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

Lecturer Attitudes and Behavioural Intentions to Use Learning Management Systems in Vietnam

This study aims to explore lecturer attitudes to, and intentions for, using a learning management system (LMS) in a Vietnamese university. Its two main purposes are to (a) identify the factors that influence lecturer attitudes and intentions to use an LMS, and (b) examine the causal relationships among the factors. To achieve this aim, the study used Davis’ (1985) technology acceptance model (TAM) as a baseline. The study expands the original model to include two constructs: perceived internet self-efficacy (PIS), and support to use (SU). The results of the study revealed that PIS was a significant direct predictor of lecturers’ perceived ease of use and behavioural intention to use an LMS. However, the support to use construct did not predict perceived ease of use. The study suggests that institutions should conduct an in-depth survey of teacher needs to assist with making well-informed decisions about developing an LMS for future emergencies

Systematic Risk in Energy Businesses: Empirical Evidence for the ASEAN

This paper is conducted to provide an additional empirical evidence in relation to the estimates of equity beta for energy businesses in the ASEAN-5 including Vietnam, Thailand, the Philippines, Malaysia, and Singapore. Listed energy companies for the period from 2005 to 2015 are used. Quantile regression, together with the OLS and LAD, has been used. Findings from this paper indicate that: (i) as long as the OLS and the LAD approaches are adopted, estimates of equity beta are relatively consistent across various research periods; (ii) estimates of equity beta appear to vary substantial across different quantiles; and (iii) estimates of equity beta have appeared to vary across research periods. However, as an overall level across time and methods, a level of risk faced by a company in the energy sector is below the average of the level of risk for the entire market for the above nations. Keywords: Beta, Listed Energy Firms, Quantile regression, ASEAN JEL Classifications: G11; G1

Equity Beta for Regulated Energy Businesses in Australia: A Revisit

This paper aims to estimate the equity beta – a key input of the Capital Asset Pricing Model, for the energy businesses in Australia in the 11-year period from 2005 to 2015. Various methods are used in this paper including Quantile Regression. Listed companies in the energy industry are considered at individual and portfolio levels. Findings from this paper are  both consistent and contrast with prior related studies: (i) energy sector in Australia face a relatively low risk level compared to the market; (ii) OLS results are higher than LAD; and (iii) QR vary across different percentiles. Keywords: Equity Beta, Quantile regression, Australia. JEL Classifications: G11; G1

Building a Model of Organizational Activities Experience in Natural Sciences under Stem Education Orientation

STEM educational-oriented Natural Science experience activities are learner-oriented activities Taking learners as the center in all natural science research activities applying engineering technology and mathematics STEM in solving practical problems Based on theoretical research on experiential activities STEM education and the characteristics of Natural Sciences the article studies the educational forces inside and outside the school From there it is proposed to build a model of organizational structure for experiential activities in Natural Sciences oriented toward STEM education The results of this research are meaningful in helping principals see the importance and significance of coordinating educational forces inside and outside the school in organizing experiential activities in Natural Sciences according to orientation STEM education for student

Primary Evaluation on Growth Performances of Stress Negative Piétrain Pigs Raised in Hai Phong Province of Vietnam

peer reviewedThe present study was carried out on 19 stress negative Piétrain pigs (Pietrain ReHal), consisting of 13 gilts and 6 young boars imported from Belgium, raised in the livestock farm of Dong Hiep (Hai Phong) in order to evaluate growth performances and their adaptability in the North of Vietnam. Results showed that the average body weight of the whole herd at 2, 4, 5.5, and 8.5 months old was 19.05, 51.05, 85.82, and 119.47 kg, respectively. During the growing periods, except the first stage, the male grew faster than the female and the pigs of the CT genotype grew faster than those of CC genotype although the difference was not significant (P>0.05). The average daily gain (ADG) was 528.56 grams for the whole herd. The ADG was higher for the male (546.48 grams) than for the female (520.29 grams), and its was higher for the CT than the CC, but the difference was not statistically significant (P>0.05). The feed conversion ratio (FCR) was 2.69 kg. The estimated lean percentage at 8.5 months old was 64.08%. The results indicate that Piétrain stress negative pigs could develop well on the farm conditions in Hai Phong, Vietnam

Using Solvent Vapor Annealing for the Enhancement of the Stability and Efficiency of Monolithic Hole-conductor-free Perovskite Solar Cells

In the last few years, perovskite solar cells have attracted enormous interest in the photovoltaic community due to their low cost of materials, tunable band gap, excellent photovoltaic properties and easy process ability at low temperature. In this work, we fabricated hole-conductor-free carbon-based perovskite solar cells with the monolithic structure: glass/FTO/bl-TiO$_{2}$/(mp-TiO$_{2}$/mp-ZrO$_{2}$/mp-carbon) perovskite. The mixed 2D/3D perovskite precursor solution composed of PbI$_{2}$, methylammonium iodide (MAI), and 5-ammoniumvaleric acid iodide (5-AVAI) was drop-casted through triple mesoporous TiO$_{2}$/ZrO$_{2}$/carbon electrode films. We found that the isopropyl alcohol (IPA) solvent vapor annealing strongly influenced on the growth of mixed 2D/3D perovskite on triple mesoscopic layers. It resulted in the better pore filling, better crystalline quality of perovskite layer, thus the improved stability and efficiency of perovskite solar cell was attributed to lower defect concentration and reduced recombination

Generalizability assessment of AI models across hospitals in a low-middle and high income country

The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income countries (HICs), which often possess the resources and infrastructure to adopt innovative healthcare technologies, LMICs confront resource limitations such as insufficient funding, outdated infrastructure, limited digital data, and a shortage of technical expertise. Consequently, many algorithms initially trained on data from non-LMIC settings are now being employed in LMIC contexts. However, the effectiveness of these systems in LMICs can be compromised when the unique local contexts and requirements are not adequately considered. In this study, we evaluate the feasibility of utilizing models developed in the United Kingdom (a HIC) within hospitals in Vietnam (a LMIC). Consequently, we present and discuss practical methodologies aimed at improving model performance, emphasizing the critical importance of tailoring solutions to the distinct healthcare systems found in LMICs. Our findings emphasize the necessity for collaborative initiatives and solutions that are sensitive to the local context in order to effectively tackle the healthcare challenges that are unique to these regions

Detection of co-infection and recombination cases with Omicron and local Delta variants of SARS-CoV-2 in Vietnam

The first nationwide outbreak of COVID-19 in Vietnam started in late April 2021 and was caused almost exclusively by a single Delta lineage, AY.57. In early 2022, multiple Omicron variants co-circulated with Delta variants and quickly became dominant. The co-circulation of Delta and Omicron happened leading to possibility of co-infection and recombination events which can be revealed by viral genomic data. From January to October 2022, a total of 1028 viral RNA samples out of 4852 positive samples (Ct < 30) were sequenced by the long pooled amplicons method on Illumina platforms. All sequencing data was analysed by the workflow for SARS-CoV-2 on CLC genomics workbench and Illumina Dragen Covid application. Among those sequenced samples, we detected a case of Delta AY.57/Omicron BA.1 co-infection and two cases of infection with Delta AY.57/Omicron BA.2 recombinants which were nearly identical and had different epidemiological characteristics. Since the AY.57 lineage circulated almost exclusively in Vietnam, these results strongly suggest domestic events of co-infection and recombination. These findings highlight the strengths of genomic surveillance in monitoring the circulating variants in the community enabling rapid identification of viral changes that may affect viral properties and evolutionary events

Implementation of point-of-care testing of C-reactive protein concentrations to improve antibiotic targeting in respiratory illness in Vietnamese primary care: a pragmatic cluster-randomised controlled trial

Background In previous trials, point-of-care testing of C-reactive protein (CRP) concentrations safely reduced antibiotic use in non-severe acute respiratory infections in primary care. However, these trials were done in a research-oriented context with close support from research staff, which could have influenced prescribing practices. To better inform the potential for scaling up point-of-care testing of CRP in respiratory infections, we aimed to do a pragmatic trial of the intervention in a routine care setting. Methods We did a pragmatic, cluster-randomised controlled trial at 48 commune health centres in Viet Nam between June 1, 2020, and May 12, 2021. Eligible centres served populations of more than 3000 people, handled 10–40 respiratory infections per week, had licensed prescribers on site, and maintained electronic patient databases. Centres were randomly allocated (1:1) to provide point-of-care CRP testing plus routine care or routine care only. Randomisation was stratified by district and by baseline prescription level (ie, the proportion of patients with suspected acute respiratory infections to whom antibiotics were prescribed in 2019). Eligible patients were aged 1–65 years and visiting the commune health centre for a suspected acute respiratory infection with at least one focal sign or symptom and symptoms lasting less than 7 days. The primary endpoint was the proportion of patients prescribed an antibiotic at first attendance in the intention-to-treat population. The per-protocol analysis included only people who underwent CRP testing. Secondary safety outcomes included time to resolution of symptoms and frequency of hospitalisation. This trial is registered with ClinicalTrials.gov, NCT03855215. Findings 48 commune health centres were enrolled and randomly assigned, 24 to the intervention group (n=18 621 patients) and 24 to the control group (n=21 235). 17 345 (93·1%) patients in the intervention group were prescribed antibiotics, compared with 20 860 (98·2%) in the control group (adjusted relative risk 0·83 [95% CI 0·66–0·93]). Only 2606 (14%) of 18 621 patients in the intervention group underwent CRP testing and were included in the per-protocol analysis. When analyses were restricted to this population, larger reductions in prescribing were noted in the intervention group compared with the control group (adjusted relative risk 0·64 [95% CI 0·60–0·70]). Time to resolution of symptoms (hazard ratio 0·70 [95% CI 0·39–1·27]) and frequency of hospitalisation (nine in the intervention group vs 17 in the control group; adjusted relative risk 0·52 [95% CI 0·23–1·17]) did not differ between groups. Interpretation Use of point-of-care CRP testing efficaciously reduced prescription of antibiotics in patients with non-severe acute respiratory infections in primary health care in Viet Nam without compromising patient recovery. The low uptake of CRP testing suggests that barriers to implementation and compliance need to be addressed before scale-up of the intervention. Funding Australian Government, UK Government, and the Foundation for Innovative New Diagnostics