194 research outputs found

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

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    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

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    A Low-Cost Synthetic Aperture Sonar System for Small Agile Vehicles

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    Development of a chipless RFID based aerospace structural health monitoring sensor system

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    Chipless Radio Frequency Identification (RFID) is modern wireless technology that has been earmarked as being suitable for low-cost item tagging/tracking. These devices do not require integrated circuitry or a battery and thus, are not only are cheap, but also easy to manufacture and potentially very robust. A great deal of attention is also being put on the possibility of giving these tags the ability to sense various environmental stimuli such as temperature and humidity. This work focusses on the potential use of chipless RFID as a sensor technology for aerospace Structural Health Monitoring. The project is focussed on the sensing of mechanical strain and temperature, with an emphasis placed on fabrication simplicity, so that the final sensor designs could be potentially fabricated in-situ using existing printing technologies. Within this project, a variety of novel chipless RFID strain and temperature sensors have been designed, fabricated and tested. A thorough discussion is also presented on the topic of strain sensor cross sensitivity, which places emphasis on issues like, transverse strain, dielectric constant variations and thermal swelling. Additionally, an exploration into other key technological challenges was also performed, with a focus on challenges such as: accurate and reliable stimulus detection, sensor polarization and multi-sensor support. Several key areas of future research have also been identified and outlined, with aims related to: Enhancing strain sensor fabrication simplicity, enhancing temperature sensor sensitivity and simplicity and developing a fully functional interrogation system

    On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

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    Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed

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

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    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

    Establishing Multi-User MIMO Communications Automatically Using Retrodirective Arrays

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    Communications in the mmWave and THz bands will be a key technological pillar for next-generation wireless networks. However, the increase in frequency results in an increase in path loss, which must be compensated for by using large antenna arrays. This introduces challenging issues due to power consumption, signalling overhead for channel estimation, hardware complexity, and slow beamforming and beam alignment schemes, which are in contrast with the requirements of next-generation wireless networks. In this paper, we propose the adoption of a retro-directive antenna array (RAA) at the user equipment (UE) side, where the signal sent by the base station (BS) is reflected towards the source after being conjugated and phase-modulated according to the UE data. By making use of modified Power Methods for the computation of the eigenvectors of the resulting round-trip channel, it is shown that, in single and multi-user multiple-input multiple-output (MIMO) scenarios, ultra-low complexity UEs can establish parallel communication links automatically with the BS in a very short time. This is done in a blind way, also by tracking fast channel variations while communicating, without the need for ADC chains at the UE as well as without explicit channel estimation and time-consuming beamforming and beam alignment schemes

    A review of commercialisation mechanisms for carbon dioxide removal

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    The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally

    Human sensing indoors in RF utilising unlabeled sensor streams

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    Indoor human sensing in radio frequencies is crucial for non-invasive, privacy-preserving digital healthcare, and machine learning is the backbone of such systems. Changes in the environment affect negatively the quality of learned mappings, which necessitates a semi-supervised approach that makes use of the unlabeled data stream to allow the learner to refine their hypothesis with time.We first explore the ambulation classification problem with frequency modulated continuous wave (FMCW) radar, replacing manual feature engineering by inductive bias in architectural choices of the neural network. We demonstrate that key ambulations: walk, bend, sit to stand and stand to sit can be distinguished with high accuracy. We then apply variational autoencoders to explore unsupervised localisation in synthetic grayscale images, finding that the goal is achievable with the choice of encoder that encodes temporal structure.Next, we evaluate temporal contrastive learning as the method of using unlabeled sensor streams in fingerprinting localisation, finding that it is a reliable method of defining a notion of pairwise distance on the data in that it improves the classification using the nearest neighbour classifier by both reducing the number of other-class items in same-class clusters, and increasing the pairwise distance contrast. Compared to the state of the art in fingerprinting localisation indoors, our contribution is that we successfully address the unsupervised domain adaptation problem.Finally, we raise the hypothesis that some knowledge can be shared between learners in different houses in a privacy-preserving manner. We adapt federated learning (FL) to the multi-residence indoor localisation scenario, which has not been done before, and propose a localfine-tuning algorithm with acceptance based on local validation error improvement. We find the tuned FL each client has a better personalised model compared to benchmark FL while keeping learning dynamics smooth for all clients

    Special Topics in Information Technology

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    This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists
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