A codificação de vídeo é um problema cuja solução deve ser projetada de acordo com as necessidades da aplicação desejada. Neste trabalho, um método de compressão de vídeo com escalabilidade é apresentado, apresentando melhorias dos formatos de compressão atuais. A escalabilidade corresponde a capacidade de extrair do bitstream completo, conjuntos eficientes de bits que são decodificados oferecendo imagens ou vídeos decodificados com uma variação (escala) segundo uma dada característica da imagem ou vídeo. O número de conjuntos que podem ser extraídos do bitstream completo definem a granularidade da escalabilidade fornecida, que pode ser muito fina ou com passos grossos. Muitas das técnicas de codificação escalável utilizam uma camada base que deve ser sempre decodificada e uma ou mais camadas superiores que permitem uma melhoria em termos de qualidade (SNR), resolução espacial e/ou resolução temporal. O esquema de codificação escalável final presente na norma MPEG-4 é uma das técnicas mais promissoras, pois pode adaptar-se às características dos canais (Internet) ou terminais que apresentam um comportamento variável ou desconhecido, como velocidade maxima de acesso, variações de largura de banda, erros de canal, etc. Apesar da norma MPEG-4 FGS se afirmar como uma alternativa viável para aplicações de distribuição de vídeo, possui uma quebra significativa de desempenho em comparação com a codificação não escalável de vídeo (perfil ASP da norma MPEG-4 Visual). Este trabalho tem por objetivo estudar novas ferramentas de codificação de vídeo introduzidas na recente norma H.264/AVC e MPEG-4 Visual, desenvolvendo um modelo que integre a escalabilidade granular presente no MPEG-4 aos avanços na área de codificação presentes no H.264/AVC. Esta estrutura de escalabilidade permite reduzir o custo em termos de eficiência da codificação escalável. Os resultados apresentados dentro de cada capítulo mostram a eficácia do método proposto bem como idéias para melhorias em trabalhos futuros. / Video encoding is a problem whose solution should be designed according to the need of intended application. This work presents a method of video compression with scalability that improves the current compression formats. Scalability represents the extracting capacity of full bitstream, efficient set of bits that are decoded to supply images or decoded videos with a variation according to a given image or video feature. A number of sets that can be extracted from full bitstream defines the supplied scalability granularity, which can be very thin or with thick steps. Most scalable video coding techniques use a base layer which must always be decoded and one or more higher layers which allow improvements in terms of quality (also known as SNR), frame/sampling rate or spatial resolution (for images and video). The MPEG-4 Fine Granularity Scalable (FGS) video coding scheme is one of the most promising techniques, because it can adapt itself to the features of channels (Internet) or terminals that present an unpredictable or unknown behavior, as maximum speed of access, variations of the bandwidth, channel errors, etc. Although the MPEG-4 FGS standard is a feasible solution for video streaming applications, it shows a significant loss of performance in comparison with non-scalable video coding, in particular the rather efficient Advanced Simple Profile defined in MPEG-4 Visual Standard. This work aims at studying new tools of video encoding introduced by the recent H.264/AVC norm and Visual MPEG-4, developing a model that integrates the granular scalability present in MPEG-4 to the coding improvements present in H.264/AVC. This new scalability structure allows cost reduction in terms of efficiency of the scalable coding. The results presented in each chapter show the effectiveness of the proposed method as well as ideas for improvements in future work.

Threshold logic gates gaining more importance in recent years due to the significant development in the switching devices. This renewed the interest in synthesis and testing of circuits with threshold logic gates. Two important synthesis considerations of threshold logic circuits are addressed namely, threshold logic function identification and reducing the total number of threshold logic gates required to represent the given boolean circuit description. A fast method to identify the given Boolean function as a threshold logic function with weight assignment is introduced. It characterizes the threshold logic function based on the modified chows parameters which results in drastic reduction in time and complexity. Experiment results shown that the proposed method is at least 10 times faster for each input and around 20 times faster for 7 and 8 input, when comparing with the algorithmic based methods. Similarly, it is 100 times faster for 8 input, when comparing with asummable method. Existing threshold logic synthesis methods decompose the larger input functions into smaller input functions and perform synthesis for them. This results in increase in the number of threshold logic gates required to represent the given circuit description. The proposed implicit synthesis methods increase the size of the functions that can be handled by the synthesis algorithm, thus the number of threshold logic gates required to implement very large input function decreases. Experiment results shown that the reduction in the TLG count is 24% in the best case and 18% on average. An automatic test pattern generation approach for transition faults on a circuit consisting of current mode threshold logic gates is introduced. The generated pattern for each fault excites the maximum propagation delay at the gate (the fault site). This is a high quality ATPG. Since current mode threshold logic gate circuits are pipelined and the combinational depth at each pipeline stage is practically one. It is experimentally shown that the fault coverage for all benchmark circuits is approximately 97%. It is also shown that the proposed method is time efficient.

A codificação de vídeo é um problema cuja solução deve ser projetada de acordo com as necessidades da aplicação desejada. Neste trabalho, um método de compressão de vídeo com escalabilidade é apresentado, apresentando melhorias dos formatos de compressão atuais. A escalabilidade corresponde a capacidade de extrair do bitstream completo, conjuntos eficientes de bits que são decodificados oferecendo imagens ou vídeos decodificados com uma variação (escala) segundo uma dada característica da imagem ou vídeo. O número de conjuntos que podem ser extraídos do bitstream completo definem a granularidade da escalabilidade fornecida, que pode ser muito fina ou com passos grossos. Muitas das técnicas de codificação escalável utilizam uma camada base que deve ser sempre decodificada e uma ou mais camadas superiores que permitem uma melhoria em termos de qualidade (SNR), resolução espacial e/ou resolução temporal. O esquema de codificação escalável final presente na norma MPEG-4 é uma das técnicas mais promissoras, pois pode adaptar-se às características dos canais (Internet) ou terminais que apresentam um comportamento variável ou desconhecido, como velocidade maxima de acesso, variações de largura de banda, erros de canal, etc. Apesar da norma MPEG-4 FGS se afirmar como uma alternativa viável para aplicações de distribuição de vídeo, possui uma quebra significativa de desempenho em comparação com a codificação não escalável de vídeo (perfil ASP da norma MPEG-4 Visual). Este trabalho tem por objetivo estudar novas ferramentas de codificação de vídeo introduzidas na recente norma H.264/AVC e MPEG-4 Visual, desenvolvendo um modelo que integre a escalabilidade granular presente no MPEG-4 aos avanços na área de codificação presentes no H.264/AVC. Esta estrutura de escalabilidade permite reduzir o custo em termos de eficiência da codificação escalável. Os resultados apresentados dentro de cada capítulo mostram a eficácia do método proposto bem como idéias para melhorias em trabalhos futuros. / Video encoding is a problem whose solution should be designed according to the need of intended application. This work presents a method of video compression with scalability that improves the current compression formats. Scalability represents the extracting capacity of full bitstream, efficient set of bits that are decoded to supply images or decoded videos with a variation according to a given image or video feature. A number of sets that can be extracted from full bitstream defines the supplied scalability granularity, which can be very thin or with thick steps. Most scalable video coding techniques use a base layer which must always be decoded and one or more higher layers which allow improvements in terms of quality (also known as SNR), frame/sampling rate or spatial resolution (for images and video). The MPEG-4 Fine Granularity Scalable (FGS) video coding scheme is one of the most promising techniques, because it can adapt itself to the features of channels (Internet) or terminals that present an unpredictable or unknown behavior, as maximum speed of access, variations of the bandwidth, channel errors, etc. Although the MPEG-4 FGS standard is a feasible solution for video streaming applications, it shows a significant loss of performance in comparison with non-scalable video coding, in particular the rather efficient Advanced Simple Profile defined in MPEG-4 Visual Standard. This work aims at studying new tools of video encoding introduced by the recent H.264/AVC norm and Visual MPEG-4, developing a model that integrates the granular scalability present in MPEG-4 to the coding improvements present in H.264/AVC. This new scalability structure allows cost reduction in terms of efficiency of the scalable coding. The results presented in each chapter show the effectiveness of the proposed method as well as ideas for improvements in future work.

A codificação de vídeo é um problema cuja solução deve ser projetada de acordo com as necessidades da aplicação desejada. Neste trabalho, um método de compressão de vídeo com escalabilidade é apresentado, apresentando melhorias dos formatos de compressão atuais. A escalabilidade corresponde a capacidade de extrair do bitstream completo, conjuntos eficientes de bits que são decodificados oferecendo imagens ou vídeos decodificados com uma variação (escala) segundo uma dada característica da imagem ou vídeo. O número de conjuntos que podem ser extraídos do bitstream completo definem a granularidade da escalabilidade fornecida, que pode ser muito fina ou com passos grossos. Muitas das técnicas de codificação escalável utilizam uma camada base que deve ser sempre decodificada e uma ou mais camadas superiores que permitem uma melhoria em termos de qualidade (SNR), resolução espacial e/ou resolução temporal. O esquema de codificação escalável final presente na norma MPEG-4 é uma das técnicas mais promissoras, pois pode adaptar-se às características dos canais (Internet) ou terminais que apresentam um comportamento variável ou desconhecido, como velocidade maxima de acesso, variações de largura de banda, erros de canal, etc. Apesar da norma MPEG-4 FGS se afirmar como uma alternativa viável para aplicações de distribuição de vídeo, possui uma quebra significativa de desempenho em comparação com a codificação não escalável de vídeo (perfil ASP da norma MPEG-4 Visual). Este trabalho tem por objetivo estudar novas ferramentas de codificação de vídeo introduzidas na recente norma H.264/AVC e MPEG-4 Visual, desenvolvendo um modelo que integre a escalabilidade granular presente no MPEG-4 aos avanços na área de codificação presentes no H.264/AVC. Esta estrutura de escalabilidade permite reduzir o custo em termos de eficiência da codificação escalável. Os resultados apresentados dentro de cada capítulo mostram a eficácia do método proposto bem como idéias para melhorias em trabalhos futuros. / Video encoding is a problem whose solution should be designed according to the need of intended application. This work presents a method of video compression with scalability that improves the current compression formats. Scalability represents the extracting capacity of full bitstream, efficient set of bits that are decoded to supply images or decoded videos with a variation according to a given image or video feature. A number of sets that can be extracted from full bitstream defines the supplied scalability granularity, which can be very thin or with thick steps. Most scalable video coding techniques use a base layer which must always be decoded and one or more higher layers which allow improvements in terms of quality (also known as SNR), frame/sampling rate or spatial resolution (for images and video). The MPEG-4 Fine Granularity Scalable (FGS) video coding scheme is one of the most promising techniques, because it can adapt itself to the features of channels (Internet) or terminals that present an unpredictable or unknown behavior, as maximum speed of access, variations of the bandwidth, channel errors, etc. Although the MPEG-4 FGS standard is a feasible solution for video streaming applications, it shows a significant loss of performance in comparison with non-scalable video coding, in particular the rather efficient Advanced Simple Profile defined in MPEG-4 Visual Standard. This work aims at studying new tools of video encoding introduced by the recent H.264/AVC norm and Visual MPEG-4, developing a model that integrates the granular scalability present in MPEG-4 to the coding improvements present in H.264/AVC. This new scalability structure allows cost reduction in terms of efficiency of the scalable coding. The results presented in each chapter show the effectiveness of the proposed method as well as ideas for improvements in future work.

Demand for wearable electronics is expected to at least triple by 2020, embracing all sorts of Internet of Everything (IoE) applications, such as activity tracking, environmental mapping, and advanced healthcare monitoring, in the purpose of enhancing the quality of life. This entails the wide availability of free-form multifunctional sensory systems (i.e “skin” platforms) that can conform to the variety of uneven surfaces, providing intimate contact and adhesion with the skin, necessary for localized and enhanced sensing capabilities. However, current wearable devices appear to be bulky, rigid and not convenient for continuous wear in everyday life, hindering their implementation into advanced and unexplored applications beyond fitness tracking. Besides, they retail at high price tags which limits their availability to at least half of the World’s population. Hence, form factor (physical flexibility and/or stretchability), cost, and accessibility become the key drivers for further developments. To support this need in affordable and adaptive wearables and drive academic developments in “skin” platforms into practical and functional consumer devices, compatibility and integration into a high performance yet low power system is crucial to sustain the high data rates and large data management driven by IoE. Likewise, scalability becomes essential for batch fabrication and precision. Therefore, I propose to develop three distinct but necessary “skin” platforms using scalable and cost effective manufacturing techniques. My first approach is the fabrication of a CMOS-compatible “silicon skin”, crucial for any truly autonomous and conformal wearable device, where monolithic integration between heterogeneous material-based sensory platform and system components is a challenge yet to be addressed. My second approach displays an even more affordable and accessible “paper skin”, using recyclable and off-the-shelf materials, targeting environmental mapping through 3D stacked arrays, or advanced personalized healthcare through the developed “paper watch” prototype. My last approach targets a harsh environment waterproof “marine skin” tagging system, using marine animals as allies to study the marine ecosystem. The “skin” platforms offer real-time and simultaneous monitoring while preserving high performance and robust behaviors under various bending conditions, maintaining system compatibility using cost-effective and scalable approaches for a tangible realization of a truly flexible wearable device.

Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research.

Ingeniero Civil Eléctrico / En tiempos modernos la envergadura del contenido multimedia avanza más rápido que el desarrollo de las tecnologías necesarias para su correcta difusión a través de la red. Es por esto que se hacen necesarios nuevos protocolos que sirvan como puente entre ambas entidades para así obtener un máximo de provecho del contenido a pesar de que la tecnología para distribuirlos aún no sea la adecuada. Es así, que dentro de las últimas tecnologías de compresión de video se encuentra Scalable Video Coding (SVC), la cual tiene por objetivo codi car distintas calidades en un único bitstream capaz de mostrar cualquiera de las calidades embebidas en éste según se reciba o no toda la información. En el caso de una conexión del tipo streaming, en donde es necesaria una uidez y delidad en ambos extremos, la tecnología SVC tiene un potencial muy grande respecto de descartar un mínimo de información para privilegiar la uidez de la transmisión. El software utilizado para la creación y manipulación de estos bitstreams SVC es Joint Scalable Video Model (JSVM). En este contexto, se desarrolla el algoritmo de deadline en Matlab, que omite informaci ón del video SVC de acuerdo a qué tan crítico sea el escenario de transmisión. En este escenario se considera la percepción de uidez del usuario como medida clave, por lo cual se prioriza mantener siempre una tasa de 30 fps a costa de una pérdida de calidad mínima. El algoritmo, omite información de acuerdo a qué tan lejos se esté de este deadline de 30 fps, si se está muy lejos, se omite información poco relevante, y si se está muy cerca, información más importante. Los resultados se contrastan con TCP y se evalúan para distintos valores de RTTs, cumpliendo totalmente el objetivo para valores menores a 150 ms que resultan en diferencias de hasta 20 s a favor del algoritmo de deadline al término de la transmisión. Esta mejora en tiempo de arribo no descarta información esencial y sólo degrada ligeramente la calidad del video en pos de mantener la tasa de 30fps. Por el contrario, en escenarios muy adversos de 300 ms en RTT, las omisiones son de gran envergadura y comprometen frames completos, en conjunto con una degradación generalizada del video y la aparición de artefactos en éste. Por tanto la propuesta cumple los objetivos en ambientes no muy adversos. Para toda la simulación se uso un video en movimiento de 352x288 y 150 frames de largo.

<div>Elasticity is an essential feature for cloud applications to handle varying and unpredictable workloads in a cost-effective way on cloud platforms. However, implementing a stateful elastic application is hard, as programmers have to: (1) reason about concurrent execution in the applications (serializability); (2) guarantee the application can process more requests with larger scale (scalability); and (3) provide elasticity management to improve performance and resource efficiency for applications (efficient elasticity management). Unfortunately, addressing all those concerns requires deep understanding and rich experience in distributed systems and cloud computing. </div><div> </div><div>In this dissertation, we provide programming support to help programmers implement their stateful elastic cloud applications in a simpler manner. Specifically, we present AEON, an actor-based programming language, and \arch, an elastic programming framework. On the one hand, AEON provides programmers with scalability and serialzability, executing actor-based programs in a serialized manner while still retaining a high degree of parallelism. Meanwhile, AEON can adjust programs' scale via fine-grained live actor migration. On the other hand, PLASMA includes (1) an elastic programming language as a second ``level'' of programming (complementing the main application programming language) for describing elasticity behaviors, and (2) a novel semantics-aware elasticity management runtime that tracks program execution and acts upon application features as suggested by elasticity behaviors. </div><div>With these, PLASMA can provide efficient elasticity management to cloud applications</div>

This paper presents a novel implementation of parallel sparse matrix-matrix multiplication using distributed memory systems on heterogeneous hardware architecture. The proposed algorithm is expected to be linearly scalable up to several thousands of processors for matrices with dimensions over 106 (million). Our approach of parallelism is based on 1D decomposition and can work for both structured and unstructured sparse matrices. The storage mechanism is based on distributed hash lists, which reduces the latency for accessing and modifying an element of the product matrix, while reducing the overall merging time of the partial results computed by the processors. Theoretically, the time and space complexity of our algorithm is linearly proportional to the total number of non-zero elements in the product matrix C. The results of the performance evaluation show that the algorithm scales much better for sparse matrices with bigger dimensions. The speedup achieved using our algorithm is much better than other existing 1D algorithms. We have been able to achieve about 500 times speedup with only 672 processors. We also identified the impact of hardware architecture on scalability.

Shrinking minimum feature size in deep sub-micron has made fabrication of progressively faster devices possible. The performance of interconnects has been a bottleneck in determining the overall performance of a chip. A reliable high-speed communication technique is necessary to improve the performance of on-chip communication. Recent publications have demonstrated that use of multiple threshold voltages improves the performance of a bus significantly. The multi-threshold capture mechanism takes advantage of predictable temporal behavior of a tightly coupled bus to predict the next state of the bus early. However, Use of multiple threshold voltages also reduces the voltage slack and consequently increases the susceptibility to noise. Reduction in supply voltage exacerbates the situation. This work proposes a novel error detection and correction encoding technique that takes advantage of the high performance of the multi-threshold capture mechanism as well as its inbuilt redundancy to achieve reliable high-speed communication while introducing considerably less amount of redundancy as compared to the conventional methods. The proposed technique utilizes graph-based algorithms to produce a set of valid code words. The algorithm takes advantage of implicit set operations using binary decision diagram to improve the scalability of the code word selection process. The code words of many crosstalk avoidance codes including the proposed error detection and correction technique exhibit a highly structured behavior. The sets of larger valid code words can be recursively formed using the sets of smaller valid code words. This work also presents a generalized framework for scalable on-chip code word generation. The proposed CODEC implementation strategy uses a structured graph to model the recursive nature of an encoding technique that facilitates scalable CODEC implementation. The non-enumerative nature of the implementation strategy makes it highly scalable. The modular nature of the CODEC also simplifies use of pipelined architecture thereby improving the throughput of the bus.