1,960 research outputs found

    Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

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    Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability

    Smiling Mind Mindfulness in Schools Program as a Classroom-Based Self-Regulation Intervention: A Case Study

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    The number of Canadian children experiencing mental health concerns, including both internalizing and externalizing difficulties, continues to be on the rise. Coincidingly, the education system in Saskatchewan continues to experience strained resources. Thus, finding an efficacious, cost-effective, and accessible mental health intervention is vital. Both internalizing (e.g., anxiety, depression) and externalizing (e.g., hyperactivity, aggression) mental health in children are correlated with poor self-regulation. Recent reviews of the literature suggest mindfulness is a promising self-regulation intervention, particularly for clinical populations, as it targets the underlying neural mechanisms related to emotion dysregulation. The current case study aimed to provide insight into the potential value of a specific mindfulness intervention, Smiling Mind, within the context of the BALANCE classroom in Saskatoon, SK. The research questions were as follows: (a) How does incorporating a mindfulness intervention into a tier-three (high support) elementary school classroom routine affect the self-regulation (e.g., ability to appropriately manage thoughts, emotions and behaviour) of students with internalizing or externalizing mental health difficulties/disorders? (b) How does a mindfulness intervention help or hinder student readjustment to the classroom setting following a prolonged absence from school due to COVID-19? And (c) What opinions, attitudes, and feelings do the students have towards incorporating mindfulness into their school day? Data sources for this study included audiotaped semi-structured interviews, a self-report measure on self-regulation, and a Daily Recording Checklist. Semi-structured interviews were completed in place of direct observations due to the COVID-19 pandemic related restrictions and the requirement of completing the research virtually. Four methods of data analysis were employed in this case study: categorical aggregation, pattern identification, direct interpretations, and naturalistic generalizations. This in-depth process led to the formation of three main themes: The Smiling Mind Program: A General Overview; Students with Exceptionalities: “Mindful Considerations”; and Responsive Teaching and Pedagogical Considerations. Results from this research could influence educators as they attempt to meet the mental health needs of all their students within an inclusive classroom environment. Having one more tool in their professional toolboxes, like the Smiling Mind Program, can empower teachers while at the same time enhance the overall well-being of their students. Additionally, future researchers will benefit from seeing how completion of an intervention case study during the COVID-19 pandemic demands flexibility, creativity and determination. The need to pivot and adapt to changing public health or school division policies and directives became the norm during this innovative study

    SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

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    The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph

    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

    Jews in East Norse Literature

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    This book explores the portrayal of Jews and Judaism in medieval Danish and Swedish literary and visual culture. Drawing on over 100 manuscripts and incunabula as well as runic inscriptions and religious art, the author describes the various, often contradictory, images ranging from antisemitism and anti-Judaism to the elevation of Jews as morally exemplary figures. It includes new editions of 54 East Norse texts with English translations

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    Intelligent interface agents for biometric applications

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    This thesis investigates the benefits of applying the intelligent agent paradigm to biometric identity verification systems. Multimodal biometric systems, despite their additional complexity, hold the promise of providing a higher degree of accuracy and robustness. Multimodal biometric systems are examined in this work leading to the design and implementation of a novel distributed multi-modal identity verification system based on an intelligent agent framework. User interface design issues are also important in the domain of biometric systems and present an exceptional opportunity for employing adaptive interface agents. Through the use of such interface agents, system performance may be improved, leading to an increase in recognition rates over a non-adaptive system while producing a more robust and agreeable user experience. The investigation of such adaptive systems has been a focus of the work reported in this thesis. The research presented in this thesis is divided into two main parts. Firstly, the design, development and testing of a novel distributed multi-modal authentication system employing intelligent agents is presented. The second part details design and implementation of an adaptive interface layer based on interface agent technology and demonstrates its integration with a commercial fingerprint recognition system. The performance of these systems is then evaluated using databases of biometric samples gathered during the research. The results obtained from the experimental evaluation of the multi-modal system demonstrated a clear improvement in the accuracy of the system compared to a unimodal biometric approach. The adoption of the intelligent agent architecture at the interface level resulted in a system where false reject rates were reduced when compared to a system that did not employ an intelligent interface. The results obtained from both systems clearly express the benefits of combining an intelligent agent framework with a biometric system to provide a more robust and flexible application

    Facial Landmark Detection Evaluation on MOBIO Database

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    MOBIO is a bi-modal database that was captured almost exclusively on mobile phones. It aims to improve research into deploying biometric techniques to mobile devices. Research has been shown that face and speaker recognition can be performed in a mobile environment. Facial landmark localization aims at finding the coordinates of a set of pre-defined key points for 2D face images. A facial landmark usually has specific semantic meaning, e.g. nose tip or eye centre, which provides rich geometric information for other face analysis tasks such as face recognition, emotion estimation and 3D face reconstruction. Pretty much facial landmark detection methods adopt still face databases, such as 300W, AFW, AFLW, or COFW, for evaluation, but seldomly use mobile data. Our work is first to perform facial landmark detection evaluation on the mobile still data, i.e., face images from MOBIO database. About 20,600 face images have been extracted from this audio-visual database and manually labeled with 22 landmarks as the groundtruth. Several state-of-the-art facial landmark detection methods are adopted to evaluate their performance on these data. The result shows that the data from MOBIO database is pretty challenging. This database can be a new challenging one for facial landmark detection evaluation.Comment: 13 pages, 10 figure

    Automates: the future of autonomous cars

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    El futur dels cotxes autònoms sembla brillant, tot i així, personatges com el mateix Elon Musk, entre d'altres, ens porten prometent que serien part de les nostres vides des de fa gairebé deu anys. Tot i això aquí seguim, amb els nostres vehicles que sí, que són genials, però de moment encara no es condueixen sols. Aquestes falses promeses i el concepte de que una màquina condueixi el cotxe per nosaltres encara genera rebuig a la majoria de la població, quan de fet més d'un 90% dels accidents de trànsit avui dia són a causa de l'error humà, i aquestes màquines seran moltes coses, però precisament humanes de moment no són. En aquest projecte s’indaga sobre l’estat actual d’aquests vehicles, que de fet certs serveis de cotxes autònoms ja ronden els carrers d’algunes de les ciutats més grans del món, com ara San Francisco. La clau és descobrir si els vehicles autònoms tenen el potencial real de convertir-se en el servei del futur. Per això, es recorre a les metodologies de Disseny de Futurs, analitzant les tendències del sector i així presentant una sèrie d'Escenaris Futurs. Aquestes metodologies ens permetran entreveure cap on ens porten els desenvolupaments actuals, per així descobrir els passos que hauríem de seguir i els que no per a una correcta i eficient implementació d'aquestes tecnologies en un futur més aviat proper que llunyà.El futuro de los coches autónomos parece brillante, aún así, personajes como el mismísimo Elon Musk, entre otros, nos llevan prometiendo que iban a ser parte de nuestras vidas desde hace ya casi diez años. Sin embargo aquí seguimos, con nuestros vehículos que sí, que son geniales, pero de momento aún no se conducen solos. Estas falsas promesas y el concepto de que una máquina conduzca el coche por nosotros aún genera rechazo en la mayoría de la población, cuando lo cierto es que más de un 90% de los accidentes de tráfico hoy en día son a causa del error humano, y estas máquinas serán muchas cosas pero precisamente humanas no son. En este proyecto se indaga sobre el estado actual de estos vehículos, que de hecho ciertos servicios de coches autónomos ya rondan las calles de algunas de las ciudades más grandes del mundo, como por ejemplo San Francisco. La clave es descubrir si los vehículos autónomos tienen el potencial real de convertirse en el servicio del futuro. Para ello, se recurre a las metodologías de Diseño de Futuros, analizando las tendencias del sector y así presentando una serie de Escenarios Futuros. Estas metodologías nos permitirán vislumbrar hacia dónde nos llevan los desarrollos actuales, para así descubrir los pasos que deberíamos seguir y los que no para una correcta y eficiente implementación de estas tecnologías en un futuro más próximo que lejano.The future of autonomous cars seems bright, even though, famous people like Elon Musk himself, among others, have been making promises around the fact that those cars would be part of our lives for almost ten years, but here we are, with our vehicles that are great, but for now they still don't drive for themselves. These false promises and the concept of a machine driving a car for us still generates rejection in the majority of the population, when the fact is that more than 90% of traffic accidents nowadays are due to human error, and these machines will be sort of things but not humans at all. This project investigates the current state of these vehicles, that in fact these autonomous car services already transit the streets of some of the largest cities in the world, cities like San Francisco. The key is to find out if autonomous vehicles have the real potential to become the service of the future. Therefore, Futures Design methodologies are used, analysing the trends of the sector and thus presenting a series of Future Scenarios. These methodologies will allow us to understand where current developments are leading us, so then we can understand the steps that we should follow as a society and those that we should not for a correct and efficient implementation of these technologies in the near future
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