Documented: Embedding and Retrieving Information from 3D Printed Objects

Documentation is an essential aspect of building interactive physical objects. For makers, documentation serves as a record that can be shared with others to demonstrate a project’s building (what and how) and decision-making (why) process. A documentation’s end-users (i.e., the makers themselves or people interested in rebuilding or learning about the project) can then self-refect on these records and take away their own lessons regarding the project. However, in the case of physical objects, we think that refecting on their documentation can be challenging since the documentation and the object are two separate artifacts. We explore this assumption in this thesis. Specifcally, we asked if embedding the documentation into the object being made will promote self-refection and whether this facilitates a deeper understanding of the object and its design process. We took three main steps to address our questions: (1) we used artifact analysis to identify the strengths and limitations of current documentation styles (i.e., text, picture, and video-based documentations) that makers typically use; (2) we conducted interviews and brainstorming sessions with professional and hobbyist makers, and asked them to determine the strengths and weaknesses of their current documentation techniques, and the improvements they envision regarding the connection between their documentation and the built object; (3) informed by our artifact analysis and interview sessions, we proposed a prototype that provides a new method to interact with an object’s documentation, which allows people to embed and retrieve documentation-related data into and from the object, respectively

Psychoanalytic Reading of Love and Desire in Somerset Maugham’s Of Human Bondage

The present study was a comprehensive psychoanalysis of the idea of love and desire in Somerset Maugham’s Of Human Bondage. The study explored the relationship Philip Carey, the main character, develops with Other people throughout the novel. To further enrich the analysis, Lacan’s theory of human love and desire was employed to provide a psychoanalytic examination of Philip Carey’s bond of love for Mildred, on the one hand, and his gradual loss of identity in his desire towards her, on the other. The study inspected the nature of Philip’s desire for Mildred and shows how he turnd to a desiring subject in his bond to her and finally reached a state of selflessness and depended heavily on Mildred as the object of his desire which drove him towards self-contempt and a masochistic denial of real facts in his life

Techniques basées sur des modèles et apprentissage machine pour la reconstruction d’image non-linéaire en tomographie optique diffuse

La tomographie optique diffuse (TOD) est une modalité d’imagerie biomédicale 3D peu dispendieuse et non-invasive qui permet de reconstruire les propriétés optiques d’un tissu biologique. Le processus de reconstruction d’images en TOD est difficile à réaliser puisqu’il nécessite de résoudre un problème non-linéaire et mal posé. Les propriétés optiques sont calculées à partir des mesures de surface du milieu à l’étude. Dans ce projet, deux méthodes de reconstruction non-linéaire pour la TOD ont été développées. La première méthode utilise un modèle itératif, une approche encore en développement qu’on retrouve dans la littérature. L’approximation de la diffusion est le modèle utilisé pour résoudre le problème direct. Par ailleurs, la reconstruction d’image à été réalisée dans différents régimes, continu et temporel, avec des mesures intrinsèques et de fluorescence. Dans un premier temps, un algorithme de reconstruction en régime continu et utilisant des mesures multispectrales est développé pour reconstruire la concentration des chromophores qui se trouve dans différents types de tissus. Dans un second temps, un algorithme de reconstruction est développé pour calculer le temps de vie de différents marqueurs fluorescents à partir de mesures optiques dans le domaine temporel. Une approche innovatrice a été d’utiliser la totalité de l’information du signal temporel dans le but d’améliorer la reconstruction d’image. Par ailleurs, cet algorithme permettrait de distinguer plus de trois temps de vie, ce qui n’a pas encore été démontré en imagerie de fluorescence. La deuxième méthode qui a été développée utilise l’apprentissage machine et plus spécifiquement l’apprentissage profond. Un modèle d’apprentissage profond génératif est mis en place pour reconstruire la distribution de sources d’émissions de fluorescence à partir de mesures en régime continu. Il s’agit de la première utilisation d’un algorithme d’apprentissage profond appliqué à la reconstruction d’images en TOD de fluorescence. La validation de la méthode est réalisée avec une mire aux propriétés optiques connues dans laquelle sont inséres des marqueurs fluorescents. La robustesse de cette méthode est démontrée même dans les situations où le nombre de mesures est limité et en présence de bruit.Abstract : Diffuse optical tomography (DOT) is a low cost and noninvasive 3D biomedical imaging technique to reconstruct the optical properties of biological tissues. Image reconstruction in DOT is inherently a difficult problem, because the inversion process is nonlinear and ill-posed. During DOT image reconstruction, the optical properties of the medium are recovered from the boundary measurements at the surface of the medium. In this work, two approaches are proposed for non-linear DOT image reconstruction. The first approach relies on the use of iterative model-based image reconstruction, which is still under development for DOT and that can be found in the literature. A 3D forward model is developed based on the diffusion equation, which is an approximation of the radiative transfer equation. The forward model developed can simulate light propagation in complex geometries. Additionally, the forward model is developed to deal with different types of optical data such as continuous-wave (CW) and time-domain (TD) data for both intrinsic and fluorescence signals. First, a multispectral image reconstruction algorithm is developed to reconstruct the concentration of different tissue chromophores simultaneously from a set of CW measurements at different wavelengths. A second image reconstruction algorithm is developed to reconstruct the fluorescence lifetime (FLT) of different fluorescent markers from time-domain fluorescence measurements. In this algorithm, all the information contained in full temporal curves is used along with an acceleration technique to render the algorithm of practical use. Moreover, the proposed algorithm has the potential of being able to distinguish more than 3 FLTs, which is a first in fluorescence imaging. The second approach is based on machine learning techniques, in particular deep learning models. A deep generative model is proposed to reconstruct the fluorescence distribution map from CW fluorescence measurements. It is the first time that such a model is applied for fluorescence DOT image reconstruction. The performance of the proposed algorithm is validated with an optical phantom and a fluorescent marker. The proposed algorithm recovers the fluorescence distribution even from very noisy and sparse measurements, which is a big limitation in fluorescence DOT imaging

Artificial Intelligence for Detection, Characterization, and Classification of Complex Visual Patterns in Medical Imaging; Applications in Pulmonary and Neuro-imaging

Medical imaging is widely used in current healthcare and research settings for various purposes such as diagnosis, treatment options, patient monitoring, longitudinal studies, etc. The two most commonly used imaging modalities in the United States are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Raw images acquired via CT or MRI need to undergo a variety of processing steps prior to being used for the purposes explained above. These processing steps include quality control, noise reduction, anatomical segmentation, tissue classification, etc. However, since medical images often include millions of voxels (smallest 3D units in the image containing information) it is extremely challenging to process them manually by relying on visual inspection and the experience of trained clinicians. In light of this, the field of medical imaging is seeking ways to automate data processing. With the impressive performance of Artificial Intelligence (AI) in the field of Computer Vision, researchers in the medical imaging community have shown increasing interest in utilizing this powerful tool to automate the task of processing medical imaging data. Despite AI’s significant contributions to the medical imaging field, large cohorts of data still remain without optimized and robust AI-based tools to process images efficiently and accurately. This thesis focuses on exploiting large cohorts of CT and MRI data to design AI-based methods for processing medical images using weakly-supervised and supervised learning strategies, as well as mathematical (and/or statistical) modeling and signal processing methods. In particular, we address four image processing problems in this thesis. Namely: 1) We propose a weakly-supervised deep learning method to automate binary quality control of diffusion MRI scans into ‘poor’ and ‘good’ quality classes; 2) We design a weakly-supervised deep learning framework to learn and detect visual patterns related to a set of different artifact categories considered in this work, in order to identify major artifact types present in dMRI volumes; 3) We develop a supervised deep learning method to classify multiple lung texture patterns with association to Emphysema disease on human lung CT scans; 4) We investigate and characterize the properties of two types of negative BOLD response elicited in human brain fMRI scans during visual stimulation using mathematical modeling and signal processing tools. Our results demonstrate that through the use of artificial intelligence and signal processing algorithms: 1) dMRI scans can be automatically categorized into two quality groups (i.e., ‘poor’ vs ‘good’) with a high classification accuracy, enabling rapid sifting of large cohorts of dMRI scans to be utilized in research or clinical settings; 2) Type of the major artifact present in ‘poor’ quality dMRI volumes can be identified robustly and automatically with high precision enabling exclusion/correction of corrupt volumes according to the artifact type contaminating them; 3) Multiple lung texture patterns related to Emphysema disease can be automatically and robustly classified across various large cohorts of CT scans enabling investigation of the disease through longitudinal studies on multiple cohorts; 4) Negative BOLD responses of different categories can be fully characterized on fMRI data collected from visual stimulation of human brain enabling researchers to better understand the human brain functionality through studying cohorts of fMRI scans

A COMPREHENSIVE ANALYSIS OF THE SPATIO-TEMPORAL VARIATION OF SATELLITE-BASED AEROSOL OPTICAL DEPTH IN MARMARA REGION OF TURKIYE DURING 2000–2021

This study investigates the spatiotemporal variability of the aerosol optical depth (AOD) in the atmosphere over the Marmara region, Turkiye. Long-term satellite observations from MODIS MAIAC AOD data spanning the period from 2000 to 2021 are utilized. Examining the temporal variations in AOD in the Marmara region, it is observed that AOD reaches its peak during spring (May) and summer (August) months, while lower AOD values are observed in winter. Specifically, between August and December, there is a significant decline in monthly mean AOD which is majorly due to particulate removal from the atmosphere via precipitation scavenging. The findings reveal that the inter-annual variability of monthly AOD variations in the Marmara region is primarily influenced by temporary Saharan dust transportation with highest deviations from 22 year averaged AOD in late winters and early springs. The findings from the analysis of seasonal spatial variation of high AOD values revealed that the high AOD area is largest in the summer with about 54% of the total area and then spring (45%) and autumn (26%). Winter has the lowest HVA with 17% of the total area. The seasonal percentage rates of HVA are due to atmospheric conditions and aerosol sources. Larger HVA in summer is due to the increase of farming practices and biomass residue burnings combined with high moisture absorption effects and high temperature. The heating-specific emissions are the main source of anthropogenic emissions over the high AOD areas during the autumn and winter and aerosols are concentrated over the urbanized centres and industrialized zones

Clinical Scoring Systems in Predicting the Outcome of Acute Upper Gastrointestinal Bleeding; a Narrative Review

Prediction of the outcome and severity of acute upper gastrointestinal bleeding (UGIB) has significant importance in patient care, disposition, and determining the need for emergent endoscopy. Recent international recommendations endorse using scoring systems for management of non-variceal UGIB patients. To date, different scoring systems have been developed for predicting the risk of 30-day mortality and re-bleeding. We have discussed the screening performance characteristics of Baylor bleeding score, the Rockall risk scoring score, Cedars-Sinai Medical Center predictive index, Glasgow Blatchford score, T-score, and AIMS65 systems, in the present review.Based on the results of this survey, there are only 3 clinical decision rules that can predict the outcome of UGIB patients, independent from endoscopy. Among these, only Glasgow Blatchford score was highly sensitive for predicting the risk of 30-day mortality and re-bleeding, simultaneously.

Agricultural land abandonment in Bulgaria: a long-term remote sensing perspective, 1950–1980

Agricultural land abandonment is a globally significant threat to the sustenance of economic, ecological, and social balance. Although the driving forces behind it can be multifold and versatile, rural depopulation and urbanization are significant contributors to agricultural land abandonment. In our chosen case study, focusing on two locations, Ruen and Stamboliyski, within the Plovdiv region of Bulgaria, we use aerial photographs and satellite imagery dating from the 1950s until 1980, in connection with official population census data, to assess the magnitude of agricultural abandonment for the first time from a remote sensing perspective. We use multi-modal data obtained from historical aerial and satellite images to accurately identify Land Use Land Cover changes. We suggest using the rubber sheeting method for the geometric correction of multi-modal data obtained from aerial photos and Key Hole missions. Our approach helps with precise sub-pixel alignment of related datasets. We implemented an iterative object-based classification approach to accurately map LULC distribution and quantify spatio-temporal changes from historical panchromatic images, which could be applied to similar images of different geographical regions

Land use and land cover mapping using deep learning based segmentation approaches and VHR Worldview-3 images

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies