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fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring 20

Fusion Learning on Multiple-Tag RFID Measurements for Respir...

引用

20th IEEE International conference on Bioinformatics and Bioengineering (BIBE)

作者： Hansen, Stephen Schwartz, Daniel Stover, Jesse Tajin, Md Abu Saleh Mongan, William M. Dandekar, Kapil R. Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)9781728195742

Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size.

关键词： sensor fusion Machine learning algorithms Signal denoising

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Multimodal sensor fusion with Differentiable Filters

Multimodal Sensor Fusion with Differentiable Filters

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

作者： Lee, Michelle A. Yi, Brent Martin-Martin, Roberto Savarese, Silvio Bohg, Jeannette Stanford Univ Stanford Artificial Intelligence Lab SAIL Stanford CA 94305 USA

ISBN: (纸本)9781728162126

Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities according to their uncertainties. Prior work has studied how to optimally fuse different sensor modalities with analytical state estimation algorithms. However, deriving the dynamics and measurement models along with their noise profile can be difficult or lead to intractable models. Differentiable filters provide a way to learn these models end-to-end while retaining the algorithmic structure of recursive filters. This can be especially helpful when working with sensor modalities that are high dimensional and have very different characteristics. In contact-rich manipulation, we want to combine visual sensing (which gives us global information) with tactile sensing (which gives us local information). In this paper, we study new differentiable filtering architectures to fuse heterogeneous sensor information. As case studies, we evaluate three tasks: two in planar pushing (simulated and real) and one in manipulating a kinematically constrained door (simulated). In extensive evaluations, we find that differentiable filters that leverage crossmodal sensor information reach comparable accuracies to unstructured LSTM models, while presenting interpretability benefits that may be important for safety-critical systems. We also release an open-source library for creating and training differentiable Bayesian filters in PyTorch, which can be found on our project website: https://***/view/multimodalfilter

关键词： State estimation

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Neural Architecture Search for Automotive Grid fusion Networks Under Embedded Hardware Constraints

Neural Architecture Search for Automotive Grid Fusion Networ...

引用

International conference on Machine Learning and applications (ICMLA)

作者： Gabor Balazs Walter Stechele Technische Universität München Munich Germany

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors-typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars.

关键词： Training Computer architecture sensor fusion Hardware Encoding Decoding Automotive engineering

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Towards modelling autonomous mobile robot localization by using sensor fusion algorithms 10

Towards modelling autonomous mobile robot localization by us...

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10th IEEE International conference on System Engineering and Technology, ICSET 2020

作者： Baharom, Abdul Khaiyum Abdul-Rahman, Shuzlina Jamali, Rafidah Mutalib, Sofianita Faculty of Computer and Mathematical Sciences Universiti Technology MARA Shah Alam Selangor40450 Malaysia Research Initiative Group of Intelligent Systems Faculty of Computer and Mathematical Sciences Universiti Technology MARA Shah Alam Selangor40450 Malaysia United Kingdom

ISBN: (纸本)9781728199108

Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes an early experiment towards modelling a low-cost and robust centimetrelevel localization for mobile robots in crowded indoor and outdoor environments. While a wide range of methods have been developed and tested on high-end hardware in autonomous vehicles, the work utilizes multiple sensor information to achieve robustness with different types of mobile robots. The application can be used by any group or organization, especially the frontliners, in managing the COVID-19 pandemic. Different Simultaneous Localization and Mapping (SLAM) algorithms, such as GMapping, Google Cartographer and Hector SLAM, are used to achieve better localization. sensor fusion strategy is applied for these SLAM packages using Real-Time Kinematic (RTK) positioning, a precise Global Navigation Satellite System (GNSS)-based sensor, by applying both Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to estimate position, velocity and attitude (PVA). The performance of the proposed algorithm will be compared against the benchmark algorithm using different sets of data in crowded places in various settings. © 2020 IEEE

关键词： Extended Kalman filters

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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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Maximizing the Probability of k-Covering an Event Location 16

Maximizing the Probability of k-Covering an Event Location

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16th IEEE International conference on Control and Automation (ICCA)

作者： Gong, Chaohui Gong, Chujun Guo, Zhong Zang, Yanwei Wang, Weilin Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China Jilin Univ Changchun Peoples R China PowerChina Huadong Engn Corp Ltd Zhejiang Res Ctr Smart Rail Transportat Hangzhou Peoples R China

ISBN: (纸本)9781728190938

In various sensor network applications like monitoring and target localization, the occurrence of an event can only be effectively located if it falls within the sensing range of at least k >= 3 sensors. In this paper, we develop an algorithm to optimize to optimize the positions and orientations of heterogeneous sensors in order to maximize the probability of an event location being k-covered. Our proposed algorithms can handle rotatable mobile sensors with directional sensing regions. We show that our proposed algorithm converges to a local optimal solution, and present numerical results to verify its performance.

关键词： Optimization sensor networks sensor fusion

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RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

RGB and LiDAR fusion based 3D Semantic Segmentation for Auto...

引用

IEEE Intelligent Transportation Systems conference (IEEE-ITSC)

作者： El Madawi, Khaled Rashed, Hazem El Sallab, Ahmad Nasr, Omar Kamel, Hanan Yogamani, Senthil Valeo R&D Cairo Cairo Egypt Cairo Univ Giza Egypt Valeo Vis Syst Galway Ireland

ISBN: (纸本)9781538670248

LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10% in both cases relative to the LiDAR only baseline.

关键词： Semantic Segmentation

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Benchmarking neural network architectures for acoustic sensor networks 13

Benchmarking neural network architectures for acoustic senso...

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13th ITG conference on Speech Communication

作者： Ebbers, Janek Heitkaemper, Jens Schmalenstroeer, Joerg Haeb-Umbach, Reinhold Department for Communication Engineering Paderborn University Germany

ISBN: (纸本)9783800747672

Due to their distributed nature wireless acoustic sensor networks offer great potential for improved signal acquisition, processing and classification for applications such as monitoring and surveillance, home automation, or hands-free telecommunication. To reduce the communication demand with a central server and to raise the privacy level it is desirable to perform processing at node level. The limited processing and memory capabilities on a sensor node, however, stand in contrast to the compute and memory intensive deep learning algorithms used in modern speech and audio processing. In this work, we perform benchmarking of commonly used convolutional and recurrent neural network architectures on a Raspberry Pi based acoustic sensor node. We show that it is possible to run medium-sized neural network topologies used for speech enhancement and speech recognition in real time. For acoustic event recognition, where predictions in a lower temporal resolution are sufficient, it is even possible to run current state-of-the-art deep convolutional models with a real-time-factor of 0.11. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach

关键词： Benchmarking

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sensor fusion and machine learning techniques to improve water cut measurements accuracy in multiphase application

Sensor fusion and machine learning techniques to improve wat...

引用

SPE Annual Technical conference and Exhibition 2020, ATCE 2020

作者： Barbariol, Tommaso Feltresi, Enrico Susto, Gian Antonio Tescaro, Diego Galvanin, Silvia University of Padova Italy Pietro Fiorentini

ISBN: (纸本)9781613997239

Objectives/Scope: A Multi-Phase Flow Meter (MPFM) performs Water Liquid Ratio (WLR) estimations using a dedicated sensor relying on one physical principle (e.g. electrical impedance). The accuracy of the WLR sensor might also be dependent on the flow properties. An approach based on Machine Learning techniques and multi-sensors data fusion has been implemented to enhance the reliability and accuracy of the WLR estimations in Multi Phase application using onboard sensor measurements of a MPFM. Methods, Procedures, Process: In order to improve the estimations of multi-phase applications, we exploit the availability of historical data collected with heterogeneous sensors;the underlying idea of the proposed approach is to exploit such data with Machine Learning supervised techniques to provide accurate measures. In this work we compare modern supervised learning approaches like Random Forest, Gradient Boosting techniques and Kernel methods. The proposed methods have a relatively simple form that can be deployed also in embedded applications. Results, Observations, Conclusions: In this work, we will show through extensive experiments that the proposed approach could improve the original estimations. The algorithms underlying the proposed approach have been trained using data collected at flow loops test facilities with different flow conditions. The best model has been chosen not only for its predictive performances, but also looking at the computational time needed to make a prediction and considering its robustness to outliers. As expected, depending on the dataset numerosity, the best performing model can change: we provide experimental results for various dataset sizes in order to help practitioners choose the best regression method depending on the available data numerosity. An additional considered aspect is the computational time of the various approaches, which may be a relevant characteristic to be evaluated before rolling out productive solutions. Novel/Additiv

关键词： Decision trees

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Stereo and Mono Depth Estimation fusion for an Improved and Fault Tolerant 3D Reconstruction 17

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

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17th IEEE International conference on Intelligent Computer Communication and Processing, ICCP 2021

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

ISBN: (纸本)9781665409766

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. © 2021 IEEE.

关键词： Fault tolerance

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