Sparse matrix multiplication on a reconfigurable many-core architecture

Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and engineering applications. In many cases sparse matrices have thousands of rows and columns where most of the entries are zero, while non-zero data is spread over the matrix. This sparsity of data locality reduces the effectiveness of data cache in general-purpose processors quite reducing their performance efficiency when compared to what is achieved with dense matrix multiplication. In this paper, we propose a parallel processing solution for SMVM in a many-core architecture. The architecture is tested with known benchmarks using a ZYNQ-7020 FPGA. The architecture is scalable in the number of core elements and limited only by the available memory bandwidth. It achieves performance efficiencies up to almost 70% and better performances than previous FPGA designs.

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.

The effect of water inorganic matrix in ibuprofen adsorption onto activated carbon for water and wastewater treatment

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

Spatial neighborhood matrix computation: Inverse distance weighted versus binary contiguity

Paper presented at Geo-Spatial Crossroad GI_Forum, Salzburg, Austria.

Matrix fractional systems

This paper addresses the matrix representation of dynamical systems in the perspective of fractional calculus. Fractional elements and fractional systems are interpreted under the light of the classical Cole–Cole, Davidson–Cole, and Havriliak–Negami heuristic models. Numerical simulations for an electrical circuit enlighten the results for matrix based models and high fractional orders. The conclusions clarify the distinction between fractional elements and fractional systems.

Factorization of elliptic boundary value problems by invariant embedding and application to overdetermined problems

Dissertação para obtenção do Grau de Doutor em Matemática

Development of mixed matrix membranes with metal - organic framework and ionic liquids for biogas upgrading

Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

The additive effects in matrix dispersed liquid crystals

Dissertation presented at Faculdade de Ciências e Tecnologia from Universidade Nova de Lisboa to obtain the degree of Master in Chemical and Biochemical Engineering

Effect of polymeric matrix on morphology and photoelectric properties of polymer-quantum dots nanocomposites

Recently, CdTe semiconductor quantum dots (QDs) have attracted great interest due to their unique properties [1]. Their dispersion into polymeric matrices would be very for several optoelectronics applications. Despite its importance, there has been relatively little work done on charge transport in the QD polymeric films [2], which is mainly affected by their structural and morphological properties. In the present work, polymer-quantum dot nanocomposites films based on optically transparent polymers in the visible spectral range and CdTe QDs with controlled particle size and emission wavelength, were prepared via solvent casting. Photoluminescent (PL) measurements indicate different emission intensity of the nanocomposites. A blue shift of the emission peak compared to that of QDs in solution occurred, which is attributed to the QDs environment changes. The morphological and structural properties of the CdTe nanocomposites were evaluated. Since better QDs dispersion was achieved, PMMA seemed to be the most promising matrix. Electrical properties measurements indicate an ohmic behavior.

Thin films composed of gold nanoparticles dispersed in a dielectric matrix: the influence of the host matrix on the optical and mechanical responses

Gold nanoparticles were dispersed in two different dielectric matrices, TiO2 and Al2O3, using magnetron sputtering and a post-deposition annealing treatment. The main goal of the present work was to study how the two different host dielectric matrices, and the resulting microstructure evolution (including both the nanoparticles and the host matrix itself) promoted by thermal annealing, influenced the physical properties of the films. In particular, the structure and morphology of the nanocomposites were correlated with the optical response of the thin films, namely their localized surface plasmon resonance (LSPR) characteristics. Furthermore, and in order to scan the future application of the two thin film system in different types of sensors (namely biological ones), their functional behaviour (hardness and Young's modulus change) was also evaluated. Despite the similar Au concentrations in both matrices (~ 11 at.%), very different microstructural features were observed, which were found to depend strongly on the annealing temperature. The main structural differences included: (i) the early crystallization of the TiO2 host matrix, while the Al2O3 one remained amorphous up to 800 °C; (ii) different grain size evolution behaviours with the annealing temperature, namely an almost linear increase for the Au:TiO2 system (from 3 to 11 nm), and the approximately constant values observed in the Au:Al2O3 system (4–5 nm). The results from the nanoparticle size distributions were also found to be quite sensitive to the surrounding matrix, suggesting different mechanisms for the nanoparticle growth (particle migration and coalescence dominating in TiO2 and Ostwald ripening in Al2O3). These different clustering behaviours induced different transmittance-LSPR responses and a good mechanical stability, which opens the possibility for future use of these nanocomposite thin film systems in some envisaged applications (e.g. LSPR-biosensors).

Development of hyaluronic acid, dextrin and extracellular matrix hydrogels for cell expansion

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)

Effect of acid or alkaline catalyst and of different capping agents on the optical properties of CdS nanoparticles incorporated within a diureasil hybrid matrix

CdS nanoparticles (NPs) were synthesized using colloidal methods and incorporated within a diureasil hybrid matrix. The surface capping of the CdS NPs by 3-mercaptopropyltrimethoxysilane (MPTMS) and 3-aminopropyltrimethoxysilane (APTMS) organic ligands during the incorporation of the NPs within the hybrid matrix has been investigated. The matrix is based on poly(ethylene oxide)/poly(propylene oxide) chains grafted to a siliceous skeleton through urea bonds and was produced by sol–gel process. Both alkaline and acidic catalysis of the sol–gel reaction were used to evaluate the effect of each organic ligand on the optical properties of the CdS NPs. The hybrid materials were characterized by absorption, steady-state and time-resolved photoluminescence spectroscopy and High Resolution Transmission Electron Microscopy (HR-TEM). The preservation of the optical properties of the CdS NPs within the diureasil hybrids was dependent on the experimental conditions used. Both organic ligands (APTMS and MPTMS) demonstrated to be crucial in avoiding the increase of size distribution and clustering of the NPs within the hybrid matrix. The use of organic ligands was also shown to influence the level of interaction between the hybrid host and the CdS NPs. The CdS NPs showed large Stokes shifts and long average lifetimes, both in colloidal solution and in the xerogels, due to the origin of the PL emission in surface states. The CdS NPs capped with MPTMS have lower PL lifetimes compared to the other xerogel samples but still larger than the CdS NPs in the original colloidal solution. An increase in PL lifetimes of the NPs after their incorporation within the hybrid matrix is related to interaction between the NPs and the hybrid host matrix.

Biological behaviour of thin films consisting of Au nanoparticles dispersed in a TiO2 dielectric matrix

In this work it was studied the possible use of thin films, composed of Au nanoparticles (NPs) embedded in a TiO2 matrix, in biological applications, by evaluating their interaction with a well-known protein, Bovine Serum Albumin (BSA), as well as with microbial cells (Candida albicans). The films were produced by one-step reactive DC magnetron sputtering followed by heat-treatment. The samples revealed a composition of 8.3 at.% of Au and a stoichiometric TiO2 matrix. The annealing promoted grain size increase of the Au NPs from 3 nm (at 300 °C) to 7 nm (at 500 °C) and a progressive crystallization of the TiO2 matrix to anatase. A broad localized surface plasmon resonance (LSPR) absorption band (λ = 580–720 nm) was clearly observed in the sample annealed at 500 °C, being less intense at 300 °C. The biological tests indicated that the BSA adhesion is dependent on surface nanostructure morphology, which in turn depends on the annealing temperature that changed the roughness and wettability of the films. The Au:TiO2 thin films also induced a significant change of the microbial cell membrane integrity, and ultimately the cell viability, which in turn affected the adhesion on its surface. The microstructural changes (structure, grain size and surface morphology) of the Au:TiO2 films promoted by heat-treatment shaped the amount of BSA adhered and affected cell viability.

Acetylated bacterial cellulose coated with urinary bladder matrix as a substrate for retinal pigment epithelium

This work evaluated the effect of acetylated bacterial cellulose (ABC) substrates coated with urinary bladder matrix (UBM) on the behavior of Retinal Pigment Epithelium (RPE), as assessed by cell adhesion, proliferation and development of cell polarity exhibiting transepithelial resistance and polygonal shaped-cells with microvilli. Acetylation of bacterial cellulose (BC) generated a moderate hydrophobic surface (around 65°) while the adsorption of UBM onto these acetylated substrates did not affect significantly the surface hydrophobicity. The ABS substrates coated with UBM enabled the development of a cell phenotype closer to that of native RPE cells. These cells were able to express proteins essential for their cytoskeletal organization and metabolic function (ZO-1 and RPE65), while showing a polygonal shaped morphology with microvilli and a monolayer configuration. The coated ABC substrates were also characterized, exhibiting low swelling effect (between 1.52.0 swelling/mm3), high mechanical strength (2048 MPa) and non-pyrogenicity (2.12 EU/L). Therefore, the ABC substrates coated with UBM exhibit interesting features as potential cell carriers in RPE transplantation that ought to be further explored.