Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Coluna de som utilizando array de transdutores de ultrasons

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Downstream processing development of enveloped viruses for clinical applications: innovative tools for rational process optimization

Dissertation presented to obtain a Ph.D. degree in Engineering and Technology Sciences, Biotechnology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Sistema de Variação de Lastro para Controlo de Movimento Vertical de Veículo Subaquático

O principal motivo para a realização deste trabalho consistiu no desenvolvimento de tecnologia robótica, que permitisse o mergulho e ascenção de grandes profundidades de uma forma eficiente. O trabalho realizado contemplou uma fase inicial de análise e estudo dos sistemas robóticos existentes no mercado, bem como métodos utilizados identificando vantagens e desvantagens em relação ao tipo de veículo pretendido. Seguiu-se uma fase de projeto e estudo mecânico, com o intuito de desenvolver um veículo com variação de lastro através do bombeamento de óleo para um reservatório exterior, para variar o volume total do veículo, variando assim a sua flutuabilidade. Para operar a grande profundidade com AUV’s é conveniente poder efetuar o trajeto up/down de forma eficiente e a variação de lastro apresenta vantagens nesse aspeto. No entanto, contrariamente aos gliders o interesse está na possibilidade de subir e descer na vertical. Para controlar a flutuabilidade e ao mesmo tempo analisar a profundidade do veículo em tempo real, foi necessario o uso de um sistema de processamento central que adquirisse a informação do sensor de pressão e comunicasse com o sistema de variação de lastro, de modo a fazer o controlo de posicionamento vertical desejado. Do ponto de vista tecnológico procurou-se desenvolver e avaliar soluções de variação de volume intermédias entre as dos gliders (poucas gramas) e as dos ROV’s workclass (dezenas ou centenas de kilogramas). Posteriormente, foi desenvolvido um simulador em matlab (Simulink) que reflete o comportamento da descida do veículo, permitindo alterar parâmetros do veículo e analisar os seus resultados práticos, de modo a poder ajustar o veículo real. Nos resultados simulados verificamos o cálculo das velocidades limite atingidas pelo veículo com diferentes coeficientes de atrito, bem como o comportamento da variação de lastro do veículo no seu deslocamento vertical. Sistema de Variação de Lastro para Controlo de Movimento Vertical de Veículo Subaquático Por fim, verificou-se ainda a capacidade de controlo do veículo para uma determinada profundiade, e foi feita a comparação entre estas simulações executadas com parâmetros muito próximos do ensaio real e os respetivos ensaios reais.

Sensor electroquímico seletivo para a Norfloxacina

A Norfloxacina (NFX) é um antibiótico antibacteriano indicado para combater bactérias Gram-negativas e amplamente utilizado para o tratamento de infeções no trato respiratório e urinário. Com a necessidade de realizar estudos clínicos e farmacológicos esenvolveram-se métodos de análise rápida e sensitiva para a determinação da Norfloxacina. Neste trabalho foi desenvolvido um novo sensor eletroquímico sensível e seletivo para a deteção da NFX. O sensor foi construído a partir de modificações efetuadas num elétrodo de carbono vítreo. Inicialmente o elétrodo foi modificado com a deposição de uma suspensão de nanotubos de carbono de paredes múltiplas (MWCNT) de modo a aumentar a sensibilidade de resposta analítica. De seguida um filme polímerico molecularmente impresso (MIP) foi preparado por eletrodeposição, a partir de uma solução contendo pirrol (monómero funcional) e NFX (template). Um elétrodo de controlo não impresso foi também preparado (NIP). Estudouse e caraterizou-se a resposta eletroquímica do sensor para a oxidação da NFX por voltametria de onda quadrada. Foram optimizados diversos parâmetros experimentais, tais como, condições ótimas de polimerização, condições de incubação e condições de extração. O sensor apresenta um comportamento linear entre a intensidade da corrente do pico e o logaritmo da concentração de NFX na gama entre 0,1 e 8μM. Os resultados obtidos apresentam boa precisão, com repetibilidade inferior a 6% e reprodutibilidade inferior a 9%. Foi calculado a partir da curva de calibração um limite de deteção de 0,2 μM O método desenvolvido é seletivo, rápido e de fácil manuseamento. O sensor molecularmente impresso foi aplicado com sucesso na deteção da NFX em amostras de urina real e água.

Using interval graphs in an order processing optimization problem

In this paper we address an order processing optimization problem known as minimization of open stacks (MOSP). We present an integer pro gramming model, based on the existence of a perfect elimination scheme in interval graphs, which finds an optimal sequence for the costumers orders.

Um sensor de peróxido de hidrogénio numa bacteria anaeróbia: a PerR de Desulfovobrio vulgaris vulgaris Hildenborough

Dissertação apresentada para obtenção do grau de Mestre em Bioquímica Estrutural e Funcional, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Cochineal, a precious source of red: cochineal dyes characterization by high performance liquid chromatography with diode array detection and principal component analysis

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

Effects of storage, processing and proteolytic digestion on microcystin-LR concentration in edible clams

Accumulation of microcystin-LR (MC-LR) in edible aquatic organisms, particularly in bivalves, is widely documented. In this study, the effects of food storage and processing conditions on the free MC-LR concentration in clams (Corbicula fluminea) fed MC-LR-producing Microcystisaeruginosa (1 × 105 cell/mL) for four days, and the bioaccessibility of MC-LR after in vitro proteolytic digestion were investigated. The concentration of free MC-LR in clams decreased sequentially over the time with unrefrigerated and refrigerated storage and increased with freezing storage. Overall, cooking for short periods of time resulted in a significantly higher concentration (P < 0.05) of free MC-LR in clams, specifically microwave (MW) radiation treatment for 0.5 (57.5%) and 1 min (59%) and boiling treatment for 5 (163.4%) and 15 min (213.4%). The bioaccessibility of MC-LR after proteolytic digestion was reduced to 83%, potentially because of MC-LR degradation by pancreatic enzymes. Our results suggest that risk assessment based on direct comparison between MC-LR concentrations determined in raw food products and the tolerable daily intake (TDI) value set for the MC-LR might not be representative of true human exposure.

Desenvolvimento de um sensor de electroencefalografia localizado

Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para complementar os requerimentos para a obtenção do grau de Mestre em Engenharia Biomédica

The Crystal Structure of the Escherichia coli Autoinducer-2 Processing Protein LsrF

PLOS ONE, 4(8):ARTe6820

Electrospun Polyaniline-Reduced Graphene Oxide Composite Nanofibers Based High Sensitive Ammonia Gas Sensor

Ammonia is an important gas in many power plants and industrial processes so its detection is of extreme importance in environmental monitoring and process control due to its high toxicity. Ammonia’s threshold limit is 25 ppm and the exposure time limit is 8 h, however exposure to 35 ppm is only secure for 10 min. In this work a brief introduction to ammonia aspects are presented, like its physical and chemical properties, the dangers in its manipulation, its ways of production and its sources. The application areas in which ammonia gas detection is important and needed are also referred: environmental gas analysis (e.g. intense farming), automotive-, chemical- and medical industries. In order to monitor ammonia gas in these different areas there are some requirements that must be attended. These requirements determine the choice of sensor and, therefore, several types of sensors with different characteristics were developed, like metal oxides, surface acoustic wave-, catalytic-, and optical sensors, indirect gas analyzers, and conducting polymers. All the sensors types are described, but more attention will be given to polyaniline (PANI), particularly to its characteristics, syntheses, chemical doping processes, deposition methods, transduction modes, and its adhesion to inorganic materials. Besides this, short descriptions of PANI nanostructures, the use of electrospinning in the formation of nanofibers/microfibers, and graphene and its characteristics are included. The created sensor is an instrument that tries to achieve a goal of the medical community in the control of the breath’s ammonia levels being an easy and non-invasive method for diagnostic of kidney malfunction and/or gastric ulcers. For that the device should be capable to detect different levels of ammonia gas concentrations. So, in the present work an ammonia gas sensor was developed using a conductive polymer composite which was immobilized on a carbon transducer surface. The experiments were targeted to ammonia measurements at ppb level. Ammonia gas measurements were carried out in the concentration range from 1 ppb to 500 ppb. A commercial substrate was used; screen-printed carbon electrodes. After adequate surface pre-treatment of the substrate, its electrodes were covered by a nanofibrous polymeric composite. The conducting polyaniline doped with sulfuric acid (H2SO4) was blended with reduced graphene oxide (RGO) obtained by wet chemical synthesis. This composite formed the basis for the formation of nanofibers by electrospinning. Nanofibers will increase the sensitivity of the sensing material. The electrospun PANI-RGO fibers were placed on the substrate and then dried at ambient temperature. Amperometric measurements were performed at different ammonia gas concentrations (1 to 500 ppb). The I-V characteristics were registered and some interfering gases were studied (NO2, ethanol, and acetone). The gas samples were prepared in a custom setup and were diluted with dry nitrogen gas. Electrospun nanofibers of PANI-RGO composite demonstrated an enhancement in NH3 gas detection when comparing with only electrospun PANI nanofibers. Was visible higher range of resistance at concentrations from 1 to 500 ppb. It was also observed that the sensor had stable, reproducible and recoverable properties. Moreover, it had better response and recovery times. The new sensing material of the developed sensor demonstrated to be a good candidate for ammonia gas determination.

Desenvolvimento de um oxímetro de pulso e de um sensor de medição de pressão arterial

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

An Enhanced WLAN Security System With FPGA Implementation for Multimedia Applications

Maintaining a high level of data security with a low impact on system performance is more challenging in wireless multimedia applications. Protocols that are used for wireless local area network (WLAN) security are known to significantly degrade performance. In this paper, we propose an enhanced security system for a WLAN. Our new design aims to decrease the processing delay and increase both the speed and throughput of the system, thereby making it more efficient for multimedia applications. Our design is based on the idea of offloading computationally intensive encryption and authentication services to the end systems’ CPUs. The security operations are performed by the hosts’ central processor (which is usually a powerful processor) before delivering the data to a wireless card (which usually has a low-performance processor). By adopting this design, we show that both the delay and the jitter are significantly reduced. At the access point, we improve the performance of network processing hardware for real-time cryptographic processing by using a specialized processor implemented with field-programmable gate array technology. Furthermore, we use enhanced techniques to implement the Counter (CTR) Mode with Cipher Block Chaining Message Authentication Code Protocol (CCMP) and the CTR protocol. Our experiments show that it requires timing in the range of 20–40 μs to perform data encryption and authentication on different end-host CPUs (e.g., Intel Core i5, i7, and AMD 6-Core) as compared with 10–50 ms when performed using the wireless card. Furthermore, when compared with the standard WiFi protected access II (WPA2), results show that our proposed security system improved the speed to up to 3.7 times.

Replication vs erasure coding in data centric storage for wireless sensor networks

In-network storage of data in wireless sensor networks contributes to reduce the communications inside the network and to favor data aggregation. In this paper, we consider the use of n out of m codes and data dispersal in combination to in-network storage. In particular, we provide an abstract model of in-network storage to show how n out of m codes can be used, and we discuss how this can be achieved in five cases of study. We also define a model aimed at evaluating the probability of correct data encoding and decoding, we exploit this model and simulations to show how, in the cases of study, the parameters of the n out of m codes and the network should be configured in order to achieve correct data coding and decoding with high probability.