Fabrication of Single Nanowire Device using Electron Beam Lithography

One dimensional nanostructure materials such as nanowires have drawn many interests among the scientific community for a wide range of applications such as field-effect transistors [1], [2], inverters[3], light-emitting diode [1], lasers [4], nanosensors [5], [6], and photodetectors [7]... Comparing with the characterization of nanowire arrays, characterizing a single nanowire will definitely provide a better understanding on new nanowire properties due to simplified behaviors of devices. Although promising theories could be drawn from those results, fabrication of test structure for single nanowire measurements cannot be easily processed using standard microfabrication techniques. Therefore, electron beam lithography integrated with photolithography technique has been used to manipulate the connection; which provides I-V characteristics, of single horizontal nanowire with a specific device. Single Si nanowire characterization could be extended to various materials for further studies. In addition to single horizontal nanowire device, single vertical nanowire structure has been fabricated. Electron beam lithography technique is mainly used to pattern well-defined nanostructures where single ZnO nanowire is grown. Optical measurement, photoluminescence, is conducted to verify ZnO nanowires. This thesis also emphasizes on fabrication process to pattern various structures such as lines, rings, and circles with different sizes from 1um to sub 100nm... They could be potential candidate to create nanodisk antenna (rings), fishnet structure (lines), and base to grown single nanowire (circle)

Si-based Germanium Tin Photodetectors for Short-Wave and Mid-Wave Infrared Detections

The demand of light-weight and inexpensive imaging system working in the infrared range keeps increasing for the last decade, especially for civil applications. Although several group IV materials such as silicon and germanium are used to realize detectors in the visible and near infrared region, they are not the efficient approach for imaging system in the short-wave infrared detection range and beyond due to bandgap limit. On the other hand, this market is heavily relied upon mature technology from III-V and II-VI elements over years, which are costly to growth and incompatible with available Si complementary metal-oxide-semiconductor (CMOS) foundries. This limits the fabrication of large scale focal plan arrays detectors in this detection range. Therefore, a material system that meets the necessary requirements has long been in demand. The Ge1-xSnx material system has been introduced as a potential solution for low-cost high-performance photodetector for short-wave infrared towards mid-infrared detections due to its compatibility with Si CMOS process and wide detection range by incorporating more Sn in the alloy. Since then, immense growth efforts have been made to improve the material quality reaching device-quality using commercial chemical vapor deposition (CVD) reactors or molecular beam epitaxy (MBE) chambers. This dissertation will develop Si-based GeSn photodetectors technology to realize low-cost high-performance focal plane arrays detectors working in the SWIR towards MIR. It began with the development of fabrication process of single element GeSn photoconductor and photodiode, followed by systematic characterization and analysis of detectors’ figures of merits to provide a more optimized structure. A peak responsivity of 20 A/W (photoconductor) and 0.34 A/W (photodiode) at 2 µm were achieved. An external quantum efficiency of 20 % was reported for the first time. The highest value of specific detectivity D* is only 3-4 times less than commercially available Extended-InGaAs detector. Surface passivation technique was also pursued to reduce surface leakage current. Finally, infrared imaging capability was demonstrated using single pixel detector. The study involves a wide range of Sn composition from 10 to 22 %

Re-examining the Cult of Personality: A Comparative Cross-national Case Study of Kim Il Sung, Mao Zedong, and Ho Chi Minh

The thesis re-examines the utility of the charismatic leader’s cult of personality as a strategic power-enhancing tool by performing a cross-national comparative case study of three Asian personality cults – Kim Il Sung, Mao Zedong, and Ho Chi Minh. To what extent did these cult leaders possess the godlike powers that the cult of personality literature implies? The thesis finds support for the conclusion that increasing deification of a leader is not always positively correlated with a leader’s godlike powers, operationalized as a leader’s unilateral decision-making powers over national policies. Kim Il Sung’s cult was manipulated by family members for their benefit. Mao Zedong attempted to wield the power of the cult of Mao during the Cultural Revolution but could not control the Red Guards. The Communist Party of Vietnam utilized the cult of Ho Chi Minh to maintain the image of national unity while ignoring his policy directives

Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.Comment: 8 pages, 8 figure

Jednoduchý odhad vzdálenosti objektu založený na kamerových záznamech

There has been a quick and effective increase in computer vision research in recent years, and this will continue. Part of this success may be attributed to the adoption and adaptation of Machine Learning methods, while other parts can be attributed to the invention of novel representations and models for specific computer vision challenges, as well as the development of cost-effective solutions. Object detection is one area that has made significant strides in recent years. Object detection has been used in a variety of applications, including robotics, consumer electronics (e.g., smart phones), security, and transportation (e.g., autonomous and assisted driving). In this thesis, the detection task is the first job completed since it enables the acquisition of further information about the identified object as well as about the surrounding scene. Once an instance of an item has been detected, it is possible to gain more information, such as the ability to identify an object and estimated its distance. It is the goal of this study to give a detailed and in-depth explanation of how to find objects and figure out how far apart they are. This thesis is primarily concerned with the creation of object distance measurement and feature extraction algorithms using the You Only Look Once (YOLO) method combined with the Triangle Similarity and Monodepth2 approach for calculating distance with a single fixed camera. The purpose of this thesis is to investigate the detection ability of the method, YOLOv4-tiny, which is one of the most common nowadays. Furthermore, it is more accurate than other detection methods and executes more quickly. The YOLO method outperforms all of the measures we looked at while still delivering a high frame rate for real-time use. Instead of picking the most appealing part of an image, the YOLO technique predicts classes and bounding boxes for the entire image in a single algorithm run. We recommend using a combination of the YOLOv4-tiny and the Triangle Similarity and a very well-known approach called Monodepth2 of the lens camera to estimate the distance between the detected item and the camera. This will allow for a more accurate measurement of the distance. Using the YOLO approach, we detect an object in an image and extract its location and width from the image. This is also known as a virtual image. The items utilized in the tests are photographs of everyday things such as bottles, people, bags, and cars,... By comparing the real and imaginary widths of an object, the triangle similarity approach will be able to determine the focal length of a camera and, as a result, determine the best distance between it and the object. At the end of the process, the linear regression approach is used to forecast the error from the observed distance.V posledních letech došlo k rychlému a efektivnímu nárůstu výzkumu v oblasti počítačového vidění, který bude pokračovat. Část tohoto úspěchu lze přičíst přijetí a přizpůsobení metod strojového učení, zatímco další část lze přičíst vynálezu nových reprezentací a modelů pro specifické problémy počítačového vidění a také vývoji nákladově efektivních řešení. Detekce objektů je jednou z oblastí, která v posledních letech dosáhla významného pokroku. Detekce objektů se používá v řadě aplikací, včetně robotiky, spotřební elektroniky (např. chytrých telefonů), bezpečnosti a dopravy (např. autonomní a asistované řízení). V této práci je detekční úloha první splněnou úlohou, protože umožňuje získat další informace o identifikovaném objektu i o okolní scéně. Jakmile je instance předmětu detekována, je možné získat další informace, například možnost identifikovat objekt a odhadnout jeho vzdálenost. Cílem této studie je podat podrobný a zevrubný výklad o tom, jak najít objekty a zjistit, jak jsou od sebe vzdáleny. Tato práce se zabývá především vytvořením algoritmů pro měření vzdálenosti objektů a extrakci prvků pomocí metody You Only Look Once (YOLO) v kombinaci s přístupem Triangle Similarity a Monodepth2 pro výpočet vzdálenosti pomocí jedné pevné kamery. Cílem této práce je prozkoumat detekční schopnost metody YOLOv4-tiny, která je v současné době jednou z nejrozšířenějších. Navíc je přesnější než ostatní metody detekce a provádí se rychleji. Metoda YOLO překonává všechna námi zkoumaná opatření a zároveň poskytuje vysokou snímkovou frekvenci pro použití v reálném čase. Namísto výběru nejatraktivnější části snímku předpovídá technika YOLO třídy a ohraničující boxy pro celý snímek v jediném běhu algoritmu. Doporučujeme použít kombinaci YOLOv4-tiny a trojúhelníkové podobnosti a velmi známého přístupu nazvaného Monodepth2 objektivu kamery pro odhad vzdálenosti mezi detekovaným předmětem a kamerou. To umožní přesnější měření vzdálenosti. Pomocí přístupu YOLO detekujeme objekt v obraze a extrahujeme z něj jeho polohu a šířku. Tento obraz je také znám jako virtuální obraz. Předměty využité v testech jsou fotografie věcí každodenní potřeby, jako jsou láhve, lidé, tašky a auta,... Porovnáním skutečné a imaginární šířky předmětu bude přístup založený na trojúhelníkové podobnosti schopen určit ohniskovou vzdálenost fotoaparátu a v důsledku toho určit nejlepší vzdálenost mezi ním a předmětem. Na konci procesu se použije přístup lineární regrese k předpovědi chyby ze zjištěné vzdálenosti.460 - Katedra informatikydobř

Hitting the jackpot: What drives casino hotel stock returns’ predictability?

Forecasting casino hotel stock prices are strategically important to key stakeholders making investment decisions. However, there is a paucity of studies that have examined the impact of macro-economic variables, sector-specific variables and firm-specific variables on casino stock returns. This research fills a research gap by examining the predictability of Australian casino hotel stock returns. The forecasting of casino hotel stock prices was modelled using econometric panel predictive regression analysis through the efficacy of 26 predictors comprising macroeconomic variables, sector-specific variables, and firm-specific variables as predictors. All eight casinos listed on the Australia Stock Exchange (ASX) were included in our sample. Our econometric panel predictive regression model found macro-economic variables with the strongest return predictability relative to the sector-specific and firm-specific variables. Our study contributes to the theoretical advancement of forecasting methodology through panel predictive regression modelling by addressing the endogeneity, persistency and heteroskedasticity of stock predictors by considering a large set of macro-economic variables, sector-specific and firm-specific variables as predictors to develop an econometric framework that suits the features of hotel financial data

Cattle as a consistently resilient agricultural commodity

This study compares a range of agricultural commodities over periods of varying economic circumstances. These commodities are examined over three categories, including returns, risk, and contribution to portfolio optimisation. Consistency in these categories is determined over four equal three-year stages which comprise pre-GFC (Global Financial Crisis), GFC, post-GFC and post-post GFC. To demonstrate resilience in the most extreme circumstances, the study uses Conditional Value at Risk (CVaR), which measures extreme risk in the tail of a distribution, as the risk measure and risk-return optimiser. The study thus provides a unique and comprehensive extreme-risk based focus which identifies and ranks the consistency of performance of agricultural commodities over a range of criteria and conditions. Cattle commodities consistently demonstrate the strongest overall performance in the categories examined

Do nonparametric measures of extreme equity risk change the parametric ordinal ranking? Evidence from Asia

There has been much discussion in the literature about how central measures of equity risk such as standard deviation fail to account for extreme tail risk of equities. Similarly, parametric measures of value at risk (VaR) may also fail to account for extreme risk as they assume a normal distribution which is often not the case in practice. Nonparametric measures of extreme risk such as nonparametric VaR and conditional value at risk (CVaR) have often been found to overcome this problem by measuring actual tail risk without applying any predetermined assumptions. However, this article argues that it is not just the actual risk of equites that is important to investor choices, but also the relative (ordinal) risk of equities compared to each other. Using an applied setting of industry portfolios in a variety of Asian countries (benchmarked to the United States), over crisis and non-crisis periods, this article finds that nonparametric measures of VaR and CVaR may provide only limited new information to investors about relative risk in the portfolios examined as there is a high degree of similarity found in relative industry risk when using nonparametric metrics as compared to central or parametric measures such as standard deviation and parametric VaR