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.

Interoperabilidade, processamento e análise visual de dados geográficos

Esta dissertação apresenta uma arquitectura interoperável que permite lidar com a obtenção, manipulação, processamento e análise de informação geográfica. A aplicação 30, implementada como parte da arquitectura, para além de permitir a visualização e manipulação de dados dentro de um ambiente 30, oferece métodos que permitem descobrir, aceder e usar geo-processos, disponíveis através de serviços Web. A interacção com o utilizador é também feita através uma abordagem que quebra a típica complexidade que a maioria dos Sistemas de Informação Geográfica apresenta. O recurso à programação visual reduz a complexidade do sistema, e permite aos operadores tirar proveito da localização e de uma abstracção de um processo complexo, onde as unidades de processamento são representadas no terreno através de componentes 30 que podem ser directamente manipuladas e ligadas de modo a criar encandeamentos complexos de processos. Estes processos podem também ser criados visualmente e disponibilizados online. ABSTRACT; This thesis presents an interoperable architecture mainly designed for manipulation, processing and geographical information analysis. The three-dimensional interface, implemented as part of the architecture, besides allowing the visualization and manipulation of spatial data within a 30 environment, offers methods for discovering, accessing and using geo-processes, available through Web Services. Furthermore, the user interaction is done through an approach that breaks the typical complexity of most Geographic information Systems. This simplicity is in general archived through a visual programming approach that allows operators to take advantage of location, and use processes through abstract representations. Thus, processing units are represented on the terrain through 30 components, which can be directly manipulated and linked to create complex process chains. New processes can also be visually created and deployed online.

Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

A GEOBIA methodology for fragmented agricultural landscapes

Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

Agroindustria rural, acción colectiva y SIALES: ¿Desarrollo o lucha contra la pobreza?

El desarrollo y reforzamiento de los sistemas agroalimentarios localizados, conjuntos de pequeñas unidades de agroindustria rural, pueden considerarse como un medio importante de reducción de la pobreza en las regiones rurales de América Latina.   Sin embargo, la validez de tal propuesta tiene que ser valorada, teniendo en cuenta los siguientes elementos: La dinámica de los SIALES radica en procesos de activación de recursos específicos, que son productos de la acción colectiva. Forman parte de cadenas con una gobernabilidad caracterizada por el dominio de actores de debajo de la cadena, tal como las grandes distribuidoras. La pobreza no se define únicamente por la falta de recursos monetarios, sino también por la falta de capacidades.   La eficacia de la acción colectiva de activación de los recursos específicos yace en la capacidad de control de estos recursos por parte de los actores involucrados, hasta con el diseño de dispositivos de exclusión acerca de ellos, lo que puede con llevar la marginalización de otros actores. Es una necesidad tanto más apremiante cuanto más ubica el SIAL en un contexto de mercado globalizado. El caso de las señales de calidad basadas en el origen territorial de los productos lo ilustra. Por otra parte, la dinámica de los SIALES puede conllevar a profundizar las capacidades, especialmente a través de procesos de aprendizaje. Las políticas públicas pueden reforzar el proceso, proporcionando bienes públicos y fomentando procesos participativos a nivel local.   Al final el desarrollo de los SIALES no constituye de por sí una garantía de reducción de las desigualdades, pero refuerza las capacidades y además es un tipo de cambio estructural, de los que se suelen asociar con la noción de desarrollo.   ABSTRACT The development of Local agri-food systems (LAS), as clusters of small rural food-processing units, can be seen as a powerful means of poverty alleviation in rural areas of Latin America.   The relevance of such a statement must, nevertheless, be assessed in the light of the following elements: LAS dynamics rest on activation processes of specific resources, as a result of collective action. LAS are part of commodity chains whose governance is characterized by the domination of downstream actors such as large retailers. Poverty does not only refer to the shortage of monetary income, but also to the absence of capacities.   The efficiency of collective action involved in the activation process of specific resources depends on the capacity of relevant actors to control the access. As a result, other actors can be cast aside in the process. It is true that these resources must be valorized in a globalized market. The case of quality signals based on geographical origin can be an example of that. On the other side, LAS dynamics can boost the development of capacities, particularly by promoting learning by doing. Public policies can channel tis process by delivering public goods and promoting participation at a local level.   All in all, the development of LAS is not per se a guarantee against the deepening of inequality. But it is a capacity-building factor and entails structural changes which are yhe essence of development processes.

Processing traits of European catfish (Silurus glanis Linnaeus, 1758) from outdoor flow-through and indoor recycling aquaculture units

The quality of fish cultured using recycling units may differ from that of fish from outdoor farming units due to a range of deviating environmental determinants. This applies not only to flesh quality but also to morphological (processing) traits. This study evaluates processing yields of sibling fish cultured in two different farming units: (i) an outdoor pond aquaculture system with a flow-through regime (24.6 ± 0.2°C), and (ii) indoor tanks using a recirculation aquaculture system (RAS; 26.0 ± 1.0°C). Clear differences were observed in the most important processing traits, i.e. skinned trunk and fillet yields, which were both significantly higher (P < 0.01) in RAS fish due to significantly smaller (P < 0.05) head weight in fish of the flow-through system. Skin represented a significantly higher (P < 0.01) proportion of total weight in both RAS males and females. The most obvious difference was in the deposited fat weight, which was significantly higher (P < 0.01) in RAS fish. Visceral fat deposits were significantly higher (P < 0.01) in females and ventral and dorsal fat deposits higher (P > 0.05) in males.

Soundcipher : a music and sound library for processing

SoundCipher is a software library written in the Java language that adds important music and sound features to the Processing environment that is widely used by media artists and otherwise has an orientation toward computational graphics. This article introduces the SoundCipher library and its features, describes its influences and design intentions, and positions it within the field of computer music programming tools. SoundCipher enables the rich history of algorithmic music techniques to be accessible within one of today’s most popular media art platforms. It also provides an accessible means for learning to create algorithmic music and sound programs.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.