My master's thesis deals with the different techniques used in fingerprints processing for identifying fingerprints. Using the software tool Visual C++ and functions of OpenCV library I programmed a separate application, that is able to select from a database of fingerprints the most consistent with a comparative fingerprint images, even when they are mutually shifted in the direction of axes X and Y. The next step in my program is to gather the edges of the fingerprint image. Those obtained using Canny edge detector. Furthermore, getting the contours of the image edges. To determine, whether the contours are the same, just compare some characteristic points of contours. Next I use a histogram function to determine the number of points for approximation of contours and evaluating compliance fingerprints. Since the processing of the input fingerprint image (or rather the approximation of the contour points) remains in the picture as black (background) and red (the approximation of the contour points), this means, that zero and the last element of the histogram represent the number of black and red points. Comparison is in percentage and is obtained by subtracting the approximated points of contours image from the original fingerprint image of approximated contour points of matched fingerprints. It determined, what percentage of red points have disappeared, so as to match two fingerprint images. If on the resulting figure is not left neither a red point, that corresponds to 100% of the fingerprints Compliance.

Cílem této práce je vyvinout metody simulací poškozování otisků prstů. V první části je kladen důraz na shrnutí stávajících znalostí v oblasti generování syntetických otisků prstů a jejich poškozování. Dále jsou uvedeny informace o otiscích prstů obecně, jejich rozpoznávání a vlivy, které otisky poškozují, včetně onemocnění kůže. Práce obsahuje návrh a implementaci aplikace SyFDaS pro generování a modulární poškozování otisků prstů. Další částí je popis metod pro poškozování vlivem průtahového režimu, zúženého snímače, poškozeného snímače, přítlaku a vlhkosti, zkreslení pokožky, bradavic, atopického ekzému a lupénky. Dále je analyzováno několik dalších typů poškození včetně falzifikátů otisků prstů. Celkově je uvedeno 43 základních poškození, která jsou vizuálně verifikována. Díky kombinování poškození je využito 1 171 typů poškození a vygenerováno 348 300 obrázků otisků prstů, které jsou vyhodnoceny čtyřmi různými metodami posuzování kvality.

The main goal of this thesis is to introduce the meaning of identification and specific meaning of personal identification and mainly fingerprint identification. There is introduction to the meaning of biometrics as a key field for fingerprint recognition. Thesis also introduces principals and types of automatic processing and automatic detection of fingerprints. An existing and succesful methods are being described in the thesis and an automatic fingerprint identification alghoritm is being realized and described. Reability of the alghoritm is tested within experimental database.

The main goal of this thesis is to introduce the meaning of term identification and term biometrics as a key field for fingerprint recognition. Thesis also introduces principals and types of automatic processing of fingerprint images and automatic fingerprints identification algorithms. An existing and successful methods are being described in the thesis and pre-processing of fingerprint images is being realized. Automatic fingerprint identification algorithm based on distance of minutiae from the core, core type and orientation field of the fingerprint is proposed. The proposed algorithm was tested on a database of fingerprint images and was statistically analysed.

This master's thesis deals with fingerprint verification. The theoretical part consist of biometry identification systems and evaluating their reliability and robustness. After that we focus on fingerprints properties needed to identification. We mention several types of fingerprint sensors, which are generaly in public use. In practical part of thesis we deal with enhancement of fingeprint image and methods of identifications. At last we created software for fingeprint identification in programming environment Matlab.

In the investigation of crimes committed by wireless users, one of the key goals is to determine the location of the mobile device at the time of the crime. Since this happens during the investigative phase after the crime is committed, we term this the post hoc geographical localization estimation problem. In this thesis, we introduce the post hoc geographical localization estimation problem and present approaches for its solution based on radio frequency (RF) fingerprinting. Motivated by the goal of establishing a crime's location with enough accuracy to obtain a search warrant, our focus is on locating a criminal mobile device in indoor environments with roughly the granularity to distiguish between two adjacent rooms, without having the ability to enter those rooms or the building to gather input data for the RF fingerprinting algorithm. While empirical performance studies of instantaneous indoor positioning systems based radio frequency (RF) fingerprinting have been presented in the literature, the core of this thesis is the first empirical study focused on the post hoc version of problem from the viewpoint of digital forensics. In this study, we set up experiments in a residential area and collect a large set of raw data in order to analyze and evaluate the algorithms, the best of which provides a mean error distance of roughly 1.4 meters. In addition, we consider enhancements to the baseline algorithms if knowledge of the blueprint of the building is available. In particular, we consider whether compensating the raw data for the attenuation caused by walls can improve algorithm performance.

The catalysis reaction performance, such as yield, is very crucial in organic chemistry. And predicting the reaction yield is still very challenging. In this thesis, machine learning is used to predict the reaction yield in a C–N cross-coupling approach. The reaction data are from the high-throughput experimental data with four variables: reactants, Pd catalysts, additives, and bases. Each reaction data will give the corresponding yield. The data are from the literature, which has been uploaded. The total data number used in machine learning is 7910. The method mainly consists of four steps. First, load the csv data and import modules. Second, encode data with molecular fingerprint or one-hot encoding. The data will be normalized if there is need. Third, split the dataset into train and test set with the size ratio of 7/3 or 8/2. Fourth, use six machine learning models to learn the data and evaluate their performance. Then, compare the prediction yield of the test set. The accuracy in prediction (RMSE value and R-squared) and running time will be considered for evaluation. By comparing the RMSE and R-squared values of different models, we can decide which one has better performance and better fitting results. Improved reaction performance, or high-performance catalysts and their characteristics may be obtained.

The most common personal authentication techniques used for identity management employ a secret PIN or password that must be remembered. The challenge, for a given user, is that a multitude of such codes must be recalled over the course of the day for transactions involving distinct computer applications. Password mania prevails. Fingerprint biometric technology is an ideal alternate solution to this password recall problem. In spite of their availability for nearly thirty years, fingerprint biometric systems still remain uncommon in public sectors of industry such as education, government, and technology. Technology has improved sufficiently that false acceptance and rejection rates are no longer valid excuses. Two proposed reasons for this lack of deployment are 1) society's misunderstanding regarding the personal privacy, security, and function of the technology; and 2) inadequate education regarding the technology. This present research was structured to test these hypotheses, and attempt to identify the major societal factors that have limited fingerprint biometric eployment in IT authentication systems. Three research approaches regarding acceptance of fingerprint biometric technology by targeted populations were used in this study, namely 1) a personal survey, 2) a personal training exercise, and 3) a web-based survey. Targeted populations included the general public in the State of Utah and its legislative members who made decisions regarding identity management legislation for state departmental functions. Objectives of this research included gaining a better understanding of 1) legislator's perceptions of why past legislation was rejected, and 2) the public's perception of the personal security of the technology. An additional objective was the confirmation that proper education on security issues improves personal confidence in and acceptance of fingerprint biometric technology.

Wireless networks are vulnerable to adversarial devices by spoofing the digital identity of valid wireless devices, allowing unauthorized devices access to the network. Instead of validating devices based on their digital identity, it is possible to use their unique "physical fingerprint" caused by changes in the signal due to deviations in wireless hardware. In this thesis, the physical fingerprint was validated by performing classification with complex-valued neural networks (NN), achieving a high level of accuracy in the process. Additionally, zero-shot learning (ZSL) was implemented to learn discriminant features to separate legitimate from unauthorized devices using outlier detection and then further separate every unauthorized device into their own cluster. This approach allows 42\% of unauthorized devices to be identified as unauthorized and correctly clustered

This is a research focusing on stimulating the comfort and safety when it comes to payment for travelling with the trains and the busses in the Swedish county Skane. The question at the issue is; can we create a system that doesn’t require the responsibility of carrying a particular tool? Can we give the passengers the possibility to control their accounts to register in a more independent way than today with Skanetrafiken? And can this be done by using fingerprint technology? My goal is mainly to observe today’s monthly and discount-card system in Skane and around the world, discover the disadvantages and design a new system as solution. The new payment system is called SmartExpress. It involves fingerprint recognition and will hopefully fit in today’s technology and have useful possibilities that will come in handy for the next generations.