Redundant Sensors for Mobile Robot Navigation

Redundant sensors are needed on a mobile robot so that the accuracy with which it perceives its surroundings can be increased. Sonar and infrared sensors are used here in tandem, each compensating for deficiencies in the other. The robot combines the data from both sensors to build a representation which is more accurate than if either sensor were used alone. Another representation, the curvature primal sketch, is extracted from this perceived workspace and is used as the input to two path planning programs: one based on configuration space and one based on a generalized cone formulation of free space.

Reflectance Map Techniques for Analyzing Surface Defects in Metal Castings

This report explores the relation between image intensity and object shape. It is shown that image intensity is related to surface orientation and that a variation in image intensity is related to surface curvature. Computational methods are developed which use the measured intensity variation across surfaces of smooth objects to determine surface orientation. In general, surface orientation is not determined locally by the intensity value recorded at each image point. Tools are needed to explore the problem of determining surface orientation from image intensity. The notion of gradient space , popularized by Huffman and Mackworth, is used to represent surface orientation. The notion of a reflectance map, originated by Horn, is used to represent the relation between surface orientation image intensity. The image Hessian is defined and used to represent surface curvature. Properties of surface curvature are expressed as constraints on possible surface orientations corresponding to a given image point. Methods are presented which embed assumptions about surface curvature in algorithms for determining surface orientation from the intensities recorded in a single view. If additional images of the same object are obtained by varying the direction of incident illumination, then surface orientation is determined locally by the intensity values recorded at each image point. This fact is exploited in a new technique called photometric stereo. The visual inspection of surface defects in metal castings is considered. Two casting applications are discussed. The first is the precision investment casting of turbine blades and vanes for aircraft jet engines. In this application, grain size is an important process variable. The existing industry standard for estimating the average grain size of metals is implemented and demonstrated on a sample turbine vane. Grain size can be computed form the measurements obtained in an image, once the foreshortening effects of surface curvature are accounted for. The second is the green sand mold casting of shuttle eyes for textile looms. Here, physical constraints inherent to the casting process translate into these constraints, it is necessary to interpret features of intensity as features of object shape. Both applications demonstrate that successful visual inspection requires the ability to interpret observed changes in intensity in the context of surface topography. The theoretical tools developed in this report provide a framework for this interpretation.

Desempenho morfoagronômico de genótipos de girassol cultivados na Chapada do Araripe, Pernambuco.

RESUMO: Com o objetivo de avaliar o desempenho agronômico de genótipos de girassol nas condições edafoclimáticas do primeiro semestre de 2015 na Chapada do Araripe, instalou-se um experimento na Estação Experimental do Instituto Agronômico de Pernambuco (IPA), no município de Araripina, Estado de Pernambuco. O delineamento foi o de blocos ao acaso, com quatro repetições e 13 tratamentos, correspondendo aos genótipos de girassol: M734, NTC 90, BRS G43, BRS G44, BRS G45, BRS G46, SYN 065, HLA 2013, HLA 2014, HLA 2015, HLA 2016, HLA 2017 e SYN 045. Avaliaram-se as seguintes características: sobrevivência final, floração inicial, maturação fisiológica, altura média do capítulo, peso de 1000 aquênios, diâmetro médio dos capítulos, produção final de aquênios, curvatura do capítulo e plantas acamadas, quebradas e atacadas por pássaros. Os genótipos apresentaram diferenças morfoagronômicas quando cultivados no primeiro semestre em condições edafoclimáticas da região do Araripe, com exceção da variável sobrevivência. O genótipo NTC 90 alcançou o maior peso de aquênios. Todos os genótipos, exceto HLA 2015, apresentaram elevado rendimento de grãos. Os caracteres plantas acamadas, quebradas, atacadas por pássaros ou a curvatura do capítulo não foram relacionadas às diferentes cultivares. ABSTRACT: The study aimed to evaluate the agronomic performance of different sunflower genotypes in edaphoclimatic conditions of Araripe region in the first semester of 2015. The experiment was established at the Experimental Station of Instituto Agronômico de Pernambuco (IPA), Araripina, Pernambuco, Brazil. Experimental design was a randomized blocks with thirteen treatments, corresponding to the sunflower genotypes: M734, NTC 90, BRS G43, BRS G44, BRS G45, BRS G46, SYN 065, HLA 2013, HLA 2014, HLA 2015, HLA 2016, HLA 2017 e SYN 045, with four replicates. The following characteristics were evaluated: final survival, early flowering, physiological maturity, average plant height, weight of 1,000 seeds, average flower diameter, final seed production, flower head curvature, lodged, broken and damaged by birds plants. The genotypes showed morphoagronomic differences when grown in the first semester of 2015 on edaphoclimatic conditions of the Araripe region, except for the variable survival. The NTC 90 genotype achieved the highest weight of head flower. All genotypes, except HLA 2015 showed high grain yield. The characters lodged, broken and damaged plants by birds or curvature of the head flower were not related to the different cultivars.

Caracteres agronômicos de híbridos de girassol em Campo Novo do Parecis, na segunda safra de verão de 2015.

RESUMO: O objetivo deste trabalho foi avaliar características agronômicos durante o desenvolvimento de híbridos de girassol cultivados na região de Campo Novo do Parecis - MT. O ensaio foi instalado e conduzido,entre os meses de fevereiro ejunho de 2015, na área experimental do setor de produção do Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso - IFMT Campus Campo Novo do Parecis - MT, cujas coordenadas geográficas são latitude S 13°40'31" longitude O 57°53'31" e altitude média de 574 m. O solo predominante é Latossolo Vermelho distrófico típico. O delineamento experimental foi em blocos casualizados com 13 tratamentos (híbridos) e 4 repetições, totalizando um total de 52 parcelas. Foram avaliadas as seguintes características agronômicas do girassol: dias para o florescimento inicial, dias para a maturação fisiológica, altura de planta, curvatura do caule, número de plantas acamadas, número de plantas quebradas e produtividade de aquênios. Os dados foram submetidos à análise de variância e ao teste de média Scott-Knott (p<0,05). O híbrido BRS G44 apresentou híbrido BRS G44 apresentou bom rendimento, ciclo precoce e porte baixo, nas condições de segunda safra de verão em Campo Novo do Parecis (MT) . Assim, este híbrido se torna boa opção para o cultivo de girassol na região. ABSTRACT: The objective of this study was to evaluate agronomic characteristics for the development of sunflower hybrids grown in the region of Campo Novo do Parecis - MT. The experiment was carried out and conducted between the months of February to June 2015 in the experimental area of the production sector of the Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso, - IFMT, Campo Novo do Parecis - MT (latitude S 13°40'31" longitude W 57°53'31" and average altitude of 574 m). The predominant soil is Typic Tropudox. The experimental design was randomized blocks with 13 treatments (hybrids) and four repetitions, resulting in 52 plots. The sunflower agronomic traits evaluated were: days to initial flowering, days to physiological maturity, plant height, stem curvature, number of lodged plants, number of broken plants and achenes productivity. Data were subjected to analysis of variance and average test Scott-Knott (p<0.05). The hybrid BRS G44 showed good yield, early maturity and low height in second summer crop conditions in Campo Novo do Parecis (MT). Thus, this hybrid is a good option for sunflower cultivation in the region.

Automatic point correspondence and registration based on linear structures

R. Marti, R. Zwiggelaar, C.M.E. Rubin, 'Automatic point correspondence and registration based on linear structures', International Journal of Pattern Recognition and Artificial Intelligence 16 (3), 331-340 (2002)

Generalized semi-geostrophic theory on a sphere

Douglas, Robert; Cullen, M.J.P.; Roulston, I.; Sewell, M.J., (2005) 'Generalized semi-geostrophic theory on a sphere', Journal of Fluid Mechanics 531 pp.123-157 RAE2008

Sistema de instrumentação mecanizada reciprocante

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

MosaicShape: Stochastic Region Grouping with Shape Prior

A novel method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework and solved by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. By this means, object segmentation and recognition are accomplished simultaneously. Within each sampling move during the simulation process,probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized parallel algorithm by Barbu and Zhu,combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.

A Quantitative Evaluation of the AVITEWRITE Model of Handwriting Learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.

The VITEWRITE Model of Handwriting Production

This article describes the VITEWRITE model for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in the outflow command to a given synergy occurs. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. Each synergy exhibits a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

A Neural Model of 3D Shape-From-Texture: Multiple-Scale Filtering, Boundary Grouping, and Surface Filling-In

A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are transformed into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive grouping network that coherently binds texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a smooth 3D surface percept. The model quantitatively simulates challenging psychophysical data about perception of prolate ellipsoids (Todd and Akerstrom, 1987, J. Exp. Psych., 13, 242). In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. Simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are also presented.

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

Flat surfaces of finite type in the 3-sphere

We introduce the notion of flat surfaces of finite type in the 3- sphere, give the algebro-geometric description in terms of spectral curves and polynomial Killing fields, and show that finite type flat surfaces generated by curves on S2 with periodic curvature functions are dense in the space of all flat surfaces generated by curves on S2 with periodic curvature functions.

The influence of thermal plasma profiles on low-frequency Alfvén eigenmode dynamics in a tokamak

The confinement of fast particles, present in a tokamak plasma as nuclear fusion products and through external heating, will be essential for any future fusion reactor. Fast particles can be expelled from the plasma through their interaction with Alfvén eigenmode (AE) instabilities. AEs can exist in gaps in the Alfvén continuum created by plasma equilibrium non-uniformities. In the ASDEX Upgrade tokamak, low-frequency modes in the frequency range from f ≈ 10 − 90kHz, including beta-induced Alfvén eigenmodes (BAEs) and lower frequency modes with mixed Alfvén and acoustic polarisations, have been observed. These exist in gaps in the Alfvén continuum opened up by geodesic curvature and finite plasma compressibility. In this thesis, a kinetic dispersion relation is solved numerically to investigate the influence of thermal plasma profiles on the evolution of these low-frequency modes during the sawtooth cycle. Using information gained from various experimental sources to constrain the equilibrium reconstructions, realistic safety factor profiles are obtained for the analysis using the CLISTE code. The results for the continuum accumulation point evolution are then compared with experimental results from ASDEX Upgrade during periods of ICRH only as well as for periods with both ICRH and ECRH applied simultaneously. It is found that the diamagnetic frequency plays an important role in influencing the dynamics of BAEs and low-frequency acoustic Alfvén eigenmodes, primarily through the presence of gradients in the thermal plasma profiles. Different types of modes that are observed during discharges heated almost exclusively by ECRH were also investigated. These include electron internal transport barrier (eITB) driven modes, which are observed to coincide with the occurrence of an eITB in the plasma during the low-density phase of the discharge. Also observed are BAE-like modes and edge-TAEs, both of which occur during the H-mode phase of the discharge.