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COOPERATIVE TRANSMISSION OF LARGE FILES OVER LORA IN MULTIMEDIA IOT NETWORKS
No full textThe increasing use of Multimedia Internet of Things (MIoT) devices in remote areas requires effective multimedia data transfer over large, energy-limited wireless networks. While Low-Power Wide-Area Networks (LPWANs), especially those utilizing LoRa (Long Range) technology, offer a promising solution with their broad coverage and energy efficiency, they are not well-suited for transmitting large amounts of multimedia data as they were originally designed for small-scale sensor data. This thesis presents a new approach that enhances the ability of LoRa networks to effectively handle large-scale multimedia data. We propose an algorithm that improves data transmission efficiency by encouraging cooperation among neighboring devices. The effectiveness of our algorithm is confirmed through detailed theoretical and practical evaluation, showing major improvements over method that evenly distributes the data between neighbors. It performs exceptionally well in challenging environments with different success rates and spreading factors, proving to be reliable under heavier data loads and achieving better overall data transmission times
DISTRIBUTED FIBER OPTICS TECHNOLOGIES FOR EFFECTIVE SHAPE SENSING
No full textThis paper gives a comprehensive study in the development of a computational model that will replicate functions of the LUNA Optical Backscatter Reflectometer (OBR) within the fiber optic shape-sensing (FOSS) framework. Fiber optic sensors have found their way to the forefront in importance due to the ability to measure sensitive strain, temperature, and curvature in structural integrity in various fields, from engineering to medicine. However, the high accuracy mostly relies on properly working tools like LUNA OBR, thus risking technical faults. This study, thus, tries digital emulation of LUNA OBR shape sensing capabilities through a MATLAB simulation in order to address the challenges presented in the malfunction of such critical equipment.
This methodology works on the development of a theoretical model towards simulating backscattering spectra—an essential element in any application of FOSS technologies. In the first place, the backscattering spectra were modeled, making some tuning on the parameters such as fiber length, attenuation coefficients, and refractive index values. Subsequently, such parameters underwent an iterative refinement by comparing the simulated results to the intended real data. A cross-correlation model has been developed that compares an individual backscattering spectrum with a combined spectrum in the study of the composition of the alignment and coherence of the signals.
This section, therefore, discusses the simulation results by validating the success of the computational model in matching real-life backscattering spectra behavior cross-correlation analyzed. This methodology proposed in this thesis is of large value not only for the continuity of research for fiber optic sensing without the physical equipment of the experiment but opens new opportunities for innovation in this field.
The paper concludes with importance in further developments of distributed fiber optic sensing technologies. This is explained in the research as one of the features of human tenacity and adaptability exercised in scientific research; significance in the exercise of overcoming such stumbling blocks as equipment failures. Further outlined in this thesis were future directions for research, which should further extend more multiplexing methods and enhance computer models for real-world applications of shape sensing
PREPARATION OF LIFEPO4 -BASED ELECTRODES WITH MAGNETO-SENSITIVE IRON OXIDE (FE2O3 ) NANOPARTICLES UNDER MAGNETIC FIELD
No full textLithium iron phosphate (LiFePO4) is one of the most widely utilized cathode materials in lithium-ion batteries (LIBs) due to its low cost and environmental safety. Despite this attribute, they still face significant challenges, including high rates and prolonged cycling. However, research has shown that utilizing a magnetic field (MF) can help tackle these issues, thereby improving the ion conductivity and inhibiting polarization of the LiFePO4 cathode. In this study, LiFePO4 cathodes were produced by subjecting them to MF (LFP-MF), while others were optimized using magnetic-sensitive Fe2O3 nanoparticle additives in two concentrations (LFP+1 wt% Fe2O3-MF and LFP+3 wt% Fe2O3-MF). The cathodes were compared to conventional LiFePO4 electrodes prepared under the same conditions but without applying MF (LFP-WMF, LFP+1% Fe2O3-WMF, and LFP+3% Fe2O3-WMF). Application of MF improved the materials' electrochemical properties, contributing to the battery's superior electrochemical performance. The lithium-ion diffusion coefficients of LFP-MF (4.1376 × 10-4 cm2 S-1), LFP+1% Fe2O3-MF (4.0614 × 10-4 cm2 S-1), and LFP+3% Fe2O3-MF (2.3021 × 10-4 cm2 S-1) were more significant than those of LiFePO4 cathodes without subjecting to the magnetic field. Furthermore, the cathodes subjected to MF had higher reversible capacity and a reduced capacity decay than those without MF at an enhanced rate capability greater than 1 C. This study discovered that an MF improves the high-rate performance of LiFePO4 cathodes
DEVELOPMENT AND OPTIMIZATION OF ML BASED COMPREHENSIVE MODELLING FRAMEWORK FOR GAN HEMTS
No full textRadio Frequency (RF) Power Amplifier (PA) is one of the most pivotal constituents
of any wireless transceivers. However, continual advancements and ever-increasing
complexity in the wireless communication technologies demand frequent innovations
in the design of RFPAs. The quality of the designed RFPAs are generally evaluated
based around two basic figures of merits namely efficiency and linearity. Thus, the
RFPAs should provide maximum power and efficiency while maintaining highly linear
operation. In literature, two primary PA design mechanisms, namely measurement- and
modeling-based techniques have been extensively utilized. Each class of technique
has pronounced merits, limitations and applications. However, owing to the seamless
integration ability of the modeling-based techniques with Computer-Aided Design
(CAD) tools, they are increasingly becoming more popular.
The design and innovation in RFPAs are excessively contingent on the measurement
facilities and the Large Signal Models (LSMs) of transistor devices. At present,
Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) technology is
regarded as an optimal microwave transistor technology for the design of RFPAs in
advanced RF/microwave and high power switching applications. This is due to their
attributes namely high energy bandgap, high saturation velocity, high electron mobility,
exceptional thermal behavior and high breakdown field. Furthermore, GaN HEMTs
manifest high power density, thus a smaller size device can be used to sustain a high
power demand. It also implies reduced lower capacitances and lower combining losses
in the design of RFPAs and Low-Noise Amplifiers (LNAs). At this point, it is essential
to mention that, in general, the available LSMs of GaN HEMTs are very specific and
therefore not readily useful for broad range of PA designs. Therefore, there is a pressing
requirement to develop accurate, reliable, efficient and robust LSMs of GaN HEMTs
which can be readily incorporated in CAD tools. Nevertheless, Small-Signal Model
(SSM) development is the first step in pursuit of developing accurate and efficient LSMs.
But, both SSMs and LSMs of GaN HEMTs are essential for the design of accurate,
efficient and reliable GaN HEMT based RFPAs. Apparently, various modeling schemes
have been exploited to develop SSMs and LSMs for GaN HEMTs, however, usually,
they are classified into three main groups, which are physics-based, Equivalent Circuit
(EC) and Behavioral Modeling (BM) frameworks.
This thesis is originated in response to the scientific and technical challenges in EC
and BM frameworks for GaN HEMTs at high frequency applications. Among these challenges, the major focuses are on the development of SSMs for GaN HEMTs, which
are simple, accurate, computational and time efficient, reliable, scalable, and CAD
adaptable. Furthermore, special attention is given to develop SSMs, which manifest
strong interpolation and extrapolation abilities. The developed SSMs are then utilized
to realize the eventual LSMs for GaN HEMTs. In order to develop SSMs and LSMs
for GaN HEMTs, which possess the above-mentioned characteristics, in this thesis,
Machine Learning (ML) based approaches have been explored and utilized because of
their superior learning, prediction, and extrapolation abilities. However, it is pertinent to
state that the ML based modelling of GaN HEMTs is still in its early exploration phase,
and various issues related to this type of modelling are unexplored and not thoroughly
discussed in literature.
It is therefore, in this thesis, an extensive appraisal and analysis of ML and
optimization based small-signal and large-signal modelling for GaN HEMTs have
been presented. In the first part of this thesis, a detailed comparative analysis of
EC based accurate, robust and efficient SSM parameter extraction methodologies for
GaN-on-Diamond HEMTs has been demonstrated. For this, initially, a Scanning-
Based Systematic (SBS) model parameter extraction approach is developed and
applied on GaN-on-Diamond HEMTs. Thereafter, marine predators algorithm,
pelican optimization algorithm and tunicate swarm algorithm, the recently developed
Optimization Algorithms (OAs), based hybrid extraction methodologies have been
developed and applied on the same GaN HEMTs. Finally, a detailed comparison of
OAs and SBS modelling schemes by using SBS extraction approach as a benchmark in
terms of reliability, accuracy, convergence behavior, complexity, execution time, and
scalability is provided and thoroughly discussed.
Accurate, efficient and CAD compatible small-signal behavioral models for GaN
HEMTs using Artificial Neural Network (ANN), Support Vector Regression (SVR)
and Gaussian Process Regression (GPR) based ML techniques have been developed,
validated and discussed in the subsequent part of this thesis. These ML based approaches
have been applied on many GaN HEMTs devices grown on Silicon (Si), Silicon Carbide
(SiC) and Diamond substrates. Furthermore, a meticulous evaluation of ANN algorithms
implemented in MATLAB, Python (using Keras, PyTorch and Scikit-learn) and R
(using H2O) for small-signal behavioral modelling of GaN HEMTs has been presented.
To establish the appropriateness of software environments in distinct application
settings, the developed models are examined on a range of metrics namely behavior
on the unseen data, training and prediction speed and ADS adaptability, and software
environments are surveyed for support and documentation, user-friendly interface,
simplicity in the model development procedure, open-access and cost. Optimization
of the hyperparameters of ML algorithms is vital to realize the best possible models.
In this context, hybrid optimized ML algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) assisted ANN, PSO and RSO assisted SVR, RSO
assisted GPR, and RSO assisted various tree-based models are explored and developed.
Finally, the developed models are evaluated on many regression tests to identify the
most fitting ML algorithms for particular applications.
Finally, the last part of the thesis presents ML based CAD adaptable advanced
models and applications. Initially, GPR, GA-ANN and RSO-Decision trees based
SSMs for GaN HEMTs are developed. Then, the integration of these developed models
with ADS are presented by inserting the developed ML based models into a design of
class-F PA. Subsequently, to examine the accuracy of the models, stability and gain
tests of the GaN HEMT based amplifier configuration are performed. Thereafter, using
the developed SSMs, a joint EC-behavioral LSM for a GaN HEMT is developed and
presented. The intrinsic drain and gate currents are modelled using GA-ANN, PSOSVR
and GPR based approaches. These current modelling approaches are compared in
terms of simplicity in the model development stage, computational efficiency, accuracy
and required time to simulate the currents. At last, LSM validation and realization using
GA-ANN based approach are demonstrated on a design of an inverse class-F PA
SYSTEM FOR EMOTION CLASSIFICATION IN INTERVIEW SETTINGS
No full textThe traditional hiring process can be time-consuming and expensive for companies, often requiring multiple interviews and lacking objectivity.
This project introduces Emotico, a web application that streamlines and enhances the hiring process. Emotico allows recruiters to post job openings and associated interview questions. Candidates then take these interviews online, with their responses evaluated by a combination of advanced technologies.
Emotico leverages OpenAI's ChatGPT-4 model to analyze the content of a candidate's answers, categorizing them and providing a score with an explanation. Additionally, Emotico incorporates emotion detection through a multimodal CNN architecture, developed by Chumachenko et al. (2022), to analyze emotions both from video and audio during video interviews. This approach provides a more well-rounded assessment of a candidate's suitability for the role.
Emotico offers significant advantages over traditional hiring methods. It streamlines the process by allowing companies to conduct interviews online and receive automated evaluations. The combination of text analysis and emotion detection offers a more comprehensive understanding of candidates, potentially reducing bias and leading to better hiring decisions.
Emotico's development can be further enhanced by incorporating additional features and refining the Machine Learning models. Exploring new avenues for emotion detection and integrating with applicant tracking systems are promising areas for future exploration
DEVELOPING AN OPTICAL FIBER BIOSENSOR FOR THE DETECTION OF KIM-1 PROTEIN
No full textKidney Injury Molecule-1 (KIM-1) is a protein within the cell membrane that experiences increased expression in the proximal tubules of kidneys following ischemia. This protein belongs to the TIM (T-cell immunoglobulin mucin) family, but unlike other TIM proteins, it is produced mainly in the renal tubular epithelial cells.
The baseline expression of KIM-1 in healthy organs is minimal, making it a valuable biomarker for the early identification of renal injury due to its significantly enhanced production during the initial stages of renal injury. Additionally, it is more sensitive than Blood Urine Nucleotide or creatinine as a marker. Recent studies have shown that urinary KIM-1 outperforms all other renal proteins in efficiency (Assadi & Sharbaf, 2019). The FDA qualified it as a biomarker of AKI, a devastating clinical problem with high mortality rates.
AKI patients have a high risk of mortality, repetitive cardiovascular events, and risk of long-term chronic kidney disease. Screening and early detection is very important. Although KIM-1 has been established as a biomarker for AKI, the limited availability of assays restricts its use in diagnosis. Therefore, an unmet clinical need exists to develop an assay specific to the given protein. In the current work, we propose the use of a novel type of optic fiber biosensor, called a semi-distributed interferometer (SDI). These fibers are characterized by their ease of manipulation, rapid fabrication, and anticipated capacity for heightened sensitivity and specificity toward KIM-1, primarily attributed to the ability of fibers for functionalization in accordance with the protein of interest.
We aim to build and characterize a semi-distributed interferometer-based optical fiber biosensor sensitive and specific to KIM-1 biomarker levels. It is hypothesized that the developed SDI-based optical fiber biosensor will considerably elevate the detection and quantification of KIM-1 levels in bodily fluids. To accomplish intended outcome, we established and optimized the optical fiber biosensor based on the SDI through functionalization, assessed its sensitivity to the KIM-1 biomarker level and made some experiments to assess its specificity towards the KIM-1 biomarker levels in the samples of artificial urine.
This study revealed that SDI-based platform can be used to detect the KIM-1 protein. This novel biosensor concept not only provides a simplified fabrication process but also offers remarkable advantages, including resistance to electromagnetic interference and the ability to function effectively in a compact and durable form factor. The utilization of this cutting-edge technology promises to revolutionize the detection of biomarkers, particularly KIM-1, in bodily fluids such as urine. Detecting such important biomarker as KIM-1 molecule using freshly developed biosensor than available technologies (ELISA, lateral flow assay) could lower the time needed to perform its measurement and analyze biological specimens having lower levels of the analyte.
The results of this study have significant implications for various fields, especially in clinical diagnostics and biomedical research. The semi-distributed interferometer-based optical fiber biosensor fabricated here could be used as a foundation for the development of sensors with the potential of earliest detection of AKI
AN OPEN HARDWARE RFID INVENTORY MANAGEMENT SYSTEM
No full textThis paper presents and evaluates a low-cost RFID reader for cost-effective multi-purpose asset tracking applications. The research explores the integration of off-the-shelf RFID and GPS modules into an ESP32 microcontroller along with some open-source pieces of software and tools to ease the system use and enhance security. The functionality of the system is evaluated through real-world testing in various scenarios. The results show that the system is capable of providing satisfactory functionalities at a low cost, thus, it offers a potential low-cost alternative to existing handheld readers
BEHIND CLOSED DOORS: THE ROLE OF INFORMAL COMMUNICATIONS IN THE SUCCESS OF KAZAKHSTANI STARTUPS
No full textThe main goal of this capstone study is to understand how informal communications influence the success and growth of Startups in Kazakhstan. An important point in the research process was also to identify what is actually meant by the term “informal communication”. Are they really casual conversations and interactions that happen outside of formal business meetings? This capstone project is focused on understanding the role that these informal interactions play in building social capital, that is, the networks and relationships that help a Startup grow. In particular, the research questions intend to identify how these communications differ from formal ones and what their impact is on the development of Startups in the Kazakhstan ecosystem. The findings reveal that informal communications are more than casual, and they may even represent strategic exchanges, which have a meaningful contribution to the generation and maintenance of social capital within the startup community. These communications have more of an informative nature in regard to shared knowledge, resources, and support for each other, in an environment that is much less official and trustful for deeper connections on a personal and professional level. What is more important is that the study found that these informal networks are critical to how investment is secured and how collaborations are created in a way that one cannot access solely from formal channels. For instance, in Kazakhstan, social relationships and trust can be invaluable, so the role of these informal communications might be irreplaceable in guiding the startup ecosystem toward growth
SCREENING FOR INHIBITORS OF ZEB1, A KEY REGULATOR OF EPITHELIAL TO MESENCHYMAL TRANSITION (EMT) IN BREAST CANCER CELLS.
No full textBackground: Breast cancer (BC) has an estimated new cases of about 2.3 million individuals and approximately 685,000 deaths in 2020, thereby making it the most common cause of mortality in women. Different subtypes of BC are categorized breast into three clinical subtypes based on the expression or lack of hormone receptors: progesterone (PR), estrogen (ER), and human epidermal growth factor receptor 2 (Her2). Despite the considerable progress made in the treatment of the various types of BC, more research is still needed to address some major obstacles in breast cancer treatment, especially those associated with poor prognosis and reduced survival rates among BC patients like chemoresistance and cancer metastasis; these processes are mediated by Zeb1, which is the key regulator of the EMT.
Methods: Cell culture was used to propagate MCF7 cell lines and do transfection. Western blot was used to assess the effects on the markers of EMT such as Zeb1 and Cdh1. Cell cycle analysis using flow cytometry was used to examine candidate inhibitors of EMT after induction of Zeb1.
Results: The induced MCF7 cells show a higher percentage of cell cycle arrests at the G1 phase than non-induced cells after treatment with candidate inhibitors.
Conclusion: Out of the three PKC inhibitors tested midostaurin, auranofin, and resveratrol; resveratrol demonstrated a more significant impact on Zeb1-expressing cells than those without expression of Zeb1 by decreasing the percentage of cells at the G1 phase, hence, resveratrol might directly interfere with the activity of Zeb
OPTIMIZATION AND RESEARCH OF OPERATING MODES OF WIND-SOLAR POWER PLANTS UNDER DIFFERENT LOAD CONFIGURATIONS
No full textThis study focuses on the design, simulation, and optimization of a hybrid wind-solar power system integrated with a Battery Management System (BMS), with the primary objective of enhancing the efficiency and sustainability of renewable energy sources. Employing MATLAB Simulink, the project models the interactions between photovoltaic (PV) panels, wind turbines, and battery storage to achieve maximal energy capture and effective storage management. The key objectives include implementing Maximum Power Point Tracking (MPPT) and passive cell balancing techniques within the BMS to optimize energy conversion and battery longevity. The methodology involved developing detailed system models and incorporating a Perturb & Observe (P&O) algorithm for MPPT, which dynamically adjusts power conversion parameters to suit changing environmental conditions. Additionally, a Machine Learning algorithm was integrated to predict energy generation, providing a sophisticated tool for enhanced energy management. Experimental results demonstrated the system’s ability to adaptively optimize operations, significantly improving the energy efficiency and operational stability of the hybrid system. The MPPT controller effectively maintained optimal power levels, while the BMS ensured uniform charge distribution among batteries, thereby prolonging their lifespan and performance. This project establishes a foundational framework for future research in hybrid renewable energy systems, suggesting that further exploration into adaptive control strategies and the integration of additional renewable sources could enhance system reliability and efficiency