Digital Commons Network^™

Antennas For Emerging Satellite Services, Jakub Przepiorowski

Doctoral

The NewSpace era has revolutionized access to space, driving advancements in satellite communication (SATCOM) systems and creating demand for low-cost, compact, and high-performance user terminals. This thesis addresses these needs by developing innovative antenna solutions tailored for user terminals designed for emerging satellite constellations and satellite Internet-of-Things (S-IoT) applications.

Power Utilization In Open Ran: Key Findings From A Usa Testbed, Saish Urumkar, Byrav Ramamurthy, Seshu Tirupathi, Sachin Sharma

Articles

Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single …

A Compact Frequency Comb Generator For Large-Format Superconducting Microwave Kinetic Inductance Detectors, Sungyoun Seo

Open Access Master's Theses

Microwave Kinetic Inductance Detectors (MKIDs) are promising superconducting detectors that achieve superior sensitivity to silicon-based photo detectors by leveraging a small bandgap of superconductors. They are advantageous for sensitive imaging instruments, particularly space telescopes. However, a large-format array with several hundreds of thousands of detectors is required for large-scale observations in space, and it encounters multiple challenges. The biggest challenge is the absence of compact, energy-efficient, and radiation-tolerant readout solutions. Of multiple functions in the MKID readout, the new frequency comb generator is presented in this thesis. It features a low on-chip memory requirement while promising the relaxed frequency resolution …

Minimalistic Communication-Based Swarm Slam, Felix B. Koch

Open Access Master's Theses

This study presents and validates Range of Communication Swarm SLAM(RCS-SLAM), a novel SLAM approach designed for multi-robot systems using minimal hardware. Unlike traditional methods that rely on range or bearing measurements, RCS-SLAM only uses the identifiers of nearby robots within communication range. In the front-end, each robot constructs an individual pose graph based on its own odometry, while recording communication events with neighboring robots. The key novelty lies in the use of one-hop communication, where robots not only detect nearby peers but also relay received identifiers, effectively acting as transmitters themselves. This mechanism extends the communication graph and enables the …

Optimal Subspace Estimation For Linear Nested Arrays: Applications And Performance Metrics, Brendan Dunn

Open Access Master's Theses

OSE (Optimal Subspace Estimation) is an algorithm that obtains an estimated subspace from structured data observed in noise. While OSE is able to obtain an accurate subspace estimate with a small number of snapshots, it has not been demonstrated how this affects the performance of many applications that leverage OSE. One such application is beamforming; Using an OSE-based beamformer, this thesis will apply common performance metrics to measure how much of an advantage an accurate subspace estimate provides. This algorithm will be compared against other widely used beamformers such as MPDR and DMR to benchmark its efficacy. Mismatch is then …

Power Saving In Open Ran By Using Advanced Cpu Scheduling Algorithm, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN is an emerging wireless technology that is gaining significant attention for its potential to enable flexi- ble, cost-efficient, and interoperable networks. Reducing power utilization in Open RAN, particularly for 5G base stations (gNodeBs) deployed in remote areas, remains a critical challenge due to limited power availability. In our previous work, we developed a CPU scheduling algorithm that optimized core allocation based on load conditions, reducing power utilization for gNodeB in a virtualized Open RAN environment. Extending our previous work, this paper introduces an advanced CPU scheduling for Open RAN designed to reduce power utilization in real hardware Open …

Demonstrating The Impact Of Cpu Scheduling On Power Consumption In Virtualized Open Ran, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN (Open Radio Access Network) is a next- generation wireless network gaining significant research interest globally due to its potential to provide a cost-efficient and scalable solution for growing network demands. Energy efficiency is an important area of focus in Open RAN deployments, as reducing power consumption while maintaining network performance is essential for sustainable wireless communication. This paper demonstrates the impact of CPU (Central Processing Unit) scheduling process priorities on power consumption and network performance in an Open RAN NodeB deployed on a testbed in the USA. The experimental results are demonstrated using two scenarios: (1) CPU Priority-Based …

Funkcjonowanie Doręczeń Elektronicznych W Ujęciu Technicznym, Michał Tabor

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

The article provides a legal-technical and market analysis of electronic delivery in Poland, concluding that while the system complies with the basic requirements of the eIDAS Regulation, it needs significant organizational and technical improvements. The author reviews the National Electronic Delivery System, the role of the designated operator and qualified trust service providers, and highlights issues with interoperability, address registration and portability, delivery mailboxes, and the hybrid delivery service. Recommended legal reforms include granting the public delivery service qualified status, enabling multiple delivery addresses for public and complex organizations, partly opening the market to commercial qualified providers, and moving supervision …

S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala

Computer Science Faculty Publications

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model’s ability to adapt to new knowledge. In this paper, we propose Structurally Stable Incremental Learning (S²IL), a FD method for CIL that mitigates forgetting by focusing on preserving the overall spatial patterns of features which promote flexible (plasticity) yet stable representations that preserve old knowledge (stability). We also demonstrate that our proposed method S²IL achieves strong incremental accuracy and outperforms other FD …

Neural Network-Based Image Compression, Atefeh Khoshkhahtinat

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of information technology and the exponential growth of digital communication have significantly increased the demand for efficient data compression techniques that reduce storage requirements, minimize bandwidth consumption, and accelerate data transmission—without substantially compromising data quality. This dissertation addresses these challenges by investigating and developing advanced learned image compression (LIC) methods, with a particular focus on lossy compression for both natural images and scientific imagery obtained from NASA’s Solar Dynamics Observatory (SDO) mission. Traditional image compression standards—such as JPEG, JPEG2000, BPG, and HEVC—rely on manually engineered transforms and heuristic rules, which often lack the adaptability required to accommodate …

Potential Of Lidar And Hyperspectral Sensing For Overcoming Challenges In Current Maritime Ballast Tank Corrosion Inspection, Sergio Pallas Enguita, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic, Richard Lebel

Electrical & Computer Engineering Faculty Publications

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral …

A Method For Ultrasound Servo Tracking For Puncture Needle, Shitong Ye, Bo Yang, Hao Quan, Shan Liu, Minyu Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of Kalman filtering uses the position …

A Proximal Policy Optimization-Based Controller For Enhanced Power Sharing In Microgrids, Seyedmohammad Hasheminasab, Armin Lotfy, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

This paper introduces a Proximal Policy Optimization (PPO)-based virtual impedance (VI) controller to enhance both power sharing and system response under disturbances in inverter-interfaced microgrids. Traditional droop control methods often face challenges due to variations in feeder impedance, which degrade performance. The proposed controller continuously updates its policy based on changes in the operating environment. The control problem is modeled as a Markov Decision Process (MDP), in which the state and action spaces are explicitly defined, and a carefully designed reward function, satisfying system criteria and constraints, guides the learning process toward achieving the desired transient and steady-state performance. By …

A Novel Intelligent Thermal Feedback Framework For Electric Motor Protection In Embedded Robotic Systems, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Cellax Nanoparticles: Taxane Nanoformulations For Solid Tumor Targeting, Lobat Tayebi, Mehran Alavi

Electrical & Computer Engineering Faculty Publications

Nanoparticles (NPs), specifically polymer-modified NPs, have illustrated unique therapeutic advantages compared to bulk materials. Cellax NPs have provided a promising approach to cancer therapy by improving drug delivery, targeting the tumor microenvironment, and potentially overcoming drug resistance, all while reducing overall toxicity compared to traditional taxane treatment. By the flash nanoprecipitation (FNP) method, docetaxel and cabazitaxel have been formulated with polyethylene glycol (PEG) modified-acetylated carboxymethylcellulose (CMC) polymer to increase biocompatibility, bioavailability, specific targeting, and modulate the tumor microenvironment. However, there are some challenges and clinical limitations related to this formulation, encompassing optimum targeted delivery to tumors, overcoming biological barriers, such …

Ai-Based Steganography Method To Enhance The Information Security Of Hidden Messages In Digital Images, Nhi Do Ngoc Huynh, Jiajun Jiang, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

With the increasing sophistication of Artificial Intelligence (AI), traditional digital steganography methods face a growing risk of being detected and compromised. Adversarial attacks, in particular, pose a significant threat to the security and robustness of hidden information. To address these challenges, this paper proposes a novel AI-based steganography framework designed to enhance the security of concealed messages within digital images. Our approach introduces a multi-stage embedding process that utilizes a sequence of encoder models, including a base encoder, a residual encoder, and a dense encoder, to create a more complex and secure hiding environment. To further improve robustness, we integrate …

Beyond Assessment Security: A Critical Policy Analysis Of Four Alternative Strategies To Uphold Academic Integrity And Adopt The Genai Transformation Of Teaching And Learning For An Accredited Engineering Degree, Sasha Nikolic, Montserrat Ros, Yasir M. Al-Abdeli, Helen Fairweather

Research outputs 2022 to 2026

Generative Artificial Intelligence (GenAI), exemplified by tools such as ChatGPT, has posed significant challenges and opportunities in the realm of academic integrity, particularly in engineering education. This commentary critically examines alternative strategies that go beyond traditional assessment security, aiming to uphold academic integrity while embracing the transformative potential of GenAI in teaching and learning. In the context of programs that rely on unit level outcomes to determine the overall student progression (not the programmatic approach to progression), this study identifies and explores four strategies that may offer potential improvements to assessment security: I-risk-level analysis, which aligns the mix of supervised …

Joint Channel Allocation And Power Control For Mission-Critical Applications In Sustainable Underwater Acoustic Networks, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) are gaining popularity for underwater communication due to their long-range capabilities. However, they experience significant challenges, including limited bandwidth and spectrum overlap with marine mammal vocalizations, which raises concerns about the sustainable and ethical use of underwater acoustic resources. This study proposes an eco-friendly approach that integrates joint channel allocation and power control to minimize harmful interference with marine life while ensuring reliable communication for mission-critical applications, such as diver safety. To balance marine protection and communication reliability, this work introduces a strategy for intelligent spectrum management, where nodes first sense the acoustic spectrum to detect …

Integrated Sensing And Covert Communications With Ris Adaptive And Non-Adaptive Modes, Jia Zhang, Dengfeng Zhang, Ke Liu, Jiande Sun, Min Li, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider an integrated sensing and covert communication (ISACC) system with a finite blocklength L aided by a reconfigurable intelligent surface (RIS) with N elements. Specifically, with the aid of RIS, a transmitter Alice is to sense the potential existence of a target, and she is also probabilistically trying to send information to a receiver Bob covertly (i.e., trying to hide her transmissions from a warden Willie). Meanwhile, Willie is to detect whether Alice is sensing the target only or conducting ISACC. We consider two RIS operation modes, i.e., a non-adaptive mode, where RIS employs a common …

Modulation Techniques For Underwater Acoustic Communication: A Comprehensive Survey, Muhammad Haziq, Quoc Viet Phung, Stefan Lachowicz, Daryoush Habibi, Iftekhar Ahmad

Research outputs 2022 to 2026

The underwater acoustic channel poses a significant challenge among communication mediums due to its time-varying dynamics, long multipath propagation, and severe Doppler shifts. Ensuring reliable data transmission under these conditions demands advanced modulation techniques, sophisticated signal processing algorithms, and high energy consumption. Moreover, underwater communication nodes typically have limited battery reserves, making them difficult and costly to replace or recharge. Additionally, as underwater communication networks expand, the risk of security threats that could compromise the system has increased, necessitating robust security measures that add further computational overhead. This comprehensive survey reviews the state-of-the-art modulation techniques for underwater acoustic communication, concentrating …

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

Power Supply Induced Jitter Analysis In High-Speed Drivers, Yifan Ding

Doctoral Dissertations

The Input/Output Buffer Information Specification (IBIS) model faces challenges in accurately simulating power-supply-induced jitter (PSIJ), particularly under nonlinear and time-varying power noise conditions with pre-driver stages. This work introduces advancements in IBIS model modification algorithms to enhance PSIJ simulation accuracy for high-speed drivers.

First, a correction coefficient-based approach was developed to adjust switching coefficients K_pu and K_pd using pre-driver DC jitter sensitivity, significantly improving model robustness under DC and AC power noise. However, its effectiveness diminished under large noise amplitudes due to coefficient shape dominance.

To address this, a simplified algorithm bypassed correction coefficient, directly correlating switching transitions with jitter …

Measurement Method And Applications Of Transfer Function In Rf Desensitization Problem, Xiangrui Su

Doctoral Dissertations

Radio frequency (RF) desensitization issues comprise two components: noise radiation sources and the transfer function from noise sources to the victim antenna. RFI is a critical challenge in modern electronic systems, particularly in densely packed environments. This work presents a comprehensive study of RFI, addressing key aspects through three novel contributions. First, a transfer function measurement method is developed for compact metallic enclosures. This method provides a precise characterization of the electromagnetic (EM) environment within confined spaces, enabling accurate identification of interference pathways. Second, an EM emission management analysis framework is proposed, leveraging transfer functions to quantify and mitigate interference …

Microwave Photonics-Assisted Interrogation Of Fiber-Optic Interferometric Sensors With Joint Frequency-Time Domain Analysis, Ruimin Jie, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber-optic interferometers are widely used in localized sensing applications due to their compact size, high sensitivity, and immunity to electromagnetic interference. In this paper, we propose and experimentally demonstrate a novel interrogation scheme for fiber-optic interferometric sensors, utilizing microwave photonics (MWP) and joint frequency-time domain analysis. As a proof of concept, a miniature fiber in-line Fabry-Perot interferometer (FPI) is integrated with a microwave photonic single-passband filter, enhanced by a dispersion compensation module to improve sensing performance. By applying an inverse Fourier transform to the system's complex frequency response, the time-domain representation of the signal is obtained, translating spectral shifts of …

Spin Mechanisms In Gd-Doped Gan Implanted With Oxygen Carbon At Room Temperature, Vishal Saravade, Amirhossein Ghods, Chuanle Zhou, Ian Ferguson

Electrical and Computer Engineering Faculty Research & Creative Works

Gadolinium-doped gallium nitride implanted with oxygen and carbon show carrier-mediated spin mechanisms at room temperature. As-grown Gd-doped GaN grown by metal-organic chemical vapor deposition using a tris(cyclopentadienyl) gadolinium precursor shows Ordinary Hall Effect and no ferromagnetism at room temperature. Upon O or C implantation in Gd-doped GaN, Anomalous Hall Effect that is indicative of carrier-mediated spin and ferromagnetism is observed. A good crystal quality is maintained even after implantation. O and C favor interstitial sites and occupy deep-level acceptor-type states in Gd-doped GaN. Room-temperature spin and ferromagnetism that is induced by gadolinium in Gd-doped GaN is activated by O and …