Electrical and Computer Engineering Commons^™

Docker Optimization Of An Automotive Sector Virtual Server Infrastructure, Leonel Hernandez, Carlos Eduardo Uc Rios

Knowledge Engineering and Data Science

Server virtualization is a powerful strategy for optimizing network infrastructure. It allows multiple virtual servers to run on a single physical server, maximizing resource utilization and improving efficiency. Deploying server virtualization using Docker technology offers a lightweight and flexible approach to optimizing network infrastructure. Docker contains package applications and their dependencies, enabling consistent and efficient deployment across various environments. Specifically, optimizing virtual server infrastructure using Docker Technology in the automotive sector focuses on improving the efficiency and management of the company's virtual server resources. By implementing Docker technology, a container platform that allows the packaging and running of applications in …

Timbre Style Transfer For Musical Instruments Acoustic Guitar And Piano Using The Generator-Discriminator Model, Widean Nagari, Joan Santoso, Esther Irawati Setiawan

Knowledge Engineering and Data Science

Music style transfer is a technique for creating new music by combining the input song's content and the target song's style to have a sound that humans can enjoy. This research is related to timbre style transfer, a branch of music style transfer that focuses on using the generator-discriminator model. This exciting method has been used in various studies in the music style transfer domain to train a machine learning model to change the sound of instruments in a song with the sound of instruments from other songs. This work focuses on finding the best layer configuration in the generator- …

A Novel Approach To Defect Detection In Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation And Model Optimization, Yusriel Ardian, Novta Danyel Irawan, Sutoko Sutoko, I Nyoman Gede Arya Astawa

Knowledge Engineering and Data Science

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian …

Transformer-Based Deep Learning Model For Sign Language Recognition, Ganzorig Batnasan

Theses

Sign language recognition research aims to develop systems and tools that can interpret and translate sign language into text or spoken language. During the past two decades, the challenges faced in this domain are multifaceted. The first and foremost challenge is the complexity of sign language, which includes intricate hand gestures, facial expressions, and body movements. Recognizing and interpreting these components accurately is challenging. The second challenge is variability among different regions and communities, leading to variations in signs and gestures. This variability poses a challenge for developing universal recognition systems.
Limited data is another challenge which makes it difficult …

A New Method To Start Loaded Induction Motors, Omar El-Sayed Mohammed Youssef

Journal of Engineering Research

Three-phase induction motors have a widespread use in industry, and starting of loaded induction motors is an important matter. However, there are few studies on motor starting using variable-frequency drives (VFDs). The existing control schemes, described by these studies, have disadvantages such as dependence on derived characteristics, adjustment of settings, design of controllers, or burden of calculations on the control processor. In this paper, a simple and effective control scheme of VFDs to start loaded motors is proposed. In this scheme, there is no dependence on any characteristic, adjustment of settings, controllers, and rotor speed sensor. By this scheme, the …

Allocation Of Phasor Measurement Units In The Wide-Area Measurement System, Samir Dawoud, Mohamed Elnemr, Alyaa Yehia

Journal of Engineering Research

WIDE Area in the power system which has been introduced to power system literature in the late 1980s. They are now commercially in power systems for purposes of monitoring, operation, and control. The wide area is a combination between the phasor measurement unit (PMU) and communication infrastructure (CI). In this paper, the location of the PMU and the CI can be optimized to involve system observability. There are a lot of methods to solve the optimal placement of PMU. The communication infrastructure is designed and implemented to collect the data from PMU and transfer data to the control center. In …

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

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 …

High Power Capacitive Wireless Power Transfer System For Future Transportation Electrification, Kodeeswaran S

Theses and Dissertations

Wireless Power Transfer (WPT) technologies, including Inductive Power Transfer (IPT) and Capacitive Power Transfer (CPT), offer promising solutions for Electric Vehicle (EV) charging applications. Compared to conventional IPT, a CPT method has no eddy current losses, lower electromagnetic interference (EMI), low system cost and weight, less misalignment problems, which gives the motivation to start this research work. The Proposed CPT method uses aluminum plates that generate electric fields by applying voltage on the plates to transfer the power from the transmitter (ground side) to the receiver (vehicle side).

This research introduces novel advancements in CPT systems to enhance power transfer …

Electrospun Nanofiber Technology Based Energy Storage And Biosensor Devices For Wearable Electronics, Bianca Seufert

USF Tampa Graduate Theses and Dissertations

As progress in the field of nanotechnology continues to evolve, it is becoming more and more prevalent to apply this small but powerful technology to portable wearable electronics. The unique scale of such a technology lends its expertise to the investigation of enzymes for biometric related diagnostics or in the development of light weight flexible energy storage devices. Furthermore, as the need for highly precise and durable wearable electronics grows, the next generation of electronics must keep pace with this development in terms of shape, flexibility, functionality, and performance.

The presented research reviews the current state of art of nanofiber …

A Gateway To Next-Generation Patient Monitoring System, Kishore Kumar Kadari

USF Tampa Graduate Theses and Dissertations

Healthcare patient monitoring is undergoing a significant digital transformation, and the integration of Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) is becoming increasingly crucial in reshaping patient care. In an era where digital technology is revolutionizing medical practices, this research aims to take a leading role in advancing a fundamental aspect of predictive and sustainable healthcare practices, enhancing patient outcomes and uplifting the practice of medicine.

This research focuses on the study of Digital Twins for precision health, which are designed to monitor and provide intricate, personalized feedback dynamically during a patient's healthcare experience. The architecture of the system is …

Development Of Novel Algorithms For The Investigation Of Small Displacement Optical Flow For Opto Kinetic Nystagmus (Okn) Detection And Large Displacement Optical Flow For Surveillance, Jaiseeli C

Theses and Dissertations

Motion is a significant component of the modern visual experience, aiding with oculomotor control, object recognition, scene interpretation, perceptual organization and the acquisition of 3D shapes. Identifying motion vectors that characterize two-dimensional image transitions, usually from video frames, is necessary to estimate motion.

Optical flow is a motion-tracking technique that estimates the trajectory and velocity of a moving object in a video by shifting each pixel between frames using displacement vectors. This work has two objectives based on small and large displacement optical flow and computes optical flow using Lucas-Kanade and Brox flow, which seem to be superior to other …

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. …

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) …

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. …

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 …

Dyvir: Virtual Reality Generated Synthetic Training Datasets For Ai, Garrett Williams

Graduate Student and Postdoctoral Fellow Symposium

Artificial Intelligence (AI) can perform complex tasks quickly such as object detection. To perform these tasks, the AI algorithms are first trained on data. However, some data such as labeled imagery of aerial objects is hard to obtain. Utilizing Virtual Reality (VR) software, a custom tool called DyViR was made to generate synthetic training datasets. Users customize the virtual environment, aerial objects, and sensor modality to produce custom-tailored datasets.

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, …

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 …

Exploring The Morphological Surface Resistance And Optical Absorption Of Thin Black Carbon Nanotube Films For Electronic And Optoelectronic Devices, Ziad Khalifa, Sameh Osama Abdellatif

Chemical Engineering

This study investigates the optical and electrical properties of thin black films of carbon nanotubes (CNTs) fabricated under various conditions to explore their potential integration as either a perfect broadband absorber or enhanced counter electrode. The study involves SEM measurements, surface resistance measurements, and UV–Vis. spectrometer analysis. The results show that the CNT thin films exhibit high electrical conductivity and strong light absorption across various wavelengths. Optically, we investigated the impact of varying the growth temperature and catalyst temperature on the absorption profile of the thin films. The fabricated and deposited CNTs showed broadband absorption spectra, reaching 92.8% of the …

A Reliable Optimal Electric Vehicle Charging Stations Allocation, Mohamed Abdelaziz, A.A. Ali, R.A. Swief, Rasha Elazab

Renewable Electrical Energy Engineering

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. …

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

Electrical & Electronic Engineering

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 …

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 …

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) …