Projecto de um conversor redutor CC-CC comutado para SoC

A evolução da tecnologia CMOS tem possibilitado uma maior densidade de integração de circuitos tornando possível o aumento da complexidade dos sistemas. No entanto, a integração de circuitos de gestão de potência continua ainda em estudo devido à dificuldade de integrar todos os componentes. Esta solução apresenta elevadas vantagens, especialmente em aplicações electrónicas portáteis alimentadas a baterias, onde a autonomia é das principais características. No âmbito dos conversores redutores existem várias topologias de circuitos que são estudadas na área de integração. Na categoria dos conversores lineares utiliza-se o LDO (Low Dropout Regulator), apresentando no entanto baixa eficiência para relações de conversão elevadas. Os conversores comutados são elaborados através do recurso a circuitos de comutação abrupta, em que a eficiência deste tipo de conversores não depende do rácio de transformação entre a tensão de entrada e a de saída. A diminuição física dos processos CMOS tem como consequência a redução da tensão máxima que os transístores suportam, impondo o estudo de soluções tolerantes a “altatensão”, com o intuito de manter compatibilidade com tensões superiores que existam na placa onde o circuito é incluído. Os sistemas de gestão de energia são os primeiros a acompanhar esta evolução, tendo de estar aptos a fornecer a tensão que os restantes circuitos requerem. Neste trabalho é abordada uma metodologia de projecto para conversores redutores CCCC comutados em tecnologia CMOS, tendo-se maximizado a frequência com vista à integração dos componentes de filtragem em circuito integrado. A metodologia incide sobre a optimização das perdas totais inerentes à comutação e condução, dos transístores de potência e respectivos circuitos auxiliares. É apresentada uma nova metodologia para o desenvolvimento de conversores tolerantes a “alta-tensão”.

A DC-DC Step-Up mu-Power Converter for Energy Harvesting Applications, Using Maximum Power PointTracking, Based on Fractional Open Circuit Voltage

A DC-DC step-up micro power converter for solar energy harvesting applications is presented. The circuit is based on a switched-capacitorvoltage tripler architecture with MOSFET capacitors, which results in an, area approximately eight times smaller than using MiM capacitors for the 0.131mu m CMOS technology. In order to compensate for the loss of efficiency, due to the larger parasitic capacitances, a charge reutilization scheme is employed. The circuit is self-clocked, using a phase controller designed specifically to work with an amorphous silicon solar cell, in order to obtain themaximum available power from the cell. This will be done by tracking its maximum power point (MPPT) using the fractional open circuit voltage method. Electrical simulations of the circuit, together with an equivalent electrical model of an amorphous silicon solar cell, show that the circuit can deliver apower of 1132 mu W to the load, corresponding to a maximum efficiency of 66.81%.

A Step-up mu-Power Converter for Solar Energy Harvesting Applications, using Hill Climbing Maximum Power Point Tracking

This paper presents a step-up micro-power converter for solar energy harvesting applications. The circuit uses a SC voltage tripler architecture, controlled by an MPPT circuit based on the Hill Climbing algorithm. This circuit was designed in a 0.13 mu m CMOS technology in order to work with an a-Si PV cell. The circuit has a local power supply voltage, created using a scaled down SC voltage tripler, controlled by the same MPPT circuit, to make the circuit robust to load and illumination variations. The SC circuits use a combination of PMOS and NMOS transistors to reduce the occupied area. A charge re-use scheme is used to compensate the large parasitic capacitors associated to the MOS transistors. The simulation results show that the circuit can deliver a power of 1266 mu W to the load using 1712 mu W of power from the PV cell, corresponding to an efficiency as high as 73.91%. The simulations also show that the circuit is capable of starting up with only 19% of the maximum illumination level.

OC/EC ratio observations in Europe: Re-thinking the approach for apportionment between primary and secondary organic carbon

This study explores a large set of OC and EC measurements in PM(10) and PM(2.5) aerosol samples, undertaken with a long term constant analytical methodology, to evaluate the capability of the OC/EC minimum ratio to represent the ratio between the OC and EC aerosol components resulting from fossil fuel combustion (OC(ff)/EC(ff)). The data set covers a wide geographical area in Europe, but with a particular focus upon Portugal, Spain and the United Kingdom, and includes a great variety of sites: urban (background, kerbside and tunnel), industrial, rural and remote. The highest minimum ratios were found in samples from remote and rural sites. Urban background sites have shown spatially and temporally consistent minimum ratios, of around 1.0 for PM(10) and 0.7 for PM(2.5).The consistency of results has suggested that the method could be used as a tool to derive the ratio between OC and EC from fossil fuel combustion and consequently to differentiate OC from primary and secondary sources. To explore this capability, OC and EC measurements were performed in a busy roadway tunnel in central Lisbon. The OC/EC ratio, which reflected the composition of vehicle combustion emissions, was in the range of 03-0.4. Ratios of OC/EC in roadside increment air (roadside minus urban background) in Birmingham, UK also lie within the range 03-0.4. Additional measurements were performed under heavy traffic conditions at two double kerbside sites located in the centre of Lisbon and Madrid. The OC/EC minimum ratios observed at both sites were found to be between those of the tunnel and those of urban background air, suggesting that minimum values commonly obtained for this parameter in open urban atmospheres over-predict the direct emissions of OC(ff) from road transport. Possible reasons for this discrepancy are explored. (C) 2011 Elsevier Ltd. All rights reserved.

Endothelial lipase correlated positively with EPA/AA ratio in RBCs membrane

The fatty acid profile of erythrocyte membranes has been considered a good biomarker for several pathologic situations. Dietary intake, digestion, absorption, metabolism, storage and exchange amongst compartments, greatly influence the fatty acids composition of different cells and tissues. Lipoprotein and hepatic lipases were also involved in fatty acid availability. In the present work we examined the correlations between fatty acid in Red Blood Cells (RBCs) membranes, the fatty acid desaturase and elongase activities, glycaemia, blood lipids, lipoproteins and apoproteins, and the endothelial lipase (EL) mass in plasma. Twenty one individuals were considered in the present study, with age >18 y. RBCs membranes were obtained and analysed for fatty acid composition by gas chromatography. The amount of fatty acids (as percentage) were analysed, and the ratios between fatty acid 16:1/16:0; 18:1/18:0; 18:0/16:0; 22:6 n-3/20:5 n-3 and 20:4 n-6/18:2 n-6 were calculated. Bivariate analysis (rs) and partial correlations were determined. SCD16 estimation activity correlated positively with BMI (rs=0.466, p=0.043) and triacylglycerols (TAG) (rs=0.483, p=0.026), and negatively with the ratio ApoA1/ApoB (rs=-0.566, p=0.007). Endothelial lipase (EL) correlated positively with the EPA/AA ratio in RBCs membranes (rs=0.524, p=0.045). After multi-adjustment for BMI, age, hs-CRP and dietary n3/n6 ratio, the correlations remained significant between EL and EPA/AA ratio. At the best of our knowledge this is the first report that correlated EL with the fatty acid profile of RBCs plasma membranes. The association found here can suggest that the enzyme may be involved in the bioavailability and distribution of n-3/n-6 fatty acids, suggesting a major role for EL in the pathophysiological mechanisms involving biomembranes’ fatty acids, such as in inflammatory response and eicosanoids metabolites pathways.

Osmolality of preterm formulas suplemented with monprotein energy supplements

Background: Addition of energy supplements to preterm formulas is an optional strategy to increase the energy intake in infants requiring fluid restriction, in conditions like bronchopulmonary dysplasia. This strategy may lead to an undesirable increase in osmolality of feeds, the maximum recommended safe limit being 400 mOsm/kg. The aim of the study was to measure the changes in osmolality of several commercialized preterm formulas after addition of glucose polymers and medium-chain triglycerides. Methods: Osmolality was measured by the freezing point depression method. Six powdered formulas with concentrations of 14 g/100 ml and 16 g/100 ml, and five ready-to-feed liquid formulas were analyzed. All formulas, were supplemented with 10% (low supplementation) or 20% (high supplementation) of additional calories, respectively, in the form of glucose polymers and medium chain triglycerides, maintaining a 1:1 glucose:lipid calorie ratio. Inter-analysis and intra-analysis coefficients of variation of the measurements were always < 3.9%. Results: The mean osmolality (mOsm/kg) of the non-supplemented formulas varied between 268.5 and 315.3 mOsm/kg, increasing by 3–5% in low supplemented formulas, and by 6–10% in high supplemented formulas. None of the formulas analyzed exceeded 352.8 mOsm/kg. Conclusion: The supplementation of preterm formulas with nonprotein energy supplements with up to 20% additional calories did not exceed the maximum recommended osmolality for neonatal feedings.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Dinuclear Mn (II,II) complexes: magnetic properties and microwave assisted oxidation of alcohols

A series of six new mixed-ligand dinuclear Mn(II, II) complexes of three different hydrazone Schiff bases (H3L1, H3L2 and H3L3), derived from condensation of the aromatic acid hydrazides benzohydrazide, 2-aminobenzohydrazide or 2-hydroxybenzohydrazide, with 2,3-dihydroxy benzaldehyde, respectively, is reported. Reactions of Mn(NO3)(2) center dot 4H(2)O with the H3L1-3 compounds, in the presence of pyridine (1 : 1 : 1 mole ratio), in methanol at room temperature, yield [Mn(H2L1)(py)(H2O)](2)(NO3)(2) center dot 2H(2)O (1 center dot 2H(2)O), [Mn(H2L2)(py)(CH3OH)](2)(NO3)(2) center dot 4H(2)O (2 center dot 4H(2)O) and [Mn(H2L3)(py)(H2O)](2)(NO3)(2) (3) respectively, whereas the use of excess pyridine yields complexes with two axially coordinated pyridine molecules at each Mn(II) centre, viz. [Mn(H2L1)(py)(2)] 2(NO3)(2) center dot H2O (4 center dot H2O), [Mn(H2L2)(py) H-O (6 center dot 2CH(3)OH), respectively. In all the complexes, the (H2L1-3)-ligand coordinates in the keto form. Complexes 1 center dot 2H(2)O, 2 center dot 4H(2)O, 4 center dot H2O, 5 center dot 2H(2)O and 6 center dot 2CH(3)OH are characterized by single crystal X-ray diffraction analysis. The complexes 1, 2 and 6, having different coordination environments, have been selected for variable temperature magnetic susceptibility measurements to examine the nature of magnetic interaction between magnetically coupled Mn(II) centres and also for exploration of the catalytic activity towards microwave assisted oxidation of alcohols. A yield of 81% (acetophenone) is obtained using a maximum of 0.4% molar ratio of catalyst relative to the substrate in the presence of TEMPO and in aqueous basic solution, under mild conditions.

Mu-Chlorido-Bridged Dimanganese(II) complexes of the schiff base derived from [2+2] condensation of 2,6-diformyl-4-methylphenol and 1,3-bis(3-aminopropyl)tetramethyldisloxane: structure, magnetismo, electrochemical behaviour, and catalytic oxidation of secondary alcohols

The reaction of 2,6-diformyl-4-methylphenol with 1,3-bis(3-aminopropyl)tetramethyldisiloxane in the presence of MnCl2 in a 1:1:2 molar ratio in methanol afforded a dinuclear -chlorido-bridged manganese(II) complex of the macrocyclic [2+2] condensation product (H2L), namely, [Mn2Cl2(H2L)(HL)]Cl center dot 3H(2)O (1). The latter afforded a new compound, namely, [Mn2Cl2(H2L)(2)][MnCl4]center dot 4CH(3)CN center dot 0.5CHCl(3 center dot)0.4H(2)O (2), after recrystallisation from 1:1 CHCl3/CH3CN. The co-existence of the free and complexed azomethine groups, phenolato donors, mu-chlorido bridges, and the disiloxane unit were well evidenced by ESI mass spectrometry and FTIR spectroscopy and confirmed by X-ray crystallography. The magnetic measurements revealed an antiferromagnetic interaction between the two high-spin (S = 5/2, g = 2) manganese(II) ions through the mu-chlorido bridging ligands. The electrochemical behaviour of 1 and 2 has been studied, and details of their redox properties are reported. Both compounds act as catalysts or catalyst precursors in the solvent-free low-power microwave-assisted oxidation of selected secondary alcohols, for example, 1-phenylethanol, cyclohexanol, 2- and 3-octanol, to the corresponding ketones in the absence of solvent. The highest yield of 72% was achieved for 1-phenylethanol by using a maximum of 1% molar ratio of catalyst relative to substrate.

µ-Chlorido-bridged dimanganese(II) complexes of the schiff base derived from [2+2] condensation of 2,6-diformyl-4-methylphenol and 1,3-bis(3-aminopropyl)tetramethyldisiloxane: structure, magnetismo, electrochemical behaviour, and catalytic oxidation of secondary alcohols

The reaction of 2,6-diformyl-4-methylphenol with 1,3-bis(3-aminopropyl)tetramethyldisiloxane in the presence of MnCl2 in a 1:1:2 molar ratio in methanol afforded a dinuclear -chlorido-bridged manganese(II) complex of the macrocyclic [2+2] condensation product (H2L), namely, [Mn2Cl2(H2L)(HL)]Cl center dot 3H(2)O (1). The latter afforded a new compound, namely, [Mn2Cl2(H2L)(2)][MnCl4]center dot 4CH(3)CN center dot 0.5CHCl(3 center dot)0.4H(2)O (2), after recrystallisation from 1:1 CHCl3/CH3CN. The co-existence of the free and complexed azomethine groups, phenolato donors, mu-chlorido bridges, and the disiloxane unit were well evidenced by ESI mass spectrometry and FTIR spectroscopy and confirmed by X-ray crystallography. The magnetic measurements revealed an antiferromagnetic interaction between the two high-spin (S = 5/2, g = 2) manganese(II) ions through the mu-chlorido bridging ligands. The electrochemical behaviour of 1 and 2 has been studied, and details of their redox properties are reported. Both compounds act as catalysts or catalyst precursors in the solvent-free low-power microwave-assisted oxidation of selected secondary alcohols, for example, 1-phenylethanol, cyclohexanol, 2- and 3-octanol, to the corresponding ketones in the absence of solvent. The highest yield of 72% was achieved for 1-phenylethanol by using a maximum of 1% molar ratio of catalyst relative to substrate.

Pentagonal parch-excited sectorized antenna for localization systems

A pentagonal patch-excited sectorized antenna (SA) suitable for 2.4-2.5 GHz localization systems was studied and developed. The integration of six patch-excited structures converges into a sectorized antenna called Hive5 that provides gain improvement compared to a patch antenna, maximum variation of 3 dB beam width over the radiation pattern and circular polarization (CP). This antenna is presented and analyzed taking into account the tap length and the flare angle. The proposed antenna in combination with a RF-Switch provides a cost effective solution for localization based on Wireless Sensor Networks (WSN) and will be used for implementing angle of arrival (AoA) techniques combined with RF fingerprinting techniques.

Severity and exposure associated with tsunami actions in urban waterfronts: the case of Lisbon, Portugal

The Tagus estuary is bordered by the largest metropolitan area in Portugal, the Lisbon capital city council. It has suffered the impact of several major tsunamis in the past, as shown by a recent revision of the catalogue of tsunamis that struck the Portuguese coast over the past two millennia. Hence, the exposure of populations and infrastructure established along the riverfront comprises a critical concern for the civil protection services. The main objectives of this work are to determine critical inundation areas in Lisbon and to quantify the associated severity through a simple index derived from the local maximum of momentum flux per unit mass and width. The employed methodology is based on the mathematical modelling of a tsunami propagating along the estuary, resembling the one occurred on the 1 November of 1755 that followed the 8.5 M-w Great Lisbon Earthquake. The employed simulation tool was STAV-2D, a shallow-flow solver coupled with conservation equations for fine solid phases, and now featuring the novelty of discrete Lagrangian tracking of large debris. Different sets of initial conditions were studied, combining distinct tidal, atmospheric and fluvial scenarios, so that the civil protection services were provided with comprehensive information to devise public warning and alert systems and post-event mitigation intervention. For the most severe scenario, the obtained results have shown a maximum inundation extent of 1.29 km at the AlcA cent ntara valley and water depths reaching nearly 10 m across Lisbon's riverfront.

Otimização de placas compósitas laminadas utilizando redes neuronais e enxames de partículas

Trabalho Final de mestrado para obtenção do grau de Mestre em engenharia Mecância

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.

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.