532 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

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Contributions to improve the technologies supporting unmanned aircraft operations

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    Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

    A novel optogenetics-based therapy for obstructive sleep apnoea

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    Obstructive sleep apnoea (OSA) is characterised by repeat upper airway narrowing and/or collapse during sleep. Many patients are sub-optimally treated due to poor tolerance or incomplete response to established therapies. We propose a novel, optogenetics-based therapy, that enables light-stimulation induced upper airway dilator muscle contractions to maintain airway patency. The primary aims of this thesis were to determine feasibility in a rodent model of OSA, and identify effective optogenetic constructs for activating upper airway muscles. Chapters 2 and 3 outline the development of a novel construct for the expression of light-sensitive proteins (opsins) in upper airway muscles, comparing two promotors and two recombinant adeno-associated virus capsids (rAAV) for optogenetic gene transfer. Results show that a muscle-specific promotor (tMCK) was superior to a non-specific promotor (CAG). With tMCK, opsin expression in the tongue was 470% greater (p=0.013, RM-ANOVA), brainstem expression was abolished, and light stimulation facilitated a 66% increase in muscle activity from that recorded during unstimulated breaths in an acute model of OSA (p<0.001, linear mixed model) (Chapter 2). Moreover, a novel, highly myotropic rAAV serotype, AAVMYO, was superior to a wild-type serotype, AAV9. The AAVMYO serotype driven by tMCK facilitated a further increase in muscle activity with light stimulation to 194% of that recorded during unstimulated breaths (p<0.001, linear mixed model) (Chapter 3). Finally, ultrasound imaging confirmed that the optimised construct was able to generate effective light-induced muscle contractions and airway dilation (Chapter 4). A secondary aim was to advance preclinical trials for the proposed therapy. To this end, a surgical protocol for chronic implantation of light delivery hardware and recording electrodes in rodents was developed (Chapter 5). The final protocol will allow us to determine the effects of acute and chronic light stimulation on opsin-expressing upper airway muscles during natural sleep. In summary, Chapters 2 to 4 provide proof-of-concept for a non-invasive optogenetics-based OSA therapy. The combination of a muscle-specific promotor and a muscle-specific viral vector presents a novel and highly effective method of inducing light sensitivity into skeletal muscle and facilitating light-evoked airway dilation. Finally, Chapter 5 commences the development of a surgical protocol that will aid ongoing preclinical trials

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Ensembles of Pruned Deep Neural Networks for Accurate and Privacy Preservation in IoT Applications

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    The emergence of the AIoT (Artificial Intelligence of Things) represents the powerful convergence of Artificial Intelligence (AI) with the expansive realm of the Internet of Things (IoT). By integrating AI algorithms with the vast network of interconnected IoT devices, we open new doors for intelligent decision-making and edge data analysis, transforming various domains from healthcare and transportation to agriculture and smart cities. However, this integration raises pivotal questions: How can we ensure deep learning models are aptly compressed and quantised to operate seamlessly on devices constrained by computational resources, without compromising accuracy? How can these models be effectively tailored to cope with the challenges of statistical heterogeneity and the uneven distribution of class labels inherent in IoT applications? Furthermore, in an age where data is a currency, how do we uphold the sanctity of privacy for the sensitive data that IoT devices incessantly generate while also ensuring the unhampered deployment of these advanced deep learning models? Addressing these intricate challenges forms the crux of this thesis, with its contributions delineated as follows: Ensyth: A novel approach designed to synthesise pruned ensembles of deep learning models, which not only makes optimal use of limited IoT resources but also ensures a notable boost in predictability. Experimental evidence gathered from CIFAR-10, CIFAR-5, and MNIST-FASHION datasets solidify its merit, especially given its capacity to achieve high predictability. MicroNets: Venturing into the realms of efficiency, this is a multi-phase pruning pipeline that fuses the principles of weight pruning, channel pruning. Its objective is clear: foster efficient deep ensemble learning, specially crafted for IoT devices. Benchmark tests conducted on CIFAR-10 and CIFAR-100 datasets demonstrate its prowess, highlighting a compression ratio of nearly 92%, with these pruned ensembles surpassing the accuracy metrics set by conventional models. FedNets: Recognising the challenges of statistical heterogeneity in federated learning and the ever-growing concerns of data privacy, this innovative federated learning framework is introduced. It facilitates edge devices in their collaborative quest to train ensembles of pruned deep neural networks. More than just training, it ensures data privacy remains uncompromised. Evaluations conducted on the Federated CIFAR-100 dataset offer a testament to its efficacy. In this thesis, substantial contributions have been made to the AIoT application domain. Ensyth, MicroNets, and FedNets collaboratively tackle the challenges of efficiency, accuracy, statistical heterogeneity arising from distributed class labels, and privacy concerns inherent in deploying AI applications on IoT devices. The experimental results underscore the effectiveness of these approaches, paving the way for their practical implementation in real-world scenarios. By offering an integrated solution that satisfies multiple key requirements simultaneously, this research brings us closer to the realisation of effective and privacy-preserved AIoT systems

    Deep learning in food category recognition

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    Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8
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