Unmixing hyperspectral intimate mixtures

Proceedings of International Conference Conference Volume 7830 Image and Signal Processing for Remote Sensing XVI Lorenzo Bruzzone Toulouse, France | September 20, 2010

A New Method to Determine the Density and Water Absorption of Fine Recycled Aggregates

The construction industry keeps on demanding huge quantities of natural resources, mainly minerals for mortars and concrete production. The depletion of many quarries and environmental concerns about reducing the dumping of construction and demolition waste in quarries have led to an increase in the procuring and use of recycled aggregates from this type of waste. If they are to be incorporated in concrete and mortars it is essential to know their properties to guarantee the adequate performance of the end products, in both mechanical and durability-related terms. Existing regulated tests were developed for natural aggregates, however, and several problems arise when they are applied to recycled aggregates, especially fine recycled aggregates (FRA). This paper describes the main problems encountered with these tests and proposes an alternative method to determine the density and water absorption of FRA that removes them. The use of sodium hexametaphosphate solutions in the water absorption test has proven to improve its efficiency, minimizing cohesion between particles and helping to release entrained air.

Parallel hyperspectral unmixing on GPUs

This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. Both techniques are highly parallelizable, which significantly reduces the computing time. A design for the commodity graphics processing units of the two methods is presented and evaluated. Experimental results obtained for simulated and real hyperspectral data sets reveal speedups up to 100 times, which grants real-time response required by many remotely sensed hyperspectral applications.

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

Invariant integrals applied to nematic liquid crystals with small Ericksen number and topological defects

Invariant integrals are derived for nematic liquid crystals and applied to materials with small Ericksen number and topological defects. The nematic material is confined between two infinite plates located at y = -h and y = h (h is an element of R+) with a semi-infinite plate at y = 0 and x < 0. Planar and homeotropic strong anchoring boundary conditions to the director field are assumed at these two infinite and semi-infinite plates, respectively. Thus, a line disclination appears in the system which coincides with the z-axis. Analytical solutions to the director field in the neighbourhood of the singularity are obtained. However, these solutions depend on an arbitrary parameter. The nematic elastic force is thus evaluated from an invariant integral of the energy-momentum tensor around a closed surface which does not contain the singularity. This allows one to determine this parameter which is a function of the nematic cell thickness and the strength of the disclination. Analytical solutions are also deduced for the director field in the whole region using the conformal mapping method. (C) 2013 Elsevier Ltd. All rights reserved.

Hyperspectral unmixing based on mixtures of Dirichlet components

This paper introduces a new unsupervised hyperspectral unmixing method conceived to linear but highly mixed hyperspectral data sets, in which the simplex of minimum volume, usually estimated by the purely geometrically based algorithms, is far way from the true simplex associated with the endmembers. The proposed method, an extension of our previous studies, resorts to the statistical framework. The abundance fraction prior is a mixture of Dirichlet densities, thus automatically enforcing the constraints on the abundance fractions imposed by the acquisition process, namely, nonnegativity and sum-to-one. A cyclic minimization algorithm is developed where the following are observed: 1) The number of Dirichlet modes is inferred based on the minimum description length principle; 2) a generalized expectation maximization algorithm is derived to infer the model parameters; and 3) a sequence of augmented Lagrangian-based optimizations is used to compute the signatures of the endmembers. Experiments on simulated and real data are presented to show the effectiveness of the proposed algorithm in unmixing problems beyond the reach of the geometrically based state-of-the-art competitors.

A numerical analysis of generalized Boussinesq-type equation using continuos/discontinuous FEM

An improved class of Boussinesq systems of an arbitrary order using a wave surface elevation and velocity potential formulation is derived. Dissipative effects and wave generation due to a time-dependent varying seabed are included. Thus, high-order source functions are considered. For the reduction of the system order and maintenance of some dispersive characteristics of the higher-order models, an extra O(mu 2n+2) term (n ??? N) is included in the velocity potential expansion. We introduce a nonlocal continuous/discontinuous Galerkin FEM with inner penalty terms to calculate the numerical solutions of the improved fourth-order models. The discretization of the spatial variables is made using continuous P2 Lagrange elements. A predictor-corrector scheme with an initialization given by an explicit RungeKutta method is also used for the time-variable integration. Moreover, a CFL-type condition is deduced for the linear problem with a constant bathymetry. To demonstrate the applicability of the model, we considered several test cases. Improved stability is achieved.

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization.

Naturally occurring radioactive materials: a method for assessing the risk of exposure

Naturally Occurring Radioactive Materials (NORM) are materials that are found naturally in the environment and contain radioactive isotopes that can cause negative effects on the health of workers who manipulate them. Present in underground work like mining and tunnel construction in granite zones, these materials are difficult to identify and characterize without appropriate equipment for risk evaluation. The assessing methods were exemplified with a case study applied to the handling and processing of phosphoric rock where one found significant amounts of radioactive isotopes and consequently elevated radon concentrations in enclosed spaces containing these materials. © 2015 Taylor & Francis Group, London.

A comprehensive high-throughput FTIR spectroscopy-based method for evaluating the transfection event: estimating the transfection efficiency and extracting associated metabolic responses

Reporter genes are routinely used in every laboratory for molecular and cellular biology for studying heterologous gene expression and general cellular biological mechanisms, such as transfection processes. Although well characterized and broadly implemented, reporter genes present serious limitations, either by involving time-consuming procedures or by presenting possible side effects on the expression of the heterologous gene or even in the general cellular metabolism. Fourier transform mid-infrared (FT-MIR) spectroscopy was evaluated to simultaneously analyze in a rapid (minutes) and high-throughput mode (using 96-wells microplates), the transfection efficiency, and the effect of the transfection process on the host cell biochemical composition and metabolism. Semi-adherent HEK and adherent AGS cell lines, transfected with the plasmid pVAX-GFP using Lipofectamine, were used as model systems. Good partial least squares (PLS) models were built to estimate the transfection efficiency, either considering each cell line independently (R 2 ≥ 0.92; RMSECV ≤ 2 %) or simultaneously considering both cell lines (R 2 = 0.90; RMSECV = 2 %). Additionally, the effect of the transfection process on the HEK cell biochemical and metabolic features could be evaluated directly from the FT-IR spectra. Due to the high sensitivity of the technique, it was also possible to discriminate the effect of the transfection process from the transfection reagent on KEK cells, e.g., by the analysis of spectral biomarkers and biochemical and metabolic features. The present results are far beyond what any reporter gene assay or other specific probe can offer for these purposes.

Toroid inductor development for a SIC DC-DC converter up to 150kW, based on finite element method

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Multiobjective optimization for node adaptation in the analysis of composite plates using a meshless collocation method

The bending of simply supported composite plates is analyzed using a direct collocation meshless numerical method. In order to optimize node distribution the Direct MultiSearch (DMS) for multi-objective optimization method is applied. In addition, the method optimizes the shape parameter in radial basis functions. The optimization algorithm was able to find good solutions for a large variety of nodes distribution.

Multiobjective optimization of viscoelastic laminated sandwich structures using the Direct MultiSearch method

A multiobjective approach for optimization of passive damping for vibration reduction in sandwich structures is presented in this paper. Constrained optimization is conducted for maximization of modal loss factors and minimization of weight of sandwich beams and plates with elastic laminated constraining layers and a viscoelastic core, with layer thickness and material and laminate layer ply orientation angles as design variables. The problem is solved using the Direct MultiSearch (DMS) solver for derivative-free multiobjective optimization and solutions are compared with alternative ones obtained using genetic algorithms.

Parallel hyperspectral compressive sensing method on GPU

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

Parallel method for sparse semisupervised hyperspectral unmixing

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