abstract: A converging operations approach using response time distribution modeling was adopted to better characterize the cognitive control dynamics underlying ongoing task cost and cue detection in event based prospective memory (PM). In Experiment 1, individual differences analyses revealed that working memory capacity uniquely predicted nonfocal cue detection, while proactive control and inhibition predicted variation in ongoing task cost of the ex-Gaussian parameter associated with continuous monitoring strategies (mu). In Experiments 2A and 2B, quasi-experimental techniques aimed at identifying the role of proactive control abilities in PM monitoring and cue detection suggested that low ability participants may have PM deficits during demanding tasks due to inefficient monitoring strategies, but that emphasizing importance of the intention can increase reliance on more efficacious monitoring strategies that boosts performance (Experiment 2A). Furthermore, high proactive control ability participants are able to efficiently regulate their monitoring strategies under scenarios that do not require costly monitoring for successful cue detection (Experiment 2B). In Experiments 3A and 3B, it was found that proactive control benefited cue detection in interference-rich environments, but the neural correlates of cue detection or intention execution did not differ when engaged in proactive versus reactive control. The results from the current set of studies highlight the importance of response time distribution modeling in understanding PM cost. Additionally, these results have important implications for extant theories of PM and have considerable applied ramifications concerning the cognitive control processes that should be targeted to improve PM abilities. / Dissertation/Thesis / Doctoral Dissertation Psychology 2015

The Diploma thesis deals with reconstruction of Missing data of an Image. It is done by the use of appropriate Mathematical theory and numerical algorithm to reconstruct missing information. The result of this implementation is the reconstruction of missing image information. The thesis also compares different numerical methods, and see which one of them perform best in terms of efficiency and accuracy of the given problem, hence it is used for the reconstruction of missing data.

This thesis presents three distinct methodologies aimed at exploring pivotal aspects within the domain of fiber optics and piezoelectric materials. The first approach offers a comprehensive exploration of three pivotal aspects within the realm of fiber optics and piezoelectric materials. The study delves into the influence of voltage variation on piezoelectric displacement, examines the effects of bending multimode fiber (MMF) on data transmission, and scrutinizes the performance of an Autoencoder in MMF image reconstruction with and without additional noise. To assess the impact of voltage variation on piezoelectric displacement, experiments were conducted by applying varying voltages to a piezoelectric material, meticulously measuring its radial displacement. The results revealed a notable increase in displacement with higher voltage, presenting implications for fiber stability and overall performance. Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an Autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The Autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.The second approach of this thesis entails a comparative investigation involving three distinct neural network models to assess their efficacy in improving image quality within optical transmissions through multimode fibers, with a specific focus on mitigating speckle patterns. Our proposed methodology integrates multimode fibers with a piezoelectric source, deliberately introducing noise into transmitted images to evaluate their performance using an autoencoder neural network. The autoencoder, trained on a dataset augmented with noise and speckle patterns, adeptly eliminates noise and reconstructs images with enhanced fidelity. Comparative analyses conducted with alternative neural network architectures, namely a single hidden layer (SHL) model and a U-Net architecture, reveal that while U-Net demonstrates superior performance in terms of image reconstruction fidelity, the autoencoder exhibits notable advantages in training efficiency. Notably, the autoencoder achieves saturation SSIM in 450 epochs and 24 minutes, surpassing the training durations of both U-Net (210 epochs, 1 hour) and SHL (160 epochs, 3 hours and 25 minutes) models. Impressively, the autoencoder's training time per epoch is six times faster than U-Net and fourteen times faster than SHL. The experimental setup involves the application of varying voltages via a piezoelectric source to induce noise, facilitating adaptation to real-world conditions. Furthermore, the study not only demonstrates the efficacy of the proposed methodology but also conducts comparative analyses with prior works, revealing significant improvements. Compared to Li et al.'s study, our methodology, particularly when utilizing the pre-trained autoencoder, demonstrates an average improvement of 15% for SSIM and 9% for PSNR in the worst-case scenario. Additionally, when compared to Lai et al.'s study employing a generative adversarial network for image reconstruction, our methodology achieves slightly superior SSIM outcomes in certain scenarios, reaching 96%. The versatility of the proposed method is underscored by its consistent performance across varying voltage scenarios, showcasing its potential applications in medical procedures and industrial inspections. This research not only presents a comprehensive and innovative approach to addressing challenges in optical image reconstruction but also signifies significant advancements compared to prior works. The final approach of this study entails employing Hermit Gaussian Functions with varying orders as activation functions within a U-Net model architecture, aiming to evaluate its effectiveness in image reconstruction. The performance of the model is rigorously assessed across five distinct voltage scenarios, and a supplementary evaluation is conducted with digit 5 excluded from the training set to gauge its generalization capability. The outcomes offer promising insights into the efficacy of the proposed methodologies, showcasing significant advancements in optical image reconstruction. Particularly noteworthy is the robust accuracy demonstrated by the higher orders of the Hermit Gaussian Function in reconstructing MMF images, even amidst the presence of noise introduced by the voltage source. However, a decline in accuracy is noted in the presence of voltage-induced noise, underscoring the imperative need for further research to bolster the model's resilience in real-world scenarios, especially in comparison to the utilization of the Rectified Linear Unit (ReLU) function.

Every moment of every day, at least hundreds of thousands of tires roll across a surface throughout the world. Tires are indisputably important in our daily life. The tire's primary component is rubber, which consumes energy when it rotates on a substrate due to the viscoelastic material's internal friction: a phenomenon referred to as rolling resistance. The interaction between the tire and the road surface is one of the most intricate and crucial phenomena in an automobile, because it is responsible for creating forces, moments, and deformation in the tire. Additionally, the road's roughness interacts with the tire and contributes significantly to its performance. This dissertation aims to develop a comprehensive physics-based model for predicting the rolling resistance of a viscoelastic material due to dynamic deformations caused by tire rotation using an analytical approach. The model was developed by proposing a Gaussian wave function propagating across a tire circumference's viscoelastic medium. The wave function was selected to describe the displacement field produced by tire-road interaction. Additionally, by adopting a multi-scale modeling technique, the model was upgraded to estimate rolling resistance while taking into account surface roughness at all length scales, from macroscopic to microscopic. Additionally, another mathematical model was developed using the Fourier series approach to evaluate the steady-state stress response and energy dissipation for any harmonic and non-harmonic periodic strain signals. Additionally, the dissertation strove to build a continuum damage mathematical model using a combined testing/modeling methodology to predict the aging of Styrene-Butadiene Rubber (SBR) after continuous exposure to the atmosphere. The obtained model was developed through the implementation of optimization techniques while formulating a mathematical model, which was then combined with a physics-based model to predict rolling resistance while taking into account rubber aging. Calibration of hyperelastic and viscoelastic material models with testing data was performed using an optimization technique that yielded sufficient results. The results of all mathematical models obtained in this dissertation are reported subsequently. The stress response of a viscoelastic material under harmonic and non-harmonic strain input yielded good agreement with the FEA model obtained using ABAQUS. The rolling resistance behavior under various operating conditions, including texture and aging effects, was reported, and the results aligned with the experimental results found in the literature. / Doctor of Philosophy / Every moment of every day, hundreds of thousands of automobile tires roll across a surface somewhere in the world. A tire is an undeniably important part of everyday life. Rubber is the tire's main component, and when it rotates on a surface, it loses energy, resulting in a force that resists motion, known as rolling resistance force. The contact between the tire and the road is one of the most complicated and important phenomena that happens in an automobile because it is responsible for the vehicle's dynamic performance in areas such as acceleration, stopping distance, and stability. Another factor that affects tire and car performance and should be taken into account is the road's roughness. This dissertation used an analytical method to come up with an accurate physics-based model for predicting the rolling resistance force of a viscoelastic material caused by tire rotation. The model was developed by assuming a Gaussian wave function would move across the tire circumference. Additionally, using a multi-scale modeling technique, the model was improved so that it could calculate the value of rolling resistance force considering surface roughness in all lengths of scale. This project also developed an additional mathematical model using the Fourier series method to determine how the stress response and energy dissipation would behave for any harmonic and nonharmonic periodic strain signals. Additionally, the dissertation presents the developing of a continuum damage mathematical model that could predict the material property of styrene-butadiene rubber (SBR) after being exposed to the air for a long time (i.e., aged). The model was developed based on experimental data and optimization techniques. This model was then combined with a physics-based model to predict rolling resistance force while taking aging into account. The material models were defined using an optimization method that yielded good results. The stress response of a viscoelastic material when it was subjected to harmonic and non-harmonic strain was in good agreement with the Finite Element Analysis (FEA) model made with ABAQUS. Rolling resistance behavior was observed, and the results were consistent with those found in the literature.

Σκοπός της παρούσας Διπλωματικής Εργασίας είναι η ανάπτυξη συγκεκριμένης μεθοδολογίας και αλγορίθμων ψηφιακής επεξεργασίας εικόνων για την αυτόματη εκτίμηση των διαμέτρων αγγείων σε οφθαλμολογικές εικόνες. Η συγκεκριμένη μέτρηση της διαμέτρου των αγγείων διαδραματίζει σημαντικό ρόλο στην έγκαιρη διάγνωση παθήσεων καθώς έχει αποδειχθεί ότι υπάρχει συσχετισμός μεταξύ των μεταβολών των τιμών των εν λόγω διαμέτρων και της εμφάνισης αλλοιώσεων στον αμφιβληστροειδή. Στα πλαίσια της εργασίας, υλοποιήθηκαν δύο μεθοδολογίες για τον υπολογισμό των διαμέτρων αγγείων οφθαλμολογικών εικόνων, οι οποίες συλλέγονται με χρήση κάμερας πυθμένα (fundus camera). Η πρώτη μεθοδολογία στηρίζεται στην εύρεση των σημείων που αποτελούν τους κεντρικούς άξονες των υπό εξέταση αγγείων με χρήση διαφορικού λογισμού. Ακολούθως, σε κάθε σημείο που ανήκει σε κεντρικό άξονα αγγείου, υπολογίζονται οι παράμετροι μιας συνάρτησης. Η εν λόγω συνάρτηση περιγράφει βέλτιστα τα επίπεδα φωτεινότητας της εικόνας κατά μήκος του ευθύγραμμου τμήματος που διέρχεται από το σημείο και είναι κάθετο στο αγγείο. H εύρεση των παραμέτρων της συνάρτησης πραγματοποιείται με χρήση τεχνικών βελτιστοποίησης. Το τελικό βήμα της μεθοδολογίας είναι η εκτίμηση της διαμέτρου των αγγείων από τις τιμές των παραμέτρων που έχουν υπολογιστεί. Η δεύτερη μεθοδολογία στηρίζεται στον αλγόριθμο που προτείνει ο P.H. Gregson. Αρχικά, πραγματοποιείται κατάτμηση της εικόνας με κατωφλίωση και εφαρμόζονται μορφολογικοί τελεστές συστολής και διαστολής στην εικόνα. Στη συνέχεια, εφαρμόζεται ο αλγόριθμος λέπτυνσης (thinning algorithm) με σκοπό την εύρεση των κεντρικών αξόνων των αγγείων και τέλος εκτιμάται η διάμετρος σε κάθε σημείο του κεντρικού άξονα με χρήση των επιπέδων του γκρίζου των εικονοστοιχείων που κείνται στην ευθεία που είναι κάθετη στο αγγείο σε κάθε σημείο του. / The scope of this Thesis is the development of a methodology and advance image processing techniques in order to automatically estimate vessel diameters in ophthalmological images. Motivation for the thesis is the fact that the measurement of vessel diameter plays significant role in the seasonable diagnosis of vascular disorders, as it is believed to be a relation between the variation in diameters and the detection of retinal disorders. In this thesis, two methodologies are developed in order to be applied in ophthalmological images that are collected by using a fundus camera. The first methodology is based on the detection of the pixels that constitute the centerlines of vessels, by using differential calculus. Specifically, at each pixel that belongs to a centerline of vessel, the parameters of a specific function are calculated. This function describes as accurately as possible the intensity levels along the segment that passes through the specific pixel and is perpendicular to the vessel. The parameters of this function are estimated using optimization techniques. The final step of the methodology is the assessment of the diameters of vessels using the values of the parameters. The second methodology is based on the algorithm that P.H.Gregson has proposed. At first, the vessels are detected by tresholding and a morphological closing algorithm is applied. Then, a thinning algorithm is used in order to detect the pixels that constitute the centerlines of the vessels and ultimately the diameter at each pixel of the centerlines is assessed using the gray levels of the pixels that constitute the segment that is perpendicular to the vessel at each specific pixel.