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Book
Entropy in Image Analysis
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ISBN: 3039210939 3039210920 Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.

Keywords

keyframes --- time-delay --- whale optimization algorithm --- multilevel thresholding --- multi-exposure image fusion --- additive manufacturing --- patch structure decomposition --- ultra-sound images --- 3D scanning --- Arimoto entropy --- contrast enhancement --- spatial filling factor --- depth maps --- image processing --- 3D prints --- differential evolution --- field of experts --- normalized divergence measure --- image privacy --- multiscale top-hat transform --- q-exponential --- texture information entropy --- diffusion --- hybrid algorithm --- Weibull statistics --- adaptive selection --- nonextensive entropy --- computer aided diagnostics --- fatty liver --- random forest --- DNA encoding --- low contrast --- entropy --- Minkowski island --- fuzzy entropy --- free-form deformations --- person re-identification --- chaotic system --- DNA computing --- pavement --- information entropy --- discrete entropy --- Tsallis statistics --- video skimming --- prime-indexed primes --- natural scene statistics (NSS) --- Hénon map --- q-sigmoid --- image entropy --- Shannon entropy --- macrotexture --- Shannon’s entropy --- binary image --- multi-feature fusion --- image analysis --- uncertainty assessment --- non-rigid registration --- hash layer --- Cantor set --- dynamic filtering --- deep neural network --- security analysis --- multiple-image encryption --- Hamming distance --- blind image quality assessment (BIQA) --- q-Gaussian --- remote sensing --- decay trend --- chaotic cryptography --- chaotic strategy --- cross-entropy loss --- random insertion --- metabolic syndrome --- sign languages --- generalized entropies --- relevance feedback --- image retrieval --- two-dimensional chaotic economic map --- cryptanalysis --- infrared images --- 3D Latin cube --- SHA-256 hash value --- gradient distributions --- structural entropy --- discrete cosine transform (DCT) --- chaotic map --- hepatic steatosis --- machine vision --- electromagnetic field optimization --- security --- image segmentation --- quantization loss --- colonoscopy --- video summarization --- permutation --- Kapur’s entropy --- surface quality assessment --- permutation-diffusion --- Ramanujan primes --- Rényi entropies --- chosen-plaintext attack --- image encryption --- dynamic index --- color image segmentation --- ultrasound --- Otsu method --- sigmoid --- reconstruction --- image information entropy --- 3-D digital imaging --- positron emission tomography --- medical imaging


Book
Machine Learning and Data Mining Applications in Power Systems
Authors: ---
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries.

Keywords

Technology: general issues --- History of engineering & technology --- Energy industries & utilities --- virtual power plant (VPP) --- power quality (PQ) --- global index --- distributed energy resources (DER) --- energy storage systems (ESS) --- power systems --- long-term assessment --- battery energy storage systems (BESS) --- smart grids --- conducted disturbances --- power quality --- supraharmonics --- 2–150 kHz --- Power Line Communications (PLC) --- intentional emission --- non-intentional emission --- mains signalling --- virtual power plant --- data mining --- clustering --- distributed energy resources --- energy storage systems --- short term conditions --- cluster analysis (CA) --- nonlinear loads --- harmonics, cancellation, and attenuation of harmonics --- waveform distortion --- THDi --- low-voltage networks --- optimization techniques --- different batteries --- off-grid microgrid --- integrated renewable energy system --- cluster analysis --- K-means --- agglomerative --- ANFIS --- fuzzy logic --- induction generator --- MPPT --- neural network --- renewable energy --- variable speed WECS --- wind energy conversion system --- wind energy --- frequency estimation --- spectrum interpolation --- power network disturbances --- COVID-19 --- time-varying reproduction number --- social distancing --- load profile --- demographic characteristic --- household energy consumption --- demand-side management --- energy management --- time series --- Hidden Markov Model --- short-term forecast --- sparse signal decomposition --- supervised dictionary learning --- dictionary impulsion --- singular value decomposition --- discrete cosine transform --- discrete Haar transform --- discrete wavelet transform --- transient stability assessment --- home energy management --- binary-coded genetic algorithms --- optimal power scheduling --- demand response --- Data Injection Attack --- machine learning --- critical infrastructure --- smart grid --- water treatment plant --- power system --- n/a --- 2-150 kHz


Book
Applications in Electronics Pervading Industry, Environment and Society : Sensing Systems and Pervasive Intelligence
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

Keywords

Technology: general issues --- model-based design --- FPGA --- HDL code generation --- wearable sensors --- embedded devices --- face recognition --- face verification --- biometric sensors --- deep learning --- distillation --- convolutional neural networks --- spatial transformer network --- video coding --- discrete cosine transform --- directional transform --- VLSI --- alternative representations to float numbers --- posit arithmetic --- Deep Neural Networks (DNNs) --- neural network activation functions --- surface electromyography --- event-driven --- functional electrical stimulation --- embedded system --- resampling --- interpolating polynomial --- polyphase filter --- digital circuit design --- ASIC --- bitmap indexing --- processing in memory --- memory wall --- big data --- internet of things --- intelligent sensors --- autonomous driving --- cyber security --- HW accelerator --- on-chip random number generator (RNG) --- SHA2 --- ASIC standard-cell --- machine learning --- edge computing --- edge analytics --- ANN --- k-NN --- SVM --- decision trees --- ARM --- X-Cube-AI --- STM32 Nucleo --- rad-hard --- PLL (phase-locked loop) --- SEE (single event effects) --- Spacefibre --- TID (total ionization dose) --- charge pump --- phase/frequency detector --- frequency divider --- ring oscillator --- LC-tank oscillator --- SpaceFibre --- rad-hard circuits --- radiation effects --- high-speed data transfer --- support attitude --- inertial measurement unit --- coal mining --- unscented Kalman filter --- quaternion --- gradient descent --- research data collection and sharing --- connected and automated driving --- deployment and field testing --- vehicular sensors --- impact assessment --- knowledge management --- collaborative project methodology --- n/a


Book
Machine Learning Methods with Noisy, Incomplete or Small Datasets
Authors: --- --- --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.

Keywords

Information technology industries --- open contours --- similarly shaped fish species --- Discrete Cosine Transform (DCT) --- Discrete Fourier Transform (DFT) --- Extreme Learning Machines (ELM) --- feature engineering --- small data-sets --- optimization --- machine learning --- preprocessing --- image generation --- weighted interpolation map --- binarization --- single sample per person --- root canal measurement --- multifrequency impedance --- data augmentation --- neural network --- functional magnetic resonance imaging --- independent component analysis --- deep learning --- recurrent neural network --- functional connectivity --- episodic memory --- small sample learning --- feature selection --- noise elimination --- space consistency --- label correlations --- empirical mode decomposition --- sparse representations --- tensor decomposition --- tensor completion --- machine translation --- pairwise evaluation --- educational data --- small datasets --- noisy datasets --- smart building --- Internet of Things (IoT) --- Markov Chain Monte Carlo (MCMC) --- ontology --- graph model --- Artificial Neural Network --- Discriminant Analysis --- dengue --- feature extraction --- sound event detection --- non-negative matrix factorization --- ultrasound images --- shadow detection --- shadow estimation --- auto-encoders --- semi-supervised learning --- prediction --- feature importance --- feature elimination --- hierarchical clustering --- Parkinson’s disease --- few-shot learning --- permutation-variable importance --- topological data analysis --- persistent entropy --- support-vector machine --- data science --- intelligent decision support --- social vulnerability --- gender-gap --- digital-gap --- COVID19 --- policy-making support --- artificial intelligence --- imperfect dataset


Book
Enabling Technology in Optical Fiber Communications: From Device, System to Networking
Authors: --- --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking.

Keywords

Research & information: general --- spectral shaping --- photonic crystal fiber cascade --- zero-dispersion frequencies spacing --- supercontinuum generation --- virtual network embedding --- converged optical networks --- network slicing --- machine learning --- software-defined network --- optical waveguide --- silicon photonics --- silicon nitride --- optical polarization --- bipolar --- optical code division multiple access --- electro-optical modulator --- free-space optics communication --- FiWi access network --- energy efficiency --- power over fiber --- TWDM-PON --- delay analysis --- frequency modulated continuous-wave (FMCW) --- light detection and ranging (LiDAR) --- instantaneous frequency --- coherent optical spectrum analyzer (COSA) --- time-frequency curve --- microstructured optical fiber --- optical fiber sensors --- refractive index sensor --- surface plasmon resonance --- optical fiber communication --- electrical dispersion compensation --- multi-channel digital backpropagation --- equalization-enhanced phase noise --- achievable information rates --- elastic optical network --- invalid spectrum rate --- advanced reservation --- defragmentation --- blocking probability --- spectrum alignment rate --- VCSEL --- neural network-based equalization --- Volterra series-based equalization --- lanthanum-aluminum silicate glass --- polarization-maintaining fiber --- fiber Bragg grating --- Sagnac interferometer --- fluorinated polyimide film --- humidity hysteresis --- optical frequency domain reflectometry --- position deviation compensation --- sub-millimeter spatial resolution --- analysis --- FPGA --- GPON --- MongoDB --- storing --- fiber optics communications --- optical security and encryption --- phase fluctuations --- Elastic Optical Network --- space division multiplexing --- routing --- coherent optical communication --- offset-quadrature amplitude modulation-based filter-bank multicarrier (FBMC/OQAM) --- blind phase noise compensation --- inter-carrier-interference (ICI) --- discrete cosine transform (DCT) --- Brillouin --- ultrafast --- distributed sensing --- pump pulse array --- resolution enhancement --- SNR --- n/a


Book
Machine Learning Methods with Noisy, Incomplete or Small Datasets
Authors: --- --- --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

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Bookmark

Abstract

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.

Keywords

Information technology industries --- open contours --- similarly shaped fish species --- Discrete Cosine Transform (DCT) --- Discrete Fourier Transform (DFT) --- Extreme Learning Machines (ELM) --- feature engineering --- small data-sets --- optimization --- machine learning --- preprocessing --- image generation --- weighted interpolation map --- binarization --- single sample per person --- root canal measurement --- multifrequency impedance --- data augmentation --- neural network --- functional magnetic resonance imaging --- independent component analysis --- deep learning --- recurrent neural network --- functional connectivity --- episodic memory --- small sample learning --- feature selection --- noise elimination --- space consistency --- label correlations --- empirical mode decomposition --- sparse representations --- tensor decomposition --- tensor completion --- machine translation --- pairwise evaluation --- educational data --- small datasets --- noisy datasets --- smart building --- Internet of Things (IoT) --- Markov Chain Monte Carlo (MCMC) --- ontology --- graph model --- Artificial Neural Network --- Discriminant Analysis --- dengue --- feature extraction --- sound event detection --- non-negative matrix factorization --- ultrasound images --- shadow detection --- shadow estimation --- auto-encoders --- semi-supervised learning --- prediction --- feature importance --- feature elimination --- hierarchical clustering --- Parkinson’s disease --- few-shot learning --- permutation-variable importance --- topological data analysis --- persistent entropy --- support-vector machine --- data science --- intelligent decision support --- social vulnerability --- gender-gap --- digital-gap --- COVID19 --- policy-making support --- artificial intelligence --- imperfect dataset


Book
Enabling Technology in Optical Fiber Communications: From Device, System to Networking
Authors: --- --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

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Bookmark

Abstract

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking.

Keywords

Research & information: general --- spectral shaping --- photonic crystal fiber cascade --- zero-dispersion frequencies spacing --- supercontinuum generation --- virtual network embedding --- converged optical networks --- network slicing --- machine learning --- software-defined network --- optical waveguide --- silicon photonics --- silicon nitride --- optical polarization --- bipolar --- optical code division multiple access --- electro-optical modulator --- free-space optics communication --- FiWi access network --- energy efficiency --- power over fiber --- TWDM-PON --- delay analysis --- frequency modulated continuous-wave (FMCW) --- light detection and ranging (LiDAR) --- instantaneous frequency --- coherent optical spectrum analyzer (COSA) --- time-frequency curve --- microstructured optical fiber --- optical fiber sensors --- refractive index sensor --- surface plasmon resonance --- optical fiber communication --- electrical dispersion compensation --- multi-channel digital backpropagation --- equalization-enhanced phase noise --- achievable information rates --- elastic optical network --- invalid spectrum rate --- advanced reservation --- defragmentation --- blocking probability --- spectrum alignment rate --- VCSEL --- neural network-based equalization --- Volterra series-based equalization --- lanthanum-aluminum silicate glass --- polarization-maintaining fiber --- fiber Bragg grating --- Sagnac interferometer --- fluorinated polyimide film --- humidity hysteresis --- optical frequency domain reflectometry --- position deviation compensation --- sub-millimeter spatial resolution --- analysis --- FPGA --- GPON --- MongoDB --- storing --- fiber optics communications --- optical security and encryption --- phase fluctuations --- Elastic Optical Network --- space division multiplexing --- routing --- coherent optical communication --- offset-quadrature amplitude modulation-based filter-bank multicarrier (FBMC/OQAM) --- blind phase noise compensation --- inter-carrier-interference (ICI) --- discrete cosine transform (DCT) --- Brillouin --- ultrafast --- distributed sensing --- pump pulse array --- resolution enhancement --- SNR --- n/a


Book
Applications in Electronics Pervading Industry, Environment and Society : Sensing Systems and Pervasive Intelligence
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

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Bookmark

Abstract

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

Keywords

Technology: general issues --- model-based design --- FPGA --- HDL code generation --- wearable sensors --- embedded devices --- face recognition --- face verification --- biometric sensors --- deep learning --- distillation --- convolutional neural networks --- spatial transformer network --- video coding --- discrete cosine transform --- directional transform --- VLSI --- alternative representations to float numbers --- posit arithmetic --- Deep Neural Networks (DNNs) --- neural network activation functions --- surface electromyography --- event-driven --- functional electrical stimulation --- embedded system --- resampling --- interpolating polynomial --- polyphase filter --- digital circuit design --- ASIC --- bitmap indexing --- processing in memory --- memory wall --- big data --- internet of things --- intelligent sensors --- autonomous driving --- cyber security --- HW accelerator --- on-chip random number generator (RNG) --- SHA2 --- ASIC standard-cell --- machine learning --- edge computing --- edge analytics --- ANN --- k-NN --- SVM --- decision trees --- ARM --- X-Cube-AI --- STM32 Nucleo --- rad-hard --- PLL (phase-locked loop) --- SEE (single event effects) --- Spacefibre --- TID (total ionization dose) --- charge pump --- phase/frequency detector --- frequency divider --- ring oscillator --- LC-tank oscillator --- SpaceFibre --- rad-hard circuits --- radiation effects --- high-speed data transfer --- support attitude --- inertial measurement unit --- coal mining --- unscented Kalman filter --- quaternion --- gradient descent --- research data collection and sharing --- connected and automated driving --- deployment and field testing --- vehicular sensors --- impact assessment --- knowledge management --- collaborative project methodology --- n/a


Book
Machine Learning and Data Mining Applications in Power Systems
Authors: ---
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries.

Keywords

Technology: general issues --- History of engineering & technology --- Energy industries & utilities --- virtual power plant (VPP) --- power quality (PQ) --- global index --- distributed energy resources (DER) --- energy storage systems (ESS) --- power systems --- long-term assessment --- battery energy storage systems (BESS) --- smart grids --- conducted disturbances --- power quality --- supraharmonics --- 2–150 kHz --- Power Line Communications (PLC) --- intentional emission --- non-intentional emission --- mains signalling --- virtual power plant --- data mining --- clustering --- distributed energy resources --- energy storage systems --- short term conditions --- cluster analysis (CA) --- nonlinear loads --- harmonics, cancellation, and attenuation of harmonics --- waveform distortion --- THDi --- low-voltage networks --- optimization techniques --- different batteries --- off-grid microgrid --- integrated renewable energy system --- cluster analysis --- K-means --- agglomerative --- ANFIS --- fuzzy logic --- induction generator --- MPPT --- neural network --- renewable energy --- variable speed WECS --- wind energy conversion system --- wind energy --- frequency estimation --- spectrum interpolation --- power network disturbances --- COVID-19 --- time-varying reproduction number --- social distancing --- load profile --- demographic characteristic --- household energy consumption --- demand-side management --- energy management --- time series --- Hidden Markov Model --- short-term forecast --- sparse signal decomposition --- supervised dictionary learning --- dictionary impulsion --- singular value decomposition --- discrete cosine transform --- discrete Haar transform --- discrete wavelet transform --- transient stability assessment --- home energy management --- binary-coded genetic algorithms --- optimal power scheduling --- demand response --- Data Injection Attack --- machine learning --- critical infrastructure --- smart grid --- water treatment plant --- power system --- n/a --- 2-150 kHz


Book
Applications in Electronics Pervading Industry, Environment and Society : Sensing Systems and Pervasive Intelligence
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

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Bookmark

Abstract

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

Keywords

model-based design --- FPGA --- HDL code generation --- wearable sensors --- embedded devices --- face recognition --- face verification --- biometric sensors --- deep learning --- distillation --- convolutional neural networks --- spatial transformer network --- video coding --- discrete cosine transform --- directional transform --- VLSI --- alternative representations to float numbers --- posit arithmetic --- Deep Neural Networks (DNNs) --- neural network activation functions --- surface electromyography --- event-driven --- functional electrical stimulation --- embedded system --- resampling --- interpolating polynomial --- polyphase filter --- digital circuit design --- ASIC --- bitmap indexing --- processing in memory --- memory wall --- big data --- internet of things --- intelligent sensors --- autonomous driving --- cyber security --- HW accelerator --- on-chip random number generator (RNG) --- SHA2 --- ASIC standard-cell --- machine learning --- edge computing --- edge analytics --- ANN --- k-NN --- SVM --- decision trees --- ARM --- X-Cube-AI --- STM32 Nucleo --- rad-hard --- PLL (phase-locked loop) --- SEE (single event effects) --- Spacefibre --- TID (total ionization dose) --- charge pump --- phase/frequency detector --- frequency divider --- ring oscillator --- LC-tank oscillator --- SpaceFibre --- rad-hard circuits --- radiation effects --- high-speed data transfer --- support attitude --- inertial measurement unit --- coal mining --- unscented Kalman filter --- quaternion --- gradient descent --- research data collection and sharing --- connected and automated driving --- deployment and field testing --- vehicular sensors --- impact assessment --- knowledge management --- collaborative project methodology --- n/a

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