A Closed-Form, Consistent and Robust Solution to Uncalibrated Photometric Stereo Via Local Diffuse Reflectance Maxima

Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction (see Fig. 1). We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.

Risk Index Score (RISc)

The main objective of this study was to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that can be translated into a simple scoring system in order to ascertain stroke cases using hospital admission medical records data. This algorithm, the Risk Index Score (RISc), was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christ (BASIC) project. The validity of the RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment accomplished by physician review of hospital admission records. The goal of this study was to develop a rapid, simple, efficient, and accurate method to ascertain the incidence of stroke from routine hospital admission hospital admission records for epidemiologic investigations. ^ The main objectives of this study were to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that could be translated into a simple scoring system to ascertain stroke cases using hospital admission medical records data. (Abstract shortened by UMI.)^

Cognitive strategies for security in wireless sensor networks

Las redes de sensores inalámbricas son uno de los sectores con más crecimiento dentro de las redes inalámbricas. La rápida adopción de estas redes como solución para muchas nuevas aplicaciones ha llevado a un creciente tráfico en el espectro radioeléctrico. Debido a que las redes inalámbricas de sensores operan en las bandas libres Industrial, Scientific and Medical (ISM) se ha producido una saturación del espectro que en pocos años no permitirá un buen funcionamiento. Con el objetivo de solucionar este tipo de problemas ha aparecido el paradigma de Radio Cognitiva (CR). La introducción de las capacidades cognitivas en las redes inalámbricas de sensores permite utilizar estas redes para aplicaciones con unos requisitos más estrictos respecto a fiabilidad, cobertura o calidad de servicio. Estas redes que aúnan todas estas características son llamadas redes de sensores inalámbricas cognitivas (CWSNs). La mejora en prestaciones de las CWSNs permite su utilización en aplicaciones críticas donde antes no podían ser utilizadas como monitorización de estructuras, de servicios médicos, en entornos militares o de vigilancia. Sin embargo, estas aplicaciones también requieren de otras características que la radio cognitiva no nos ofrece directamente como, por ejemplo, la seguridad. La seguridad en CWSNs es un aspecto poco desarrollado al ser una característica no esencial para su funcionamiento, como pueden serlo el sensado del espectro o la colaboración. Sin embargo, su estudio y mejora es esencial de cara al crecimiento de las CWSNs. Por tanto, esta tesis tiene como objetivo implementar contramedidas usando las nuevas capacidades cognitivas, especialmente en la capa física, teniendo en cuenta las limitaciones con las que cuentan las WSNs. En el ciclo de trabajo de esta tesis se han desarrollado dos estrategias de seguridad contra ataques de especial importancia en redes cognitivas: el ataque de simulación de usuario primario (PUE) y el ataque contra la privacidad eavesdropping. Para mitigar el ataque PUE se ha desarrollado una contramedida basada en la detección de anomalías. Se han implementado dos algoritmos diferentes para detectar este ataque: el algoritmo de Cumulative Sum y el algoritmo de Data Clustering. Una vez comprobado su validez se han comparado entre sí y se han investigado los efectos que pueden afectar al funcionamiento de los mismos. Para combatir el ataque de eavesdropping se ha desarrollado una contramedida basada en la inyección de ruido artificial de manera que el atacante no distinga las señales con información del ruido sin verse afectada la comunicación que nos interesa. También se ha estudiado el impacto que tiene esta contramedida en los recursos de la red. Como resultado paralelo se ha desarrollado un marco de pruebas para CWSNs que consta de un simulador y de una red de nodos cognitivos reales. Estas herramientas han sido esenciales para la implementación y extracción de resultados de la tesis. ABSTRACT Wireless Sensor Networks (WSNs) are one of the fastest growing sectors in wireless networks. The fast introduction of these networks as a solution in many new applications has increased the traffic in the radio spectrum. Due to the operation of WSNs in the free industrial, scientific, and medical (ISM) bands, saturation has ocurred in these frequencies that will make the same operation methods impossible in the future. Cognitive radio (CR) has appeared as a solution for this problem. The networks that join all the mentioned features together are called cognitive wireless sensor networks (CWSNs). The adoption of cognitive features in WSNs allows the use of these networks in applications with higher reliability, coverage, or quality of service requirements. The improvement of the performance of CWSNs allows their use in critical applications where they could not be used before such as structural monitoring, medical care, military scenarios, or security monitoring systems. Nevertheless, these applications also need other features that cognitive radio does not add directly, such as security. The security in CWSNs has not yet been explored fully because it is not necessary field for the main performance of these networks. Instead, other fields like spectrum sensing or collaboration have been explored deeply. However, the study of security in CWSNs is essential for their growth. Therefore, the main objective of this thesis is to study the impact of some cognitive radio attacks in CWSNs and to implement countermeasures using new cognitive capabilities, especially in the physical layer and considering the limitations of WSNs. Inside the work cycle of this thesis, security strategies against two important kinds of attacks in cognitive networks have been developed. These attacks are the primary user emulator (PUE) attack and the eavesdropping attack. A countermeasure against the PUE attack based on anomaly detection has been developed. Two different algorithms have been implemented: the cumulative sum algorithm and the data clustering algorithm. After the verification of these solutions, they have been compared and the side effects that can disturb their performance have been analyzed. The developed approach against the eavesdropping attack is based on the generation of artificial noise to conceal information messages. The impact of this countermeasure on network resources has also been studied. As a parallel result, a new framework for CWSNs has been developed. This includes a simulator and a real network with cognitive nodes. This framework has been crucial for the implementation and extraction of the results presented in this thesis.

Avaliações e testes dos métodos MASW e ReMi por meio do tratamento de dados empíricos e sintéticos em um programa de processamento e inversão desenvolvido em MATLAB e sua implicação em um problema geológico na Bacia de Taubaté

Os métodos de ondas superficiais com ênfase nas ondas Rayleigh foram utilizados como o núcleo desse trabalho de Doutorado. Inicialmente, as ondas Rayleigh foram modeladas permitindo o estudo de sensibilidade de suas curvas de dispersão sob diferentes configurações de parâmetros físicos representando diversos modelos de camadas, em que pôde ser observado parâmetros com maior e menor sensibilidade e também alguns efeitos provocados por baixas razões de Poisson. Além disso, na fase de inversão dos dados a modelagem das ondas Rayleigh foi utilizada para a construção da função objeto, que agregada ao método de mínimos quadrados, a partir do método de Levenberg-Marquardt, permitiu a implementação de um algoritmo de busca local responsável pela inversão de dados das ondas superficiais. Por se tratar de um procedimento de busca local, o algoritmo de inversão foi complementado por uma etapa de pré-inversão com a geração de um modelo inicial para que o procedimento de inversão fosse mais rápido e eficiente. Visando uma eficiência ainda maior do procedimento de inversão, principalmente em modelos de camadas com inversão de velocidades, foi implementado um algoritmo de pós-inversão baseado em um procedimento de tentativa e erro minimizando os valores relativos da raiz quadrada do erro quadrático médio (REQMr) da inversão dos dados. Mais de 50 modelos de camadas foram utilizados para testar a modelagem, a pré-inversão, inversão e pós-inversão dos dados permitindo o ajuste preciso de parâmetros matemáticos e físicos presentes nos diversos scripts implementados em Matlab. Antes de inverter os dados adquiridos em campo, os mesmos precisaram ser tratados na etapa de processamento de dados, cujo objetivo principal é a extração da curva de dispersão originada devido às ondas superficiais. Para isso, foram implementadas, também em Matlab, três metodologias de processamento com abordagens matemáticas distintas. Essas metodologias foram testadas e avaliadas com dados sintéticos e reais em que foi possível constatar as virtudes e deficiências de cada metodologia estudada, bem como as limitações provocadas pela discretização dos dados de campo. Por último, as etapas de processamento, pré-inversão, inversão e pós-inversão dos dados foram unificadas para formar um programa de tratamento de dados de ondas superficiais (Rayleigh). Ele foi utilizado em dados reais originados pelo estudo de um problema geológico na Bacia de Taubaté em que foi possível mapear os contatos geológicos ao longo dos pontos de aquisição sísmica e compará-los a um modelo inicial existente baseado em observações geomorfológicas da área de estudos, mapa geológico da região e informações geológicas globais e locais dos movimentos tectônicos na região. As informações geofísicas associadas às geológicas permitiram a geração de um perfil analítico da região de estudos com duas interpretações geológicas confirmando a suspeita de neotectônica na região em que os contatos geológicos entre os depósitos Terciários e Quaternários foram identificados e se encaixaram no modelo inicial de hemi-graben com mergulho para Sudeste.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

On a Direct Approach to the Solution of Inverse Optical Problems

The evaluation from experimental data, of physical quantities, which enter into the electromagnetic Maxwell equations, is described as inverse optical problem. The functional relations between the dependent and independent variables are of transcendental character and numeric procedures for evaluation of the unknowns are largely used. Herein, we discuss a direct approach to the solution, illustrated by a specific example of determination of thin films optical constants from spectrophotometric data. New algorithm is proposed for the parameters evaluation, which does not need an initial guess of the unknowns and does not use iterative procedures. Thus we overcome the intrinsic deficiency of minimization techniques, such as gradient search methods, Simplex methods, etc. The price of it is a need of more computing power, but our algorithm is easily implemented in structures such as grid clusters. We show the advantages of this approach and its potential for generalization to other inverse optical problems.

Fibre impairment compensation using artificial neural network equalizer for high-capacity coherent optical OFDM signals

We propose an artificial neural network (ANN) equalizer for transmission performance enhancement of coherent optical OFDM (C-OOFDM) signals. The ANN equalizer showed more efficiency in combating both chromatic dispersion (CD) and single-mode fibre (SMF)-induced non-linearities compared to the least mean square (LMS). The equalizer can offer a 1.5 dB improvement in optical signal-to-noise ratio (OSNR) compared to LMS algorithm for 40 Gbit/s C-OOFDM signals when considering only CD. It is also revealed that ANN can double the transmission distance up to 320 km of SMF compared to the case of LMS, providing a nonlinearity tolerance improvement of ∼0.7 dB OSNR.

Direct sequence spread spectrum based PWM strategy for harmonic reduction and communication

Switched mode power supplies (SMPSs) are essential components in many applications, and electromagnetic interference is an important consideration in the SMPS design. Spread spectrum based PWM strategies have been used in SMPS designs to reduce the switching harmonics. This paper proposes a novel method to integrate a communication function into spread spectrum based PWM strategy without extra hardware costs. Direct sequence spread spectrum (DSSS) and phase shift keying (PSK) data modulation are employed to the PWM of the SMPS, so that it has reduced switching harmonics and the input and output power line voltage ripples contain data. A data demodulation algorithm has been developed for receivers, and code division multiple access (CDMA) concept is employed as communication method for a system with multiple SMPSs. The proposed method has been implemented in both Buck and Boost converters. The experimental results validated the proposed DSSS based PWM strategy for both harmonic reduction and communication.

Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.

Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Free-flight experiments in LISA Pathfinder

The LISA Path finder mission will demonstrate the technology of drag-free test masses for use as inertial references in future space-based gravitational wave detectors. To accomplish this, the Path finder spacecraft will perform drag-free flight about a test mass while measuring the acceleration of this primary test mass relative to a second reference test mass. Because the reference test mass is contained within the same spacecraft, it is necessary to apply forces on it to maintain its position and attitude relative to the spacecraft. These forces are a potential source of acceleration noise in the LISA Path finder system that are not present in the full LISA con figuration. While LISA Path finder has been designed to meet it's primary mission requirements in the presence of this noise, recent estimates suggest that the on-orbit performance may be limited by this 'suspension noise'. The drift-mode or free-flight experiments provide an opportunity to mitigate this noise source and further characterize the underlying disturbances that are of interest to the designers of LISA-like instruments. This article provides a high-level overview of these experiments and the methods under development to analyze the resulting data.

Motif detection inspired by immune memory

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorithm searches from a completely neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of motifs successfully in both cases, and the value of these motifs is discussed.