Optics-Based Quantum Information and Sensing Platforms Utilizing the Nitrogen-Vacancy Center in Diamond

Thesis (Ph.D.)--University of Washington, 2016-08

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Electric field strengths and ion trajectories in sharp-edge field ionization sources

On the presumption that a sharp edge may be represented by a hyperbola, a conformal transformation method is used to derive electric field equations for a sharp edge suspended above a flat plate. A further transformation is then introduced to give electric field components for a sharp edge suspended above a thin slit. Expressions are deduced for the field strength at the vertex of the edge in both arrangements. The calculated electric field components are used to compute ion trajectories in the simple edge/flat-plate case. The results are considered in relation to future study of ion focusing and unimolecular decomposition of ions in field ionization mass spectrometers.

Development of a whole cell biochip for toxicant detection.

In the development of biosensors for ecotoxicity testing it is desirable to produce a small, portable system that can be used in the field. Toxicity testing using bioluminescence is widely used in the laboratory utilising natural and genetically modified (lux/ luc-marked) bacteria and other microorganisms. It is currently not possible to use genetically manipulated microorganisms in field testing and a biosensor, therefore, that incorporates naturally luminescent organisms may be preferred. In the development of a biosensor it is aimed to use the naturally luminescent bacterium Vibrio fischeri as a toxicity detection system on a chip. The bacterium will be immobilised in a polymeric matrix. Current work deals with the optimisation of light output and light preservation within the bacterium prior to immobilisation in polyvinyl alcohol. An examination of a range of physicochemical conditions within the polymer will be made, including cell density, thickness of polymer film, growth and light induction environment, and, preservation conditions, in order to develop a testing system giving consistent results over the lifetime of the biosensor. Data will be presented on light production using different culture media for the growth of V. fischeri and retention of light under immobilised conditions. .

'Immune System Approaches to Intrusion Detection - A Review'

Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.

Immune System Approaches to Intrusion Detection - A Review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.

'Immune System Approaches to Intrusion Detection - A Review'

Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

Optimizing Western Blots for the Detection of Endogenous α-Synuclein in the Enteric Nervous System

International audience

Immune System Approaches to Intrusion Detection - A Review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.

Hibridación in situ fluorescente (FISH)

Background: At the end of 80s, cloning technologies with the increase of the antibodies’ sensibility made easier the development of technologies based on Fluorescence in situ Hibridation (FISH). Nowadays, It’s widely used in the field of basic investigation as much as clinic diagnostic. Method: FISH is a technique that combines molecular biology with histochemistry way to detect specific nucleotide sequences so that chromosome’s section or even whole chromosome can be marked on metaphases cells (cell in division) and on attached cellular nucleus. This detection is realized using DNA fluorescence probes (marked with fluorophores), that can be different according to the structures manage to detect: large single-locus probes, small unique-sequence probes, chromosome- or region-specific “paints” or repetitive sequence probes and genomic DNA probes. Some of the applications of this technique is that can be so useful in the detection of numerical and structural chromosomal alterations such as polyploidies or genomic rearrangement, to mapping metaphases cells and even to detect bacteria or another type of microorganism. In addition, FISH allows us to monitoring diseases (antitumor therapies, quantification of genomic altered cells…) and the precise location of chromosomic broken spots on tumor searching for new genes involved in cancer and detect and map interested known genes. Conclusion: FISH has many advantages ahead of conventional cytogenetic techniques (bands G karyotype) overall at the time of establish a clinic diagnostic to detect tumors and chromosomic aberration, presenting a higher sensibility and specificity as well as being a relative quick technique (24 hours).

Mapping and modulating spatial attention asymmetries in young and older adults

Healthy young adults demonstrate a group-level, systematic preference for stimuli presented in the left side of space relative to the right (‘pseudoneglect’) (Bowers & Heilman, 1980). This results in an overestimation of features such as size, brightness, numerosity and spatial frequency in the left hemispace, probably as a result of right cerebral hemisphere dominance for visuospatial attention. This spatial attention asymmetry is reduced in the healthy older population, and can be shifted entirely into right hemispace under certain conditions. Although this rightward shift has been consistently documented in behavioural experiments, there is very little neuroimaging evidence to explain this effect at a neuroanatomical level. In this thesis, I used behavioural methodology and electroencephalography (EEG) to map spatial attention asymmetries in young and older adults. I then use transcranial direct current stimulation (tDCS) to modulate these spatial biases, with the aim of assessing age-related differences in response to tDCS. In the first of three experiments presented in this thesis, I report in Chapter Two that five different spatial attention tasks provide consistent intra-task measures of spatial bias in young adults across two testing days. There were, however, no inter-task correlations between the five tasks, indicating that pseudoneglect is at least partially driven by task-dependent patterns of neural activity. In Chapter Three, anodal tDCS was applied separately to the left (P5) and right (P6) posterior parietal cortex (PPC) in young and older adults, with an aim to improve the detection of stimuli appearing in the contralateral visual field. There were no age differences in response to tDCS, but there were significant differences depending on baseline performance. Relative to a sham tDCS protocol, tDCS applied to the right PPC resulted in maintained visual detection across both visual fields in adults who were good at the task at baseline. In contrast, left PPC tDCS resulted in reduced detection sensitivity across both visual fields in poor performers. Finally, in Chapter Four, I report a right-hemisphere lateralisation of EEG activity in young adults that was present for long (but not short) landmark task lines. In contrast, older adults demonstrated no lateralised activity for either line length, thus providing novel evidence of an age-related reduction of hemispheric asymmetry in older adults. The results of this thesis provide evidence of a highly complex set of factors that underlie spatial attention asymmetries in healthy young and older adults.

Loading and damage modelling for an integrated SHM system of a prestressed concrete railway bridge in Naples

The thesis explores recent technology developments in the field of structural health monitoring and its application to railway bridge projects. It focuses on two main topics. First, service loads and effect of environmental actions are modelled. In particular, the train moving load and its interaction with rail track is considered with different degrees of detail. Hence, results are compared with real-time experimental measurements. Secondly, the work concerns the identification, definition and modelling process of damages for a prestressed concrete railway bridge, and their implementation inside FEM models. Along with a critical interpretation of the in-field measurements, this approach results in the development of undamaged and damaged databases for the AI-aided detection of anomalies and the definition of threshold levels to prompt automatic alert interventions. In conclusion, an innovative solution for the development of the railway weight-in-motion system is proposed.

Convergence Of A Specialized Root Trait In Plants From Nutrient-impoverished Soils: Phosphorus-acquisition Strategy In A Nonmycorrhizal Cactus.

In old, phosphorus (P)-impoverished habitats, root specializations such as cluster roots efficiently mobilize and acquire P by releasing large amounts of carboxylates in the rhizosphere. These specialized roots are rarely mycorrhizal. We investigated whether Discocactus placentiformis (Cactaceae), a common species in nutrient-poor campos rupestres over white sands, operates in the same way as other root specializations. Discocactus placentiformis showed no mycorrhizal colonization, but exhibited a sand-binding root specialization with rhizosheath formation. We first provide circumstantial evidence for carboxylate exudation in field material, based on its very high shoot manganese (Mn) concentrations, and then firm evidence, based on exudate analysis. We identified predominantly oxalic acid, but also malic, citric, lactic, succinic, fumaric, and malonic acids. When grown in nutrient solution with P concentrations ranging from 0 to 100 μM, we observed an increase in total carboxylate exudation with decreasing P supply, showing that P deficiency stimulated carboxylate release. Additionally, we tested P solubilization by citric, malic and oxalic acids, and found that they solubilized P from the strongly P-sorbing soil in its native habitat, when the acids were added in combination and in relatively low concentrations. We conclude that the sand-binding root specialization in this nonmycorrhizal cactus functions similar to that of cluster roots, which efficiently enhance P acquisition in other habitats with very low P availability.

The use of fluorescence in situ hybridization in the diagnosis of hidden mosaicism: apropos of three cases of sex chromosome anomalies

FISH has been used as a complement to classical cytogenetics in the detection of mosaicism in sex chromosome anomalies. The aim of this study is to describe three cases in which the final diagnosis could only be achieved by FISH. Case 1 was an 8-year-old 46,XY girl with normal female genitalia referred to our service because of short stature. FISH analysis of lymphocytes with probes for the X and Y centromeres identified a 45,X/46,X,idic(Y) constitution, and established the diagnosis of Turner syndrome. Case 2 was a 21-month-old 46,XY boy with genital ambiguity (penile hypospadias, right testis, and left streak gonad). FISH analysis of lymphocytes and buccal smear identified a 45,X/46,XY karyotype, leading to diagnosis of mixed gonadal dysgenesis. Case 3 was a 47,XYY 19-year-old boy with delayed neuromotor development, learning disabilities, psychological problems, tall stature, small testes, elevated gonadotropins, and azoospermia. FISH analysis of lymphocytes and buccal smear identified a 47,XYY/48,XXYY constitution. Cases 1 and 2 illustrate the phenotypic variability of the 45,X/46,XY mosaicism, and the importance of detection of the 45,X cell line for proper management and follow-up. In case 3, abnormal gonadal function could be explained by the 48,XXYY cell line. The use of FISH in clinical practice is particularly relevant when classical cytogenetic analysis yields normal or uncertain results in patients with features of sex chromosome aneuploidy. Arq Bras Endocrinol Metab. 2012;56(8):545-51