691 resultados para Problem-based learning.
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The amplitude-modulation (AM) and phase-modulation (PM) of an amplitude-modulated frequency-modulated (AM-FM) signal are defined as the modulus and phase angle, respectively, of the analytic signal (AS). The FM is defined as the derivative of the PM. However, this standard definition results in a PM with jump discontinuities in cases when the AM index exceeds unity, resulting in an FM that contains impulses. We propose a new approach to define smooth AM, PM, and FM for the AS, where the PM is computed as the solution to an optimization problem based on a vector interpretation of the AS. Our approach is directly linked to the fractional Hilbert transform (FrHT) and leads to an eigenvalue problem. The resulting PM and AM are shown to be smooth, and in particular, the AM turns out to be bipolar. We show an equivalence of the eigenvalue formulation to the square of the AS, and arrive at a simple method to compute the smooth PM. Some examples on synthesized and real signals are provided to validate the theoretical calculations.
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Among the intelligent safety technologies for road vehicles, active suspensions controlled by embedded computing elements for preventing rollover have received a lot of attention. The existing models for synthesizing and allocating forces in such suspensions are conservatively based on the constraints that are valid until no wheels lift off the ground. However, the fault tolerance of the rollover-preventive systems can be enhanced if the smart/active suspensions can intervene in the more severe situation in which the wheels have just lifted off the ground. The difficulty in computing control in the last situation is that the vehicle dynamics then passes into the regime that yields a model involving disjunctive constraints on the dynamics. Simulation of dynamics with disjunctive constraints in this context becomes necessary to estimate, synthesize, and allocate the intended hardware realizable forces in an active suspension. In this paper, we give an algorithm for the previously mentioned problem by solving it as a disjunctive dynamic optimization problem. Based on this, we synthesize and allocate the roll-stabilizing time-dependent active suspension forces in terms of sensor output data. We show that the forces obtained from disjunctive dynamics are comparable with existing force allocations and, hence, are possibly realizable in the existing hardware framework toward enhancing the safety and fault tolerance.
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Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.
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Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).
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Este informe recoge las guías del docente y del estudiante para la puesta en marcha, seguimiento continuo y evaluación de la asignatura Ingeniería del Software del segundo curso del Grado en Ingeniería Informática. Todo ello basado en metodologías activas, concretamente la metodología de Aprendizaje Basado en Proyectos (ABP, o PBL de Project Based Learning). El trabajo publicado en este informe es el resultado obtenido por los autores dentro del programa de formación del profesorado en metodologías activas (ERAGIN), auspiciado por el Vicerrectorado de Calidad e Innovación Docente de la Universidad del País Vasco (UPV/EHU).
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Se presentan herramientas y material de apoyo para aplicar la metodología de aprendizaje cooperativo en la docencia universitaria, con una orientación especial a ingeniería.
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Networks for Knowledge (n4k Ltd) are a Work Based Learning organisation which specialises in Early Years Education. Training and Development Manager, Elaine Wareing has developed the use of Facebook and Twitter to promote peer learning and interaction beyond the classroom. It also allows trainers to discuss ideas and challengers with a wider group of learners. This has allowed practitioners across a wide geographical area to share their thoughts and ideas together on some of the subjects relating to early years practice.
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Headjogs is a well established hair academy within the Essex and East of London region. This work-based learning provider is run by Stephen Daly, Director, and Debbie Scott. Around six months ago, Debbie participated in the e-Guides programme, where she gained the expertise to implement innovative technology into the curriculum. Since then they have received Association of Learning Providers (ALP) Learner Innovation Grant funding with which they plan to reinforce their innovative approach to hairdressing.
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Work-based learning provider, KEITS has improved efficiencies and institutional effectiveness with the introduction of Blackberry mobile devices to all their assessors. They have saved time and money on paper and travel, diversified evidence capture and improved their overall engagement with learners.
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Crossing the Threshold, one of a series of advice and guidance publications, is designed to support use of the online e-Portfolio Implementation Toolkit and video case studies by those involved in wide-scale implementation of e-portfolios in their institutions. As the resources address the needs of both managers and practitioners, the publication has relevance for a wide range of readers in further and higher education and work-based learning. To assist the planning and effective management of a large-scale e-portfolio implementation, Crossing the Threshold follows the stages of an implementation journey with insights and guidance drawn from the toolkit and its supporting case studies. Links are provided throughout the publication to more detailed information in the two online resources.
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Effective Practice in a Digital Age is designed for those in further and higher education who aim to enhance the student learning experience through apt and imaginative uses of technology. A visually rich publication, Effective Practice in a Digital Age outlines key aspects of designing learning in a technology-rich context and is structured to address the needs of experienced practitioners as well as those new to technology-based learning and teaching – the ten newly researched case studies offer a choice of pathways reflecting the diversity of approaches taken by practitioners in current UK practice.
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Duración (en horas): Más de 50 horas. Destinatario: Estudiante y Docente
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XX1 CUIEET - Congreso Universitario de Innovación Educativa en las Enseñanzas Técnicas, Valencia, 2013.
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240 p.
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Os recentes avanços tecnológicos fizeram aumentar o nível de qualificação do pesquisador em epidemiologia. A importância do papel estratégico da educação não pode ser ignorada. Todavia, a Associação Brasileira de Pós-graduação em Saúde Coletiva (ABRASCO), no seu último plano diretor (2005-2009), aponta uma pequena valorização na produção de material didático-pedagógico e, ainda, a falta de uma política de desenvolvimento e utilização de software livre no ensino da epidemiologia. É oportuno, portanto, investir em uma perspectiva relacional, na linha do que a corrente construtivista propõe, uma vez que esta teoria tem sido reconhecida como a mais adequada no desenvolvimento de materiais didáticos informatizados. Neste sentido, promover cursos interativos e, no bojo destes, desenvolver material didático conexo é oportuno e profícuo. No âmbito da questão política de desenvolvimento e utilização de software livre no ensino da epidemiologia, particularmente em estatística aplicada, o R tem se mostrado um software de interesse emergente. Ademais, não só porque evita possíveis penalizações por utilização de software comercial sem licença, mas também porque o franco acesso aos códigos e programação o torna uma ferramenta excelente para a elaboração de material didático em forma de hiperdocumentos, importantes alicerces para uma tão desejada interação docentediscente em sala de aula. O principal objetivo é desenvolver material didático em R para os cursos de bioestatística aplicada à análise epidemiológica. Devido a não implementação de certas funções estatísticas no R, também foi incluída a programação de funções adicionais. Os cursos empregados no desenvolvimento desse material fundamentaram-se nas disciplinas Uma introdução à Plataforma R para Modelagem Estatística de Dados e Instrumento de Aferição em Epidemiologia I: Teoria Clássica de Medidas (Análise) vinculadas ao departamento de Epidemiologia, Instituto de Medicina Social (IMS) da Universidade do Estado do Rio de Janeiro (UERJ). A base teórico-pedagógica foi definida a partir dos princípios construtivistas, na qual o indivíduo é agente ativo e crítico de seu próprio conhecimento, construindo significados a partir de experiências próprias. E, à ótica construtivista, seguiu-se a metodologia de ensino da problematização, abrangendo problemas oriundos de situações reais e sistematizados por escrito. Já os métodos computacionais foram baseados nas Novas Tecnologias da Informação e Comunicação (NTIC). As NTICs exploram a busca pela consolidação de currículos mais flexíveis, adaptados às características diferenciadas de aprendizagem dos alunos. A implementação das NTICs foi feita através de hipertexto, que é uma estrutura de textos interligados por nós ou vínculos (links), formando uma rede de informações relacionadas. Durante a concepção do material didático, foram realizadas mudanças na interface básica do sistema de ajuda do R para garantir a interatividade aluno-material. O próprio instrutivo é composto por blocos, que incentivam a discussão e a troca de informações entre professor e alunos.