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Magnetic signal denoising based on auxiliary sensor array and deep noise reconstruction

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Wang, Xiaoxian Zhang, Shiwu Song, Juncai Liu, Yongbin Lu, Siliang Anhui Univ Coll Elect & Informat Engn Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230026 Peoples R China Anhui Univ Sch Internet Hefei 230601 Peoples R China Anhui Univ Coll Elect Engn & Automat Hefei 230601 Peoples R China

Magnetic field measurement technologies are extensively applied in nondestructive testing, quality inspection, and fault diagnosis of machines and equipment. Magnetic signals are highly susceptible to noise interference, and how to effectively conduct signal denoising under strong noise disturbance remains a challenge. This study designs a novel auxiliary sensor array comprising center and distributed satellite sensors for magnetic noise reconstruction and signal denoising. The signals of the center and satellite sensors are highly correlative owing to the propagation of the magnetic noise through space. An attention-based long short term memory model is designed and trained to establish the mapping relationship among the center and satellite signals. The noise of the center sensor can then be reconstructed from those of the satellite sensors in the procedure of magnetic field measurement. Finally, the denoised signal of the center sensor is obtained by subtracting the noisy signal with the reconstructed noise. The effectiveness and superiority of the proposed method are validated by experiments and compared with the conventional methods. The designed auxiliary sensor array and algorithm are easily implemented with remarkable robustness, flexibility, and scalability, thereby showing greatly potential use for magnetic signal denoising in a variety of mechatronic systems.

关键词： Magnetic signal denoising Auxiliary sensor array Deep noise reconstruction multisensor data fusion Deep neural networks

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Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

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SENSORS 2022年第17期22卷 6513-6513页

作者： AlSahly, Abdullah M. Hassan, Mohammad Mehedi Saleem, Kashif Alabrah, Amerah Rodrigues, Joel J. P. C. King Saud Univ Dept Informat Syst Coll Comp & Informat Sci Riyadh 11543 Saudi Arabia King Saud Univ Ctr Excellence Informat Assurance CoEIA Riyadh 11653 Saudi Arabia China Univ Petr East China Coll Comp Sci & Technol Qingdao 266555 Peoples R China Inst Telecomunicacoes P-6201001 Covilha Portugal

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations;furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation-in terms of either positioning or trackin

关键词： blueprint filter algorithm indoor localization indoor tracking machine learning multisensor data fusion smartphone sensor virtual IMU Web of Things

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Covariance Intersection fusion with element-wise partial knowledge of correlation

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AUTOMATICA 2022年 139卷 110168-110168页

作者： Ajgl, Jiri Straka, Ondrej Univ West Bohemia Fac Sci Appl European Ctr Excellence New Technol Informat Soc Plzen 30614 Czech Republic Univ West Bohemia Fac Sci Appl Dept Cybernet Plzen 30614 Czech Republic

Covariance Intersection fusion is a linear rule for combining estimates. If the cross-correlation matrix of the errors of two estimates is unknown, the rule is bound-optimal. This paper elaborates the case when some elements of the cross-correlation matrix are known. Techniques for constructing a family of upper bounds of the joint mean square error matrix are introduced. All configurations for the fusion of up to four estimates are considered explicitly. The techniques are also applicable for the fusion of more than four estimates. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Covariance Intersection Distributed estimation multisensor data fusion Partially known correlation

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Intelligent Multi‐modal data Processing 1

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丛书名： The Wiley Series in intelligent signal and data processing

2021年

作者： Soham Sarkar Abhishek Basu Siddhartha Bhattacharyya

ISBN: (数字)9781119571452

ISBN: (纸本)9781119571384

A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal data Processing contains a review of the most recent applications of data processing. The Editors and contributors – noted experts on the topic – offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal data Processing brings together in one volume the range of multi-modal data processing.

关键词： Optical data processing Multimodal user interfaces (Computer systems) Artificial intelligence Digital watermarking multisensor data fusion

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Fractional Gabor Convolutional Network for Multisource Remote Sensing data Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhao, Xudong Tao, Ran Li, Wei Philips, Wilfried Liao, Wenzhi Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Ghent IMEC Res Grp Image Proc & Interpretat B-9000 Ghent Belgium Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China Flemish Inst Technol Res VITO Sustainable Mat Management B-2400 Mol Belgium Univ Ghent Dept TELIN B-9000 Ghent Belgium

Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have been developed to enhance and extract the spatially and spectrally semantic information in the hyperspectral image (HSI) and light detection and ranging (LiDAR) data processing and analysis. However, an abundance of redundant information and sometimes a lack of discriminative features reduce the efficiency and effectiveness of multisource classification methods. This article proposes a fractional Gabor convolutional network (FGCN), focusing on efficient feature fusion and comprehensive feature extraction. First, the proposed FGCN uses Octave convolution layers to perform multisource information fusion and preserve discriminative information. Second, fractional Gabor convolutional (FGC) layers are proposed to extract multiscale, multidirectional, and semantic change features. The completeness and discrimination of the multisource features using different FGC kernels are improved, which yield robust feature extraction against semantic changes. Finally, the fractional Gabor feature and spectral feature are combined with two weighting factors which can be learned during the network training. Experimental results and comparisons with state-of-the-art multisource classification methods indicate the effectiveness of the proposed FGCN. With the FGCN, we can obtain an 89.90x0025;overall accuracy on the challenging Muufl Gulfport (MUUFL) data set, with an improvement of 3x0025;over state-of-the-art methods.

关键词： Feature extraction Laser radar Convolution Hyperspectral imaging Stacking Semantics Redundancy Fractional Gabor convolutional network (FGCN) hyperspectral image (HSI) light detection and ranging (LiDAR) multisensor data fusion

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The Negation of a Probability Distribution: A Generalized Approach

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CYBERNETICS AND SYSTEMS 2022年

作者： Verma, Rajkumar Rohtagi, Barkha Univ Talca Inst Invest Interdisciplinaria Talca Chile Univ Talca Sch Econ & Business Talca Chile Univ Chile Dept Management Control & Informat Syst Santiago Chile KIET Grp Inst Dept Appl Sci Ghaziabad Uttar Pradesh India

The probability distribution function has been extensively used in several application areas to represent uncertain information. The negation of a probability distribution, proposed by Yager, provides a new approach to characterize uncertain information more effectively. It is also noted that the negation of a probability distribution can be generated a whole set of possible probability distributions. In the present communication, we propose a new generalized method to obtain the negation of a probability distribution. The paper discusses several important properties associated with the proposed negation of the probability distribution. It is interesting to note that the defined generalized negation of a probability distribution converges to the uniform distribution and reduces to Yager's negation as a limiting case. By considering different values of the parameter, a whole set of negations associated with a probability distribution can be generated, which follow different paths to approach convergence. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed approach.

关键词： multisensor data fusion negation of probability distribution target recognition

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Audiovisual Speaker Tracking Using Nonlinear Dynamical Systems With Dynamic Stream Weights

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 1065-1078页

作者： Schymura, Christopher Kolossa, Dorothea Ruhr Univ Bochum Fac Elect Engn & Informat Technol Inst Commun Acoust Cognit Signal Proc Grp D-44801 Bochum Germany

data fusion plays an important role in many technical applications that require efficient processing of multimodal sensory observations. A prominent example is audiovisual signal processing, which has gained increasing attention in automatic speech recognition, speaker localization and related tasks. If appropriately combined with acoustic information, additional visual cues can help to improve the performance in these applications, especially under adverse acoustic conditions. A dynamic weighting of acoustic and visual streams based on instantaneous sensor reliability measures is an efficient approach to data fusion in this context. This article presents a framework that extends the well-established theory of nonlinear dynamical systems with the notion of dynamic stream weights for an arbitrary number of sensory observations. It comprises a recursive state estimator based on the Gaussian filtering paradigm, which incorporates dynamic stream weights into a framework closely related to the extended Kalman filter. Additionally, a convex optimization approach to estimate oracle dynamic stream weights in fully observed dynamical systems utilizing a Dirichlet prior is presented. This serves as a basis for a generic parameter learning framework of dynamic stream weight estimators. The proposed system is application-independent and can be easily adapted to specific tasks and requirements. A study using audiovisual speaker tracking tasks is considered as an exemplary application in this work. An improved tracking performance of the dynamic stream-weight-based estimation framework over state-of-the-art methods is demonstrated in the experiments.

关键词： Robot sensing systems Bayes methods Reliability Acoustics State estimation Visualization Kalman filters multisensor data fusion dynamical systems dynamic stream weights Gaussian filter audiovisual speaker tracking

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Sensor Combination Selection for Human Gait Phase Segmentation Based on Lower Limb Motion Capture With Body Sensor Network

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Li, Jie Liu, Xiaofeng Wang, Zhelong Zhou, Xu Wang, Ziyang Hohai Univ Coll IoT Engn Changzhou 213022 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

Gait phase contains rich kinematic information of lower limbs, which has great reference significance for rehabilitation medicine, assistive design, and identity recognition. This research presents a wearable gait phase segmentation method based on lower limb motion capture (MoCap) technique. In our method, a body sensor network (BSN) covering the whole lower limbs was established to capture the motion data of human gait, and a 3-D lower limb dynamic model is created to reconstruct lower limb movements through multisensor data fusion. Six gait events are labeled by the lower limb dynamic model. Then, a deep classification network combining temporal convolutional network (TCN) and long-short-term memory (LSTM) is proposed to segment the six gait phases as pattern classification. In addition, different sensor combinations for gait phase segmentation were also evaluated to select optimal sensor layouts. Detection performance is evaluated using metrics of accuracy, specificity, recall, and F1 score, and the averaged performance values are 98.9%, 98.9%, 98.8%, and 98.9%, respectively. The overall experimental results demonstrate that our proposed method can well address the issue of gait phase segmentation and provide spatial-temporal parameters for further gait analysis.

关键词： Motion capture Motion segmentation Gyroscopes Quaternions Inertial sensors Estimation data collection Body area network gait phase segmentation long short-term memory (LSTM) multisensor data fusion temporal convolutional network (TCN)

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An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: A heatwave event in Naples

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SCIENCE OF THE TOTAL ENVIRONMENT 2022年 805卷 150130-150130页

作者： Oliveira, Ana Lopes, Antonio Niza, Samuel Soares, Amilcar Univ Lisbon IN Ctr Innovat Technol & Policy Res Inst Super Te Lisbon Portugal Univ Lisbon Ctr Estudos Geog IGOT Inst Geog & Ordenantento Terr Lisbon Portugal Univ Lisbon CERENA Inst Super Tecn Lisbon Portugal

Southern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for an-ticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough under-standing of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corre-sponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning ap-proach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps - built-up infrastructures up -take heat during the day which is released back into the atmosphere, during the night, whereas vegetation land sur-faces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important ex-planatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In fu -ture research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temp

关键词： Urban climate adaptation Heatwave Urban Heat Island Land surface temperature Local climate zones Random forest multisensor data fusion Satellite thermal imagery

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An Underground Pipeline Mapping Method Based on fusion of Multisource data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhou, Xiren Chen, Qiuju Jiang, Bingbing Chen, Huanhuan Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Engn Hangzhou 311121 Peoples R China

There is a need to map underground pipelines due to nonavailable existing pipeline maps caused by poor management of statutory records and insufficient updating of documentation whenever pipeline construction or rerouting occurs. By fusing multisource data, a novel method to map underground pipelines is proposed in this article. Statutory records of the underground pipelines are converted into the initial pipeline map. Pipeline information obtained from manhole covers and remote sensing technologies are normalized into the pipeline dataset composed of detected points. The probabilistic pipeline mapping model (PPMM) is then proposed to map the buried pipelines from the conducted pipeline dataset, with or without statutory pipeline records. In this model, each detected point is classified into the specific pipeline that most likely generates the data of this point, and detected points generated from the same pipeline are fit to revise the pipelines' locations and directions. The above classification and fitting operations are performed iteratively, and PPMM would output the pipeline map with the highest probability. Experimental studies on real-world datasets are conducted and analyzed, and the obtained results demonstrate the effectiveness of the proposed method.

关键词： Pipelines data models Probabilistic logic Remote sensing Global Positioning System Soft sensors Mathematical models Buried pipeline mapping ground-penetrating radar (GPR) multisensor data fusion probabilistic model

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