255 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

    Get PDF
    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    A Routine and Post-disaster Road Corridor Monitoring Framework for the Increased Resilience of Road Infrastructures

    Get PDF

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

    Full text link
    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

    Get PDF
    The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics

    K-Means and Alternative Clustering Methods in Modern Power Systems

    Get PDF
    As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

    D4.2 Intelligent D-Band wireless systems and networks initial designs

    Get PDF
    This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

    Direct communication radio Iinterface for new radio multicasting and cooperative positioning

    Get PDF
    Cotutela: Universidad de defensa UNIVERSITA’ MEDITERRANEA DI REGGIO CALABRIARecently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its advantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sideline aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology

    Towards Developing a Digital Twin Implementation Framework for Manufacturing Systems

    Get PDF
    This research studies the implementation of digital twins in manufacturing systems. Digital transformation is relevant due to changing manufacturing techniques and user demands. It brings new business opportunities, changes organizations, and allows factories to compete in the digital era. Nevertheless, digital transformation presents many uncertainties that could bring problems to a manufacturing system. Some potential problems are loss of data, cybersecurity threats, unpredictable behavior, and so on. For instance, there are doubts about how to integrate the physical and virtual spaces. Digital twin (DT) is a modern technology that can enable the digital transformation of manufacturing companies. DT works by collecting real-time data of machines, products, and processes. DT monitors and controls operations in real-time helping in the identification of problems. It performs simulations to improve manufacturing processes and end-products. DT presents several benefits for manufacturing systems. It gives feedback to the physical system, increases the system’s reliability and availability, reduces operational risks, helps to achieve organizational goals, reduces operations and maintenance costs, predicts machine failures, etc. DT presents all these benefits without affecting the system’s operation. xv This dissertation analyzes the implementation of digital twins in manufacturing systems. It uses systems thinking methods and tools to study the problem space and define the solution space. Some of these methods are the conceptagon, systemigram, and the theory of inventive problem solving (TRIZ in Russian acronym). It also uses systems thinking tools such as the CATWOE, the 9-windows tool, and the ideal final result (IFR). This analysis gives some insights into the digital twin implementation issues and potential solutions. One of these solutions is to build a digital twin implementation framework Next, this study proposes the development of a small-scale digital twin implementation framework. This framework could help users to create digital twins in manufacturing systems. The method to build this framework uses a Model-Based Systems Engineering approach and the systems engineering “Vee” model. This framework encompasses many concepts from the digital twin literature. The framework divides these concepts along three spaces: physical, virtual, and information. It also includes other concepts such as digital thread, data, ontology, and enabling technologies. Finally, this dissertation verifies the correctness of the proposed framework. The verification process shows that the proposed framework can develop digital twins for manufacturing systems. For that purpose, this study creates a process digital twin simulation using the proposed framework. This study presents a mapping and a workflow diagram to help users use the proposed framework. Then, it compares the digital twin simulation with the digital twin user and system requirements. The comparison finds that the proposed framework was built right

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

    Get PDF
    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Machine Learning Use-Cases in C-ITS Applications

    Get PDF
    In recent years, the development of Cooperative Intelligent Transportation Systems (C-ITS) have witnessed significant growth thus improving the smart transportation concept. The ground of the new C-ITS applications are machine learning algorithms. The goal of this paper is to give a structured and comprehensive overview of machine learning use-cases in the field of C-ITS. It reviews recent novel studies and solutions on CITS applications that are based on machine learning algorithms. These works are organised based on their operational area, including self-inspection level, inter-vehicle level and infrastructure level. The primary objective of this paper is to demonstrate the potential of artificial intelligence in enhancing C-ITS applications
    • …
    corecore