Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. 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. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

An approach to absorptive capacity based on the Portuguese textile industry

The main purpose of this research is to identify the hidden knowledge and learning mechanisms in the organization in order to disclosure the tacit knowledge and transform it into explicit knowledge. Most firms usually tend to duplicate their efforts acquiring extra knowledge and new learning skills while forgetting to exploit the existing ones thus wasting one life time resources that could be applied to increase added value within the firm overall competitive advantage. This unique value in the shape of creation, acquisition, transformation and application of learning and knowledge is not disseminated throughout the individual, group and, ultimately, the company itself. This work is based on three variables that explain the behaviour of learning as the process of construction and acquisition of knowledge, namely internal social capital, technology and external social capital, which include the main attributes of learning and knowledge that help us to capture the essence of this symbiosis. Absorptive Capacity provides the right tool to explore this uncertainty within the firm it is possible to achieve the perfect match between learning skills and knowledge needed to support the overall strategy of the firm. This study has taken in to account a sample of the Portuguese textile industry and it is based on a multisectorial analysis that makes it possible a crossfunctional analysis to check on the validity of results in order to better understand and capture the dynamics of organizational behavior.

An approach to absorptive capacity based on the Portuguese textile industry(Final version)

The main purpose of this research is to identify the hidden knowledge and learning mechanisms in the organization in order to disclosure the tacit knowledge and transform it into explicit knowledge. Most firms usually tend to duplicate their efforts acquiring extra knowledge and new learning skills while forgetting to exploit the existing ones thus wasting one life time resources that could be applied to increase added value within the firm overall competitive advantage. This unique value in the shape of creation, acquisition, transformation and application of learning and knowledge is not disseminated throughout the individual, group and, ultimately, the company itself. This work is based on three variables that explain the behaviour of learning as the process of construction and acquisition of knowledge, namely internal social capital, technology and external social capital, which include the main attributes of learning and knowledge that help us to capture the essence of this symbiosis. Absorptive Capacity provides the right tool to explore this uncertainty within the firm it is possible to achieve the perfect match between learning skills and knowledge needed to support the overall strategy of the firm. This study has taken in to account a sample of the Portuguese textile industry and it is based on a multisectorial analysis that makes it possible a crossfunctional analysis to check on the validity of results in order to better understand and capture the dynamics of organizational behavior.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Risk Aversion in a Mixed-Integer Nonlinear Approach to Support Decision-Making for a Hydro Power Producer

In this paper, a mixed-integer nonlinear approach is proposed to support decision-making for a hydro power producer, considering a head-dependent hydro chain. The aim is to maximize the profit of the hydro power producer from selling energy into the electric market. As a new contribution to earlier studies, a risk aversion criterion is taken into account, as well as head-dependency. The volatility of the expected profit is limited through the conditional value-at-risk (CVaR). The proposed approach has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems.

Wavelength Selective a-SiC:H p-i-n/p-i-n Heterostructure for Fluorescent Proteins Detection

In this paper we present results on the optimization of multilayered a-SiC:H heterostructures that can be used as optical transducers for fluorescent proteins detection using the Fluorescence Resonance Energy Transfer approach. Double structures composed by pin based aSiC:H cells are analyzed. The color discrimination is achieved by ac photocurrent measurement under different externally applied bias. Experimental data on spectral response analysis, current-voltage characteristics and color and transmission rate discrimination are reported. An electrical model, supported by a numerical simulation gives insight into the device operation. Results show that the optimized a-SiC:H heterostructures act as voltage controlled optical filters in the visible spectrum. When the applied voltages are chosen appropriately those optical transducers can detect not only the selective excitation of specimen fluorophores, but also the subsequent weak acceptor fluorescent channel emission.

Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain

This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain We propose a novel mixed-integer nonlinear programming (MINLP) approach, considering hydroelectric power generation as a nonlinear function of water discharge and of the head. As a new contribution to eat her studies, we model the on-off behavior of the hydro plants using integer variables, in order to avoid water discharges at forbidden areas Thus, an enhanced STHS is provided due to the more realistic modeling presented in this paper Our approach has been applied successfully to solve a test case based on one of the Portuguese cascaded hydro systems with a negligible computational time requirement.

A comparative benchmarking analysis of main Iberian container terminals: a DEA approach

Benchmarking is an important tool to organisations to improve their productivity, product quality, process efficiency or services. From Benchmarking the organisations could compare their performance with competitors and identify their strengths and weaknesses. This study intends to do a benchmarking analysis on the main Iberian Sea ports with a special focus on their container terminals efficiency. To attain this, the DEA (data envelopment analysis) is used since it is considered by several researchers as the most effective method to quantify a set of key performance indicators. In order to reach a more reliable diagnosis tool the DEA is used together with the data mining in comparing the sea ports operational data of container terminals during 2007.Taking into account that sea ports are global logistics networks the performance evaluation is essential to an effective decision making in order to improve their efficiency and, therefore, their competitiveness.

Síntese e fotosensibilização de nanotubos de titanatos

Nesta tese é descrita a preparação de nanotubos de titanatos (TNT) via síntese hidrotérmica alcalina, usando uma nova metodologia que evita a utilização de TiO2 cristalino como precursor. Foi estudada a influência da substituição sódio/protão na estrutura, morfologia e propriedades ópticas dos materiais preparados. Os resultados mostraram que a substituição Na+ → H+ resulta numa redução na distância intercamadas dos TNTs, tendo sido medidos valores entre 1.13±0.03 nm e 0.70±0.02 nm para aquele parâmetro. O comportamento óptico dos TNTs foi estudado na região UV-vis, estimando-se um hiato óptico de energia 3.27±0.03 eV para a amostra com maior teor de sódio enquanto que para a amostra protonada foi determinado um valor de 2.81±0.02 eV. Estes valores mostram que a troca iónica Na+ → H+ teve influência no desvio da banda de absorção dos TNTs para a região do visível próximo. A actividade fotocatalítica dos TNTs na degradação do corante rodamina 6G (R6G) foi posteriormente estudada. Verificou-se que, apesar de a amostra com maior teor de sódio ter sido a que exibiu maior capacidade para adsorver o R6G, foi a amostra protonada que apresentou a actividade catalítica mais elevada na fotodegradação deste corante. Numa segunda fase, e com o objectivo de preparar novos materiais nanoestruturados fotosensíveis, procedeu-se à decoração dos TNTs protonados com semicondutores (SC) nanocristalinos usando um método novo. Para o efeito os TNTs foram decorados com nanocristalites de ZnS, CdS e Bi2S3. Foi estudada a influência do tipo de semicondutor na estrutura, morfologia e propriedades ópticas dos SC/TNTs obtidos. Verificou-se que, para qualquer dos semicondutores usados no processo de decoração, a estrutura dos TNTs é preservada e não ocorre segregação do SC. Verificou-se ainda que a morfologia dos nanocompósitos preparados depende fortemente da natureza do semicondutor. No que respeita ao comportamento óptico destes materiais, foram determinados hiatos ópticos de energia 3.67±0.03 eV, 2.47±0.03 eV e 1.35±0.01 eV para as amostras ZnS/TNT, CdS/TNT e Bi2S3/TNT, respectivamente. Estes resultados mostram que através do processo de decoração de TNTs com semicondutores podem ser preparados materiais nanocompósitos inovadores, com propriedades ópticas novas e/ou pré-definidas numa gama alargada do espectro electromagnético.

Implementação de WDM com dispositivos semicondutores

Este trabalho utiliza uma estrutura pin empilhada, baseada numa liga de siliceto de carbono amorfo hidrogenado (a-Si:H e/ou a-SiC:H), que funciona como filtro óptico na zona visível do espectro electromagnético. Pretende-se utilizar este dispositivo para realizar a demultiplexagem de sinais ópticos e desenvolver um algoritmo que permita fazer o reconhecimento autónomo do sinal transmitido em cada canal. O objectivo desta tese visa implementar um algoritmo que permita o reconhecimento autónomo da informação transmitida por cada canal através da leitura da fotocorrente fornecida pelo dispositivo. O tema deste trabalho resulta das conclusões de trabalhos anteriores, em que este dispositivo e outros de configuração idêntica foram analisados, de forma a explorar a sua utilização na implementação da tecnologia WDM. Neste trabalho foram utilizados três canais de transmissão (Azul – 470 nm, Verde – 525 nm e Vermelho – 626 nm) e vários tipos de radiação de fundo. Foram realizadas medidas da resposta espectral e da resposta temporal da fotocorrente do dispositivo, em diferentes condições experimentais. Variou-se o comprimento de onda do canal e o comprimento de onda do fundo aplicado, mantendo-se constante a intensidade do canal e a frequência de transmissão. Os resultados obtidos permitiram aferir sobre a influência da presença da radiação de fundo e da tensão aplicada ao dispositivo, usando diferentes sequências de dados transmitidos nos vários canais. Verificou-se, que sob polarização inversa, a radiação de fundo vermelho amplifica os valores de fotocorrente do canal azul e a radiação de fundo azul amplifica o canal vermelho e verde. Para polarização directa, apenas a radiação de fundo azul amplifica os valores de fotocorrente do canal vermelho. Enquanto para ambas as polarizações, a radiação de fundo verde, não tem uma grande influência nos restantes canais. Foram implementados dois algoritmos para proceder ao reconhecimento da informação de cada canal. Na primeira abordagem usou-se a informação contida nas medidas de fotocorrente geradas pelo dispositivo sob polarização inversa e directa. Pela comparação das duas medidas desenvolveu-se e testou-se um algoritmo que permite o reconhecimento dos canais individuais. Numa segunda abordagem procedeu-se ao reconhecimento da informação de cada canal mas com aplicação de radiação de fundo, tendo-se usado a informação contida nas medidas de fotocorrente geradas pelo dispositivo sob polarização inversa sem aplicação de radiação de fundo com a informação contida nas medidas de fotocorrente geradas pelo dispositivo sob polarização inversa com aplicação de radiação de fundo. Pela comparação destas duas medidas desenvolveu-se e testou-se o segundo algoritmo que permite o reconhecimento dos canais individuais com base na aplicação de radiação de fundo.

A hybrid approach to multi-agent decision-making

In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective 'versus' individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii)joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement

A practical approach for profit-based unit commitment with emission limitations

This paper proposes a practical approach for profit-based unit commitment (PBUC) with emission limitations. Under deregulation, unit commitment has evolved from a minimum-cost optimisation problem to a profit-based optimisation problem. However, as a consequence of growing environmental concern, the impact of fossil-fuelled power plants must be considered, giving rise to emission limitations. The simultaneous address of the profit with the emission is taken into account in our practical approach by a multiobjective optimisation (MO) problem. Hence, trade-off Curves between profit and emission are obtained for different energy price profiles, in a way to aid decision-makers concerning emission allowance trading. Moreover, a new parameter is presented, ratio of change, and the corresponding gradient angle, enabling the proper selection of a compromise commitment for the units. A case study based on the standard IEEE 30-bus system is presented to illustrate the proficiency Of Our practical approach for the new competitive and environmentally constrained electricity supply industry.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Scheduling of head-dependent cascaded hydro systems: Mixed-integer quadratic programming approach

This paper is on the problem of short-term hydro, scheduling, particularly concerning head-dependent cascaded hydro systems. We propose a novel mixed-integer quadratic programming approach, considering not only head-dependency, but also discontinuous operating regions and discharge ramping constraints. Thus, an enhanced short-term hydro scheduling is provided due to the more realistic modeling presented in this paper. Numerical results from two case studies, based on Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach.

Chaos and crises in a model for cooperative hunting: A symbolic dynamics approach

In this work we investigate the population dynamics of cooperative hunting extending the McCann and Yodzis model for a three-species food chain system with a predator, a prey, and a resource species. The new model considers that a given fraction sigma of predators cooperates in prey's hunting, while the rest of the population 1-sigma hunts without cooperation. We use the theory of symbolic dynamics to study the topological entropy and the parameter space ordering of the kneading sequences associated with one-dimensional maps that reproduce significant aspects of the dynamics of the species under several degrees of cooperative hunting. Our model also allows us to investigate the so-called deterministic extinction via chaotic crisis and transient chaos in the framework of cooperative hunting. The symbolic sequences allow us to identify a critical boundary in the parameter spaces (K, C-0) and (K, sigma) which separates two scenarios: (i) all-species coexistence and (ii) predator's extinction via chaotic crisis. We show that the crisis value of the carrying capacity K-c decreases at increasing sigma, indicating that predator's populations with high degree of cooperative hunting are more sensitive to the chaotic crises. We also show that the control method of Dhamala and Lai [Phys. Rev. E 59, 1646 (1999)] can sustain the chaotic behavior after the crisis for systems with cooperative hunting. We finally analyze and quantify the inner structure of the target regions obtained with this control method for wider parameter values beyond the crisis, showing a power law dependence of the extinction transients on such critical parameters.