Digital Commons Network^™

Design, Optimization, And Techno-Economic Analysis Of A Standalone Pv-Wind-Battery Microgrid For A Remote Load In Kurdistan Region Of Iraq, Harwan Mohammed Taha, Laith Akram Mohammed

Mansoura Engineering Journal

Standalone microgrids play a crucial role in rural areas where grid electricity delivery is difficult and expensive. This research verifies the effectiveness of a standalone microgrid in a rugged area, Pers Mountain in Akre district, Duhok governorate, Iraqi Kurdistan. A first hybrid renewable energy system (HRES)-based standalone microgrid is presented to supply a remote load. Solar and wind energy are employed for their reliability, sustainability, and abundance, with battery storage ensuring continuous power when solar and wind are unavailable. Simulation results demonstrate reliable power supply during summer months (loss of power supply probability < 5%), with the system operating across three modes — PV-dominant daytime, combined wind-battery evening, and battery-discharge nighttime. The inverter achieved minimum total harmonic distortion of 1.53% for both current and voltage outputs, below the IEEE 519 limit. A full-year hourly particle swarm optimization (PSO) sizing analysis assessed annual reliability and each source's economic contribution: the original fixed sizing yields an annual loss of power supply probability (LPSP) of 26.80% (expected energy not served ~1,471 kWh/year), motivating a PSO-optimized PV-battery configuration achieving annual LPSP of 5% (~274 kWh/year unserved) at comparable cost. The site's wind resource is reasonable (13.8% capacity factor), but the fixed tower and controller cost outweighs its contribution at this small scale. The proposed HRES totals $11,960–$16,317 depending on configuration, approximately 28–38 times lower than grid extension ($455,600) to the site, while producing zero CO₂ emissions and avoiding approximately 4,189 kg/year versus an equivalent HOMER Pro-simulated diesel generator.

A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced By Grey Wolf Optimization For Dc–Dc Boost Converters With Hardware-In-Loop Validation, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

This paper proposes a universal hybrid model-free quantum–transfer learning controller with enhanced online grey wolf optimization algorithm (GWO–QTL) for DC–DC boost converter. This system has the characteristics of non-minimum phase behavior, parasitic effects, and fractional-order dynamics because of high frequency operation. These characteristics make analytical modeling complicated and make it difficult to have a single traditional controller that will operate reliably over different converter types. This motivates the creation of a unified model-free control framework that is able to learn directly from the behavior of the converter without relying on the topology specific models. Reinforcement learning, where an agent interacts …

Optimizing The Size And Siting Of Distributed Generation In Unbalanced Distribution Systems With Multi-Objective Reptile Search Algorithm, Pema Dorji, Chimi Pelden Dorji, Stefan Lachowicz, Octavian Bass

Research outputs 2022 to 2026

This paper presents the Multi-Objective Reptile Search algorithm for identifying ideal rating and location of distributed generation in unbalanced grids, focusing on minimizing power losses, total costs, and carbon emissions. The proposed methodology integrates DIgSILENT PowerFactory and Python platforms to evaluate unbalanced IEEE distribution feeders under varying power factor conditions. The results demonstrate that optimal DG configurations involve strategically positioning multiple units to enable both active and reactive power injection, significantly improving overall system performance across multiple objectives and voltage deviation index. The analysis identifies an optimal PF range between 0.75 and 0.89, with unity power factor operations yielding suboptimal …

Drone Endurance In Hydrogen Fuel Cell Hybrid Technologies: Power Architectures And Design Strategies, Yaw Chong Tak, Tarek Abedin, Johnny Koh Siaw Paw, Jagadeesh Pasupuleti, Tan Jian Ding, Tiong Sieh Kiong, K. Kadirgama, F. Benedict, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

This study delivers an exhaustive exploration of novel and hybrid power systems for Unmanned Aerial Vehicles (UAVs) aimed at improving endurance, efficiency, and mission performance. In the wake of increasing requirements for long-endurance and high-performance UAVs, traditional battery systems are limited by their energy density and lifetime. To overcome this, the research compares four primary power sources: hydrogen fuel cells, lithium-based batteries, photovoltaic cells, and supercapacitors, with a focus on their hybrid architecture integration. The originality of this research is in the relative comparison of these power sources in multi-mode UAV operations, with an emphasis on their performance, energy management …

C2p-M: Critical Connection Protection In Multiplex Graphs, Conggai Li, Wei Ni, Ming Ding, Youyang Qu, Jianjun Chen, Wenjie Zhang, Thierry Rakotoarivelo

Research outputs 2022 to 2026

Multiplex graphs represent diverse real-world interactions among entities, where multiple relationship types coexist within the same set of entities. These graphs introduce privacy risks, as data collectors can exploit cross-layer dependencies to infer hidden and sensitive connections. In this work, we propose a C2P-M framework that identifies and protects critical connections while preserving the structural information in multiplex graphs. Unlike conventional methods for single-layer graphs that perturb all edges uniformly, C2P-M selectively protects critical connections, maintaining the analytical usability of the graph. To achieve this, we introduce the multiplex p-cohesion model, which incorporates new score functions that account for both …

A Review On Underwater Beamforming: Techniques, Challenges, And Future Directions, Ruba Zaheer, Quoc Viet Phung, Iftekhar Ahmad, Asma Aziz, Daryoush Habibi, Yue Rong, Walid K. Hasan

Research outputs 2022 to 2026

This paper comprehensively reviews recent advancements in Underwater Beamforming (UWB) systems, highlighting its pivotal role in underwater communication, sensing, and environmental monitoring. It explores the various beamforming applications, ranging from maritime surveillance to marine life monitoring, and indicates its significance in enhancing signal clarity, spatial resolution, and noise suppression in underwater acoustic environments. The unique challenges posed by the underwater environment that introduce complexities into the beamforming process such as non-stationary noise interference, severe signal attenuation, multipath propagation, and dynamic environmental variability are thoroughly discussed. The review systematically discusses and examines conventional, adaptive, and learning-based beamforming techniques, analyzing their strengths, …

Guitar Amplifier Directivity, Rachel C. Edelman, Brian E. Anderson, Samuel D. Bellows, Timothy W. Leishman

Directivity

No abstract provided.

Ev Charging And V2g Operation For Distribution System Vpp Including Model Predictive Control, Rosemary E. Alden, Simone Silvestri, Malcolm D. Mcculloch, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Future smart grid virtual power plants (VPPs) are considered for development based on industry communication standards for electric vehicle (EV) chargers such as Open Charge Point Protocol (OCPP), IEC 15118, and IEC 61851. To support research and development of computationally intelligent controls for distributed EV batteries, a python-based API OpenDSS VPP framework is utilized with thousands of experimental smart meter profiles, the IEEE 123 node test feeder, and hundreds of national survey-based EV modules for conventional and optimal charging and vehicle-to-grid (V2G) control development to mitigate any voltage violations and reduce peak load. A methodology is proposed for model-predictive control …

Covert Performance Of Star-Ris Aided Thz Communication System With Rsma And Phase Errors, Xiangbin Yu, Yue Zhou, Shihao Yan, Yun Rui, Xiaoyu Dang, Chau Yuen, Mohsen Guizani

Research outputs 2022 to 2026

In this paper, the covert performance of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided Terahertz (THz) communication systems with rate-splitting multiple access (RSMA) over the generalized α-μ fading channel is studied, where the discrete phase error and noise power uncertainty are considered. By means of the Gaussian approximation and Gamma distribution, the closed-form outage probability (OP) of the legitimate users is firstly derived. Then, the covert performance of the system is analyzed, and average detection error probability (ADEP) is deduced for the warden, and resultant closed-form ADEP is obtained. By minimizing the ADEP, the optimal detection threshold …

Covert Transmission For Active Ris-Aided Full-Duplex Uav Integrated Sensing, Communication, And Computation Systems, Qi Zhang, Wei Gao, Chuan Liu, Yu Yao, Shihao Yan, Feng Shu, Shi Jin

Research outputs 2022 to 2026

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone …

Design Of Artificial Interference Signal Waveforms For Covert Communication Aided By Multiple Friendly Nodes, Xuyang Zhao, Wei Guo, Yongchao Wang, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider a covert communication scenario with multiple friendly interference nodes. The goal is to hide a legitimate communication link from a transmitter to a receiver under a warden’s surveillance. Firstly, we propose a novel strategy for generating artificial noise (AN) signals and formulate a corresponding design problem, aiming to minimize the adverse effects of AN on the legitimate receiver while enhancing communication covertness. Specifically, we optimize the basis matrix for AN signal space using statistical information of the involved channel coefficients, when precise channel state information are unavailable. Secondly, we analyze the geometric structure of the …

Integrating Environmental Awareness In Underwater Acoustic Networks: A Comprehensive Review, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi, Walid K. Hasan, Ruba Zaheer

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) are critical for enabling long-range underwater communication, supporting a wide range of applications, such as environmental monitoring, resource exploration, disaster prevention and marine security. Unfortunately, UANs operate within a limited acoustic spectrum, where these spectrums often overlap with those used by marine animals for communication, navigation and foraging. Additionally, anthropogenic noise from industrial activities contributes to acoustic pollution, intensifying this problem. This frequency overlap poses significant risks to marine life. Therefore, this review highlights the urgent need to develop environmentally aware UANs that minimize harmful acoustic interference with marine mammals, fish and invertebrates. Further, it focuses …

Sensing-Then-Beamforming: Robust Transmission Design For Ris-Empowered Integrated Sensing And Covert Communication, Xingyu Zhao, Min Li, Ming Min Zhao, Shihao Yan, Min Jian Zhao

Research outputs 2022 to 2026

Traditional covert communication often relies on the knowledge of the warden's channel state information, which is inherently challenging to obtain due to the non-cooperative nature and potential mobility of the warden. The integration of sensing and communication technology provides a promising solution by enabling the legitimate transmitter to sense and track the warden, thereby enhancing transmission covertness. In this paper, we develop a framework for sensing-then-beamforming in reconfigurable intelligent surface (RIS)-empowered integrated sensing and covert communication (ISACC) systems, where the transmitter (Alice) estimates and tracks the mobile aerial warden's channel using sensing echo signals while simultaneously sending covert information to …

A Novel Deep Transfer Learning-Based Adaptive Cascade Pi Controller Enhanced By Reinforcement Learning Algorithm And Snake Optimization For Robust Speed Regulation Of Brushless Dc Motors, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) are common in electric cars, industrial automation, and robotics because of their high efficiency, high torque control, and compact size. Nevertheless, strong speed and current regulation is not easily attained because of system variation, load variations and the shortcomings of traditional fixed-gain proportional-integral (PI) controllers. In this paper, a new snake optimization-assisted deep transfer learning-based reinforcement learning (SOA-DTL-RL)-based adaptive cascade PI controller is proposed that combines transfer learning with fast adaptation, Reinforcement learning with real-time optimization, and snake optimization with optimal initial gain selection to guarantee the robust speed and current regulation in BLDC motors. The proposed …

Optimizing Ev Battery Charging Using Fuzzy Logic In The Presence Of Uncertainties And Unknown Parameters, Minhaz Uddin Ahmed, Md Ohirul Qays, Stefan Lachowicz, Parvez Mahmud

Research outputs 2022 to 2026

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system …

Super-Resolution Learning Inspired Spectral-Spatial Correlation Network For Hyperspectral Target Detection, Jiaping Zhong, Yunsong Li, Jianxin Li, Yanzi Shi, Weiying Xie, Paolo Gamba

Research outputs 2022 to 2026

Hyperspectral target detection (HTD) aims at extracting targets from complex backgrounds while overcoming noise interference. Existing deep learning models for HTD usually suffer from low spatial resolution and unitary representation, especially in space-borne platforms. Super-resolution, as a critical technology to enhance the spatial details, could effectively address the aforementioned issue. To make super-resolution absolutely pose positive effects on target detection, this paper proposes an end-to-end novel super-resolution learning inspired spectral-spatial correlation network for hyperspectral target detection (SR-HTD) from the perspective of spatial and spectral regularization to achieve high-precision detection. Specifically, we designed a Spatial Correlation Aggregation (SCA) module inspired by …

A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Enhanced For Speed Regulation Of Brushless Dc Motor, Seyyed Morteza Ghamari, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better …

Renewable And Affordable Energy For Apartments. Inquiry Into Renewable And Affordable Energy For Apartments, Nishadi Ruwandima Weerasinghe Mudiyanselage, Asma Aziz

Research outputs 2022 to 2026

Recent literature identifies a persistent disparity between detached housing and apartments in accessing renewable and affordable electricity. While rooftop solar PV deployment in Australia  has expanded rapidly since 2017, access to these technologies in multi-unit dwellings (MUDs) remains limited, resulting in higher electricity costs for apartment residents compared with standalone houses. This inequity is increasingly significant given rising electricity prices and the growing share of Australians living in apartments.

Code-Net++: An Attention-Guided Deep Learning Framework With Grad-Cam-Based Explainability For Covid-19 Detection Using Chest X-Ray Images, Fareesa Amina, Dr Krishnanaik Vankdoth

Mansoura Engineering Journal

Chest radiograph imaging has emerged as a practical and scalable diagnostic modality for respiratory diseases, including COVID-19. However, accurate discrimination of COVID-19 manifestations from other pulmonary abnormalities remains challenging because of low contrast, imaging noise, and overlapping radiographic patterns. This work presents CODE-NET++, an enhanced attention-guided deep learning framework with Grad-CAM-based explainability for reliable COVID-19 detection using chest X-ray images. The proposed framework integrates adaptive trilateral filtering for image enhancement, Reverse Edge Attention Network (RE-Net) for lesion-aware segmentation, and an Enhanced LinkNet architecture with dilated convolutions for multiscale feature extraction and classification. Grad-CAM-based explainable artificial intelligence visualization is incorporated to …

State-Dependent Queueing For Adaptive Signal Control: A Simulation-Based Performance Evaluation, Shaimaa Alseddiek, Usama Elrawy Shahdah, Hala B. Nafea, Hossam El-Din Moustafa, El-Said Ahmed Marzouk, Mohamed M. Ashour

Mansoura Engineering Journal

Urban traffic congestion persists as a critical challenge to transportation system efficiency, sustainability, and safety. Traditional queuing models utilizing fixed service rates inadequately represent the dynamic feedback between congestion and capacity in real vehicular flow. State-Dependent Queuing Models (SDQMs) address this limitation by modelling service rate as a function of queue length or density. This research advances SDQM application for adaptive traffic signal control through development of a calibrated state-dependent departure rate implemented within a microscopic simulation environment using SUMO and TraCI. Six control strategies including fixed-time, actuated, and two SDQM variants were evaluated across traffic demands ranging from undersaturated …

Multi-Scale Sensor-Aware Variational Autoencoders For Adaptive Iot Security: Integrating Drift Calibration And Cyberattack Detection, Md Kamal Hossain, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Existing approaches to Industrial Internet of Things (IoT) anomaly detection treat sensor drift calibration and cyberattack detection as separate problems, overlooking their strong interdependence and the compound failure modes that arise when drift dynamics are exploited by stealthy adversaries. Industrial Internet of IoT sensor networks therefore require a unified framework that jointly models both phenomena. This paper proposes the first unified multi-scale sensor-aware variational autoencoder (MS-VAE) framework that jointly models sensor drift and malicious activity within shared latent representations, enabling integrated calibration and security monitoring. The framework introduces three key components: (i) a multi-scale latent architecture that captures short-term anomalies …

Nanomagnet Based Straintronic Devices For Unconventional Computing: Simulation And Performance Analysis, Rahnuma Rahman

Theses and Dissertations

Nanomagnetic devices are of great interest in digital hardware because of their non-volatility and dynamic ability to change magnetization but suffer from high switching error rates and temperature sensitivity. Magnetostrictive nanomagnets that utilize strain to switch between stable magnetization states encoding bit information are of interest since they are extremely energy efficient as piezoelectric layers can be used to rotate magnetization that have switching energies in the range of attojoules. Their stochasticity can also be useful in probabilistic, analog, neuromorphic, and collective computing systems, where occasional switching errors are not devastating. The dissertation extends spintronics beyond conventional computing schemes by …

Ontological Runtime Monitoring For Mission Engineering, Alexander R. Will

Theses and Dissertations

The advent of the Urban Air Mobility (UAM) concept will bring low-altitude aviation to civilians through passenger and cargo transport. However, the prospective vehicles in UAM studies are predominantly autonomous, raising questions about their efficacy and stability in densely populated urban areas. At the same time, extensive work has been performed to define a new branch of systems engineering that focuses on runtime behavior, synchronization, communication, and task allocation.This field is known as "mission engineering". Mission engineering has been deployed in military scenarios to model human-autonomy cooperation. By applying the concepts from this field to UAM, full systems-of-systems can be …

Dielectric Grating Integrated Hg1-Xcdxte (Mercury Cadmium Telluride) Infrared Photodetectors For High Operating Temperature, Mohammad Ariful Hoque Sojib

Theses and Dissertations

Mercury cadmium telluride (Hg1-xCdxTe, MCT) infrared photodetectors typically require cryogenic cooling to suppress thermal noise, limiting their adoption in size, weight, power, and cost (SWaP-C) constrained military and commercial systems. This dissertation investigates dielectric grating array integration as an extrinsic route to high operating temperature (HOT) detection in Auger-suppressed MCT architectures. Finite element modeling shows that a grating assisted light coupling into a subwavelength (1.5 µm) Hg0.762Cd0.238Te mid-wave infrared layer acting as an absorptive waveguide, achieves photocarrier generation rate comparable to a conventional bulk (e.g., 5 µm) layer. For long-wave infrared detectors, coupled FDTD and drift-diffusion modeling, together with a …

Pillpetz: A Smarter Way To Encourage Medication Adherence In Children, John H. Begley

CMC Senior Theses

Medication adherence is usually framed as a problem of patient behavior, but this thesis argues that it is equally a problem of design. Children who take daily medication face barriers that adults often do not: developing executive function, dependence on caregivers, shifting school and home routines, privacy concerns, and stigma around being perceived as different. The standard prescription bottle, by contrast, was designed primarily for dispensing efficiency, safety, and accidental ingestion prevention—not for sustained daily use by a developing child.

This thesis proposes PillPetz, a smart pill case and digital companion that uses routine, play, dose confirmation, and caregiver-connected support …

Estimating Sediment Properties Using A New Source Level Function For Wind-Driven Underwater Sound Derived From Long-Term Archival Data, S Bruce Martin, Martin Siderius

Electrical and Computer Engineering Faculty Publications and Presentations

Wind-driven breaking waves generate the background sound throughout the ocean. An accurate source level for wind-driven breaking waves is needed for estimating the ambient sound levels needed for sound exposure modeling, environmental assessments, and assessing the detection performance of sonars. Previous models applied a constant roll-off of sound levels at -16 dB/decade at all wind speeds, and these models' source levels were flat at frequencies below ∼1000 Hz due to a lack of measurements. Here, we analyzed 16 long-term archival datasets with limited anthropogenic sound sources to estimate the wind-driven source level down to 100 Hz. We estimated the site-specific …

Robust Broadband Characterization Of Flexible Absorbers, Sheet Material, And Liquids Using A Coaxial Structure, Joseph Christopher Stecher

Masters Theses

Modern high-frequency measurement systems require reliable calibration and sample positioning to ensure measurement fidelity. This thesis presents three studies addressing practical limitations in broadband material parameter extraction and instrumentation.

The first study introduces a modified Nicolson–Ross–Weir (NRW) technique for flexible, compression-sensitive materials from 100 MHz to 18 GHz. Rigid 3D-printed spacers ensure precise sample positioning, and a T-matrix–based de-embedding procedure removes spacer effects. Validation using microstrip measurements and full-wave simulation confirms accurate permittivity extraction across compression levels.

The second study extends NRW to sheet materials enabling accurate material characterization. Independent validation using toroidal inductors with leakage correction and parallel-plate capacitors …

Incremental Cluster Validity Indices And Their Role In Interpreting Lifelong Learning Systems, Niklas Max Melton

Doctoral Dissertations

Clustering and supervised learning are often treated as distinct paradigms, yet both rely on structure in feature space. This dissertation investigates the relationship between cluster validity indices (CVIs) and supervised learning in real-time and lifelong learning settings where data arrive incrementally and cannot be revisited. Across four studies, it develops methods for online cluster validation, uses supervised learning to improve their interpretability, and applies these ideas to evaluating performance degradation in continual learning.

The first study extends incremental cluster validity indices (iCVIs), enabling widely used validation metrics to operate in streaming environments. Experiments on synthetic and real-world datasets show systematic …

Architecting A Complex Adaptive System Model For Selecting Policies To Reduce Kidney Discard, Lirim Ashiku

Doctoral Dissertations

"The kidney allocation system is a complex, evolving system involving multiple heterogeneous agents. Each agent exhibits emergent behavior that may not fully align with the complex system goals. Therefore, there is a need for a transdisciplinary systems approach to visualize the interdependency among agents and understand the dominant patterns that shape the kidney allocation system.

First, this research presented an incremental hierarchical system engineering approach in identifying the agents’ needs and behaviors toward the complex systems’ goal of maximizing deceased donor kidney utilization and reducing kidney discard. The hierarchical systems approach linked with model-based system engineering aided in eliciting agents’ …

Near-Field Scan Method For Radiated Susceptibility Analysis And The Design And Optimization Of A Miniaturized Spiral Antenna For Ultra-Wideband Applications, Mckennan Edward Starkey

Masters Theses

"The work presented in this thesis consists of two separate topics. The first topic is a new method for the characterization of radiated susceptibility of electronic devices, while the second topic is on antenna design. The first topic proposes an approach to experimentally determine locations within an electronic system that are well coupled to external far-field radiators. This method is performed by using a differential magnetic field probe to sweep across the target device while measuring the total radiated power (TRP) produced by the device in a mode stirred tent. Scan locations where the TRP is larger than the background …