Growth and characterization of binary and pseudo-binary 3-5 compounds exhibiting non-linear optical behavior. Undergraduate research opportunities in microgravity science and technology

In line with the specified objectives, a Bridgman-type growth configuration in which unavoidable end effects - conventionally leading to growth interface relocation - are compensated by commensurate input-power changes is developed; the growth rate on a microscale is predictable and unaffected by changes in heat transfer conditions. To permit quantitative characterization of the growth furnace cavity (hot-zone), a 3-D thermal field mapping technique, based on the thermal image, is being tested for temperatures up to 1100 C. Computational NIR absorption analysis was modified to now permit characterization of semi-insulating single crystals. Work on growth and characterization of bismuth-silicate was initiated. Growth of BSO (B12SiO20) for seed material by the Czochralski technique is currently in progress. Undergraduate research currently in progress includes: ground based measurements of the wetting behavior (contact angles) of semiconductor melts on substrates consisting of potential confinement materials for solidification experiments in a reduced gravity environment. Hardware modifications required for execution of the wetting experiments in a KC-135 facility are developed

An Analysis of Optical Contributions to a Photo-Sensor's Ballistic Fingerprints

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Luk\'a\v{s}, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a "ballistic" fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature. In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.Comment: 16 pages, 9 figures, preprint for journal submission, paper is based on a thesis chapte

Effect of Ductile Damage Evolution in Sheet Metal Forming: Experimental and Numerical Investigations

The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as necking, fracture, springback, buckling and wrinkling. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. In the first part of this work, we focus on the traditional identification of the constitutive law using oriented tensile tests (0°, 45°, and 90° with respect to the rolling direction). A Digital Image Correlation (DIC) method is used in order to measure the displacements on the surface of the specimen and to analyze the necking evolution and the instability along the shear band. Therefore, bulge tests involving a number of die shapes (circular and elliptic) were developed. In a second step, a mixed numerical–experimental method is used for the identification of the plastic behavior of the stainless steel metal sheet. The initial parameters of the inverse identification were extracted from a uniaxial tensile test. The optimization procedure uses a combination of a Monte-Carlo and a Levenberg-Marquardt algorithm. In the second part of this work, according to some results obtained by SEM (Scaning Electron Microscopy) of the crack zones on the tensile specimens, a Gurson Tvergaard Needleman (GTN) ductile model of damage has been selected for the numerical simulations. This model was introduced in order to give informations concerning crack initiations during hydroforming. At the end of the paper, experimental and numerical comparisons of sheet metal forming applications are presented and validate the proposed approach

Automated Optical Inspection and Image Analysis of Superconducting Radio-Frequency Cavities

The inner surface of superconducting cavities plays a crucial role to achieve highest accelerating fields and low losses. For an investigation of this inner surface of more than 100 cavities within the cavity fabrication for the European XFEL and the ILC HiGrade Research Project, an optical inspection robot OBACHT was constructed. To analyze up to 2325 images per cavity, an image processing and analysis code was developed and new variables to describe the cavity surface were obtained. The accuracy of this code is up to 97% and the PPV 99% within the resolution of 15.63 $\mu \mathrm{m}$. The optical obtained surface roughness is in agreement with standard profilometric methods. The image analysis algorithm identified and quantified vendor specific fabrication properties as the electron beam welding speed and the different surface roughness due to the different chemical treatments. In addition, a correlation of $\rho = -0.93$ with a significance of $6\,\sigma$ between an obtained surface variable and the maximal accelerating field was found

Source identification for mobile devices, based on wavelet transforms combined with sensor imperfections

One of the most relevant applications of digital image forensics is to accurately identify the device used for taking a given set of images, a problem called source identification. This paper studies recent developments in the field and proposes the mixture of two techniques (Sensor Imperfections and Wavelet Transforms) to get better source identification of images generated with mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms can jointly serve as good forensic features to help trace the source camera of images produced by mobile phones. Furthermore, the model proposed here can also determine with high precision both the brand and model of the device

A vision-based system for inspecting painted slates

Purpose – This paper describes the development of a novel automated vision system used to detect the visual defects on painted slates. Design/methodology/approach – The vision system that has been developed consists of two major components covering the opto-mechanical and algorithmical aspects of the system. The first component addresses issues including the mechanical implementation and interfacing the inspection system with the development of a fast image processing procedure able to identify visual defects present on the slate surface. Findings – The inspection system was developed on 400 slates to determine the threshold settings that give the best trade-off between no false positive triggers and correct defect identification. The developed system was tested on more than 300 fresh slates and the success rate for correct identification of acceptable and defective slates was 99.32 per cent for defect free slates based on 148 samples and 96.91 per cent for defective slates based on 162 samples. Practical implications – The experimental data indicates that automating the inspection of painted slates can be achieved and installation in a factory is a realistic target. Testing the devised inspection system in a factory-type environment was an important part of the development process as this enabled us to develop the mechanical system and the image processing algorithm able to perform slate inspection in an industrial environment. The overall performance of the system indicates that the proposed solution can be considered as a replacement for the existing manual inspection system. Originality/value – The development of a real-time automated system for inspecting painted slates proved to be a difficult task since the slate surface is dark coloured, glossy, has depth profile non-uniformities and is being transported at high speeds on a conveyor. In order to address these issues, the system described in this paper proposed a number of novel solutions including the illumination set-up and the development of multi-component image-processing inspection algorithm

Detection of amblyopia utilizing generated retinal reflexes

Investigation confirmed that GRR images can be consistently obtained and that these images contain information required to detect the optical inequality of one eye compared to the fellow eye. Digital analyses, electro-optical analyses, and trained observers were used to evaluate the GRR images. Two and three dimensional plots were made from the digital analyses results. These plotted data greatly enhanced the GRR image content, and it was possible for nontrained observers to correctly identify normal vs abnormal ocular status by viewing the plots. Based upon the criteria of detecting equality or inequality of ocular status of a person's eyes, the trained observer correctly identified the ocular status of 90% of the 232 persons who participated in this program

Multispectral images of peach related to firmness and maturity at harvest

wo multispectral maturity classifications for red soft-flesh peaches (‘Kingcrest’, ‘Rubyrich’ and ‘Richlady’ n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak)

Multi-scale gapped smoothing algorithm for robust baseline-free damage detection in optical infrared thermography

Flash thermography is a promising technique to perform rapid non-destructive testing of composite materials. However, it is well known that several difficulties are inherently paired with this approach, such as non-uniform heating, measurement noise and lateral heat diffusion effects. Hence, advanced signal-processing techniques are indispensable in order to analyze the recorded dataset. One such processing technique is Gapped Smoothing Algorithm, which predicts a gapped pixel’s value in its sound state from a measurement in the defected state by evaluating only its neighboring pixels. However, the standard Gapped Smoothing Algorithm uses a fixed spatial gap size, which induces issues to detect variable defect sizes in a noisy dataset. In this paper, a Multi-Scale Gapped Smoothing Algorithm (MSGSA) is introduced as a baseline-free image processing technique and an extension to the standard Gapped Smoothing Algorithm. The MSGSA makes use of the evaluation of a wide range of spatial gap sizes so that defects of highly different dimensions are identified. Moreover, it is shown that a weighted combination of all assessed spatial gap sizes significantly improves the detectability of defects and results in an (almost) zero-reference background. The technique thus effectively suppresses the measurement noise and excitation non-uniformity. The efficiency of the MSGSA technique is evaluated and confirmed through numerical simulation and an experimental procedure of flash thermography on carbon fiber reinforced polymers with various defect sizes