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1. Process Mining Workshops. ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers.

Author

Montali, Marco, Montali, Marco, Senderovich, Arik, and Weidlich, Matthias

Subjects
Business mathematics & systems, Data mining, Health & safety aspects of IT, Information technology: general issues, Machine learning, business process management, conformance checking, data science, deep learning, event data, health informatics, knowledge graphs, machine learning, predictive process monitoring, process analytics, process discovery, process mining, process querying, streaming analytics
Abstract

Summary: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23-28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: - 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) - 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) - 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) - 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) - 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) - 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) - 1st International Workshop on Education meets Process Mining (EduPM) - 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)

2. Non-invasive Medical Devices for Detection and Monitoring within Healthcare.

Author

Priefer, Ronny and Priefer, Ronny

Subjects
History of engineering & technology, Technology: general issues, COVID-19, Cu2O nanomaterial, NIR technology, OpenPose, SARS-CoV-2, SERS spectroscopy, SVM, Squeeze U-net, acetone, arteriovenous fistula, assistive feeding devices, bioinstrumentation, biomaterials, biosensor, biosensors, blood pressure (BP), breath analysis, breathalyzer, cellulose paper, chitosan, chronic kidney disease, deep learning, diabetes management, diagnosis, dual-channel, electrical stimulation systems, electrochemical sensor, electromagnetic sensing, electromyography, electronic nose, facial pain, fusion deep learning, glucose, glucose sensor, harmonic ratio, iso-block postural identity, jaw muscles, ketogenic diet, layer-by-layer, mHealth, medical devices, n/a, nanocomposite, nanomaterials, near-infrared, non-enzymatic sensor, non-invasive, optical spectroscopy, oral diagnostics, pediatric population, photoacoustic spectroscopy, photoplethysmogram (PPG), point of care testing, portable, rapid diagnostics, real time, salivary biomarkers, salivary conductivity, salivary diagnostics, self-assembly, semi-conductor metal oxides, sensors, smoking, temporomandibular joint disorders, transcutaneous electrical nerve stimulation, tremor, vascular wall motion monitor, volatile organic compounds, wearable orthoses
Abstract

Summary: Biosensors, especially non-invasive ones, are an area of extreme importance and fantastic advancements. As nanotechnology continually becomes more and more refined, its applications in a multitude of areas also grow. One area that has seen an explosion of innovations is healthcare. Such biosensors have allowed for the diagnosis, monitoring, and even treatment of a myriad of different disease states. This reprint highlights many of the past, current, and potential future advancements.

3. Sustainable Mobility and Transport.

Author

Inkinen, Tommi, Inkinen, Tommi, Wilson, Mark, and Yigitcanlar, Tan

Subjects
Film, TV & radio, BERT, Finland, Gauteng province, Mallorca mobility, Russia, academic research, battery electric vehicle, bidirectional electric vehicle charging, business models, charging infrastructure, club convergence, deep learning, electric vehicle, electric vehicle incentives, electric vehicle policy, emission, energy harvesting, environmental impact, green transport strategy, high-speed railway, hybrid electric vehicle, income gap, journalism, life cycle analysis, logistic regression, logistics, magazine, mobile energy, mobile-energy-as-a-service (MEaaS), mobility, modal choice, natural language processing (NLP), newspaper, nonlinear time-varying factor model, passenger car, piezoelectric, regulation, regulations, renewable energy resource, research review, shared mobility, simulation, smart cities, smart city, supply chains, sustainability, sustainable mobility, topic modelling, transport, transport planning, transportation, trucks, two-wheelers, urban electromobility
Abstract

Summary: This Special Issue is dedicated to sustainable mobility and transport, with a special focus on technological advancements. Global transport systems are significant sources of air, land, and water emissions. A key motivator for this Special Issue was the diversity and complexity of mitigating transport emissions and industry adaptions towards increasingly stricter regulation. Originally, the Special Issue called for papers devoted to all forms of mobility and transports. The papers published in this Special Issue cover a wide range of topics, aiming to increase understanding of the impacts and effects of mobility and transport in working towards sustainability, where most studies place technological innovations at the heart of the matter. The goal of the Special Issue is to present research that focuses, on the one hand, on the challenges and obstacles on a system-level decision making of clean mobility, and on the other, on indirect effects caused by these changes.

4. Quantitative Methods for Economics and Finance.

Author

Trinidad-Segovia, J.E., Sánchez-Granero, Miguel Ángel, and Trinidad-Segovia, J.E.

Subjects
Coins, banknotes, medals, seals (numismatics), Bitcoin, Chinese listed companies, DCC, DEA, EGARCH, EVT, Ethereum, FD4 approach, GARCH, Hurst exponent, Markov Chain Monte Carlo simulation, P 500, P500, Ripple, S&amp, SRA approach, Tobin's q, United States, VaR, academic cheating, asset pricing, autoregressive integrated moving average (ARIMA), bilateral investment treaties, biotechnological firms, bitcoin, cash flow management, centered model, co-movement, cointegration, commodity prices, computational finance, copula, copulas, corporate prudential risk, correlation risk premium, cryptocurrency, decision-making process, decreasing impatience, deep learning, deep recurrent convolutional neural networks, delay, derivation, detection, discount, dispersion trading, dynamically simulated autoregressive distributed lag (DYS-ARDL), econometrics, eigenvalues, elasticity, energy consumption, ensemble empirical mode decomposition (EEMD), essential multicollinearity, financial distress, financial distress prediction, financial markets, forecasting, foreign direct investment, futures prices, generalized Pareto distribution, genetic algorithm (GA), gold, historical simulation approach, hurst exponent, induced risk aversion, informality, intercept, intertemporal choice, liquidity constraints, liquidity risk, local optima vs. local minima, long memory, macroeconomic propagation, mean square error, multicollinearity, multiperiod financial management, multiple periods, non-linear macroeconomic modelling, non-parametric efficiency, noncentered model, nonessential multicollinearity, number of factors, option arbitrage, pairs trading, peaks-over-threshold, pharmaceutical industry, policy uncertainty, precautionary savings, probability, probability of volatility cluster, productivity, profitability, raise regression, regional trade agreements, risk, scale economies, stock prices, structural gravity model, student t-copula, support vector regression (SVR), tax evasion, the financial accelerator, threshold regression, unconstrained distributed lag model, variance inflation factor, volatility cluster, volatility series, volatility trading
Abstract

Summary: This book is a collection of papers for the Special Issue "Quantitative Methods for Economics and Finance" of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice.

5. Applications in Electronics Pervading Industry, Environment and Society. Sensing Systems and Pervasive Intelligence.

Author

Saponara, Sergio, Bellotti, Francesco, De Gloria, Alessandro, and Saponara, Sergio

Subjects
Technology: general issues, ANN, ARM, ASIC, ASIC standard-cell, Deep Neural Networks (DNNs), FPGA, HDL code generation, HW accelerator, LC-tank oscillator, PLL (phase-locked loop), SEE (single event effects), SHA2, STM32 Nucleo, SVM, SpaceFibre, Spacefibre, TID (total ionization dose), VLSI, X-Cube-AI, alternative representations to float numbers, autonomous driving, big data, biometric sensors, bitmap indexing, charge pump, coal mining, collaborative project methodology, connected and automated driving, convolutional neural networks, cyber security, decision trees, deep learning, deployment and field testing, digital circuit design, directional transform, discrete cosine transform, distillation, edge analytics, edge computing, embedded devices, embedded system, event-driven, face recognition, face verification, frequency divider, functional electrical stimulation, gradient descent, high-speed data transfer, impact assessment, inertial measurement unit, intelligent sensors, internet of things, interpolating polynomial, k-NN, knowledge management, machine learning, memory wall, model-based design, n/a, neural network activation functions, on-chip random number generator (RNG), phase/frequency detector, polyphase filter, posit arithmetic, processing in memory, quaternion, rad-hard, rad-hard circuits, radiation effects, resampling, research data collection and sharing, ring oscillator, spatial transformer network, support attitude, surface electromyography, unscented Kalman filter, vehicular sensors, video coding, wearable sensors
Abstract

Summary: 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.

6. Image and Video Processing and Recognition Based on Artificial Intelligence.

Author

Park, Kang Ryoung, Kim, Euntai, Lee, Sangyoun, and Park, Kang Ryoung

Subjects
Technology: general issues, GC-LSTM model, HKPolyU-DB, K-nearest neighbors, Krawtchouk polynomials, Marr wavelets, RANSAC, SDUMLA-HMT-DB, Tchebichef polynomials, action recognition, active learning, armature, artificial image generation, artificial intelligence, autoencoders, bag of deep features, binarized statistical image features, biometrics, body orientation, bounding box regression, brain computer interface, building extraction, camera position, channel interaction, character recognition, common spatial patterns, computer vision, continuous wavelet transform, convolutional neural network, convolutional neural networks, crowd counting, cycle-consistent adversarial networks, data augmentation, deep convolutional neural networks, deep learning, depth map, domain adaptation, edges to photos, emotion recognition, entropy and response, epidermis, face image analysis, face parsing, face recognition, facial attributes classification, fast approximation, feature distillation, finger position, finger-vein recognition, fully convolutional networks, generative adversarial net, generative adversarial network, generative adversarial network (GAN), generative models, global context, graph matching, guidance, helicopter footage, heterogeneous database, high-resolution remote sensing image, homotopy iterative hard thresholding, image de-raining, image processing, image pyramid, image-to-image conversion, infrared circumferential scanning system, joint attention, label to photos, lighting, local context, loss function, malignant thyroid nodule, mask R-CNN, medical image fusion, monocular depth estimation, multi resolution network, multi-person, multi-scale decomposition, multi-task, multitask learning, normalized cross-correlation, object recognition, optimization, orthogonal moments, orthogonal polynomials, pathology, pose estimation, prediction system, presentation attack detection, presentation attack face images, remote sensing, residual network, satellite image, semantic segmentation, semi-supervised learning, single-sample face recognition, skin, social robotics, sparse coding, super-resolution, support vector machine, surface defect detection, surface inspection, target recognition, transfer learning, typhoon, ultrasound image, unobserved database, vehicle recognition, weighted binary cross-entropy loss
Abstract

Summary: This book includes 23 published papers on Special issues of "Image and Video Processing and Recognition Based on Artificial Intelligence" in the journal Sensors. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition.

7. Human Health Engineering Volume II.

Author

Aerts, Jean Marie and Aerts, Jean Marie

Subjects
Technology: general issues, APS, Electrocardiogram (ECG), Individual verification, Interval based LDA, Monte-Carlo method (MMC), UE-FMA, Vater-Pacini corpuscles, anorexia nervosa, automated assessment, biometrics, body posture, clinical evaluation, closed-loop insulin pump, convolutional neural network, deep learning, driving fatigue, driving phobia, efficient estimator, electrodermal activity, electroencephalography, electromyography sensors, feature extraction, frequency-domain analysis, gait analysis, haptic feedback, inkjet printing, local muscle vibration, low back pain, measurement uncertainty, n/a, nonlinear analysis, organizational model, physiological signal, piezo-fluid-structural coupled simulation, pinch force, plantar visualization, post-traumatic stress disorder, postural control, proprioceptors, pulling force, rehabilitation, remote fetal monitor, respiratory rate, response frequency, robotics, serious games, silver nanoparticles, slip onset, smoothness, standard deviation, strain gauge, stretchable and wearable sensors, stroke, stumbling, technological devices, time-domain analysis, upper limb impairment, upper limb rehabilitation, valveless micropump, vibratory stimulation device, virtual reality, virtual reality exposure therapy, wearable sensors
Abstract

Summary: In this Special Issue on "Human Health Engineering Volume II", we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being.

8. Hyperspectral Imaging and Applications.

Author

Chang, Chein-I, Chang, Chein-I, Song, Meiping, Wu, Chao-Cheng, and Zhang, Junping

Subjects
History of engineering & technology, Technology: general issues, 90° yaw imaging, AHS, AVIRIS, Africa, Dunhuang site, Gram-Schmidt orthogonalization, HyMap, KSVD, Otsu's method, SVM, adaptive window, agroforestry, airborne laser scanning, algebraic multigrid methods, anomaly detection, band expansion process (BEP), band grouping, band selection, band selection (BS), band subset selection (BSS), biodiversity, class imbalance, classification, composite kernel, constrained energy minimization, constrained energy minimization (CEM), correlation band expansion process (CBEP), data fusion, data integration, data unmixing, data-guided constraints, deep belief networks, deep learning, deep pipelined background statistics, endmember extraction, ensemble learning, evenness, fire severity, graph, hashing ensemble, hierarchical feature, high-level synthesis, hyperspectral, hyperspectral classification, hyperspectral compression, hyperspectral detection, hyperspectral image, hyperspectral image (HSI), hyperspectral image classification, hyperspectral imagery, hyperspectral images (HSIs), hyperspectral imaging, hyperspectral pansharpening, hyperspectral unmixing, image enhancement, image fusion, imaging spectroscopy, in situ measurements, intrinsic image decomposition, irradiance-based method, iterative CEM (ICEM), iterative algorithm, label propagation, linearly constrained minimum variance (LCMV), local abundance, local summation RX detector (LS-RXD), lossy compression, machine learning, mineral mapping, minimum noise fraction, multiscale spatial information, multiscale union regions adaptive sparse representation (MURASR), nonlinear band expansion (NBE), nonnegative matrix factorization, nuclear norm, on-board compression, optical spectral region, orthogonal projections, panchromatic, panchromatic image, parallel processing, peatland, progressive sample processing (PSP), prototype space, raw material, real-time processing, recursive anomaly detection, reflectance-based method, remote sensing, rolling guidance filtering (RGF), rotation forest, semi-supervised learning, semi-supervised local discriminant analysis, sequential LCMV-BSS (SQ LCMV-BSS), sliding window, sparse coding, sparse unmixing, sparseness, spectral mixture analysis, spectral variability, spectral-spatial classification, sprout detection, structure tensor, successive LCMV-BSS (SC LCMV-BSS), superpixel, target detection, terrestrial hyperspectral imaging, texture feature enhancement, thermal infrared spectral region, tree species, tree-based ensemble, vegetation type, vicarious calibration, vineyard, water stress, weighted fusion, weighted least squares filter
Abstract

Summary: Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

9. Design and Application of Agricultural Equipment in Tillage System.

Author

Ucgul, Mustafa, Chang, Chung-Liang, and Ucgul, Mustafa

Subjects
History of engineering & technology, Technology: general issues, DBSCAN, DEM, DEM contact models, DEM-MBD coupling, EDEM, GMM, HMCVT, I-GA, I-PSO algorithm, I-SA algorithm, Kmeans, MBD, MBD-DEM bidirectional coupling model, adaptive neuro-fuzzy inference system, agricultural, agricultural machine, agricultural machinery, analytical force prediction model, anti-blocking and row-sorting, calibration, cavitating law, cohesive and frictional soils, collision restitution coefficient, compaction, compound planter, control strategy, corn seed, correction of characteristics, cotton recovery device, coupled simulation, deep learning, deflector optimization, disc, disc blade, disc seeder, discrete element, discrete element method, discrete element method (DEM), discrete element modeling, discrete element simulation, ditching, dry direct-seeded rice, dual motor coupling drive, dual vs. single tyres, durability calculation method, electric tractor, electrical tractor, experiment, flat disc, fluid analysis, force prediction, full-factorial test, fuzzy inference system, generalization ability, geometric principle, hole-forming device, hydro-mechanical continuously variable transmission, image processing, improved genetic algorithm, key components, machine vision, mechanical control, motor efficiency, multi-body dynamics (MBD), n/a, neural networks, no-till sowing, no-tillage, no-tillage sowing, optimal design, optimization design, parameter identification, parameter match, parameter optimization, parameters optimization, plasma-hardening surface, plough, ploughshares, post-harvest, property, prototype, quality improvement, rapeseed transplanting, residual film recovery device, residual film recovery machine, response surface regression model, rice combine harvester, rut depth, sandy soil, seed offset, seed-soil, seedbed clearing and shaping, seeding, seeding furrow, semi-analytical model, simulation, simulation experiments, single evaluation index modeling method, smart agriculture, soil, soil bearing capacity, soil cover, soil displacement, soil dynamics, soil failure, soil forces, soil separation spiral, soil-covering thickness, soil-tool interaction, sowing strip cleaning, soybean, spiral discharge straw, spring-tine, stalk cutting, steering control, stone removal rate, strip farming, stubble management, support vector regression, throwing device, throwing width, tillage, tilling depth, topsoil burial, traction, tractive efficiency, tractor, traffic, two-wheeled robot trailer, tyre size and inflation pressure, unmanned, virtual simulation, wear, weeder, well-cellar cavitating mechanism, wind blades
Abstract

Summary: Agricultural productivity should increase to meet the growing food demand. Tillage is defined as the mechanical manipulation of agricultural soil, and it is an extremely vital part of crop production, particularly for seedbed preparation and weed control. Tillage operations are carried out using mechanical force, commonly with a tractor-drawn tool to achieve the cutting, inversion, pulverization, and disturbance of soil. A significant part of the energy (from fossil fuels) used in crop production is expended in tillage. This energy use results in greenhouse gas emissions. It is essential that we reduce energy use (hence, greenhouse gas emissions) to achieve sustainable farming practices and improve crop production and design new tillage tools or optimize the existing tools. Although the design and evaluation of tillage tools are generally carried out using analytical methods and field experiments, with recent technological improvements, computer technology has been used for the design and evaluation of tillage tools. Additionally, sensor technology can improve the efficiency of tillage tools. This Special Issue collated innovative papers that make a significant contribution to the design and application of agricultural equipment in tillage systems. It involved original research and review papers from different research fields, such as agricultural engineering, engineering simulation, and precision agriculture.

10. Advances in Intelligent Vehicle Control.

Author

Cabrera, Juan A. and Cabrera, Juan A.

Subjects
History of engineering & technology, Technology: general issues, 3D multiple object detection, ADAS, EV charging scheduling, GNSS receivers, Internet of Things, Internet of connected vehicles, Kalman filter, LQR controller, RTK corrections, VMS, active air suspension, binary linear programming, binary quadratic programming, controller area network, curriculum learning, cybersecurity, deep learning, discrete-time sliding-mode current control (DSMCC), disturbance observer design, double lane change, driver vehicle system, dynamic SLAM, electric vehicle (EV), electric vehicles, electrical vehicles, energy management, environment perception, heterogeneous networking, heterogeneous vehicular communication, high efficiency, image processing, in-vehicle network, inertial sensors, intelligent mobility, intrusion detection, machine learning, model-based control, motorcycle lean angle, multiple object tracking, n/a, noninverting buck-boost converter, nonlinear height control, output constraints, random road excitation, reinforcement learning, roll angle estimator, safety optimization, semantics, sensor redundancy, sim-to-real world, transfer learning, tyre thermodynamics, tyre wear, vehicle control, vehicle dynamic potential, vehicle localization, vehicle safety, vehicular ad hoc networks, weather influence, wide bandwidth control
Abstract

Summary: This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems.

11. Bioinformatics Applications Based On Machine Learning.

Author

Chamoso, Pablo, Chamoso, Pablo, González-Briones, Alfonso, Mohamad, Mohd, and Rodriguez, Sara

Subjects
Technology: general issues, Affective Computing, Arabidopsis thaliana, Artificial Neural Networks, CTX-M, Decision table, Harmony Search, Machine Learning, PART, Particle Swarm Optimization, Weka, bi-directional LSTM, bioinformatics, biological functions detection, classification, clinical data, clinical implications, cluster, computer vision, convolutional neural network, data mining, deep learning, derivative-free optimization, detection, diabetes, dynamic models, ear detection, epidemiology, evolutionary computation, evolutionary computing, feature selection, forecast, fuel cell, gene clustering, genetic programming, glycolysis, gradient boosting, hybrid systems, hydrogen energy, image recognition, informative genes, intelligent systems, machine learning, metabolism, metagenomics, metrics, mitochondrial protein, parameter estimation, personality assessment, plasmodium falciparum, power management, real-life patients, regression, swarm intelligence, transposable elements, video analysis, yeast
Abstract

Summary: The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

12. Visible Light Communication (VLC)

Author

Chen, Chen and Chen, Chen

Subjects
History of engineering & technology, Technology: general issues, ADO-OFDM, BLSTM, FSO, LED, LiFi, Monte Carlo method, NOMA triangle, POF, RGB LED, Ricean K-factor, VLC, autoencoder (AE), bandwidth limitation, bit error rate, bit error ratio, carrierless amplitude and phase (CAP) modulation, channel modeling, channel propagation characteristics, chaotic encryption, cluster characteristics, coefficient approximation, constant transmission efficiency, deep learning, delay spread (DS), dimming control, dual-mode index modulation (DM), energy efficiency (EE), error performance, free-space communication, full-duplex, gamma-gamma function, index modulation, intelligent reflecting surface (IRS), light-emitting diode (LED), long-reach, maximum likelihood sequence estimation, mmWave communications, n/a, neural network (NN), non-orthogonal multiple access (NOMA), nonlinear equalization, nonlinearity, orthogonal frequency division multiplexing, orthogonal frequency division multiplexing (OFDM), orthogonal frequency-division multiplexing, pairwise coding (PWC), path loss, peak-to-average-power ratio (PAPR), photon counting, power efficiency, pre-distorted enhanced, predistortion, reservoir computing, sampling frequency offset, successive interference cancellation, superposition constellation adjustment, temporal dispersion, the number of mirrors, transceiver design, underwater optical wireless communication (UOWC), underwater wireless optical communication, vehicular visible light communication (VVLC), visible light communication, visible light communication (VLC), visible light communications, visible light communications (VLC), visible light positioning (VLP)
Abstract

Summary: Visible light communication (VLC) using light-emitting diodes (LEDs) or laser diodes (LDs) has been envisioned as one of the key enabling technologies for 6G and Internet of Things (IoT) systems, owing to its appealing advantages, including abundant and unregulated spectrum resources, no electromagnetic interference (EMI) radiation and high security. However, despite its many advantages, VLC faces several technical challenges, such as the limited bandwidth and severe nonlinearity of opto-electronic devices, link blockage and user mobility. Therefore, significant efforts are needed from the global VLC community to develop VLC technology further. This Special Issue, "Visible Light Communication (VLC)", provides an opportunity for global researchers to share their new ideas and cutting-edge techniques to address the above-mentioned challenges. The 16 papers published in this Special Issue represent the fascinating progress of VLC in various contexts, including general indoor and underwater scenarios, and the emerging application of machine learning/artificial intelligence (ML/AI) techniques in VLC.

13. Renewable Energy Resource Assessment and Forecasting.

Author

Galanis, George and Galanis, George

Subjects
Research & information: general, ANFIS, Granger causality, Kalman filtering, Kalman-Bayesian filter, Markov chains, WRF, anthropogenic waste processing, biofuel, biomass, biotechnology, central solar receiver, clearness coefficient, climate policy, complex terrain, concentrated solar, concentrating solar power, cross border trading, deep learning, deformable models, developing countries, different horizontal resolution, direct normal irradiance, economic efficiency, electric energy demand, electricity trading, energy, energy resource assessment, forecast, forecast errors, forecasting, functional statistics, global horizontal irradiance (GHI), heat supply of industrial processes, high-resolution, momentum sink, nowcasting, numerical weather prediction model, open channel flows, operational strategies, particle swarm optimization, photovoltaic solar energy, prediction, ramp rates, renewable energy, renewable energy forecasting, renewable energy sources, risk analysis, shape-invariant model, shark algorithm, shock-capturing capability, short-term forecasts, solar collectors, solar irradiance forecasts, solar irradiation, solar radiation, solar resource, spot prices, sustainable development, system advisor model, the European Green Deal, thermochemical, thrust force coefficient, tidal-stream energy, unbounded flow, validation, weather research and forecasting model, wind
Abstract

Summary: In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on 'Renewable Energy Resource Assessment and Forecasting' aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today's technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources.

14. Process Control and Smart Manufacturing for Industry 4.0.

Author

Yurish, Sergey Y. and Yurish, Sergey Y.

Subjects
Chemistry, Research & information: general, 3D printing, COVID-19, FDM, H-Swish, Industry 4.0, NDT, RSW, acoustic emission, agent-based, batch process, beetle antennae search algorithm, business compass, community innovation governance, community transformation, condition monitoring, core data model, coupling and coordination analysis, cyclone, data fusion, deep learning, drive train, earliness, electrode tip-dressing, electrode wear, energy consumption, energy-saving, equipment selection decision, erosion, forklift AGV, gas dynamics, gas-solid, grey wolf algorithm, industrial robotic manipulator, injection molding, lean implementation, lean manufacturing, lean principles, low-carbon, machine learning, magnetic particle inspection, make-to-order, master production scheduling, mathematical programming, mechatronics line, multi-mode, multi-phase, mushrooming, optimization, overtime, partial least squares, personal protection, phase private data model, phase rule filter, plateau forming, process monitoring, processing time, product morphology, production control mechanisms, production processes, protective face shields, pull principle, quality control, rapid prototyping, resistance spot welding, semantic segmentation, separator, steel alloys, storage system, sustainable blank dimension design, tardiness, ternary space, vibration, visual servoing system, wheeled mobile robot
Abstract

Summary: This reprint, entitled "Process Control and Smart Manufacturing for Industry 4.0", contains the extended papers from the Series of annual IFSA conferences on Automation, Robotics and Communications for Industry 4.0/5.0 (ARCI) on the following topics: Process Automation, Process Control and Monitoring, Design Principles in Industry 4.0, Smart Manufacturing and Technologies, Smart Factories, Machine Learning and Artificial Intelligence in Manufacturing. The reprint contains 13 chapters written by 54 ARCI conference participants from seven countries: China, Croatia, Denmark, Germany, Italy, Poland and Romania. This reprint will inform readers of cutting-edge developments in the field and provide effective starting points and a road map for further research and development. All chapters follow the same structure: firstly, an introduction to the specific topic under study; secondly, a description of the field, including sensing or/and measuring applications. Each chapter ends with a curated list of references, including books, journals, conference proceedings and websites. "Process Control and Smart Manufacturing for Industry 4.0" is intended for researchers and scientists from academia and industry, as well as for postgraduate students.

15. Approximate Bayesian Inference.

Author

Alquier, Pierre and Alquier, Pierre

Subjects
Mathematics & science, Research & information: general, Approximate Bayesian Computation, Bayesian inference, Bayesian sampling, Bayesian statistics, Bethe free energy, Edward-Sokal coupling, Gaussian, Gibbs posterior, Hamilton Monte Carlo, Kullback-Leibler divergence, Langevin Monte Carlo, Langevin dynamics, Laplace approximations, MCMC, MCMC-SAEM, Markov Chain Monte Carlo, Markov chain, Markov chain Monte Carlo, Markov kernels, Monte Carlo integration, PAC-Bayes, PAC-Bayes theory, Riemann Manifold Hamiltonian Monte Carlo, Sequential Monte Carlo, Stiefel manifold, approximate Bayesian computation, approximate Bayesian computation (ABC), bifurcation, complex systems, control variates, data imputation, data streams, deep learning, differential evolution, differential privacy (DP), discrete state space, dynamical systems, entropy, ergodicity, expectation-propagation, factor graphs, fixed-form variational Bayes, generalisation bounds, gradient descent, greedy algorithm, hyperparameters, integrated nested laplace approximation, machine learning, mean-field, message passing, meta-learning, network modeling, network variability, neural networks, no free lunch theorems, non-reversible dynamics, online learning, online optimization, particle flow, principal curves, priors, probably approximately correct, regret bounds, robustness, sequential Monte Carlo, sequential learning, sleeping experts, sparse vector technique (SVT), statistical learning theory, statistical mechanics, stochastic gradients, stochastic volatility, thinning, variable flow, variational Bayes, variational approximations, variational free energy, variational inference, variational message passing
Abstract

Summary: Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis-Hastings algorithm of the Gibbs sampler. These algorithms target the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC-Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.

16. Networks, Communication, and Computing Vol. 2.

Author

Farago, Andras and Farago, Andras

Subjects
Information technology industries, NP-complete, NP-hard, UAV placement, ant colony optimization algorithms, approximation, attention mechanism, cellular networks, clustering routing algorithms, communication traffic prediction, deep learning, deformable convolution, disjoint connecting paths, energy efficiency, intelligent traffic management, metaheuristics, network design, network lifetime, polynomial time algorithm, reinforcement learning, routing, systematic review, the next generation of telecommunication (5G), underwater wireless sensor networks, unmanned aerial vehicles, unsplittable flow
Abstract

Summary: Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields.

17. Mathematical Modeling and Simulation in Mechanics and Dynamic Systems.

Author

Scutaru, Maria Luminița, Pruncu, Catalin I., and Scutaru, Maria Luminița

Subjects
Mathematics & science, Research & information: general, "holographic implementations", ABAQUS, ANN, Artificial Neural Network (ANN), COVID-19, Catalonia, Computational Fluid Dynamics (CFD), Convolutional Neural Network (CNN), Deep Learning (DL), Euler-Poincaré equation, HT-PEMFC, Lagrange-d'Alembert principle, Lie group, Monte Carlo simulation, PEM fuel cell, SARS-CoV-2, SDL, SEIRD (Susceptible, Exposed, Infected and Recovered and Death), SL (2R) group, Sine-Gordon equations, T-stress, X-FEM, actual systems, apolar transport, artificial neural network, autonomous feature extraction, code-based modelling approach, complex system dynamics, computational solutions, computer simulations, control, crack, cubics, deep learning, differentiability, dimensional analysis, double Lorentzian spectrum, ecological analysis, ecological coefficient of performance, exergy analysis, extended finite element method, extended iso-geometric analysis, finite time thermodynamic optimization, fractal Schrödinger regimes, fractal hydrodynamic regimes, fractal kink, fractal soliton, geometric analogy, harmonic mapping, harmonic mapping principle, heat transfer, helicopter model, hidden symmetries, high temperature proton exchange membrane fuel cell, interphase section, irreversibility, machine learning, mechanics, model law, modeling, n/a, nanowire cantilever, non-conservative dynamical system, notch, numerical analysis, partial aging in standby, percolation onset, period doubling scenario, photovoltaics, pipe, pipeline, polymer CNTs systems, power density, qualitative and quantitative verification of simulation model, similarity theory, solar energy, state probability functions, stochastic model, straight bar, stress difference method (SDM), system of transcendental equation, thermodynamic efficiency, thermophotovoltaics, transfer learning, turbulent flow
Abstract

Summary: The present book contains the 16 papers accepted and published in the Special Issue "Mathematical Modeling and Simulation in Mechanics and Dynamic Systems" of the MDPI "Mathematics" journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity.

18. Low Back Pain (LBP)

Author

Denaro, Vincenzo, Denaro, Vincenzo, Iavicoli, Sergio, Russo, Fabrizio, and Vadalà, Gianluca

Subjects
Environmental science, engineering & technology, Technology: general issues, African American, Australian method, CT, EMG, Google Trends, Latino, Low Back Pain Scale, MRI, Neurac, ROC curve, Wikipedia, X-ray, absenteeism, anthropometry, artificial intelligence, back pain, back problems, bone cancer, bone metastasis, canal stenosis, chronic, cognitive behavioral therapy, computer aided diagnosis, computer vision, core stability, cross-sectional studies, decision support systems, deep learning, depression, digital image processing, disability, disk herniation, epidemiology, epidural steroid injections, ergonomics, fear of pain, fear-avoidance beliefs, gender difference, handling task, hip-knee line, infodemiology, insurance, kinesiophobia, knowledge, low back pain, low back pain (LBP), low-back pain, lower extremity, lumbalgia, lumbar decompression, lumbar fusion, lumbar instability, lumbar rotation, lumbar translation, lumbosacral radicular pain, lung cancer, machine learning, manual therapy, maximum voluntary isometric contraction, meta-analysis, mindfulness-based stress reduction, musculoskeletal disorders, musculoskeletal pain, non-radiological lumbar instability, occupational back-support exoskeleton, occupational disorder, occupational health, occupational safety and health, older adults, orthopaedics, pain, patient care, pelvic incidence, physical activity, population attributable fraction, postural stability, prevention, prostate cancer, public health, radiography, radiomics, rehabilitation nurses, reliability, return to work, review, risk assessment, satisfaction, screening tool, seasonality, sedentary behaviour, sensitivity, signature, specificity, spinal load, standing position, sub-threshold lumbar instability, systematic review, underserved, unemployment, unexpected external postural perturbations, wearable assistive device, work ability, workers, workload, workplace, workplace interventions
Abstract

Summary: Low back pain (LBP) is a major public health problem, being the most commonly reported musculoskeletal disorder (MSD) and the leading cause of compromised quality of life and work absenteeism. Indeed, LBP is the leading worldwide cause of years lost to disability, and its burden is growing alongside the increasing and aging population. The etiology, pathogenesis, and occupational risk factors of LBP are still not fully understood. It is crucial to give a stronger focus to reducing the consequences of LBP, as well as preventing its onset. Primary prevention at the occupational level remains important for highly exposed groups. Therefore, it is essential to identify which treatment options and workplace-based intervention strategies are effective in increasing participation at work and encouraging early return-to-work to reduce the consequences of LBP. The present Special Issue offers a unique opportunity to update many of the recent advances and perspectives of this health problem. A number of topics will be covered in order to attract high-quality research papers, including the following major areas: prevalence and epidemiological data, etiology, prevention, assessment and treatment approaches, and health promotion strategies for LBP. We have received a wide range of submissions, including research on the physical, psychosocial, environmental, and occupational perspectives, also focused on workplace interventions.

19. Classic versus deep learning approaches to address computer vision challenges : a study of faint edge detection and multispectral image registration

Author

Ofir, Yehonatan Nati and Nebel, Jean-Christophe

Subjects
006.3, deep learning, computer vision, faint edge detection, multispectral image registration
Abstract

Computer Vision involves many challenging problems. While early work utilized classic methods, in recent years solutions have often relied on deep neural networks. In this study, we explore those two classes of methods through two applications that are at the limit of the ability of current computer vision algorithms, i.e., faint edge detection and multispectral image registration. We show that the detection of edges at a low signal-to-noise ratio is a demanding task with proven lower bounds. The introduced method processes straight and curved edges in nearly linear complexity. Moreover, performance is of high quality on noisy simulations, boundary datasets, and real images. However, in order to improve accuracy and runtime, a deep solution was also explored. It utilizes a multiscale neural network for the detection of edges in binary images using edge preservation loss. The second group of work that is considered in this study addresses multispectral image alignment. Since multispectral fusion is particularly informative, robust image alignment algorithms are required. However, as this cannot be carried out by single-channel registration methods, we propose a traditional approach that relies on a novel edge descriptor using a feature-based registration scheme. Experiments demonstrate that, although it is able to align a wide field of spectral channels, it lacks robustness to deal with every geometric transformation. To that end, we developed a deep approach for such alignment. Contrarily to the previously suggested edge descriptor, our deep approach uses an invariant representation for spectral patches via metric learning that can be seen as a teacher-student method. All those pieces of work are reported in five published papers with state-of-the-art experimental results and proven theory. As a whole, this research reveals that, while traditional methods are rooted in theoretical principles and are robust to a wide field of images, deep approaches are faster to run and achieve better performance if, not only sufficient training data are available, but also they are of the same image type as the data on which they are applied.

Published
2021