394 resultados para image spectroscopy


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Purpose Arbitrary numbers of corneal confocal microscopy images have been used for analysis of corneal subbasal nerve parameters under the implicit assumption that these are a representative sample of the central corneal nerve plexus. The purpose of this study is to present a technique for quantifying the number of random central corneal images required to achieve an acceptable level of accuracy in the measurement of corneal nerve fiber length and branch density. Methods Every possible combination of 2 to 16 images (where 16 was deemed the true mean) of the central corneal subbasal nerve plexus, not overlapping by more than 20%, were assessed for nerve fiber length and branch density in 20 subjects with type 2 diabetes and varying degrees of functional nerve deficit. Mean ratios were calculated to allow comparisons between and within subjects. Results In assessing nerve branch density, eight randomly chosen images not overlapping by more than 20% produced an average that was within 30% of the true mean 95% of the time. A similar sampling strategy of five images was 13% within the true mean 80% of the time for corneal nerve fiber length. Conclusions The “sample combination analysis” presented here can be used to determine the sample size required for a desired level of accuracy of quantification of corneal subbasal nerve parameters. This technique may have applications in other biological sampling studies.

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In 2010, the State Library of Queensland (SLQ) donated their out-of-copyright Queensland images to Wikimedia Commons. One direct effect of publishing the collections at Wikimedia Commons is the ability of general audiences to participate and help the library in processing the images in the collection. This paper will discuss a project that explored user participation in the categorisation of the State Library of Queensland digital image collections. The outcomes of this project can be used to gain a better understanding of user participation that lead to improving access to library digital collections. Two techniques for data collection were used: documents analysis and interview. Document analysis was performed on the Wikimedia Commons monthly reports. Meanwhile, interview was used as the main data collection technique in this research. The data collected from document analysis was used to help the researchers to devise appropriate questions for interviews. The interviews were undertaken with participants who were divided into two groups: SLQ staff members and Wikimedians (users who participate in Wikimedia). The two sets of data collected from participants were analysed independently and compared. This method was useful for the researchers to understand the differences between the experiences of categorisation from both the librarians’ and the users’ perspectives. This paper will provide a discussion on the preliminary findings that have emerged from each group participant. This research provides preliminary information about the extent of user participation in the categorisation of SLQ collections in Wikimedia Commons that can be used by SLQ and other interested libraries in describing their digital content by their categorisations to improve user access to the collection in the future.

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This paper presents a reactive collision avoidance method for small unmanned rotorcraft using spherical image-based visual servoing. Only a single point feature is used to guide the aircraft in a safe spiral like trajectory around the target, whilst a spherical camera model ensures the target always remains visible. A decision strategy to stop the avoidance control is derived based on the properties of spiral like motion, and the effect of accurate range measurements on the control scheme is discussed. We show that using a poor range estimate does not significantly degrade the collision avoidance performance, thus relaxing the need for accurate range measurements. We present simulated and experimental results using a small quad rotor to validate the approach.

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Typical flow fields in a stormwater gross pollutant trap (GPT) with blocked retaining screens were experimentally captured and visualised. Particle image velocimetry (PIV) software was used to capture the flow field data by tracking neutrally buoyant particles with a high speed camera. A technique was developed to apply the Image Based Flow Visualization (IBFV) algorithm to the experimental raw dataset generated by the PIV software. The dataset consisted of scattered 2D point velocity vectors and the IBFV visualisation facilitates flow feature characterisation within the GPT. The flow features played a pivotal role in understanding gross pollutant capture and retention within the GPT. It was found that the IBFV animations revealed otherwise unnoticed flow features and experimental artefacts. For example, a circular tracer marker in the IBFV program visually highlighted streamlines to investigate specific areas and identify the flow features within the GPT.

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Particle collections from the stratosphere via either the JSC Curatorial Program or the U2 Program (NASA Ames) occur between 16km and 19km altitude and are usually part of ongoing experiments to measure parameters related to the aerosol layer. Fine-grained aerosols (<0.1µm) occur in the stratosphere up to 35km altitude and are concentrated between 15km and 25km altitude[1]. All interplanetary dust particles (IDP's) from these stratospheric collections must pass through this aerosol layer before reaching the collection altitude. The major compounds in this aerosol layer are sulfur rich particulates (<0.1µm) and gases and include H2S04, OCS, S02 and CS2 [2].In order to assess possible surface reactions of interplanetary dust particles (IDP's) with ambient aerosols in the stratosphere, we have initiated a Surface Auger Microprobe (SAM) and electron microscope study of selected particles from the JSC Cosmic Dust Collection.

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The application of epoxy embedding and microtomy to individual chondritic interplanetary dust particles (lOP's)(Bradley and Brownlee, 1986a) provides not only higher precision in thin-film elemental analyses (Bradley and Brownlee, 19861:1), but also allows a wealth of other important techniques for the micro-characterization of these primitive extraterrestrial materials. For example, individual sections (e.g. 100 nm thick) or a series of sections, can be examined using image analysis techniques which utilize either transmitted or scanned secondary electron images, or alternatively, secondary X-ray spectra collected concurrently from a given region of sample. Individual particles, or groups of particles with similar image characteristics can then be rapidly identified using conventional grey-scale/particle recognition techniques for each microtomed section of lOP. This type of image analysis provides a suitable method for determination of particle size and shape distribution as well as porosity throughout the aggregate.

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Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

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Some minerals are colloidal and show no X-ray diffraction patterns. Vibrational spectroscopy offers one of the few methods for the assessment of the structure of these types of mineral. Among this group of minerals is kemmlitzite (Sr,Ce)Al3(AsO4)(SO4)(OH)6. The objective of this research is to determine the molecular structure of the mineral kemmlitzite using vibrational spectroscopy. Raman microscopy offers a useful method for the analysis of such colloidal minerals. Raman and infrared bands are attributed to the AsO43- , SO42- and water stretching vibrations. The Raman spectrum is dominated by a very intense sharp band at 984 cm-1 assigned to the SO42- symmetric stretching mode. Raman bands at 690, 772 and 825 cm-1 may be assigned to the AsO43- antisymmetric and symmetric stretching modes. Raman bands observed at 432 and 465 cm-1 are attributable to the doubly degenerate 2 (SO4)2- bending mode. Vibrational spectroscopy is important in the assessment of the molecular structure of the kemmlitzite, especially when the mineral is non-diffracting or poorly diffracting.

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Since the first destination image studies were published in the early 1970s, the field has become one of the most popular in the tourism literature. While reviews of the destination image literature show no commonly agreed conceptualisation of the construct, researchers have predominantly used structured questionnaires for measurement. There has been criticism that the way some of these scales have been selected means a greater likelihood of attributes being irrelevant to participants. This opens up the risk of stimulating uninformed responses. The issue of uninformed response was first raised as a source of error 60 years ago. However, there has been little, if any, discussion in relation to destination image measurement, studies of which often require participants to provide opinion-driven rather than fact-based responses. This paper reports the trial of a ‘don’t know’ (DK) non-response option for participants in two destination image questionnaires. It is suggested the use of a DK option provides participants with an alternative to i) skipping the question, ii) using the scale midpoint to denote neutrality, or iii) providing an uninformed response. High levels of DK usage by participants can then alert the marketer of the need to improve awareness of destination performance for potential salient attributes.

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The structure of the borate mineral sakhaite Ca12Mg4(BO3)7(CO3)4Cl(OH)2·H2O, a borate–carbonate of calcium and magnesium has been assessed using vibrational spectroscopy. Assignment of bands is undertaken by comparison with the data from other published results. Intense Raman band at 1134 cm−1 with a shoulder at 1123 cm−1 is assigned to the symmetric stretching mode. The Raman spectrum displays bands at 1479, 1524 and 1560 cm−1 which are assigned to the antisymmetric stretching vibrations. The observation of multiple carbonate stretching bands supports the concept that the carbonate units are non-equivalent. The Raman band at 968 cm−1 with a shoulder at 950 cm−1 is assigned to the symmetric stretching mode of trigonal boron. Raman bands at 627 and 651 cm−1 are assigned to the out-of-plane bending modes of trigonal and tetrahedral boron. Raman spectroscopy coupled with infrared spectroscopy enables the molecular structure of the mineral sakhaite to be assessed.

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Boracite is a magnesium borate mineral with formula: Mg3B7O13Cl and occurs as blue green, colorless, gray, yellow to white crystals in the orthorhombic – pyramidal crystal system. An intense Raman band at 1009 cm−1 was assigned to the BO stretching vibration of the B7O13 units. Raman bands at 1121, 1136, 1143 cm−1 are attributed to the in-plane bending vibrations of trigonal boron. Four sharp Raman bands observed at 415, 494, 621 and 671 cm−1 are simply defined as trigonal and tetrahedral borate bending modes. The Raman spectrum clearly shows intense Raman bands at 3405 and 3494 cm−1, thus indicating that some Cl anions have been replaced with OH units. The molecular structure of a natural boracite has been assessed by using vibrational spectroscopy.

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Current diagnostic methods for assessing the severity of articular cartilage degenerative conditions, such as osteoarthritis, are inadequate. There is also a lack of techniques that can be used for real-time evaluation of the tissue during surgery to inform treatment decision and eliminate subjectivity. This book, derived from Dr Afara’s doctoral research, presents a scientific framework that is based on near infrared (NIR) spectroscopy for facilitating the non-destructive evaluation of articular cartilage health relative to its structural, functional, and mechanical properties. This development is a component of the ongoing research on advanced endoscopic diagnostic techniques in the Articular Cartilage Biomechanics Research Laboratory of Professor Adekunle Oloyede at Queensland University of Technology (QUT), Brisbane Australia.

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Henmilite is a triclinic mineral with the crystal structure consisting of isolated B(OH)4 tetrahedra, planar Cu(OH)4 groups and Ca(OH)3 polyhedra. The structure can also be viewed as having dimers of Ca polyhedra connected to each other through 2B(OH) tetrahedra to form chains parallel to the C axis. The structure of the mineral has been assessed by the combination of Raman and infrared spectra. Raman bands at 902, 922, 951, and 984 cm−1 and infrared bands at 912, 955 and 998 cm−1 are assigned to stretching vibrations of tetragonal boron. The Raman band at 758 cm−1 is assigned to the symmetric stretching mode of tetrahedral boron. The series of bands in the 400–600 cm−1 region are due to the out-of-plane bending modes of tetrahedral boron. Two very sharp Raman bands are observed at 3559 and 3609 cm−1. Two infrared bands are found at 3558 and 3607 cm−1. These bands are assigned to the OH stretching vibrations of the OH units in henmilite. A series of Raman bands are observed at 3195, 3269, 3328, 3396, 3424 and 3501 cm−1 are assigned to water stretching modes. Infrared spectroscopy also identified water and OH units in the henmilite structure. It is proposed that water is involved in the structure of henmilite. Hydrogen bond distances based upon the OH stretching vibrations using a Libowitzky equation were calculated. The number and variation of water hydrogen bond distances are important for the stability off the mineral.

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Vibrational spectroscopy has been used to assess the structure of kotoite a borate mineral of magnesium which is isostructural with jimboite. The mineral is orthorhombic with point group: 2/m 2/m 2/m. The mineral has the potential as a new memory insulator material. The mineral has been characterised by a combination of Raman and infrared spectroscopy. The Raman spectrum is dominated by a very intense band at 835 cm−1, assigned to the symmetric stretching mode of tetrahedral boron. Raman bands at 919, 985 and 1015 cm−1 are attributed to the antisymmetric stretching modes of tetrahedral boron. Kotoite is strictly an hydrous borate mineral. An intense Raman band observed at 3559 cm−1 is attributed to the stretching vibration of hydroxyl units, more likely to be associated with the borate mineral hydroxyborate. The lack of observation of water bending modes proves the absence of water in the kotoite structure.