Silicon carbide (SiC) is currently under development for high power bipolar devices such as insulated gate bipolar transistors (IGBTs). A major issue for these devices is the charge carrier lifetime, which, in the absence of structural defects such as dislocations, is influenced by point defects and their associated deep levels. These defects provide energy levels within the bandgap and may act as either recombination or trapping centers, depending on whether they interact with both conduction and valence band or only one of the two bands. Of all deep levels know in 4H-SiC, the intrinsic carbon vacancy related Z1/2 is the most problematic since it is a very effective recombination center which is unavoidably formed during growth. Its concentration in the epilayer can be decreased for the production of high voltage devices by injecting interstitial carbon, for example by oxidation, which, however, results in the formation of other new deep levels. Apart from intrinsic crystal flaws, extrinsic defects such as transition metals may also produce deep levels within the bandgap, which in literature have so far only been shown to produce trapping effects. The focus of the thesis is the transient electrical and optical characterization of deep levels in SiC and their influence on the carrier lifetime. For this purpose, deep level transient spectroscopy (DLTS) and minority carrier transient spectroscopy (MCTS) variations were used in combination with time-resolved photoluminescence (TRPL). Paper 1 deals with a lifetime limiting deep level related to Fe-incorporation in n-type 4H-SiC during growth and papers 2 and 3 focus on identifying the main intrinsic recombination center in p-type 4H-SiC. In paper 4, the details of the charge carrier capture behavior of the deeper donor levels of the carbon vacancy, EH6/7, are investigated. Paper 5 deals with trapping effects created by unwanted incorporation of high amounts of boron during growth of n-type 4H-SiC which hinders the measurement of the carrier lifetime by room temperature TRPL. Finally, paper 6 is concerned with the characterization of oxidation-induced deep levels created in n- and p-type 4H- and 6H-SiC as a side-product of lifetime improvement by oxidation. In paper 1, the appearance of a new recombination center in n-type 4H-SiC, the RB1 level is discussed and the material is analyzed using room temperature TRPL, DLTS and pnjunction DLTS. The level appears to originate from a reactor contamination with Fe, a transition metal that generally leads to the formation of several trapping centers in the bandgap. Here it is found that under specific circumstances beneficial to the growth of high-quality material with a low Z1/2 concentration, the Fe incorporation also creates an additional recombination center capable of limiting the carrier lifetime. In paper 2, all deep levels found in p-type 4H-SiC grown at Linköping University which are accessible by DLTS and MCTS are investigated with regard to their efficiency as recombination centers. We find that none of the detectable levels is able to reduce carrier lifetime in p-type significantly, which points to the lifetime killer being located in the top half of the bandgap and having a large hole to electron capture cross section ratio (such as Z1/2, which is found in n-type material), making it undetectable by DLTS and MCTS. Paper 3 compares carrier lifetimes measured by temperature-dependent TRPL measurements in n- and p-type 4H-SiC and it is shown that the lifetime development over a large temperature range (77 - 1000 K) is similar in both types. This is interpreted as a further indication that the carbon vacancy related Z1/2 level is the main lifetime killer in p-type. In paper 4, the hole and electron capture cross sections of the near midgap deep levels EH6/7 are characterized. Both levels are capable of rapid electron capture but have only small hole capture rates, making them insignificant as recombination centers, despite their advantageous position near midgap. Minority carrier trapping by boron, which is both a p-type dopant and an unavoidable contaminant in 4H-SiC grown by CVD, is investigated in paper 5. Since even the shallow boron acceptor levels are relatively deep in the bandgap, minority trap and-release effects are detectable in room-temperature TRPL measurements. In case a high density of boron exists in n-type 4H-SiC, for example leached out from damaged graphite reactor parts during growth, we demonstrate that these trapping effects may be misinterpreted in room temperature TRPL measurements as a long free carrier lifetime. Paper 6 uses MCTS, DLTS, and room temperature TRPL to characterize the oxidation induced deep levels ON1 and ON2 in n- and p-type 4H- and their counterparts OS1-OS3 in 6H-SiC. The levels are found to all be positive-U, coupled two-levels defects which trap electrons efficiently but exhibit very inefficient hole capture once the defect is fully occupied by electrons. It is shown that these levels are incapable of significantly influencing carrier lifetime in epilayers which underwent high temperature lifetime enhancement oxidations. Due to their high density after oxidation and their high thermal stability they may, however, act to compensate n-type doping in low-doped material.

Thesis (M. Sc.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 45-48).

L'accroissement rapide des données numériques vidéographiques fait de la compréhension automatiquedes vidéos un enjeu de plus en plus important. Comprendre de manière automatique une vidéo recouvrede nombreuses applications, parmi lesquelles l'analyse du contenu vidéo sur le web, les véhicules autonomes,les interfaces homme-machine. Cette thèse présente des contributions dans deux problèmes majeurs pourla compréhension automatique des vidéos : la détection d'actions supervisée par des données web, et la localisation d'actions humaines.La détection d'actions supervisées par des données web a pour objectif d'apprendre à reconnaître des actions dans des contenus vidéos sur Internet, sans aucune autre supervision. Nous proposons une approche originaledans ce contexte, qui s'appuie sur la synergie entre les données visuelles (les vidéos) et leur description textuelle associée, et ce dans le but d'apprendre des classifieurs pour les événements sans aucune supervision. Plus précisément, nous télechargeons dans un premier temps une base de données vidéos à partir de requêtes construites automatiquement en s'appuyant sur la description textuelle des événéments, puis nous enlevons les vidéos téléchargées pour un événement, et dans laquelle celui-ci n'apparaït pas. Enfin, un classifieur est appris pour chaque événement. Nous montrons l'importance des deux étapes principales, c'est-à-dire la créations des requêtes et l'étape de suppression des vidéos, par des résutatsquantitatifs. Notre approche est évaluée dans des conditions difficiles, où aucune annotation manuelle n'est disponible, dénotées EK0 dans les challenges TrecVid. Nous obtenons l'état de l'art sur les bases de donnéesMED 2011 et 2013.Dans la seconde partie de notre thèse, nous nous concentrons sur la localisation des actions humaines, ce qui implique de reconnaïtre à la fois les actions se déroulant dans la vidéo, comme par exemple "boire" ou "téléphoner", et leur étendues spatio-temporelles. Nous proposons une nouvelle méthode centrée sur la personne, traquant celle-ci dans les vidéos pour en extraire des tubes encadrant le corps entier, même en cas d'occultations ou dissimulations partielles. Deux raisons motivent notre approche. La première est qu'elle permet de gérer les occultations et les changements de points de vue de la caméra durant l'étape de localisation des personnes, car celle-ci estime la position du corps entier à chaque frame. La seconde est que notre approche fournit une meilleure grille de référence que les tubes humains standards (c'est-à-dire les tubes qui n'encadrent que les parties visibles) pour extraire de l'information sur l'action. Le coeur de notre méthode est un réseau de neurones convolutionnel qui apprend à générer des propositions de parties du corps humain. Notre algorithme de tracking connecte les détections temporellement pour extraire des tubes encadrant le corps entier. Nous évaluons notre nouvelle méthode d'extraction de tubes sur une base de données difficile, DALY, et atteignons l'état de l'art. / With the rapid growth of digital video content, automaticvideo understanding has become an increasingly important task. Video understanding spansseveral applications such as web-video content analysis, autonomous vehicles, human-machine interfaces (eg, Kinect). This thesismakes contributions addressing two major problems in video understanding:webly-supervised action detection and human action localization.Webly-supervised action recognition aims to learn actions from video content on the internet, with no additional supervision. We propose a novel approach in this context, which leverages thesynergy between visual video data and the associated textual metadata, to learnevent classifiers with no manual annotations. Specifically, we first collect avideo dataset with queries constructed automatically from textual descriptionof events, prune irrelevant videos with text and video data, and then learn thecorresponding event classifiers. We show the importance of both the main steps of our method, ie,query generation and data pruning, with quantitative results. We evaluate this approach in the challengingsetting where no manually annotated training set is available, i.e., EK0 in theTrecVid challenge, and show state-of-the-art results on MED 2011 and 2013datasets.In the second part of the thesis, we focus on human action localization, which involves recognizing actions that occur in a video, such as ``drinking'' or ``phoning'', as well as their spatial andtemporal extent. We propose a new person-centric framework for action localization that trackspeople in videos and extracts full-body human tubes, i.e., spatio-temporalregions localizing actions, even in the case of occlusions or truncations.The motivation is two-fold. First, it allows us to handle occlusions and camera viewpoint changes when localizing people, as it infers full-body localization. Second, it provides a better reference grid for extracting action information than standard human tubes, ie, tubes which frame visible parts only.This is achieved by training a novel human part detector that scores visibleparts while regressing full-body bounding boxes, even when they lie outside the frame. The core of our method is aconvolutional neural network which learns part proposals specific to certainbody parts. These are then combined to detect people robustly in each frame.Our tracking algorithm connects the image detections temporally to extractfull-body human tubes. We evaluate our new tube extraction method on a recentchallenging dataset, DALY, showing state-of-the-art results.

abstract: With the emergence of edge computing paradigm, many applications such as image recognition and augmented reality require to perform machine learning (ML) and artificial intelligence (AI) tasks on edge devices. Most AI and ML models are large and computational heavy, whereas edge devices are usually equipped with limited computational and storage resources. Such models can be compressed and reduced in order to be placed on edge devices, but they may loose their capability and may not generalize and perform well compared to large models. Recent works used knowledge transfer techniques to transfer information from a large network (termed teacher) to a small one (termed student) in order to improve the performance of the latter. This approach seems to be promising for learning on edge devices, but a thorough investigation on its effectiveness is lacking. The purpose of this work is to provide an extensive study on the performance (both in terms of accuracy and convergence speed) of knowledge transfer, considering different student-teacher architectures, datasets and different techniques for transferring knowledge from teacher to student. A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student. But other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact. For example, a smaller and shorter network, trained with knowledge transfer on Caltech 101 achieved a significant improvement of 7.36\% in the accuracy and converges 16 times faster compared to the same network trained without knowledge transfer. On the other hand, smaller network which is thinner than the teacher network performed worse with an accuracy drop of 9.48\% on Caltech 101, even with utilization of knowledge transfer. / Dissertation/Thesis / Masters Thesis Computer Science 2018

At the Castle Creek study area, levee deposits are well-exposed over an area of ~2.6 km wide and ~90 m thick. This provides an opportunity to describe their lateral and vertical lithological changes, and accordingly details about their reservoir geometry and stratal continuity. Here, levee deposits are divided vertically into packages, each consisting of a sand-rich lower part overlain sharply by a mud-rich upper part. Each lower part displays a consistent thickening then thinning trend laterally away from its genetically related channel. The characteristics of these packages suggest that they were controlled by recurring changes in the structure of channellized flows, which in turn was controlled by grain size and grain sorting. This ultimately was controlled by short-term changes in relative sea level. Moreover, some mud- and sand-rich strata are rich in residual carbon suggesting that mid-fan levees can serve as source rocks for hydrocarbon generation, and also reservoirs.

Please read the abstract in the section 00front of this document / Dissertation (M Eng (Mining Engineering))--University of Pretoria, 2007. / Mining Engineering / unrestricted

This thesis tackles the problem of understanding deep neural network with piece- wise linear activation functions. We leverage tropical geometry, a relatively new field in algebraic geometry to characterize the decision boundaries of a single hidden layer neural network. This characterization is leveraged to understand, and reformulate three interesting applications related to deep neural network. First, we give a geo- metrical demonstration of the behaviour of the lottery ticket hypothesis. Moreover, we deploy the geometrical characterization of the decision boundaries to reformulate the network pruning problem. This new formulation aims to prune network pa- rameters that are not contributing to the geometrical representation of the decision boundaries. In addition, we propose a dual view of adversarial attack that tackles both designing perturbations to the input image, and the equivalent perturbation to the decision boundaries.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22580号 / 情博第717号 / 新制||情||123(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 黒橋 禎夫, 教授 田島 敬史, 教授 田中 克己(京都大学 名誉教授) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Coherent Raman microscopy (CRM) is a powerful nonlinear optical imaging technique based on contrast via Raman active molecular vibrations. CRM has been used in domains ranging from biology to medicine to geology in order to provide quick, sensitive, chemical-specific, and label-free 3D sectioning of samples. The Raman contrast is usually obtained by combining two ultrashort pulse input beams, known as Pump and Stokes, whose frequency difference is adjusted to the Raman vibrational frequency of interest. CRM can be used in conjunction with other imaging modalities such as second harmonic generation, fluorescence, and third harmonic generation microscopy, resulting in a multimodal imaging technique that can capture a massive amount of data. Two fundamental elements are crucial in CRM. First, a laser source which is broadband, stable, rapidly tunable, and low in noise. Second, a strategy for image analysis that can handle denoising and material classification issues in the relatively large datasets obtained by CRM techniques. Stimulated Raman Scattering (SRS) microscopy is a subset of CRM techniques, and this thesis is devoted entirely to it. Although Raman imaging based on a single vibrational resonance can be useful, non-resonant background signals and overlapping bands in SRS can impair contrast and chemical specificity. Tuning over the Raman spectrum is therefore crucial for target identification, which necessitates the use of a broadband and easily tunable laser source. Although supercontinuum generation in a nonlinear fibre could provide extended tunability, it is typically not viable for some CRM techniques, specifically in SRS microscopy. Signal acquisition schemes in SRS microscopy are focused primarily on detecting a tiny modulation transfer between the Pump and Stokes input laser beams. As a result, very low noise source is required. The primary and most important component in hyperspectral SRS microscopy is a low-noise broadband laser source. The second problem in SRS microscopy is poor signal-to-noise (SNR) ratios in some situations, which can be caused by low target-molecule concentrations in the sample and/or scattering losses in deep-tissue imaging, as examples. Furthermore, in some SRS imaging applications (e.g., in vivo), fast imaging, low input laser power or short integration time is required to prevent sample photodamage, typically resulting in low contrast (low SNR) images. Low SNR images also typically suffer from poorly resolved spectral features. Various de-noising techniques have been used to date in image improvement. However, to enable averaging, these often require either previous knowledge of the noise source or numerous images of the same field of view (under better observing conditions), which may result in the image having lower spatial-spectral resolution. Sample segmentation or converting a 2D hyperspectral image to a chemical concentration map, is also a critical issue in SRS microscopy. Raman vibrational bands in heterogeneous samples are likely to overlap, necessitating the use of chemometrics to separate and segment them. We will address the aforementioned issues in SRS microscopy in this thesis. To begin, we demonstrate that a supercontinuum light source based on all normal dispersion (ANDi) fibres generates a stable broadband output with very low incremental source noise. The ANDi fibre output's noise power spectral density was evaluated, and its applicability in hyperspectral SRS microscopy applications was shown. This demonstrates the potential of ANDi fibre sources for broadband SRS imaging as well as their ease of implementation. Second, we demonstrate a deep learning neural net model and unsupervised machine-learning algorithm for rapid and automated de-noising and segmentation of SRS images based on a ten-layer convolutional autoencoder: UHRED (Unsupervised Hyperspectral Resolution Enhancement and De-noising). UHRED is trained in an unsupervised manner using only a single (“one-shot”) hyperspectral image, with no requirements for training on high quality (ground truth) labelled data sets or images.

In this exciting era of artificial intelligence and machine learning, the success of AlphaGo, AlphaZero, and MuZero has generated a great interest in deep reinforcement learning, especially under self-play settings. The methods used by AlphaZero are finding their ways to be more useful than before in many different application areas, such as clinical medicine, intelligent military command decision support systems, and recommendation systems. While specific methods of reinforcement learning with selfplay have found their place in application domains, there is much to be explored from existing reinforcement learning methods not originally intended for self-play settings. This thesis focuses on evaluating performance of existing reinforcement learning techniques in self-play settings. In this research, we trained and evaluated the performance of two deep reinforcement learning algorithms with self-play settings on game environments, such as the games Connect Four and Chess. We demonstrate how a simple on-policy, policy-based method, such as REINFORCE, shows signs of learning, whereas an off-policy value-based method such as Deep Q-Networks does not perform well with self-play settings in the selected environments. The results show that REINFORCE agent wins 85% of the games after training against a random baseline agent and 60% games against the greedy baseline agent in the game Connect Four. The agent’s strength from both techniques was measured and plotted against different baseline agents. We also investigate the impact of selected significant hyper-parameters in the performance of the agents. Finally, we provide our recommendation for these hyper-parameters’ values for training deep reinforcement learning agents in similar environments. / Graduate