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Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing.

machine learning --- neural networks --- gait analysis --- embedded system --- NTV --- NTC --- low-power --- low-voltage memory and clocking circuits --- minimum-energy design --- power-performance --- resilient adaptive computing --- edge devices --- power management --- energy efficiency --- near-threshold computing (NTC) --- deep neural network (DNN) --- accelerators --- timing error --- AI --- tensor processing unit (TPU) --- multiply and accumulate (MAC) --- reliability --- Near-Threshold Computing --- functional unit --- performance optimization --- cross-layer optimization

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Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing.

Technology: general issues --- machine learning --- neural networks --- gait analysis --- embedded system --- NTV --- NTC --- low-power --- low-voltage memory and clocking circuits --- minimum-energy design --- power-performance --- resilient adaptive computing --- edge devices --- power management --- energy efficiency --- near-threshold computing (NTC) --- deep neural network (DNN) --- accelerators --- timing error --- AI --- tensor processing unit (TPU) --- multiply and accumulate (MAC) --- reliability --- Near-Threshold Computing --- functional unit --- performance optimization --- cross-layer optimization

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The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge.

Technology: general issues --- centrifugal pump --- double hidden layer --- Levenberg–Marquardt algorithm --- performance prediction --- thermal energy storage --- stratification --- dynamic simulation --- heating --- double-channel sewage pump --- critical wall roughness --- numerical calculation --- external characteristics --- axial-flow pump --- impeller --- approximation model --- optimization design --- multi-disciplinary --- blade slot --- orthogonal test --- numerical simulation --- Francis turbine --- anti-cavity fins --- draft tube --- vortex rope --- low flow rates --- internal flow characteristics --- unsteady pressure --- energy recovery --- turboexpander --- throttling valves --- CFD --- modelling techniques --- Kaplan turbine --- draft tube optimization --- CFD analysis --- DOE --- response surface --- single-channel pump --- CFD-DEM coupling method --- particle features and behaviors --- solid-liquid two-phase flows --- computational fluid dynamics (CFD) --- artificial neural network (ANN) --- subcooled boiling flows --- uncertainty quantification (UQ) --- Monte Carlo dropout --- deep ensemble --- deep neural network (DNN) --- intake structures --- physical hydraulic model --- free surface flow --- free surface vortices --- vertical pump --- design considerations --- magnetocaloric effect --- coefficient of performance --- refrigeration --- capacity --- mathematical modelling --- energy systems

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The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge.

Technology: general issues --- centrifugal pump --- double hidden layer --- Levenberg–Marquardt algorithm --- performance prediction --- thermal energy storage --- stratification --- dynamic simulation --- heating --- double-channel sewage pump --- critical wall roughness --- numerical calculation --- external characteristics --- axial-flow pump --- impeller --- approximation model --- optimization design --- multi-disciplinary --- blade slot --- orthogonal test --- numerical simulation --- Francis turbine --- anti-cavity fins --- draft tube --- vortex rope --- low flow rates --- internal flow characteristics --- unsteady pressure --- energy recovery --- turboexpander --- throttling valves --- CFD --- modelling techniques --- Kaplan turbine --- draft tube optimization --- CFD analysis --- DOE --- response surface --- single-channel pump --- CFD-DEM coupling method --- particle features and behaviors --- solid-liquid two-phase flows --- computational fluid dynamics (CFD) --- artificial neural network (ANN) --- subcooled boiling flows --- uncertainty quantification (UQ) --- Monte Carlo dropout --- deep ensemble --- deep neural network (DNN) --- intake structures --- physical hydraulic model --- free surface flow --- free surface vortices --- vertical pump --- design considerations --- magnetocaloric effect --- coefficient of performance --- refrigeration --- capacity --- mathematical modelling --- energy systems

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Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods.

Technology: general issues --- filter --- finite memory structure --- infinite memory structure --- smoother --- target tracking --- Indoor Positioning System --- WLAN --- C-Means --- K-Means --- Access Point Selection --- RSS-fingerprint --- smartphone --- pedestrian dead reckoning --- heading estimation --- autoregressive model --- adaptive Kalman filter --- indoor localization --- Wi-Fi received signal strength indicator (RSSI) --- semisupervised learning --- feature extraction --- mobile fingerprinting --- trajectory learning --- localization --- hybrid localization --- Bluetooth Low Energy --- extended kalman filter --- internet of things --- proximity sensors --- smartphone sensors --- pedestrian dead reckoning (PDR) --- Wi-Fi indoor positioning --- sensor fusion frameworks --- Kalman filter --- location fingerprinting --- trilateration --- received signal strength indication (RSSI) --- indoor positioning --- 5G system --- hybrid positioning --- geometric dilution of precision --- closed-form solution --- Cramer-Rao lower bound --- visually impaired (VI) --- computer vision --- deep learning --- multi-label convolutional support vector machine (M-CSVM) --- assistive technology --- visually impaired --- navigational system --- indoor navigation --- markers --- mobile robots --- wireless sensor network --- time of arrival (TOA) --- NLOS --- modified probabilistic data association (MPDA) --- indoor location recognition --- received signal strength (RSS) --- Wi-Fi fingerprint positioning --- deep neural network (DNN) --- optimization methods --- adaptive filter --- hidden Markov models (HMM) --- I/O detection --- GPS signal --- machine learning --- positioning applications. --- PDR --- geomagnetic positioning --- particle filter --- genetic algorithm --- Wi-Fi fine timing measurement --- NLOS identification --- Gaussian model --- carrier phase --- differential pseudolite system --- extended Kalman filter --- reliability --- integrity monitoring --- transparent obstacle recognition --- reflection noise --- laser range finder --- path planning --- mobile robot --- automated data acquisition --- remote sensing technologies --- automated progress reporting --- data fusion --- tracking resources --- bearing estimation --- azimuth estimation --- signal processing --- position estimation --- photodiode array --- indoor ranging algorithm --- channel state information --- received signal strength indicator --- VPR --- fusion navigation --- UWB --- multi-path detection --- NLOS and MP discrimination --- SVM --- random forest --- multilayer perceptron --- LOS --- DWM1000 --- fingerprinting --- smart buildings --- mobile devices --- indoor localization technologies --- model based techniques --- quality control