Polishing Mineral Specimens by the Use of an Optical Polishing Machine

The problem in this investigation was to determine if mineral specimens mounted in bakelite, or lucite, could be polished for microscopic examination by the use of an optical polishing machine, and if this method would cut down the length of time required to polish specimens by the methods now in use.

Influência de protocolos de jateamento na rugosidade da superfície de uma cerâmica de zircônia tetragonal parcialmente estabilizada por ítria

Objective: To evaluate the influence of different air abrasion protocols on the surface roughness of an yttria-stabilized polycrystalline tetragonal zirconia) (Y-TZP) ceramic, as well as the surface topography of the ceramic after the treatment. Method: Fifty-four specimens (7.5×4×7.5mm) obtained from two ceramic blocks (LAVA, 3M ESPE) were flattened with fine-grit sandpaper and subjected to sintering in the ceramic system's specific firing oven. Next, the specimens were embedded in acrylic resin and the surfaces to be treated were polished in a polishing machine using sandpapers of decreasing abrasion (600- to 1,200-grit) followed by felt discs with 10μm and 3μm polishing pastes and colloidal silica. The specimens were then randomly assigned to 9 groups, according to factors particle and pressure(n=6): Gr1- control; Gr2- Al 2O 3(50μm)/2.5 bar; Gr3- Al 2O 3(110μm)/2.5 bar; Gr4- SiO 2(30μm)/2.5 bar; Gr5- SiO 2(30μm)/2.5 bar; Gr6- Al 2O 3(50μm)/3.5 bar; Gr7- Al2O3(110μm)/3.5 bar; Gr8- SiO 2(30μm)/3.5 bar; Gr9- SiO 2(30μm)/3.5 bar. After treatments, surface roughness was analyzed by a digital optical profilometer and the morphology was examined by scanning electron microscopy (SEM). Data (μm) were subjected to statistical analysis by Dunnett's test (5%), two-way ANOVA and Tukey's test (5%). Results: The type of particle (p=0.0001) and the pressure (p=0.0001) used in the air abrasion protocols influenced the surface roughness values among the experimental groups (ANOVA). The mean surface roughness values (μm) obtained for the experimental groups (Gr2 to Gr9) were, respectively: 0.37 D; 0.56 BC; 0.46 BC; 0.48 CD; 0.59 BC; 0.82 A; 0.53B CD; 0.67 AB. The SEM analysis revealed that Al 2O 3, regardless of the particle size and pressure used, caused damage to the surface of the specimens, as it produced superficial damages on the ceramic, in the form of grooves and cracks. Conclusion: Al2O3 (110 μm/3.5 bar) air abrasion promoted the highest surface roughness on the ceramics, but it does not mean that this protocol promotes better ceramic-cement union compared to the other air abrasion protocols.

Biaxial flexural strength of CAD/CAM ceramics.

Aim of the study was to evaluate the biaxial flexural strength of ceramics processed using the Cerec inLab system. The hypothesis was that the flexural strength would be influenced by the type of ceramic. Ten samples (ISO 6872) of each ceramic (N.=50/n.=10) were made using Cerec inLab (software Cerec 3D) (Ø:15 mm, thickness: 1.2 mm). Three silica-based ceramics (Vita Mark II [VM], ProCad [PC] and e-max CAD ECAD]) and two yttria-stabilized tetragonal-zirconia-polycrystalline ceramics (Y-TZP) (e-max ZirCad [ZrCAD] and Vita In-Ceram 2000 YZ Cubes [VYZ]) were tested. The samples were finished with wet silicone carbide papers up to 1200-grit and polished in a polishing machine with diamond paste (3 μm). The samples were then submitted to biaxial flexural strength testing in a universal testing machine (EMIC), 1 mm/min. The data (MPa) were analyzed using the Kruskal-Wallis and Dunn (5%) tests. Scanning electronic microscopy (SEM) was performed on a representative sample from each group. The values (median, mean±sd) obtained for the experimental groups were: VM (101.7, 102.1±13.65 MPa), PC (165.2, 160±34.7 MPa), ECAD (437.2, 416.1±50.1 MPa), ZrCAD (804.2, 800.8±64.47 MPa) and VYZ (792.7, 807±100.7 MPa). The type of ceramic influenced the flexural strength values (P=0.0001). The ceramics ECADa, e-max ZrCADa and VYZa presented similar flexural strength values which were significantly higher than the other groups (PCb and VM IIb), which were similar statistically between them (Dunn's test). The hypothesis was accepted. The polycrystalline ceramics (Y-TZP) should be material chosen for make FPDs because of their higher flexural strength values.

Influência das propriedades petrográficas na qualidade do polimento de rochas ornamentais

Pós-graduação em Geologia Regional - IGCE

Effect of grinding with diamond-disc and -bur on the mechanical behavior of a Y-TZP ceramic

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Optical properties of semiconductor quantum-well material using photonic crystal fabricated by micro-fabrication machine

Two-dimensional photonic crystals in near infrared region were fabricated by using the focused ion beam ( FIB) method and the method of electron-beam lithography (EBL) combined with dry etching. Both methods can fabricate perfect crystals, the method of FIB is simple,the other is more complicated. It is shown that the material with the photonic crystal fabricated by FIB has no fluorescence,on the other hand, the small-lattice photonic crystal made by EBL combined with dry etching can enhance the extraction efficiency two folds, though the photonic crystal has some disorder. The mechanisms of the enhanced-emission and the absence of emission are also discussed.

Polishing-related optical anisotropy of semi-insulating GaAs studied by reflectance difference spectroscopy

The strong in-plane optical anisotropy of (001) semi-insulating GaAs, which comes from the submicron region under the surface, has been observed by reflectance difference spectroscopy. The optical anisotropy can be explained by the anisotropic strain that is introduced by the asymmetric distribution of 60 degrees dislocations during surface polishing. The simulated spectra reproduce the line shape of the experimental ones. The simulations show that the anisotropic strain is typically about 2.3x10(-4). (C) 2000 American Institute of Physics. [S0021-8979(00)01315-3].

Kerr-induced nonlinearity reduction in coherent optical OFDM by low complexity support vector machine regression-based equalization

We experimentally demonstrate 7-dB reduction of nonlinearity penalty in 40-Gb/s CO-OFDM at 2000-km using support vector machine regression-based equalization. Simulation in WDM-CO-OFDM shows up to 12-dB enhancement in Q-factor compared to linear equalization.

Downtime reduction analysis of the Australia Post flat mail optical character reader

Machine downtime, whether planned or unplanned, is intuitively costly to manufacturing organisations, but is often very difficult to quantify. The available literature showed that costing processes are rarely undertaken within manufacturing organisations. Where cost analyses have been undertaken, they generally have only valued a small proportion of the affected costs, leading to an overly conservative estimate. This thesis aimed to develop a cost of downtime model, with particular emphasis on the application of the model to Australia Post’s Flat Mail Optical Character Reader (FMOCR). The costing analysis determined a cost of downtime of $5,700,000 per annum, or an average cost of $138 per operational hour. The second section of this work focused on the use of the cost of downtime to objectively determine areas of opportunity for cost reduction on the FMOCR. This was the first time within Post that maintenance costs were considered along side of downtime for determining machine performance. Because of this, the results of the analysis revealed areas which have historically not been targeted for cost reduction. Further exploratory work was undertaken on the Flats Lift Module (FLM) and Auto Induction Station (AIS) Deceleration Belts through the comparison of the results against two additional FMOCR analysis programs. This research has demonstrated the development of a methodical and quantifiable cost of downtime for the FMOCR. This has been the first time that Post has endeavoured to examine the cost of downtime. It is also one of the very few methodologies for valuing downtime costs that has been proposed in literature. The work undertaken has also demonstrated how the cost of downtime can be incorporated into machine performance analysis with specific application to identifying high costs modules. The outcome of this report has both been the methodology for costing downtime, as well as a list of areas for cost reduction. In doing so, this thesis has outlined the two key deliverables presented at the outset of the research.

Learning to avoid indoor obstacles from optical flow

Optical flow (OF) is a powerful motion cue that captures the fusion of two important properties for the task of obstacle avoidance − 3D self-motion and 3D environmental surroundings. The problem of extracting such information for obstacle avoidance is commonly addressed through quantitative techniques such as time-to-contact and divergence, which are highly sensitive to noise in the OF image. This paper presents a new strategy towards obstacle avoidance in an indoor setting, using the combination of quantitative and structural properties of the OF field, coupled with the flexibility and efficiency of a machine learning system.The resulting system is able to effectively control the robot in real-time, avoiding obstacles in familiar and unfamiliar indoor environments, under given motion constraints. Furthermore, through the examination of the networks internal weights, we show how OF properties are being used toward the detection of these indoor obstacles.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.