A series of pyrene end-labeled monodisperse poly(ethylene oxide)s (PEO(X)-Py2 where X represents the number average molecular weight (Mn) of the PEOs and equals 2, 5, 10 and 16.5 K) and one pyrene mono-labeled PEO (PEO(2K)-Py1) were synthesized and characterized in solution using fluorescence. First, the end-to-end cyclization (EEC) of PEO(X)-Py2 was investigated in seven organic solvents with viscosities (η) ranging from 0.32 to 1.92 mPa•s. The classical Birks scheme was used to globally fit the pyrene monomer and excimer fluorescence decays. The fraction of pyrenes that did not form excimer (ffree) was found to increase with increasing η and Mn. This result was contrary to the assumptions made by Birks’ scheme. To account for this, ffree was assumed to represent the fraction of PEO chains other than the monolabeled polymer impurities that cannot accomplish EEC. A fluorescence blob model (FBM) was applied to handle this assumption in the process of excimer formation for the PEO(X)-Py2 samples in solution. The radius of a blob, Rblob, in organic solvents was determined according to the results retrieved from the FBM. To quantitatively account for the existence of pyrene impurity in pyrene-labeled macromolecules, known amounts of PEO(2K)-Py1 were added into a PEO(2K)-Py2 solution and the fluorescence decays were fitted globally according to the Birks scheme and “model free” (MF) analysis to verify the validation of the MF analysis. The MF analysis was then applied to determine the amounts of 1-pyrenebutyric acid (PyBA) that had been added to a solution of pyrene end-labeled fourth generation dendritic hybrid (Py16-G4-PS). The results demonstrated that the contribution from unwanted fluorescent species could be isolated and quantitatively accounted for by fitting the fluorescence decays of the pyrene monomer and excimer globally with the MF analysis. Since the PEO(X)-Py2 samples form hydrophobic pyrene aggregates in aqueous solution, a sequential model (SM) was proposed to characterize the pyrene excimer formation of PEO(X)-Py2 in water at different polymer concentration (CP). The capture distance over which the pyrenyl end-groups experience hydrophobic forces in water was determined by assuming that the end-to-end distances of the PEO(X)-Py2 samples adopt a Gaussian distribution and that the fraction of pyrenes that are aggregated (fE0) determined by the sequential model corresponds to the fraction of PEO(X)-Py2 chains whose end-to-end distance is smaller than the hydrophobic capture distance. Since a surfactant can interact with a hydrophobically modified water-soluble polymer in aqueous solution, the interactions taking place between PEO(X)-Py2 and sodium dodecyl sulfate (SDS) were investigated at a low PEO(X)-Py2 concentration. The pyrene monomer and excimer fluorescence decays of the PEO(X)-Py2 and SDS solutions were acquired at various SDS concentrations and globally fitted according to the MF analysis to retrieve the parameters that described the kinetics of pyrene excimer formation. At high SDS concentrations above the critical micelle concentration (CMC), the pyrene end-groups of the short-chain samples (PEO(2K)-Py2 and PEO(5K)-Py2) were incorporated inside the same micelle and excimer was formed intramolecularly, while most pyrene groups of the long-chain samples (PEO(10K)-Py2 and PEO(16.5K)-Py2) were isolated into different micelles. Lastly, both the rheological properties and fluorescence behavior of a pyrene-labeled hydrophobically-modified alkali-swellable emulsion (Py-HASE) polymer in basic aqueous solution with SDS were studied. Furthermore, a joint experimental setup that combined a rheometer and a steady-state fluorometer was applied to investigate at the molecular level the effect that a shearing force had on the polymeric network. However, despite the dramatic decrease in solution viscosity with increasing shear rate, no change in the fluorescence spectra was detected, suggesting that changes in the polymeric network that affected the balance of intra- versus intermolecular pyrene associations did not impact the process of excimer formation. Together the experiments described in this thesis represent the broadest set of examples found in the scientific literature where information on the dynamics and level of association of pyrene-labeled polymers has been retrieved through the quantitative analysis of the fluorescence decays acquired with pyrene-labeled polymers in solution.

The biological function of a protein is determined by its amino acid sequence, structure, and internal dynamics. In turn the prediction of a protein structure from its folding pathway involves the characterization of the dynamics of the polypeptide backbone. This study addresses how the internal dynamics of arborescent polypeptides are affected by increased crowding of the interior of these branched polymer molecules. Linear, comb-branched, and arborescent poly(L-glutamic acid) (PGA) samples were analyzed by 1H NMR spectroscopy to determine their chain conformation. The PGA chains of these constructs were shown to adopt α-helical and random coil conformations in N,N-dimethylformamide (DMF) and in dimethyl sulfoxide (DMSO), respectively. The hydrodynamic diameter (Dh) of the arborescent PGAs, determined using dynamic light scattering measurements, increased with increasing generation number and when the side-chains adopted random coil instead of α-helical conformations. The PGA samples were labelled with 1-pyrenemethylamine to determine how their structure affected the internal dynamics of the arborescent polymers in solution, from the analysis of their fluorescence spectra and decays. For each pyrene-labelled polymeric construct excimer formation increased with increasing pyrene content, and the efficiency of excimer formation increased with the generation number due to the increased density of the macromolecules. Comparison of the time-resolved fluorescence results acquired in DMF and in DMSO demonstrated that the helical conformation led to slower chain dynamics in DMF and that despite the higher viscosity of DMSO, the polypeptide side-chains were more mobile as a consequence of the random coil conformation of the linear PGA segments. These results suggest that the formation of structural motives inside a polypeptide slows down its internal dynamics.

Honeybees have a rich genetic diversity in Anatolia. This is reflected in the presence of numerous subspecies of honeybee in Turkey. In METU, Department of Biology, honeybee populations of different regions in Turkey are investigated in order to characterize population variation in these regions. A total of 23 length and angle features belonging to the honeybee hindlegs and forewings are measured in these studies using a microscope and a monitor. These measurements are carried out by placing rulers on the monitor that shows the honeybee image and getting the length and angle features. However, performing measurements in this way is a time consuming process and is open to human-dependent errors. In this thesis, a &ldquo / semi-automated honeybee feature extraction system&rdquo / is presented. The aim is to increase the efficiency by decreasing the time spent on handling these measurements and by increasing the accuracy of measured hindleg and forewing features. The problem is studied from the acquisition of the microscope images, to the feature extraction of the honeybee features. In this scope, suitable methods are developed for segmentation of honeybee hindleg and forewing images. Within intermediate steps, blob analysis is utilized, and edges of the forewing and hindlegs are thinned using skeletonization. Templates that represent the forewing and hindleg edges are formed by either Bezier Curves or Polynomial Interpolation. In the feature extraction phase, Active Contour (Snake) algorithm is applied to the images in order to find the critical points using these templates.

Road safety is a serious problem in many countries and affects the lives of many people. Improving road safety starts with the drivers, and the best way to make them change their habits is to offer incentives for better, safer driving styles. This project aims to make that possible by offering a means to calculate a quantified indicator of how safe a driver's habits are. This is done by developing an on-board, visual road-sign recognition system that can be coupled with a vehicle tracking system to determine how often a driver violates the rules of the road. The system detects stop signs, red traffic lights and speed limit signs, and outputs this data in a format that can be read by a vehicle tracking system, where it can be combined with speed information and sent to a central database where the driver safety rating can be calculated. Input to the system comes from a simple, standard dashboard mounted camera within the vehicle, which generates a continuous stream of images of the scene directly in front of the vehicle. The images are subjected to a number of cascaded detection sub-systems to determine if any of the target objects (road signs) appear within that video frame. The detection system software had to be optimized for minimum false positive detections, since those will unfairly punish the driver, and it also had to be optimized for speed to run on small hardware that can be installed in the vehicle. The first stage of the cascaded system consists of an image detector that detects circles within the image, since traffic lights and speed signs are circular and a stop sign can be approximated by a circle when the image is blurred or the resolution is lowered. The second stage is a neural network that is trained to recognize the target road sign in order to determine which road sign was found, or to eliminate other circular objects found in the image frame. The output of the neural network is then sent through an iterative filter with a majority voted output to eliminate detection 'jitter' and the occasional incorrect classifier output. Object tracking is applied to the 'good' detection outputs and used as an additional input for the detection phase on the next frame. In this way the continuity and robustness of the image detector are improved, since the object tracker indicates to it where the target object is most likely to appear in the next frame, based on the track it has been following through previous frames. In the final stage the detection system output is written to the chosen pins of the hardware output port, from where the detection output can be indicated to the user and also used as an input to the vehicle tracking system. To find the best detection approach, some methods found in literature were studied and the most likely candidates compared. The scale invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms are too slow compared to the cascaded approach to be used for real-time detection on an in-vehicle hardware platform. In the cascaded approach used, different detection stage algorithms are tested and compared. The Hough circle transform is measured against blob detection on stop signs and speed limit signs. On traffic light state detection two approaches are tested and compared, one based on colour information and the other on direct neural network classification. To run the software in the user's vehicle, an appropriate hardware platform is chosen. A number of promising hardware platforms were studied and their specifications compared before the best candidate was selected and purchased for the project. The developed software was tested on the selected hardware in a vehicle during real public road driving for extended periods and under various conditions. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Syftet med denna uppsats var att jämföra två olika databashanteringssystem, MongoDB samt MariaDB, utifrån specifika krav från en uppdragsgivare gällande prestanda samt komplexitet. Då MariaDB är ett SQL-databashanteringssystem och MongoDB ett NoSQLdatabashanteringssystem som bygger på olika databasmodeller behandlas data på olika sätt, vilket ligger som grund till jämförelsen mellan de olika databashanteringssystemen. Uppsatsen fokuserar på att utifrån fyra olika tester, två prestandatestet och två tester som jämför komplexiteten, kunna jämföra databashanteringssystemen MariaDB och MongoDB. Dessa databashanteringssystem ställdes emot de angivna kraven från uppdragsgivaren för att se vilket av dem som är bäst lämpat. Två olika applikationer utvecklades med hjälp av C# och användes under testerna för att utföra testerna. Efter att testerna utförts rekommenderades MongoDB till uppdragsgivaren på grund av den prestandafördel som testerna visade på i långsiktig användning av systemet. Även komplexiteten för MongoDB visade sig vara mindre vilket stärker rekommendationen ytterligare.

This thesis presents a method of tracking and correcting for the swaying of a central receiver tower in concentrated solar production plants. The method uses a camera with image processing algorithms to detect movement of the center of the tower. A prototype was constructed utilizing a CMOS camera connected to a microcontroller to control the movements of three surrounding heliostats. The prototype uses blob-tracking algorithms to detect and correct for movements of a colored model target. The model was able to detect movements in the tower with average error of 0.32 degrees, and was able to correctly orient the surrounding heliostats to within 1.2 and 2.6 degrees of accuracy while testing indoors and outdoors, respectively.

Denna kandidatuppsats berör databaser i javabaserade bildhanteringssystem där bilderna lagras och hämtas som binära objekt. I MySQL och en del andra databashanterare kallas detta format för Blob(Binary large object). För att bildhanteringssystemet skall fungera bra krävs det att man använder en snabb databas. Syftet har varit att av ett urval databaser utse den databas som är snabbast i avseende på svarstider för hämtning av bilder som lagras som binära objekt i databaser. Databaserna är de fyra välkända databashanterarna MySQL, MariaDB, PostGreSQL och MongoDB. Testerna har utförts med databaserna integrerade i Javabaserade klient-server moduler för att så mycket som möjligt spegla de villkor som förekommer i ett bildhanteringssystem. De testverktyg som har använts är JMeter som är en avancerad applikation för mätning av svarstider och PerfMon som övervakar åtgång av systemresurser. MongoDB var den snabbaste bilddatabasen men det finns många osäkerhetsfaktorer som måste beaktas vilket också beskrivs i denna kandidatuppsats. Trots att många åtgärder för att motverka osäkerhetsfaktorerna har gjorts, förblir mätosäkerheten stor. Mer åtgärder för att isolera databasernas del av svarstiderna i ett klient-server system måste göras. Förslag på åtgärder redogörs i denna kandidatuppsats. / This bachelor thesis concerns databases in Java-based imaging system where the images are stored and retrieved as binary objects. In MySQL and in some other database management systems this format is called Blob (Binary Large Object). For the imaging system to work well, it is necessary to use a fast database. The aim has been that out of a sample of databases designate the database that is the fastest in terms of response times for downloading images stored as binary objects in databases. The databases are the four well-known database management systems MySQL, MariaDB, PostgreSQL and MongoDB. The tests have been conducted with the databases integrated into Java-based client-server modules in order to as much as possible mirror the conditions prevailing in an imaging system. The test tool that has been used is JMeter which is an advanced application for measuring response times and PerfMon to monitor the consumption of system resources. MongoDB was the fastest image database, but there are many uncertainties that must be considered, which is also explained in this bachelor thesis. Although many measures to counter the uncertainties have been made, the measurement uncertainty remains big. Further measures to isolate the database part of the response times in a client-server system must be made. Proposed measures are described in this bachelor thesis.

Il est maintenant accepté expérimentalement que les filaments de plasma alignés sur le champ magnétique, appelés “blobs”, jouent un rôle important dans le transport dans le plasma de bord. Dans cette thèse, les phénomènes fondamentaux du transport dans le plasma de bord sont étudiés en mettant l'accent sur le phénomène de filaments plasma. Dans un premier temps, les mécanismes de propagation de blobs uniques sont envisagés. Puis la génération de blobs par la turbulence de bord est étudiée, et le transport turbulent est discuté entant que phénomène collectif. Des particularités du transport turbulent, incluant les blobs auto-organisés, sont reliées à un transport de type Bohm (c'est à dire des perturbations avec des corrélations radiales longues, et un coefficient de transport effectif quisuit la dépendance Bohm). De plus, en prolongement de ce travail, un effort initial vers une transposition du transport non-local au plasmade bord est décrite. / Recently, it has been accepted that magnetic field aligned plasma filaments, referred to as "blobs" play important roles in the transport of Scrape-off Layer (SoL) plasmas. In this thesis, putting an emphasis on the plasma blob phenomenon, we study fundamental processes of SoL transport using numerical simulation. At first, weinvestigate the propagation mechanisms of single and isolated blobs.Next, we study the generation of blobs from edge turbulence, and discuss the SoL turbulent transport as a collective phenomenon. Features of turbulent transport, which includes the self-organized blobs in SoL, are identified as Bohm-like transport (i.e., the perturbation with long radial correlations and the effective transport coefficient that follows the dependence of Bohm-like transport). Additionally, as an advancement of study, we describe an initial effort to extend the view of non local transport to edge plasmas.

This report shows that a reliable motion detection is needed to make an accurate prediction of future activity. Several experiments are carried out to obtain information about the object ́s behaviour and the best settings for the motion detection. A moving object is captured using two cameras, for two image sequences, and motion detection is applied to the stereoscopic data. Background subtraction algorithm followed by image segmentation algorithm, morphology algorithm, and blob analy- sis are performed on the images to find the coordinates for the centroid of the moving object. Two models are created to make a statistical inter- pretation of the data: one model for the height over the width and one statistical model for the distance between the cameras and the moving object over the width. The mean and standard deviation values are calculated to make a reliable interpretation of the captured images and the moving object. The Kalman filter is used for the prediction of future activity. The filters of the statistical models are trained with the first coordinates of the detected balls, and the next coordinates are predicted.

abstract: Recent advances in medical imaging technology have greatly enhanced imaging based diagnosis which requires computational effective and accurate algorithms to process the images (e.g., measure the objects) for quantitative assessment. In this dissertation, one type of imaging objects is of interest: small blobs. Example small blob objects are cells in histopathology images, small breast lesions in ultrasound images, glomeruli in kidney MR images etc. This problem is particularly challenging because the small blobs often have inhomogeneous intensity distribution and indistinct boundary against the background. This research develops a generalized four-phased system for small blob detections. The system includes (1) raw image transformation, (2) Hessian pre-segmentation, (3) feature extraction and (4) unsupervised clustering for post-pruning. First, detecting blobs from 2D images is studied where a Hessian-based Laplacian of Gaussian (HLoG) detector is proposed. Using the scale space theory as foundation, the image is smoothed via LoG. Hessian analysis is then launched to identify the single optimal scale based on which a pre-segmentation is conducted. Novel Regional features are extracted from pre-segmented blob candidates and fed to Variational Bayesian Gaussian Mixture Models (VBGMM) for post pruning. Sixteen cell histology images and two hundred cell fluorescent images are tested to demonstrate the performances of HLoG. Next, as an extension, Hessian-based Difference of Gaussians (HDoG) is proposed which is capable to identify the small blobs from 3D images. Specifically, kidney glomeruli segmentation from 3D MRI (6 rats, 3 humans) is investigated. The experimental results show that HDoG has the potential to automatically detect glomeruli, enabling new measurements of renal microstructures and pathology in preclinical and clinical studies. Realizing the computation time is a key factor impacting the clinical adoption, the last phase of this research is to investigate the data reduction technique for VBGMM in HDoG to handle large-scale datasets. A new coreset algorithm is developed for variational Bayesian mixture models. Using the same MRI dataset, it is observed that the four-phased system with coreset-VBGMM has similar performance as using the full dataset but about 20 times faster. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2015