Semiautomatic Detection of Scoliotic Rib Borders From Posteroanterior Chest Radiographs

3-D assessment of scoliotic deformities relies on an accurate 3-D reconstruction of bone structures from biplanar X-rays, which requires a precise detection and matching of anatomical structures in both views. In this paper, we propose a novel semiautomated technique for detecting complete scoliotic rib borders from PA-0° and PA-20° chest radiographs, by using an edge-following approach with multiple-path branching and oriented filtering. Edge-following processes are initiated from user starting points along upper and lower rib edges and the final rib border is obtained by finding the most parallel pair among detected edges. The method is based on a perceptual analysis leading to the assumption that no matter how bent a scoliotic rib is, it will always present relatively parallel upper and lower edges. The proposed method was tested on 44 chest radiographs of scoliotic patients and was validated by comparing pixels from all detected rib borders against their reference locations taken from the associated manually delineated rib borders. The overall 2-D detection accuracy was 2.64 ± 1.21 pixels. Comparing this accuracy level to reported results in the literature shows that the proposed method is very well suited for precisely detecting borders of scoliotic ribs from PA-0° and PA-20° chest radiographs.

An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps

The application of automatic segmentation methods in lesion detection is desirable. However, such methods are restricted by intensity similarities between lesioned and healthy brain tissue. Using multi-spectral magnetic resonance imaging (MRI) modalities may overcome this problem but it is not always practicable. In this article, a lesion detection approach requiring a single MRI modality is presented, which is an improved method based on a recent publication. This new method assumes that a low similarity should be found in the regions of lesions when the likeness between an intensity based fuzzy segmentation and a location based tissue probabilities is measured. The usage of a normalized similarity measurement enables the current method to fine-tune the threshold for lesion detection, thus maximizing the possibility of reaching high detection accuracy. Importantly, an extra cleaning step is included in the current approach which removes enlarged ventricles from detected lesions. The performance investigation using simulated lesions demonstrated that not only the majority of lesions were well detected but also normal tissues were identified effectively. Tests on images acquired in stroke patients further confirmed the strength of the method in lesion detection. When compared with the previous version, the current approach showed a higher sensitivity in detecting small lesions and had less false positives around the ventricle and the edge of the brain

On the edge: how well do fire mitigation strategies work on the urban fringe?

The reduction of loss of lives and assets during bushfire is one of the primary aims or lire management agencies. Traditional fire mitigation strategies include strategic fire hreaks, static water points, management of ignition sources, rapid detection and local response, air attack, and fuel reduction burning. There have been few quantitative studies that assess the success or these strategies. We need to promote 'new' strategies more focused on human hehaviour and community preparedness.
DeJcndable space provides our best strategy for reducing losses during major bushfires. The size or the defendable space depends on the type of house to be defended, who is defending it, and the spatial context of the property. In the urban fi'inge, remnant vegetation on private property often has
high conservation values, and application of traditional mitigation strategies, as well as the vegetation modification required to achieve defendable space, may have significant impacts on conservation and biodiversity values.

Detection of curved edges at subpixel accuracy using deformable models

One approach to the detection of curves at subpixel accuracy involves the reconstruction of such features from subpixel edge data points. A new technique is presented for reconstructing and segmenting curves with subpixel accuracy using deformable models. A curve is represented as a set of interconnected Hermite splines forming a snake generated from the subpixel edge information that minimizes the global energy functional integral over the set. While previous work on the minimization was mostly based on the Euler-Lagrange transformation, the authors use the finite element method to solve the energy minimization equation. The advantages of this approach over the Euler-Lagrange transformation approach are that the method is straightforward, leads to positive m-diagonal symmetric matrices, and has the ability to cope with irregular geometries such as junctions and corners. The energy functional integral solved using this method can also be used to segment the features by searching for the location of the maxima of the first derivative of the energy over the elementary curve set.

Detection and Modeling of Boundary Layer Height

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.

A study of acoustic emission technique for concrete damage detection

The Acoustic emission (AE) technique, as one of non-intrusive and nondestructive evaluation techniques, acquires and analyzes the signals emitting from deformation or fracture of materials/structures under service loading. The AE technique has been successfully applied in damage detection in various materials such as metal, alloy, concrete, polymers and other composite materials. In this study, the AE technique was used for detecting crack behavior within concrete specimens under mechanical and environmental frost loadings. The instrumentations of the AE system used in this study include a low-frequency AE sensor, a computer-based data acquisition device and a preamplifier linking the AE sensor and the data acquisition device. The AE system purchased from Mistras Group was used in this study. The AE technique was applied to detect damage with the following laboratory tests: the pencil lead test, the mechanical three-point single-edge notched beam bending (SEB) test, and the freeze-thaw damage test. Firstly, the pencil lead test was conducted to verify the attenuation phenomenon of AE signals through concrete materials. The value of attenuation was also quantified. Also, the obtained signals indicated that this AE system was properly setup to detect damage in concrete. Secondly, the SEB test with lab-prepared concrete beam was conducted by employing Mechanical Testing System (MTS) and AE system. The cumulative AE events and the measured loading curves, which both used the crack-tip open displacement (CTOD) as the horizontal coordinate, were plotted. It was found that the detected AE events were qualitatively correlated with the global force-displacement behavior of the specimen. The Weibull distribution was vii proposed to quantitatively describe the rupture probability density function. The linear regression analysis was conducted to calibrate the Weibull distribution parameters with detected AE signals and to predict the rupture probability as a function of CTOD for the specimen. Finally, the controlled concrete freeze-thaw cyclic tests were designed and the AE technique was planned to investigate the internal frost damage process of concrete specimens.

Appendix A. Whole-rock major, trace and rare earth element analyses of the Ulubey volcanics, Appendix B. Analytical standards and detection limits for analyses

Collisional and post-collisional volcanic rocks in the Ulubey (Ordu) area at the western edge of the Eastern Pontide Tertiary Volcanic Province (EPTVP) in NE Turkey are divided into four suites; Middle Eocene (49.4-44.6 Ma) aged Andesite-Trachyandesite (AT), Trachyandesite-Trachydacite-Rhyolite (TTR), Trachydacite-Dacite (TD) suites, and Middle Miocene (15.1 Ma) aged Trachybasalt (TB) suite. Local stratigraphy in the Ulubey area starts with shallow marine environment sediments of the Paleocene-Eocene time and then continues extensively with sub-aerial andesitic to rhyolitic and rare basaltic volcanism during Eocene and Miocene time, respectively. Petrographically, the volcanic rocks are composed primarily of andesites/trachyandesites, with minor trachydacites/rhyolites, basalts/trachybasalts and pyroclastics, and show porphyric, hyalo-microlitic porphyric and rarely glomeroporphyric, intersertal, intergranular, fluidal and sieve textures. The Ulubey (Ordu) volcanic rocks indicate magma evolution from tholeiitic-alkaline to calc-alkaline with medium-K contents. Primitive mantle normalized trace element and chondrite normalized rare earth element (REE) patterns show that the volcanic rocks have moderate light rare earth element (LREE)/heavy rare earth element (HREE) ratios relative to E-Type MORB and depletion in Nb, Ta and Ti. High Th/Yb ratios indicate parental magma(s) derived from an enriched source formed by mixing of slab and asthenospheric melts previously modified by fluids and sediments from a subduction zone. All of the volcanic rocks share similar incompatible element ratios (e.g., La/Sm, Zr/Nb, La/Nb) and chondrite-normalized REE patterns, indicating that the basic to acidic rocks originated from the same source. The volcanic rocks were produced by the slab dehydration-induced melting of an existing metasomatized mantle source, and the fluids from the slab dehydration introduced significant large ion lithophile element (LILE) and LREE to the source, masking its inherent HFSE-enriched characteristics. The initial 87Sr/86Sr (0.7044-0.7050) and eNd (-0.3 to +3.4) ratios of the volcanics suggest that they originated from an enriched lithospheric mantle source with low Sm/Nd ratios. Integration of the geochemical, petrological and isotopical with regional and local geological data suggest that the Tertiary volcanic rocks from the Ulubey (Ordu) area were derived from an enriched mantle, which had been previously metasomatized by fluids derived from subducted slab during Eocene to Miocene in collisional and post-collisional extension-related geodynamic setting following Late Mesozoic continental collision between the Eurasian plate and the Tauride-Anatolide platform.

Smart aeronautical structures: development and experimental validation of a structural health monitoring system for damage detection

En muchas áreas de la ingeniería, la integridad y confiabilidad de las estructuras son aspectos de extrema importancia. Estos son controlados mediante el adecuado conocimiento de danos existentes. Típicamente, alcanzar el nivel de conocimiento necesario que permita caracterizar la integridad estructural implica el uso de técnicas de ensayos no destructivos. Estas técnicas son a menudo costosas y consumen mucho tiempo. En la actualidad, muchas industrias buscan incrementar la confiabilidad de las estructuras que emplean. Mediante el uso de técnicas de última tecnología es posible monitorizar las estructuras y en algunos casos, es factible detectar daños incipientes que pueden desencadenar en fallos catastróficos. Desafortunadamente, a medida que la complejidad de las estructuras, los componentes y sistemas incrementa, el riesgo de la aparición de daños y fallas también incrementa. Al mismo tiempo, la detección de dichas fallas y defectos se torna más compleja. En años recientes, la industria aeroespacial ha realizado grandes esfuerzos para integrar los sensores dentro de las estructuras, además de desarrollar algoritmos que permitan determinar la integridad estructural en tiempo real. Esta filosofía ha sido llamada “Structural Health Monitoring” (o “Monitorización de Salud Estructural” en español) y este tipo de estructuras han recibido el nombre de “Smart Structures” (o “Estructuras Inteligentes” en español). Este nuevo tipo de estructuras integran materiales, sensores, actuadores y algoritmos para detectar, cuantificar y localizar daños dentro de ellas mismas. Una novedosa metodología para detección de daños en estructuras se propone en este trabajo. La metodología está basada en mediciones de deformación y consiste en desarrollar técnicas de reconocimiento de patrones en el campo de deformaciones. Estas últimas, basadas en PCA (Análisis de Componentes Principales) y otras técnicas de reducción dimensional. Se propone el uso de Redes de difracción de Bragg y medidas distribuidas como sensores de deformación. La metodología se validó mediante pruebas a escala de laboratorio y pruebas a escala real con estructuras complejas. Los efectos de las condiciones de carga variables fueron estudiados y diversos experimentos fueron realizados para condiciones de carga estáticas y dinámicas, demostrando que la metodología es robusta ante condiciones de carga desconocidas. ABSTRACT In many engineering fields, the integrity and reliability of the structures are extremely important aspects. They are controlled by the adequate knowledge of existing damages. Typically, achieving the level of knowledge necessary to characterize the structural integrity involves the usage of nondestructive testing techniques. These are often expensive and time consuming. Nowadays, many industries look to increase the reliability of the structures used. By using leading edge techniques it is possible to monitoring these structures and in some cases, detect incipient damage that could trigger catastrophic failures. Unfortunately, as the complexity of the structures, components and systems increases, the risk of damages and failures also increases. At the same time, the detection of such failures and defects becomes more difficult. In recent years, the aerospace industry has done great efforts to integrate the sensors within the structures and, to develop algorithms for determining the structural integrity in real time. The ‘philosophy’ has being called “Structural Health Monitoring” and these structures have been called “smart structures”. These new types of structures integrate materials, sensors, actuators and algorithms to detect, quantify and locate damage within itself. A novel methodology for damage detection in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (Principal Component Analysis) and other dimensional reduction techniques. The use of fiber Bragg gratings and distributed sensing as strain sensors is proposed. The methodology have been validated by using laboratory scale tests and real scale tests with complex structures. The effects of the variable load conditions were studied and several experiments were performed for static and dynamic load conditions, demonstrating that the methodology is robust under unknown load conditions.

High Resolution Detection of Mechanical Forces Exerted by Locomoting Fibroblasts on the Substrate

We have developed a new approach to detect mechanical forces exerted by locomoting fibroblasts on the substrate. Cells were cultured on elastic, collagen-coated polyacrylamide sheets embedded with 0.2-μm fluorescent beads. Forces exerted by the cell cause deformation of the substrate and displacement of the beads. By recording the position of beads during cell locomotion and after cell removal, we discovered that most forces were radially distributed, switching direction in the anterior region. Deformations near the leading edge were strong, transient, and variable in magnitude, consistent with active local contractions, whereas those in the posterior region were weaker, more stable, and more uniform, consistent with passive resistance. Treatment of cells with cytochalasin D or myosin II inhibitors caused relaxation of the forces, suggesting that they are generated primarily via actin–myosin II interactions; treatment with nocodazole caused no immediate effect on forces. Immunofluorescence indicated that the frontal region of strong deformation contained many vinculin plaques but no apparent concentration of actin or myosin II filaments. Strong mechanical forces in the anterior region, generated by locally activated myosin II and transmitted through vinculin-rich structures, likely play a major role in cell locomotion and in mechanical signaling with the surrounding environment.

A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection.

Hierarchically grouped 2D local features applied to edge contour localisation

One of the most significant research topics in computer vision is object detection. Most of the reported object detection results localise the detected object within a bounding box, but do not explicitly label the edge contours of the object. Since object contours provide a fundamental diagnostic of object shape, some researchers have initiated work on linear contour feature representations for object detection and localisation. However, linear contour feature-based localisation is highly dependent on the performance of linear contour detection within natural images, and this can be perturbed significantly by a cluttered background. In addition, the conventional approach to achieving rotation-invariant features is to rotate the feature receptive field to align with the local dominant orientation before computing the feature representation. Grid resampling after rotation adds extra computational cost and increases the total time consumption for computing the feature descriptor. Though it is not an expensive process if using current computers, it is appreciated that if each step of the implementation is faster to compute especially when the number of local features is increasing and the application is implemented on resource limited ”smart devices”, such as mobile phones, in real-time. Motivated by the above issues, a 2D object localisation system is proposed in this thesis that matches features of edge contour points, which is an alternative method that takes advantage of the shape information for object localisation. This is inspired by edge contour points comprising the basic components of shape contours. In addition, edge point detection is usually simpler to achieve than linear edge contour detection. Therefore, the proposed localization system could avoid the need for linear contour detection and reduce the pathological disruption from the image background. Moreover, since natural images usually comprise many more edge contour points than interest points (i.e. corner points), we also propose new methods to generate rotation-invariant local feature descriptors without pre-rotating the feature receptive field to improve the computational efficiency of the whole system. In detail, the 2D object localisation system is achieved by matching edge contour points features in a constrained search area based on the initial pose-estimate produced by a prior object detection process. The local feature descriptor obtains rotation invariance by making use of rotational symmetry of the hexagonal structure. Therefore, a set of local feature descriptors is proposed based on the hierarchically hexagonal grouping structure. Ultimately, the 2D object localisation system achieves a very promising performance based on matching the proposed features of edge contour points with the mean correct labelling rate of the edge contour points 0.8654 and the mean false labelling rate 0.0314 applied on the data from Amsterdam Library of Object Images (ALOI). Furthermore, the proposed descriptors are evaluated by comparing to the state-of-the-art descriptors and achieve competitive performances in terms of pose estimate with around half-pixel pose error.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.