Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications.

Fully Automatic Segmentation of Hip CT Images

Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

Flujo de potencia entre estructuras complejas sometidas a excitaciones de alta frecuencia y aplicaciones a la detección de daño estructural

El tema central de investigación en esta Tesis es el estudio del comportamientodinámico de una estructura mediante modelos que describen la distribución deenergía entre los componentes de la misma y la aplicación de estos modelos parala detección de daños incipientes.Los ensayos dinámicos son un modo de extraer información sobre las propiedadesde una estructura. Si tenemos un modelo de la estructura se podría ajustar éstepara que, con determinado grado de precisión, tenga la misma respuesta que elsistema real ensayado. Después de que se produjese un daño en la estructura,la respuesta al mismo ensayo variará en cierta medida; actualizando el modelo alas nuevas condiciones podemos detectar cambios en la configuración del modeloestructural que nos condujeran a la conclusión de que en la estructura se haproducido un daño.De este modo, la detección de un daño incipiente es posible si somos capacesde distinguir una pequeña variación en los parámetros que definen el modelo. Unrégimen muy apropiado para realizar este tipo de detección es a altas frecuencias,ya que la respuesta es muy dependiente de los pequeños detalles geométricos,dado que el tamaño característico en la estructura asociado a la respuesta esdirectamente proporcional a la velocidad de propagación de las ondas acústicas enel sólido, que para una estructura dada es inalterable, e inversamente proporcionala la frecuencia de la excitación. Al mismo tiempo, esta característica de la respuestaa altas frecuencias hace que un modelo de Elementos Finitos no sea aplicable enla práctica, debido al alto coste computacional.Un modelo ampliamente utilizado en el cálculo de la respuesta de estructurasa altas frecuencias en ingeniería es el SEA (Statistical Energy Analysis). El SEAaplica el balance energético a cada componente estructural, relacionando la energíade vibración de estos con la potencia disipada por cada uno de ellos y la potenciatransmitida entre ellos, cuya suma debe ser igual a la potencia inyectada a cadacomponente estructural. Esta relación es lineal y viene caracterizada por los factoresde pérdidas. Las magnitudes que intervienen en la respuesta se consideranpromediadas en la geometría, la frecuencia y el tiempo.Actualizar el modelo SEA a datos de ensayo es, por lo tanto, calcular losfactores de pérdidas que reproduzcan la respuesta obtenida en éste. Esta actualización,si se hace de manera directa, supone la resolución de un problema inversoque tiene la característica de estar mal condicionado. En la Tesis se propone actualizarel modelo SEA, no en término de los factores de pérdidas, sino en términos deparámetros estructurales que tienen sentido físico cuando se trata de la respuestaa altas frecuencias, como son los factores de disipación de cada componente, susdensidades modales y las rigideces características de los elementos de acoplamiento.Los factores de pérdidas se calculan como función de estos parámetros. Estaformulación es desarrollada de manera original en esta Tesis y principalmente sefunda en la hipótesis de alta densidad modal, es decir, que en la respuesta participanun gran número de modos de cada componente estructural.La teoría general del método SEA, establece que el modelo es válido bajounas hipótesis sobre la naturaleza de las excitaciones externas muy restrictivas,como que éstas deben ser de tipo ruido blanco local. Este tipo de carga es difícil dereproducir en condiciones de ensayo. En la Tesis mostramos con casos prácticos queesta restricción se puede relajar y, en particular, los resultados son suficientementebuenos cuando la estructura se somete a una carga armónica en escalón.Bajo estas aproximaciones se desarrolla un algoritmo de optimización por pasosque permite actualizar un modelo SEA a un ensayo transitorio cuando la carga esde tipo armónica en escalón. Este algoritmo actualiza el modelo no solamente parauna banda de frecuencia en particular sino para diversas bandas de frecuencia demanera simultánea, con el objetivo de plantear un problema mejor condicionado.Por último, se define un índice de daño que mide el cambio en la matriz depérdidas cuando se produce un daño estructural en una localización concreta deun componente. Se simula numéricamente la respuesta de una estructura formadapor vigas donde producimos un daño en la sección de una de ellas; como se tratade un cálculo a altas frecuencias, la simulación se hace mediante el Método delos Elementos Espectrales para lo que ha sido necesario desarrollar dentro de laTesis un elemento espectral de tipo viga dañada en una sección determinada. Losresultados obtenidos permiten localizar el componente estructural en que se haproducido el daño y la sección en que éste se encuentra con determinado grado deconfianza.AbstractThe main subject under research in this Thesis is the study of the dynamic behaviourof a structure using models that describe the energy distribution betweenthe components of the structure and the applicability of these models to incipientdamage detection.Dynamic tests are a way to extract information about the properties of astructure. If we have a model of the structure, it can be updated in order toreproduce the same response as in experimental tests, within a certain degree ofaccuracy. After damage occurs, the response will change to some extent; modelupdating to the new test conditions can help to detect changes in the structuralmodel leading to the conclusión that damage has occurred.In this way incipient damage detection is possible if we are able to detect srnallvariations in the model parameters. It turns out that the high frequency regimeis highly relevant for incipient damage detection, because the response is verysensitive to small structural geometric details. The characteristic length associatedwith the response is proportional to the propagation speed of acoustic waves insidethe solid, but inversely proportional to the excitation frequency. At the same time,this fact makes the application of a Finite Element Method impractical due to thehigh computational cost.A widely used model in engineering when dealing with the high frequencyresponse is SEA (Statistical Energy Analysis). SEA applies the energy balance toeach structural component, relating their vibrational energy with the dissipatedpower and the transmitted power between the different components; their summust be equal to the input power to each of them. This relationship is linear andcharacterized by loss factors. The magnitudes considered in the response shouldbe averaged in geometry, frequency and time.SEA model updating to test data is equivalent to calculating the loss factorsthat provide a better fit to the experimental response. This is formulated as an illconditionedinverse problem. In this Thesis a new updating algorithm is proposedfor the study of the high frequency response regime in terms of parameters withphysical meaning such as the internal dissipation factors, modal densities andcharacteristic coupling stiffness. The loss factors are then calculated from theseparameters. The approach is developed entirely in this Thesis and is mainlybased on a high modal density asumption, that is to say, a large number of modescontributes to the response.General SEA theory establishes the validity of the model under the asumptionof very restrictive external excitations. These should behave as a local white noise.This kind of excitation is difficult to reproduce in an experimental environment.In this Thesis we show that in practical cases this assumption can be relaxed, inparticular, results are good enough when the structure is excited with a harmonicstep function.Under these assumptions an optimization algorithm is developed for SEAmodel updating to a transient test when external loads are harmonic step functions.This algorithm considers the response not only in a single frequency band,but also for several of them simultaneously.A damage index is defined that measures the change in the loss factor matrixwhen a damage has occurred at a certain location in the structure. The structuresconsidered in this study are built with damaged beam elements; as we are dealingwith the high frequency response, the numerical simulation is implemented witha Spectral Element Method. It has therefore been necessary to develop a spectralbeam damaged element as well. The reported results show that damage detectionis possible with this algorithm, moreover, damage location is also possible withina certain degree of accuracy.

Structural health monitoring network system with wireless communications inside closed aerospace structures

Structural Health Monitoring (SHM) requires integrated "all in one" electronic devices capable of performing analysis of structural integrity and on-board damage detection in aircraft?s structures. PAMELA III (Phased Array Monitoring for Enhanced Life Assessment, version III) SHM embedded system is an example of this device type. This equipment is capable of generating excitation signals to be applied to an array of integrated piezoelectric Phased Array (PhA) transducers stuck to aircraft structure, acquiring the response signals, and carrying out the advanced signal processing to obtain SHM maps. PAMELA III is connected with a host computer in order to receive the configuration parameters and sending the obtained SHM maps, alarms and so on. This host can communicate with PAMELA III through an Ethernet interface. To avoid the use of wires where necessary, it is possible to add Wi-Fi capabilities to PAMELA III, connecting a Wi-Fi node working as a bridge, and to establish a wireless communication between PAMELA III and the host. However, in a real aircraft scenario, several PAMELA III devices must work together inside closed structures. In this situation, it is not possible for all PAMELA III devices to establish a wireless communication directly with the host, due to the signal attenuation caused by the different obstacles of the aircraft structure. To provide communication among all PAMELA III devices and the host, a wireless mesh network (WMN) system has been implemented inside a closed aluminum wingbox. In a WMN, as long as a node is connected to at least one other node, it will have full connectivity to the entire network because each mesh node forwards packets to other nodes in the network as required. Mesh protocols automatically determine the best route through the network and can dynamically reconfigure the network if a link drops out. The advantages and disadvantages on the use of a wireless mesh network system inside closed aerospace structures are discussed.

Detección de patrones y anomalías espectrales del terreno mediante espectrometría de imagen de alta resolución : reconocimiento, optimización y evaluación multiescenario

El objeto de esta Tesis doctoral es el desarrollo de una metodologia para la deteccion automatica de anomalias a partir de datos hiperespectrales o espectrometria de imagen, y su cartografiado bajo diferentes condiciones tipologicas de superficie y terreno. La tecnologia hiperespectral o espectrometria de imagen ofrece la posibilidad potencial de caracterizar con precision el estado de los materiales que conforman las diversas superficies en base a su respuesta espectral. Este estado suele ser variable, mientras que las observaciones se producen en un numero limitado y para determinadas condiciones de iluminacion. Al aumentar el numero de bandas espectrales aumenta tambien el numero de muestras necesarias para definir espectralmente las clases en lo que se conoce como Maldicion de la Dimensionalidad o Efecto Hughes (Bellman, 1957), muestras habitualmente no disponibles y costosas de obtener, no hay mas que pensar en lo que ello implica en la Exploracion Planetaria. Bajo la definicion de anomalia en su sentido espectral como la respuesta significativamente diferente de un pixel de imagen respecto de su entorno, el objeto central abordado en la Tesis estriba primero en como reducir la dimensionalidad de la informacion en los datos hiperespectrales, discriminando la mas significativa para la deteccion de respuestas anomalas, y segundo, en establecer la relacion entre anomalias espectrales detectadas y lo que hemos denominado anomalias informacionales, es decir, anomalias que aportan algun tipo de informacion real de las superficies o materiales que las producen. En la deteccion de respuestas anomalas se asume un no conocimiento previo de los objetivos, de tal manera que los pixeles se separan automaticamente en funcion de su informacion espectral significativamente diferenciada respecto de un fondo que se estima, bien de manera global para toda la escena, bien localmente por segmentacion de la imagen. La metodologia desarrollada se ha centrado en la implicacion de la definicion estadistica del fondo espectral, proponiendo un nuevo enfoque que permite discriminar anomalias respecto fondos segmentados en diferentes grupos de longitudes de onda del espectro, explotando la potencialidad de separacion entre el espectro electromagnetico reflectivo y emisivo. Se ha estudiado la eficiencia de los principales algoritmos de deteccion de anomalias, contrastando los resultados del algoritmo RX (Reed and Xiaoli, 1990) adoptado como estandar por la comunidad cientifica, con el metodo UTD (Uniform Targets Detector), su variante RXD-UTD, metodos basados en subespacios SSRX (Subspace RX) y metodo basados en proyecciones de subespacios de imagen, como OSPRX (Orthogonal Subspace Projection RX) y PP (Projection Pursuit). Se ha desarrollado un nuevo metodo, evaluado y contrastado por los anteriores, que supone una variacion de PP y describe el fondo espectral mediante el analisis discriminante de bandas del espectro electromagnetico, separando las anomalias con el algortimo denominado Detector de Anomalias de Fondo Termico o DAFT aplicable a sensores que registran datos en el espectro emisivo. Se han evaluado los diferentes metodos de deteccion de anomalias en rangos del espectro electromagnetico del visible e infrarrojo cercano (Visible and Near Infrared-VNIR), infrarrojo de onda corta (Short Wavelenght Infrared-SWIR), infrarrojo medio (Meadle Infrared-MIR) e infrarrojo termico (Thermal Infrared-TIR). La respuesta de las superficies en las distintas longitudes de onda del espectro electromagnetico junto con su entorno, influyen en el tipo y frecuencia de las anomalias espectrales que puedan provocar. Es por ello que se han utilizado en la investigacion cubos de datos hiperepectrales procedentes de los sensores aeroportados cuya estrategia y diseno en la construccion espectrometrica de la imagen difiere. Se han evaluado conjuntos de datos de test de los sensores AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) y MASTER (MODIS/ASTER Simulator). Se han disenado experimentos sobre ambitos naturales, urbanos y semiurbanos de diferente complejidad. Se ha evaluado el comportamiento de los diferentes detectores de anomalias a traves de 23 tests correspondientes a 15 areas de estudio agrupados en 6 espacios o escenarios: Urbano - E1, Semiurbano/Industrial/Periferia Urbana - E2, Forestal - E3, Agricola - E4, Geologico/Volcanico - E5 y Otros Espacios Agua, Nubes y Sombras - E6. El tipo de sensores evaluados se caracteriza por registrar imagenes en un amplio rango de bandas, estrechas y contiguas, del espectro electromagnetico. La Tesis se ha centrado en el desarrollo de tecnicas que permiten separar y extraer automaticamente pixeles o grupos de pixeles cuya firma espectral difiere de manera discriminante de las que tiene alrededor, adoptando para ello como espacio muestral parte o el conjunto de las bandas espectrales en las que ha registrado radiancia el sensor hiperespectral. Un factor a tener en cuenta en la investigacion ha sido el propio instrumento de medida, es decir, la caracterizacion de los distintos subsistemas, sensores imagen y auxiliares, que intervienen en el proceso. Para poder emplear cuantitativamente los datos medidos ha sido necesario definir las relaciones espaciales y espectrales del sensor con la superficie observada y las potenciales anomalias y patrones objetivos de deteccion. Se ha analizado la repercusion que en la deteccion de anomalias tiene el tipo de sensor, tanto en su configuracion espectral como en las estrategias de diseno a la hora de registrar la radiacion prodecente de las superficies, siendo los dos tipos principales de sensores estudiados los barredores o escaneres de espejo giratorio (whiskbroom) y los barredores o escaneres de empuje (pushbroom). Se han definido distintos escenarios en la investigacion, lo que ha permitido abarcar una amplia variabilidad de entornos geomorfologicos y de tipos de coberturas, en ambientes mediterraneos, de latitudes medias y tropicales. En resumen, esta Tesis presenta una tecnica de deteccion de anomalias para datos hiperespectrales denominada DAFT en su variante de PP, basada en una reduccion de la dimensionalidad proyectando el fondo en un rango de longitudes de onda del espectro termico distinto de la proyeccion de las anomalias u objetivos sin firma espectral conocida. La metodologia propuesta ha sido probada con imagenes hiperespectrales reales de diferentes sensores y en diferentes escenarios o espacios, por lo tanto de diferente fondo espectral tambien, donde los resultados muestran los beneficios de la aproximacion en la deteccion de una gran variedad de objetos cuyas firmas espectrales tienen suficiente desviacion respecto del fondo. La tecnica resulta ser automatica en el sentido de que no hay necesidad de ajuste de parametros, dando resultados significativos en todos los casos. Incluso los objetos de tamano subpixel, que no pueden distinguirse a simple vista por el ojo humano en la imagen original, pueden ser detectados como anomalias. Ademas, se realiza una comparacion entre el enfoque propuesto, la popular tecnica RX y otros detectores tanto en su modalidad global como local. El metodo propuesto supera a los demas en determinados escenarios, demostrando su capacidad para reducir la proporcion de falsas alarmas. Los resultados del algoritmo automatico DAFT desarrollado, han demostrado la mejora en la definicion cualitativa de las anomalias espectrales que identifican a entidades diferentes en o bajo superficie, reemplazando para ello el modelo clasico de distribucion normal con un metodo robusto que contempla distintas alternativas desde el momento mismo de la adquisicion del dato hiperespectral. Para su consecucion ha sido necesario analizar la relacion entre parametros biofisicos, como la reflectancia y la emisividad de los materiales, y la distribucion espacial de entidades detectadas respecto de su entorno. Por ultimo, el algoritmo DAFT ha sido elegido como el mas adecuado para sensores que adquieren datos en el TIR, ya que presenta el mejor acuerdo con los datos de referencia, demostrando una gran eficacia computacional que facilita su implementacion en un sistema de cartografia que proyecte de forma automatica en un marco geografico de referencia las anomalias detectadas, lo que confirma un significativo avance hacia un sistema en lo que se denomina cartografia en tiempo real. The aim of this Thesis is to develop a specific methodology in order to be applied in automatic detection anomalies processes using hyperspectral data also called hyperspectral scenes, and to improve the classification processes. Several scenarios, areas and their relationship with surfaces and objects have been tested. The spectral characteristics of reflectance parameter and emissivity in the pattern recognition of urban materials in several hyperspectral scenes have also been tested. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) and MASTER (MODIS/ASTER Simulator) have been used in this research. It is assumed that there is not prior knowledge of the targets in anomaly detection. Thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by the image segmentation. Several experiments on different scenarios have been designed, analyzing the behavior of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. Results and their consequences in unsupervised classification processes are discussed. Detection of spectral anomalies aims at extracting automatically pixels that show significant responses in relation of their surroundings. This Thesis deals with the unsupervised technique of target detection, also called anomaly detection. Since this technique assumes no prior knowledge about the target or the statistical characteristics of the data, the only available option is to look for objects that are differentiated from the background. Several methods have been developed in the last decades, allowing a better understanding of the relationships between the image dimensionality and the optimization of search procedures as well as the subpixel differentiation of the spectral mixture and its implications in anomalous responses. In other sense, image spectrometry has proven to be efficient in the characterization of materials, based on statistical methods using a specific reflection and absorption bands. Spectral configurations in the VNIR, SWIR and TIR have been successfully used for mapping materials in different urban scenarios. There has been an increasing interest in the use of high resolution data (both spatial and spectral) to detect small objects and to discriminate surfaces in areas with urban complexity. This has come to be known as target detection which can be either supervised or unsupervised. In supervised target detection, algorithms lean on prior knowledge, such as the spectral signature. The detection process for matching signatures is not straightforward due to the complications of converting data airborne sensor with material spectra in the ground. This could be further complicated by the large number of possible objects of interest, as well as uncertainty as to the reflectance or emissivity of these objects and surfaces. An important objective in this research is to establish relationships that allow linking spectral anomalies with what can be called informational anomalies and, therefore, identify information related to anomalous responses in some places rather than simply spotting differences from the background. The development in recent years of new hyperspectral sensors and techniques, widen the possibilities for applications in remote sensing of the Earth. Remote sensing systems measure and record electromagnetic disturbances that the surveyed objects induce in their surroundings, by means of different sensors mounted on airborne or space platforms. Map updating is important for management and decisions making people, because of the fast changes that usually happen in natural, urban and semi urban areas. It is necessary to optimize the methodology for obtaining the best from remote sensing techniques from hyperspectral data. The first problem with hyperspectral data is to reduce the dimensionality, keeping the maximum amount of information. Hyperspectral sensors augment considerably the amount of information, this allows us to obtain a better precision on the separation of material but at the same time it is necessary to calculate a bigger number of parameters, and the precision lowers with the increase in the number of bands. This is known as the Hughes effects (Bellman, 1957) . Hyperspectral imagery allows us to discriminate between a huge number of different materials however some land and urban covers are made up with similar material and respond similarly which produces confusion in the classification. The training and the algorithm used for mapping are also important for the final result and some properties of thermal spectrum for detecting land cover will be studied. In summary, this Thesis presents a new technique for anomaly detection in hyperspectral data called DAFT, as a PP's variant, based on dimensionality reduction by projecting anomalies or targets with unknown spectral signature to the background, in a range thermal spectrum wavelengths. The proposed methodology has been tested with hyperspectral images from different imaging spectrometers corresponding to several places or scenarios, therefore with different spectral background. The results show the benefits of the approach to the detection of a variety of targets whose spectral signatures have sufficient deviation in relation to the background. DAFT is an automated technique in the sense that there is not necessary to adjust parameters, providing significant results in all cases. Subpixel anomalies which cannot be distinguished by the human eye, on the original image, however can be detected as outliers due to the projection of the VNIR end members with a very strong thermal contrast. Furthermore, a comparison between the proposed approach and the well-known RX detector is performed at both modes, global and local. The proposed method outperforms the existents in particular scenarios, demonstrating its performance to reduce the probability of false alarms. The results of the automatic algorithm DAFT have demonstrated improvement in the qualitative definition of the spectral anomalies by replacing the classical model by the normal distribution with a robust method. For their achievement has been necessary to analyze the relationship between biophysical parameters such as reflectance and emissivity, and the spatial distribution of detected entities with respect to their environment, as for example some buried or semi-buried materials, or building covers of asbestos, cellular polycarbonate-PVC or metal composites. Finally, the DAFT method has been chosen as the most suitable for anomaly detection using imaging spectrometers that acquire them in the thermal infrared spectrum, since it presents the best results in comparison with the reference data, demonstrating great computational efficiency that facilitates its implementation in a mapping system towards, what is called, Real-Time Mapping.

A computational model coupling mechanics and electrophysiology in spinal cord injury

Traumatic brain injury and spinal cord injury have recently been put under the spotlight as major causes of death and disability in the developed world. Despite the important ongoing experimental and modeling campaigns aimed at understanding the mechanics of tissue and cell damage typically observed in such events, the differenti- ated roles of strain, stress and their corresponding loading rates on the damage level itself remain unclear. More specif- ically, the direct relations between brain and spinal cord tis- sue or cell damage, and electrophysiological functions are still to be unraveled. Whereas mechanical modeling efforts are focusing mainly on stress distribution and mechanistic- based damage criteria, simulated function-based damage cri- teria are still missing. Here, we propose a new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate. This multiscale approach provides a new framework for damage evaluation directly relating neuron mechanics and electrophysiological properties, thus provid- ing a link between mechanical trauma and subsequent func- tional deficits.

Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.

A computational model coupling mechanics and electrophysiology in spinal cord injury

Traumatic brain injury and spinal cord injury have recently been put under the spotlight as major causes of death and disability in the developed world. Despite the important ongoing experimental and modeling campaigns aimed at understanding the mechanics of tissue and cell damage typically observed in such events, the differentiated roles of strain, stress and their corresponding loading rates on the damage level itself remain unclear. More specifically, the direct relations between brain and spinal cord tissue or cell damage, and electrophysiological functions are still to be unraveled. Whereas mechanical modeling efforts are focusing mainly on stress distribution and mechanistic-based damage criteria, simulated function-based damage criteria are still missing. Here, we propose a new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate. This multiscale approach provides a new framework for damage evaluation directly relating neuron mechanics and electrophysiological properties, thus providing a link between mechanical trauma and subsequent functional deficits

Análisis dinámico experimental y numérico de una presa bóveda : ajuste del modelo

El objetivo de este trabajo es analizar las propiedades dinámicas de una presa bóveda de doble curvatura (presa de La Tajera, Guadalajara) para ajustar un modelo de elementos finitos. Para ello se han utilizado acelerómetros de alta sensibilidad sincronizados inalámbricamente. Se han obtenido las frecuencias, amortiguamientos y formas modales frente a los efectos de las acciones de tipo ambiental (viento, paso de vehículos). Se ha modelado mediante elementos finitos la presa y su cimiento incorporando el efecto del nivel del embalse. Con las propiedades dinámicas de la estructura halladas numéricamente se ha realizado un plan de medidas en los puntos que se consideraban más significativos. Tras realizar las medidas, se ha procedido al análisis de resultados mediante un Análisis Modal Operacional. Ello permite estimar los parámetros modales (frecuencias, amortiguamientos y formas modales) experimentalmente y se ha valorado el alcance de los mismos. Posteriormente viene la parte fundamental de este trabajo, que es el ajuste del modelo de elementos finitos inicial considerando el comportamiento dinámico obtenido experimentalmente. El modelo actualizado puede utilizarse dentro de un sistema de detección de daños, o por ejemplo, para el estudio del comportamiento ante un sismo considerando la interacción presa-embalse-cimiento. The purpose of this paper is to study the dynamic characteristics of a double curvature arch dam (La Tajera arch dam) for a Finite Element Model Updating. To achieve it, high sensitivity accelerometers synchronized wirelessly have been used. The system modal dampings, natural frequencies mode shapes are identified using output only identification techniques under environmental loads (wind, vehicles). Firstly, a finite element model of the dam-reservoir-foundation system was created. Once the dynamic properties of the structure were numerically obtained, a testing plan was then carried out identifying the most significant test points. After the measurements were carried out, an Operational Modal Analysis was performed to obtain experimentally the structure dynamic properties: natural frequencies, modal dampings and mode shapes. experimentally and to assess its reach. Then, the finite element model updating of the initial model was carried out to match the recorded dynamic behavior. The updated model may be used within a structural health monitoring and damage detection system or, as it is proposed on this thesis, for the analysis of the seismic response of arch dam-reservoir-foundation coupled systems

Cartels: the Probability of Getting Caught in the European Union. Bruges European Economic Research (BEER) Papers 12/March 2008

In 1991, Bryant and Eckard estimated the annual probability that a cartel would be detected by the US Federal authorities, conditional on being detected, to be at most between 13 % and 17 %. 15 years later, we estimated the same probability over a European sample and we found an annual probability that falls between 12.9 % and 13.3 %. We also develop a detection model to clarify this probability. Our estimate is based on detection durations, calculated from data reported for all the cartels convicted by the European Commission from 1969 to the present date, and a statistical birth and death process model describing the onset and detection of cartels.

Phosphoregulators: Protein kinases and protein phosphatases of mouse

With the completion of the human and mouse genome sequences, the task now turns to identifying their encoded transcripts and assigning gene function. In this study, we have undertaken a computational approach to identify and classify all of the protein kinases and phosphatases present in the mouse gene complement. A nonredundant set of these sequences was produced by mining Ensembl gene predictions and publicly available cDNA sequences with a panel of InterPro domains. This approach identified 561 candidate protein kinases and 162 candidate protein phosphatases. This cohort was then analyzed using TribeMCL protein sequence similarity clustering followed by CLUSTALV alignment and hierarchical tree generation. This approach allowed us to (1) distinguish between true members of the protein kinase and phosphatase families and enzymes of related biochemistry, (2) determine the structure of the families, and (3) suggest functions for previously uncharacterized members. The classifications obtained by this approach were in good agreement with previous schemes and allowed us to demonstrate domain associations with a number of clusters. Finally, we comment on the complementary nature of cDNA and genome-based gene detection and the impact of the FANTOM2 transcriptome project.

Pitch jump probability measures for the analysis of snoring sounds in apnea

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The gold standard of diagnosis, called polysomnography (PSG), requires a full-night hospital stay connected to over ten channels of measurements requiring physical contact with sensors. PSG is inconvenient, expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. Diagnostic systems intent on using snore-related sounds (SRS) face the tough problem of how to define a snore. In this paper, we present a working definition of a snore, and propose algorithms to segment SRS into classes of pure breathing, silence and voiced/unvoiced snores. We propose a novel feature termed the 'intra-snore-pitch-jump' (ISPJ) to diagnose OSA. Working on clinical data, we show that ISPJ delivers OSA detection sensitivities of 86-100% while holding specificity at 50-80%. These numbers indicate that snore sounds and the ISPJ have the potential to be good candidates for a take-home device for OSA screening. Snore sounds have the significant advantage in that they can be conveniently acquired with low-cost non-contact equipment. The segmentation results presented in this paper have been derived using data from eight patients as the training set and another eight patients as the testing set. ISPJ-based OSA detection results have been derived using training data from 16 subjects and testing data from 29 subjects.

Conception, fabrication et caractérisation d'un biocapteur SPR à base de guides d'ondes photoniques sur substrat de verre

Résumé : Malgré le nombre croissant de capteurs dans les domaines de la chimie et la biologie, il reste encore à étudier en profondeur la complexité des interactions entre les différentes molécules présentes lors d’une détection à l’interface solide-liquide. Dans ce cadre, il est de tout intérêt de croiser différentes méthodes de détection afin d’obtenir des informations complémentaires. Le principal objectif de cette étude est de dimensionner, fabriquer et caractériser un détecteur optique intégré sur verre basé sur la résonance plasmonique de surface, destiné à terme à être combiné avec d’autres techniques de détection, dont un microcalorimètre. La résonance plasmonique de surface est une technique reconnue pour sa sensibilité adaptée à la détection de surface, qui a l’avantage d’être sans marquage et permet de fournir un suivi en temps réel de la cinétique d’une réaction. L’avantage principal de ce capteur est qu’il a été dimensionné pour une large gamme d’indice de réfraction de l’analyte, allant de 1,33 à 1,48. Ces valeurs correspondent à la plupart des entités biologiques associées à leurs couches d’accroche dont les matrices de polymères, présentés dans ce travail. Étant donné que beaucoup d’études biologiques nécessitent la comparaison de la mesure à une référence ou à une autre mesure, le second objectif du projet est d’étudier le potentiel du système SPR intégré sur verre pour la détection multi-analyte. Les trois premiers chapitres se concentrent sur l’objectif principal du projet. Le dimensionnement du dispositif est ainsi présenté, basé sur deux modélisations différentes, associées à plusieurs outils de calcul analytique et numérique. La première modélisation, basée sur l’approximation des interactions faibles, permet d’obtenir la plupart des informations nécessaires au dimensionnement du dispositif. La seconde modélisation, sans approximation, permet de valider le premier modèle approché et de compléter et affiner le dimensionnement. Le procédé de fabrication de la puce optique sur verre est ensuite décrit, ainsi que les instruments et protocoles de caractérisation. Un dispositif est obtenu présentant des sensibilités volumiques entre 1000 nm/RIU et 6000 nm/RIU suivant l’indice de réfraction de l’analyte. L’intégration 3D du guide grâce à son enterrage sélectif dans le verre confère au dispositif une grande compacité, le rendant adapté à la cointégration avec un microcalorimètre en particulier. Le dernier chapitre de la thèse présente l’étude de plusieurs techniques de multiplexage spectral adaptées à un système SPR intégré, exploitant en particulier la technologie sur verre. L’objectif est de fournir au moins deux détections simultanées. Dans ce cadre, plusieurs solutions sont proposées et les dispositifs associés sont dimensionnés, fabriqués et testés.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.