ESTCube-1 nanosatelliidi alams usteemide ja tarkvara disain ja karakteriseerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneElektrilise päikesepurje tehnoloogia võimaldaks kosmosesondidel navigeerida planeetidevahelises ruumis ilma kütuseta, kasutades vaid päikesetuult ja elektrienergiat. Küll aga on tehnoloogiliselt keerukas päikesepurje purjetraadi väljakerimine, mis eeldab kosmosesondi pöörlemapanekut. 2013. aasta 7. mail maalähedasele orbiidile läkitatud tudengisatelliit ESTCube-1 oli esimene satelliit elektrilise päikesepurje katsetusmooduliga. Satelliit seati edukalt vajaliku pöörlemiskiirusega pöörlema, kuid purje väljakerimine ebaõnnestus mehaanilise tõrke tõttu katsetusmooduli motoriseeritud purjepoolis. ESTCube-1 pöörlemapanekut ja päikesepurje katsetusmooduli juhtimist võimaldasid satelliidi pardaarvuti ja seda ümbritsevad liidesed, mille arendamise ja valideerimise tulemustele keskendub antud väitekiri. Pardaarvuti kogus mõõdiseid satelliidi asendi anduritelt, juhtis magnetmähiseid ning lülitas missioonilasti purjepooli mootorit, purjepooli kõrgepinge toiteplokki ja elektronkiirgureid. Lisaks vahendas pardaarvuti pilte pardakaamerast ning salvestas mõõtmistulemusi satelliidi alamsüsteemidelt et need hiljem maajaamale edastada. Satelliidi kaheaastase eluea jooksul ei täheldatud missioonikriitilisi tõrkeid pardaarvuti ega selle liideste töös. ESTCube-1 missioon aitas edukalt tõsta elektrilise päikesepurje tehnoloogia komponentide valmidusastet tulevasteks missioonideks.Electrical solar wind sail (E-sail) technology would enable propellantless interplanetary navigation of space probes, using just solar wind and electricity. One of the main challenges of the technology is E-sail tether deployment, for which the space probe would be spun to a high angular rate. Launched on May 7th, 2013, the Estonian student satellite ESTCube-1 was the first spacecraft with an E-sail experiment payload. While the satellite was successfully spun to the spin rate necessary for the experiment, the motorised reel technology used on the payload proved immature for tether deployment. ESTCube-1 spin-up and payload control were enabled by the spacecraft on-board computer. This thesis is focused on the results of the development and in-orbit validation of the on-board computer and its interfaces to other related systems on the satellite. The on-board computer collected measurements from spacecraft attitude sensors, controlled its magnetic torquers, mediated camera images and stored telemetry from various subsystems for later transmission. The on-board computer also toggled the tether reel motor, electron emitters and controlled the high voltage supply for the E-sail tether. Throughout the two-year lifetime of the spacecraft, no mission-critical issues were encountered in the operation of the on-board computer or its interfaces. The ESTCube-1 mission successfully improved the technological readiness of E-sail components for future missions.https://www.ester.ee/record=b524281

Analog design for manufacturability: lithography-aware analog layout retargeting

As transistor sizes shrink over time in the advanced nanometer technologies, lithography effects have become a dominant contributor of integrated circuit (IC) yield degradation. Random manufacturing variations, such as photolithographic defect or spot defect, may cause fatal functional failures, while systematic process variations, such as dose fluctuation and defocus, can result in wafer pattern distortions and in turn ruin circuit performance. This dissertation is focused on yield optimization at the circuit design stage or so-called design for manufacturability (DFM) with respect to analog ICs, which has not yet been sufficiently addressed by traditional DFM solutions. On top of a graph-based analog layout retargeting framework, in this dissertation the photolithographic defects and lithography process variations are alleviated by geometrical layout manipulation operations including wire widening, wire shifting, process variation band (PV-band) shifting, and optical proximity correction (OPC). The ultimate objective of this research is to develop efficient algorithms and methodologies in order to achieve lithography-robust analog IC layout design without circuit performance degradation

Quantum Chemistry in Nanoscale Environments: Insights on Surface-Enhanced Raman Scattering and Organic Photovoltaics

The understanding of molecular effects in nanoscale environments is becoming increasingly relevant for various emerging fields. These include spectroscopy for molecular identification as well as in finding molecules for energy harvesting. Theoretical quantum chemistry has been increasingly useful to address these phenomena to yield an understanding of these effects. In the first part of this dissertation, we study the chemical effect of surface-enhanced Raman scattering (SERS). We use quantum chemistry simulations to study the metal-molecule interactions present in these systems. We find that the excitations that provide a chemical enhancement contain a mixed contribution from the metal and the molecule. Moreover, using atomistic studies we propose an additional source of enhancement, where a transition metal dopant surface could provide an additional enhancement. We also develop methods to study the electrostatic effects of molecules in metallic environments. We study the importance of image-charge effects, as well as field-bias to molecules interacting with perfect conductors. The atomistic modeling and the electrostatic approximation enable us to study the effects of the metal interacting with the molecule in a complementary fashion, which provides a better understanding of the complex effects present in SERS. In the second part of this dissertation, we present the Harvard Clean Energy project, a high-throughput approach for a large-scale computational screening and design of organic photovoltaic materials. We create molecular libraries to search for candidates structures and use quantum chemistry, machine learning and cheminformatics methods to characterize these systems and find structure-property relations. The scale of this study requires an equally large computational resource. We rely on distributed volunteer computing to obtain these properties. In the third part of this dissertation we present our work related to the acceleration of electronic structure methods using graphics processing units. This hardware represents a change of paradigm with respect to the typical CPU device architectures. We accelerate the resolution-of-the-identity Moller-Plesset second-order perturbation theory algorithm using graphics cards. We also provide detailed tools to address memory and single-precision issues that these cards often present

Design Development Test and Evaluation (DDT and E) Considerations for Safe and Reliable Human Rated Spacecraft Systems

A team directed by the NASA Engineering and Safety Center (NESC) collected methodologies for how best to develop safe and reliable human rated systems and how to identify the drivers that provide the basis for assessing safety and reliability. The team also identified techniques, methodologies, and best practices to assure that NASA can develop safe and reliable human rated systems. The results are drawn from a wide variety of resources, from experts involved with the space program since its inception to the best-practices espoused in contemporary engineering doctrine. This report focuses on safety and reliability considerations and does not duplicate or update any existing references. Neither does it intend to replace existing standards and policy

The Telecommunications and Data Acquisition Report

This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA

Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning

The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is increased, so is the need for large amounts of labeled data to train the model. This is associated with a costly human annotation effort. To address this concern, with the long-term goal of leveraging the abundance of cheap unlabeled data, we explore methods of unsupervised "pre-training." In particular, we propose to use self-supervised automatic image colorization. We show that traditional methods for unsupervised learning, such as layer-wise clustering or autoencoders, remain inferior to supervised pre-training. In search for an alternative, we develop a fully automatic image colorization method. Our method sets a new state-of-the-art in revitalizing old black-and-white photography, without requiring human effort or expertise. Additionally, it gives us a method for self-supervised representation learning. In order for the model to appropriately re-color a grayscale object, it must first be able to identify it. This ability, learned entirely self-supervised, can be used to improve other visual tasks, such as classification and semantic segmentation. As a future direction for self-supervision, we investigate if multiple proxy tasks can be combined to improve generalization. This turns out to be a challenging open problem. We hope that our contributions to this endeavor will provide a foundation for future efforts in making self-supervision compete with supervised pre-training.Comment: Ph.D. thesi

Visual region understanding: unsupervised extraction and abstraction

The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer representations and high level human descriptions. In this research we explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions and their behaviours. For this purpose we address issues related to (i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a) a unified region merging method and spatiotemporal region reduction, (b) shape representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm and trajectory plots for the obtained region classes. 1 Specifically, we formulate a region merging spatial segmentation mechanism that combines and adapts features shown previously to be individually useful, namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across standard metrics and datasets validate their performance

Methods and instrumentation for raman characterization of bladder cancer tumor

High incidence and recurrence rates make bladder cancer the most common malignant tumor in the urinary system. Cystoscopy is the gold standard test used for diagnosis, nevertheless small flat tumors might be missed, and the procedure still represents discomfort to patients and high recurrence can result from of urethral injuries. During cystoscopy, suspicious tumors are detected through white light endoscopy and resected tissue is further examined by histopathology. after resection, the pathologist provides information on the differentiation of the cells and the penetration depth of the tumor in the tissue, known as grading and staging of tumor, respectively. During cystoscopy, information on tumor grading and morphological depth characterization can assist onsite diagnosis and significantly reduce the amount of unnecessarily resected tissue. Recently, new developments in optical imaging and spectroscopic approaches have been demonstrated to improve the results of standard techniques by providing real-time detection of macroscopic and microscopic biomedical information. Different applications to detect anomalies in tissues and cells based on the chemical composition and structure at the microscopic level have been successfully tested. There is, nevertheless, the need to cope with the demands for clinical translation. This doctoral thesis presents the investigations, clinical studies and approaches applied to filling the main open research questions when applying Raman spectroscopy as a diagnostic tool for bladder cancer tumor grading and general Raman spectroscopy-based oncological clinical studies

Third International Symposium on Space Mission Operations and Ground Data Systems, part 2

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The symposium papers focus on improvements in the efficiency, effectiveness, and quality of data acquisition, ground systems, and mission operations. New technology, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations. This volume covers expert systems, systems development tools and approaches, and systems engineering issues