IMaR Research Centre / Lero The Irish Software Research Centre Institute of Technology Tralee Tralee Ireland

ISBN: (数字)9781728190808

ISBN: (纸本)9781728190815

Over the last 10 years, huge advances have been made in the areas of sensor technologies and processing platforms, pushing forward developments in the field of autonomous vehicles, mostly represented by self-driving cars. However, the complexity of these systems has been also increased in terms of the hardware and software within them, especially for the perception stage in which the goal is to create a reliable representation of the vehicle and the world. In order to manage this complexity, several architectural models have been proposed as guidelines to design, develop, operate and deploy self-driving solutions for real applications. In this work, a review on autonomous driving architectures is presented, classifying them into technical or functional architectures depending on the domain of their definition. In addition, the perception stage of self-driving solutions is analysed as a component of the architectures, detailing into the sensing part and how data fusion is used to perform localisation, mapping and object detection. Finally, the paper is concluded with additional thoughts on the actual status and future trends in the field.

关键词： Software Computer architecture Autonomous vehicles Robot sensing systems Hardware Standards Real-time systems

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Stereo and Mono Depth Estimation fusion for an Improved and Fault Tolerant 3D Reconstruction

Stereo and Mono Depth Estimation Fusion for an Improved and ...

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IEEE International conference on Intelligent Computer Communication and Processing (ICCP)

作者： Mircea Paul Muresan Marchis Raul Sergiu Nedevschi Radu Danescu Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (纸本)9781665409773

Depth estimation approaches are crucial for environment perception in applications like autonomous driving or driving assistance systems. Solutions using cameras have always been preferred to other depth estimation methods, due to low sensor prices and their ability to extract rich semantic information from the scene. Monocular depth estimation algorithms using CNNs may fail to reconstruct due to unknown geometric properties of certain objects or scenes, which may not be present during the training stage. Furthermore, stereo reconstruction methods, may also fail to reconstruct some regions for various other reasons, like repetitive surfaces, untextured areas or solar flares to name a few. To mitigate the reconstruction issues that may appear, in this paper we propose two refinement approaches that eliminate regions which are not correctly reconstructed. Moreover, we propose an original architecture for combining the mono and stereo results in order to obtain improved disparity maps. The proposed solution is designed to be fault tolerant such that if an image is not correctly acquired or is corrupted, the system is still able to reconstruct the environment. The proposed approach has been tested on the KITTI dataset in order to illustrate its performance.

关键词： Training Fault tolerance Surface reconstruction Three-dimensional displays Fault tolerant systems Semantics Estimation

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Event-Based Re-configurable Hierarchical Processors for Smart Image sensors 30

Event-Based Re-configurable Hierarchical Processors for Smar...

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30th IEEE International conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Bhowmik, Pankaj Pantho, Jubaer Hossain Bobda, Christophe Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9781728116013

This paper presents a reconfigurable hierarchical hardware architecture at the pixel and region level for smart image sensors to accelerate machine vision applications. This architecture maintains hierarchical processing that begins at the pixel level. It reduces the computational burden on the sequential processor and accelerates the overall processing. There are three hierarchical layers, and each layer passes only the relevant information to the next layer by removing redundant information. This method facilitates the final processor to respond if there is an event in the scene. Identifying relevant information is complex, and we adapted different bio-inspired algorithms in the hierarchical layers to meet this purpose. Besides, these processors are made runtime reconfigurable to different applications, and it adds flexibility after fabrication. This hierarchical processing breaks the traditional sequential image processing and introduces parallelism for the machine vision applications. We evaluate the design in FPGA and achieve the GDSII file in ASIC platform at 800MHz. Simulation results show that the area overhead and power penalty for adding reconfiguration feature stay in an acceptable range. Besides, removing redundant information 84.01% and 96.91% dynamic power can be saved at the pixel-level and region-level, respectively.

关键词： Pixel Level Processing Reconfigurability Predictive Coding Attention Module Smart Image sensor FPGA ASIC

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