Intelligent sampling for the measurement of structured surfaces

Uniform sampling in metrology has known drawbacks such as coherent spectral aliasing and a lack of efficiency in terms of measuring time and data storage. The requirement for intelligent sampling strategies has been outlined over recent years, particularly where the measurement of structured surfaces is concerned. Most of the present research on intelligent sampling has focused on dimensional metrology using coordinate-measuring machines with little reported on the area of surface metrology. In the research reported here, potential intelligent sampling strategies for surface topography measurement of structured surfaces are investigated by using numerical simulation and experimental verification. The methods include the jittered uniform method, low-discrepancy pattern sampling and several adaptive methods which originate from computer graphics, coordinate metrology and previous research by the authors. By combining the use of advanced reconstruction methods and feature-based characterization techniques, the measurement performance of the sampling methods is studied using case studies. The advantages, stability and feasibility of these techniques for practical measurements are discussed

Vehicle path verification using wireless sensor networks

Path Verification is a problem where a verifier would like to determine how closely a vehicle actually traversed a path that it claims to have traversed. This problem has critical significances in terms of vehicle mobility. Mobile nodes can be patrols officers or cab drivers, while respective verifiers can be police dispatchers or cab operators. In this paper, we design a sensor network assisted technique for vehicle path verification. In our design, a number of static wireless sensors placed in road segments will serve as witnesses and certify vehicles as they move. Post movement, these witness certificates will be utilized by the verifier to derive the actual path of a suspect vehicle. The challenge now is how to compare a Claimed Path as reported by the vehicle and the Actual Path derived from witness certificates. In this paper, we design a simple, yet effective technique for comparing similarity between two vehicle paths. Our technique extends from Continuous Dynamic Time Warping, which involves constructing a universal manifold from the two paths and then finding the geodesic on the resulting polygonal surface (shortest path along the surface) which is a diagonal from the origin of the surface to the terminal point. This distance is analogous to the Fréchet distance and yields a good measure of the similarity between two paths. Using simulations and real experiments, we demonstrate the performance of our technique from the perspective of detecting false paths claims from correct ones. We also design light-weight cryptographic techniques to prevent vehicle masquerading and certificate forging attacks. A proof of concept experiment was conducted on the streets of Rolla, Missouri. A sensor grid was established on a small section of Rolla and a vehicle with a transmitter was driven through the grid many times. The analysis of the data yielded results consistent with the expected ones --Abstract, page iii

Efficient sampling of Bayesian posteriors and predictive distributions in χEFT

In this thesis I employ Bayesian statistics to quantify parametric and epistemic uncertainties in chiral effective field theories (χEFT) and propagate these forward to predictions of observables in low-energy nuclear physics. Two primary sources of uncertainty---experimental errors and the theoretical error induced by the truncation of the EFT at up to next-to-next-to-leading-order---are modelled and accounted for in the posterior distributions of the unknown low-energy constants (LECs) that govern interaction strengths in χEFT. These posteriors are computationally challenging to extract and I therefore introduce an advanced Markov chain Monte Carlo (MCMC) algorithm, known as Hamiltonian Monte Carlo, and investigate its performance. I compare its sampling efficiency to standard MCMC algorithms and find reductions in computation time by factors around 3-6 in the present work. I exploit the extracted posteriors to produce predictive distributions for neutron-proton and proton-proton scattering cross sections below and above the pion production threshold and check the consistency of the model predictions against empirical data and higher-order point estimates. I find that the predictive distributions provide reliable credibility intervals as long as the size of the truncation error is estimated from expansion coefficients at next-to-leading-order and above. The LEC posteriors are also central to uncertainty quantification in few- and manybody systems, and as part of a larger collaboration I explore constraints on three-nucleon forces imposed by light-nuclei observables

Analysis of Brain Magnetic Resonance Images: Voxel-Based Morphometry and Pattern Classification Approaches

This thesis aims to examine two types of elaboration techniques of brain magnetic resonance imaging (MRI) data: the voxel-based morphometry (VBM) and the support vector machine (SVM) approaches. While the VBM is a standard and well-established mass-univariate method, the SVM multivariate analysis has been rarely implemented to investigate brain MRI data. An improvement of our knowledge on the pattern classication approach is necessary to be achieved, both to assess its exploratory capability and to point out advantages and disadvantages with respect to the more largely used VBM approach. Despite these methods are potentially suitable to investigate a large variety of neurological and neuropsychiatric disorders, in the present study they have been employed with the purpose of detecting neuroanatomical and gender-related abnormalities in children with autism spectrum disorders (ASD). In fact, the dierences in the neuroanatomy of young children with ASD are an intriguing and still poor investigated issue. After a description of the physical principles of nuclear magnetic resonance and an overview of magnetic resonance imaging, we specied the two algorithms that represent the object of the current study: voxel-based morphometry and support vector machines classication methods. Hence, we described the theoretical principles they are based on, pointing out schemes and procedures employed to implement these analysis approaches. Then, we examined the application of VBM and SVM methods to an opportunely chosen sample of MRI data. A total of 152 structural MRI scans were selected. Specically, our dataset was composed by 76 ASD children and 76 matched controls in the 2-7 year age range. The images were preprocessed applying the SPM8 algorithm, based on the dieomorphic anatomical registration through exponentiated lie algebra (DARTEL) procedure. The resulting grey matter (GM) segments were analyzed by applying the conventional voxel-wise two-sample t-test VBM analysis and employing the stringent family-wise error (FWE) rate correction according to random gaussian elds theory. The same preprocessed GM segments were then analyzed using the SVM pattern classication approach, that presents the advantage of intrinsically taking into account interregional correlations. Moreover, this technique would allow investigations about the predictive value of structural MRI scans. In fact, the SVM classication capability can be quantied in terms of the area under the receiver operating characteristic curve (AUC). The leave-pair-out cross- validation protocol has been adopted to evaluate the classication performance. The recursive feature elimination (RFE) procedure has been implemented both to reduce the large number of features in the classication problem and to enhance the classication capability. The SVM-RFE allows also to localize the most discriminant voxels and to visualize them in a discrimination map. However, the pattern classication method was not employed to predict the class membership of undiagnosed subjects, but as a gure of merit allowing to determine an optimal threshold on the discrimination maps, where possible between-group structural dierences are encoded. With the aim of strengthening the SVM-based methods applied to brain data and to guarantee reliability and reproducibility of the results, we set up the following tests: 1. We evaluated the consistency among all discrimination maps, each obtained from one of the SVM leave-pair-out cross-validation steps, within the chosen range of number of retained features employed. 2. We assessed the dependency on the population of the training set within the cross- validation procedure. In this way we became able to check for the stability of our statistical results with respect to the number of subjects employed during the learning phase. Furthermore, we can evaluate the classication performances for dierent cross- validation schemes. Among the results we obtained, we found that SVMs applied to GM scans correctly discriminate ASD male and female individuals with respect to controls with an AUC above the 87% with a fraction of retained voxels in the 0.4-29% range. By choosing as operative point of the system that corresponding to the lower amount of signicant voxels (0.4% of the total number of voxels) we obtained a sensitivity of 82% and a specicity of 80%. The resulting discrimination maps showed some signicant regions where an excess of GM characterizes the ASD subjects with respect to the matched control group. These regions seemed to be consistent with those obtained from the VBM analysis, nevertheless the SVM analysis highlighted a larger number of interesting gender-specic discriminating regions. Hence, multivariate methods based on the SVM could contribute not only to distinguish ASD from control children, but also to disentangle the gender specicity of ASD brain alterations, consistently with respect to the mass-univariate approach. Achieving a better AUC could make possible to employ the pattern recognition approach not only to individuate brain regions discriminating between patients and controls, but also to predict the class membership of undiagnosed subjects, thus facilitating the early diagnosis of the ASD pathology

Experimental Synthetic Aperture Radar with Dynamic Metasurfaces

We investigate the use of a dynamic metasurface as the transmitting antenna for a synthetic aperture radar (SAR) imaging system. The dynamic metasurface consists of a one-dimensional microstrip waveguide with complementary electric resonator (cELC) elements patterned into the upper conductor. Integrated into each of the cELCs are two diodes that can be used to shift each cELC resonance out of band with an applied voltage. The aperture is designed to operate at K band frequencies (17.5 to 20.3 GHz), with a bandwidth of 2.8 GHz. We experimentally demonstrate imaging with a fabricated metasurface aperture using existing SAR modalities, showing image quality comparable to traditional antennas. The agility of this aperture allows it to operate in spotlight and stripmap SAR modes, as well as in a third modality inspired by computational imaging strategies. We describe its operation in detail, demonstrate high-quality imaging in both 2D and 3D, and examine various trade-offs governing the integration of dynamic metasurfaces in future SAR imaging platforms

Bridge damage detection using an intelligent engineering system

This thesis concerns the design of an algorithm that is capable to detect structural damage in civil infrastructure bridges. The algorithm, which will be dubbed Damage Diagnostics System throughout the thesis, is the software component of a broader Bridge Health Monitoring System. This broader system integrates software and hardware,such as sensors and data acquisition components...The rationale for the Structural Damage Diagnosis is based on the principle of the structural vibration testing. The Health Monitoring System captures the vibration signals, as the bridge responds to excitation from various sources. The purpose of the Diagnostic System is to extract information from the vibration signals concerning the damage condition of the bridge. This system will identify and quantify the damage, by examining the shifts in the vibration signature. This can be performed with a comparison between the actual vibration signal and the vibration behavior of the undamaged bridge --Introduction, page 1