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Advances in Multi-Sensor Information Fusion: Theory and Applications 2017

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ISBN: 9783038429333 9783038429340 Year: Pages: VIII, 560 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Electrical and Nuclear Engineering
Added to DOAB on : 2018-06-26 15:21:03
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The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor.Multi-sensor information fusion has been a key issue in sensor research since the 1970s and it has been applied in many fields, such as geospatial information systems, business intelligence, oceanography, discovery science, intelligent transport systems, wireless sensor networks, etc. Recently, thanks to the vast development in sensor and computer memory technologies, more and more sensors are being used in practical systems and a large amount of measurement data are recorded and restored, which may actually be the "time series big data". For example, sensors in machines and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization.The goal of this Special Issue is to report on innovative ideas and solutions for the methods of multi-sensor information fusion in the emerging applications era, focusing on development, adoption and applications.

Multi-Sensor Information Fusion

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ISBN: 9783039283026 9783039283033 Year: Pages: 602 DOI: 10.3390/books978-3-03928-303-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:09
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This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.

Keywords

linear regression --- covariance matrix --- data association --- sensor fusing --- SLAM --- multi-sensor data fusion --- conflicting evidence --- Dempster–Shafer evidence theory --- belief entropy --- similarity measure --- data classification --- fault diagnosis --- Bar-Shalom Campo --- Covariance Projection method --- data fusion --- distributed architecture --- Kalman filter --- linear constraints --- inconsistent data --- user experience evaluation --- user experience measurement --- eye-tracking --- facial expression --- galvanic skin response --- EEG --- interaction tracker --- self-reporting --- user experience platform --- mix-method approach --- image fusion --- multi-focus --- weight maps --- gradient domain --- fast guided filter. --- Dempster-Shafer evidence theory (DST) --- uncertainty measure --- open world --- closed world --- Deng entropy --- extended belief entropy --- sensor data fusion --- orthogonal redundant inertial measurement units --- data fusion architectures --- sensors bias --- fire source localization --- dynamic optimization --- global information --- the Range-Point-Range frame --- the Range-Range-Range frame --- sensor array --- SINS/DVL integrated navigation --- unscented information filter --- square root --- state probability approximation --- most suitable parameter form --- deep learning --- data preprocessing --- Human Activity Recognition (HAR) --- Internet of things (IoT) --- Industry 4.0 --- trajectory reconstruction --- low-cost sensors --- embedded systems --- powered two wheels (PTW) --- safe trajectory --- data fusion --- health management decision --- grey group decision-making --- health reliability degree --- maintenance decision --- sensor system --- least-squares filtering --- least-squares smoothing --- networked systems --- random parameter matrices --- random delays --- packet dropouts --- multi-sensor system --- multi-sensor information fusion --- particle swarm optimization --- sensor data fusion algorithm --- distributed intelligence system --- multi-sensor time series --- deep learning --- machine health monitoring --- time-distributed ConvLSTM model --- spatiotemporal feature learning --- optimal estimate --- unknown inputs --- distributed fusion --- augmented state Kalman filtering (ASKF) --- soft sensor --- coefficient of determination maximization strategy --- expectation maximization (EM) algorithm --- Gaussian mixture model (GMM) --- alumina concentration --- multi-sensor joint calibration --- high-dimensional fusion data (HFD) --- supervoxel --- Gaussian density peak clustering --- sematic segmentation --- multisensor data fusion --- multitarget tracking --- GMPHD --- sonar network --- RFS --- attitude estimation --- Kalman filter --- land vehicle --- magnetic angular rate and gravity (MARG) sensor --- quaternion --- yaw estimation --- network flow theory --- multitarget tracking --- spectral clustering --- A* search algorithm --- RTS smoother --- integer programming --- Surface measurement --- multi-sensor measurement --- surface modelling --- data fusion --- Gaussian process --- multi-sensor network --- observable degree analysis --- information fusion --- nonlinear system --- hybrid adaptive filtering --- weighted fusion estimation --- square-root cubature Kalman filter --- information filter --- surface quality control --- multi-sensor data fusion --- cutting forces --- vibration --- acoustic emission --- signal feature extraction methods --- predictive modeling techniques --- attitude --- orientation --- estimation --- Kalman filter --- quaternion --- manifold --- image registration --- evidential reasoning --- belief functions --- uncertainty --- DoS attack --- industrial cyber-physical system (ICPS) --- security zones --- mimicry security switch strategy --- fixed-point filter --- extended Kalman filter --- nested iterative method --- Steffensen’s iterative method --- convergence condition --- vehicular localization --- target positioning --- high-definition map --- vehicle-to-everything --- intelligent and connected vehicles --- intelligent transport system --- image registration --- non-rigid feature matching --- local structure descriptor --- Gaussian mixture model --- aircraft pilot --- workload --- multi-source data fusion --- fuzzy neural network --- principal component analysis --- parameter learning --- drift compensation --- domain adaption --- feature representations --- electronic nose --- data fusion --- dual gating --- MEMS accelerometer and gyroscope --- cardiac PET --- out-of-sequence --- multi-target tracking --- random finite set --- gaussian mixture probability hypothesis density --- multisensor system --- Gaussian process regression --- Bayesian reasoning method --- Dempster–Shafer evidence theory (DST) --- uncertainty measure --- novel belief entropy --- multi-sensor data fusion --- decision-level sensor fusion --- electronic nose --- subspace alignment --- interference suppression --- transfer --- evidence combination --- time-domain data fusion --- object classification --- uncertainty --- multirotor UAV --- precision landing --- artificial marker --- pose estimation --- sensor fusion --- camera --- LiDAR --- calibration --- plane matching --- ICP --- projection --- data fusion --- data registration --- adaptive distance function --- complex surface measurement --- Gaussian process model --- Dempster–Shafer evidence theory --- conflict measurement --- mutual support degree --- Hellinger distance --- Pignistic vector angle --- multi-sensor data fusion --- multi-environments --- state estimation --- unmanned aerial vehicle

Data Acquisition and Processing in Cultural Heritage

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ISBN: 9783039217403 9783039217410 Year: Pages: 276 DOI: 10.3390/books978-3-03921-741-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:09
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Advances in the knowledge of the tangible components (position, size, shape) and intangible components (identity, habits) of an historic building or site involves fundamental and complex tasks in any project related to the conservation of cultural heritage (CH). In recent years, new geotechnologies have proven their usefulness and added value to the field of cultural heritage (CH) in the tasks of recording, modeling, conserving, and visualizing. In addition, current developments in building information modeling (HBIM), allow integration and simulation of different sources of information, generating a digital twin of any complex CH construction. As a result, experts in the area have increased the number of available sensors and methodologies. However, the quick evolution of geospatial technologies makes it necessary to revise their use, integration, and application in CH. This process is difficult to adopt, due to the new options which are opened for the study, analysis, management, and valorization of CH. Therefore, the aim of the present Special Issue is to cover the latest relevant topics, trends, and best practices in geospatial technologies and processing methodologies for CH sites and scenarios as well as to introduce the new tendencies. This book originates from the Special Issue “Data Acquisition and Processing in Cultural Heritage”, focusing primarily on data and sensor integration for CH; documentation/restoration in CH; heritage 3D documentation and modeling of complex CH sites; drone inspections in CH; software development in CH; and augmented reality in CH. It is hoped that this book will provide the advice and guidance required for any CH professional, making the best possible use of these sensors and methods in CH.

Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

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ISBN: 9783039213757 9783039213764 Year: Pages: 344 DOI: 10.3390/books978-3-03921-376-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR

Keywords

Vehicle-to-X communications --- Intelligent Transport Systems --- VANET --- DSRC --- Geobroadcast --- multi-sensor --- fusion --- deep learning --- LiDAR --- camera --- ADAS --- object tracking --- kernel based MIL algorithm --- Gaussian kernel --- adaptive classifier updating --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- joystick --- two-wheeled --- terrestrial vehicle --- path planning --- infinity norm --- p-norm --- kinematic control --- navigation --- actuation systems --- maneuver algorithm --- automated driving --- cooperative systems --- communications --- interface --- automated-manual transition --- driver monitoring --- visual tracking --- discriminative correlation filter bank --- occlusion --- sub-region --- global region --- autonomous vehicles --- driving decision-making model --- the emergency situations --- red light-running behaviors --- ethical and legal factors --- T-S fuzzy neural network --- road lane detection --- map generation --- driving assistance --- autonomous driving --- real-time object detection --- autonomous driving assistance system --- urban object detector --- convolutional neural networks --- machine vision --- biological vision --- deep learning --- convolutional neural network --- Gabor convolution kernel --- recurrent neural network --- enhanced learning --- autonomous vehicle --- crash injury severity prediction --- support vector machine model --- emergency decisions --- relative speed --- total vehicle mass of the front vehicle --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- drowsiness detection --- smart band --- electrocardiogram (ECG) --- photoplethysmogram (PPG) --- recurrence plot (RP) --- convolutional neural network (CNN) --- squeeze-and-excitation --- residual learning --- depthwise separable convolution --- blind spot detection --- machine learning --- neural networks --- predictive --- vehicle dynamics --- electric vehicles --- FPGA --- GPU --- parallel architectures --- optimization --- panoramic image dataset --- road scene --- object detection --- deep learning --- convolutional neural network --- driverless --- autopilot --- deep leaning --- object detection --- generative adversarial nets --- image inpainting --- n/a

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

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ISBN: 9783038976981 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Signal Processing and Analysis of Electrical Circuit

Authors: --- ---
ISBN: 9783039282944 9783039282951 Year: Pages: 604 DOI: 10.3390/books978-3-03928-295-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:09
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This Special Issue with 35 published articles shows the significance of the topic “Signal Processing and Analysis of Electrical Circuit”. This topic has been gaining increasing attention in recent times. The presented articles can be categorized into four different areas: signal processing and analysis methods of electrical circuits; electrical measurement technology; applications of signal processing of electrical equipment; fault diagnosis of electrical circuits. It is a fact that the development of electrical systems, signal processing methods, and circuits has been accelerating. Electronics applications related to electrical circuits and signal processing methods have gained noticeable attention in recent times. The methods of signal processing and electrical circuits are widely used by engineers and scientists all over the world. The constituent papers represent a significant contribution to electronics and present applications that can be used in industry. Further improvements to the presented approaches are required for realizing their full potential.

Keywords

VMD --- signal analysis --- ICEEMD --- IMF --- random noise --- attenuation --- commutator motor --- fault diagnosis --- method --- technique --- signal processing --- acoustic --- Inner and outer product --- tensor --- FPGA --- hardware --- direct digital synthesizer (DDS) --- frequency tuning word (FTW) --- stability --- accuracy --- demodulation --- phase-locked loop --- Chebyshev filter --- measurement noise suppression --- class AB operation --- CMOS --- current mirror --- current buffer --- quasi floating gate --- low power --- CMOS --- dynamic comparator --- offset calibration --- high speed --- low noise --- low power --- ADC --- all-pass filter --- CMOS --- time delay --- broadband --- true-time-delay --- electrochemical impedance spectroscopy --- FRA --- multichannel acquisition --- impedance spectrometry --- microcontroller --- intelligent vehicles --- LiDAR odometry --- range sensing --- simultaneous localization and mapping (SLAM) --- tilt sensor --- sensor data fusion --- complementary filters --- overlap-add processing --- spectral analysis --- variational mode decomposition (VMD) --- duffing chaotic oscillator (DCO) --- permutation entropy (PE) --- feature extraction --- frequency characteristic --- underwater acoustic signal --- ship-radiated noise --- PM/PWM --- capacitance-to-time conversion --- differential capacitive sensor --- brick-wall filter --- fuzzy logic --- induction motor --- Shannon entropy --- short-circuit fault --- APF --- CNFET --- pole-frequency --- chirality --- phase angle --- tuning --- DC–DC converter --- switched capacitor --- power management integrated circuit --- CMOS technology --- frequency standard comparator --- dual Mixing Time difference --- phase difference --- correlation function --- chebyshev polynomial --- compressed sensing --- estimated sparsity --- least squares solution --- stochastic gradient --- reconstruction probability --- direct position determination --- array signal processing --- Doppler shifts --- matrix eigen-perturbation theory --- system errors --- Cramér–Rao bound --- differential power analysis (DPA), SIMON --- fault injection --- double rate --- power randomization --- intention of movement classification --- EMG-Signals --- Support Vector Machines --- asynchronous --- delay cell --- passive resistor --- SAR ADC --- loop delay circuit --- Resistance-to-Period converter --- robust read-out circuits --- ratiometric technique --- Fresnel lens --- wingbeat --- insects --- optoelectronics --- bees --- wasps --- fruit flies --- e-traps --- analog-to-digital converter --- successive approximation register --- direct sampling --- time-interleaved --- channel-selection-embedded bootstrap --- segmented pre-quantization and bypass --- LVDS --- high-speed serial interface --- transmitter --- receiver --- low-power --- image fusion --- multi-sensor fusion --- night vision --- hierarchical heterogeneous multi-DAG workflow --- multigroup scan --- ultrasonic phased array --- heterogeneous earliest finish time --- resolver --- discrete wavelet transform --- singular value decomposition --- automatic calibration --- noise reduction --- ripple voltage measurement --- DAC --- comparator --- peak-ripple estimation --- binary search --- low-cost --- secret image sharing --- digital image --- n-out-of-n scheme --- color palette --- colluder attack --- image restoration --- impulse noise --- ADMM --- HOCTVL1 --- spatially adapted regularization parameter --- rod electrode --- electrostatic induction --- method of images --- induced charge --- induced current --- 3D-IC design --- NILT --- TSV noise coupling --- RDL --- chain matrix --- interconnect line --- n/a

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