A fast parallel hyperspectral coded aperture algorithm for compressive sensing using OpenCL

In this paper, we develop a fast implementation of an hyperspectral coded aperture (HYCA) algorithm on different platforms using OpenCL, an open standard for parallel programing on heterogeneous systems, which includes a wide variety of devices, from dense multicore systems from major manufactures such as Intel or ARM to new accelerators such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), the Intel Xeon Phi and other custom devices. Our proposed implementation of HYCA significantly reduces its computational cost. Our experiments have been conducted using simulated data and reveal considerable acceleration factors. This kind of implementations with the same descriptive language on different architectures are very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

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.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

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.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. 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, specially 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 thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

Algoritmo paralelo para determinação de autovalores de matrizes hermitianas

Dissertação (mestrado)–Universidade de Brasília, Universidade UnB de Planaltina, Programa de Pós-Graduação em Ciência de Materiais, 2015.

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.

Load balancing for constraint solving with GPUs

Solving a complex Constraint Satisfaction Problem (CSP) is a computationally hard task which may require a considerable amount of time. Parallelism has been applied successfully to the job and there are already many applications capable of harnessing the parallel power of modern CPUs to speed up the solving process. Current Graphics Processing Units (GPUs), containing from a few hundred to a few thousand cores, possess a level of parallelism that surpasses that of CPUs and there are much less applications capable of solving CSPs on GPUs, leaving space for further improvement. This paper describes work in progress in the solving of CSPs on GPUs, CPUs and other devices, such as Intel Many Integrated Cores (MICs), in parallel. It presents the gains obtained when applying more devices to solve some problems and the main challenges that must be faced when using devices with as different architectures as CPUs and GPUs, with a greater focus on how to effectively achieve good load balancing between such heterogeneous devices.

Towards a Multi-device Constraints Solver

To reduce the amount of time needed to solve the most complex Constraint Satisfaction Problems (CSPs) usually multi-core CPUs are used. There are already many applications capable of harnessing the parallel power of these devices to speed up the CSPs solving process. Nowadays, the Graphics Processing Units (GPUs) possess a level of parallelism that surpass the CPUs, containing from a few hundred to a few thousand cores and there are much less applications capable of solving CSPs on GPUs, leaving space for possible improvements. This article describes the work in progress for solving CSPs on GPUs and CPUs and compares results with some state-of-the-art solvers, presenting already some good results on GPUs.

Safe and Collaborative Navigation & Interaction With Mobile Manipulators in Domestic Appliance Test Labs

Safe collaboration between a robot and human operator forms a critical requirement for deploying a robotic system into a manufacturing and testing environment. In this dissertation, the safety requirement for is developed and implemented for the navigation system of the mobile manipulators. A methodology for human-robot co-existence through a 3d scene analysis is also investigated. The proposed approach exploits the advance in computing capability by relying on graphic processing units (GPU’s) for volumetric predictive human-robot contact checking. Apart from guaranteeing safety of operators, human-robot collaboration is also fundamental when cooperative activities are required, as in appliance test automation floor. To achieve this, a generalized hierarchical task controller scheme for collision avoidance is developed. This allows the robotic arm to safely approach and inspect the interior of the appliance without collision during the testing procedure. The unpredictable presence of the operators also forms dynamic obstacle that changes very fast, thereby requiring a quick reaction from the robot side. In this aspect, a GPU-accelarated distance field is computed to speed up reaction time to avoid collision between human operator and the robot. An automated appliance testing also involves robotized laundry loading and unloading during life cycle testing. This task involves Laundry detection, grasp pose estimation and manipulation in a container, inside the drum and during recovery grasping. A wrinkle and blob detection algorithms for grasp pose estimation are developed and grasp poses are calculated along the wrinkle and blobs to efficiently perform grasping task. By ranking the estimated laundry grasp poses according to a predefined cost function, the robotic arm attempt to grasp poses that are more comfortable from the robot kinematic side as well as collision free on the appliance side. This is achieved through appliance detection and full-model registration and collision free trajectory execution using online collision avoidance.

Interference mitigation techniques for cloud-RAN deployments in 5G and beyond systems

The deployment of ultra-dense networks is one of the most promising solutions to manage the phenomenon of co-channel interference that affects the latest wireless communication systems, especially in hotspots. To meet the requirements of the use-cases and the immense amount of traffic generated in these scenarios, 5G ultra-dense networks are being deployed using various technologies, such as distributed antenna system (DAS) and cloud-radio access network (C-RAN). Through these centralized densification schemes, virtualized baseband processing units coordinate the distributed access points and manage the available network resources. In particular, link adaptation techniques are shown to be fundamental to overall system operation and performance enhancement. The core of this dissertation is the result of an analysis and a comparison of dynamic and adaptive methods for modulation and coding scheme (MCS) selection applied to the latest mobile telecommunications standards. A novel algorithm based on the proportional-integral-derivative (PID) controller principles and block error rate (BLER) target has been proposed. Tests were conducted in a 4G and 5G system level laboratory and, by means of a channel emulator, the performance was evaluated for different channel models and target BLERs. Furthermore, due to the intrinsic sectorization of the end-users distribution in the investigated scenario, a preliminary analysis on the joint application of users grouping algorithms with multi-antenna and multi-user techniques has been performed. In conclusion, the importance and impact of other fundamental physical layer operations, such as channel estimation and power control, on the overall end-to-end system behavior and performance were highlighted.

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.