Energy Management and Optimization for Video Decoders based on Functional-oriented Reconfiguration

In recent years, the increasing sophistication of embedded multimedia systems and wireless communication technologies has promoted a widespread utilization of video streaming applications. It has been reported in 2013 that youngsters, aged between 13 and 24, spend around 16.7 hours a week watching online video through social media, business websites, and video streaming sites. Video applications have already been blended into people daily life. Traditionally, video streaming research has focused on performance improvement, namely throughput increase and response time reduction. However, most mobile devices are battery-powered, a technology that grows at a much slower pace than either multimedia or hardware developments. Since battery developments cannot satisfy expanding power demand of mobile devices, research interests on video applications technology has attracted more attention to achieve energy-efficient designs. How to efficiently use the limited battery energy budget becomes a major research challenge. In addition, next generation video standards impel to diversification and personalization. Therefore, it is desirable to have mechanisms to implement energy optimizations with greater flexibility and scalability. In this context, the main goal of this dissertation is to find an energy management and optimization mechanism to reduce the energy consumption of video decoders based on the idea of functional-oriented reconfiguration. System battery life is prolonged as the result of a trade-off between energy consumption and video quality. Functional-oriented reconfiguration takes advantage of the similarities among standards to build video decoders reconnecting existing functional units. If a feedback channel from the decoder to the encoder is available, the former can signal the latter changes in either the encoding parameters or the encoding algorithms for energy-saving adaption. The proposed energy optimization and management mechanism is carried out at the decoder end. This mechanism consists of an energy-aware manager, implemented as an additional block of the reconfiguration engine, an energy estimator, integrated into the decoder, and, if available, a feedback channel connected to the encoder end. The energy-aware manager checks the battery level, selects the new decoder description and signals to build a new decoder to the reconfiguration engine. It is worth noting that the analysis of the energy consumption is fundamental for the success of the energy management and optimization mechanism. In this thesis, an energy estimation method driven by platform event monitoring is proposed. In addition, an event filter is suggested to automate the selection of the most appropriate events that affect the energy consumption. At last, a detailed study on the influence of the training data on the model accuracy is presented. The modeling methodology of the energy estimator has been evaluated on different underlying platforms, single-core and multi-core, with different characteristics of workload. All the results show a good accuracy and low on-line computation overhead. The required modifications on the reconfiguration engine to implement the energy-aware manager have been assessed under different scenarios. The results indicate a possibility to lengthen the battery lifetime of the system in two different use-cases.

Finite element code-based modeling of a multi-feature isolation system and passive alleviation of possible inner pounding

The existing seismic isolation systems are based on well-known and accepted physical principles, but they are still having some functional drawbacks. As an attempt of improvement, the Roll-N-Cage (RNC) isolator has been recently proposed. It is designed to achieve a balance in controlling isolator displacement demands and structural accelerations. It provides in a single unit all the necessary functions of vertical rigid support, horizontal flexibility with enhanced stability, resistance to low service loads and minor vibration, and hysteretic energy dissipation characteristics. It is characterized by two unique features that are a self-braking (buffer) and a self-recentering mechanism. This paper presents an advanced representation of the main and unique features of the RNC isolator using an available finite element code called SAP2000. The validity of the obtained SAP2000 model is then checked using experimental, numerical and analytical results. Then, the paper investigates the merits and demerits of activating the built-in buffer mechanism on both structural pounding mitigation and isolation efficiency. The paper addresses the problem of passive alleviation of possible inner pounding within the RNC isolator, which may arise due to the activation of its self-braking mechanism under sever excitations such as near-fault earthquakes. The results show that the obtained finite element code-based model can closely match and accurately predict the overall behavior of the RNC isolator with effectively small errors. Moreover, the inherent buffer mechanism of the RNC isolator could mitigate or even eliminate direct structure-tostructure pounding under severe excitation considering limited septation gaps between adjacent structures. In addition, the increase of inherent hysteretic damping of the RNC isolator can efficiently limit its peak displacement together with the severity of the possibly developed inner pounding and, therefore, alleviate or even eliminate the possibly arising negative effects of the buffer mechanism on the overall RNC-isolated structural responses.

3D simulation of complex shading affecting PV systems taking benefit from the power of graphics cards developed for the video game industry

Shading reduces the power output of a photovoltaic (PV) system. The design engineering of PV systems requires modeling and evaluating shading losses. Some PV systems are affected by complex shading scenes whose resulting PV energy losses are very difficult to evaluate with current modeling tools. Several specialized PV design and simulation software include the possibility to evaluate shading losses. They generally possess a Graphical User Interface (GUI) through which the user can draw a 3D shading scene, and then evaluate its corresponding PV energy losses. The complexity of the objects that these tools can handle is relatively limited. We have created a software solution, 3DPV, which allows evaluating the energy losses induced by complex 3D scenes on PV generators. The 3D objects can be imported from specialized 3D modeling software or from a 3D object library. The shadows cast by this 3D scene on the PV generator are then directly evaluated from the Graphics Processing Unit (GPU). Thanks to the recent development of GPUs for the video game industry, the shadows can be evaluated with a very high spatial resolution that reaches well beyond the PV cell level, in very short calculation times. A PV simulation model then translates the geometrical shading into PV energy output losses. 3DPV has been implemented using WebGL, which allows it to run directly from a Web browser, without requiring any local installation from the user. This also allows taken full benefits from the information already available from Internet, such as the 3D object libraries. This contribution describes, step by step, the method that allows 3DPV to evaluate the PV energy losses caused by complex shading. We then illustrate the results of this methodology to several application cases that are encountered in the world of PV systems design. Keywords: 3D, modeling, simulation, GPU, shading, losses, shadow mapping, solar, photovoltaic, PV, WebGL

Development of a visual object tracking system using a compressive sensing framework for modeling the appearance

En el presente trabajo se aborda el problema del seguimiento de objetos, cuyo objetivo es encontrar la trayectoria de un objeto en una secuencia de video. Para ello, se ha desarrollado un método de seguimiento-por-detección que construye un modelo de apariencia en un dominio comprimido usando una nueva e innovadora técnica: “compressive sensing”. La única información necesaria es la situación del objeto a seguir en la primera imagen de la secuencia. El seguimiento de objetos es una aplicación típica del área de visión artificial con un desarrollo de bastantes años. Aun así, sigue siendo una tarea desafiante debido a varios factores: cambios de iluminación, oclusión parcial o total de los objetos y complejidad del fondo de la escena, los cuales deben ser considerados para conseguir un seguimiento robusto. Para lidiar lo más eficazmente posible con estos factores, hemos propuesto un algoritmo de tracking que entrena un clasificador Máquina Vector Soporte (“Support Vector Machine” o SVM en sus siglas en inglés) en modo online para separar los objetos del fondo de la escena. Con este fin, hemos generado nuestro modelo de apariencia por medio de un descriptor de características muy robusto que describe los objetos y el fondo devolviendo un vector de dimensiones muy altas. Por ello, se ha implementado seguidamente un paso para reducir la dimensionalidad de dichos vectores y así poder entrenar nuestro clasificador en un dominio mucho menor, al que denominamos domino comprimido. La reducción de la dimensionalidad de los vectores de características se basa en la teoría de “compressive sensing”, que dice que una señal con poca dispersión (pocos componentes distintos de cero) puede estar bien representada, e incluso puede ser reconstruida, a partir de un conjunto muy pequeño de muestras. La teoría de “compressive sensing” se ha aplicado satisfactoriamente en este trabajo y diferentes técnicas de medida y reconstrucción han sido probadas para evaluar nuestros vectores reducidos, de tal forma que se ha verificado que son capaces de preservar la información de los vectores originales. También incluimos una actualización del modelo de apariencia del objeto a seguir, mediante el reentrenamiento de nuestro clasificador en cada cuadro de la secuencia con muestras positivas y negativas, las cuales han sido obtenidas a partir de la posición predicha por el algoritmo de seguimiento en cada instante temporal. El algoritmo propuesto ha sido evaluado en distintas secuencias y comparado con otros algoritmos del estado del arte de seguimiento, para así demostrar el éxito de nuestro método.

Desenvolvimento de video educativo para a aprendizagem do autocateterismo vesical intermitente

A bexiga neurogênica é uma disfunção vesical decorrente principalmente da lesão medular. O cateterismo vesical intermitente é o tratamento mais indicado na atualidade, deve ser realizado de 4 a 6 vezes ao dia, durante toda a vida, visando a proteção do trato urinário superior e a aquisição da continência urinária. Na reabilitação desses indivíduos, a autocateterização vesical é um desafio enfrentado na busca pela autonomia, privacidade, inserção social e participação. Os vídeos educativos são utilizados para o aprendizado do autocateterismo em vários países, por serem de fácil utilização e acesso via internet. Apesar disso, não existem vídeos realizados para o contexto brasileiro, levando em consideração os cateteres urinários e a técnica utilizada no Brasil. Este estudo teve como objetivo desenvolver e validar um vídeo educativo para a realização do autocateterismo vesical intermitente limpo. Trata-se de um estudo quantitativo, desenvolvido em duas fases: 1ª fase com a avaliação de vídeos educativos públicos direcionados para a aprendizagem do autocateterismo vesical intermitente com a técnica limpa; e 2ª Fase, com o desenvolvimento e validação de um vídeo educativo voltado para aprendizagem do autocateterismo. O levantamento dos vídeos utilizou um site de compartilhamento de vídeos utilizando o descritor \"autocateterismo\". Os vídeos foram avaliados por três juízes da área de saúde. O processo de desenvolvimento e validação do roteiro do vídeo educativo utilizou questionários previamente ratificados. Participaram dessas etapas, respectivamente, 18 e 17 juízes experts em reabilitação e/ou no ensino em saúde. O levantamento mostrou que apenas 3,5% (172) do total de vídeos disponíveis no site pesquisado eram voltados para o aprendizado do autocateterismo no contexto brasileiro. Seis vídeos eram específicos para o autocateterismo, dos quais quatro tinham informações desatualizadas ou incorretas, apenas dois atingiram a pontuação aceitável. Na validação do roteiro observou-se um predomínio de participantes do sexo feminino (94,44%), com idade de 30 a 60 anos, dos quais 72,22% possuíam mestrado e 50% atuavam há mais de cinco anos na área de reabilitação. O roteiro foi considerado validado com 96,29% das respostas dos juízes \"concordo\" ou \"concordo totalmente\" nas questões referentes ao quesito objetivo, 91,09% para quesito conteúdo, 98,12% em relação ao quesito relevância, 75% quanto ao quesito ambiente, 71,11% no quesito linguagem verbal e 92,70% referente à inclusão de tópicos. A produção do vídeo contou com uso de tecnologia 3D e apoio de uma equipe técnica especializada. No que se refere à validação do conteúdo do vídeo educativo, o conteúdo do vídeo foi considerado validado com 100% dos juízes que responderam \"concordo\" ou \"concordo totalmente\" nas questões referentes à funcionalidade, 86,27% referentes à usabilidade, 97,06% no quesito eficiência, 100% para técnica audiovisual, 94,11% quanto ao ambiente e 97,05% procedimento. O vídeo educativo foi avaliado positivamente tanto pela qualidade das informações quanto pela didática do ensino, mostrando a relevância da validação de materiais educativos. A expectativa é disseminar o vídeo educativo em diferentes centros de reabilitação e Universidades, visando propagar e tornar o conhecimento sobre a temática mais acessível à sociedade e aos profissionais de saúde, em especial os de reabilitação. Além de incentivar e embasar metodologicamente o desenvolvimento de outros vídeos educativos na área da saúde

A Comparative Study of Registration Methods for RGB-D Video of Static Scenes

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

Self-Reported Fall and Associate Risk Factors Among Korean American Adults In Washington State

Thesis (Master's)--University of Washington, 2016-06

A longitudinal investigation of self-other discrimination and the emergence of mirror self-recognition

The aim in this study was to investigate the association between infants' developing interest in their self-image and the onset of mirror self-recognition (MSR). A longitudinal study was conducted with 98 infants who were seen at intervals of 3 months from 9-24 months of age. At each session, the infants were administered a preferential-looking test whereby they were presented with a video image of themselves alongside a video image of a same-aged peer in two conditions, unmarked and marked. From the 12-month session onwards, the infants were also administered a version of the standard mark test of MSR. The infants showed a significant preference for looking at images of themselves in both conditions coincident with the onset of MSR. This result indicates that developing an interest in the self-image is an important component in the development of MSR. (C) 2003 Elsevier Science Inc. All rights reserved.

Modeling the molecular weight distribution of block copolymer formation in a reversible addition-fragmentation chain transfer mediated living radical polymerization

Block copolymers have become an integral part of the preparation of complex architectures through self-assembly. The use of reversible addition-fragmentation chain transfer (RAFT) allows blocks ranging from functional to nonfunctional polymers to be made with predictable molecular weight distributions. This article models block formation by varying many of the kinetic parameters. The simulations provide insight into the overall polydispersities (PDIs) that will be obtained when the chain-transfer constants in the main equilibrium steps are varied from 100 to 0.5. When the first dormant block [polymer-S-C(Z)=S] has a PDI of 1 and the second propagating radical has a low reactivity to the RAFT moiety, the overall PDI will be greater than 1 and dependent on the weight fraction of each block. When the first block has a PDI of 2 and the second propagating radical has a low reactivity to the RAFT moiety, the PDI will decrease to around 1.5 because of random coupling of two broad distributions. It is also shown how we can in principle use only one RAFT agent to obtain block copolymers with any desired molecular weight distribution. We can accomplish this by maintaining the monomer concentration at a constant level in the reactor over the course of the reaction. (c) 2005 Wiley Periodicals, Inc.

Mirror self-recognition beyond the face

Three studies (N=144) investigated how toddlers aged 18 and 24 months pass the surprise-mark test of self-recognition. In Study 1, toddlers were surreptitiously marked in successive conditions on their legs and faces with stickers visible only in a mirror. Rates of sticker touching did not differ significantly between conditions. In Study 2, toddlers failed to touch a sticker on their legs that had been disguised before being marked. In Study 3, having been given 30-s exposure to their disguised legs before testing, toddlers touched the stickers on their legs and faces at equivalent levels. These results suggest that toddlers pass the mark test based on expectations about what they look like, expectations that are not restricted to the face.

The development of self-recognition in mirrors and live videos

By 24-months of age most children show mirror self-recognition. When surreptitiously marked on their forehead and then presented with a mirror, they explore their own head for the unexpected mark. Here we demonstrate that self-recognition in mirrors does not generalize to other visual feedback. We tested 80 children on mirror and live video versions of the task. Whereas 90% of 24-month olds passed the mirror version, only 35% passed the video version. Seventy percent of 30-month olds showed video selfrecognition and only by age 36-months did the pass rate on the video version reach 90%. It remains to be y 24-months of age most children show mirror self-recognition. When surreptitiously marked on their forehead and then presented with a mirror, they explore their own head for the unexpected mark. Here we demonstrate that self-recognition in mirrors does not generalize to other visual feedback. We tested 80 children on mirror and live video versions of the task. Whereas 90% of 24-month olds passed the mirror version, only 35% passed the video version. Seventy percent of 30-month olds showed video selfrecognition and only by age 36-months did the pass rate on the video version reach 90%. It remains to be

Industry evolution and cross-sectoral skill transfers:a comparative analysis of the video game industry in Japan, the United States, and the United Kingdom

In this paper we explore the interrelationship between technological progress and the formation of industry-specific skills by analysing the evolution of the video-game industry in three countries: Japan, the United States, and the United Kingdom. We argue that the cross-sectoral transfer of skills occurs differently depending on national contexts, such as the social legitimacy and strength of preexisting industries, the socioeconomic status of entrepreneurs or pioneer firms in an emerging industry, and the sociocultural cohesiveness between the preexisting and emerging industries. Each country draws on a different set of creative resources, which results in a unique trajectory. Whereas Japan's video-game industry emerged out of corporate sponsorships in arcades, toys, and consumer electronics industries and drew skills from the comic book and animated-film sectors, the video-game industry in the United States evolved from arcades and personal computers. In the United Kingdom the video-game industry developed bottom-up, through a process of skills formation in the youth culture of 'bedroom coders' that nurtured self-taught programmers in their teens throughout the country.

Laser beam self-focusing in the atmosphere

We propose to exploit a self-focusing effect in the atmosphere to assist delivering powerful laser beams from orbit to the ground. We demonstrate through numerical modeling that when the self-focusing length is comparable with the atmosphere height the spot size on the ground can be reduced well below the diffraction limits without beam quality degradation. The density variation suppresses beam filamentation and provides the self-focusing of the beam as a whole. The use of light self-focusing in the atmosphere can greatly relax the requirements for the orbital optics and ground receivers.