abstract: Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors. / Dissertation/Thesis / Ph.D. Electrical Engineering 2013

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Cuando las personas interactúan con los ordenadores, hay mucha información que no se proporciona a propósito. Mediante el estudio de estas interacciones implícitas es posible entender qué características de la interfaz de usuario son beneficiosas (o no), derivando así en implicaciones para el diseño de futuros sistemas interactivos. La principal ventaja de aprovechar datos implícitos del usuario en aplicaciones informáticas es que cualquier interacción con el sistema puede contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de tener que interrumpir al usuario para que envíe información explícitamente sobre un tema que en principio no tiene por qué guardar relación con la intención de utilizar el sistema. Por el contrario, en ocasiones las interacciones implícitas no proporcionan datos claros y concretos. Por ello, hay que prestar especial atención a la manera de gestionar esta fuente de información. El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto al diseño como al desarrollo de aplicaciones que puedan reaccionar consecuentemente a las interacciones implícitas del usuario, y 2) proporcionar una serie de metodologías para la evaluación de dichos sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la adecuación del marco de trabajo de la tesis. Resultados empíricos con usuarios reales demuestran que aprovechar la interacción implícita es un medio tanto adecuado como conveniente para mejorar de múltiples maneras los sistemas interactivos. / Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803

A construção de material didático é bastante difícil, mas avaliá-lo é ainda mais complexo. Saber como o aluno absorveu o conteúdo, como reagiu a ele e quanto tempo foi gasto durante o uso de cada objeto de aprendizagem pode ajudar a refletir se o conteúdo está adequado às necessidades deste aluno. Além disso, considerar as diferentes características cognitivas de aprendizado e, conseqüentemente, possibilitar adaptações através de ajustes no conteúdo didático digital auxiliaria a avaliação do professor em um curso disponível através da web. Este trabalho propõe um sistema de acompanhamento (MSys) que, através do monitoramento do nível de utilização das atividades desenvolvidas pelos alunos, deverá apresentar para o professor, assim como para o próprio aluno, resultados resumidos e comparativos do acompanhamento. Apesar de diversos ambientes do tipo LMS (Learning Management System) em uso atualmente estarem bastante difundidos, não tem sido dado a importância devida para este tipo de ferramenta capaz de oferecer um acompanhamento detalhado de atividades dos alunos. Este trabalho discute como a ferramenta aqui proposta foi concebida, desde sua arquitetura, que utiliza padrões atuais de construção de conteúdo digital, definindo requisitos funcionais para o seu desenvolvimento e exibindo resultados de uma simulação que comprova esta concepção. Além disso, é apresentado um protótipo da ferramenta, validando as interfaces de captura e exibição dos resultados e mostrando que é possível integrá-la a sistemas de aprendizado eletrônico, trazendo benefícios à avaliação do professor. / E-learning content creation is not an easy task, but its evaluation is even more complex. In order to evaluate if the content is adequate to the students needs, it would be helpful to know how the student assimilated the learning content, how he reacted to it and the period of time spent on the learning object. Besides, considering the different cognitive features of learning and the possibility of adjustment of the didactic content, it could help teacher\'s evaluation in an available online course. This work describes a monitoring system (Msys) that tracks the level of utilization of student\'s activities, and presents summarized and comparative results to the teacher and the student. Even though the widespread use of several LMS (Learning Management System) environments today, the importance of tools capable of offering a detailed monitoring of student\'s activities has not been recognized. This work focus on how the tool was created using current standards on digital content construction, defining functional requirements to its development and presenting simulation results that is a proof-ofconcept. Also, it is presented a prototype of the tool, validating the interfaces of capture and results presentation, showing that is possible to integrate it to online learning systems, bringing benefits to the teacher evaluation.

A construção de material didático é bastante difícil, mas avaliá-lo é ainda mais complexo. Saber como o aluno absorveu o conteúdo, como reagiu a ele e quanto tempo foi gasto durante o uso de cada objeto de aprendizagem pode ajudar a refletir se o conteúdo está adequado às necessidades deste aluno. Além disso, considerar as diferentes características cognitivas de aprendizado e, conseqüentemente, possibilitar adaptações através de ajustes no conteúdo didático digital auxiliaria a avaliação do professor em um curso disponível através da web. Este trabalho propõe um sistema de acompanhamento (MSys) que, através do monitoramento do nível de utilização das atividades desenvolvidas pelos alunos, deverá apresentar para o professor, assim como para o próprio aluno, resultados resumidos e comparativos do acompanhamento. Apesar de diversos ambientes do tipo LMS (Learning Management System) em uso atualmente estarem bastante difundidos, não tem sido dado a importância devida para este tipo de ferramenta capaz de oferecer um acompanhamento detalhado de atividades dos alunos. Este trabalho discute como a ferramenta aqui proposta foi concebida, desde sua arquitetura, que utiliza padrões atuais de construção de conteúdo digital, definindo requisitos funcionais para o seu desenvolvimento e exibindo resultados de uma simulação que comprova esta concepção. Além disso, é apresentado um protótipo da ferramenta, validando as interfaces de captura e exibição dos resultados e mostrando que é possível integrá-la a sistemas de aprendizado eletrônico, trazendo benefícios à avaliação do professor. / E-learning content creation is not an easy task, but its evaluation is even more complex. In order to evaluate if the content is adequate to the students needs, it would be helpful to know how the student assimilated the learning content, how he reacted to it and the period of time spent on the learning object. Besides, considering the different cognitive features of learning and the possibility of adjustment of the didactic content, it could help teacher\'s evaluation in an available online course. This work describes a monitoring system (Msys) that tracks the level of utilization of student\'s activities, and presents summarized and comparative results to the teacher and the student. Even though the widespread use of several LMS (Learning Management System) environments today, the importance of tools capable of offering a detailed monitoring of student\'s activities has not been recognized. This work focus on how the tool was created using current standards on digital content construction, defining functional requirements to its development and presenting simulation results that is a proof-ofconcept. Also, it is presented a prototype of the tool, validating the interfaces of capture and results presentation, showing that is possible to integrate it to online learning systems, bringing benefits to the teacher evaluation.

In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo-feature extraction to 3D laser scanners and radio frequency identification (RFID) tags. A multi-sensor data fusion model that utilizes multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, many existing fusion models are based on data fusion at the sensor and object levels and are therefore incapable of capturing critical information regarding a number of activities and processes on a construction site, particularly those related to non-structural trades such as welding, inspection, and installation activities. In this research, a workflow based data fusion framework is developed for construction progress, quality and productivity assessment. The developed model is based on tracking construction activities as well as objects, in contrast to the existing sensor-based models that are focussed on tracking objects. Data sources include high frequency automated technologies including 3D imaging and ultra-wide band (UWB) positioning. Foreman reports, schedule information, and other data sources are included as well. Data fusion and management process workflow implementation via a distributed computing network and archiving using a cloud-based architecture are both illustrated. Validation was achieved using a detailed laboratory experimental program as well as an extensive field implementation project. The field implementation was conducted using five months of data acquired on the University of Waterloo Engineering VI construction project, yielding promising results. The data fusion processes of this research provide more accurate and more reliable progress and earned value estimates for construction project activities, while the developed data management processes enable the secure sharing and management of construction research data with the construction industry stakeholders as well as with researchers from other institutions.

In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo-feature extraction to 3D laser scanners and radio frequency identification (RFID) tags. A multi-sensor data fusion model that utilizes multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, many existing fusion models are based on data fusion at the sensor and object levels and are therefore incapable of capturing critical information regarding a number of activities and processes on a construction site, particularly those related to non-structural trades such as welding, inspection, and installation activities. In this research, a workflow based data fusion framework is developed for construction progress, quality and productivity assessment. The developed model is based on tracking construction activities as well as objects, in contrast to the existing sensor-based models that are focussed on tracking objects. Data sources include high frequency automated technologies including 3D imaging and ultra-wide band (UWB) positioning. Foreman reports, schedule information, and other data sources are included as well. Data fusion and management process workflow implementation via a distributed computing network and archiving using a cloud-based architecture are both illustrated. Validation was achieved using a detailed laboratory experimental program as well as an extensive field implementation project. The field implementation was conducted using five months of data acquired on the University of Waterloo Engineering VI construction project, yielding promising results. The data fusion processes of this research provide more accurate and more reliable progress and earned value estimates for construction project activities, while the developed data management processes enable the secure sharing and management of construction research data with the construction industry stakeholders as well as with researchers from other institutions.

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The amount surveillance footage recorded each day is too large for human operators to analyze. A video summary system to process and refine this video data would prove beneficial in many instances. This work defines the problem in terms of its inputs, outputs and sub-problems, identifies suitable techniques and existing works as well as describes a design of such system. The system is implemented, and the results are examined.

No description available.