2,780 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

    Enhancing robustness in video recognition models : Sparse adversarial attacks and beyond

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    Recent years have witnessed increasing interest in adversarial attacks on images, while adversarial video attacks have seldom been explored. In this paper, we propose a sparse adversarial attack strategy on videos (DeepSAVA). Our model aims to add a small human-imperceptible perturbation to the key frame of the input video to fool the classifiers. To carry out an effective attack that mirrors real-world scenarios, our algorithm integrates spatial transformation perturbations into the frame. Instead of using the norm to gauge the disparity between the perturbed frame and the original frame, we employ the structural similarity index (SSIM), which has been established as a more suitable metric for quantifying image alterations resulting from spatial perturbations. We employ a unified optimisation framework to combine spatial transformation with additive perturbation, thereby attaining a more potent attack. We design an effective and novel optimisation scheme that alternatively utilises Bayesian Optimisation (BO) to identify the most critical frame in a video and stochastic gradient descent (SGD) based optimisation to produce both additive and spatial-transformed perturbations. Doing so enables DeepSAVA to perform a very sparse attack on videos for maintaining human imperceptibility while still achieving state-of-the-art performance in terms of both attack success rate and adversarial transferability. Furthermore, built upon the strong perturbations produced by DeepSAVA, we design a novel adversarial training framework to improve the robustness of video classification models. Our intensive experiments on various types of deep neural networks and video datasets confirm the superiority of DeepSAVA in terms of attacking performance and efficiency. When compared to the baseline techniques, DeepSAVA exhibits the highest level of performance in generating adversarial videos for three distinct video classifiers. Remarkably, it achieves an impressive fooling rate ranging from 99.5% to 100% for the I3D model, with the perturbation of just a single frame. Additionally, DeepSAVA demonstrates favorable transferability across various time series models. The proposed adversarial training strategy is also empirically demonstrated with better performance on training robust video classifiers compared with the state-of-the-art adversarial training with projected gradient descent (PGD) adversary

    Reliable Sensor Intelligence in Resource Constrained and Unreliable Environment

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    The objective of this research is to design a sensor intelligence that is reliable in a resource constrained, unreliable environment. There are various sources of variations and uncertainty involved in intelligent sensor system, so it is critical to build reliable sensor intelligence. Many prior works seek to design reliable sensor intelligence by developing robust and reliable task. This thesis suggests that along with improving task itself, task reliability quantification based early warning can further improve sensor intelligence. DNN based early warning generator quantifies task reliability based on spatiotemporal characteristics of input, and the early warning controls sensor parameters and avoids system failure. This thesis presents an early warning generator that predicts task failure due to sensor hardware induced input corruption and controls the sensor operation. Moreover, lightweight uncertainty estimator is presented to take account of DNN model uncertainty in task reliability quantification without prohibitive computation from stochastic DNN. Cross-layer uncertainty estimation is also discussed to consider the effect of PIM variations.Ph.D

    National Report for the IAG of the IUGG 2019-2022

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    Major results of researches conducted by Russian geodesists in 2019-2022 on the topics of the International Association of Geodesy (IAG) of the International Union of Geodesy and Geophysics (IUGG) are presented in this issue. This report is prepared by the Section of Geodesy of the National Geophysical Committee of Russia. In the report prepared for the XXVII General Assembly of IUGG (Germany, Berlin, 11-20 July 2023), the results of principal researches in geodesy, geodynamics, gravimetry, in the studies of geodetic reference frame creation and development, Earth's shape and gravity field, Earth's rotation, geodetic theory, its application and some other directions are briefly described. For some objective reasons not all results obtained by Russian scientists on the field of geodesy are included in the report.Comment: Misprint in the title of the arXiv record has been corrected. The submission content is not affecte

    Exploring the Reported Strengths and Limitations of Aboriginal and Torres Strait Islander Health Research: A Narrative Review of Intervention Studies

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    High quality intervention research is needed to inform evidence-based practice and policy for Aboriginal and Torres Strait Islander communities. We searched for studies published from 2008–2020 in the PubMed database. A narrative review of intervention literature was conducted, where we identified researcher reported strengths and limitations of their research practice. A total of 240 studies met inclusion criteria which were categorised as evaluations, trials, pilot interventions or implementation studies. Reported strengths included community engagement and partnerships; sample qualities; Aboriginal and Torres Strait Islander involvement in research; culturally appropriate and safe research practice; capacity building efforts; providing resources or reducing costs for services and communities; understanding local culture and context; and appropriate timelines for completion. Reported limitations included difficulties achieving the target sample size; inadequate time; insufficient funding and resources; limited capacity of health workers and services; and inadequate community involvement and communication issues. This review highlights that community consultation and leadership coupled with appropriate time and funding, enables Aboriginal and Torres Strait Islander health intervention research to be conducted. These factors can enable effective intervention research, and consequently can help improve health and wellbeing outcomes for Aboriginal and Torres Strait Islander people.Romany McGuffog, Jamie Bryant, Kade Booth, Felicity Collis, Alex Brown, Jaquelyne T. Hughes, Catherine Chamberlain, Alexandra McGhie, Breanne Hobden, and Michelle Kenned

    Decoding of EEG signals reveals non-uniformities in the neural geometry of colour

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    The idea of colour opponency maintains that colour vision arises through the comparison of two chromatic mechanisms, red versus green and yellow versus blue. The four unique hues, red, green, blue, and yellow, are assumed to appear at the null points of these the two chromatic systems. Here we hypothesise that, if unique hues represent a tractable cortical state, they should elicit more robust activity compared to other, non-unique hues. We use a spatiotemporal decoding approach to report that electroencephalographic (EEG) responses carry robust information about the tested isoluminant unique hues within a 100-350 ms window from stimulus onset. Decoding is possible in both passive and active viewing tasks, but is compromised when concurrent high luminance contrast is added to the colour signals. For large hue-differences, the efficiency of hue decoding can be predicted by mutual distance in a nominally uniform perceptual colour space. However, for small perceptual neighbourhoods around unique hues, the encoding space shows pivotal non-uniformities which suggest that anisotropies in neurometric hue-spaces may reflect perceptual unique hues

    Datascape: speculative city for data to inhabit

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    La creciente exponencial de datos está afectando profundamente el entorno físico, y su proliferación descontrolada continuará reconfigurando y alterando el paisaje. Sin embargo, estas afectaciones pueden no ser inmediatamente aparentes. Este proyecto tiene como objetivo explorar y representar visualmente el impacto de la producción masiva de datos en el paisaje físico, diseñando una ciudad especulativa para que los datos la habiten para ilustrar las posibles implicaciones de esta posibilidad en el futuro. El objetivo es estudiar el estado actual de la producción de datos, comprender sus efectos en el medio ambiente y crear una representación visual que resalte las posibles consecuencias del crecimiento de los datos. A través de la investigación, el análisis y el diseño, este proyecto pretende contribuir al discurso sobre las implicaciones ambientales de la producción de datos y ofrecer ideas sobre los posibles escenarios futuros que esperan a nuestro entorno construido. Se invita a realizar un examen crítico de nuestras prácticas digitales y se fomenta un enfoque responsable y sostenible en la producción y consumo de datos.The exponential rise in data is profoundly affecting the physical environment, and its uncontrolled proliferation will continue to reshape and alter the landscape. However, these impacts may not be immediately apparent. This project aims to explore and visually represent the impact of massive data production on the physical landscape by designing a speculative city for data to inhabit, illustrating the potential implications of this impact in the future. The objective is to study the current state of data production, understand its effects on the environment, and create a compelling visual representation that highlights the potential consequences of data growth. Through research, analysis, and design, this project aims to contribute to the discourse on the environmental implications of data production and offer insights into potential future scenarios that await our built environment. It calls for a critical examination of our digital practices and encourages responsible and sustainable approaches to data production and consumption

    Spatiotemporal Integration of Early Visual Processing: Visual Phenomena beyond the Critical Fusion Frequency

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    Department of Biomedical Engineering (Human Factors Engineering)Our visual system integrates continuous signals to construct visual representation. This integration process involves combining visual inputs across both space and time. This spatiotemporal integration is inherently linked to motion. The movements of our eyes play a fundamental role in shaping our visual perception by spatially shifting the images projected on our retinas. The significance of considering the effects of eye movements on visual perception, resulting from these spatial modulations, has been consistently emphasized. In the first part of the study, we focused on examining the influence of eye movements on the temporal sensitivity of the human visual system. Specifically, we assessed participants' ability to detect the flickering visual stimuli with and without the inclusion of eye movements. The findings revealed that the effects of eye movements varied depending on the spatial features of the stimuli. When eye movements were incorporated, participants were able to perceive flickering edges beyond their temporal limit, surpassing the critical fusion frequency (CFF). Furthermore, a significant positive correlation was observed between temporal sensitivity and the extent of eye movements executed during stimulus presentation. To further elucidate the alterations in visual perception caused by the spatial characteristics of visual stimuli and the shifted retinal image resulting from eye movements, we developed a spatiotemporal integration model. This model aimed to emulate the spatiotemporal integration process occurring in our early visual system and was employed to encompass the observations made in this study. In the second part of the study, we investigated the phenomena of visual persistence and the sense of reality associated with moving objects that move at a speed comparable to our eye movements (approximately 100 degrees per second). Introducing a Speedline, which connected two consecutively presented objects spatially, mitigated the smear effect on moving objects in terms of the range of visual persistence and perceived length. Additionally, the presence of the Speedline enhanced the resemblance between the moving objects in the display and the motion of objects in the real world, thereby amplifying the sense of reality in the object motion. Overall, this study offers valuable insights into how eye movements impact visual perception, considering the spatial features of objects. The predictions made by the developed model suggest that the observed behavioral patterns, such as elevated temporal sensitivity to stimuli containing spatial edges, are outcomes of the spatiotemporal integration process occurring in our early visual system. By investigating the intricate relationship between eye movements, object motion, spatial characteristics of visual stimuli, and visual perception, we deepen our understanding of how visual system processes and integrates visual information.clos

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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