The ongoing deployment of 5G networks is a key enabler for realizing upcoming services at scale, including the massive deployment of the Internet of Things, providing communications to support autonomous vehicles, and supporting the realization of smart cities. However, as the limitations of existing approaches become apparent, the need for next-generation technologies is growing, in terms of improving spectral efficiency, increasing network data rates and densification, supporting local/wide area wireless coordination, and more. In this Special Issue, we present some technologies to meet these demanding requirements and facilitate coordinating wireless networks to support future Beyond 5G networks.

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This reprint focuses on the analytical and experimental research of advanced array signal processing techniques, which are significant to the promotion of radar and sonar signal processing. The advanced array signal processing methods for target detection, imaging, and recognition using radar and sonar in a wide range of complex adverse environments with strong background noise/jamming are of particular interest, especially in terms of waveform design, synthetic aperture radar/sonar, direction of arrival estimation, adaptive beamforming, multiple-input multiple-output, and deep learning for target detection.

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Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

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Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

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Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

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The present reprint contains 33 articles accepted and published in the Special Issue entitled “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022” in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.

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The relationship between individuality and aggregation is an important topic in Complex Systems Science, as both aspects are facets of emergence. This problem has generally been addressed by adopting a classical individual- versus population-level approach, in which boundaries emerge in segregated communities. More specifically, boundaries delimiting and interconnecting aggregates are at play. It is, therefore, crucial to define the properties of complex systems correctly, such as generic agent-based models, with which to simulate communities situated in grid- and scale-free network environments. To do this, complexities may be resolved through simulation, modeling and analysis techniques, which help provide confidence regarding the behavior of such systems, especially of those operating in dynamic environments or under unexpected constraints. Moreover, modeling and simulation help reduce the risks and costs involved in the design and development of validation tests.

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The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors.

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Machine learning and deep learning have made tremendous progress over the last decade and have become the de facto standard across a wide range of image, video, text, and sound processing domains, from object recognition to image generation. Recently, deep learning and deep reinforcement learning have begun to develop end-to-end training to solve more complex operation research and combinatorial optimization problems, such as covering problems, vehicle routing problems, traveling salesman problems, scheduling problems, and other complex problems requiring general simulations. These methods also sometimes include classic search and optimization algorithms for machine learning, such as Monte Carlo Tree Search in AlphaGO. The present reprint contains all of the articles accepted and published in the Special Issue of Mathematics entitled "Advances in Machine Learning and Mathematical Modeling for Optimization Problems”. The articles presented in this Special Issue provide insights into related fields, including models, performance evaluation and improvements, and application developments. We hope that readers will benefit from the insights of these papers and contribute to these rapidly growing areas. We also hope that this Special Issue will shed light on major developments in the area of machine learning and mathematical modeling for optimization problems and that it will attract the attention of the scientific community to pursue further investigations, leading to the rapid implementation of these techniques.

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Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.

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Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

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This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

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Artificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids.

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The aim of this reprint is to address increasingly complex human problems by utilizing various sensors to collect data, enabling the formulation of solutions through deep learning and artificial intelligence (AI). This trend creates a high demand for sensors while presenting new challenges in developing sensor devices and applications across various fields, such as healthcare, manufacturing, agriculture, transportation, construction, and environmental monitoring. For instance, in environmental monitoring, AI-integrated sensors rapidly analyze large datasets to identify real-time patterns and trends, enhancing weather forecasting accuracy by gathering data from multiple sources. In industrial settings, AI-enhanced sensors optimize manufacturing by monitoring equipment health, predicting failures, and proactively scheduling maintenance. This reprint compiles contributions on AI and sensor technology, sharing ideas, designs, applications, and deployment experiences across various fields, including smart manufacturing, construction, autonomous vehicles, traffic monitoring, object recognition, image classification, speech processing, and human behavior analysis.

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The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management.

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The automotive industry has always been synonymous with research and innovation, but nowadays the industry is adding pressure and is establishing the agenda of the researchers from the field. Visions have been provided, and the hardware and the software exist; the only question remaining is: “who is going to deliver”? To answer this question, we encouraged scientists, researchers, industry specialists, and academics to share their vision of autonomous vehicles. What will the platform look like? What kind of hardware and software is most suitable? Who will make the connection between these two interdependent environments (and how), so that in the end the AI will define the process? These are the pressing issues of the current moment, and this Special Issue will help all those interested in the topic to promote their vision and ideas. Since the automotive field does not belong to a classical scientific field but has become an independent self-made scientific branch, all those who felt that they could contribute to this highly dynamic environment were requested to join in promoting their particular research or reviews in this Special Issue coordinated from both academia and the industry.

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Aircraft electrification is one of the most important and strategic initiatives currently supporting the innovation of the aviation industry. This manifests in the well-known more-electric aircraft concept (with the ultimate aim of achieving the all-electric long-term target), which aims to gradually replace onboard systems based on mechanical, hydraulic, or pneumatic power sources with electrically powered ones to reduce the weight and costs, optimize energy, and increase the eco-compatibility and reliability of future aircrafts.A key technological enabler for pursuing these challenging objectives is electro-mechanical actuation. The applicability of electro-mechanical actuators (EMAs) in aerospace has been proved in terms of dynamic performances, but it still entails several concerns in terms of reliability/safety and operation in a harsh environment. In civil aircrafts, EMAs are often avoided for safety-critical functions (flight controls, brakes, landing gears, and nose wheel steering), essentially because the statistical database on the components' fault modes is poor.This Special Issue is thus focused on advancements and innovations in the design, modelling/simulation, architectural definition, reliability/safety analysis, control, condition-monitoring, and experimental testing of EMAs developed for safety-critical aerospace applications. The research papers included in this Special Issue will undoubtedly contribute to progress towards the objective of more electric flights.

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The reprint “Energy-efficient communication networks and systems” gives insights into the range of problems in the field of obtaining more energy-efficient communication networks and systems. The reprint contains the outcomes of the “Special Issue on energy-efficient communication networks and systems” organized for the Sensors journal published by the Multidisciplinary Digital Publishing Institute (MDPI). The 12 high-quality papers published in the frame of the Sensors journal Special Issue have been collected and reproduced in this book, demonstrating significant achievements in the field to which this reprint is dedicated.Published papers consolidated in this reprint are self-contained peer-reviewed scientific works. Among published papers, one paper is editorial, one paper is a review type of paper, and the remaining ten articles are research articles. The papers are authored by 40 authors coming from academia and industry. The published articles target the improvement of energy efficiency in the fields of fiber-wireless, vehicle-to-vehicle/infrastructure, sensors, satellites, 5G, virtual elements, and cognitive radio networks. The contributions of published papers provide valuable insights into the latest research and technologies for improving the energy efficiency of communication networks and systems.This reprint can serve the readers as a source of information when needed in their industrial, teaching and/or research and development activities since it presents the recent advances in different fields related to improving the energy efficiency of communication networks and systems.

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The advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have served as examples of the success of internet of things and artificial intelligence deployment in transportation research. Topics include collision avoidance for surface ships, indoor localization, vehicle authentication, traffic signal control, path-planning of unmanned ships, driver drowsiness and stress detection, vehicle density estimation, maritime vessel flow forecast, and vehicle license plate recognition. High-performance computing services have become more affordable in recent years, which triggered the adoption of deep-learning-based approaches to increase the performance standards of artificial intelligence models. Nevertheless, it has been pointed out by various researchers that traditional shallow-learning-based approaches usually have an advantage in applications with small datasets. The book can provide information to government officials, researchers, and practitioners. In each article, the authors have summarized the limitations of existing works and offered valuable information on future research directions.

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This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics.

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This Special Issue aims at reporting current investigations on emerging materials and devices taking up the challenge of pursuing a significant improvement in the energy performance of buildings and indoor comfort. What is the contribution of innovative technologies in the epochal transition to low environmental impact buildings? This is the question addressed in this Special Issue, in order to offer a wide and heterogeneous amount of data to readers, along with results of high scientific impact concerning the application of innovative technologies in construction. The 2015 Paris Agreement on climate change following the COP 21 Conference on Climate Change, organized by United Nations, required the States to reduce carbon emissions in the building stock. In the European Union, almost 50% of final energy consumption is used for heating and cooling; out of this huge amount, 80% is used in buildings. It makes sense, then, that the Union’s goals are inherently linked to the real effort to renovate the building stock. To do this, in the EU and worldwide, the priority is to enhance energy efficiency, by deploying low-cost renewable energies and innovative technologies, especially those derived from recent achievements in the field of nanomaterials research, with special reference to building integration of novel technologies, spanning from chromogenics to semitransparent photovoltaics, super-insulating materials, and phase change materials. Articles here proposed deal with every construction or plant component of the building organism, taking advantage of novel technologies to improve their performance, from the envelope to structures, HVAC, and other technical systems, as well as indoor climate analyses in buildings and indoor environmental quality (IEQ), as well as visual comfort indoors.

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