Estudo da homogeneidade e discriminação de madeiras através de espectroscopia de imagens, imagens digitais e ferramentas quimiométricas

Tese (doutorado)–Universidade de Brasília, Instituto de Química, Programa de Pós-Graduação em Química, 2016.

Self-healing thermal annealing: surface morphological restructuring control of GaN nanorods

With advances in nanolithography and dry etching, top-down methods of nanostructuring have become a widely used tool for improving the efficiency of optoelectronics. These nano dimensions can offer various benefits to the device performance in terms of light extraction and efficiency, but often at the expense of emission color quality. Broadening of the target emission peak and unwanted yellow luminescence are characteristic defect-related effects due to the ion beam etching damage, particularly for III–N based materials. In this article we focus on GaN based nanorods, showing that through thermal annealing the surface roughness and deformities of the crystal structure can be “self-healed”. Correlative electron microscopy and atomic force microscopy show the change from spherical nanorods to faceted hexagonal structures, revealing the temperature-dependent surface morphology faceting evolution. The faceted nanorods were shown to be strain- and defect-free by cathodoluminescence hyperspectral imaging, micro-Raman, and transmission electron microscopy (TEM). In-situ TEM thermal annealing experiments allowed for real time observation of dislocation movements and surface restructuring observed in ex-situ annealing TEM sampling. This thermal annealing investigation gives new insight into the redistribution path of GaN material and dislocation movement post growth, allowing for improved understanding and in turn advances in optoelectronic device processing of compound semiconductors.

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and compared with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour.

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

Snap-shot multispectral imaging of vascular dynamics in a mouse window-chamber model.

Understanding tumor vascular dynamics through parameters such as blood flow and oxygenation can yield insight into tumor biology and therapeutic response. Hyperspectral microscopy enables optical detection of hemoglobin saturation or blood velocity by either acquiring multiple images that are spectrally distinct or by rapid acquisition at a single wavelength over time. However, the serial acquisition of spectral images over time prevents the ability to monitor rapid changes in vascular dynamics and cannot monitor concurrent changes in oxygenation and flow rate. Here, we introduce snap shot-multispectral imaging (SS-MSI) for use in imaging the microvasculature in mouse dorsal-window chambers. By spatially multiplexing spectral information into a single-image capture, simultaneous acquisition of dynamic hemoglobin saturation and blood flow over time is achieved down to the capillary level and provides an improved optical tool for monitoring rapid in vivo vascular dynamics.

Modelling of urban sensible heat flux at multiple spatial scales : a demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach

Urbanization related alterations to the surface energy balance impact urban warming (‘heat islands’), the growth of the boundary layer, and many other biophysical processes. Traditionally, in situ heat flux measures have been used to quantify such processes, but these typically represent only a small local-scale area within the heterogeneous urban environment. For this reason, remote sensing approaches are very attractive for elucidating more spatially representative information. Here we use hyperspectral imagery from a new airborne sensor, the Operative Modular Imaging Spectrometer (OMIS), along with a survey map and meteorological data, to derive the land cover information and surface parameters required to map spatial variations in turbulent sensible heat flux (QH). The results from two spatially-explicit flux retrieval methods which use contrasting approaches and, to a large degree, different input data are compared for a central urban area of Shanghai, China: (1) the Local-scale Urban Meteorological Parameterization Scheme (LUMPS) and (2) an Aerodynamic Resistance Method (ARM). Sensible heat fluxes are determined at the full 6 m spatial resolution of the OMIS sensor, and at lower resolutions via pixel aggregation and spatial averaging. At the 6 m spatial resolution, the sensible heat flux of rooftop dominated pixels exceeds that of roads, water and vegetated areas, with values peaking at ∼ 350 W m− 2, whilst the storage heat flux is greatest for road dominated pixels (peaking at around 420 W m− 2). We investigate the use of both OMIS-derived land surface temperatures made using a Temperature–Emissivity Separation (TES) approach, and land surface temperatures estimated from air temperature measures. Sensible heat flux differences from the two approaches over the entire 2 × 2 km study area are less than 30 W m− 2, suggesting that methods employing either strategy maybe practica1 when operated using low spatial resolution (e.g. 1 km) data. Due to the differing methodologies, direct comparisons between results obtained with the LUMPS and ARM methods are most sensibly made at reduced spatial scales. At 30 m spatial resolution, both approaches produce similar results, with the smallest difference being less than 15 W m− 2 in mean QH averaged over the entire study area. This is encouraging given the differing architecture and data requirements of the LUMPS and ARM methods. Furthermore, in terms of mean study QH, the results obtained by averaging the original 6 m spatial resolution LUMPS-derived QH values to 30 and 90 m spatial resolution are within ∼ 5 W m− 2 of those derived from averaging the original surface parameter maps prior to input into LUMPS, suggesting that that use of much lower spatial resolution spaceborne imagery data, for example from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is likely to be a practical solution for heat flux determination in urban areas.

Properties of seawater and particulate matter from a WETLabs ALFA hyperspectral laser spectrofluorometer mounted on the continuous surface water sampling system during the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides continuous measurements made with an Aquatic Laser Fluorescence Analyzer (ALFA) (Chekalyuk et al., 2014), connected in-line to the TARA flow through system during 2013. The ALFA instrument provides dual-wavelength excitation (405 and 514 nm) of laser-stimulated emission (LSE) for spectral and temporal analysis. It offers in vivo fluorescence assessments of phytoplankton pigments, biomass, photosynthetic yield (Fv/Fm), phycobiliprotein (PBP)-containing phytoplankton groups, and chromophoric dissolved organic matter (CDOM) (Chekalyuk and Hafez, 2008; 2013A). Spectral deconvolution (SDC) is used to assess the overlapped spectral bands of aquatic fluorescence constituents and water Raman scattering (R). The Fv/Fm measurements are spectrally corrected for non-chlorophyll fluorescence background produced by CDOM and other constituents (Chekalyuk and Hafez, 2008). The sensor was cleaned weakly following the manufacturer recommended protocol.

FPGA-based architecture for hyperspectral endmember extraction

Hyperspectral instruments have been incorporated in satellite missions, providing data of high spectral resolution of the Earth. This data can be used in remote sensing applications, such as, target detection, hazard prevention, and monitoring oil spills, among others. In most of these applications, one of the requirements of paramount importance is the ability to give real-time or near real-time response. Recently, onboard processing systems have emerged, in order to overcome the huge amount of data to transfer from the satellite to the ground station, and thus, avoiding delays between hyperspectral image acquisition and its interpretation. For this purpose, compact reconfigurable hardware modules, such as field programmable gate arrays (FPGAs) are widely used. This paper proposes a parallel FPGA-based architecture for endmember’s signature extraction. This method based on the Vertex Component Analysis (VCA) has several advantages, namely it is unsupervised, fully automatic, and it works without dimensionality reduction (DR) pre-processing step. The architecture has been designed for a low cost Xilinx Zynq board with a Zynq-7020 SoC FPGA based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data sets collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low cost embedded systems, opening new perspectives for onboard hyperspectral image processing.