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This book presents applications on the ultrasonic wave for material characterization and nondestructive evaluations. It could be of interest to the researchers and students who are studying on the fields of ultrasonic waves.

Technology: general issues --- tomographic imaging --- DHB algorithm --- noncontact --- air-coupled transducer --- steel wire rod --- nonlinear ultrasonic --- EMAT --- guided wave --- ultrasonic sensors --- GPS --- surface monitoring --- image processing --- dynamic time warping --- piezoelectric materials --- transversely isotropic material --- IEEE resonance method --- resonant and anti-resonant frequencies --- dynamic non-destructive evaluation --- acoustic wave --- scattering --- random rough surface --- impedance

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This Special Issue contains a series of excellent research works on telecommunications and signal processing, selected from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP) which was held on July 4–6, 2018, in Athens, Greece. The conference was organized in cooperation with the IEEE Region 8 (Europe, Middle East, and Africa), IEEE Greece Section, IEEE Czechoslovakia Section, and IEEE Czechoslovakia Section SP/CAS/COM Joint Chapter by seventeen universities from the Czech Republic, Hungary, Turkey, Taiwan, Japan, Slovak Republic, Spain, Bulgaria, France, Slovenia, Croatia, and Poland, for academics, researchers, and developers, and serves as a premier international forum for the annual exchange and promotion of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors worldwide. This collection of 10 papers is highly recommended for researchers, and believed to be interesting, inspiring, and motivating for readers in their further research.

similarity measure --- dynamic time warping --- n/a --- Least Absolute Shrinkage and Selection Operator (LASSO) --- multispectral information --- transmission convergence layer --- 3D segmentation --- micrographia --- MATLAB --- neural network --- wireless communication --- identification --- interference alignment --- Parkinson’s disease dysgraphia --- NG-PON2 --- timing --- GPON --- semantic segmentation --- fractional-order filters --- maximum likelihood criterion --- kinematic analysis --- multitemporal data --- fractional calculus --- multi-hop relay network --- u-net --- interference leakage --- Richardson iteration --- activation process --- acoustic analysis --- follow-up study --- fractional-order derivative --- electrocardiogram (ECG) --- deep learning --- security --- modulo M quasi-stationary --- cognitive radio --- low-pass filters --- time-interleaved analog-to-digital converter (TIADC) --- sample-and-hold (S/H) mismatch --- authentication --- pattern recognition --- online handwriting --- sparse inference --- Taylor series --- EPON --- open-source --- spine --- machine learning --- brain --- signal representation --- magnitude responses --- Chebyshev filters --- XG-PON --- phonation --- hypokinetic dysarthria --- Parkinson’s disease --- overcomplete multi-scale dictionary construction --- Parkinson's disease dysgraphia --- Parkinson's disease

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In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included.

smart homes --- Internet of Things (IoT) --- Wi-Fi --- human monitoring --- behavioral analysis --- ambient assisted living --- intelligent luminaires --- wireless sensor network --- indoor localisation --- indoor monitoring --- Graphics Processing Units (GPUs) --- CUDA --- OpenMP --- OpenCL --- K-means --- brain cancer detection --- hyperspectral imaging --- unsupervised clustering --- impaired sensor --- Structural Health Monitoring --- Time of Flight --- subharmonics --- Cascaded-Integrator-Comb (CIC) filter --- FPGA --- fixed point math --- data adaptive demodulator --- motion estimation --- inertial sensors --- simulation --- spline function --- Kalman filter --- eHealth --- software engineering --- gesture recognition --- Dynamic Time Warping --- Hidden Markov Model --- usability --- Cramér–Rao lower bound (CRLB) --- human motion --- Inertial Measurement Unit (IMU) --- Time of Arrival (TOA) --- wearable sensors --- endothelial dysfunction --- photoplethysmography --- machine learning --- computer-assisted screening --- sleep pose recognition --- keypoints feature matching --- Bayesian inference --- near-infrared images --- scale invariant feature transform --- heartbeat classification --- arrhythmia --- denoising autoencoder --- autoencoder --- deep learning --- auditory perception --- biometrics --- computer vision --- web control access --- web security --- human–computer interaction --- n/a --- Cramér-Rao lower bound (CRLB) --- human-computer interaction

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This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

Technology: general issues --- History of engineering & technology --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification

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The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders.

History of engineering & technology --- inertial measurement units --- gait analysis --- biomedical signal processing --- pattern recognition --- step detection --- physiological signals --- Parkinson’s disease --- pathological gait --- turning analysis --- wearable sensors --- mobile gait analysis --- wearables --- inertial sensors --- traumatic brain injury --- dynamic balance --- gait disorders --- gait patterns --- head injury --- gait symmetry --- gait smoothness --- acceleration --- machine learning --- classification --- accelerometer --- GAITRite --- multi-regression normalization --- SVM --- random forest classifier --- balance --- gait --- transcranial direct current stimulation --- wearable electronics --- IMUs --- cueing --- posture --- rehabilitation --- cerebellar ataxia --- movement analysis --- personalized medicine --- stroke --- asymmetry --- trunk --- reliability --- validity --- aging --- reactive postural responses --- yaw perturbation --- kinematics --- postural stability --- dynamic posturography --- multiple sclerosis --- gait metrics --- test-retest reliability --- sampling frequency --- accelerometry --- autocorrelation --- harmonic ratio --- six-minute walk --- back school --- inertial sensor --- lower back pain --- stability --- timed up and go test --- gait assessment --- tri-axial accelerometer --- CV --- healthy subjects --- test-retest --- trajectory reconstruction --- stride segmentation --- dynamic time warping --- pedestrian dead-reckoning --- near falls --- loss of balance --- pre-impact fall detection --- activities of daily life --- bio-signals --- EEG --- EMG --- wireless sensors --- posturography --- Alzheimer’s disease --- vestibular syndrome --- diagnosis --- symptoms monitoring --- wearable --- home-monitoring --- inertial measurement units --- gait analysis --- biomedical signal processing --- pattern recognition --- step detection --- physiological signals --- Parkinson’s disease --- pathological gait --- turning analysis --- wearable sensors --- mobile gait analysis --- wearables --- inertial sensors --- traumatic brain injury --- dynamic balance --- gait disorders --- gait patterns --- head injury --- gait symmetry --- gait smoothness --- acceleration --- machine learning --- classification --- accelerometer --- GAITRite --- multi-regression normalization --- SVM --- random forest classifier --- balance --- gait --- transcranial direct current stimulation --- wearable electronics --- IMUs --- cueing --- posture --- rehabilitation --- cerebellar ataxia --- movement analysis --- personalized medicine --- stroke --- asymmetry --- trunk --- reliability --- validity --- aging --- reactive postural responses --- yaw perturbation --- kinematics --- postural stability --- dynamic posturography --- multiple sclerosis --- gait metrics --- test-retest reliability --- sampling frequency --- accelerometry --- autocorrelation --- harmonic ratio --- six-minute walk --- back school --- inertial sensor --- lower back pain --- stability --- timed up and go test --- gait assessment --- tri-axial accelerometer --- CV --- healthy subjects --- test-retest --- trajectory reconstruction --- stride segmentation --- dynamic time warping --- pedestrian dead-reckoning --- near falls --- loss of balance --- pre-impact fall detection --- activities of daily life --- bio-signals --- EEG --- EMG --- wireless sensors --- posturography --- Alzheimer’s disease --- vestibular syndrome --- diagnosis --- symptoms monitoring --- wearable --- home-monitoring