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GPU ACCELERATED MICROWAVE KINETIC INDUCTANCE DETECTOR (MKID) READOUT SYSTEM
No full textMicrowave Kinetic Inductance Detectors
(MKIDs) have proven to be valuable instruments for detecting weak signals in the
microwave and millimeter-wave spectrum
ranges in many scientific domains. Their novel
operating principle, which is based on the
detection of kinetic inductance changes in
superconducting resonators, has led to applications in astronomy, quantum computing,
and materials science. The efficient reading
of MKID arrays, on the other hand, creates
computational challenges and frequently necessitates the use of advanced data processing
techniques. While effective, traditional readout
systems based on Field Programmable Gate
Arrays (FPGA) have limitations in terms of
flexibility and development simplicity. This
study looks into the possibility of Graphics
Processing Unit (GPU) acceleration to overcome these difficulties. Using an example from
current research, this research demonstrates
how GPU acceleration can improve MKID
readout systems, improve performance, and
facilitate adjustments. In addition to speeding
up development, the integration of GPUs
opens up novel opportunities for MKID
applications across scientific disciplines
Blended learning adoption in Kazakhstan
No full textThis study investigates the impact of secondary school teachers’ attitudes towards
technology on the adoption of blended learning in Northern Kazakhstan, guided by the
main research question: “What are the attitudes of secondary school teachers in
Kazakhstan towards the use of technology in education?”. I used a qualitative research
design, and it was informed by the Technology Acceptance Model and Social Cognitive
Theory. I conducted in-depth interviews with ten teachers, selected through stratified
sampling from various subjects and experience levels. The thematic analysis revealed a
clear connection between teachers’ positive attitudes towards technology and the adoption
of blended learning. The participants recognized technology’s potential to enhance student
engagement in education but highlighted challenges such as technical difficulties and
unequal access to technology. Moreover, the resistance towards technology integration
demonstrated by some participants was rooted in a preference for traditional methods and
concerns over technology’s impact and further compounded by a significant gap in
professional development. The combination resulted in the hesitancy in adopting blended
learning approaches. The findings also suggest that participants who view technology
positively are more likely to support and adopt blended learning approaches. This indicates
that positive attitudes towards technology can significantly enhance the integration of
blended learning methods. These attitudes were influenced by factors such as prior
technology experiences, training, and peer support. Overall, the findings call for the urgent
need for targeted professional development and infrastructure improvements to address
these barriers and enhance digital literacy among teachers, facilitating more effective
blended learning environments
OPTICAL FIBER BIOSENSORS FOR THE DETECTION OF PATHOGENS
No full textWastewater-based epidemiology (WBE), which offers real-time insights into the frequency and spread of infectious diseases within communities, is gaining recognition as a crucial tool in public health. One of the benefits of WBE is its ability to detect and mitigate possible outbreaks by means of proactive monitoring of virus particles in wastewater.
The necessity for biosensing devices that can identify viral infections in liquid samples has increased in light of the current COVID-19 epidemic. Because optical fiber biosensors (OFBs) have the special qualities of optical fibers that allow them to detect biological chemicals with great sensitivity and speed, they have become the industry leader in response to this urgency. Optical fiber biosensors have great potential for use in biotechnology, environmental monitoring, and medical diagnostics, among other fields. They are one of the extremely useful applications in the ongoing fight against infectious illnesses because of their adaptability and plasticity.
By concentrating on the creation and improvement of a fiber-optic biosensor especially designed for the detection of viral particles in liquid medium, our work aims to further the field of optical fiber biosensing technology. In our latest publication Bekmurzayeva et al. (2024) we emphasized the advantage of using label-free optical fiber biosensors for the detection of various analytes. Besides, in our other work that was published in 2023 (Kazhiyev et al., 2023) we introduced a semi-distributed interferometers (SDI) fiber-optic sensors for high sensitivity refractive index detection. In this thesis work we aim to use SDI sensors along with a biofunctionalization strategy to fabricate a sensing network that will be able to detect poxvirus in real-time conditions. To achieve this, we attempt to use anti-L1, anti-A27, and anti-A33 antibodies, which have a strong affinity for poxviruses. By immobilizing these antibodies onto the biosensor surface, we intend to create a selective platform capable of capturing and detecting viral particles present in wastewater and domestic environments.
Building upon previous research that has demonstrated promising results in detecting poxviruses using similar antibody-based biosensors explained in the paper of Seitkamal et al. (2023), our study endeavors to expand the scope of application to real-world scenarios. In addition, we performed successful experiments at State University of Milan, Italy, that confirm that the using the same antibodies we could detect vaccinia virus with 10^3 to 10^8 PFU/mL concentration, and limit of detection ~4000 PFU/mL. This result was achieved within a NATO Science for Peace and Security program (grant G5486), which has been completed in May 2023. Through collaborative efforts and innovative methodologies, we aim to validate the efficacy and reliability of our biosensor in detecting viruses in diverse environmental settings. Additionally, we will explore the long-term performance of the antibodies in continuous monitoring applications, ensuring the sustainability and effectiveness of the biosensor over extended periods.
Furthermore, our research extends beyond mere detection capabilities, as we envision the development of optimized sensor networks that can be strategically deployed for indoor and outdoor surveillance. These sensor networks, equipped with our fiber-optic biosensors, have the potential to serve as early warning systems, alerting authorities to the presence of viral pathogens and enabling timely intervention measures to curb outbreaks. Since we observed a similar behavior between detection of virus and detection of its proteins, we aim at continuing this research proving the detection in wastewaters and domestic environment, evaluating the duration of the antibodies in long-term measurement. We confirm that we will mimic the viral particles by using the proteins already available in our laboratories as their affinity and specificity has already been proven and reported. Our work is essentially an attempt to use optical fiber biosensors to their full potential for improving monitoring technologies which in turn will greatly contribute to WBE. Our mission is to do our part to the development of robust, scalable, and reliable biosensing technologies that can protect communities from the threat of infectious diseases by utilising state-of-the-art innovations and creative techniques
DEVELOPMENT OF ULTRA-HIGH PERFORMANCE GEOPOLYMER MORTAR FOR CONSTRUCTION 3D PRINTING
No full textThe construction industry faces a pressing need for sustainable materials that offer superior mechanical properties, durability, and printability. This research addresses this need by developing an ultra-high-performance geopolymer mortar specifically tailored for construction 3D printing. The study systematically identifies and evaluates suitable materials for geopolymer mortar through an extensive literature review and material selection process. A design of experiments (DOE) approach is employed to vary key parameters in geopolymer mortar formulations, followed by rigorous experimental validation.
The research methodology involves the preparation of three different geopolymer compositions (G1, G2, G3) and testing for setting time, compressive strength, flexural strength, and buildability. Buildability testing includes viscosity analysis, shape retention testing, and extrudability testing to evaluate suitability for 3D printing applications. The study also tests 3D printed geopolymers for compressive strength and conducts a comparative analysis of composition development.
Main research results indicate that G2 consistently exhibits the highest compressive and flexural strength across all tested durations, with shorter setting times compared to G1 and G3. However, printed geopolymers demonstrate lower compressive strength compared to molded counterparts, with G2 showing relatively higher retention. Additionally, G2 displays more favorable viscosity characteristics and good extrudability, suggesting suitability for smoother extrusion and better layer formation during printing.
The findings of this research have significant industrial implications, offering a sustainable alternative to conventional construction materials. The development of ultra-high-performance geopolymer mortar for 3D printing applications enables faster and more efficient construction processes while reducing material wastage and enhancing structural performance. This research contributes to advancements in sustainable construction practices, paving the way for a more environmentally friendly and economically viable future in the construction industry
DESIGN AND IMPLEMENTATION OF FIBER OPTIC INTERFEROMETER FOR PROTEIN INTERACTION ANALYSIS
No full textThis capstone project particularly focuses on developing a Semidistributed Interferometer made of optic fiber, geared to protein interactions. This work consists of the design of the interferometer, calibration, and functionalization steps. This will describe data processing on the acquired data using MATLAB and study the behavior of the protein through both static and dynamic measurements. This will thus lead to very high sensitivity of the interferometer, robust methodologies of calibration, optimized techniques of functionalization, and tools in data processing that will further allow the extraction of meaningful insight from interferometer data. Ultimately, the project targets performing the studies of protein interactions using the interferometer developed in the project and hence brings out a set of cellular processes and disease mechanisms into light
PERFORMANCE ENHANCEMENT IN RIS-AIDED WIRELESS NETWORKS
No full textThis study explores the performance of a cooperative non-orthogonal multiple access (CNOMA) downlink wireless communication system, aided by a novel technology called si- multaneously transmitting and reflecting re- configurable intelligent surfaces (STAR-RISs), operating over Nakagami-m fading channels. The evaluation focuses on outage probability (OP), spectral efficiency (SE), and energy effi- ciency (EE) as key performance indicators. The mathematically intractable probability density and cumulative distribution functions of the near user are approximated using the Gamma moment-matching method. This enables the derivation of closed-form expressions for OP and ergodic rates of users, which are used to determine SE and EE. Monte-Carlo simulations validate the analytical results. A comparison with OMA and NOMA is provided to assess CNOMA’s effectiveness, revealing that the co- operative link can enhance SE and EE in the STAR-RIS-based NOMA network
BLIND SOURCE SEPARATION FOR AUTOMATIC MUSIC TRANSCRIPTION
No full textThe primary objective of this project is to develop methods aimed to the conduct the blind signal separation of musical notes with Nonnegative Matrix Factorization (NMF). This is motivated by the fact that music signals are often recorded with a single microphone, hence, there is a need to develop the Automatic Music Transcription (AMT) methods that could mitigate this assumption and produce the desirable separation result. Therefore, this project report presents the rank estimation method for determination of number of musical notes in the recording. It is motivated by the fact that most of the research works on NMF assume \emph{a priori} knowledge regarding the rank of factorization which may not be available in most of the real world scenarios. As a result, the Weighted Singular Value Thresholding based on Stein's Unbiased Risk Estimate (WSVT-SURE) in which rank estimation is performed by non-uniform shrinkage of singular values via weight vector is presented. We also introduce gradient optimization of a smooth approximation of WSVT-SURE (GWSVT-SURE) to estimate the optimal threshold parameter. In the context of AMT, the proposed algorithms allow one to estimate the number of musical note components in the recordings. The proposed algorithms have been evaluated with the polyphonic piano music excerpts. It is observed that the proposed WSVT-SURE algorithm reaches significant improvement in the estimation performance, while GWSVT-SURE shows substantial savings in the computational cost
ASTRA BUS PER ASPERA: INFLUENCE OF SOCIAL INTERACTIONS IN COMMUNITY BUSES ON IDENTITY FORMATION
No full textPublic transportation is very important for people, especially in big cities like
Astana. People of all ages are using public buses, and forced to interact with each
other one way or another on a daily basis. The study focuses on those interactions in
public transport, and its influence on identity formation of people. The study is
qualitative, consisting of 2 parts: participant observation and interviews. The
participants were chosen using snowball sampling, and distributed into 3 age groups:
young adults (18-30), middle-aged people (30-60), and elderly (60+). The content was
analyzed by using the theory of symbolic interactionism. The result showed that
public transport is not viewed as a place for public interactions. Instead it is a place of
solitude despite the limited space the bus creates. Majority of the people prefer to
keep to themselves, instead of interacting with strangers or even acquaintances. Public
interactions are initiated only when needed, and only for the sake of everybody else’s
comfor
INVERSE DYNAMIC MODEL IDENTIFICATION OF A BIPEDAL ROBOT USING ARTIFICIAL NEURAL NETWORKS
No full textThe principal aim is to contribute to the identification of the inverse dynamics of the bipedal
robot by training several Artificial Neural Network (ANN) models. These include Feedfor-
ward Neural Networks (FFNN), Long Short-Term Memory(LSTM), and Recurrent Neural
Networks (RNN). The project will compare these models to determine the best performer in
terms of learning the inverse dynamics of the bipedal robot, considering both performance and
computational complexity. The main tools for the achievement of the task are CoppeliaSim
simulation and Python programming languag