Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Synchronisation, détection et égalisation de modulation à phase continue dans des canaux sélectifs en temps et en fréquence

Si les drones militaires connaissent un développement important depuis une quinzaine d’année, suivi depuis quelques années par les drones civiles dont les usages ne font que se multiplier, en réalité les drones ont un siècle avec le premier vol d’un avion équipé d’un système de pilotage automatique sur une centaine de kilomètre en 1918. La question des règles d’usage des drones civiles sont en cours de développement malgré leur multiplication pour des usages allant de l’agriculture, à l’observation en passant par la livraison de colis. Ainsi, leur intégration dans l’espace aérien reste un enjeu important, ainsi que les standards de communication avec ces drones dans laquelle s’inscrit cette thèse. Cette thèse vise en effet à étudier et proposer des solutions pour les liens de communications des drones par satellite.L’intégration de ce lien de communication permet d’assurer la fiabilité des communications et particulièrement du lien de Commande et Contrôle partout dans le monde, en s’affranchissant des contraintes d’un réseau terrestre (comme les zones blanches). En raison de la rareté des ressources fréquentielles déjà allouées pour les futurs systèmes intégrant des drones, l’efficacité spectrale devient un paramètre important pour leur déploiement à grande échelle et le contexte spatiale demande l’utilisation d’un système de communication robuste aux non-linéarités. Les Modulations à Phase Continue permettent de répondre à ces problématiques. Cependant, ces dernières sont des modulations non-linéaire à mémoire entraînant une augmentation de la complexité des récepteurs. Du fait de la présence d’un canal multi-trajet (canal aéronautique par satellite), le principal objectif de cette thèse est de proposer des algorithmes d’égalisation (dans le domaine fréquentiel pour réduire leur complexité) et de synchronisation pour CPM adaptés à ce concept tout en essayant de proposer une complexité calculatoire raisonnable. Dans un premier temps, nous avons considéré uniquement des canaux sélectifs en fréquence et avons étudier les différents égaliseurs de la littérature. En étudiant leur similitudes et différences, nous avons pu développer un égaliseur dans le domaine fréquentiel qui proposant les mêmes performances a une complexité moindre. Nous proposons également des méthodes d’estimation canal et une méthode d’estimation conjointe du canal et de la fréquence porteuse. Dans un second temps nous avons montré comment étendre ces méthodes à des canaux sélectifs en temps et fréquence permettant ainsi de conserver une complexité calculatoire raisonnable

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

Nouvelle forme d'onde et récepteur avancé pour la télémesure des futurs lanceurs

Les modulations à phase continue (CPMs) sont des méthodes de modulations robuste à la noncohérence du canal de propagation. Dans un contexte spatial, les CPM sont utilisées dans la chaîne de transmission de télémesure de la fusée. Depuis les années 70, la modulation la plus usitée dans les systèmes de télémesures est la modulation CPFSK continuous phase frequency shift keying filtrée. Historiquement, ce type de modulation est concaténée avec un code ReedSolomon (RS) afin d'améliorer le processus de décodage. Côté récepteur, les séquences CPM non-cohérentes sont démodulées par un détecteur Viterbi à sortie dure et un décodeur RS. Néanmoins, le gain du code RS n'est pas aussi satisfaisant que des techniques de codage moderne capables d'atteindre la limite de Shannon. Actualiser la chaîne de communication avec des codes atteignant la limite de Shannon tels que les codes en graphe creux, implique deremanier l’architecture du récepteur usuel pour un détecteur à sortie souple. Ainsi, on propose dans cette étude d' élaborer un détecteur treillis à sortie souple pour démoduler les séquences CPM non-cohérentes. Dans un deuxième temps, on concevra des schémas de pré-codages améliorant le comportement asymptotique du récepteur non-cohérent et dans une dernière étape on élabora des codes de parité à faible densité (LDPC) approchant la limite de Shannon

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment

De animais a máquinas : humanos tecnicamente melhores nos imaginários de futuro da convergência tecnológica

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Sociais, Departamento de Sociologia, 2020.O tema desta investigação é discutir os imaginários sociais de ciência e tecnologia que emergem a partir da área da neuroengenharia, em sua relação com a Convergência Tecnológica de quatro disciplinas: Nanotecnologia, Biotecnologia, tecnologias da Informação e tecnologias Cognitivas - neurociências- (CT-NBIC). Estas áreas desenvolvem-se e são articuladas por meio de discursos que ressaltam o aprimoramento das capacidades físicas e cognitivas dos seres humanos, com o intuito de construir uma sociedade melhor por meio do progresso científico e tecnológico, nos limites das agendas de pesquisa e desenvolvimento (P&D). Objetivos: Os objetivos nesse cenário, são discutir as implicações éticas, econômicas, políticas e sociais deste modelo de sistema sociotécnico. Nos referimos, tanto as aplicações tecnológicas, quanto as consequências das mesmas na formação dos imaginários sociais, que tipo de relações se estabelecem e como são criadas dentro desse contexto. Conclusão: Concluímos na busca por refletir criticamente sobre as propostas de aprimoramento humano mediado pela tecnologia, que surgem enquanto parte da agenda da Convergência Tecnológica NBIC. No entanto, as propostas de melhoramento humano vão muito além de uma agenda de investigação. Há todo um quadro de referências filosóficas e políticas que defendem o aprimoramento da espécie, vertentes estas que se aliam a movimentos trans-humanistas e pós- humanistas, posições que são ao mesmo tempo éticas, políticas e econômicas. A partir de nossa análise, entendemos que ciência, tecnologia e política estão articuladas, em coprodução, em relação às expectativas de futuros que são esperados ou desejados. Ainda assim, acreditamos que há um espaço de diálogo possível, a partir do qual buscamos abrir propostas para o debate público sobre questões de ciência e tecnologia relacionadas ao aprimoramento da espécie humana.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The subject of this research is to discuss the social imaginaries of science and technology that emerge from the area of neuroengineering in relation with the Technological Convergence of four disciplines: Nanotechnology, Biotechnology, Information technologies and Cognitive technologies -neurosciences- (CT-NBIC). These areas are developed and articulated through discourses that emphasize the enhancement of human physical and cognitive capacities, the intuition it is to build a better society, through the scientific and technological progress, at the limits of the research and development (R&D) agendas. Objectives: The objective in this scenery, is to discuss the ethic, economic, politic and social implications of this model of sociotechnical system. We refer about the technological applications and the consequences of them in the formation of social imaginaries as well as the kind of social relations that are created and established in this context. Conclusion: We conclude looking for critical reflections about the proposals of human enhancement mediated by the technology. That appear as a part of the NBIC technologies agenda. Even so, the proposals of human enhancement go beyond boundaries that an investigation agenda. There is a frame of philosophical and political references that defend the enhancement of the human beings. These currents that ally to the transhumanism and posthumanism movements, positions that are ethic, politic and economic at the same time. From our analysis, we understand that science, technology and politics are articulated, are in co-production, regarding the expected and desired futures. Even so, we believe that there is a space of possible dialog, from which we look to open proposals for the public discussion on questions of science and technology related to enhancement of human beings

Brain-computer interfaces: barriers and opportunities to widespread clinical adoption

Brain-computer Interface (BCI) is an emerging neurotechnology with potential applications involving primarily neurological disorders. There is a rising interest in the use of BCI to address current unmet clinical needs from patients. Despite their therapeutic potential, BCI use is still mostly limited to research stages and its translation into mainstream clinical applications and widespread adoption is lagging. This study revises the current potential clinical applications of BCIs in humans, attempts to understand barriers and opportunities to wider clinical adoption and draws health policy and management implications of BCIs use in medical practice. The methodology followed a two-step approach which included a systematic review of potential clinical applications of BCIs and a qualitative study, using focus group method, to understand and integrate professionals’ experiences, perceptions, thoughts and feelings on the wide clinical adoption of BCIs. Focus groups included professionals from the medical, engineering and management field. BCI clinical applications with more clinical evidence include neurorehabilitation with non-invasive devices and the control of assistive devices with invasive BCIs. Nowadays, several barriers to wider clinical adoption of BCIs, including technological, seem addressable. However, systemic barriers from the health systems to innovation and technological interventions need a comprehensive and multidisciplinary approach to enhance their adoption. Professionals from medicine, engineering and management, working in collaboration in healthcare contexts, are some of the stakeholders important to change the current vision of healthcare towards innovation.As interfaces cérebro-computador (BCI) são uma Neurotecnologia emergente com potencial para serem aplicadas no âmbito clínico, nomeadamente em condições de foro neurológico. Existe um interesse crescente no uso desta tecnologia para ir de encontro às necessidades clínicas de doentes com poucas soluções de tratamento e apoio médico. Apesar das potencialidades das BCI para serem usadas em contexto clínico em humanos, as suas aplicações têm-se limitado a contextos específicos de pesquisa e sem transição para a área da saúde com consequente adoção enquanto ferramenta terapêutica. Com este trabalho pretende-se rever as aplicações clínicas atuais destes dispositivos em humanos, perceber quais as barreiras e oportunidades para a sua adoção em contextos clínicos e retirar ilações do uso de BCI para políticas de saúde e gestão de inovação na prática médica. A metodologia foi dividida em duas fases, que incluíram uma revisão sistemática das potenciais aplicações clínicas de BCI e um estudo qualitativo, usando focus groups, para melhor perceber e integrar as experiências, perceções, ideias e sentimentos de profissionais em relação à adoção de BCI na prática clínica comum. Os focus groups incluíram profissionais das áreas médica, de engenharia e de gestão. As aplicações clínicas com maior nível de evidência para a clínica incluem a neuroreabilitação com BCI não-invasivos e o controlo de dispositivos de assistência com BCI invasivos. Atualmente, diversas barreiras à implementação de BCI em contexto clínico, incluindo o desenvolvimento tecnológico, parecem ser possíveis de ultrapassar num prazo razoável. Contudo, barreiras sistemáticas à inovação e intervenções tecnológicas no âmbito dos sistemas de saúde, apresentam-se como um problema mais complexo e necessitarão de uma abordagem mais globalizada e multidisciplinar para tornar possível a adoção de BCI na prática clínica. Para atingir este objetivo e ultrapassar estas barreiras, profissionais das áreas de medicina, engenharia e gestão devem colaborar e trabalhar em conjunto em contextos de saúde, contribuindo para uma mudança de cultura e tornando os sistemas de saúde mais abertos à inovação