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In the last decade, sleep spindles have attracted steadily increasing attention. This interest is motivated by the many intriguing relationships between spindles and various diseases (e.g., schizophrenia, Parkinson, Alzheimer, autism, mental retardation), recovery processes (e.g., post brain stroke), and cognitive faculties (e.g., memory consolidation, intelligence, dream recall, sleep preservation). Nonetheless, a methodological wall has impeded the study of sleep spindles. Their investigation rests heavily on our ability to reliably and consistently identify spindle patterns from background EEG activity, a task involving many obstacles, including: a fuzzy definition of spindles, low inter-expert agreement on their scoring, lack of consensus on standard techniques for their automated detection, low reproducibility of observed characteristics and correlates, unavailability of large, standardized, high-quality databases, and inconsistencies in the methods used to evaluate the performance of automated detectors. The primary aims of this research topic were to bring together world-class researchers on a project designed to facilitate exchanges on methodological difficulties encountered in assessing sleep spindles and to promote standardized spindle-related resources. In preparing their contributions, authors were encouraged to use existing – or to propose new – publicly available resources for assessing sleep spindles. To allow fair and accurate comparison of reported results, the authors were also encouraged to validate their tools on a common benchmark. A database containing expert spindle scoring (i.e., the Montreal Archive of Sleep Studies) was made publicly available for that purpose.

Neural oscillations --- Electroencephalography (EEG) --- Sleep --- Sleep Spindles --- Memory --- IQ --- sigma waves --- automatic detection --- biomarker --- Open access

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In the last decade, sleep spindles have attracted steadily increasing attention. This interest is motivated by the many intriguing relationships between spindles and various diseases (e.g., schizophrenia, Parkinson, Alzheimer, autism, mental retardation), recovery processes (e.g., post brain stroke), and cognitive faculties (e.g., memory consolidation, intelligence, dream recall, sleep preservation). Nonetheless, a methodological wall has impeded the study of sleep spindles. Their investigation rests heavily on our ability to reliably and consistently identify spindle patterns from background EEG activity, a task involving many obstacles, including: a fuzzy definition of spindles, low inter-expert agreement on their scoring, lack of consensus on standard techniques for their automated detection, low reproducibility of observed characteristics and correlates, unavailability of large, standardized, high-quality databases, and inconsistencies in the methods used to evaluate the performance of automated detectors. The primary aims of this research topic were to bring together world-class researchers on a project designed to facilitate exchanges on methodological difficulties encountered in assessing sleep spindles and to promote standardized spindle-related resources. In preparing their contributions, authors were encouraged to use existing – or to propose new – publicly available resources for assessing sleep spindles. To allow fair and accurate comparison of reported results, the authors were also encouraged to validate their tools on a common benchmark. A database containing expert spindle scoring (i.e., the Montreal Archive of Sleep Studies) was made publicly available for that purpose.

Neural oscillations --- Electroencephalography (EEG) --- Sleep --- Sleep Spindles --- Memory --- IQ --- sigma waves --- automatic detection --- biomarker --- Open access --- Neural oscillations --- Electroencephalography (EEG) --- Sleep --- Sleep Spindles --- Memory --- IQ --- sigma waves --- automatic detection --- biomarker --- Open access

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The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.

Technology: general issues --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group

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The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.

Technology: general issues --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group

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The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research.

Technology: general issues --- History of engineering & technology --- underground mining --- ventilation systems --- methane release hazard --- spontaneous combustion hazard --- safety --- copper ore comminution --- hydro-jet mill --- high-pressure water jet --- deep underground min --- IoT devices --- smartphone --- gas hazards --- Industry 4.0 in mining --- ball mills --- energy efficiency --- internal dynamics --- DEM model --- resonance oscillations --- measurements --- drilling rig --- process monitoring --- operational cycles --- sound measurement --- electric current acquisition --- threshold-based segmentation --- envelope spectrum --- safety function --- electro-hydraulic control system --- powered roof support --- textile conveyor belts --- multiply conveyor belts --- multiply belt splices --- laboratory splice tests --- fluid coupling --- ball mill --- electric motor --- drive system --- grinding --- energetic efficiency --- surface mining --- mining equipment --- multiple criteria decision making (MCDM) --- ELECTRE III --- thermal imaging --- belt conveyor --- diagnostics --- mechanical failure --- preventative maintenance --- well reamer --- rock mass --- intensification of production --- nonlinear law --- completion of wells --- innovative technology --- UAV --- acoustic signal processing --- Euclidean distance --- underground mine --- search and rescue --- drilling --- pipe --- drill bit --- manufacturing error --- material --- model --- elastic modulus --- computational fluid dynamics --- numerical model geometry --- laser scanning --- machine diagnostics --- LHD --- engine oil pressure data --- oil pump wear --- statistical analysis --- convergence functions --- numerical modeling --- high horizontal stress --- excavations stability assessment --- mobile laser scanning --- Velodyne LiDAR --- Riegl scanning system --- open pit mine --- underground laboratory --- new ways of mine reclamation --- sustainability --- circular economy --- radar interferometry --- subsidence trough --- automatic detection --- mathematical morphology --- underground mining --- ventilation systems --- methane release hazard --- spontaneous combustion hazard --- safety --- copper ore comminution --- hydro-jet mill --- high-pressure water jet --- deep underground min --- IoT devices --- smartphone --- gas hazards --- Industry 4.0 in mining --- ball mills --- energy efficiency --- internal dynamics --- DEM model --- resonance oscillations --- measurements --- drilling rig --- process monitoring --- operational cycles --- sound measurement --- electric current acquisition --- threshold-based segmentation --- envelope spectrum --- safety function --- electro-hydraulic control system --- powered roof support --- textile conveyor belts --- multiply conveyor belts --- multiply belt splices --- laboratory splice tests --- fluid coupling --- ball mill --- electric motor --- drive system --- grinding --- energetic efficiency --- surface mining --- mining equipment --- multiple criteria decision making (MCDM) --- ELECTRE III --- thermal imaging --- belt conveyor --- diagnostics --- mechanical failure --- preventative maintenance --- well reamer --- rock mass --- intensification of production --- nonlinear law --- completion of wells --- innovative technology --- UAV --- acoustic signal processing --- Euclidean distance --- underground mine --- search and rescue --- drilling --- pipe --- drill bit --- manufacturing error --- material --- model --- elastic modulus --- computational fluid dynamics --- numerical model geometry --- laser scanning --- machine diagnostics --- LHD --- engine oil pressure data --- oil pump wear --- statistical analysis --- convergence functions --- numerical modeling --- high horizontal stress --- excavations stability assessment --- mobile laser scanning --- Velodyne LiDAR --- Riegl scanning system --- open pit mine --- underground laboratory --- new ways of mine reclamation --- sustainability --- circular economy --- radar interferometry --- subsidence trough --- automatic detection --- mathematical morphology