Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms

The identification of people by measuring some traits of individual anatomy or physiology has led to a specific research area called biometric recognition. This thesis is focused on improving fingerprint recognition systems considering three important problems: fingerprint enhancement, fingerprint orientation extraction and automatic evaluation of fingerprint algorithms. An effective extraction of salient fingerprint features depends on the quality of the input fingerprint. If the fingerprint is very noisy, we are not able to detect a reliable set of features. A new fingerprint enhancement method, which is both iterative and contextual, is proposed. This approach detects high-quality regions in fingerprints, selectively applies contextual filtering and iteratively expands like wildfire toward low-quality ones. A precise estimation of the orientation field would greatly simplify the estimation of other fingerprint features (singular points, minutiae) and improve the performance of a fingerprint recognition system. The fingerprint orientation extraction is improved following two directions. First, after the introduction of a new taxonomy of fingerprint orientation extraction methods, several variants of baseline methods are implemented and, pointing out the role of pre- and post- processing, we show how to improve the extraction. Second, the introduction of a new hybrid orientation extraction method, which follows an adaptive scheme, allows to improve significantly the orientation extraction in noisy fingerprints. Scientific papers typically propose recognition systems that integrate many modules and therefore an automatic evaluation of fingerprint algorithms is needed to isolate the contributions that determine an actual progress in the state-of-the-art. The lack of a publicly available framework to compare fingerprint orientation extraction algorithms, motivates the introduction of a new benchmark area called FOE (including fingerprints and manually-marked orientation ground-truth) along with fingerprint matching benchmarks in the FVC-onGoing framework. The success of such framework is discussed by providing relevant statistics: more than 1450 algorithms submitted and two international competitions.

Continuous, Passive Liquid-Liquid Extraction and Emulsion Separation Within Microfluidic and Millifluidic Devices

Liquid-liquid extraction (LLE) is a method used to separate compounds based on their relative activity in two immiscible phases. By significantly reducing the scale of liquid-liquid extraction to the micro- and milli-fluidic levels, this separation process can bemade suitable for low volume, high value materials.

Skin Lesion Extraction And Its Application

In this thesis, I study skin lesion detection and its applications to skin cancer diagnosis. A skin lesion detection algorithm is proposed. The proposed algorithm is based color information and threshold. For the proposed algorithm, several color spaces are studied and the detection results are compared. Experimental results show that YUV color space can achieve the best performance. Besides, I develop a distance histogram based threshold selection method and the method is proven to be better than other adaptive threshold selection methods for color detection. Besides the detection algorithms, I also investigate GPU speed-up techniques for skin lesion extraction and the results show that GPU has potential applications in speeding-up skin lesion extraction. Based on the skin lesion detection algorithms proposed, I developed a mobile-based skin cancer diagnosis application. In this application, the user with an iPhone installed with the proposed application can use the iPhone as a diagnosis tool to find the potential skin lesions in a persons' skin and compare the skin lesions detected by the iPhone with the skin lesions stored in a database in a remote server.

Groundwater Under the Butte Hill - The East Camp Mining System and the Berkeley Pit

This lecture discusses monitoring activities of the Berkeley Pit for the past 31 years at the Montana Bureau of Mines and Geology in Butte, Montana.

Ending Exploitation: Natural Resource Extraction and 1970s Legislature – Tom Towe & Harrison Fagg “In the Crucible of Change”

Abstract to be posted.

A new sensitive method for the quantification of glyoxal and methylglyoxal in snow and ice by stir bar sorptive extraction and liquid desorption-HPLC-ESI-MS

In this study, the development of a new sensitive method for the analysis of alpha-dicarbonyls glyoxal (G) and methylglyoxal (MG) in environmental ice and snow is presented. Stir bar sorptive extraction with in situ derivatization and liquid desorption (SBSE-LD) was used for sample extraction, enrichment, and derivatization. Measurements were carried out using high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). As part of the method development, SBSE-LD parameters such as extraction time, derivatization reagent, desorption time and solvent, and the effect of NaCl addition on the SBSE efficiency as well as measurement parameters of HPLC-ESI-MS/MS were evaluated. Calibration was performed in the range of 1–60 ng/mL using spiked ultrapure water samples, thus incorporating the complete SBSE and derivatization process. 4-Fluorobenzaldehyde was applied as internal standard. Inter-batch precision was <12 % RSD. Recoveries were determined by means of spiked snow samples and were 78.9 ± 5.6 % for G and 82.7 ± 7.5 % for MG, respectively. Instrumental detection limits of 0.242 and 0.213 ng/mL for G and MG were achieved using the multiple reaction monitoring mode. Relative detection limits referred to a sample volume of 15 mL were 0.016 ng/mL for G and 0.014 ng/mL for MG. The optimized method was applied for the analysis of snow samples from Mount Hohenpeissenberg (close to the Meteorological Observatory Hohenpeissenberg, Germany) and samples from an ice core from Upper Grenzgletscher (Monte Rosa massif, Switzerland). Resulting concentrations were 0.085–16.3 ng/mL for G and 0.126–3.6 ng/mL for MG. Concentrations of G and MG in snow were 1–2 orders of magnitude higher than in ice core samples. The described method represents a simple, green, and sensitive analytical approach to measure G and MG in aqueous environmental samples.

Matrix Porewater In Crystalline Rocks: Extraction and Analysis

The chemical and isotopic characterization of porewater residing in the inter- and intragranular pore space of the low-permeability rock matrix is an important component with respect to the site characterization and safety assessment of potential host rocks for a radioactive waste disposal. The chemical and isotopic composition of porewater in such low permeability rocks has to be derived by indirect extraction techniques applied to naturally saturated rock material. In most of such indirect extraction techniques – especially in case of rocks of a porosity below about 2 vol.% – the original porewater concentrations are diluted and need to be back-calculated to in-situ concentrations. This requires a well-defined value for the connected porosity – accessible to different solutes under in-situ conditions. The derivation of such porosity values, as well as solute concentrations, is subject to various perturbations during drilling, core sampling, storage and experiments in the laboratory. The present study aims to demonstrate the feasibility of a variety of these techniques to charac-terize porewater and solute transport in crystalline rocks. The methods, which have been de-veloped during multiple porewater studies in crystalline environments, were applied on four core samples from the deep borehole DH-GAP04, drilled in the Kangerlussuaq area, Southwest Greenland, as part of the joint NWMO–Posiva–SKB Greenland Analogue Project (GAP). Potential artefacts that can influence the estimation of in situ porewater chemistry and isotopes, as well as their controls, are described in detail in this report, using specific examples from borehole DH-GAP04

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

Arsenic speciation in edible alga samples by microwave-assisted extraction and high performance liquid chromatography coupled to atomic fluorescence spectrometry

Twelve commercially available edible marine algae from France, Japan and Spain and the certified reference material (CRM) NIES No. 9 Sargassum fulvellum were analyzed for total arsenic and arsenic species. Total arsenic concentrations were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) after microwave digestion and ranged from 23 to 126 μg g−1. Arsenic species in alga samples were extracted with deionized water by microwave-assisted extraction and showed extraction efficiencies from 49 to 98%, in terms of total arsenic. The presence of eleven arsenic species was studied by high performance liquid chromatography–ultraviolet photo-oxidation–hydride generation atomic–fluorescence spectrometry (HPLC–(UV)–HG–AFS) developed methods, using both anion and cation exchange chromatography. Glycerol and phosphate sugars were found in all alga samples analyzed, at concentrations between 0.11 and 22 μg g−1, whereas sulfonate and sulfate sugars were only detected in three of them (0.6-7.2 μg g−1). Regarding arsenic toxic species, low concentration levels of dimethylarsinic acid (DMA) (<0.9 μg g−1) and generally high arsenate (As(V)) concentrations (up to 77 μg g−1) were found in most of the algae studied. The results obtained are of interest to highlight the need to perform speciation analysis and to introduce appropriate legislation to limit toxic arsenic species content in these food products.

Determination of selected pharmaceutical compounds in biosolids bysupported liquid extraction and gas chromatography?tandem massspectrometry

In this work, an analytical method was developed for the determination of pharmaceutical drugs inbiosolids. Samples were extracted with an acidic mixture of water and acetone (1:2, v/v) and supportedliquid extraction was used for the clean-up of extracts, eluting with ethyl acetate:methanol (90:10, v/v).The compounds were determined by gas chromatography?tandem mass spectrometry using matrix-match calibration after silylation to form their t-butyldimethylsilyl derivatives. This method presentsvarious advantages, such as a fairly simple operation for the analysis of complex matrices, the use ofinexpensive glassware and low solvent volumes. Satisfactory mean recoveries were obtained with thedeveloped method ranging from 70 to 120% with relative standard deviations (RSDs) ? 13%, and limitsof detection between 0.5 and 3.6 ng g?1. The method was then successfully applied to biosolids samplescollected in Madrid and Catalonia (Spain). Eleven of the sixteen target compounds were detected in thestudied samples, at levels up to 1.1 ?g g?1(salicylic acid). Ibuprofen, caffeine, paracetamol and fenofibratewere detected in all of the samples analyzed.

3D Feature Extraction and Modelling for SLAM

Paper submitted to the 39th International Symposium on Robotics ISR 2008, Seoul, South Korea, October 15-17, 2008.