Sensitivity and reliability of objective image analysis compared to subjective grading of bulbar hyperaemia

Aims: To establish the sensitivity and reliability of objective image analysis in direct comparison with subjective grading of bulbar hyperaemia. Methods: Images of the same eyes were captured with a range of bulbar hyperaemia caused by vasodilation. The progression was recorded and 45 images extracted. The images were objectively analysed on 14 occasions using previously validated edge-detection and colour-extraction techniques. They were also graded by 14 eye-care practitioners (ECPs) and 14 non-clinicians (NCb) using the Efron scale. Six ECPs repeated the grading on three separate occasions Results: Subjective grading was only able to differentiate images with differences in grade of 0.70-1.03 Efron units (sensitivity of 0.30-0.53), compared to 0,02-0.09 Efron units with objective techniques (sensitivity of 0.94-0.99). Significant differences were found between ECPs and individual repeats were also inconsistent (p<0.001). Objective analysis was 16x more reliable than subjective analysis. The NCLs used wider ranges of the scale but were more variable than ECPs, implying that training may have an effect on grading. Conclusions: Objective analysis may offer a new gold standard in anterior ocular examination, and should be developed further as a clinical research tool to allow more highly powered analysis, and to enhance the clinical monitoring of anterior eye disease.

One's own country and familiar places in the mind's eye:different topological representations for navigational and non-navigational contents

Visual mental imagery is a process that draws on different cognitive abilities and is affected by the contents of mental images. Several studies have demonstrated that different brain areas subtend the mental imagery of navigational and non-navigational contents. Here, we set out to determine whether there are distinct representations for navigational and geographical images. Specifically, we used a Spatial Compatibility Task (SCT) to assess the mental representation of a familiar navigational space (the campus), a familiar geographical space (the map of Italy) and familiar objects (the clock). Twenty-one participants judged whether the vertical or the horizontal arrangement of items was correct. We found that distinct representational strategies were preferred to solve different categories on the SCT, namely, the horizontal perspective for the campus and the vertical perspective for the clock and the map of Italy. Furthermore, we found significant effects due to individual differences in the vividness of mental images and in preferences for verbal versus visual strategies, which selectively affect the contents of mental images. Our results suggest that imagining a familiar navigational space is somewhat different from imagining a familiar geographical space. © 2014 Elsevier Ireland Ltd.

In situ diagnostics and prognostics of solder fatigue in IGBT modules for electric vehicle drives

This paper proposes an in situ diagnostic and prognostic (D&P) technology to monitor the health condition of insulated gate bipolar transistors (IGBTs) used in EVs with a focus on the IGBTs' solder layer fatigue. IGBTs' thermal impedance and the junction temperature can be used as health indicators for through-life condition monitoring (CM) where the terminal characteristics are measured and the devices' internal temperature-sensitive parameters are employed as temperature sensors to estimate the junction temperature. An auxiliary power supply unit, which can be converted from the battery's 12-V dc supply, provides power to the in situ test circuits and CM data can be stored in the on-board data-logger for further offline analysis. The proposed method is experimentally validated on the developed test circuitry and also compared with finite-element thermoelectrical simulation. The test results from thermal cycling are also compared with acoustic microscope and thermal images. The developed circuitry is proved to be effective to detect solder fatigue while each IGBT in the converter can be examined sequentially during red-light stopping or services. The D&P circuitry can utilize existing on-board hardware and be embedded in the IGBT's gate drive unit.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Re-imagining Yerevan in the post-Soviet era: Urban symbolism and narratives of the nation in the landscape of Armenia's capital

The urban landscape of Yerevan has experienced tremendous changes since the collapse of the Soviet Union and Armenia’s independence in 1991. Domestic and foreign investments have poured into Yerevan’s building sector, converting many downtown neighborhoods into sleek modern districts that now cater to foreign investors, tourists, and the newly rich Armenian nationals. Large portions of the city’s green parks and other public spaces have been commercialized for private and exclusive use, creating zones that are accessible only to the affluent. In this dissertation I explore the rapidly transforming landscape of Yerevan and its connections to the development of contemporary Armenian national identity. This research was guided by principles of ethnographic inquiry, and I employed diverse methods, including document and archival research, structured and semi-structured interviews and content analysis of news media. I also used geographic information systems (GIS) and satellite images to represent and visualize the stark transformations of spaces in Yerevan. Informed by and contributing to three literatures—on the relationship between landscape and identity formation, on the construction of national identity, and on Soviet and post-Soviet cities—this dissertation investigates how messages about contemporary Armenian national identity are being expressed via the transforming landscape of Armenia’s national capital. In it I describe the ways in which abrupt transformations have resulted in the physical and symbolic eviction of residents, introducing fierce public debates about belonging and exclusion within the changing urban context. I demonstrate that the new additions to Yerevan’s landscape and the symbolic messages that they carry are hotly contested by many long-time residents, who struggle for inclusion of their opinions and interests in the process of re-imagining their national capital. This dissertation illustrates many of the trends that are apparent in post-Soviet and post-Socialist space, while at the same time exposing some unique characteristics of the Armenian case.

Rigid and Non-rigid Point-based Medical Image Registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Land-cover detection and landscape structure analysis in the Pachitea Basin, Peruvian Amazon

Classification procedures, including atmospheric correction satellite images as well as classification performance utilizing calibration and validation at different levels, have been investigated in the context of a coarse land-cover classification scheme for the Pachitea Basin. Two different correction methods were tested against no correction in terms of reflectance correction towards a common response for pseudo-invariant features (PIF). The accuracy of classifications derived from each of the three methods was then assessed in a discriminant analysis using crossvalidation at pixel, polygon, region, and image levels. Results indicate that only regression adjusted images using PIFs show no significant difference between images in any of the bands. A comparison of classifications at different levels suggests though that at pixel, polygon, and region levels the accuracy of the classifications do not significantly differ between corrected and uncorrected images. Spatial patterns of land-cover were analyzed in terms of colonization history, infrastructure, suitability of the land, and landownership. The actual use of the land is driven mainly by the ability to access the land and markets as is obvious in the distribution of land cover as a function of distance to rivers and roads. When considering all rivers and roads a threshold distance at which disproportional agro-pastoral land cover switches from over represented to under represented is at about 1km. Best land use suggestions seem not to affect the choice of land use. Differences in abundance of land cover between watersheds are more prevailing than differences between colonist and indigenous groups.

The SCUBA HAlf degree extragalactic survey - III. Identification of radio and mid-infrared counterparts to submillimetre galaxies

Determining an accurate position for a submillimetre (submm) galaxy (SMG) is the crucial step that enables us to move from the basic properties of an SMG sample - source counts and 2D clustering - to an assessment of their detailed, multiwavelength properties, their contribution to the history of cosmic star formation and their links with present-day galaxy populations. In this paper, we identify robust radio and/or infrared (IR) counterparts, and hence accurate positions, for over two-thirds of the SCUBA HAlf-Degree Extragalactic Survey (SHADES) Source Catalogue, presenting optical, 24-μm and radio images of each SMG. Observed trends in identification rate have given no strong rationale for pruning the sample. Uncertainties in submm position are found to be consistent with theoretical expectations, with no evidence for significant additional sources of error. Employing the submm/radio redshift indicator, via a parametrization appropriate for radio-identified SMGs with spectroscopic redshifts, yields a median redshift of 2.8 for the radio-identified subset of SHADES, somewhat higher than the median spectroscopic redshift. We present a diagnostic colour-colour plot, exploiting Spitzer photometry, in which we identify regions commensurate with SMGs at very high redshift. Finally, we find that significantly more SMGs have multiple robust counterparts than would be expected by chance, indicative of physical associations. These multiple systems are most common amongst the brightest SMGs and are typically separated by 2-6 arcsec, similar to 15-20/sin i kpc at z~ 2, consistent with early bursts seen in merger simulations.

Macroparasites of allis shad (Alosa alosa) and twaite shad (Alosa fallax) of the Western Iberian Peninsula Rivers : ecological, phylogenetic and zoonotic insights

Acknowledgments The authors sincerely thank M.N. Cueto, J.M. Antonio and M.E. Garci of the ECOBIOMAR group at IIM-CSIC for molecular analysis, technical support and quality images of some parasites. M. Bao is supported by a PhD grant from the University of Aberdeen and also by financial support of the contract from the EU Project PARASITE (grant number 312068). A. Roura is supported by BFundación Barrié de la Maza^ postdoctoral fellowship and a Securing Food, Water and the Environment Research Focus Area grant (La Trobe University). This study was partially supported by a PhD grant from the Portuguese Foundation for Science and Technology (FCT) (SFRH/BD/4892/2008) and partially supported by the European Regional Development Fund (ERDF) through the COMPETE—Operational Competitiveness Programme and national funds through FCT—Foundation for Science and Technology, under the project BPEst-C/MAR/LA0015/2013. The authors thank the staff of the Station of Hydrobiology of the USC BEncoro do Con^ due their participation in the surveys, with special mention to J. Sánchez for separating digenean fauna existing in the stomachs of A. fallax. This work has been partially supported by the project 10PXIB2111059PR of the Xunta de Galicia and the project MIGRANET of the Interreg IV B SUDOE (South-West Europe) Territorial Cooperation Programme (SOE2/P2/E288). D.J. Nachón is supported by a PhD grant from the Xunta de Galicia (PRE/2011/198)

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Forging Iconographies and Casting Colonialism: Monuments and Memories in Ontario, 1850-2001

Commemorations are a critical window for exploring the social, political, and cultural trends of a specific time period. Over the past two centuries, the commemorative landscape of Ontario reaffirmed the inclusion/exclusion of particular racial groups. Intended as static markers to the past, monuments in particular visually demonstrated the boundaries of a community and acted as ongoing memorials to existing social structures. Using a specific type of iconography and visual language, the creators of monuments imbued the physical markers of stone and bronze with racialized meanings. As builders were connected with their own time periods and social contexts, the ideas behind these commemorations shifted. Nonetheless, creators were intent on producing a memorial that educated present and future generations on the boundaries of their “imagined communities.” This dissertation considers the carefully chosen iconographies of Ontario’s monuments and how visual symbolism was attached to historical memory. Through the examination of five case studies, this dissertation examines the shifting commemorative landscape of Ontario and how memorials were used to mark the boundaries of communities. By integrating the visual analysis of monuments and related images, it bridges a methodological and theoretical gap between history and art history. This dissertation opens an important dialogue between these fields of study and demonstrates how monuments themselves are critical “documents” of the past.

Multi-Scale Visualization and Interpretation of Geological Relationships

Visualization and interpretation of geological observations into a cohesive geological model are essential to Earth sciences and related fields. Various emerging technologies offer approaches to multi-scale visualization of heterogeneous data, providing new opportunities that facilitate model development and interpretation processes. These include increased accessibility to 3D scanning technology, global connectivity, and Web-based interactive platforms. The geological sciences and geological engineering disciplines are adopting these technologies as volumes of data and physical samples greatly increase. However, a standardized and universally agreed upon workflow and approach have yet to properly be developed. In this thesis, the 3D scanning workflow is presented as a foundation for a virtual geological database. This database provides augmented levels of tangibility to students and researchers who have little to no access to locations that are remote or inaccessible. A Web-GIS platform was utilized jointly with customized widgets developed throughout the course of this research to aid in visualizing hand-sized/meso-scale geological samples within a geologic and geospatial context. This context is provided as a macro-scale GIS interface, where geophysical and geodetic images and data are visualized. Specifically, an interactive interface is developed that allows for simultaneous visualization to improve the understanding of geological trends and relationships. These developed tools will allow for rapid data access and global sharing, and will facilitate comprehension of geological models using multi-scale heterogeneous observations.

Nagaland: Borders, Boundaries, Belonging: Contested spaces and disputed narratives

In 2011 I travelled to three of the ‘Seven Sister’ states of old Assam, Nagaland, Meghalaya & Assam. My journey to this remote and politically sensitive region, bordering Chinese occupied Tibet, Bangladesh and Myanmar was prompted by my father’s experiences in the region during WW2 in the Burma Campaign and brought into sharp relief on-going themes in my work, the impact the past has on the present, the relationship of time and place, identity and memory and the transcultural experiences caused by war, colonisation and migration. The drawings I made on location, the objects I collected and the notes and photographs I took formed the basis of the bookwork: NAGALAND borders boundaries belonging. When making the finished work the material quality of the object and the processes by which it was made become very important. The historical resonance of the medium and the time consuming nature of the process reflect the embedding of form and idea, and paid homage to the material culture of the Naga hill tribes. The bookwork was hand-bound, handset and printed by letterpress. Some spreads were printed in 6 colours and the book took over a year to produce. I see my practice as echoing that of generations of Lady travellers; embracing the need to journey, be in a liminal space, to have a plan but not be afraid to divert from it. To be alone, take a sketchbook and make images is, for me, the definition of the itinerant illustrator; one who travels widely in geographic space, visual forms and ideas, in order to get lost and find the unlooked for.

"Lancea", Lusitanian Word, and the "Lancienses" Ethnogenesis

Analysis of the word lancea, of Hispanic origin after Varro, and of place names, people´s names and personal names derived from it. It confirms that the spear was the most important weapon in the Bronze Age, belonging to the iuventus and used as heroic and divine symbol. This analysis confirms also the personality of the Lusitanians, a people related to the Celts but with more archaic archaeological, linguistic and cultural characteristics originated in the tradition of the Atlantic Bronze in the II millennium BC. It is also relevant to better know the organisation of Broze and Iron Age societies and the origin of Indo-Europeans peoples in Western Europe and of pre-Roman peoples of Iberia.