3-D Vision and Figure-Ground Separation by Visual Cortex

A neural network theory of :3-D vision, called FACADE Theory, is described. The theory proposes a solution of the classical figure-ground problem for biological vision. It does so by suggesting how boundary representations and surface representations are formed within a Boundary Contour System (BCS) and a Feature Contour System (FCS). The BCS and FCS interact reciprocally to form 3-D boundary and surface representations that arc mutually consistent. Their interactions generate 3-D percepts wherein occluding and occluded object completed, and grouped. The theory clarifies how preattentive processes of 3-D perception and figure-ground separation interact reciprocally with attentive processes of spatial localization, object recognition, and visual search. A new theory of stereopsis is proposed that predicts how cells sensitive to multiple spatial frequencies, disparities, and orientations are combined by context-sensitive filtering, competition, and cooperation to form coherent BCS boundary segmentations. Several factors contribute to figure-ground pop-out, including: boundary contrast between spatially contiguous boundaries, whether due to scenic differences in luminance, color, spatial frequency, or disparity; partially ordered interactions from larger spatial scales and disparities to smaller scales and disparities; and surface filling-in restricted to regions surrounded by a connected boundary. Phenomena such as 3-D pop-out from a 2-D picture, DaVinci stereopsis, a 3-D neon color spreading, completion of partially occluded objects, and figure-ground reversals are analysed. The BCS and FCS sub-systems model aspects of how the two parvocellular cortical processing streams that join the Lateral Geniculate Nucleus to prestriate cortical area V4 interact to generate a multiplexed representation of Form-And-Color-And-Depth, or FACADE, within area V4. Area V4 is suggested to support figure-ground separation and to interact. with cortical mechanisms of spatial attention, attentive objcect learning, and visual search. Adaptive Resonance Theory (ART) mechanisms model aspects of how prestriate visual cortex interacts reciprocally with a visual object recognition system in inferotemporal cortex (IT) for purposes of attentive object learning and categorization. Object attention mechanisms of the What cortical processing stream through IT cortex are distinguished from spatial attention mechanisms of the Where cortical processing stream through parietal cortex. Parvocellular BCS and FCS signals interact with the model What stream. Parvocellular FCS and magnocellular Motion BCS signals interact with the model Where stream. Reciprocal interactions between these visual, What, and Where mechanisms arc used to discuss data about visual search and saccadic eye movements, including fast search of conjunctive targets, search of 3-D surfaces, selective search of like-colored targets, attentive tracking of multi-element groupings, and recursive search of simultaneously presented targets.

Normal and Amnesis Learning, Recognition, and Memory by a Neural Model of Cortico-Hippocampal Interactions

The processes by which humans and other primates learn to recognize objects have been the subject of many models. Processes such as learning, categorization, attention, memory search, expectation, and novelty detection work together at different stages to realize object recognition. In this article, Gail Carpenter and Stephen Grossberg describe one such model class (Adaptive Resonance Theory, ART) and discuss how its structure and function might relate to known neurological learning and memory processes, such as how inferotemporal cortex can recognize both specialized and abstract information, and how medial temporal amnesia may be caused by lesions in the hippocampal formation. The model also suggests how hippocampal and inferotemporal processing may be linked during recognition learning.

Evanescent field sensing: optical nanofibres, whispering-gallery-mode microspherical and microbubble resonators

In this thesis, the evanescent field sensing techniques of tapered optical nanofibres and microspherical resonators are investigated. This includes evanescent field spectroscopy of a silica nanofibre in a rubidium vapour; thermo-optical tuning of Er:Yb co-doped phosphate glass microspheres; optomechanical properties of microspherical pendulums; and the fabrication and characterisation of borosilicate microbubble resonators. Doppler-broadened and sub-Doppler absorption spectroscopic techniques are performed around the D2 transition (780.24 nm) of rubidium using the evanescent field produced at the waist of a tapered nanofibre with input probe powers as low as 55 nW. Doppler-broadened Zeeman shifts and a preliminary dichroic atomic vapour laser lock (DAVLL) line shape are also observed via the nanofibre waist with an applied magnetic field of 60 G. This device has the potential for laser frequency stabilisation while also studying the effects of atom-surface interactions. A non-invasive thermo-optical tuning technique of Er:Yb co-doped microspheres to specific arbitrary wavelengths is demonstrated particularly to 1294 nm and the 5S1/2F=3 to 5P3/2Fʹ=4 laser cooling transition of 85Rb. Reversible tuning ranges of up to 474 GHz and on resonance cavity timescales on the order of 100 s are reported. This procedure has prospective applications for sensing a variety of atomic or molecular species in a cavity quantum electrodynamics (QED) experiments. The mechanical characteristics of a silica microsphere pendulum with a relatively low spring constant of 10-4 Nm-1 are explored. A novel method of frequency sweeping the motion of the pendulum to determine its natural resonance frequencies while overriding its sensitivity to environmental noise is proposed. An estimated force of 0.25 N is required to actuate the pendulum by a displacement of (1-2) μm. It is suggested that this is of sufficient magnitude to be experienced between two evanescently coupled microspheres (photonic molecule) and enable spatial trapping of the micropendulum. Finally, single-input borosilicate microbubble resonators with diameters <100 μm are fabricated using a CO2 laser. Optical whispering gallery mode spectra are observed via evanescent coupling with a tapered fibre. A red-shift of (4-22) GHz of the resonance modes is detected when the hollow cavity was filled with nano-filtered water. A polarisation conversion effect, with an efficiency of 10%, is observed when the diameter of the coupling tapered fibre waist is varied. This effect is also achieved by simply varying the polarisation of the input light in the tapered fibre where the efficiency is optimised to 92%. Thus, the microbubble device acts as a reversible band-pass to band-stop optical filter for cavity-QED, integrated solid-state and semiconductor circuit applications.

Averroes y las cosas justas por naturaleza

En su comentario a la Ética a Nicómaco, Averroes se ocupó del pasaje donde Aristóteles distingue entre las cosas que son justas por naturaleza y aquéllas que lo son en virtud de la ley (V, 7 1134b18-1135a5). Su comentario es particularmente breve, pero plantea algunas dificultades importantes, como su alusión a un derecho naturale legale, que, según Leo Strauss, vendría a ser simplemente un derecho positivo de aceptación general. En este artículo se busca caracterizar lo justo natural y lo justo positivo en el comentario de Averroes y mostrar el alcance de la variación de los criterios propios de la justicia positiva.

Entropy-Based Approach to Analyze and Classify Mineral Aggregates

This paper presents an automatic method to detect and classify weathered aggregates by assessing changes of colors and textures. The method allows the extraction of aggregate features from images and the automatic classification of them based on surface characteristics. The concept of entropy is used to extract features from digital images. An analysis of the use of this concept is presented and two classification approaches, based on neural networks architectures, are proposed. The classification performance of the proposed approaches is compared to the results obtained by other algorithms (commonly considered for classification purposes). The obtained results confirm that the presented method strongly supports the detection of weathered aggregates.

Uma contribuição ao estudo das categorias internas e de sua proliferação em redes ARTMAP

ART networks present some advantages: online learning; convergence in a few epochs of training; incremental learning, etc. Even though, some problems exist, such as: categories proliferation, sensitivity to the presentation order of training patterns, the choice of a good vigilance parameter, etc. Among the problems, the most important is the category proliferation that is probably the most critical. This problem makes the network create too many categories, consuming resources to store unnecessarily a large number of categories, impacting negatively or even making the processing time unfeasible, without contributing to the quality of the representation problem, i. e., in many cases, the excessive amount of categories generated by ART networks makes the quality of generation inferior to the one it could reach. Another factor that leads to the category proliferation of ART networks is the difficulty of approximating regions that have non-rectangular geometry, causing a generalization inferior to the one obtained by other methods of classification. From the observation of these problems, three methodologies were proposed, being two of them focused on using a most flexible geometry than the one used by traditional ART networks, which minimize the problem of categories proliferation. The third methodology minimizes the problem of the presentation order of training patterns. To validate these new approaches, many tests were performed, where these results demonstrate that these new methodologies can improve the quality of generalization for ART networks

Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (c) 2005 Elsevier B.V. All rights reserved.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

Formulação do controle preventivo em sistemas de distribuição de energia elétrica baseada na lógica fuzzy e redes neurais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise da estabilidade transitória via rede neural Art-Artmap fuzzy Euclidiana modificada com treinamento continuado

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise da estabilidade estática de tensão de sistemas elétricos de potência usando uma rede neural baseada na teoria da ressonância adaptativa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Detecção, classificação e localização de faltas de curto-circuito em sistemas de distribuição de energia elétrica usando sistemas inteligentes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sistema inteligente para a predição de grupo de risco de evasão discente

Pós-graduação em Engenharia Elétrica - FEIS