Remediação de solos contaminados por compostos farmacêuticos

O recente surgimento de nanopartículas de ferro valente-zero (nFZV), um material com elevada capacidade de remediação de solos por via de reacções de oxidação/redução pode ser uma opção viável para a remoção de fármacos do solo. A sua aplicação já é uma realidade em alguns tipos de solos contaminados por compostos específicos e, com este trabalho, procura-se estudar a sua capacidade de remediação de solos contaminados por compostos farmacêuticos, recorrendo-se a uma tecnologia “verde” de síntese destas nanopartículas. Esta tecnologia é bastante recente, ainda não aplicada no campo de trabalho, que se baseia no uso de folhas de certas árvores para produzir extratos naturais que reduzem o ferro (III) a ferro zero valente, formando nFZV. Desta forma procedeu-se, à escala laboratorial, ao estudo da eficiência das nFZV na degradação de um fármaco – paracetamol – e comparou-se com a eficiência demonstrada por oxidantes, muito utilizados hoje em dia em casos de remediação in situ como o permanganato de potássio, o peróxido de hidrogénio, o persulfato de sódio e o reagente de Fenton. O estudo foi efectuado em dois meios diferentes: solução aquosa e solo arenoso. De forma muito sucinta, o estudo baseou-se na introdução dos oxidantes/nFZV em soluções/solos contaminados com paracetamol e consequente monitorização do processo de remediação através de cromatografia líquida de alta eficiência. Nos ensaios com soluções aquosas contaminadas com paracetamol, o permanganato de potássio e o reagente de Fenton revelaram capacidade para degradar o paracetamol, atingindo mesmo um grau de degradação de 100%. O persulfato de sódio também demonstrou uma capacidade de degradação do paracetamol, chegando a atingir 99% de degradação, mas apenas recorrendo ao uso de um volume de oxidante elevado quando comparado com os outros dois oxidantes já referidos. Por outro lado, o peróxido de hidrogénio não demonstrou qualquer capacidade de degradação do paracetamol, pelo que o seu uso não passou desta fase. Verificou-se também que o uso de ferro granulado para o tratamento de água contaminada com paracetamol revelou resultados diferentes dos observados no uso de nFZV, obtendo-se eficiências de 87%. Existiram dificuldades analíticas na quantificação do paracetamol, especificamente relacionadas com o uso do extracto de folhas de amoreira, cuja composição continha substâncias que causaram dificuldades acentuadas na análise dos cromatogramas. Por fim, um pequeno teste de combinação do reagente de Fenton com os fenómenos de biodegradação resultantes dos microrganismos presentes em folhas do extracto de chá preto demonstrou que este pode ser uma área que pode e deve ser mais estudada. Desta forma, a utilização das nFZV para o tratamento de água contaminada com paracetamol não permitiu a retirada de conclusões seguras sobre a capacidade que as nFZV produzidas com extractos de folhas de amoreira e de chá preto têm de degradação do paracetamol. Nos testes de remediação de solos contaminados os resultados demonstraram que, mais uma vez, tanto o permanganato de potássio como o reagente de Fenton se revelam como os melhores oxidantes para a degradação do paracetamol, obtendo-se a degradação total do paracetamol. Por outro lado, voltou a ser necessário uma elevada quantidade de persulfato de sódio quando comparada com os dois anteriores, para que ocorra a degradação desta mesma quantidade de paracetamol, demonstrando mais uma vez que, apesar de não ideal, o persulfato demonstra capacidade de degradação do paracetamol.

Algorithm-oriented design of efficient many-core architectures applied to dense matrix multiplication

Recent integrated circuit technologies have opened the possibility to design parallel architectures with hundreds of cores on a single chip. The design space of these parallel architectures is huge with many architectural options. Exploring the design space gets even more difficult if, beyond performance and area, we also consider extra metrics like performance and area efficiency, where the designer tries to design the architecture with the best performance per chip area and the best sustainable performance. In this paper we present an algorithm-oriented approach to design a many-core architecture. Instead of doing the design space exploration of the many core architecture based on the experimental execution results of a particular benchmark of algorithms, our approach is to make a formal analysis of the algorithms considering the main architectural aspects and to determine how each particular architectural aspect is related to the performance of the architecture when running an algorithm or set of algorithms. The architectural aspects considered include the number of cores, the local memory available in each core, the communication bandwidth between the many-core architecture and the external memory and the memory hierarchy. To exemplify the approach we did a theoretical analysis of a dense matrix multiplication algorithm and determined an equation that relates the number of execution cycles with the architectural parameters. Based on this equation a many-core architecture has been designed. The results obtained indicate that a 100 mm(2) integrated circuit design of the proposed architecture, using a 65 nm technology, is able to achieve 464 GFLOPs (double precision floating-point) for a memory bandwidth of 16 GB/s. This corresponds to a performance efficiency of 71 %. Considering a 45 nm technology, a 100 mm(2) chip attains 833 GFLOPs which corresponds to 84 % of peak performance These figures are better than those obtained by previous many-core architectures, except for the area efficiency which is limited by the lower memory bandwidth considered. The results achieved are also better than those of previous state-of-the-art many-cores architectures designed specifically to achieve high performance for matrix multiplication.

Cloud-assisted Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for medical and non-medical applications. WBANs consist of a number of miniaturized, portable, and autonomous sensor nodes that are used for long-term health monitoring of patients. These sensor nodes continuously collect information of patients, which are used for ubiquitous health monitoring. In addition, WBANs may be used for managing catastrophic events and increasing the effectiveness and performance of rescue forces. The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is expected to play a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing (WBAN-cloud) will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud will enable users (including physicians and nurses) to globally access the processing and storage infrastructure at competitive costs. Because WBANs forward useful and life-critical information to the cloud – which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering

FPGA-based architecture for hyperspectral unmixing

This paper proposes an FPGA-based architecture for onboard hyperspectral unmixing. This method based on the Vertex Component Analysis (VCA) has several advantages, namely it is unsupervised, fully automatic, and it works without dimensionality reduction (DR) pre-processing step. The architecture has been designed for a low cost Xilinx Zynq board with a Zynq-7020 SoC FPGA based on the Artix-7 FPGA programmable logic and tested using real hyperspectral datasets. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low cost embedded systems.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Parallel hyperspectral compressive sensing method on GPU

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Parallel hyperspectral coded aperture for compressive sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.

The discovery of three lost ‘Salting’ carpets: science as a tool for revealing their history

Thesis presented at the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, to obtain a Master degree in Conservation and Restoration,Specialization in Textiles

System-on-chip field-programmable gate array design for onboard real-time hyperspectral unmixing

Hyperspectral instruments have been incorporated in satellite missions, providing large amounts of data of high spectral resolution of the Earth surface. This data can be used in remote sensing applications that often require a real-time or near-real-time response. To avoid delays between hyperspectral image acquisition and its interpretation, the last usually done on a ground station, onboard systems have emerged to process data, reducing the volume of information to transfer from the satellite to the ground station. For this purpose, compact reconfigurable hardware modules, such as field-programmable gate arrays (FPGAs), are widely used. This paper proposes an FPGA-based architecture for hyperspectral unmixing. This method based on the vertex component analysis (VCA) and it works without a dimensionality reduction preprocessing step. The architecture has been designed for a low-cost Xilinx Zynq board with a Zynq-7020 system-on-chip FPGA-based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low-cost embedded systems, opening perspectives for onboard hyperspectral image processing.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Projecto e implementação de osciloscópio digital baseado na norma IEEE1451.0

Os osciloscópios digitais são utilizados em diversas áreas do conhecimento, assumindo-se no âmbito da engenharia electrónica, como instrumentos indispensáveis. Graças ao advento das Field Programmable Gate Arrays (FPGAs), os instrumentos de medição reconfiguráveis, dadas as suas vantagens, i.e., altos desempenhos, baixos custos e elevada flexibilidade, são cada vez mais uma alternativa aos instrumentos tradicionalmente usados nos laboratórios. Tendo como objectivo a normalização no acesso e no controlo deste tipo de instrumentos, esta tese descreve o projecto e implementação de um osciloscópio digital reconfigurável baseado na norma IEEE 1451.0. Definido de acordo com uma arquitectura baseada nesta norma, as características do osciloscópio são descritas numa estrutura de dados denominada Transducer Electronic Data Sheet (TEDS), e o seu controlo é efectuado utilizando um conjunto de comandos normalizados. O osciloscópio implementa um conjunto de características e funcionalidades básicas, todas verificadas experimentalmente. Destas, destaca-se uma largura de banda de 575kHz, um intervalo de medição de 0.4V a 2.9V, a possibilidade de se definir um conjunto de escalas horizontais, o nível e declive de sincronismo e o modo de acoplamento com o circuito sob análise. Arquitecturalmente, o osciloscópio é constituído por um módulo especificado com a linguagem de descrição de hardware (HDL, Hardware Description Language) Verilog e por uma interface desenvolvida na linguagem de programação Java®. O módulo é embutido numa FPGA, definindo todo o processamento do osciloscópio. A interface permite o seu controlo e a representação do sinal medido. Durante o projecto foi utilizado um conversor Analógico/Digital (A/D) com uma frequência máxima de amostragem de 1.5MHz e 14 bits de resolução que, devido às suas limitações, obrigaram à implementação de um sistema de interpolação multi-estágio com filtros digitais.

Desenvolvimento de Métodos de Extração de Resíduos de Antimicrobianos e Quantificação em Amostras Alimentares

O leite é um alimento complexo, pela sua composição rico em água, proteínas, lípidos, vitaminas e minerais. Devido ao seu alto valor nutricional é fundamental para a amamentação de crianças e animais em crescimento, pois fornece componentes fundamentais para o desenvolvimento e manutenção da saúde. Os antimicrobianos são amplamente utilizados como uma medida terapêutica no tratamento de infeções bacterianas, profilaxia e como promotores de crescimento (aditivos). A presença de resíduos de antimicrobianos no leite pode representar riscos para a saúde humana, como reações alérgicas em indivíduos hipersensíveis e resistências. Os objetivos deste estudo são o desenvolvimento de novos métodos de limpeza e de pré-concentração para amostras de leite, por meio de extração em fase sólida (SPE), com a finalidade de realizar uma melhor identificação e quantificação de antimicrobiana por Cromatografia Líquida de Alta Performance (HPLC). Todos os métodos desenvolvidos são de fácil execução, com taxas de recuperação dos agentes antimicrobianos viáveis, com uma percentagem de recuperação a partir de 85%. O método cromatográfico utilizado para a deteção e quantificação (HPLC-DAD) têm os limites de deteção (LD) entre 2.43ng / mL e 1.62ng / mL e os limites de quantificação (LQ) entre 7,36 ng / mL e 4.92 ng / mL, o que significa este método vai de encontro às diretrizes estipuladas pela União Europeia para os agentes antimicrobianos estudados. A combinação dos métodos propostos de limpeza e pré-concentração por SPE e multirresíduo por HPLC-DAD permite, por conseguinte, a deteção e quantificação de resíduos de antibióticos no leite, tornando esta uma alternativa importante e útil no processo de controlo de qualidade para a indústria de alimentos e outras área.

Analysis of early synthetic dyes with HPLC-DAD-MS: an important database for analysis of colorants used in cultural heritage

Presented thesis at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles