101 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

    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

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    Naval Postgraduate School Academic Catalog - February 2023

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    Present and Future of Gravitational Wave Astronomy

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    The first detection on Earth of a gravitational wave signal from the coalescence of a binary black hole system in 2015 established a new era in astronomy, allowing the scientific community to observe the Universe with a new form of radiation for the first time. More than five years later, many more gravitational wave signals have been detected, including the first binary neutron star coalescence in coincidence with a gamma ray burst and a kilonova observation. The field of gravitational wave astronomy is rapidly evolving, making it difficult to keep up with the pace of new detector designs, discoveries, and astrophysical results. This Special Issue is, therefore, intended as a review of the current status and future directions of the field from the perspective of detector technology, data analysis, and the astrophysical implications of these discoveries. Rather than presenting new results, the articles collected in this issue will serve as a reference and an introduction to the field. This Special Issue will include reviews of the basic properties of gravitational wave signals; the detectors that are currently operating and the main sources of noise that limit their sensitivity; planned upgrades of the detectors in the short and long term; spaceborne detectors; a data analysis of the gravitational wave detector output focusing on the main classes of detected and expected signals; and implications of the current and future discoveries on our understanding of astrophysics and cosmology

    Remote Sensing Data Compression

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    A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

    Electronics for Sensors

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    The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces

    Agricultural Monitoring System using Images through a LPWAN Network

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    Internet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Gobernación de Boyacá, Colfuturo, Colciencias, Universidad Santo Tomás, Pontificia Universidad JaverianaInternet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0002-3554-1531https://scholar.google.com/citations?user=5_dx9REAAAAJ&hl=eshttps://scienti.minciencias.gov.co/cvlac/EnRecursoHumano/query.d

    Cyber Security and Critical Infrastructures

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    This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

    Naval Postgraduate School Academic Catalog - September 2022

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