Matrix assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOFMS) is used to study several inorganic coordination complexes and a variety of compounds from cyanobacteria. Also presented is a discussion of TOFMS instrumentation and the improvements in resolution and instrument automation that have lead to widespread and diverse applications of MALDI-TOFMS in all areas of science. The feasibility of using direct laser desorption/ionization (LDI) TOFMS to detect trace elements in a variety of glass samples using a lithium borate fusion technique for sample preparation is investigated. The result of the fusion technique is a homogeneous incorporation of the analytical sample into a glass. The fusion technique is investigated as a way to eliminate matrix effects in direct LDI-TOFMS analysis. However, the high concentration and low ionization potential of lithium suppress ionization of the elements of interest. The detection limits of elements in glass samples were not at the trace level. Therefore the technique is not as useful as well-established analytical methods like X-ray fluorescence and inductively coupled plasma mass spectrometry. Direct laser ablation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis of four inorganic coordination complexes are discussed. The compounds studied include [Ir(dpp)2Cl2](PF6), {[(bpy)2Ru(dpp)]2RuCl2}(PF6)4, [(tpy)Ru(tpp)Ru(tpp)RhCl3](PF6)4 and {[(bpy)2Ru(dpp)]2IrCl2}(PF6)5 (dpp = 2,3-bis-(2'-pyridyl)-pyrazine, bpy = 2,2'-bipyridine, tpy = 2,2',6',2"-terpyradine, tpp = 2,3,5,6,-tetrakis-(2'-pyridyl)-pyrazine). Spectral intensities and fragmentation patterns are compared and evaluated for several instrument parameters, matrices, and matrix-to-analyte ratios. Direct ablation and MALDI mass spectra of the monometallic complex showed the same ion peaks and differed only in the relative peak intensities. Direct ablation of the trimetallic complexes produced only low-mass fragments containing one metal atom at most. MALDI spectra of the trimetallic complexes exhibited little fragmentation in the high-mass region (>1500 Da) and less fragmentation in the low-mass region compared to direct laser ablation. Proper matrix selection for MALDI analysis was vital, as was an appropriate matrix-to-analyte ratio. The results demonstrate the applicability of MALDI-TOF mass spectrometry for the structural characterization of labile inorganic coordination complexes. A correlation is made between the gas-phase redox chemistry in the MALDI plume and the solution phase electrochemistry for this series of complexes. MALDI-TOFMS was also used to study compounds isolated from cyanobacteria. A MALDI screening method has been developed to detect the presence of scytonemin, a UV-absorbing pigment. Detection of scytonemin is accomplished by a simple solvent extraction of cyanobacteria in the desiccated state with subsequent MALDI-TOFMS analysis. The method is rapid and semi-quantitative. Cyanobacteria is the only known organism to produce scytonemin, and it is only produced when the organism is subjected to UV stress. Laboratory-grown cultures were subjected to different amounts of UV radiation, and the screening method was used to detect the presence or absence of scytonemin. Cultures grown under ambient conditions (low UV) did not show the presence of scytonemin, while those grown under UV lamps did show the detectable scytonemin. Because scytonemin acts as a biomarker for UV stress, the MALDI screening method could find application in molecular ecology studies of cyanobacteria. Peptide mass fingerprinting is used to monitor the isolation of the water stress protein from N. commune. The protein is produced by recombinant growth in E. coli in order to assess the role of Wsp in the desiccation tolerance of N. commune. The results show that SDS-PAGE and Western blot analysis are not sufficient to detect the presence of Wsp after purification using ion-exchange chromatography. Three E. coli proteins were identified in the same molecular weight range as Wsp and one of them cross-reacts with the series of antibodies used for the Western blot. The presence of contaminating proteins that cross-react with the immuno assay make it difficult to determine which fractions contained Wsp. Peptide mass fingerprints were obtained for a series of fractions collected after ion-exchange chromatography to pinpoint the location of Wsp. Peptide mass fingerprinting was also used to monitor the stability of the clone and results show that the clone is modified over a six month period. / Ph. D.

Unlike the conventional "lock-and-key" sensor design in which one sensor is finely tuned to respond to one analyte, the sensor array approach employs multiple sensors in which one sensor responds to many analytes. Consequently, signal processing algorithms must be used to identify the analyte present from the array's response. The analyte identification process becomes significantly more complicated when a mixture of analytes is presented to the sensor array. Conventional methods that are employed in gas mixture identification are plagued by several design issues like: complexity, scalability, and flexibility. This thesis derives and develops a novel method, fingerprint-based ICA (FICA), to extract and identify individual analytes from a sensor array's response to a gas mixture of the analytes. FICA is a simple, flexible, and scalable signal processing system that employs independent components analysis (ICA) to extract and identify individual analytes present in a gas mixture; separation and identification of gas mixtures using ICA has not been investigated previously. FICA takes a fundamentally different approach that reflects the underlying property of gas mixtures: gas mixtures are composed of individual analyte responses. Conventional signal processing methods that identify gas mixtures have been developed and implemented in this work; this helps us understand the drawbacks in the conventional approach. FICA's performance is compared to the performance of conventional methods using metric like error rate and false positives rate. Properties like flexibility, scalability, and the data requirements for both conventional methods and FICA are examined. Results obtained in this work indicates that FICA results in lower error rates, and it's performance is better than conventional methods like multi-stage multi-stage support vector machines, and PCR. Furthermore, FICA provides the most simple, scalable, and flexible signal processing system. / Master of Science

Identification of an individual from discriminating features of the friction ridge surface is one of the oldest and most commonly used biometric techniques. Methods for identification span from tedious, although highly accurate, manual examination to much faster Automated Fingerprint Identification Systems (AFIS). While automatic fingerprint recognition has grown in popularity due to the speed and accuracy of matching minutia features of good quality plain-to-rolled prints, the performance is less than impressive when matching partial fingerprints. For some applications, including forensic analysis where partial prints come in the form of latent prints, it is not always possible to obtain high-quality image samples. Latent prints, which are lifted from a surface, are typically of low quality and low fingerprint surface area. As a result, the overlapping region in which to find corresponding features in the genuine matching ten-print is reduced; this in turn reduces the identification performance. Image quality also can vary substantially during image capture in applications with a high throughput of subjects having limited training, such as in border control. The rushed image capture leads to an overall acceptable sample being obtained where local image region quality may be low. We propose an improvement to the reliability of features detected in exemplar prints in order to reduce the likelihood of an unreliable overlapping region corresponding with a genuine partial print. A novel approach is proposed for detecting minutiae in low quality image regions. The approach has demonstrated an increase in match performance for a set of fingerprints from a well-known database. While the method is effective at improving match performance for all of the fingerprint images in the database, a more significant improvement is observed for a subset of low quality images. In addition, a novel method for fingerprint analysis using a sequence of fingerprint images is proposed. The approach uses the sequence of images to extract and track minutiae for temporal analysis during a single impression, reducing the variation in image quality during image capture. Instead of choosing a single acceptable image from the sequence based on a global measure, we examine the change in quality on a local level and stitch blocks from multiple images based on the optimal local quality measures. / Ph. D.

Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today. Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matching features from fingerprint images with poor quality. Images where the fingerprint pattern is heavily degraded usually inhibit the performance of an AFIS system. The performance of AFIS systems is also reduced when matching fingerprints of individuals with large age variations. This doctoral thesis presents contributions within the field of fingerprint image enhancement, segmentation and minutiae detection. The reliability of the extracted fingerprint features is highly dependent on the quality of the obtained fingerprints. Unfortunately, it is not always possible to have access to high quality fingerprints. Therefore, prior to the feature extraction, an enhancement of the quality of fingerprints and a segmentation are performed. The segmentation separates the fingerprint pattern from the background and thus limits possible sources of error due to, for instance, feature outliers. Most enhancement and segmentation techniques are data-driven and therefore based on certain features extracted from the low quality fingerprints at hand. Hence, different types of processing, such as directional filtering, are employed for the enhancement. This thesis contributes by proposing new research both for improving fingerprint matching and for the required pre-processing that improves the extraction of features to be used in fingerprint matching systems. In particular, the majority of enhancement and segmentation methods proposed herein are adaptive to the characteristics of each fingerprint image. Thus, the methods are insensitive towards sensor and fingerprint variability. Furthermore, introduction of the higher order statistics (kurtosis) for fingerprint segmentation is presented. Segmentation of the fingerprint image reduces the computational load by excluding background regions of the fingerprint image from being further processed. Also using a neural network to obtain a more robust minutiae detector with a patch rejection mechanism for speeding up the minutiae detection is presented in this thesis.

This dissertation focuses on person identification problems and proposes solutions to overcome those problems. First part is about fingerprint features extraction algorithm performance evaluation. Modifications to a known synthesis algorithm are proposed to make it fast and suitable for performance evaluation. Matching of deformed fingerprints is discussed in the second part of the work. New fingerprint matching algorithm that uses local structures and does not perform fingerprint alignment is proposed to match deformed fingerprints. The use of group delay features of linear prediction model for speaker recognition is proposed in the third part of the work. New similarity metric that uses group delay features is described. It is demonstrated that automatic speaker recognition system with proposed features and similarity metric outperforms traditional speaker identification systems . Multibiometrics using fingerprints and voice is addressed in the last part of the dissertation. / Penkiose disertacijos darbo dalyse nagrinėjamos žmogaus identifikavimo pagal pirštų atspaudus ir balsą problemos ir siūlomi jų sprendimai. Pirštų atspaudų požymių išskyrimo algoritmų kokybės įvertinimo problemą siūloma spręsti panaudojant sintezuotus pirštų atspaudus. Darbe siūlomos žinomo pirštų atpaudų sintezės algoritmo modifikacijos, kurios leidžia sukurti piršto atspaudo vaizdą su iš anksto nustatytomis charakteristikomis ir požymiais bei pagreitina sintezės procesą. Pirštų atspaudų požymių palyginimo problemos yra aptartos ir naujas palyginimo algoritmas yra siūlomas deformuotų pirštų palyginimui. Algoritmo kokybė yra įvertinta ant viešai prieinamų ir vidinių duomenų bazių. Naujas asmens identifikavimo pagal balsą metodas remiantis tiesinės prognozės modelio grupinės delsos požymiais ir tų požymių palyginimo metrika kokybės prasme lenkia tradicinius asmens identifikavimo pagal balsą metodus. Pirštų ir balso įrašų nepriklausomumas yra irodytas ir asmens atpažinimas pagal balsą ir pirštų atspaudus kartu yra pasiūlytas siekiant išspręsti bendras biometrinių sistemų problemas.

Proton exchange membrane fuel cells (PEMFC) are attractive energy sources for power trains in vehicles because of their low operating temperature that enables fast start-up and high power densities. Cost reduction and durability are the key issues to be solved before PEMFCs can be successfully commercialized. The major portion of fuel cell cost is associated with the catalyst layer which is typically comprised of carbon-supported Pt and ionomer. The degradation of the catalyst layer is one of the major failure modes that can cause voltage degradation and limit the service life of the fuel cell stack during operation. To develop a highly durable and better performing catalyst layer, topics such as the causes for the degradation, modes of failure, different mechanisms and effect of degradation on fuel cell performance must be studied thoroughly. Key degradation modes of catalyst layer are carbon corrosion and Pt dissolution. These two modes change the electrode structure in the membrane electrode assembly (MEA) and result in catalyst layer thinning, CO2 evolution, Pt deposition in the membrane and Pt agglomeration. Alteration of the electrode morphology can lead to voltage degradation. Accelerated stress tests (ASTs) which simulate the conditions and environments to which fuel cells are subject, but which can be completed in a timely manner, are commonly used to investigate the degradation of the various components. One of the current challenges in employing these ASTs is to relate the performance loss under a given set of conditions to the various life-limiting factors and material changes. In this study, various degradation modes of the cathode catalyst layer are isolated to study their relative impact on performance loss ‗Fingerprints‘ of identifiable performance losses due to carbon corrosion are developed for MEAs with 0.4 mg cm−2 cathode platinum loadings. The fingerprint is used to determine the extent of performance loss due to carbon corrosion and Pt dissolution in cases where both mechanisms operate. This method of deconvoluting the contributions to performance loss is validated by comparison to the measured performance losses when the catalyst layer is subjected to an AST in which Pt dissolution is predominant. The limitations of this method iv are discussed in detail. The developed fingerprint suggests that carbon loss leading to CO2 evolution during carbon corrosion ASTs contributes to performance loss of the cell. A mechanistic model for carbon corrosion of the cathode catalyst layer based on one appearing in the literature is developed and validated by comparison of the predicted carbon losses to those measured during various carbon corrosion ASTs. Practical use of the model is verified by comparing the predicted and experimentally observed performance losses. Analysis of the model reveals that the reversible adsorption of water and subsequent oxidation of the carbon site onto which water is adsorbed is the main cause of the current decay during ASTs. Operation of PEM fuel cells at higher cell temperatures and lower relative humidities accelerates Pt dissolution in the catalyst layer during ASTs. In this study, the effects of temperature and relative humidity on MEA degradation are investigated by applying a newly developed AST protocol in which Pt dissolution is predominant and involves the application of a potentiostatic square-wave pulse with a repeating pattern of 3s at 0.6 V followed by 3s at 1.0 V. This protocol is applied at three different temperatures (40°C, 60°C and 80°C) to the same MEA. A diagnostic signature is developed to estimate kinetic losses by making use of the effective platinum surface area (EPSA) obtained from cyclic voltammograms. The analysis indicates that performance degradation occurs mainly due to the loss of Pt in electrical contact with the support and becomes particularly large at 80°C. This Pt dissolution AST protocol is also investigated at three different relative humidities (100%, 50% and 0%). Electrochemical impedance spectroscopy measurements of the MEAs show an increase in both the polarization and ohmic resistances during the course of the AST. Analysis by cyclic voltammetry shows a slight increase in EPSA when the humidity increases from 50% to 100%. The proton resistivity of the ionomer measured by carrying out impedance measurements on MEAs with H2 being fed on the anode side and N2 on the cathode side is found to increase by the time it reaches its end-of-life state when operated under 0 % RH conditions.

Our purpose with this thesis was to find biometric methods that can be used in access control of mobile access. The access control has two parts. Firstly, to validate the identity of the caller and, secondly, to ensure the validated user is not changed during the session that follows. Any solution to the access control problem is not available today, which means that anyone can get access to the mobile phone and the Internet. Therefore we have researched after a solution that can solve this problem but also on how to secure that no one else can take over an already validated session. We began to search for biometric methods that are available today to find them that would be best suited together with a mobile phone. After we had read information about them we did choose three methods for further investigation. These methods were Fingerprint Recognition, Iris Scan and Speaker Verification. Iris Scan is the method that is best suited to solve the authentication problem. The reasons for this are many. One of them is the uniqueness and stability of the iris, not even identical twins or the pair of the same individual has the same iris minutiae. The iris is also very protected behind eyelids, cornea and the aqueous humor and therefore difficult to damage. When it comes to the method itself, is it one of the most secure methods available today. One of the reasons for this is that the equal error rate is better than one in a million. However, this rate can be even better. It all depends on the Hamming Distance, which is a value that show how different the saved and temporarily template are, and what it is set to. To solve our session authentication, which was to make sure that no one else could take over a connected mobile phone, a sensor plate is the answer. This sensor will be able to sense for touch, heat and pulse. These three sensor measurements will together secure a validated session since the mobile phone will disconnect if the sensor looses its sensor data. There are, however, technological and other challenges to be solved before our proposed solutions will become viable. We address some of these issues in our thesis.

<p>In this study, any change in fingerprint performance, image quality and minutiae count for infants in three different age groups was evaluated (0-6, 7-12, and >12 months). This was done to determine whether there is a difference in performance between infant age groups for a fingerprint recognition system.</p> <p>The purpose of this research was to determine whether there is a difference in infant fingerprint performance and image quality metrics, between three different age groups (0-6, 7-12, and >12 months old), using the same optical sensor? The data used for this secondary analysis was collected as part of a longitudinal multimodal infant study, using the Digital Persona U.are.U 4500. DET curves, zoo analysis, and image quality metrics were used to evaluate performance and quality factored by infant age group.</p><p>This study found that there was a difference in image quality and minutiae count, genuine and impostor match scores, and performance error rates (EER) between the three age groups. Therefore, quality and performance were dependent on age. While there was a difference in performance between age groups, there was generally stability for subjects who overlapped between multiple age groups. Difference in performance was most likely due to the difference in physical characteristics between subjects in each age group, rather than individual instability. The results showed that it could potentially be feasible to use fingerprint recognition for children over the age of 12 months.</p>

The research was prompted by constant criticism of the Independent Electoral Commission (IEC) because of its poor voter registration and voters’ roll. Free and fair elections are the strong arm of democracy. The identified research problem was investigated by means of qualitative research. A thorough literature study from various sources of information, a case file analysis and structured interviews were used to gather the required data. The interviews were conducted with fingerprint experts, a data processor and constituency electoral assistants from the Lesotho Mounted Police Service, the National Identity and Civil Registry at the Ministry of Home Affairs and the IEC respectively. It was found that using fingerprints and the Automated Fingerprint Identification System (AFIS) can assist in investigations and in combating voter’s fraud, as fingerprints are unique. AFIS can be utilised for various purposes by institutions in the private and public sectors. Like any apparatus, AFIS has its own strengths and weaknesses. / Criminology and Security Science / M. Tech. (Forensic investigation)

Penkiose disertacijos darbo dalyse nagrinėjamos asmens identifikavimo pagal pirštų atspaudus ir balsą problemos ir siūlomi jų sprendimai. Pirštų atspaudų požymių išskyrimo algoritmų kokybės įvertinimo problemą siūloma spręsti panaudojant sintezuotus pirštų atspaudus. Darbe siūlomos žinomo pirštų atpaudų sintezės algoritmo modifikacijos, kurios leidžia sukurti piršto atspaudo vaizdą su iš anksto nustatytomis charakteristikomis ir požymiais bei pagreitina sintezės procesą. Pirštų atspaudų požymių palyginimo problemos yra aptartos ir naujas palyginimo algoritmas yra siūlomas deformuotų pirštų palyginimui. Algoritmo kokybė yra įvertinta naudojant viešai prieinamas ir vidines duomenų bazes. Naujas asmens identifikavimo pagal balsą metodas remiantis tiesinės prognozės modelio grupinės delsos požymiais ir tų požymių palyginimo metrika kokybės prasme lenkia tradicinius asmens identifikavimo pagal balsą metodus. Pirštų ir balso įrašų nepriklausomumas yra irodytas ir asmens atpažinimas pagal balsą ir pirštų atspaudus kartu yra pasiūlytas siekiant išspręsti bendras biometrinių sistemų problemas. / This dissertation focuses on person identification problems and proposes solutions to overcome those problems. First part is about fingperprint feaures extraction algorithm performance evaluaiton. Modifications to a known synthesis algorithm are proposed to make it fast and suitable for performance evaluation. Matching of deformed fingerprints is discussed in the second part of the work. New fingerprint matching algorithm that uses local structures and does not perform fingerprint alignment is proposed to match deformed fingerprints. The use of group delay features of linear prediciton model for speaker identification is proposed in the third part of the work. New similarity metric that uses group delay features is described. It is demonstrated that automatic speaker identification system with proposed features and similarity metric outperforms traditional speaker identification systems. Multibiometrics using fingerprints and voice is adressed in the last part of the dissertation.