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This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is des

Parallel processing (Electronic computers) --- Neural networks (Computer science) --- Back propagation (Artificial intelligence) --- Transputers.

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The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components.

History of engineering & technology --- state of charge (SOC) --- joint estimation --- lithium-ion battery --- variational Bayesian approximation --- dual extended Kalman filter (DEKF) --- measurement statistic uncertainty --- electric vehicles --- renewable energy sources --- microgrid --- economic dispatching --- capacity allocation --- cooperative optimization --- SOC --- second-order RC model --- model parameter optimization --- AUKF --- small-signal modeling --- battery energy storage system --- battery management system --- control --- stability --- dynamic response --- wireless power --- state-of-charge --- electric vehicle --- LiFePO4 batteries --- state of charge (SoC) --- Butler–Volmer equation --- Arrhenius --- Peukert --- coulomb efficiency --- back propagation neural network (BPNN) --- torque and battery distribution --- particle swarm optimization --- air-cooled BTMS --- compact lithium ion battery module --- ANN --- battery electric vehicles --- battery management --- hybrid energy storage --- n/a --- Butler-Volmer equation

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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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This Special Issue is entitled “Environmental Sustainability in Maritime Infrastructures”. Oceans and coastal areas are essential in our lives from several different points of view: social, economic, and health. Given the importance of these areas for human life, not only for the present but also for the future, it is necessary to plan future infrastructures, and maintain and adapt to the changes the existing ones. All of this taking into account the sustainability of our planet. A very significant percentage of the world's population lives permanently or enjoys their vacation periods in coastal zones, which makes them very sensitive areas, with a very high economic value and as a focus of adverse effects on public health and ecosystems. Therefore, it is considered very relevant and of great interest to launch this Special Issue to cover any aspects related to the vulnerability of coastal systems and their inhabitants (water pollution, coastal flooding, climate change, overpopulation, urban planning, waste water, plastics at sea, effects on ecosystems, etc.), as well as the use of ocean resources (fisheries, energy, tourism areas, etc.).

Technology: general issues --- floating offshore wind --- concrete wind platform --- economic feasibility --- IRR --- NPV --- LCOE --- feasibility study --- offshore wind --- levelized cost of energy (LCOE) --- wave energy --- software --- EU ETS --- Emission allowances --- Greenhouse gas emissions --- Transparency --- Accounting regulation --- tidal current energy --- life cycle assessment --- ISO --- greenhouse gases emissions --- port infrastructure --- carbon footprint --- offshore waste disposal facility --- hazard analysis --- risk matrix --- subsystem --- environmental impact --- ocean renewable energy --- OTEC --- environmental and social impacts --- energy production --- renewable energy --- zero emissions port --- wave energy converter --- young mangroves --- mangrove restoration --- portable reef design --- field observation --- Amami Oshima --- geographic information system --- back-propagation neural network --- rainfall --- historical flood --- prediction --- formal planning --- informal planning --- spatial planning process --- coastal area spatial planning --- planning levels --- community involvement --- territorial community --- coastal communities --- coastal fisheries --- dry fish --- livelihood --- vulnerability --- AHP --- urban regeneration --- littoral landscape --- Mediterranean architecture --- sustainable mobility --- transport infrastructure --- greenway

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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.

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