Engineering Commons^™

Optimization Of An Off-Grid Pv/Biogas Hybrid Energy System For Electrification: A Case Study In A Rural Area In Egypt, Ibrahim A. Elzahy, Ahmed Refaat, Samir Dawoud

Journal of Engineering Research

The increasing demand for energy in remote or rural areas, coupled with the environmental benefits and cost-effectiveness of renewable energy sources, has led to a growing interest in off-grid energy systems. This paper proposes an optimized off-grid energy system to supply energy demand for a village and a desalination reverse osmosis water plant (RO) in Shalateen, Egypt, using a combination of photovoltaic (PV) and biomass technologies. To achieve minimizing Loss of Power Supply Probability (LPSP) and Total Net Present Cost (TNPC), two decision variables are optimized using the Harmony Search algorithm implemented in MATLAB. The reliability of a designed system …

Recent Progress And Challenges Of Implantable Biodegradable Biosensors, Fahmida Alam, Md Ashfaq Ahmed, Ahmed Hasnain Jalal, Ishrak Siddiquee, Rabeya Zinnat Adury, G M Mehedi Hossain, Nezih Pala

Electrical and Computer Engineering Faculty Publications and Presentations

Implantable biosensors have evolved to the cutting-edge technology of personalized health care and provide promise for future directions in precision medicine. This is the reason why these devices stand to revolutionize our approach to health and disease management and offer insights into our bodily functions in ways that have never been possible before. This review article tries to delve into the important developments, new materials, and multifarious applications of these biosensors, along with a frank discussion on the challenges that the devices will face in their clinical deployment. In addition, techniques that have been employed for the improvement of the …

Protecting Return Address Integrity For Risc-V Via Pointer Authentication, Yuhe Zhao

Masters Theses

Embedded systems based on lightweight microprocessors are becoming more prevalent in various applications. However, the security of them remains a significant challenge due to the limited resources and exposure to external threats. Especially, some of these devices store sensitive data and control critical devices, making them high-value targets for attackers. Software security is particularly important because attackers can easily access these devices on the internet and obtain control of them by injecting malware.

Return address (RA) hijacking is a common software attack technique used to compromise control flow integrity (CFI) by manipulating memory, such as return-to-libc attacks. Several methods have …

Blockchain Design For A Secure Pharmaceutical Supply Chain, Zhe Xu

Masters Theses

In the realm of pharmaceuticals, particularly during the challenging times of the COVID-19 pandemic, the supply chain for drugs has faced significant strains. The increased demand for vaccines and therapeutics has revealed critical weaknesses in the current drug supply chain management systems. If not addressed, these challenges could lead to severe societal impacts, including the rise of counterfeit medications and diminishing trust in government authorities.

The study identified that more than the current strategies, such as the Drug Supply Chain Security Act (DSCSA) in the U.S., which focuses on unique authentication and traceability codes for prescription drugs, is needed to …

Recent Advances In Solar Photo(Electro)Catalytic Nitrogen Fixation, Jun-Bo Ma, Sheng Lin, Zhiqun Lin, Lan Sun, Chang-Jian Lin

Journal of Electrochemistry

Ammonia (NH₃) is an essential chemical in modern society. It is currently produced in industry by the Haber-Bosch process using H₂ and N₂ as reactants in the presence of iron-based catalysts at high-temperature (400–600 ^oC) and extremely highpressure (20–40 MPa) conditions. However, its efficiency is limited to 10% to 15%. At the same time, a large amount of energy is consumed, and CO₂ emission is inevitably. The development of a sustainable, clean, and environmentally friendly energy system represents a key strategy to address energy crisis and environmental pollution, ultimately aiming to achieve carbon neutrality. …

Effect Of Fabrication Parameters On The Ferroelectricity Of Hafnium Zirconium Oxide Films: A Statistical Study, Guillermo A. Salcedo, Ahmad E. Islam, Elizabeth Reichley, Michael Dietz, Christine M. Schubert Kabban, Kevin D. Leedy, Tyson C. Back, Weison Wang, Andrew Green, Timothy S. Wolfe, James M. Sattler

Faculty Publications

Ferroelectricity in hafnium zirconium oxide (Hf_1−xZr_xO₂) and the factors that impact it have been a popular research topic since its discovery in 2011. Although the general trends are known, the interactions between fabrication parameters and their effect on the ferroelectricity of Hf_1−xZr_xO₂ require further investigation. In this paper, we present a statistical study and a model that relates Zr concentration (x), film thickness (t_f), and annealing temperature (T_a) with the remanent polarization (P_r) in tungsten (W)-capped Hf_1−xZr_xO₂. …

The Determination Of Pzc And Differential Capacitance Curve Of Platinum-Alkaline Polymer Electrolyte Interfaces, Chen-Xi Liu, Ze-Ping Zou, Mei-Xue Hu, Yu Ding, Yu Gu, Shuai Liu, Wen-Jing Nan, Yi-Chang Ma, Zhao-Bin Chen, Dong-Ping Zhan, Qiu-Gen Zhang, Lin Zhuang, Jia-Wei Yan, Bing-Wei Mao

Journal of Electrochemistry

Alkaline polymer electrolyte (APE) is the core component of modern alkaline hydrogen and oxygen fuel cells, and its single ion conductor nature makes the "electrode/APE" interfaces different from the conventional "electrode/solution" interfaces in terms of ion distribution, electrical double layer structure and polarization behavior. Due to the complexity of the APE and the associated solid-solid interfaces, fundamental investigations are challenging and deeper understanding of the structures and properties of such interfaces is in the infant stage. In this work, we aim to investigate the double layer structure from the aspects of differential capacitance curve and potential of zero charge (PZC) …

Investigation Of Gas Dynamics In Water And Oil-Based Muds Using Das, Dts, And Dss Measurements, Temitayo S. Adeyemi

LSU Master's Theses

Reliable prediction of gas migration velocity, void fraction, and length of gas-affected region in water and oil-based muds is essential for effective planning, control, and optimization of drilling operations. However, there is a gap in our understanding of gas behavior and dynamics in water and oil-based muds. This is a consequence of the use of experimental systems that are not representative of field-scale conditions. This study seeks to bridge the gap via the well-scale deployment of distributed fiber-optic sensors for real-time monitoring of gas behavior and dynamics in water and oil-based mud. The aforementioned parameters were estimated in real-time using …

Watercave: Virtual Reality For Engaging The Next Generation In Wastewater Management, Leeza Duller

Graduate Student and Postdoctoral Fellow Symposium

Within the next five to ten years, a significant portion of water and wastewater personnel are expected to retire, posing a substantial challenge for the utilities industry. The potential shortage of experienced workers poses inherent risks to the continuity of water services. WaterCAVE emerges as one of several tools designed to address this impending issue by engaging and inspiring the next generation to consider careers in the wastewater industry. This virtual reality (VR) simulation utilizes a ten-sided immersive display and was designed using Unity's 3D real-time development platform. Students from various educational levels, ranging from Pre-K to 12, are invited …

Incorporating Machine Learning With Satellite Data To Support Critical Infrastructure Measurement And Sustainable Development, Aggrey Muhebwa

Doctoral Dissertations

Under the umbrella concept of Artificial Intelligence (AI) for good, recent advances in machine learning and large-scale data analysis have opened new opportunities to solve humanity’s most pressing challenges. Improvements in computation complexity and advances in AI (e.g., Vision Transformers) have led to faster and more effective techniques for extracting high-dimensional patterns from large-scale heterogeneous datasets (big data). Further, as satellite data become increasingly available at varying temporal-spatial resolutions, AI tools are helping us to better understand the underlying causes of environmental and socioeconomic changes at an unprecedented scale, ushering in an era of data-driven decision-making to support sustainable and …

Fpga Security Techniques With Applications To Cloud And Multi-Tenant Use Cases, Xiang Li

Doctoral Dissertations

Field programmable gate arrays (FPGAs) are integrated circuits that consist of programmable logic that a user can configure and deploy for applications such as hardware emulation and accelerating high performance computing. In recent years, the emergence of FPGAs in the cloud has led to research on multi-tenant FPGAs. In a multi-tenant scenario, the same FPGA fabric is shared among multiple users, or among multiple untrusting IP cores. Multi-tenancy has economic benefits, largely due to improvements in resource utilization, but also brings new security concerns since the tenants could behave maliciously. Although the tenants sharing an FPGA are logically isolated from …

A Ground-Based L-Band Radar System For Monitoring Forest Temporal Dynamics, Xingjian Chen

Doctoral Dissertations

L-band FMCW radar is implemented for monitoring forest dynamics. It took short-term and long-term measurements with an internal calibration system that guarantees stability and precision. The radar data is compared to in-situ measurement, which infers causal relationships between radar backscatter signal and forest physiology index such as tree dielectric. This paper explains the relationship between radar signals and environmental components such as precipitation based on the measurement. The radar demonstrates some interesting observations, for example, trees' daily activity and freeze-thaw process.

Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …

Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …

Value Of Service-Oriented Multi-Service Provisioning And Resource Allocation In Integrated Localization, Sensing And Communication Systems, Biwei Li

Electronic Thesis and Dissertation Repository

The unprecedented proliferation of wireless infrastructures and their ongoing convergence with diverse industrial Internet of Things (IoT) applications introduce new demands for upcoming wireless networks. In response to such diversity of demands, envisioned future wireless networks must have multiple beyond communication capabilities, such as localization and sensing. To efficiently utilize, allocate, and manage these capabilities, the integration of localization, sensing, and communication (ILSAC) within a unified wireless system structure is of utmost importance. However, the seamless integration of ILSAC into intricate network infrastructures is encumbered by critical challenges, including high-accuracy localization/sensing algorithm, efficient resource management and allocation scheme, and robust …

Development Demand, Power Energy Consumption And Green And Low-Carbon Transition For Computing Power In China, Xiaohong Chen, Liaoying Cao, Jiaolong Chen, Jinghui Zhang, Wenzhi Cao, Yangjie Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a critical digital infrastructure, computing power has become the core productivity and a new engine driving economic growth in the digital economy. Nevertheless, the power-hungry nature of computing/data centers, representing the computing infrastructure, consumes a significant amount of electrical energy. Currently, China’s economy is transitioning from high-speed growth to high-quality development. It is imperative to study how to coordinate the development of computing power while ensuring its safety and achieving green and low-carbon goals. Based on an overview of the current status of computing power development, this study predicts the future demand for computing power in China, analyzes the …

Maximizing Ev Profit And Grid Stability Through Virtual Power Plant Considering V2g, A. Selim Türkoğlu, H. Cihan Güldorum, Ibrahim Sengor

Dept. of Electrical & Electronic Engineering Publications

The electrification of transportation through the widespread adoption of electric vehicles (EVs) has raised substantial concerns within the realm of power grid operations. This concern predominantly stems from the elevated electricity demand brought about by the surging population of EVs, consequently exerting strain on the power grid infrastructure which can be reduced with vehicle-to-grid (V2G) technology integration. To address this issue, this paper delves further into the realm of grid integration by introducing a Virtual Power Plant (VPP) concept to enhance the synergy between EVs and power grid. This study aims to compare different realistic objectives, ranging from total active …

On The Use Of Machine Learning And Data-Transformation Methods To Predict Hydration Kinetics And Strength Of Alkali-Activated Mine Tailings-Based Binders, Sahil Surehali, Taihao Han, Jie Huang, Aditya Kumar, Narayanan Neithalath

Electrical and Computer Engineering Faculty Research & Creative Works

The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement …

Intelligent Protection Scheme Using Combined Stockwell-Transform And Deep Learning-Based Fault Diagnosis For The Active Distribution System, Latha Maheswari Kandasamy, Kanakaraj Jaganathan

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time-domain simulations are carried out in EMTP-RV to extract time-synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase-detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis …

Consensus-Based Virtual Leader Tracking Algorithm For Flight Formation Control Of Swarm Uavs, Berat Yıldız, Akif Durdu, Ahmet Kayabaşi

Turkish Journal of Electrical Engineering and Computer Sciences

Technological developments in industrial areas also impact unmanned aerial vehicles (UAVs). Recent improvements in both software and hardware have significantly increased the use of many UAVs in social and military fields. In particular, the widespread use of these vehicles in social areas such as entertainment, shipping, transportation, and delivery and military areas such as surveillance, tracking, and offensive measures has accelerated the research on swarm systems. This study examined the previous investigations on swarm UAVs and aimed to create a more efficient algorithm. The effectiveness of the proposed algorithm was compared with other leader-based applications. A swarm consisting of 5 …

Lower Data Attacks On Advanced Encryption Standard, Orhun Kara

Turkish Journal of Electrical Engineering and Computer Sciences

The Advanced Encryption Standard (AES) is one of the most commonly used and analyzed encryption algorithms. In this work, we present new combinations of some prominent attacks on AES, achieving new records in data requirements among attacks, utilizing only 2 4 and 2 16 chosen plaintexts (CP) for 6-round and 7-round AES 192/256, respectively. One of our attacks is a combination of a meet-in-the-middle (MiTM) attack with a square attack mounted on 6-round AES-192/256 while another attack combines an MiTM attack and an integral attack, utilizing key space partitioning technique, on 7-round AES-192/256. Moreover, we illustrate that impossible differential (ID) …

Cascade Controller Design Via Controller Synthesis For Load Frequency Control Of Electrical Power Systems, Yavuz Güler, Mustafa Nalbantoğlu, Ibrahim Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

The regulation of tie-line electricity flow and frequency of electrical power systems (EPS) is crucial for ensuring their robustness to parameter changes and efficient management of disturbances. To this end, a novel cascade control design approach utilizing a serial Proportional-Integral-Derivative controller with a filter (PIDF) is proposed in this paper. The parameters of the controllers are derived analytically, and it is employed in both loops of the cascade control system to regulate the Load Frequency Control (LFC) of EPS. The implementation of PIDF controllers in both loops is utilized in the cascade control scheme for various power systems featuring different …

Advanced Hyperthermia Treatment: Optimizing Microwave Energy Focus For Breast Cancer Therapy, Burak Acar, Tuba Yilmaz Abdolsaheb, Ali Yapar

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a fast antenna phase optimization scheme to enable microwave power focusing for breast cancer hyperthermia. The power focusing is achieved through the maximization of the deposited electric field on the target malignant tumor tissue. To do so, a malignant breast tumor, the surrounding breast medium, and the skin of the breast are modeled as a cylindrical structure composed of eccentric cylinders, and electric field distribution is computed analytically in terms of cylindrical harmonics. This approach minimized the computational cost and simplified the breast medium model. To ensure applicability across various breast types, the dielectric properties (DPs) of …

Atomic Comagnetometer Gyroscopes For Inertial Navigation Systems: A Review, Murat Salim Karabinaoglu, Bekir Çakir, Mustafa Engin Başoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, developments in quantum sensing, laser, and atomic sensor technologies have also enabled advancement in the field of quantum navigation. Atomic-based gyroscopes have emerged as one of the most critical atomic sensors in this respect. In this review, a brief technology statement of spin exchange relaxation free (SERF) and nuclear magnetic resonance (NMR) type atomic comagnetometer gyroscope (CG) is presented. Related studies in the literature have been gathered, and the fundamental compositions of CGs with technical basics are presented. A comparison of SERF and NMR CGs is provided. A basic simulation of SERF CG was carried out because …

Uncovering And Mitigating Spurious Features In Domain Generalization, Saeed Karimi, Hamdi̇ Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we …

Miniature Optical Fiber Fabry-Perot Interferometer Based On A Single-Crystal Metal-Organic Framework For The Detection And Quantification Of Benzene And Ethanol At Low Concentrations In Nitrogen Gas, Farhan Mumtaz, Bohong Zhang, Narasimman Subramaniyam, Mohammad Roman, Peter Holtmann, Abhishek Prakash Hungund, Ryan O'Malley, Thomas M. Spudich, Michael Davis, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This study reports for the first time, to the best of our knowledge, a real-time detection of ultralow-concentration chemical gases using fiber-optic technology, combining a miniaturized Fabry-Perot interferometer (FPI) with metal-organic frameworks (MOFs). The sensor consists of a short and thick-walled silica capillary segment spliced to a lead-in single-mode fiber (SMF), housing a tiny single crystal of HKUST-1 MOF, imparting chemo selectivity features. Ethanol and benzene gases were tested, resulting in a shift in the FPI interference signal. The sensor demonstrated high sensitivity, detecting ethanol gas concentrations (EGCs) with a sensitivity of 0.428 nm/ppm between 24.9 and 40.11 ppm and …

6d Single-Fluorogen Orientation-Localization Microscopy For Elucidating The Architecture Of Beta-Sheet Assemblies And Biomolecular Condensates, Tingting Wu, Weiyan Zhou, Jai S. Rudra, Rohit V. Pappu, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

We develop six-dimensional single-molecule orientation-localization microscopy (SMOLM) to measure the 3D positions and 3D orientations simultaneously of single fluorophores. We show how careful optimization of phase and polarization modulation components can encode phase, polarization, and angular spectrum information from each fluorescence photon into a microscope’s dipole-spread function. We used the transient binding and blinking of Nile red (NR) to characterize the helical structure of fibrils formed by designed amphipathic peptides, KFE8^L and KFE8^D, and the pathological amyloid-beta peptide Aβ42. We also deployed merocyanine 540 to uncover the interfacial architectures of biomolecular condensates.

Chatreview: A Chatgpt-Enabled Natural Language Processing Framework To Study Domain-Specific User Reviews, Brittany Ho, Ta'rhonda Mayberry, Khanh Linh Nguyen, Manohar Dhulipala, Vivek Krishnamani Pallipuram

All Faculty Articles - School of Engineering and Computer Science

We present ChatReview, a ChatGPT-enabled natural language processing framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework …

Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Biomedical Datasets Distill Many Mechanisms Of Human Diseases, Linking Diseases To Genes And Phenotypes (Signs And Symptoms Of Disease), Genetic Mutations To Altered Protein Structures, And Altered Proteins To Changes In Molecular Functions And Biological Processes. It Is Desirable To Gain New Insights From These Data, Especially With Regard To The Uncovering Of Hierarchical Structures Relating Disease Variants. However, Analysis To This End Has Proven Difficult Due To The Complexity Of The Connections Between Multi-Categorical Symbolic Data. This Article Proposes Symbolic Tree Adaptive Resonance Theory (START), With Additional Supervised, Dual-Vigilance (DV-START), And Distributed Dual-Vigilance (DDV-START) Formulations, For The Clustering Of …

Numerical Back Analysis Of An Underground Bulk Mining Operation Using Distributed Optical Fiber Sensors For Model Calibration, Samuel Nowak, Taghi Sherizadeh, Mina Esmaeelpour, Paul Brooks, Dogukan Guner, Kutay Karadeniz

Mining Engineering Faculty Research & Creative Works

Numerical Modeling Of Complex Underground Engineering Projects Such As Caverns, Tunnels, And Bulk Mining Zones Is An Essential Part Of The Design Phase. Large-Scale Models Require Significant Reductions In Complexity From The Real-World Scenario, Which Often Leads To Low Confidence In The Model Output. In This Work, A Mine-Scale Numerical Model Is Developed To Simulate A Room And Pillar Extraction Mining Operation. The Model Inputs Are Calibrated Through The Comparison Of The Model Response To Pillar Extraction In An Analogous Mine Geometry With Measured Strain Values Collected Using A Novel Distributed Optical Fiber Strain Sensing Technique After Pillar Extraction. Calibration …