An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

This paper presents an effective method for the detection of a fingerprint’s reference point by analyzing fingerprint ridges’ curvatures. The proposed approach is a multi-stage system. The first step extracts the fingerprint ridges from an image and transforms them into chains of discrete points. In the second step, the obtained chains of points are processed by a dedicated algorithm to detect corners and other points of highest curvature on their planar surface. In a series of experiments we demonstrate that the proposed method based on this algorithm allows effective determination of fingerprint reference points. Furthermore, the proposed method is relatively simple and achieves better results when compared with the approaches known from the literature. The reference point detection experiments were conducted using publicly available fingerprint databases FVC2000, FVC2002, FVC2004 and NIST

A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic

The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of biometric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

Elastin-like Polypeptide Linkers for Single-Molecule Force Spectroscopy

Single-molecule force spectroscopy (SMFS) is by now well established as a standard technique in biophysics and mechanobiology. In recent years, the technique has benefitted greatly from new approaches to bioconjugation of proteins to surfaces. Indeed, optimized immobilization strategies for biomolecules and refined purification schemes are being steadily adapted and improved, which in turn has enhanced data quality. In many previously reported SMFS studies, poly(ethylene glycol) (PEG) was used to anchor molecules of interest to surfaces and/or cantilever tips. The limitation, however, is that PEG exhibits a well-known trans-trans-gauche to all-trans transition, which results in marked deviation from standard polymer elasticity models such as the worm-like chain, particularly at elevated forces. As a result, the assignment of unfolding events to protein domains based on their corresponding amino acid chain lengths is significantly obscured. Here, we provide a solution to this problem by implementing unstructured elastin-like polypeptides as linkers to replace PEG. We investigate the suitability of tailored elastin-like polypeptides linkers and perform direct comparisons to PEG, focusing on attributes that are critical for single-molecule force experiments such as linker length, monodispersity, and bioorthogonal conjugation tags. Our results demonstrate that by avoiding the ambiguous elastic response of mixed PEG/peptide systems and instead building the molecular mechanical systems with only a single bond type with uniform elastic properties, we improve data quality and facilitate data analysis and interpretation in force spectroscopy experiments. The use of all-peptide linkers allows alternative approaches for precisely defining elastic properties of proteins linked to surfaces

Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet

Multiplexed single molecule observation and manipulation of engineered biomolecules

Molecular processes in organisms are often enabled by structural elements resilient to mechanical forces. For instance, the microbial and hierarchical cellulosome protein system comprises enzymes and the receptor-ligand complexes Cohesin-Dockerin (Coh-Doc), that act in concert for the efficient hydrolysis of plant polysaccharides. The Coh-Doc complexes can withstand remarkably high forces to keep host cells and enzymes bound to their substrates in the extreme environmental conditions the microorganisms frequently live in. This work focuses on the investigation of mechanical stability of such biomolecules on the single-molecule level. The highly symmetric binding interface of the Coh-Doc type I complex from Clostridium thermocellum, enables two different binding conformations withcomparable affinity and similar strength. I was able to show that both conformations exist in the wild-type molecules and are occupied under native conditions. I further characterized one of the strongest non-covalent protein complexes known, Coh-Doc type III from Ruminococcus flavefaciens by elucidating the pivotal role of the adjacent xModule domain for the mechanical stabilization of the whole complex and the role of the bimodal rupture force distribution. Such large forces impair accuracy of measured contour length increments in unfolding studies by inducing conformational changes in poly-ethylene glycol (PEG) linkers in aqueous buffer systems. This problemwas solved by introducing elastin-like polypeptides (ELP) as surface tethers. Having a peptide backbone similar to that of unfolded proteins, ELP linkers do not alter accuracy of the single-molecule force spectroscopy (SMFS) assay. To provide high throughput and precise comparability, I worked on a microfluidic platform for the in vitro protein synthesis and immobilization. The Coh-Doc system was hereby integrated as a binding handle for multiplexed measurements of mechanostability. Employing a single AFM probe to measure multiple different molecules facilitates force precision required to shed light onto molecular mechanisms down to the level of single amino acids. I also applied the Coh-Doc complex to a purely protein based single-molecule cut and paste assay for the bottom-up assembly of molecular systems for quick phenotyping of spatial arrangements. With this system, interactions in enzymatic synergies can be studied by defined positioning patterns on the single molecule level. To understand and design force responses of complex systems, I complemented the investigation of protein systems with SMFS studies on DNA Origami structures. The results of SMFS on DNA were compared to a simulation framework. Despite their difference in force loading rates, both methods agree well within their results, enabling better fundamental understanding of complex molecular superstructures.Molekulare Prozesse in Organismenwerden oft von Strukturelementen ermöglicht, die mechanischen Kräften standhalten können. Ein Beispiel hierfür ist das mikrobielle und hierarchisch aufgebaute Proteinsystem des Zellulosoms. Enzyme und die Rezeptor-Liganden Komplexe Cohesin-Dockerin (Coh-Doc) arbeiten hierbei für die effiziente Hydrolyse von pflanzlichen Polysacchariden zusammen. Die Coh-Doc Komplexe können bemerkenswerten Kräften standhalten, um in den extremen Umweltbedingungen, in denen die Mikroorganismen teilweise leben, die Wirtszellen und Enzyme an ihre Substrate binden zu können. Die vorliegende Arbeit untersucht den Einfluss von mechanischer Kraft auf solche Biomoleküle mittels Einzelmolekülmessungen. Die hohe Symmetrie des Bindeinterfaces des Coh-Doc Typ I Komplexes aus Clostridium thermocellum ermöglicht zwei verschiedene Konformationen, die vergleichbare Affinität und Stärke aufweisen. Im Rahmen dieser Arbeit konnte ich beide in denWildtyp-Molekülen und unter nativen Bedingungen nachweisen. Eines der stärksten bekannten nicht-kovalenten Rezeptor-Liganden Systeme, Coh- Doc Typ III aus Ruminococcus flavefaciens wurde charakterisiert, und die Kernrolle des benachbarten xModuls für die Stabilität des gesamten Komplexes sowie die Rolle der bimodalen Kraftverteilung untersucht. Solch hohe Kräfte vermindern die Genauigkeit der gemessenenKonturlängeninkremente von Proteinentfaltungen, indem sie Konformationsänderungen der Poly- Ethylenglykol (PEG) Oberflächenanker in wässrigen Puffersystemen verursachen. Mit Elastin-ähnlichen Polypeptiden (ELP) als Anker wurde dieses Problem gelöst: durch die Ähnlichkeit des Peptid-Rückgrates von ELPs mit dem entfaltener Proteine beeinflussen diese die Genauigkeit des Experiments nicht. Für die Optimierung von Messdurchsatz und Vergleichbarkeit entwickelte ich an einer Mikrofluidik-Plattform zur in vitro Proteinsynthese und -immobilisierung. Das Coh-Doc System wurde hierbei als Binde-Molekül für gemultiplexte Messungen integriert. Die dadurch ermöglichte Nutzung einer einzigen AFM Messsonde für die Messung verschiedener Moleküle erlaubt die nötige Kraftpräzision, um molekulare Mechanismen bis auf die Ebene einzelner Aminosäuren aufzuklären. Des weiteren habe ich den Coh-Doc Komplex in einem rein auf Proteininteraktionen basierten ’Cut and Paste’ Assay für den modularen Aufbau molekularer Systeme implementiert. Dieses ermöglicht schnelle Phänotypisierung geometrischer Anordnunungen und die Untersuchung von Wechselwirkung zwischen Enzymen mittels definierter Positionierung auf Einzelmolekülebene. Um die Kraftantwort komplexer Systeme besser verstehen und letztendlich gestalten zu können, ergänzte ich die Untersuchung von Proteinsystemen mit derer von DNA-Origami Strukturen. Die Ergebnisse der Kraftspektroskopie an DNA wurden mit Computersimulationen verglichen, und trotz des großen Unterschieds ihrer Ladungsraten stimmen beide Methoden gut überein. Dadruch legen sie die Grundlagen für ein besseres Verständnis komplexer molekularer Superstrukturen

Damage and repair identification in reinforced concrete beams modelled with various damage scenarios using vibration data

This research aims at developing a novel vibration-based damage identification technique that can efficiently be applied to real-time large data for detection, classification, localisation and quantification of the potential structural damage