Digital Commons Network^™

Enhancing Energy Efficiency In Kubernetes Cluster Through Energy-Aware Resource Scheduling By Node Labeling Assignment, Soundharya Moorthi

Chulalongkorn University Theses and Dissertations (Chula ETD)

As the demand for cloud-native infrastructure grows, managing energy becomes increasingly essential for small-scale and large-scale Kubernetes clusters. This research introduces two scheduling strategies to enhance energy usage in Kubernetes clusters: the energy-aware resource scheduler and the adaptive energy-aware resource scheduler. Both approaches aim to reduce energy consumption by dynamically managing node states and workload assignments based on resource utilization. Both schedulers are implemented as extenders on top of the default Kubernetes scheduler. A comparative evaluation against the default Kubernetes scheduler and the bin packing scheduler, using power and energy utilization metrics, demonstrates that while the energy-aware scheduler achieves notable …

An Attention-Guided Image Super-Resolution For Scene Text, Tithnorakneath Em

Chulalongkorn University Theses and Dissertations (Chula ETD)

Scene Text Image Super-Resolution (STISR) is crucial in enhancing text recognition for low-resolution images, often degraded due to environmental and device limitations. While many existing STISR approaches leverage super-resolution techniques to refine image quality, they frequently need specialized mechanisms to extract text-specific features. Most existing methods were based on feature extraction methods that missed critical details, such as edges and text-background contrasts, that are usually important for recognition. In this work, we propose a new feature extraction method that uses Channel Attention and Spatial Attention mechanisms to emphasize features related to text, namely edges, contours, and spatial alignment. We add …

A Study Of Electric Vehicle Charging To Mitigate Impacts On A Power Distribution System, Samran Saleem

Chulalongkorn University Theses and Dissertations (Chula ETD)

Electric vehicles continue to gain rapid acceptance in the global market thus reshaping the way power distribution systems function today. The study examines how EV’s charging affects the voltage quality, voltage unbalance, overloading the system as well as performance of the distribution grids. MATLAB/Simulink simulations have been done using the modified IEEE 13 node test system on Node 634, Node 692, and Node 611 to analyze four charging cases including no charging (Case A) and evening peak (Case B) and early morning (Case C) and overnight off-peak (Case D) charging to assess EVs charging impact on domestic power grid. The …

A Novel Computational Analysis Of Boundary-Driven Two-Dimensional Heat Flow With Internal Heat Generation, Muhammad Abid, Madiha Bibi, Nasir Yasin, Muhammad Shahid

Mathematics & Statistics Faculty Publications

Accurate numerical solution of parabolic and elliptic partial differential equations governing two-dimensional heat transfer is critical for engineering simulations but computationally challenging. This work employs key numerical techniques finite differences, conjugate gradients, and Crank-Nicolson time stepping to solve the heat diffusion equation and analyze method performance. The Poisson equation is discretized using second-order central finite differences and solved with the conjugate gradient approach to determine the steady state solution. The transient heat equation is integrated in time via the Crank-Nicolson implicit scheme, also utilizing conjugate gradients. The methods effectively compute solutions matching analytical and boundary conditions. Convergence and stability are …

Improved Peec Modeling Of Antennas Through Time-Dependent Partial Elements, Fabrizio Loreto, Giuseppe Pettanice, Martin Stumpf, Albert E. Ruehli, Jonas Ekman, Giulio Antonini

Electrical and Computer Engineering Faculty Research & Creative Works

Over the Last Twenty Years, the Evolution of Communications Has Extended the Systems' Operating Frequency Range to the Tens of GHz. in This Framework, Time Domain Integral Equation-Based (TDIE) Methods for Antenna Modeling Have Gained Increasing Interest. among Them, the Partial Elements Equivalent Circuit (PEEC) Method Turns Out to Be Attractive for its Capability to Provide Compact Circuit Models. Similarly to Other Integral Equation-Based Methods, Like the Method of Moments (MoM) in the Time Domain (TD), the PEEC Method Can Suffer from Late-Time Instabilities. This Work Shows that a Rigorous Computation of the Time-Dependent Partial Elements Leads to an Improved …

Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley

Engineering Management & Systems Engineering Faculty Publications

Generative AI (GenAI) serves as a powerful tool that can create a wide range of content, including but not limited to text, speech, images, code, videos, and 3D models. ChatGPT stands out as a particularly appealing Generative Pretrained Transformer (GPT) model that offers supplementary capabilities through GPTs and plugins. These extensions enable users to engage with the chatbot and improve its functionality, surpassing mere content generation. Our study delves into the potential of ChatGPT, specifically GPT-4, to expedite the creation of diagrams to support the system architecting process. To this end, we explored the use of ChatGPT's Diagrams Show Me …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

Photoluminescence Switching In Quantum Dots Connected With Carboxylic Acid And Thiocarboxylic Acid End-Group Diarylethene Molecules, Ephraiem S. Sarabamoun, Pramod Aryal, Jonathan M. Bietsch, Maurice Curran, Sugandha Verma, Grayson Johnson, Lucy U. Yoon, Amelia G. Reid, Esther H. R. Tsai, Charles W. Machan, Christopher Paolucci, Guijun Wang, Joshua J. Choi

Chemistry & Biochemistry Faculty Publications

We contrast the switching of photoluminescence (PL) of PbS quantum dots (QDs) cross-linked with photochromic diarylethene molecules with different end groups, 4,4′-(1-cyclopentene-1,2-diyl)bis[5-methyl-2-thiophenecarboxylic acid] (1C) and 4,4′-(1-cyclopentene-1,2-diyl)bis[5-methyl-2-thiophenethiocarboxylic acid] (2T). Our results show that the QDs cross-linked with the carboxylic acid end group molecules (1C) exhibit a greater amount of switching in photoluminescence intensity compared to QDs cross-linked with the thiocarboxylic acid end group (2T). We also demonstrate that regardless of the molecule used, greater switching amounts are observed for smaller quantum dots. Varying these parameters allows for the fabrication of photoswitches with tunable PL change. We relate these observations to the …

Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals, Srinivasa Rao Ippili

Theses and Dissertations--Mechanical and Aerospace Engineering

In various industries, the early detection of faults in rotating machinery is crucial to prevent system failures and ensure customer satisfaction. Typically, vibration measurement and diagnosis are employed for fault detection, but this process faces challenges in automation due to the complexity of installing and maintaining accelerometers, particularly in end-of-line quality control or pre-installed machinery health assessments. Acoustic signals, as a form of mechanical wave, offer an alternative for monitoring machinery while in operation. Unlike accelerometers, acoustic transducers are non-contact and easy to set up, enabling real-time data collection without interrupting equipment operation. However, utilizing acoustic signals in manufacturing poses …

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Application Of Fusion Based Deep Learning Models To Improve Millimeter Wave Beamforming, Abishek Subramanian

Dissertations, Master's Theses and Master's Reports

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: …

Developing Robust Autonomous Vehicles With Ros, Dylan J. Kangas

Dissertations, Master's Theses and Master's Reports

The demand for autonomous vehicles (AVs) is rising across both military and civilian sectors. These unmanned systems offer numerous advantages, such as improved efficiency, safety, and adaptability. Addressing this demand requires the development of resilient and versatile autonomous vehicles crucial for the transport and reconnaissance markets.

The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Factors such as sensor noise, erroneous readings, and deliberate attacks should all be considered when developing a robust autonomous system. This work aims to quantify the degradation of sensor data which causes mapping …

Modeling Of Inverter-Based Resources For Hardware In The Loop Testing Of Protection And Control Schemes, Md Aamir Rahmani

Dissertations, Master's Theses and Master's Reports

High penetration of inverter-based resources (IBR) may cause the misoperation of protective relays due to the dynamic nature of fault currents fed by the IBR during short-circuit faults. Misoperation may cause damage to power system equipment and affect the reliability of the power system. This dissertation presents an in-depth investigation into the modeling of IBR in the context of transmission line fault scenarios to understand and analyze the characteristics of fault currents fed by the IBR. The research objectives encompass the development of reliable IBR models capable of accurately replicating fault current behaviors, designing IBR operation control schemes, and implementing …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …

An Fpga-Based Eit System For Deep Space Medical Imaging, Kendall R. Farnham

Dartmouth College Ph.D Dissertations

Dangers associated with high radiation and microgravity exposure in space are critical challenges inhibiting us from exploring deep space and pursuing long-duration missions, as current medical systems are unable to monitor, diagnose, or treat tissue injury within physical spacecraft constraints and communication limits. Ultrasound (US) is the current imaging system used on the International Space Station, but this technology relies on telemedical support (or onboard artificial intelligence/autonomous capabilities) for both operation and diagnosis, posing challenges for crews isolated in deep space. Electrical impedance tomography (EIT) is a non-invasive, non-ionizing technology that produces images of the electrical properties of tissues and …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Distance Estimation Based On Step Frequency Using Accelerometer Data, Rami M. Al-Naimat, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In recent years, smartphone sensors have become one of the most important and easily available sensors to facilitate people's lives, especially in health care and positioning (indoor environments). However, the data coming from smartphone sensors can be distorted during the user’s movement such as irrelevant movements, walk mode, and speed of walking. This distortion (noise) impairs the estimated distance accuracy (accumulative error) which increases with increasing walking distance. In addition, the accuracy of the distance traveled is affected by the user's speed, as the speed affects the step length. This work proposes a novel approach for calculating step length in …

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain

VMASC Publications

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …

Moisture-Controlled Triboelectrification During Coffee Grinding, Joshua Méndez Harper, Yong-Hyun Kim, Robin E. Bumbaugh, Connor S. Mcdonald, Christopher H. Hendon, Elana J. Cope, Leif E. Lindberg, Justin Pham, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

Triboelectrification is the physical process where materials acquire surface charge from frictional interactions at their interfaces.The magnitude of charge depends on the interfacial material composition and can be harnessed in emergent technologies for energy generation.

The mechanism of electrostatic accumulation is complex and is further obscured in granular materials where collisions are sufficiently energetic to cause fracturing. In this “fractoelectric” regime, crack initiation and propagation are thought to charge particles through transfer of electrons and/or ions at the hot crack interface.

Whether a material’s charging is dominated by tribo- or fractoelectrification, fracture-generated granular flows often comprise particles whose surface charge …

A Magnetic Constant Torque Mechanism, Gozde Sivka, Dawei Che, Bert Dechant, Colton Bruce, Jonathan Z. Bird

Electrical and Computer Engineering Faculty Publications and Presentations

This paper presents the design of a new type of magnetic constant torque mechanism. The constant torque is shown to be created over a prescribed ±65° stroke length and is shown to be highly uniform. The value of the constant torque is adjustable through the relative axial motion of the rotors. The constant torque is created using a simple magnet arrangement which makes use of the axial transverse-flux path of one rotor's magnetic field relative to the azimuthal field flow of the fixed secondary rotor. It is shown that the use of segmented magnets introduces torque ripple.

Optimizing Large Language Models And Multimodal Approaches For Biomedical Publication And Satellite Imagery, Youngsun Jang

Electronic Theses and Dissertations

This dissertation comprises two main sections: the first focuses on natural language processing (NLP) for extracting key information from scientific literature using large language models (LLMs), and the second addresses remote sensing for detecting natural disasters, such as floods, from satellite imagery using a multimodal approach. The first section investigates methods to enhance Transformer-based models in classifying and extracting information from biomedical scientific publications. Key contributions include the development of a custom dataset for classification and Question and Answering (Q&A) tasks, fine-tuning Transformer models like the Bidirectional Encoder Representations from Transformers (BERT) and addressing multi-span answer issues with the TAg-based …

Lidar From The Sky: Uav Integration And Fusion Techniques For Advanced Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct …

Design Of Experiment Analysis On Multiple Pim Sources In An Rf Antenna System, Shengxuan Xia, Yuchu He, Haicheng Zhou, Hanfeng Wang, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Passive intermodulation (PIM) has been identified as one of the common root causes for receiving sensitivity degradation (desense) on radiofrequency (RF) antennas working in frequency-division duplex mode. The component-level PIM characterization and simulation have been well-established over the years. However, the study has been using an ideal 50 Ω transmission line system while antenna modules are more complicated than a simple transmission line structure. Moreover, the metallic contacts can exist in multiple locations with different connection topologies in real applications. This paper provides a method to simulate the PIM performance caused by the metallic contacts in a practical environment. In …

Smart Roadside Safety Vest, Nicholas Huttinger, Emily Stapleton, Susannah Smith

Williams Honors College, Honors Research Projects

The objective of this project is to design a vest to be worn by pedestrians and cyclists, or anyone who may be working near traffic. This vest will utilize an LED display on the back of the vest to increase the visibility of the wearer and convey information to nearby drivers. The vest will also be able to detect and alert the wearer to approaching vehicles. If it is detected that the wearer has been involved in an accident, the vest will send a message to the wearer’s designated emergency contact(s), which will include the GPS coordinates of the wearer …

Self-Watering Plant Device, Nick Sabetta

Williams Honors College, Honors Research Projects

Many homeowners enjoy having plants around their homes. However, life can quickly become busy and stressful. This often makes it difficult to keep with their house plants. The goal of this project is to design a device that will be able to help take care of house plants. The device will be able to water the plant when the soil moisture is detected. The device will also monitor the air temperature as well as the sunlight levels that the plant is receiving. All of the collected data will then be sent via Bluetooth to a smartphone. The app will alert …

Smart Dog Door, Andrew Shetler, William Boissoneault, Jacob Stump, Benjamin Charlson

Williams Honors College, Honors Research Projects

With modern life getting busier and more complex, many have turned to autonomous assistance for taking care of pets. The objective of this project is to design and prototype a device and application that will allow only certain dogs to enter and exit through a dog door while making the owners aware of their dog's activity. The dog door will utilize wireless communication from the dog to the door to control the status of the door’s lock and update the database that will send updates to the application. The application will keep track of when the dog enters and exits …

Schaeffler Icvd Coating Machine, Louis Mcgrath

Williams Honors College, Honors Research Projects

Chemical Vapor Deposition (CVD) is mutual technology used to deposit thin film through a gaseous phase by vaporizing the solid materials. This conventional process usually requires high thermal stability of the materials, which is not applicable for most of the polymeric materials. Therefore, a novel process, initiated Chemical Vapor Deposition (iCVD) is developed by introducing the gaseous monument and initiator to form the thin film in-situ. By adjusting the free-radical polymerization in the vapor phase, a variety of thin and uniform polymer films can be achieved. Depending on the chemistry, iCVD has many categories. This report explains the design …

Midi Recorder And Learning Tool, Caden Dees, Saikishore Gowrishankar, Cameron Johnson, Benjamin Bolyard

Williams Honors College, Honors Research Projects

The project's goal is to create a device that can record MIDI files from any standard 88-key piano as well as teach piano learners how to play songs.