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检索条件"任意字段=Sensor Fusion: Architectures, Algorithms and Applications II"

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A low-cost, open-source, and distributed ocean profiling sensor node 17

A low-cost, open-source, and distributed ocean profiling sen...

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17th IEEE sensors applications Symposium (SAS)

作者： Rico, Andres Prasad, Suparnamaaya Larson, Kent MIT Media Lab Cambridge MA 02139 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665409810

ISBN: (纸本)9781665409810

Ocean exploration is emerging as a critical field of research for understanding and combating climate change. While coastal waters house vital marine ecosystems, such as coral reefs, they are still vastly unexplored. sensor nodes in coastal waters can give local communities and researchers relevant data for understanding the relationship between climate change, blue-economy activities, and ecosystem degradation. We present and validate a sensor node that can be opportunistically used by non-industrial fishing boats in order to collectively source ocean profiles containing temperature, light, and acidity levels at multiple depths. We describe the complete system architecture and validate our design with deployments in three different types of water bodies. First, we validate the device's temperature, light, and depth sensors in a submersion within a controlled pool environment. We then evaluate the software-based power cycling for intermittent data acquisition algorithms through a river deployment. Finally, we demonstrate the complete system's functionality and possible ways to incorporate acidity sensing with an open ocean deployment. In addition to field testing, we describe and test possible architectures for adding networking capabilities through a WiFi-based system. We conclude that the device's electrical and mechanical characteristics significantly contribute towards enabling low-cost, community-sourced data acquisition for ocean coastal water profiling.

关键词： Ocean IoT Opportunistic Sensing sensor fusion

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An Overview of Factors Influencing the Mass Adoption of Self-Driving Vehicles 9

An Overview of Factors Influencing the Mass Adoption of Self...

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IEEE 9th International Conference on Power Electronics Systems and applications (PESA)

作者： Li, Zilin Cheng, Ka Wai Eric Chan, Kevin K. W. Hong Kong Polytech Univ Dept Elect Engn Power Elect Res Ctr Hong Kong Peoples R China

ISBN: (纸本)9781665486682

A self-driving vehicle used to be found in science fiction but would promisingly and gradually become a reality in our daily life in the foreseeable future. Self-driving or autonomous vehicles can bring about disruptive changes to the current transportation system, commuting ways, the concept of vehicle ownership, risk management, legal liability, regulations, urban planning, etc. A highly/fully self-driving vehicle is one of the most complex man-made systems and involves various aspects of modern society. Nevertheless, the public seems to lack a clear enough understanding of self-driving vehicles. To further the development of self-driving technologies, it is necessary to develop a comprehensive understanding of multifaceted factors that could hinder the mass adoption of self-driving vehicles. Therefore, this paper intends to present an overview of some technical factors that could influence the advancement of self-driving vehicles. First, this paper reviews state-of-the-art architectures of a self-driving vehicle. Various sensor technologies are followed with a discussion of their limitations. As two essential software subsystems for realizing a self-driving vehicle, the perception and decision-making systems are presented with an overview of their major tasks and challenges. This paper is concluded with a summary of the technical challenges, being a helpful resource for engineers, the public, policy-makers, and social researchers.

关键词： autonomous vehicles architectures camera Lidar overview Radar self-driving car sensor technologies sensor fusion algorithms

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Micro-strain gauge endoscopic tactile sensor using two sensing elements for tissue manipulation

Micro-strain gauge endoscopic tactile sensor using two sensi...

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sensor fusion: architectures, algorithms, and applications IV

作者： Dargahi, J. Normandeau, M. Milne, J.A. Parameswaran, M. Payandeh, S. Univ of New Brunswick Saint John Canada

Currently available commercial endoscopic graspers do not have any built-in sensors. Thus, the surgeon does not have any tactile feed-back to manipulate tissues safely. This paper reports on the design, fabrication and testing of a semiconductor micro-strain gauge endoscopic tactile sensor. This sensor consists of two semiconductor micro-strain gauge sensors which are positioned at the back face of an endoscopic grasper. It can measure the magnitude and the position of the applied force with only two sensing elements. The amplification system for the strain gauge is also designed and fabricated. It is shown that when a force is applied to the endoscopic grasper, the magnitude of the applied force can be visually seen in an LED device. The position of the applied force is obtained by combining the output from two insulated strain gauges. We have shown that the grasper operates in a wet environment. It exhibits high force sensitivity, large dynamic range, and good linearity. The sensor is integrated with a commercial endoscopic tool. The advantages and disadvantages of the system are also discussed.

关键词： Microsensors

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Visual and Inertial Multi-Rate Data fusion for Motion Estimation via Pareto-Optimization

Visual and Inertial Multi-Rate Data Fusion for Motion Estima...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Loianno, Giuseppe Lippiello, Vincenzo Fischione, Carlo Siciliano, Bruno Univ Naples Federico II PRISMA Lab Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

ISBN: (纸本)9781467363587

Motion estimation is an open research field in control and robotic applications. sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical model-based techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

关键词： Kalman filters Pareto optimisation computational complexity motion estimation robots sensor fusion statistical analysis

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MEFE: A Multi-fEature Knowledge fusion and Evaluation Method Based on BERT 20th

MEFE: A Multi-fEature Knowledge Fusion and Evaluation Method...

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20th International Conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Ji, Yimu Hu, Lin Liu, Shangdong Xu, Zhengyang Liu, Yanlan Liu, Kaihang Tang, Shuning Liu, Qiang Xiao, Wan Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun InstHigh Performance Comp & Bigdata Nanjing 210003 Jiangsu Peoples R China Nanjing Ctr HPC China Nanjing 210003 Jiangsu Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Educ Sci & Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Knowledge fusion is an important part of constructing a knowledge graph. In recent years, with the development of major knowledge bases, the integration of multi-source knowledge bases is the focus and difficulty in the field of knowledge fusion. Due to the large differences in knowledge base structure, the efficiency and accuracy of fusion are not high. In response to this problem, this paper proposes MEFE (Multi-fEature Knowledge fusion and Evaluation Method) based on BERT. MEFE comprehensively considers the attributes, descriptions and category characteristics of entities to perform knowledge fusion on multi-source knowledge bases. Firstly, MEFE uses entity category tags to build a category dictionary. Then, it vectorizes the category tags based on the dictionary and clusters the entities according to the category tags. Finally it uses BERT (Bidirectional Encoder Representation from Transformers) to calculate the entity similarity for the entity pairs in the same group. We calculate entity redundancy rate and information loss rate of knowledge base according to the fusion result, so as to evaluate the quality of the knowledge base. Experiments show that MEFE effectively improves the efficiency of knowledge fusion through clustering, and the use of BERT promotes the accuracy of fusion.

关键词： Multi-source knowledge base Knowledge fusion Vectorization of category labels BERT Quality evaluation

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Visual Information Processing ii

Visual Information Processing II

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Visual Information Processing ii 1993

The proceedings contain 46 papers. The topic discussed include: aliasing as noise: a quantitative and qualitative assessment;satellite image processing using a hammering neural network;edge-detection method using visual perception;multiscale planar-curve descriptor using biorthogonal wavelet transform;spatial transformation architectures with applications: an introduction;statistical model of the noise in speckle pattern and suboptimal digital image filtering method;rectangularly and hexagonally sampled imaging-system-fidelity analysis;and neuromorphic infrared focal plane performs sensor fusion on-plane local-contrast-enhancement spatial and temporal filtering.

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Study of a modified Dempster-Shafer approach using an expected utility interval decision rule

Study of a modified Dempster-Shafer approach using an expect...

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Proceedings of the 1999 sensor fusion: architectures, algorithms, and applications iiI

作者： Cheaito, Ali Lecours, Michel Bosse, Eloi Universite Laval Que Canada

The Combination operation of the conventional Dempster-Shafer algorithm has a tendency to increase exponentially the number of propositions involved in bodies of evidence by creating new ones. The aim of this paper is to explore a 'modified Dempster-Shafer' approach of fusing identity declarations emanating from different sources which include a number of radars, IFF and ESM systems in order to limit the explosion of the number of propositions. We use a non-ad hoc decision rule based on the expected utility interval (EUI) to select the most probable object in a comprehensive Platform Data Base (PDB) containing all the possible identity values that a potential target may take. We study the effect of the redistribution of the confidence levels of the eliminated propositions which otherwise overload the real-time data fusion system;these eliminated confidence levels can in particular be assigned to ignorance, or uniformly added to the remaining propositions and the ignorance. A scenario has been selected to demonstrate the performance of our modified Dempster-Shafer method of evidential reasoning.

关键词： sensor data fusion

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Leveraging Deep Learning on Multimodal sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins

Leveraging Deep Learning on Multimodal Sensor Data for Wirel...

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作者： Salehihikouei, Batool Northeastern University

学位级别：Ph.D., Doctor of Philosophy

With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots, respectively. In this dissertation, we present methods, where these sensors are re-purposed to assist network management in wireless communication, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods, where the multimodal sensor information is used for two applications: (i) beamforming at the mmWave band and (ii) joint optimization of the navigation and network management in warehouse environments. In the first part, we study multimodal beamforming methods for mmWave vehicular networks. First, we present deep learning fusion algorithms, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System), camera, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy, compared to using single modalities. Second, we study the trade-off between the accuracy and cost of different learning strategies and demonstrate that federated learning is the most successful learning strategy, with respect to the communication overhead. Third, we propose algorithms to further optimize the communication overhead by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. Fourth, we propose a modality-agnostic deep learning paradigm that operates on any possible combination of sensor modalities. In part two, we propose using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity, which is typically used for studying the impa

关键词： Network resource management Reinforcement learning Deep learning algorithms sensor modalities Federated learning systems

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Mitigation of Motion Artifacts in Pulse Oximetry through Redundant sensors 25

Mitigation of Motion Artifacts in Pulse Oximetry through Red...

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25th IEEE Conference on Signal Processing - algorithms, architectures, Arrangements, and applications (IEEE SPA)

作者： Alkhoury, Ludvik Choi, Jiwon Wang, Chizhong Mahoney, Sean Shender, Barry S. Hrebien, Leonid Kam, Moshe New Jersey Inst Technol Dept Elect & Comp Engn Newark Coll Engn Newark NJ 07102 USA Athena GTX Regulatory Affairs Dept Athena IA 50113 USA Naval Air Warfare Ctr Aircraft Div Human Syst Dept Patuxent River MD 20670 USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

Photoplethysmography (PPG) signals are used in pulse oximetry to calculate peripheral capillary oxygen saturation (SpO(2)) levels. It is well known that the accuracy of the calculated SpO(2) is susceptible to motion noise. Using field data collected from human subjects during rest and while exhibiting medium to high-intensity physical exercises, we show how sensor redundancy can mitigate motion effects by employing two channels of red and infrared PPG signals. We demonstrate the advantage of redundancy using two SpO(2) calculation methods. These methods are widely used "Red over Infrared" (RoI) method and the Discrete Saturation Transform (DST) algorithm both, assisted by pre-filtering by a heart-rate tuned comb filter. The data were collected from individuals at rest and while exercising. SpO(2) levels are calculated for two channels, each using a red and an infrared PPG signal. We integrated the SpO(2) computed from the two channels with a Kalman filter (KF) and computed the SpO(2) mean absolute error (MAE) from each channel individually and after integrating the two channels. The SpO(2) MAEs of each one of the two channels separately were greater (and statistically significant) than the SpO(2) MAE obtained from the two channels integrated with a KF. We observed that when two channels of PPG (red and infrared) signals were available, then combining SpO(2) levels obtained from these two channels exhibited a smoother and more reliable estimate of the blood oxygen saturation level as compared the SpO(2) levels computed from each channel separately. The reason is that if the data for the two channels are collected at sufficient physical distance from one another, the motion artifacts affecting the two channels are uncorrelated. The KF is then able to use past measurements and modeling of the dynamics to attenuate the effect of the motion artifacts.

关键词： pulse oximetry peripheral capillary oxygen saturation (SpO(2)) electrocardiography (ECG) motion artifact motion resistant oximetry data fusion comb filter red over infrared Discrete Saturation Transform (DST) Kalman Filter wearable unit multiple photoplethysmography (PPG) channels

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Trajectory Optimisation for Avionics-Based GNSS Integrity Augmentation System 35

Trajectory Optimisation for Avionics-Based GNSS Integrity Au...

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35th IEEE/AIAA Digital Avionics Systems Conference (DASC)

作者： Sabatini, Roberto Moore, Terry Hill, Chris Gardi, Alessandro Ramasamy, Subramanian Gilgien, Michael RMIT Univ Sch Engn Melbourne Vic 3000 Australia Univ Nottingham Nottingham Geospatial Inst Nottingham NG7 2TU England Cranfield Univ Dept Aerosp Engn Cranfield MK43 0AL Beds England

ISBN: (纸本)9781509025237

In this paper, trajectory optimisation algorithms developed specifically for the Global Navigation satellite System (GNSS) Avionics-Based Integrity Augmentation (ABIA) system are presented. The ABIA system is designed to increase the levels of integrity and accuracy (as well as continuity in multi-sensor data fusion architectures) of GNSS in a variety of mission-and safety-critical aviation applications. The trajectory optimisation algorithms can be employed both for flight planning as well as real-time optimisation of manned and unmanned aircraft for all flight phases. Three and six Degrees-of-Freedom (3-DoF/6-DoF) aircraft dynamics models are adopted to generate a number of feasible flight trajectories that also satisfy the GNSS constraints. A detailed simulation case study is presented to evaluate the performance of trajectory optimisation algorithms for GNSS integrity augmentation using an AIRBUS A320 3-DoF aircraft dynamics model. Results confirm that the employed trajectory optimisation algorithms are capable of supporting high-integrity tasks when GNSS is used as the primary source of navigation data.

关键词： Avionics Based Integrity Augmentation Trajectory Optimisation Caution and Wanirng Flags GNSS Integrity Mission-and Safety-Critical GNSS applications

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