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Cryptography lies at the heart of most technologies deployed today for secure communications. At the same time, mathematics lies at the heart of cryptography, as cryptographic constructions are based on algebraic scenarios ruled by group or number theoretical laws. Understanding the involved algebraic structures is, thus, essential to design robust cryptographic schemes. This Special Issue is concerned with the interplay between group theory, symmetry and cryptography. The book highlights four exciting areas of research in which these fields intertwine: post-quantum cryptography, coding theory, computational group theory and symmetric cryptography. The articles presented demonstrate the relevance of rigorously analyzing the computational hardness of the mathematical problems used as a base for cryptographic constructions. For instance, decoding problems related to algebraic codes and rewriting problems in non-abelian groups are explored with cryptographic applications in mind. New results on the algebraic properties or symmetric cryptographic tools are also presented, moving ahead in the understanding of their security properties. In addition, post-quantum constructions for digital signatures and key exchange are explored in this Special Issue, exemplifying how (and how not) group theory may be used for developing robust cryptographic tools to withstand quantum attacks.

NP-Completeness --- protocol compiler --- post-quantum cryptography --- Reed–Solomon codes --- key equation --- euclidean algorithm --- permutation group --- t-modified self-shrinking generator --- ideal cipher model --- algorithms in groups --- lightweight cryptography --- generalized self-shrinking generator --- numerical semigroup --- pseudo-random number generator --- symmetry --- pseudorandom permutation --- Berlekamp–Massey algorithm --- semigroup ideal --- algebraic-geometry code --- non-commutative cryptography --- provable security --- Engel words --- block cipher --- cryptography --- beyond birthday bound --- Weierstrass semigroup --- group theory --- braid groups --- statistical randomness tests --- group-based cryptography --- alternating group --- WalnutDSA --- Sugiyama et al. algorithm --- cryptanalysis --- digital signatures --- one-way functions --- key agreement protocol --- error-correcting code --- group key establishment

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In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.

History of engineering & technology --- EM-algorithm --- multi-GNSS --- PPP --- process noise --- observation covariance matrix --- extended Kalman filter --- machine learning --- GNSS phase bias --- sequential quasi-Monte Carlo --- variance reduction --- autoregressive processes --- ARMA-process --- colored noise --- continuous process --- covariance function --- stochastic modeling --- time series --- elementary error model --- terrestrial laser scanning --- variance-covariance matrix --- terrestrial laser scanner --- stochastic model --- B-spline approximation --- Hurst exponent --- fractional Gaussian noise --- generalized Hurst estimator --- very long baseline interferometry --- sensitivity --- internal reliability --- robustness --- CONT14 --- Errors-In-Variables Model --- Total Least-Squares --- prior information --- collocation vs. adjustment --- mean shift model --- variance inflation model --- outlierdetection --- likelihood ratio test --- Monte Carlo integration --- data snooping --- GUM analysis --- geodetic network adjustment --- stochastic properties --- random number generator --- Monte Carlo simulation --- 3D straight line fitting --- total least squares (TLS) --- weighted total least squares (WTLS) --- nonlinear least squares adjustment --- direct solution --- singular dispersion matrix --- laser scanning data

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In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.

History of engineering & technology --- EM-algorithm --- multi-GNSS --- PPP --- process noise --- observation covariance matrix --- extended Kalman filter --- machine learning --- GNSS phase bias --- sequential quasi-Monte Carlo --- variance reduction --- autoregressive processes --- ARMA-process --- colored noise --- continuous process --- covariance function --- stochastic modeling --- time series --- elementary error model --- terrestrial laser scanning --- variance-covariance matrix --- terrestrial laser scanner --- stochastic model --- B-spline approximation --- Hurst exponent --- fractional Gaussian noise --- generalized Hurst estimator --- very long baseline interferometry --- sensitivity --- internal reliability --- robustness --- CONT14 --- Errors-In-Variables Model --- Total Least-Squares --- prior information --- collocation vs. adjustment --- mean shift model --- variance inflation model --- outlierdetection --- likelihood ratio test --- Monte Carlo integration --- data snooping --- GUM analysis --- geodetic network adjustment --- stochastic properties --- random number generator --- Monte Carlo simulation --- 3D straight line fitting --- total least squares (TLS) --- weighted total least squares (WTLS) --- nonlinear least squares adjustment --- direct solution --- singular dispersion matrix --- laser scanning data

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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.

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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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.

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