Iterative CT reconstruction from few projections for the nondestructive post irradiation examination of nuclear fuel assemblies

The core components (e.g. fuel assemblies, spacer grids, control rods) of the nuclear reactors encounter harsh environment due to high temperature, physical stress, and a tremendous level of radiation. The integrity of these elements is crucial for safe operation of the nuclear power plants. The Post Irradiation Examination (PIE) can reveal information about the integrity of the elements during normal operations and off‐normal events. Computed tomography (CT) is a tool for evaluating the structural integrity of elements non-destructively. CT requires many projections to be acquired from different view angles after which a mathematical algorithm is adopted for reconstruction. Obtaining many projections is laborious and expensive in nuclear industries. Reconstructions from a small number of projections are explored to achieve faster and cost-efficient PIE. Classical reconstruction algorithms (e.g. filtered back projection) cannot offer stable reconstructions from few projections and create severe streaking artifacts. In this thesis, conventional algorithms are reviewed, and new algorithms are developed for reconstructions of the nuclear fuel assemblies using few projections. CT reconstruction from few projections falls into two categories: the sparse-view CT and the limited-angle CT or tomosynthesis. Iterative reconstruction algorithms are developed for both cases in the field of compressed sensing (CS). The performance of the algorithms is assessed using simulated projections and validated through real projections. The thesis also describes the systematic strategy towards establishing the conditions of reconstructions and finds the optimal imaging parameters for reconstructions of the fuel assemblies from few projections. --Abstract, page iii

An Investigation of Less Export of Readymade Garments to Non-Traditional Markets by Bangladesh and Justification of Increasing Export

This paper focuses on the current condition of readymade garments export by Bangladesh to well-established export destinations and nontraditional destinations. The main objective of this study is to find out ways of increasing Bangladesh’s export earnings. The RMG industry has set an export target of US $50 billion by 2021 for which new export destinations must be explored. Bangladesh, however, is still far behind the target due to some constraints. Another objective of this study is to identify these constraints and justify the reasons to explore the non-traditional markets. This study was conducted using secondary data obtained from various trusted national sources. These data were analyzed and showcased using statistical tools to interpret the discussed topic

Pupil Localisation and Eye Centre Estimation using Machine Learning and Computer Vision

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye-frames within the input image. We then use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye-frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along-with a standardized distance metric we introduce first time in this study. Proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare and automated deception detection

Mathematical Logic Establishment for Automated Trash Controlling in Carding Machine

In this paper, it is intended to establish a mathematical logic for the purpose of removal of trash in an automatic way and removal of trash will refer to the cleaning process of carding machine. But the selection of degree of cleaning has to be optimum considering other process factors like fiber loss, fiber rupturing, neps generation etc. Higher degree of cleaning causes higher degree of fiber loss. And in spinning mill, fiber loss means money loss as raw cotton purchasing cost consumes 50% to 60% of total manufacturing cost of yarn in terms of Bangladesh. Alongside, fiber loss is affected the cleaning system because the system is designed for fiber cleaning, not for fiber loss. So, it is necessary to measure the performance of the system. For this purpose, we have chosen two terms, ‘Cleaning Efficiency (C.E.)’ to measure the degree of cleaning & ‘Effective Cleaning (E.C.)’ to measure the performance of the system. In this paper, it is intended to describe a relationship between these two terms, graphical expression of the individuals, a way to calculate the force applied by mechanical means and the force required to clean. The ultimate result of this project is to find the relationship between the surface speed of taker-in and the trash weight of output material

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers, characterized by the rapid proliferation of immature white blood cells (WBCs). These atypical cells can overwhelm healthy cells, leading to severe health consequences. Early and accurate detection of ALL is vital for effective treatment and improving survival rates. Traditional diagnostic methods are time-consuming, costly, and prone to errors. The paper proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques to enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes various transfer learning models like ResNet101V2, VGG19, InceptionV3, and InceptionResNetV2 for classifying ALL. The methodology includes using the Local Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and reliability of the AI system's predictions. This approach is critical for overcoming the "black box" nature of AI, where decisions made by models are often opaque and unaccountable. The paper highlights that the proposed method using the InceptionV3 model achieved an impressive 98.38% accuracy, outperforming other tested models. The results, verified by the LIME algorithm, showcase the potential of this method in accurately identifying ALL, providing a valuable tool for medical practitioners. The research underscores the impact of explainable artificial intelligence (XAI) in medical diagnostics, paving the way for more transparent and trustworthy AI applications in healthcare

Novel Framework for Outdoor Mobility Assistance and Auditory Display for Visually Impaired People

Outdoor mobility of Visually Impaired People (VIPs) has always been challenging due to the dynamically varying scenes and environmental states. Variety of systems have been introduced to assist VIPs’ mobility that include sensor mounted canes and use of machine intelligence. However, these systems are not reliable when used to navigate the VIPs in dynamically changing environments. The associated challenges are the robust sensing and avoiding diverse types of obstacles, dynamically modelling the changing environmental states (e.g. moving objects, road-works), and effective communication to interpret the environmental states and hazards. In this paper, we propose an intelligent wearable auditory display framework that will process real-time video and multi-sensor data streams to: a) identify the type of obstacles, b) recognize the surrounding scene/objects and corresponding attributes (e.g. geometry, size, shape, distance from user), c) automatically generate the descriptive information about the recognized obstacle/objects and attributes, d) produce accurate, precise and reliable spatial information and corresponding instructions in audio-visual form to assist and navigate VIPs safely with or without the assistance of traditional means

Observation of room temperature gate tunable quantum confinement effect in photodoped junctionless MOSFET

In the pursuit of room temperature quantum hardware, our study introduces a gate voltage tunable quantum wire within a tri-gated n-type junctionless MOSFET. The application of gate voltage alters the parabolic potential well of the tri-gated junctionless MOSFET, enabling modification of the nanowire's potential well profile. In the presence of light, photogenerated electrons accumulate at the center of the junctionless nanowire, aligning with the modified potential well profile influenced by gate bias. These carriers at the center are far from interfaces and experience less interfacial noise. Therefore, such clean photo-doping shows clear, repeatable peaks in current for specific gate biases compared to the dark condition, considering different operating drain-to-source voltages at room temperature. We propose that photodoping-induced subband occupation of gate tunable potential well of the nanowire is the underlying phenomenon responsible for this kind of observation. This study reveals experimental findings demonstrating gate-induced switching from semi-classical to the quantum domain, followed by the optical occupancy of electronic sub-bands at room temperature. We developed a compact model based on the Nonequilibrium Green's function formalism to understand this phenomenon in our illuminated device better. This work reveals the survival of the quantum confinement effect at room temperature in such semi-classical transport.Comment: 12 pages, 6 figure

Design and Hardware Implementation Considerations of Modified Multilevel Cascaded H-Bridge Inverter for Photovoltaic System

Inverters are an essential part in many applications including photovoltaic generation. With the increasing penetration of renewable energy sources, the drive for efficient inverters is gaining more and more momentum. In this paper, output power quality, power loss, implementation complexity, cost, and relative advantages of the popular cascaded multilevel H-bridge inverter and a modified version of it are explored. An optimal number of levels and the optimal switching frequency for such inverters are investigated, and a five-level architecture is chosen considering the trade-offs. This inverter is driven by level shifted in-phase disposition pulse width modulation technique to reduce harmonics, which is chosen through deliberate testing of other advanced disposition pulse width modulation techniques. To reduce the harmonics further, the application of filters is investigated, and an LC filter is applied which provided appreciable results. This system is tested in MATLAB/Simulink and then implemented in hardware after design and testing in Proteus ISIS. The general cascaded multilevel H-bridge inverter design is also implemented in hardware to demonstrate a novel low-cost MOSFET driver build for this study. The hardware setups use MOSFETs as switching devices and low-cost ATmega microcontrollers for generating the switching pulses via level shifted in-phase disposition pulse width modulation. This implementation substantiated the effectiveness of the proposed design

Suppression of inflammatory mediators by aqueous leaf extract of Crotalaria verrucosa: in vivo and in vitro analysis

Background: Crotalaria verrucosa is a traditional plant frequently prescribed by the tribes for its medicinal value against inflammation. The present study was designed to investigate the scientific basis for medicinal value in inflammation by in vivo and in vitro analysis.Methods: Anti-inflammatory activity of the plant’s leaf was evaluated by two in vivo methods - carrageenan induced rat paw edema and xylene induced mice ear edema. Moreover, in vitro analysis was performed through heat induced hemolysis and heat induced protein denaturation methods.Results: The inflammation produced by carrageenan and xylene were effectively suppressed by the aqueous leaf extract of C. verrucosa (CVAQ) at 600 mg/kg body weight which was comparable to the standards. In heat induced hemolysis test the extract was able to inhibit the lysis up to 70% at 500 µg/ml whereas in heat induced protein denaturation test it reduces the percentage till 69% at the same concentration.Conclusions: The findings suggested that CVAQ possess moderate to high anti-inflammatory activity when applied in low to high concentrated doses. However, the study can only conclude from this basic evaluation that the extract needs to be further investigated for identifying the potential compound which contributed to such medicinal value of the plant