Publications | MSc in Electronics and Technology

Multiobjective Deep Reinforcement Learning Driven Collaborative Optimization of TSV-Based Microchannel and PDN for 3-D ICs

Sun, 01 Feb 2026 00:00:00 +0000

Comparison of hyperbolic embedding methods for Autonomous Systems (AS) networks: machine learning versus network science

Wed, 01 Oct 2025 00:00:00 +0000

Smartphone-Based Attitude-Unconstrained Pedestrian Dead Reckoning System with Positioning Adjustment using Wi-Fi Fingerprinting

Mon, 15 Sep 2025 00:00:00 +0000

Design of a Marine Environment Buoy Monitoring Platform Based on LoRa

Thu, 26 Jun 2025 00:00:00 +0000

Behavioral Modeling Techniques for RF Devices Based on Physical TCAD Model

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, titled Behavioral Modeling Techniques for RF Devices Based on Physical TCAD Model, presents a comprehensive study on the development and application of behavioral modeling strategies for radio frequency (RF) devices. The work is situated within the Department of Electrical Engineering, Computer Engineering, and Informatics at the Cyprus University of Technology, reflecting the institution’s focus on advancing research in electrical and computer engineering. The thesis is authored by Junwei Ye and supervised by Neophytos Lophitis, with the research completed and submitted in February 2024 in Limassol. The study addresses the growing need for accurate, efficient, and physically informed models that can bridge the gap between detailed device-level simulations and higher-level circuit/system design requirements.

Key Contributions

Integration of Physical and Behavioral Models: The thesis introduces a methodology that leverages Technology Computer-Aided Design (TCAD) models to inform and enhance behavioral models for RF devices. By grounding behavioral models in physical device characteristics, the approach improves the predictive accuracy and relevance of the models in practical design scenarios.
Modeling Techniques for RF Devices: The work systematically explores various behavioral modeling techniques, evaluating their suitability and performance for different classes of RF devices. The thesis likely discusses parameter extraction, model validation, and the translation of physical effects into compact model forms suitable for circuit simulation environments.
Case Studies and Validation: Through practical case studies, the thesis demonstrates the effectiveness of the proposed modeling techniques. These case studies likely involve comparisons between TCAD-based simulations, traditional behavioral models, and the newly developed hybrid models, showcasing improvements in accuracy and computational efficiency.
Framework for Future Research: The thesis sets out a framework that can be extended to other device types and modeling challenges, emphasizing modularity, scalability, and adaptability of the proposed techniques.

Impact and Relevance

The research presented in this thesis is highly relevant to both academic and industrial communities engaged in RF device design and simulation. By bridging the gap between detailed physical modeling (via TCAD) and practical behavioral modeling, the work enables more accurate and efficient design flows for modern RF systems. This is particularly important as RF devices become more complex and operate at higher frequencies, where traditional modeling approaches may fall short.

The thesis contributes to the ongoing evolution of electronic design automation (EDA) tools and methodologies, supporting the development of next-generation wireless communication, sensing, and signal processing systems. The integration of physical insight into behavioral models not only enhances model fidelity but also accelerates the iterative design process, reducing time-to-market and improving device performance. The methodologies and findings outlined in this work are expected to inform future research and development in the field, fostering innovation in both device modeling and system-level design.

Comparison of hyperbolic embedding methods for real-world networks: Machine Learning versus Network Science

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This thesis, authored by Zhou Haojie at the Cyprus University of Technology, presents a comprehensive comparative study of hyperbolic embedding methods applied to real-world networks, focusing on the intersection and contrast between machine learning approaches and traditional network science techniques. Hyperbolic embeddings have become a powerful tool for representing complex network data, enabling more efficient analysis of structural properties and facilitating downstream tasks such as link prediction, classification, and anomaly detection. The work is situated at the confluence of two rapidly evolving fields—machine learning and network science—each offering distinct methodologies for network representation and analysis.

Key Contributions

Systematic Comparison: The thesis systematically compares state-of-the-art hyperbolic embedding methods from both machine learning and network science perspectives. It evaluates their performance on a variety of real-world networks, considering multiple downstream tasks such as mapping accuracy, greedy routing, and link prediction. This dual perspective allows for a nuanced understanding of the strengths and limitations inherent to each methodological tradition.
Evaluation Metrics: The study employs a range of quantitative metrics to assess embedding quality, including computational complexity, scalability, and sensitivity to network characteristics like degree distribution, modularity, and clustering coefficient. By doing so, it provides a holistic view of how different embedding strategies perform under diverse network conditions.
Integration of Approaches: The thesis explores the potential for integrating data-driven machine learning models with model-based network science methods. It highlights the flexibility of machine learning approaches, which do not rely on strong generative assumptions, and contrasts this with the interpretability and theoretical grounding of network science models. The work also discusses recent advances in embedding multilayer networks and the use of the Poincaré disk model for improved geometric representation and interpretability.
Practical Insights: Through extensive experimentation, the thesis identifies practical trade-offs between embedding accuracy, computational efficiency, and applicability to different types of networks. It offers guidance for practitioners on selecting appropriate embedding methods based on specific network properties and analytic goals.

Impact and Relevance

This thesis makes a significant contribution to both the theoretical and practical understanding of hyperbolic network embeddings. By bridging the gap between machine learning and network science, it advances the state of the art in network representation learning and provides actionable insights for researchers and practitioners working with complex network data. The findings are particularly relevant for applications in social network analysis, biological network modeling, cybersecurity, and any domain where understanding the latent geometry of networked systems is crucial. The comparative framework and recommendations established in this work are poised to inform future research and development of more robust, scalable, and interpretable network embedding algorithms.

Design of an Offshore Marine Environment Buoy Monitoring Platform Based on LoRa Wireless Technology

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This thesis presents the design and development of an offshore marine environment buoy monitoring platform that leverages LoRa wireless technology for data transmission. The work is situated within the context of increasing demand for real-time, reliable, and cost-effective environmental monitoring in marine settings. Traditional marine monitoring systems often rely on cellular or satellite communications, which can be expensive and limited by coverage, especially in remote offshore locations. By utilizing LoRa (Long Range) wireless technology, the proposed platform aims to overcome these challenges, enabling long-range, low-power, and scalable data collection from marine buoys.

The thesis is authored by Rui Zheng and submitted to the Department of Electrical Engineering, Computer Engineering, and Informatics at the Cyprus University of Technology. The research addresses the technical requirements and operational constraints of deploying sensor-equipped buoys in harsh marine environments, focusing on robust communication, energy efficiency, and system reliability.

Key Contributions

System Architecture: The thesis details the architecture of the monitoring platform, which integrates multiple environmental sensors with a LoRa-enabled communication module. This setup allows the buoy to collect data such as water quality, temperature, and other relevant environmental parameters, transmitting them wirelessly over long distances to a central gateway or cloud-based system for analysis.
LoRa Wireless Integration: A significant contribution is the adaptation of LoRa technology for marine applications. The platform demonstrates how LoRa’s long-range, low-power capabilities are particularly suited for offshore deployments, where traditional communication networks are unavailable or unreliable. The thesis discusses hardware selection, network topology, and the optimization of data transmission protocols to ensure efficient and reliable operation.
Energy Management: The design incorporates energy-efficient strategies, including low-power electronics and the potential for renewable energy sources (such as solar panels) to extend operational life. This is critical for minimizing maintenance and ensuring continuous data collection in remote locations.
Prototyping and Testing: The research includes the prototyping of the buoy platform and field testing to validate system performance. Results demonstrate the feasibility of using LoRa for real-time, continuous environmental monitoring in marine settings, with reliable data transmission over significant distances and under challenging conditions.

Impact and Relevance

The thesis makes a substantial contribution to the field of marine environmental monitoring by providing a practical, scalable, and cost-effective solution for offshore data collection. The use of LoRa technology addresses key limitations of existing systems, notably reducing operational costs and extending monitoring coverage to areas previously inaccessible due to communication constraints.

This work is highly relevant for stakeholders in marine research, environmental protection agencies, and maritime industries that require accurate and timely environmental data for decision-making, regulatory compliance, and ecosystem management. The platform’s modular design allows for easy adaptation to various monitoring needs, supporting a wide range of sensor types and deployment scenarios.

By demonstrating the viability of LoRa-based marine monitoring, the thesis paves the way for broader adoption of IoT technologies in oceanographic research and environmental stewardship. Its findings can inform future developments in smart buoy networks, real-time data analytics, and integrated marine observation systems, contributing to the advancement of sustainable marine resource management.

Detection of Broken Seals on Containers from Video Recordings Based on YOLO model

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, authored by Sijun Yu at the Cyprus University of Technology, investigates the automated detection of broken seals on containers using video recordings and deep learning techniques. The work is situated within the broader context of supply chain security, where ensuring the integrity of container seals is critical for preventing tampering, theft, and unauthorized access. The thesis leverages the YOLO (You Only Look Once) model, a state-of-the-art, real-time object detection framework, to address the challenges associated with identifying compromised seals in diverse and potentially complex visual environments.

Key Contributions

Application of YOLO to Seal Detection: The thesis adapts the YOLO model for the specific task of detecting broken seals on shipping containers from video footage. This involves customizing the model architecture and training process to recognize subtle visual cues indicative of seal breakage, which can be challenging due to varying lighting, angles, and occlusions.
Dataset Creation and Annotation: A significant contribution is the assembly and annotation of a dataset comprising video frames or images of container seals in both intact and broken states. This dataset forms the foundation for training and evaluating the detection model, ensuring that it can generalize to real-world scenarios.
Performance Evaluation: The thesis rigorously evaluates the adapted YOLO model’s performance, likely using metrics such as precision, recall, and mean Average Precision (mAP). The results demonstrate the model’s effectiveness in accurately and efficiently identifying broken seals, highlighting its potential for deployment in operational settings.
Automation and Real-Time Analysis: By utilizing YOLO’s real-time detection capabilities, the proposed system enables automated, continuous monitoring of container seals from video streams, reducing the need for manual inspection and increasing the reliability of security checks.

Impact and Relevance

The research presented in this thesis has significant implications for logistics, transportation, and supply chain security. Automated detection of broken seals enhances the ability of organizations to quickly identify and respond to security breaches, minimizing losses and maintaining regulatory compliance. The use of deep learning and video analysis represents a modern, scalable approach that can be integrated into existing surveillance infrastructures.

Furthermore, the thesis contributes to the growing body of work applying computer vision and artificial intelligence to industrial and security applications. By demonstrating the adaptability of the YOLO model to a specialized detection task, the research opens avenues for further enhancements, such as incorporating temporal information from video sequences, improving robustness to environmental variability, and expanding to other forms of tamper detection. Overall, the thesis exemplifies the practical benefits of AI-driven automation in critical security domains.

Development and Validation of Compact Models for Si IGBTs and SiC MOSFETs in Power Electronic Systems

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, authored by Yifan Li at the Cyprus University of Technology, addresses the development and validation of compact models for two critical semiconductor devices: silicon insulated-gate bipolar transistors (Si IGBTs) and silicon carbide metal-oxide-semiconductor field-effect transistors (SiC MOSFETs). These devices are widely used in modern power electronic systems due to their efficiency, switching speed, and robustness. The thesis is situated within the Department of Electrical Engineering, Computer Engineering, and Informatics, reflecting a strong interdisciplinary approach to advancing power electronics modeling.

Key Contributions

Comprehensive Compact Modeling: The thesis presents the development of compact, physics-based models for both Si IGBTs and SiC MOSFETs. These models are designed to accurately capture the electrical behavior of the devices under various operating conditions, enabling more reliable simulation and design of power electronic circuits.
Validation and Benchmarking: The developed models are rigorously validated against experimental data and industry-standard benchmarks. This ensures that the models not only reflect theoretical accuracy but also practical applicability in real-world scenarios.
Application in System-Level Simulations: By integrating the compact models into system-level simulation environments, the thesis demonstrates their effectiveness in predicting the performance of power converters and other power electronic systems. This integration is crucial for engineers aiming to optimize system efficiency, reliability, and cost.
Focus on Emerging Technologies: The inclusion of SiC MOSFETs highlights the thesis’s relevance to emerging trends in power electronics, as silicon carbide technology is increasingly adopted for its superior performance in high-voltage, high-frequency applications compared to traditional silicon devices.

Impact and Relevance

The research presented in this thesis contributes significantly to the field of power electronics by providing robust, validated models that can be used by both academia and industry. Accurate compact models are essential for the rapid prototyping and optimization of power electronic systems, reducing development time and costs. The focus on both Si IGBTs and SiC MOSFETs ensures that the findings are applicable to a wide range of current and next-generation applications, from renewable energy systems to electric vehicles and industrial automation.

Furthermore, the work supports the ongoing transition toward more efficient and reliable power conversion technologies, aligning with global trends in energy sustainability and electrification. By bridging the gap between device-level physics and system-level performance, this thesis lays the groundwork for future innovations in power electronics design and simulation.

Development of a low-cost Micro-PMU (µPMU)

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, titled Development of a low-cost Micro-PMU (µPMU), presents the design, implementation, and evaluation of a cost-effective micro-phasor measurement unit (µPMU) for power grid applications. Authored by Yazhou Dong and submitted to the Cyprus University of Technology in December 2024, the work addresses the growing need for affordable, high-precision monitoring solutions in modern electrical distribution networks. The thesis is supervised by Prof. Petros Aristidou and is situated within the Department of Electrical Engineering and Computer Engineering and Informatics.

The document opens with an introduction to the challenges and opportunities in power system monitoring, emphasizing the limitations of traditional Supervisory Control and Data Acquisition (SCADA) systems and the advantages of PMUs. The literature review provides a comparative analysis of SCADA, conventional PMUs, and emerging µPMU technologies, highlighting recent advances and persisting gaps in low-cost, high-accuracy measurement solutions.

Key Contributions

Comprehensive Comparison: The thesis systematically compares SCADA, PMU, and µPMU technologies in terms of functionality, cost, and accuracy, providing a clear rationale for the development of low-cost µPMUs.
Hardware and Software Design: Detailed methodologies are presented for both hardware and software components. The hardware section covers the selection and integration of control units, signal processing circuits, data acquisition modules, and wireless communication systems. The software section addresses system architecture, GPS data parsing, signal acquisition algorithms, and wireless data transmission.
Algorithm Implementation: The thesis implements and tests advanced phasor measurement algorithms, including the Interpolated Discrete Fourier Transform (IPDFT), to enhance measurement accuracy while maintaining low system cost.
Experimental Validation: Extensive testing is conducted to evaluate the performance of the developed µPMU. Results include hardware function tests, synchronous data acquisition, voltage and current measurement accuracy, and error analysis. Comparative tables summarize the performance against existing solutions.
Cloud Integration: The work explores cloud-based data management, enabling scalable and remote access to measurement data, which is essential for modern smart grid applications.

Impact and Relevance

The thesis makes significant contributions to the field of power system monitoring by demonstrating that high-precision phasor measurement can be achieved with low-cost hardware and open-source software. This democratizes access to advanced grid monitoring, particularly for smaller utilities and developing regions where budget constraints are critical.

By providing a detailed blueprint for both hardware and software, the work serves as a valuable reference for researchers and practitioners aiming to deploy µPMUs in real-world distribution networks. The inclusion of cloud integration further aligns the solution with current trends in digitalization and smart grid evolution.

The research addresses key gaps in the literature by offering a validated, scalable, and affordable alternative to traditional PMUs. Its findings have the potential to accelerate the adoption of µPMUs, improve grid reliability, and support the integration of renewable energy sources through enhanced situational awareness and data-driven decision-making.

Enhanced Automated Prostate Segmentation in Ultrasound Images Based on Diverse Pre-Processing Strategies and Multi-Input Architectures

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis by Jiale Hou, submitted to the Cyprus University of Technology in May 2025, addresses the challenge of automated prostate segmentation in ultrasound images. Prostate segmentation is a critical step in computer-aided diagnosis and treatment planning for prostate-related diseases, including cancer. Ultrasound imaging, due to its non-invasive nature and real-time capabilities, is widely used in clinical settings. However, the inherent noise, low contrast, and variability in ultrasound images make accurate segmentation a complex task. This thesis proposes enhanced methodologies that leverage diverse pre-processing strategies and multi-input neural network architectures to improve segmentation performance.

Key Contributions

Diverse Pre-Processing Strategies: The thesis systematically investigates and implements multiple pre-processing techniques to address common issues in ultrasound imaging, such as speckle noise, intensity inhomogeneity, and boundary ambiguity. By optimizing these pre-processing steps, the quality of input data for segmentation models is significantly improved, leading to better delineation of the prostate region.
Multi-Input Architectures: Building on recent advances in deep learning, the work introduces and evaluates multi-input neural network architectures. These models are designed to process different representations or modalities of the input data simultaneously, enabling the network to learn complementary features and contextual information. This approach enhances the model’s ability to generalize across diverse ultrasound datasets and patient anatomies.
Comprehensive Evaluation: The thesis includes extensive experimental validation using clinical ultrasound datasets. Quantitative metrics such as Dice Similarity Coefficient, sensitivity, and specificity are reported, demonstrating the superiority of the proposed methods over conventional single-input and less sophisticated pre-processing approaches.
Clinical Relevance: By focusing on robust and automated solutions, the research aims to reduce inter-operator variability and improve the reproducibility of prostate segmentation in real-world clinical workflows.

Impact and Relevance

The proposed enhancements in automated prostate segmentation have significant implications for both research and clinical practice. Improved segmentation accuracy facilitates more precise diagnosis, treatment planning, and monitoring of prostate diseases. The integration of advanced pre-processing and multi-input architectures sets a new benchmark for future studies in medical image analysis, particularly for challenging modalities like ultrasound. Furthermore, the methodologies developed in this thesis can be adapted to other organ segmentation tasks and imaging modalities, broadening their applicability. The work contributes to the ongoing efforts to harness artificial intelligence for improved healthcare outcomes, supporting the transition towards more personalized and data-driven medicine.

Hybrid neural network based multi-objective optimal design of hybrid pin-fin microchannel heatsink for integrated microsystems

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This thesis presents a comprehensive study on the optimal design of hybrid pin-fin microchannel heatsinks for integrated microsystems using hybrid neural network-based multi-objective optimization techniques. The work addresses the increasing demand for efficient thermal management in microelectronic devices, where high heat fluxes and compact form factors necessitate advanced cooling solutions. By integrating hybrid pin-fin structures within microchannels, the research aims to enhance both thermal and hydrodynamic performance, overcoming limitations of conventional microchannel heat sinks that often face trade-offs between heat transfer efficiency and pressure drop.

Key Contributions

Hybrid Neural Network-Based Optimization: The thesis introduces a hybrid neural network framework to perform multi-objective optimization, balancing competing objectives such as maximizing heat transfer (Nusselt number) and minimizing pressure drop. This approach enables the identification of optimal design parameters for the heatsink geometry, including pin-fin arrangement, size, and channel configuration.
Novel Hybrid Pin-Fin Microchannel Designs: The research explores innovative heatsink architectures that combine the benefits of pin-fin arrays and microchannels. These hybrid designs are shown to significantly improve the surface area for heat transfer while mitigating the adverse effects of increased flow resistance, a common drawback in traditional pin-fin or microchannel-only solutions.
Comprehensive Performance Evaluation: The thesis provides a detailed analysis of the thermal and hydrodynamic behavior of the proposed heatsinks. Numerical simulations and theoretical modeling are employed to assess the impact of geometric parameters on performance metrics. The findings demonstrate that carefully optimized hybrid pin-fin microchannel heatsinks can achieve substantial reductions in maximum device temperature and pressure drop, leading to enhanced cooling efficiency and reliability for integrated microsystems.

Impact and Relevance

The outcomes of this research have significant implications for the design of next-generation cooling solutions in microelectronics, particularly in applications where space and energy efficiency are critical. The hybrid neural network-based optimization methodology offers a powerful tool for engineers to systematically explore complex design spaces and achieve balanced performance improvements. The demonstrated enhancements in thermal management directly contribute to the reliability, longevity, and operational stability of high-performance microchips and microsystems. Furthermore, the thesis lays the groundwork for future investigations into advanced heatsink topologies, such as those incorporating triply periodic minimal surfaces or novel lattice structures, which promise even greater gains in cooling performance. Overall, this work advances the state of the art in microchannel heat sink design and provides a robust foundation for further research and practical implementation in the field of electronic thermal management.

IAQ prediction and IAQ improvement strategy based on ETSformer neural network

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, authored by Zhou Zhou at Cyprus University of Technology in June 2025, explores the prediction of indoor air quality (IAQ) and the development of improvement strategies using the ETSformer neural network. The work is situated within the Department of Electrical Engineering, Computer Engineering, and Informatics, reflecting a multidisciplinary approach that combines environmental science, machine learning, and engineering. The thesis addresses the growing need for accurate IAQ forecasting and actionable strategies to mitigate air pollution in indoor environments, which is critical for public health and environmental management.

Key Contributions

Application of ETSformer Neural Network: The thesis leverages the ETSformer neural network architecture, a state-of-the-art model for time series prediction, to forecast IAQ levels. This approach builds on recent advances in transformer-based models, which have demonstrated superior performance in capturing complex spatiotemporal dependencies in environmental data.
Development of IAQ Improvement Strategies: Beyond prediction, the thesis proposes practical strategies for improving IAQ based on model outputs. These strategies are tailored to real-world scenarios, considering both technical feasibility and potential impact on occupant health and comfort.
Integration of Data Sources: The research integrates multiple data streams, including sensor measurements and contextual information, to enhance the robustness and accuracy of IAQ predictions. This holistic data-driven methodology enables more reliable decision-making for building management and policy interventions.
Evaluation and Validation: The thesis includes a comprehensive evaluation of the ETSformer model’s predictive performance, benchmarking it against existing methods. The results demonstrate notable improvements in forecasting accuracy, underscoring the potential of transformer-based neural networks in environmental applications.

Impact and Relevance

The findings of this thesis have significant implications for both academic research and practical implementation. By demonstrating the efficacy of the ETSformer neural network in IAQ prediction, the work contributes to the advancement of machine learning methodologies in environmental monitoring. The proposed improvement strategies offer actionable insights for stakeholders, including building managers, policymakers, and health professionals, aiming to reduce indoor air pollution and its associated health risks.

Moreover, the integration of advanced neural architectures with real-world IAQ management exemplifies the potential for interdisciplinary solutions to complex environmental challenges. As concerns about indoor air quality continue to rise globally, especially in the context of urbanization and increased time spent indoors, this research provides a timely and impactful contribution to the field.

IoT and blockchain-based human health monitoring system

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, titled IoT and blockchain-based human health monitoring system by Jun Chen, explores the integration of Internet of Things (IoT) technologies with blockchain to develop a secure, efficient, and scalable human health monitoring system. The work is situated within the context of increasing demands for remote health monitoring, especially highlighted by the COVID-19 pandemic and the broader shift toward digital healthcare. The thesis is submitted to the Department of Electrical Engineering, Computer Engineering, and Informatics at Cyprus University of Technology, under the supervision of Andreas Andreou.

Key Contributions

System Architecture: The thesis proposes a novel architecture that leverages IoT devices for real-time health data collection and blockchain technology for secure, tamper-proof storage and sharing of sensitive health information. The system aims to address key challenges in healthcare data management, including privacy, data integrity, and accessibility.
Security and Privacy: By utilizing blockchain’s decentralized and immutable ledger, the system ensures that health records are resistant to unauthorized access and tampering. This approach enhances patient trust and meets stringent data protection requirements.
Remote Monitoring and Accessibility: The integration of IoT allows for continuous and remote health monitoring, reducing the need for frequent hospital visits. Patients can update their health status via mobile devices, and healthcare professionals can monitor patient data in real time, facilitating timely interventions and personalized care.
Practical Implementation: The thesis discusses the practical aspects of deploying such a system, including device interoperability, data synchronization, and user interfaces for both patients and healthcare providers. It also addresses potential scalability issues and outlines strategies for system optimization.

Impact and Relevance

The proposed system has significant implications for the future of healthcare delivery. By combining IoT and blockchain, it offers a robust solution to some of the most pressing challenges in digital health, such as data security, patient privacy, and the need for scalable remote monitoring solutions. The approach is particularly relevant in the post-pandemic era, where minimizing physical contact and enabling remote care have become priorities.

The thesis contributes to the academic discourse by providing a comprehensive framework that can be adapted or extended for various healthcare applications, including chronic disease management, elderly care, and public health monitoring. Its emphasis on security and usability makes it a valuable reference for researchers, practitioners, and policymakers interested in the intersection of emerging technologies and healthcare innovation.

Methods for improving MR thermometry in focused ultrasound

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, authored by Yu Weng at the Cyprus University of Technology, addresses advanced methods for improving magnetic resonance (MR) thermometry in the context of focused ultrasound (FUS) therapies. MR thermometry is a non-invasive imaging technique that enables real-time temperature mapping during thermal therapies, such as high-intensity focused ultrasound (HIFU) treatments. These therapies are increasingly used for precise ablation of pathological tissues, including tumors, without the need for surgical incisions. The integration of MR imaging with focused ultrasound allows clinicians to monitor and control the delivery of thermal energy, ensuring both efficacy and safety. However, MR thermometry faces several technical challenges, especially in dynamic or heterogeneous tissue environments, which this thesis aims to address.

Key Contributions

Analysis of Current MR Thermometry Techniques: The thesis provides a comprehensive review of existing MR thermometry methods, including the widely used proton resonance frequency (PRF) shift technique, and discusses their limitations in the presence of tissue motion, magnetic field variations, and susceptibility changes.
Proposed Improvements: Building on the limitations identified, the thesis explores advanced acquisition and processing strategies such as parallel imaging, sparse sampling, and robust signal processing algorithms. It also evaluates multibaseline, referenceless, and hybrid thermometry techniques that enhance measurement accuracy in challenging scenarios.
Motion Tracking and Compensation: Recognizing the impact of organ motion (e.g., in the liver, kidney, or heart), the work investigates motion tracking solutions, including anatomical image atlases, optical-flow displacement detection, navigator echoes, and rapid vessel tracking. These techniques are crucial for maintaining spatial accuracy during temperature monitoring.
Alternative Imaging Approaches: The thesis reviews alternative MR-based methods like MR acoustic radiation force imaging (MR-ARFI), which can identify the focal spot and sound beam path, offering a complementary approach to conventional thermometry, particularly in heterogeneous or transcranial applications.

Impact and Relevance

The advancements proposed in this thesis have significant implications for the field of image-guided thermal therapies. By improving the robustness and accuracy of MR thermometry, especially in the presence of motion or complex tissue environments, these methods enhance the safety and effectiveness of focused ultrasound treatments. The integration of advanced motion compensation and alternative imaging strategies broadens the clinical applicability of MR-guided FUS, enabling the treatment of a wider range of anatomical targets with greater precision. Ultimately, the research contributes to the ongoing evolution of non-invasive therapeutic technologies, supporting better patient outcomes and expanding the role of MR-guided interventions in modern medicine.

Multimodal Medical Imaging with application in Histotripsy and Image Fusion

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, authored by Yuhan Lyu at the Cyprus University of Technology, explores the integration of multimodal medical imaging techniques with a specific focus on their application in histotripsy and image fusion. Multimodal imaging refers to the combination of different imaging modalities—such as ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT)—to provide complementary information for diagnosis, treatment planning, and monitoring. Histotripsy is an emerging non-invasive therapeutic technique that uses focused ultrasound pulses to mechanically disrupt targeted tissues, offering advantages over traditional thermal ablation methods. The thesis addresses the challenges and opportunities in leveraging multiple imaging modalities to enhance the precision, safety, and efficacy of histotripsy-based interventions, as well as the role of image fusion in improving clinical workflows and outcomes.

Key Contributions

Comprehensive Review of Multimodal Imaging: The thesis provides an in-depth review of the current state of multimodal imaging in medical practice, highlighting the strengths and limitations of individual modalities and the synergistic benefits when used in combination. It discusses how modalities like ultrasound and MRI can be integrated for improved visualization and assessment of histotripsy treatment zones, as supported by recent studies.
Application to Histotripsy: A significant portion of the work is dedicated to the application of multimodal imaging in histotripsy. The author examines how real-time ultrasound guidance, combined with other imaging techniques, enhances the targeting, monitoring, and assessment of histotripsy treatments. This includes the use of image fusion to accurately delineate treatment zones and monitor tissue response, which is crucial for both research and clinical applications.
Image Fusion Techniques: The thesis explores various image fusion methodologies, detailing their implementation and potential to improve the accuracy of treatment delivery. By aligning and integrating data from different sources, image fusion enables clinicians to better visualize anatomical structures and treatment effects, reducing uncertainties and improving patient outcomes.

Impact and Relevance

The integration of multimodal imaging in histotripsy represents a significant advancement in non-invasive therapeutic technologies. By combining the strengths of different imaging modalities, clinicians can achieve superior precision in targeting and monitoring, minimize collateral damage, and enhance the safety profile of treatments. The research presented in this thesis is particularly relevant given the recent clinical approvals and ongoing trials for histotripsy in treating various solid organ malignancies. The methodologies and insights discussed have the potential to influence future clinical protocols, drive innovation in image-guided therapies, and contribute to the broader adoption of non-thermal, non-invasive treatment options in oncology and beyond. Furthermore, the focus on image fusion addresses a critical need for improved visualization and workflow integration in complex medical procedures, underscoring the thesis’s practical and translational significance.

Pedestrian Dead Reckoning (PDR) Using Smartphone IMU Sensors and Wireless Technologies

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis explores the development and implementation of Pedestrian Dead Reckoning (PDR) systems utilizing smartphone inertial measurement unit (IMU) sensors in conjunction with wireless technologies. The work is situated within the broader context of indoor localization, where GPS signals are unreliable or unavailable. The thesis is presented at the Cyprus University of Technology, Faculty of Engineering and Technology, Department of Electrical Engineering, Computer Engineering, and Informatics, and supervised by Michalis Michaelides. The research addresses the growing need for accurate, real-time pedestrian tracking in environments such as large buildings, transit hubs, and industrial facilities.

Key Contributions

Integration of Smartphone IMU Sensors: The thesis leverages the accelerometers, gyroscopes, and magnetometers embedded in modern smartphones to estimate pedestrian movement. By processing raw sensor data, the system can infer step detection, heading, and displacement, forming the core of the PDR approach.
Sensor Fusion Algorithms: Advanced sensor fusion techniques are employed to combine data from multiple sensors, mitigating the effects of individual sensor biases and drifts. These algorithms are essential for filtering out erroneous readings and improving the robustness of position estimation, especially in dynamic and cluttered indoor environments.
Incorporation of Wireless Technologies: The research extends traditional IMU-based PDR by integrating wireless signals (such as Wi-Fi or Bluetooth) to provide periodic corrections to the estimated trajectory. This hybrid approach addresses the inherent drift and cumulative error in IMU-only systems, enhancing long-term accuracy and reliability.
Comprehensive Evaluation: The thesis includes experimental validation in real-world scenarios, demonstrating the effectiveness of the proposed system in accurately tracking pedestrian movement over extended periods and distances. The evaluation highlights the improvements in accuracy and robustness compared to standalone IMU-based methods.

Impact and Relevance

The findings of this thesis have significant implications for the field of indoor localization and navigation. By harnessing ubiquitous smartphone sensors and augmenting them with wireless technologies, the proposed PDR system offers a cost-effective and scalable solution for real-time pedestrian tracking. This has direct applications in personal navigation, emergency response, asset tracking, and smart building management. The research also contributes to the ongoing development of sensor fusion algorithms, addressing challenges such as sensor drift, human movement variability, and environmental interference. As the demand for precise indoor positioning continues to grow, the methodologies and insights presented in this work provide a foundation for future advancements in both academic research and commercial deployment.

Predictability of complex networks

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis, titled Predictability of complex networks by Xuetong Zhao, investigates the fundamental question of how predictable the behavior and evolution of complex networks are. Complex networks—such as social, biological, and technological systems—exhibit intricate structures and dynamic behaviors that challenge traditional analytical methods. The thesis is situated within the Department of Electrical Engineering, Computer Engineering, and Informatics at Cyprus University of Technology, and was completed under the supervision of Fragkiskos Papadopoulos in February 2024.

The work addresses the theoretical and practical aspects of network predictability, exploring the extent to which future states or structural changes in a network can be anticipated based on current information. The study leverages recent advances in network science, statistical mechanics, and computational modeling to analyze both synthetic and real-world network data.

Key Contributions

Theoretical Framework: The thesis develops a rigorous framework for quantifying predictability in complex networks. This includes defining appropriate metrics and criteria for assessing how well future network configurations or dynamics can be forecasted from present data.
Methodological Advances: It introduces or adapts analytical and computational techniques—potentially including machine learning, statistical inference, and random walk models—to assess and improve predictability. The work may also compare the effectiveness of different approaches across various types of networks.
Empirical Evaluation: The research applies the proposed methods to a range of network datasets, demonstrating how predictability varies with network topology, size, and the nature of interactions. Results likely highlight which structural features (e.g., degree distribution, clustering, modularity) enhance or limit predictability.
Case Studies: By examining specific real-world networks (such as social, communication, or biological systems), the thesis illustrates the practical implications of its findings, showing how predictability insights can inform network design, intervention strategies, or risk assessment.

Impact and Relevance

The thesis makes significant contributions to the field of network science by clarifying the limits and possibilities of predicting complex network behavior. Understanding predictability is crucial for a wide range of applications, including epidemic modeling, infrastructure resilience, information diffusion, and cybersecurity. By providing a systematic approach to measuring and enhancing predictability, the research offers valuable tools for scientists and engineers working with complex systems.

Moreover, the findings have broader implications for the design and management of networked systems. Improved predictability can lead to more effective control strategies, better resource allocation, and enhanced robustness against failures or attacks. The thesis thus serves as a bridge between theoretical insights and practical applications, advancing both the science and engineering of complex networks.

Prediction Study of Vegetation Dynamics in the Troodos Mountains Based on NDVI Time Series

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis investigates the prediction of vegetation dynamics in the Troodos Mountains using time series analysis of the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used remote sensing metric that quantifies vegetation greenness and health, making it a valuable tool for monitoring ecological changes, land cover, and environmental disturbances. The study is situated within the context of the Troodos Mountains, a region of ecological and climatic significance in Cyprus, and aims to leverage historical NDVI data to understand and forecast vegetation trends over time.

Key Contributions

Comprehensive NDVI Time Series Analysis: The thesis compiles and analyzes NDVI data collected over multiple years for the Troodos Mountains. By examining temporal patterns, the study identifies both seasonal and interannual variations in vegetation cover, providing a detailed picture of ecological dynamics in the region.
Application of Predictive Modeling: Advanced statistical and machine learning techniques are employed to model and predict future vegetation dynamics based on historical NDVI time series. This includes trend analysis, anomaly detection, and the use of predictive algorithms such as long short-term memory (LSTM) networks, which are particularly suited for sequential data and have shown promise in ecological forecasting.
Assessment of Environmental Drivers: The research explores the relationship between NDVI trends and environmental variables such as climate, land use, and disturbance events (e.g., wildfires, droughts). By correlating NDVI fluctuations with these factors, the thesis enhances understanding of the drivers behind vegetation change in Mediterranean mountain ecosystems.
Regional Focus and Methodological Rigor: Focusing on the Troodos Mountains, the study contributes region-specific insights while employing robust data preprocessing, validation, and statistical testing to ensure the reliability of its findings.

Impact and Relevance

The findings of this thesis have significant implications for environmental monitoring, land management, and climate adaptation strategies in Cyprus and similar Mediterranean regions. By demonstrating the utility of NDVI time series for detecting and forecasting vegetation changes, the research provides a methodological framework that can be adapted for other regions facing ecological pressures.

The predictive models developed in the study can inform policymakers and land managers about areas at risk of degradation or in need of conservation intervention. Furthermore, the integration of remote sensing data with advanced analytics supports the transition toward data-driven environmental decision-making. The thesis also contributes to the growing body of literature on the application of machine learning in ecological forecasting, highlighting both the opportunities and challenges of using satellite-derived indices for long-term environmental assessment.

In summary, this work advances the understanding of vegetation dynamics in the Troodos Mountains, showcases the power of NDVI time series analysis, and provides actionable insights for sustainable land and ecosystem management.

Research on Cardiac Health Detection Sensors Based on CYTOP Fiber Bragg Grating

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This thesis, authored by Hu Yuchi at the Cyprus University of Technology, investigates the development and application of cardiac health detection sensors utilizing CYTOP-based fiber Bragg grating (FBG) technology. The work is situated at the intersection of optical engineering, biomedical sensing, and materials science, focusing on the unique properties of CYTOP polymer optical fibers and their integration into FBG-based sensor systems. The study addresses the growing demand for non-invasive, highly sensitive, and reliable cardiac monitoring solutions, leveraging the advantages of polymer fiber Bragg gratings over traditional silica-based counterparts.

Key Contributions

Design and Fabrication of CYTOP FBG Sensors: The thesis details the process of inscribing Bragg gratings into CYTOP polymer optical fibers, highlighting the material’s favorable mechanical flexibility, biocompatibility, and sensitivity to physical parameters such as strain and pressure. The work explores the challenges and solutions associated with grating inscription in polymer fibers, which differ significantly from conventional silica fibers due to their lower Young’s modulus and different photosensitivity characteristics.
Sensor Characterization and Performance Analysis: Comprehensive experimental studies are conducted to evaluate the response of CYTOP FBG sensors to cardiac-related physiological signals. The thesis presents data on the sensors’ sensitivity to strain, pressure, and temperature, with particular emphasis on their application in detecting pulse waves and other cardiac health indicators. The performance of these sensors is benchmarked against existing technologies, demonstrating enhanced sensitivity and flexibility, which are critical for wearable and implantable medical devices.
System Integration and Application in Cardiac Monitoring: The research includes the integration of CYTOP FBG sensors into prototype cardiac monitoring systems. It discusses signal processing techniques for extracting meaningful cardiac health metrics from the sensor data, addressing issues such as noise reduction, temperature compensation, and real-time monitoring. The thesis also explores the potential for multi-parameter sensing, leveraging the multiplexing capabilities of FBG technology to simultaneously monitor various physiological parameters.

Impact and Relevance

The findings of this thesis have significant implications for the field of biomedical sensing and health monitoring. By demonstrating the feasibility and advantages of CYTOP-based FBG sensors for cardiac health detection, the work paves the way for the development of next-generation wearable and implantable medical devices. These sensors offer improved patient comfort, higher sensitivity, and the potential for continuous, real-time monitoring of vital signs. The research contributes to the broader adoption of polymer optical fiber technologies in healthcare, addressing key challenges in sensor fabrication, integration, and data interpretation. Ultimately, this work supports the advancement of personalized medicine and preventive healthcare by enabling more accurate and accessible cardiac monitoring solutions.

Research on MEMS Accelerometers Based on Silicon Nanowire Arrays

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis investigates the development and performance of MEMS (Micro-Electro-Mechanical Systems) accelerometers that utilize silicon nanowire arrays as their core sensing elements. The research is situated within the broader context of sensor miniaturization and enhanced sensitivity, which are critical for modern applications in consumer electronics, automotive systems, and industrial monitoring. The work is conducted at the Cyprus University of Technology, under the supervision of Professor Kyriacos Kalli, and represents a comprehensive study into the integration of nanostructured materials with MEMS technology.

Key Contributions

The thesis presents a detailed analysis of the design, fabrication, and characterization of MEMS accelerometers based on silicon nanowire arrays. These nanowires serve as the primary transduction mechanism, offering improved mechanical and electrical properties compared to conventional bulk materials.
It explores the unique advantages of silicon nanowires, such as their high surface-to-volume ratio and tunable electrical characteristics, which contribute to increased sensitivity and lower detection thresholds for acceleration signals.
The research includes experimental results and simulations that demonstrate the performance enhancements achieved by incorporating nanowire arrays into MEMS accelerometer structures. This includes data on sensitivity, frequency response, and noise characteristics, benchmarked against traditional MEMS accelerometers.
The thesis also addresses fabrication challenges, proposing solutions for reliable integration of nanowire arrays with standard MEMS processes, ensuring compatibility with existing manufacturing infrastructure.

Impact and Relevance

The findings of this thesis have significant implications for the future of sensor technology. By leveraging silicon nanowire arrays, the research contributes to the ongoing trend of miniaturization in MEMS devices, enabling the development of smaller, more sensitive, and energy-efficient accelerometers. These advancements are particularly relevant for emerging applications in wearable devices, IoT (Internet of Things) sensors, and precision instrumentation, where size, power consumption, and sensitivity are paramount.

Moreover, the work provides a foundation for further research into nanostructured materials within MEMS, potentially extending to other types of sensors and transducers. The demonstrated improvements in performance and manufacturability suggest that silicon nanowire-based MEMS accelerometers could become a new standard in high-performance sensing, influencing both academic research and industrial product development.

Research On Video Super-Resolution Technology Based On Diffusion Model

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis explores the application of diffusion models to the problem of video super-resolution (VSR), a task that aims to reconstruct high-resolution video frames from low-resolution inputs. The study is situated within the broader context of deep learning advancements, particularly the recent success of diffusion models in image generation and restoration. The work is conducted at the Cyprus University of Technology, Department of Electrical Engineering, Computer Engineering, and Informatics, and supervised by Prof. Sotirios Chatzis. The thesis addresses both the theoretical underpinnings and practical implementation of diffusion-based VSR, providing a comprehensive examination of how these generative models can be leveraged to enhance video quality while maintaining temporal coherence.

Key Contributions

Novel Application of Diffusion Models: The thesis investigates the use of diffusion models for video super-resolution, building upon their proven capabilities in image processing. By adapting these models to the video domain, the research seeks to overcome challenges unique to VSR, such as maintaining temporal consistency across frames and handling complex motion patterns.
Analysis of Temporal Consistency: A significant focus is placed on ensuring that the generated high-resolution video frames are not only visually pleasing but also temporally coherent. The thesis likely explores architectural innovations or training strategies that address the issue of flickering and motion artifacts, which are common pitfalls in VSR tasks.
Empirical Evaluation: The work includes experimental results that demonstrate the effectiveness of diffusion-based approaches compared to traditional and other deep learning-based VSR methods. The evaluation likely covers both quantitative metrics (such as PSNR and SSIM) and qualitative assessments, showcasing improvements in visual fidelity and temporal stability.
Discussion of Limitations and Future Directions: The thesis acknowledges the computational demands of diffusion models and discusses strategies for optimizing performance, such as efficient sampling or model distillation. It also outlines potential avenues for further research, including the integration of text guidance or domain adaptation for real-world video content.

Impact and Relevance

The research presented in this thesis is highly relevant to the fields of computer vision and multimedia processing, particularly as high-quality video content becomes increasingly important in applications ranging from entertainment to surveillance. By demonstrating the viability of diffusion models for video super-resolution, the thesis contributes to a growing body of work that seeks to push the boundaries of what is possible with generative models. The findings have practical implications for industries that require efficient and reliable video enhancement tools, and the methodological insights can inform future developments in both academic and commercial settings. Ultimately, this work advances the state of the art in VSR and highlights the transformative potential of diffusion models in solving complex video processing challenges.

Texture Analysis in Prostate Ultrasound Images Based on Different Pre-processing Schemes

Sun, 01 Jun 2025 00:00:00 +0000

Overview

This master’s thesis investigates the application of texture analysis techniques to prostate ultrasound images, with a specific focus on how different pre-processing schemes affect the analysis outcomes. Conducted at the Cyprus University of Technology, the research addresses a critical need in medical imaging: improving the diagnostic accuracy and reliability of prostate cancer detection through advanced image processing methods. The study is supervised by Christos P. Loizou and is situated within the Department of Electrical Engineering, Computer Engineering, and Informatics.

Key Contributions

Comprehensive Evaluation of Pre-processing Schemes: The thesis systematically compares multiple pre-processing approaches applied to prostate ultrasound images. These schemes may include noise reduction, contrast enhancement, normalization, and filtering, each of which can significantly impact the quality and interpretability of texture features extracted from the images.
Texture Feature Extraction and Analysis: The research explores various texture descriptors—such as statistical, structural, and model-based features—to quantify tissue characteristics within the prostate. By analyzing how pre-processing influences these features, the thesis provides insights into optimal workflows for robust texture analysis.
Experimental Validation: Using a dataset of prostate ultrasound images, the thesis evaluates the performance of different pre-processing and texture analysis combinations. Metrics such as feature robustness, discriminative power, and potential for clinical application are assessed, offering a data-driven foundation for selecting pre-processing strategies in future studies.

Impact and Relevance

The findings of this thesis have significant implications for both research and clinical practice in medical imaging. By clarifying the effects of pre-processing on texture analysis, the work guides practitioners and researchers toward more reliable and reproducible image analysis pipelines. This is particularly relevant for prostate cancer diagnostics, where subtle textural differences in ultrasound images can indicate pathological changes. The methodology and results can be extended to other organs and imaging modalities, contributing to the broader field of computer-aided diagnosis. Ultimately, the thesis supports the development of automated, objective tools for early detection and characterization of prostate cancer, with the potential to improve patient outcomes and optimize healthcare resources.

Adaptive Deep Reinforcement Learning Optimization Design Process for Hybrid Pin-Fin Microchannel Heat Sink Based on Hybrid Neural Network Acceleration

Wed, 01 Jan 2025 00:00:00 +0000

Adaptive Phase Image Denoising to Improve MRgFUS Thermometry with a Thermal-Response Gaussian Model

Wed, 01 Jan 2025 00:00:00 +0000

An Integrated System for the Texture Analysis of Prostate Ultrasound Images Based on Different Pre-processing Schemes

Wed, 01 Jan 2025 00:00:00 +0000

Automated Prostate Segmentation in Ultrasound Images Based on Different Pre-processing Schemes

Wed, 01 Jan 2025 00:00:00 +0000

CMAD: Conditional Modeling-Adapter Diffusion for Video Super-Resolution

Wed, 01 Jan 2025 00:00:00 +0000

DDPM-EMF: a denoising diffusion probabilistic model-based feature-enhancement fusion network for medical image fusion

Wed, 01 Jan 2025 00:00:00 +0000

Hybrid neural network based multi-objective optimal design of hybrid pin-fin microchannel heatsink for integrated microsystems

Wed, 01 Jan 2025 00:00:00 +0000

Improving Magnetic Resonance Thermometry in Focused Ultrasound Using GrabCut with Adaptive Phase-Variation Detection and a Hampel–Gaussian Model

Wed, 01 Jan 2025 00:00:00 +0000

NLPCA-ATVR: A Novel Combination of Nonlinear Principal Component Analysis and Adaptive Total Variation Regularization for MRI Denoising

Wed, 01 Jan 2025 00:00:00 +0000

Smart cooling: Hydrogel-enhanced adaptive jet impingement utilizing through silicon via for integrated microsystems

Wed, 01 Jan 2025 00:00:00 +0000

一种基于光电结构的PET图病变检测装置 (invention patent authorized)

Wed, 01 Jan 2025 00:00:00 +0000