Excitatory–inhibitory network in the visual cortex: Psychophysical evidence

At early stages in visual processing cells respond to local stimuli with specific features such as orientation and spatial frequency. Although the receptive fields of these cells have been thought to be local and independent, recent physiological and psychophysical evidence has accumulated, indicating that the cells participate in a rich network of local connections. Thus, these local processing units can integrate information over much larger parts of the visual field; the pattern of their response to a stimulus apparently depends on the context presented. To explore the pattern of lateral interactions in human visual cortex under different context conditions we used a novel chain lateral masking detection paradigm, in which human observers performed a detection task in the presence of different length chains of high-contrast-flanked Gabor signals. The results indicated a nonmonotonic relation of the detection threshold with the number of flankers. Remote flankers had a stronger effect on target detection when the space between them was filled with other flankers, indicating that the detection threshold is caused by dynamics of large neuronal populations in the neocortex, with a major interplay between excitation and inhibition. We considered a model of the primary visual cortex as a network consisting of excitatory and inhibitory cell populations, with both short- and long-range interactions. The model exhibited a behavior similar to the experimental results throughout a range of parameters. Experimental and modeling results indicated that long-range connections play an important role in visual perception, possibly mediating the effects of context.

Neonatal lead exposure impairs development of rodent barrel field cortex

Childhood exposure to low-level lead can permanently reduce intelligence, but the neurobiologic mechanism for this effect is unknown. We examined the impact of lead exposure on the development of cortical columns, using the rodent barrel field as a model. In all areas of mammalian neocortex, cortical columns constitute a fundamental structural unit subserving information processing. Barrel field cortex contains columnar processing units with distinct clusters of layer IV neurons that receive sensory input from individual whiskers. In this study, rat pups were exposed to 0, 0.2, 1, 1.5, or 2 g/liter lead acetate in their dam's drinking water from birth through postnatal day 10. This treatment, which coincides with the development of segregated columns in the barrel field, produced blood lead concentrations from 1 to 31 μg/dl. On postnatal day 10, the area of the barrel field and of individual barrels was measured. A dose-related reduction in barrel field area was observed (Pearson correlation = −0.740; P < 0.001); mean barrel field area in the highest exposure group was decreased 12% versus controls. Individual barrels in the physiologically more active caudoventral group were affected preferentially. Total cortical area measured in the same sections was not altered significantly by lead exposure. These data support the hypothesis that lead exposure may impair the development of columnar processing units in immature neocortex. We demonstrate that low levels of blood lead, in the range seen in many impoverished inner-city children, cause structural alterations in a neocortical somatosensory map.

Caracterização tecnológica do minério de ouro da Mina Morro do Ouro - Paracatu, MG.

As unidades de beneficiamento de minério de ouro buscam cada vez mais uma produção de baixo custo e maximização dos ganhos financeiros. A caracterização tecnológica está inserida em uma abordagem multidisciplinar que permite agregar conhecimento, alternativas de otimização e redução nos custos de operação. Inserida como uma ferramenta na caracterização tecnológica, a análise de imagens automatizada tem importante papel no setor mineral principalmente pela rapidez das análises, robustez estatística e confiabilidade nos resultados. A técnica pode ser realizada por meio de imagens adquiridas em microscópio eletrônico de varredura, associada a microanálises químicas sendo utilizada em diversas etapas de um empreendimento mineiro. Este estudo tem como objetivo a caraterização tecnológica de minério de ouro da Mina Morro do Ouro, Minas Gerais na qual foi utilizado a técnica de análise de imagens automatizada por MLA em um conjunto de 88 amostras. Foi possível identificar que 90% do ouro está na fração acima de 0,020 mm; o quartzo e mica representam cerca de 80% da massa total do minério; os sulfetos apresentam diâmetro de círculo equivalente entre 80 e 100 ?m e são representados por pirita e arsenopirita, com pirrotita, calcopirita, esfalerita e galena subordinada. Também foi possível observar que o ouro está majoritariamente associado à pirita e arsenopirita e com o aumento de teor de arsênio, cresce a parcela de ouro associado à arsenopirita. As medianas das distribuições de tamanho dos grãos de ouro apresentam um valor médio de 19 ?m. Verificou-se que a composição dos grãos de ouro é bastante diversa, em média 77% de ouro e 23% de prata. Para material abaixo de 0,50 mm observa-se uma parcela expressiva de perímetro exposto dos grãos de ouro (média 73%); o ouro incluso (21% do total dos grãos de ouro) está associado a pirita e arsenopirita, sendo que em 14 das 88 amostras este valor pode superar 40% do total de ouro contido. A ferramenta da análise de imagens automatizada mostrou-se bastante eficiente definindo características particulares o que fornece de forma objetiva subsídios para os trabalhos de planejamento de mina e processamento mineral.

Resolution limits to object tracking with subpixel accuracy

Subpixel methods increase the accuracy and efficiency of image detectors, processing units, and algorithms and provide very cost-effective systems for object tracking. Published methods achieve resolution increases up to three orders of magnitude. In this Letter, we demonstrate that this limit can be theoretically improved by several orders of magnitude, permitting micropixel and submicropixel accuracies. The necessary condition for movement detection is that one single pixel changes its status. We show that an appropriate target design increases the probability of a pixel change for arbitrarily small shifts, thus increasing the detection accuracy of a tracking system. The proposal does not impose severe restriction on the target nor on the sensor, thus allowing easy experimental implementation.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

Use of Costas arrays in subpixel metrology

Subpixel methods increase the accuracy and efficiency of image detectors, processing units, and algorithms and provide very cost-effective systems for object tracking. A recently proposed method permits micropixel and submicropixel accuracies providing certain design constraints on the target are met. In this paper, we explore the use of Costas arrays - permutation matrices with ideal auto-ambiguity properties - for the design of such targets.

High performance numerical modeling of ultra-short laser pulse propagation based on multithreaded parallel hardware

The focus of this study is development of parallelised version of severely sequential and iterative numerical algorithms based on multi-threaded parallel platform such as a graphics processing unit. This requires design and development of a platform-specific numerical solution that can benefit from the parallel capabilities of the chosen platform. Graphics processing unit was chosen as a parallel platform for design and development of a numerical solution for a specific physical model in non-linear optics. This problem appears in describing ultra-short pulse propagation in bulk transparent media that has recently been subject to several theoretical and numerical studies. The mathematical model describing this phenomenon is a challenging and complex problem and its numerical modeling limited on current modern workstations. Numerical modeling of this problem requires a parallelisation of an essentially serial algorithms and elimination of numerical bottlenecks. The main challenge to overcome is parallelisation of the globally non-local mathematical model. This thesis presents a numerical solution for elimination of numerical bottleneck associated with the non-local nature of the mathematical model. The accuracy and performance of the parallel code is identified by back-to-back testing with a similar serial version.

Low-Power Tracking Image Sensor Based on Biological Models of Attention

This paper presents implementation of a low-power tracking CMOS image sensor based on biological models of attention. The presented imager allows tracking of up to N salient targets in the field of view. Employing "smart" image sensor architecture, where all image processing is implemented on the sensor focal plane, the proposed imager allows reduction of the amount of data transmitted from the sensor array to external processing units and thus provides real time operation. The imager operation and architecture are based on the models taken from biological systems, where data sensed by many millions of receptors should be transmitted and processed in real time. The imager architecture is optimized to achieve low-power dissipation both in acquisition and tracking modes of operation. The tracking concept is presented, the system architecture is shown and the circuits description is discussed.

Multi-threaded parallel numerical modelling of femtosecond pulse propagation in laser machining

Femtosecond laser microfabrication has emerged over the last decade as a 3D flexible technology in photonics. Numerical simulations provide an important insight into spatial and temporal beam and pulse shaping during the course of extremely intricate nonlinear propagation (see e.g. [1,2]). Electromagnetics of such propagation is typically described in the form of the generalized Non-Linear Schrdinger Equation (NLSE) coupled with Drude model for plasma [3]. In this paper we consider a multi-threaded parallel numerical solution for a specific model which describes femtosecond laser pulse propagation in transparent media [4, 5]. However our approach can be extended to similar models. The numerical code is implemented in NVIDIA Graphics Processing Unit (GPU) which provides an effitient hardware platform for multi-threded computing. We compare the performance of the described below parallel code implementated for GPU using CUDA programming interface [3] with a serial CPU version used in our previous papers [4,5]. © 2011 IEEE.

Costs and benefits of orthographic inconsistency in reading:evidence from a cross-linguistic comparison

We compared reading acquisition in English and Italian children up to late primary school analyzing RTs and errors as a function of various psycholinguistic variables and changes due to experience. Our results show that reading becomes progressively more reliant on larger processing units with age, but that this is modulated by consistency of the language. In English, an inconsistent orthography, reliance on larger units occurs earlier on and it is demonstrated by faster RTs, a stronger effect of lexical variables and lack of length effect (by fifth grade). However, not all English children are able to master this mode of processing yielding larger inter-individual variability. In Italian, a consistent orthography, reliance on larger units occurs later and it is less pronounced. This is demonstrated by larger length effects which remain significant even in older children and by larger effects of a global factor (related to speed of orthographic decoding) explaining changes of performance across ages. Our results show the importance of considering not only overall performance, but inter-individual variability and variability between conditions when interpreting cross-linguistic differences.

Produção do espaço e reestruturação produtiva do setor de laticínio no Rio Grande do Norte

Since the 1970s, Brazil has gone through several changes in its economic and productive structures, which have symbiotic relationship with the organization and dynamics of the Brazilian territory. This set of economic, social and technical-scientific transformations developed in the amid the productive capital restructuring, a process that occurs on a global scale, but that effective with particularities in different places. Adopting this presuposition the present research had as main objective analyze the productive restructuring of the dairy sector in Rio Grande do Norte, highlighting its relationship with production process / organization of space and its impact on the social relations of production. The adopted methodology to elaborate of this study was based on the achievement a bibliographic review with regard to proceedings of production of space and productive restructuring, document research about the dynamics of the dairy sector in Rio Grande do Norte, as well as on regulatory instructions governing the dairy production in Brazil, we achieve parallel secondary data collection, with official organs such as IBGE, EMATER and SINDLEITE. Another important methodological resource was the realization of the field research, which enabled us to empirically understand the distinct realities lived by agents acting on milk production system in Rio Grande do Norte. The analyzes performed nevertheless evidence that the restructuring process in the dairy sector is fomented, greatly by state,that finance, encourages and normatizes the production of milk in the country. In the specific case of Rio Grande do Norte, this process is boosted by the creation of "Programa do Leite," which by constituting of an institutional market, contributes to the strengthening and expansion of industries, the detriment of the artisanal processing sector. Nevertheless family farmers continue to act in the activity, be only producing and trading fresh milk, supplying milk to processing units, mediating the production of their peers or by the craft benefiting milk in traditional cheese factories presents in the entire state of Rio Grande do Norte. The results reveal that it is a complex web of social relations of production that are established at the heart of laticinista activity in the Rio Grande Norte, these are summarily marked by relations of competition and complementarity between industrial and artisanal processing of milk

Computational fluid dynamics (CFD) based approach to consequence assessment of accidental release of hydrocarbon during storage and transportation

This thesis investigated the risk of accidental release of hydrocarbons during transportation and storage. Transportation of hydrocarbons from an offshore platform to processing units through subsea pipelines involves risk of release due to pipeline leakage resulting from corrosion, plastic deformation caused by seabed shakedown or damaged by contact with drifting iceberg. The environmental impacts of hydrocarbon dispersion can be severe. Overall safety and economic concerns of pipeline leakage at subsea environment are immense. A large leak can be detected by employing conventional technology such as, radar, intelligent pigging or chemical tracer but in a remote location like subsea or arctic, a small chronic leak may be undetected for a period of time. In case of storage, an accidental release of hydrocarbon from the storage tank could lead pool fire; further it could escalate to domino effects. This chain of accidents may lead to extremely severe consequences. Analyzing past accident scenarios it is observed that more than half of the industrial domino accidents involved fire as a primary event, and some other factors for instance, wind speed and direction, fuel type and engulfment of the compound. In this thesis, a computational fluid dynamics (CFD) approach is taken to model the subsea pipeline leak and the pool fire from a storage tank. A commercial software package ANSYS FLUENT Workbench 15 is used to model the subsea pipeline leakage. The CFD simulation results of four different types of fluids showed that the static pressure and pressure gradient along the axial length of the pipeline have a sharp signature variation near the leak orifice at steady state condition. Transient simulation is performed to obtain the acoustic signature of the pipe near leak orifice. The power spectral density (PSD) of acoustic signal is strong near the leak orifice and it dissipates as the distance and orientation from the leak orifice increase. The high-pressure fluid flow generates more noise than the low-pressure fluid flow. In order to model the pool fire from the storage tank, ANSYS CFX Workbench 14 is used. The CFD results show that the wind speed has significant contribution on the behavior of pool fire and its domino effects. The radiation contours are also obtained from CFD post processing, which can be applied for risk analysis. The outcome of this study will be helpful for better understanding of the domino effects of pool fire in complex geometrical settings of process industries. The attempt to reduce and prevent risks is discussed based on the results obtained from the numerical simulations of the numerical models.

Two-Path All-pass Based Half-Band Infinite Impulse Response Decimation Filters and the Effects of Their Non-Linear Phase Response on ECG Signal Acquisition

This paper is based on the novel use of a very high fidelity decimation filter chain for Electrocardiogram (ECG) signal acquisition and data conversion. The multiplier-free and multi-stage structure of the proposed filters lower the power dissipation while minimizing the circuit area which are crucial design constraints to the wireless noninvasive wearable health monitoring products due to the scarce operational resources in their electronic implementation. The decimation ratio of the presented filter is 128, working in tandem with a 1-bit 3rd order Sigma Delta (ΣΔ) modulator which achieves 0.04 dB passband ripples and -74 dB stopband attenuation. The work reported here investigates the non-linear phase effects of the proposed decimation filters on the ECG signal by carrying out a comparative study after phase correction. It concludes that the enhanced phase linearity is not crucial for ECG acquisition and data conversion applications since the signal distortion of the acquired signal, due to phase non-linearity, is insignificant for both original and phase compensated filters. To the best of the authors’ knowledge, being free of signal distortion is essential as this might lead to misdiagnosis as stated in the state of the art. This article demonstrates that with their minimal power consumption and minimal signal distortion features, the proposed decimation filters can effectively be employed in biosignal data processing units.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

USE OF STARTER CULTURES TO IMPROVE PORTUGUESE TRADITIONAL SAUSAGES

In Mediterranean countries, such as Portugal, traditional dry-fermented sausages are highly appreciated. They are often still being manufactured in small processing units, according to traditional procedures. The aims of the present study were to evaluate the effect of different starter cultures and their optimal concentration, to reduce the microbial load in end-products, with the purpose to improve the sausages’ safety, without deteriorating sensory acceptability. pH, aw, colour, texture and microbiological profile were assessed. On the other hand, a sensory panel evaluated the products. Based on the first results, S. xylosus and L. sakei were chosen to be inoculated together with a yeast strain. In the mixed starter culture experiment, a food safety issue arose probably related to the higher aw value (0.91). The presence of Salmonella spp. detected in a few end-products sausages did not allow a full sensory evaluation in the mixed starter culture experiment. However, in the two preliminary experiments, the use of starter cultures did not depreciate the panellists’ overall appreciation and products acceptability.