Design and development of a robotic tape applicator : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University

The work described in this thesis is on the design, operation and testing of a programmable adhesive tape applicator 'EziStick'. The system demonstrates a mechatronics system comprised of mechanical, electronic and computer systems. 'EziStick' is capable of identifying any tape edge and then initialising and loading the tape over the applicator foot for successful application of tape. The tape tension monitoring system will allow continuous monitoring of the tape tension during its application. 'EziStick' is currently attached to the end of a robot arm to enhance its work envelope. The system is controlled via a low cost microcontroller and it is highly modular and transportable. 'EziStick' may be attached to the end of any robot (machines) with various degrees of freedom. In this way the cost of the system is adjusted by the complexity of the application. The experimental results showed that there is a relationship between the tape application speed and the quality of its application. Although the current prototype is designed for the application of aluminium tape, tests have indicated that other types of tape can be used in 'EziStick'

I Know It\u27s You: Touch Behavioral Characteristics Recognition on Smartphone Based on Pattern Password

In recent years, pattern password has been widely used for user authentication on smartphones and other mobile devices in addition to the traditional password protection approach. However, pattern password authentication mechanism is incapable of protecting users from losses when a user\u27s login credential information is stolen. We propose an identity verification scheme based on user’s touching behaviors when inputting a pattern password on the smartphone screen. By exploiting the biometrical features, such as position, pressure, size, and time when a user inputs a pattern password to a smartphone, the proposed user verification mechanism can validate whether the user is the true owner of the smartphone. We adopted fuzzy logic, artificial neural network, and support vector machine, to build classifiers, using the behavioral data collected from 10 users. The experimental results show that all the three algorithms have significant recognition capacity, and the fuzzy logic algorithm is the best one with its false acceptance rate and false rejection rate as 4.7% and 4.468% respectively

Color demosaicing using variance of color differences

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Principles and theory of protein-based pattern formation

Biological systems perform functions by the orchestrated interplay of many small components without a "conductor." Such self-organization pervades life on many scales, from the subcellular level to populations of many organisms and whole ecosystems. On the intracellular level, protein-based pattern formation coordinates and instructs functions like cell division, differentiation and motility. A key feature of protein-based pattern formation is that the total numbers of the involved proteins remain constant on the timescale of pattern formation. The overarching theme of this thesis is the profound impact of this mass-conservation property on pattern formation and how one can harness mass conservation to understand the underlying physical principles. The central insight is that changes in local densities shift local reactive equilibria, and thus induce concentration gradients which, in turn, drive diffusive transport of mass. For two-component systems, this dynamic interplay can be captured by simple geometric objects in the (low-dimensional) phase space of chemical concentrations. On this phase-space level, physical insight can be gained from geometric criteria and graphical constructions. Moreover, we introduce the notion of regional (in)stabilities, which allows one to characterize the dynamics in the highly nonlinear regime reveals an inherent connection between Turing instability and stimulus-induced pattern formation. The insights gained for conceptual two-component systems can be generalized to systems with more components and several conserved masses. In the minimal setting of two diffusively coupled "reactors," the full dynamics can be embedded in the phase-space of redistributed masses where the phase space flow is organized by surfaces of local reactive equilibria. Building on the phase-space analysis for two component systems, we develop a new approach to the important open problem of wavelength selection in the highly nonlinear regime. We show that two-component reaction–diffusion systems always exhibit uninterrupted coarsening (the continual growth of the characteristic length scale) of patterns if they are strictly mass conserving. Selection of a finite wavelength emerges due to weakly broken mass-conservation, or coupling to additional components, which counteract and stop the competition instability that drives coarsening. For complex dynamical phenomena like wave patterns and the transition to spatiotemporal chaos, an analysis in terms of local equilibria and their stability properties provides a powerful tool to interpret data from numerical simulations and experiments, and to reveal the underlying physical mechanisms. In collaborations with different experimental labs, we studied the Min system of Escherichia coli. A central insight from these investigations is that bulk-surface coupling imparts a strong dependence of pattern formation on the geometry of the spatial confinement, which explains the qualitatively different dynamics observed inside cells compared to in vitro reconstitutions. By theoretically studying the polarization machinery in budding yeast and testing predictions in collaboration with experimentalists, we found that this functional module implements several redundant polarization mechanisms that depend on different subsets of proteins. Taken together, our work reveals unifying principles underlying biological self-organization and elucidates how microscopic interaction rules and physical constraints collectively lead to specific biological functions.Biologische Systeme führen Funktionen durch das orchestrierte Zusammenspiel vieler kleiner Komponenten ohne einen "Dirigenten" aus. Solche Selbstorganisation durchdringt das Leben auf vielen Skalen, von der subzellulären Ebene bis zu Populationen vieler Organismen und ganzen Ökosystemen. Auf der intrazellulären Ebene koordiniert und instruieren proteinbasierte Muster Funktionen wie Zellteilung, Differenzierung und Motilität. Ein wesentliches Merkmal der proteinbasierten Musterbildung ist, dass die Gesamtzahl der beteiligten Proteine auf der Zeitskala der Musterbildung konstant bleibt. Das übergreifende Thema dieser Arbeit ist es, den tiefgreifenden Einfluss dieser Massenerhaltung auf die Musterbildung zu untersuchen und Methoden zu entwickeln, die Massenerhaltung nutzen, um die zugrunde liegenden physikalischen Prinzipien von proteinbasierter Musterbildung zu verstehen. Die zentrale Erkenntnis ist, dass Änderungen der lokalen Dichten lokale reaktive Gleichgewichte verschieben und somit Konzentrationsgradienten induzieren, die wiederum den diffusiven Transport von Masse antreiben. Für Zweikomponentensysteme kann dieses dynamische Wechselspiel durch einfache geometrische Objekte im (niedrigdimensionalen) Phasenraum der chemischen Konzentrationen erfasst werden. Auf dieser Phasenraumebene können physikalische Erkenntnisse durch geometrische Kriterien und grafische Konstruktionen gewonnen werden. Darüber hinaus führen wir den Begriff der regionalen (In-)stabilität ein, der es erlaubt, die Dynamik im hochgradig nichtlinearen Regime zu charakterisieren und einen inhärenten Zusammenhang zwischen Turing-Instabilität und stimulusinduzierter Musterbildung aufzuzeigen. Die für konzeptionelle Zweikomponentensysteme gewonnenen Erkenntnisse können auf Systeme mit mehr Komponenten und mehreren erhaltenen Massen verallgemeinert werden. In der minimalen Fassung von zwei diffusiv gekoppelten "Reaktoren" kann die gesamte Dynamik in den Phasenraum umverteilter Massen eingebettet werden, wobei der Phasenraumfluss durch Flächen lokaler reaktiver Gleichgewichte organisiert wird. Aufbauend auf der Phasenraumanalyse für Zweikomponentensysteme entwickeln wir einen neuen Ansatz für die wichtige offene Fragestellung der Wellenängenselektion im hochgradig nichtlinearen Regime. Wir zeigen, dass "coarsening" (das stetige wachsen der charakteristischen Längenskala) von Mustern in Zweikomponentensystemen nie stoppt, wenn sie exakt massenerhaltend sind. Die Selektion einer endlichen Wellenlänge entsteht durch schwach gebrochene Massenerhaltung oder durch Kopplung an zusätzliche Komponenten. Diese Prozesse wirken der Masseumverteilung, die coarsening treibt, entgegen und stoppen so das coarsening. Bei komplexen dynamischen Phänomenen wie Wellenmustern und dem Übergang zu raumzeitlichen Chaos bietet eine Analyse in Bezug auf lokale Gleichgewichte und deren Stabilitätseigenschaften ein leistungsstarkes Werkzeug, um Daten aus numerischen Simulationen und Experimenten zu interpretieren und die zugrunde liegenden physikalischen Mechanismen aufzudecken. In Zusammenarbeit mit verschiedenen experimentellen Labors haben wir das Min-System von Escherichia coli untersucht. Eine zentrale Erkenntnis aus diesen Untersuchungen ist, dass die Kopplung zwischen Volumen und Oberfläche zu einer starken Abhängigkeit der Musterbildung von der räumlichen Geometrie führt. Das erklärt die qualitativ unterschiedliche Dynamik, die in Zellen im Vergleich zu in vitro Rekonstitutionen beobachtet wird. Durch die theoretische Untersuchung der Polarisationsmaschinerie in Hefezellen, kombiniert mit experimentellen Tests theoretischer Vorhersagen, haben wir herausgefunden, dass dieses Funktionsmodul mehrere redundante Polarisationsmechanismen implementiert, die von verschiedenen Untergruppen von Proteinen abhängen. Zusammengenommen beleuchtet unsere Arbeit die vereinheitlichenden Prinzipien, die der intrazellulären Selbstorganisation zugrunde liegen, und zeigt, wie mikroskopische Interaktionsregeln und physikalische Bedingungen gemeinsam zu spezifischen biologischen Funktionen führen

Universal Demosaicking of Color Filter Arrays

A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the luminance component at each pixel, and thus, avoids the associated estimation error. Instead, we directly use the acquired CFA color intensity at each pixel as an input component. Two independent chrominance components are estimated at each pixel based on the interpixel chrominance in the window, which is captured with the difference of CFA color values between the pixel of interest and its neighbors. Two mechanisms are employed for the accurate estimation: distance-related and edge-sensing weighting to reflect the confidence levels of the inter-pixel chrominance components, and pseudoinverse-based estimation from the components in a window. Then from the acquired CFA color component and two estimated chrominance components, the three primary colors are reconstructed by a linear color transform, which is optimized for the least transform error. Our experiments show that the proposed method is much better than other published universal demosaicking methods.National Key Basic Research Project of China (973 Program) [2015CB352303, 2011CB302400]; National Natural Science Foundation (NSF) of China [61071156, 61671027]SCI(E)[email protected]; [email protected]; [email protected]; [email protected]

Two-layer ensemble of deep learning models for medical image segmentation. [Article]

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification

SINGLE-CELL DYNAMICS OF CORE PLURIPOTENCY FACTORS IN HUMAN PLURIPOTENT STEM CELLS

Human pluripotent stem cell therapy is a novel approach to cellular therapeutics that can treat and model multiple diseases, accelerate regenerative medicine, and progress drug discovery. To induce pluripotency in somatic cells through the process of reprogramming, core pluripotency TFs are upregulated to force a somatic cell to adopt a pluripotent cell fate. From this hiPSC state, these cells can be differentiated to any cell type and used for downstream research and clinical applications. These pluripotency transcription factors (TFs) are OCT4, SOX2, and NANOG, which form a core signaling network critical for maintaining stem cell pluripotency and self-renewal. Currently, the spatiotemporal expression dynamics of these pluripotency TFs throughout differentiation and reprogramming is unclear, limiting our understanding of stem cell fate decisions. We investigated the temporal dynamics of pluripotency TFs underlying stem cell pluripotency and elucidated these dynamics during differentiation and reprogramming while potentially improving reprogramming strategies for clinical applications. We initially combined CRISPR/Cas9-mediated gene editing with microraft array technology to generate human embryonic stem cell lines with endogenously tagged fluorophores for the pluripotent TFs. We used time-lapse imaging on a SOX2/NANOG reporter to reveal that pluripotent stem cells show gastrulation-like patterning without direct chemical or spatial induction. Directed differentiation to the three primary germ layers—endoderm, mesoderm, and ectoderm—revealed distinct spatiotemporal patterns of SOX2 and NANOG expression in single cells. Finally, we captured dynamic changes in cell morphology during ectoderm differentiation corresponding to the formation of neural rosettes.To elucidate pluripotent TF reactivation during reprogramming, the SOX2/NANOG reporter was differentiated to various cell types and then reprogrammed back into a pluripotent state. We found that the cells initially underwent morphological changes, then had subsequent inverse pluripotent TF reactivation, showing pluripotency recovery happens in reverse order from which it was lost. We then utilized the differentiated reporter cells to perform a screen of chemical combinations that can reactivate the pluripotency TFs. The results from this work provide insight into pluripotency TF dynamics during differentiation and reprogramming as well as knowledge for generating live cell reporters and a potential reprogramming alternative for future stem cell applications.Doctor of Philosoph