Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T17:06:49Z No. of bitstreams: 2 Dissertacao Telmo Silva Filho.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:23:59Z (GMT) No. of bitstreams: 2 Dissertacao Telmo Silva Filho.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-13T13:23:59Z (GMT). No. of bitstreams: 2 Dissertacao Telmo Silva Filho.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-27 / A Análise de Dados Simbólicos lida com tipos de dados complexos, capazes de modelar a variabilidade interna dos dados e dados imprecisos. Dados simbólicos intervalares surgem naturalmente de valores como variação de temperatura diária, pressão sanguínea, entre outros. Esta dissertação introduz um algoritmo de Learning Vector Quantization para dados simbólicos intervalares, que usa uma distância Euclidiana intervalar ponderada e generalizada para medir a distância entre instâncias de dados e protótipos. A distância proposta tem quatro casos especiais. O primeiro caso é a distância Euclidiana intervalar e tende a modelar classes e clusters com formas esféricas. O segundo caso é uma distância intervalar baseada em protótipos que modela subregiões não-esféricas e de tamanhos similares dentro das classes. O terceiro caso permite à distância lidar com subregiões não-esféricas e de tamanhos variados dentro das classes. O último caso permite à distância modelar classes desbalanceadas, compostas de subregiões de várias formas e tamanhos. Experimentos são feitos para avaliar os desempenhos do Learning Vector Quantization intervalar proposto, usando todos os quatro casos da distância proposta. Três conjuntos de dados intervalares sintéticos e um conjunto de dados intervalares reais são usados nesses experimentos e seus resultados mostram a utilidade de uma distância localmente ponderada.

This master thesis consists of implementation and evaluation of an AEC, Acoustic Echo Canceller, algorithm in a floating-point architecture. The most important question this thesis will try to answer is to determine benefits or drawbacks of using a floating-point architecture, relative a fixed-point architecture, to do AEC. In a telephony system there is two common forms of echo, line echo and acoustic echo. Acoustic echo is introduced by sound emanating from a loudspeaker, e.g. in a handsfree or speakerphone, being picked up by a microphone and then sent back to the source. The problem with this feedback is that the far-end speaker will hear one, or multiple, time-delayed version(s) of her own speech. This time-delayed version of speech is usually perceived as both confusing and annoying unless removed by the use of AEC. In this master thesis the performance of a floating-point version of a normalized least-mean-square AEC algorithm was evaluated in an environment designed and implemented to approximate live telephony calls. An instruction-set simulator and assembler available at the initiation of this master thesis were extended to enable; zero-overhead loops, modular addressing, post-increment of registers and register-write forwarding. With these improvements a bit-true assembly version was implemented capable of real-time AEC requiring 15 million instructions per second. A solution using as few as eight mantissa bits, in an external format used when storing data in memory, was found to have an insignificant effect on the selected AEC implementation’s performance. Due to the relatively low memory requirement of the selected AEC algorithm, the use of a small external format has a minor effect on the required memory size. In total this indicates that the possible reduction of the memory requirement and related energy consumption, does not justify the added complexity and energy consumption of using a floating-point architecture for the selected algorithm. Use of a floating-point format can still be advantageous in speech-related signal processing when the introduced time delay by a subband, or a similar frequency domain, solution is unacceptable. Speech algorithms that have high memory use and small introduced delay requirements are a good candidate for a floating-point digital signal processor architecture.

I investigate some algebras and calculi naturally associated with the symplectic and metric Clifford algebras. In particular, I reformulate the well known Lepage decomposition for the symplectic exterior algebra in geometrical form and present some new results relating to the simple subspaces of the decomposition. I then present an analogous decomposition for the symmetric exterior algebra with a metric. Finally, I extend this symmetric exterior algebra into a new calculus for the symmetric differential forms on a pseudo-Riemannian manifold. The importance of this calculus lies in its potential for the description of bosonic systems in Quantum Theory.

A área de reconhecimento de objetos tem assistido a um impressionante progresso na última década. O estudo de descritores, aliado à estratégias de amostragem usando quantizações espaciais e a combinação de classificadores têm permeado o estado da arte nos últimos anos. Neste trabalho é proposta uma nova quantização espacial com número arbitrário de níveis e subdivisões arbitrárias de regiões. Regiões adjacentes possuem sobreposição gerando redundância na representação destas regiões de fronteiras e, assim, evitando as quebras que acontecem nas pirâmides espaciais tradicionais que prejudicam a interpretação das formas. Apesar de melhorar o desempenho da abordagem do saco de palavras, as pirâmides espaciais não são robustas a variações na orientação dos objetos na imagem. Foi também proposto neste trabalho, uma divisão espacial utilizando regiões circulares concêntricas que aumentam a robustez a rotação dos objetos na imagem em aproximadamente 80% quando comparada às pirâmides espaciais. Além das novas divisões espaciais, é proposto neste trabalho um novo descritor baseado na aplicação de granulometria morfológica no mapa de bordas da imagem original. Este descritor foi utilizado na criação de modelos de classes em aplicações de categorização de objetos utilizando uma base de dados pública com resultados superiores aos do melhor descritor baseado em bordas reportado pela literatura. Todas estas novas técnicas propostas foram utilizadas em um problema desafiador de categorização de objetos de classes muito parecidas. Foi utilizado um subconjunto da base de pássaros Caltech-UCSD Birds-200 2011 com resultados comparáveis aos melhores resultados reportados pela literatura. A abordagem proposta cria uma classificação de dois níveis e utiliza modelos específicos por classe o que é intuitivo, pois cada espécie de pássaro possui características muito sutis que as diferenciam das demais espécies testadas. Vários descritores são utilizados na criação dos modelos de classes e uma combinação de classificadores gera a rotulação final para a amostra. O descritor proposto neste trabalho esteve presente no melhor modelo de 11 das 13 classes testadas e o resultado final obtido pela técnica de categorização proposta é o melhor resultado utilizando a abordagem do saco de palavras. / The object recognition area has experienced an impressive progress in the last decade. The study of descriptors, together with a sampling strategy using spatial quantization and the combination of classifiers have been presented in the state of art in recent years. This work proposes a new spatial quantizations with an arbitrary number of levels and divisions in each level. Adjacent regions have overlapping areas that generate redundant representation and avoid breakages in the structures that are in their border regions as it happens in the traditional spatial pyramids and impairs the correct interpretation of these structures. Despite spatial pyramids to improve the performance of the bag-of-words approach in object recognition, they are not robust to changes in object orientation in the image. It was also proposed, in this work, a spatial division using concentric circular regions that is almost 80% more robust to rotation of objects when compared to the spatial pyramids using rectangular divisions. In addition to the new spatial division of the image, it is proposed a new granulometric-based descriptor that it is applied to the map of edges of the original image. This descriptor was used in the building of categorys models for object categorization in a public database and showed a better performance than the most used edge-based descriptor reported in literature. All these new proposed techniques were used in a challenge problem of object categorization of very similar classes. It was used a subset of the public database Caltech-UCSD Birds-200 2011 and the method obtained results compared to the best results reported in the literature. The proposed approach uses a 2-level classification and builds class-specific models that are an intuitive way to model the species of birds as very subtle characteristics differ in each tested class of birds. Many descriptors are used in the building of models of species and a combination of classifiers generates the final label for a tested sample. The descriptor proposed here were presented in 11 of 13 best models of birds classes. The final result obtained by the proposed object categorization method is the best one using the bag-of-words approach.

This thesis includes three separate research projects focusing on computer vision principles and deep learning pattern recognition problems. Chapter 3 entails color quantization applications using traditional Kmeans clustering techniques and random selection of color techniques within the red, green, blue (RGB) color space to maintain a high-quality image while significantly reducing image file size. Chapter 4 consists of a handwriting character recognition algorithm using backpropagation to classify 70,000 handwritten values from US Census Bureau employees and high school students. Chapter 5 proposes a novel classification technique for 109,446 unique heartbeat samples to identify areas of interest and assist medical professionals in diagnosing heart problems.

Purpose: This article provides an empirical identification of groups of marketing scholars who share common beliefs about the role of science and the logic of scientific discovery. Design: We use Topology Representing Network quantization to empirically identify classes of marketing researchers within a representative sample of marketing professors. Findings: We find six distinct classes of marketing scholars. They differ with regard to popularity (size) and productivity (levels of publication output). Comparing the sub-samples of German-speaking and US respondents shows cross-cultural differences. Value: The study enhances our understanding of the current scientific orientation(s) of marketing. It may help to motivate marketing scholars to ponder on their own positions and assist them in judging where they may belong. Future comparisons over time would give us indication about the future of the academic discipline of marketing.(author's abstract)

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Due to the rapid growth of the communication market, a large amount of research is in process toward a high speed and high resolution sigma-delta A/D converter. This dissertation focuses on the design of a single-stage sigma-delta A/D converter with very low oversampling ratio for the wireless application. An architecture for a multibit single-stage delta-sigma A/D converter with two-step quantization is proposed. Both the MSB and LSB signals produced by the two-step quantization are fed back simultaneously to all integrator stages, making it suitable for low oversampling ratios. The two-step ADC avoids the problem that the complexity of an internal flash ADC increases exponentially with each added bit. A segmented architecture with coarse/fine DEM and DAC is proposed to reduce the complexity of DEM and DAC due to the large internal quantizer. The consequence of the segmentation, mismatch between coarse and fine DACs can be noise-shaped by using a digital requantization (REQ) algorithm. A second-order single-stage sigma-delta A/D converter with 8-bit two-step inner quantization is proposed in this dissertation, which employs the feed-forward branches to reduce the integrator output swing. The proposed modulator is implemented with TSMC 0.25 μm mixed-signal process, using the switched-capacitor circuit. The measured system achieves the dynamic range of 70 dB under an oversampling ratio of 16 with the REQ algorithm reducing the noise floor in the signal bandwidth by 20 dB.

Dividing up the range of analog values into discrete binary values during the analog to digital conversion process forces us to incur a rounding error. See what that error looks and sounds like in this episode of Geek Author.

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