Engineering Commons^™

Trends In The Development And Improvement Of Glass Furnaces And Monitoring And Control Systems, N.R Yusupbekov, O'Tkir U. Xolmanov

Chemical Technology, Control and Management

The theory and practice of establishing industrial automation systems for glass furnaces and installations as they are right now are taken into consideration. Comparative analysis is based on publications over the last 20 years in the field being studied. On the basis of an analysis of the review materials, generalizations are made that form the basis for proposals aimed at modernizing and improving the technology and instrumentation of fuel combustion processes in gas-burning furnaces and installations, as well as monitoring and control systems for technological processes occurring in glass furnaces and installations.

Normalized Linearly-Combined Chaotic System: Design, Analysis, Implementation And Application, Md Sakib Hasan, Anurag Dhungel, Partha Sarathi Paul, Maisha Sadia, Md Razuan Hossain

Faculty and Student Publications

This work presents a general framework for developing a multi-parameter 1-D chaotic system for uniform and robust chaotic operation across the parameter space. This is important for diverse practical applications where parameter disturbance may cause degradation or even complete disappearance of chaotic properties. The wide uninterrupted chaotic range and improved chaotic properties are demonstrated with the aid of stability analysis, bifurcation diagram, Lyapunov exponent (LE), Kolmogorov entropy, Shannon entropy, and correlation coefficient. We also demonstrate the proposed system’s amenability to cascading for further performance improvement. We introduce an efficient Field-Programmable Gate Array (FPGA)-based implementation and validate its chaotic properties using …

Dynamic Spectrum Management In 3d Networks Inspired By Biological Systems, Jordan Anthony Patrizi

Electrical and Computer Engineering ETDs

In this thesis, the challenge of dynamic spectrum management is treated following an economic-based perspective. In particular, a novel bioinspired spectrum allocation from the Network Service Providers (NSPs) to the users is introduced, exploiting the theory of symbiosis under the free competition. The overall objective is to determine the NSPs optimal bandwidth prices to maximize their profit, while satisfying the users bandwidth needs. The aforementioned spectrum management framework is analyzed while exploiting a novel 3D network architecture consisting of High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground gNBs NSPs, each one of them presenting different operational characteristics and …

Research Progress And Performance Improvement Strategies Of Hard Carbon Anode Materials For Sodium-Ion Batteries, Xiu-Ping Yin, Yu-Feng Zhao, Jiu-Jun Zhang

Journal of Electrochemistry

This paper systematically summarizes the research progress of hard carbon anode materials in sodium ion batteries(SIBs) and the development of the corresponding sodium storage mechanism in recent years, and reviews the performance improvement strategies of hard carbon materials from the aspects of structural design and electrolyte regulation. The effects of the selection of precursors, carbonization temperature, pretreatment, pore formers, heteroatom doping, material compounding, electrolyte regulation and pre-sodiumization on the sodium storage performance of hard carbon anode materials are briefly described. This paper provides new insights into the design, synthesis and electrolyte
matching of high-performance and low-cost hard carbon materials, and …

Preparation And Electrocatalytic Performance Of Feni-Cop/Nc Bifunctional Catalyst, Si-Miao Liu, Jing-Jiao Zhou, Shi-Jun Ji, Zhong-Sheng Wen

Journal of Electrochemistry

Rechargeable zinc-air batteries have gradually attracted much attention worldwide due to their high capacity, high energy density and low price. Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) correspond to the charging and discharging processes in rechargeable zinc-air battery, respectively. At present, commercial Pt/C and IrO₂ catalysts hinder the large-scale application of zinc-air batteries due to low reserves, high prices and poor stability. Therefore, exploring high performance, low cost and high stability with dual functional catalysts is important for the development of rechargeable zinc-Air batteries. The metal-organic frameworks (MOFs) have high specific surface area, structural stability, good catalytic …

Surface Modifications Of Lini0.96Co0.02Mn0.02O2 With Tungsten Oxide And Phosphotungstic Acid, Gang Zhao, Zheng-Liang Gong, Yi-Xiao Li, Yong Yang

Journal of Electrochemistry

With the rapid development of electric vehicles, enormous demands are made for higher energy density, better cycling performance and lower cost of lithium-ion batteries (LIBs). As an important high capacity cathode material for LIBs, the high nickel layered oxide material LiNi_0.8Co_0.1Mn_0.1O₂(NCM811) can reach an energy density of 760 Wh·kg^-1. The ultra-high nickel ternary positive electrode material (LiNi_1-x-yCo_xMn_yO₂, x ≥ 0.90) has a specific capacity of more than 210 mAh·g^-1, and can realize higher energy density. Besides, an ultra-high nickel material …

Analysis Of The Pros And Cons Of The Acquisition Of Training Ships By Metis, Jeremiah Yaw Frimpong

World Maritime University Dissertations

No abstract provided.

Multiple Reflections And The Near-Field Effects On A Metamaterial Quarter-Wave Plate, James Davis, Durdu Güney

Michigan Tech Publications

Metamaterial-based quarter-wave plates (QWPs) have emerged as promising candidates for advanced polarization control in a variety of optical applications, owing to their unique properties, such as ultra-thin profiles and tailored spectral responses. We design an ultra-thin, high-efficiency, and broadband QWP in transmission mode based on a TiO2" role="presentation" style="box-sizing: border-box; max-height: none; display: inline; line-height: normal; font-size: 13.2px; font-size-adjust: none; overflow-wrap: normal; text-wrap-mode: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; color: rgb(34, 34, 34); font-family: Arial, Arial, Helvetica, sans-serif; position: relative;">22/Au grating structure. We show that multiple reflections and …

Novel Fpga Speed Grade Authentication Method For Counterfeit Identification, Ana Maria Camacho Negrete

USF Tampa Graduate Theses and Dissertations

Today's society runs on modern microelectronics. Whether it is our computers, smartphones and tablets, navigation systems, and even automobiles, the impact of inferior components on our society's functioning is a well-known and serious challenge.Inferior components, in this context, refer to non-specification compliant parts that are known to enter the global supply chain either due to manufacturing/quality control errors or intentional component counterfeiting by non-OEM (original equipment manufacturer) sources. This thesis discusses the latter, which is a well-documented and growing challenge that poses a serious risk to every sector of our society. This has been further exacerbated by the global pandemic-driven …

Experimental Evaluation Of Airflow Guiding Components For Wind-Driven Single-Sided Natural Ventilation: A Comparative Study In A Test Chamber, Nima Najafi Ziarani, Malcolm J. Cook, Paul D. O’Sullivan

Publications

Single-sided ventilation is a frequently employed ventilation strategy in contemporary structures, yet its effectiveness is often mediocre, underscoring the need for solutions to enhance its performance. To evaluate the effect of louvres and guide vanes on the effective ventilation rate, 21 different louvre configurations and four guide vane designs were compared with a plain opening for single-sided ventilation in a full-scale experimental test chamber under three different magnitudes of local wind velocity. Ventilation rates were measured using the tracer gas decay method with 12 NDIR CO2 sensors. The findings, obtained under specific test conditions, indicate that deeper openings result in …

Directional Microwave Emission From Femtosecond-Laser Illuminated Linear Arrays Of Superconducting Rings, Thomas J. Bullard, Kyle Frische, Charlie Ebbing, Stephen J. Hageman, John Morrison, John Bulmer, Enam A. Chowdury, Michael L. Dexter, Timothy J. Haugan, Anil K. Patnaik

Faculty Publications

We examine the electromagnetic emission from two photo-illuminated linear arrays composed of inductively charged superconducting ring elements. The arrays are illuminated by an ultrafast infrared laser that triggers microwave broadband emission detected in the 1–26 GHz range. Based on constructive interference from the arrays a narrowing of the forward radiation lobe is observed with increasing element count and frequency demonstrating directed GHz emission. Results suggest that higher frequencies and a larger number of elements are achievable leading to a unique pulsed array emitter concept that can span frequencies from the microwave to the terahertz (THz) regime.

Dynamic Mechanism Of Science Based Technological Innovation And Industrial Evolution—Take Semiconductor, Digital Computer And Radio Technologies As Examples, Yi Zhang, Qiang Yan

Bulletin of Chinese Academy of Sciences (Chinese Version)

By studying the technological innovation and industrial development process of semiconductor, digital computer and radio, this study analyzes the path, conditions and force of science-based technological innovation and its industrialization, establishes a chain reaction model of large-scale technological innovation and diffusion, and compares it with market-based technological innovation. It is found that the large-scale aggregation of scientific research institutions and industrial laboratories accelerates the speed of technological innovation, and diffuses along two paths of scientific research institutions to enterprises and enterprises to enterprises, forming a chain reaction of large-scale technological innovation. Strategic demand is the basic driving force for the …

Generation Of Dna Oligomers With Similar Chemical Kinetics Via In-Silico Optimization, Michael Tobiason, Bernard Yurke, William L. Hughes

Electrical and Computer Engineering Faculty Publications and Presentations

Networks of interacting DNA oligomers are useful for applications such as biomarker detection, targeted drug delivery, information storage, and photonic information processing. However, differences in the chemical kinetics of hybridization reactions, referred to as kinetic dispersion, can be problematic for some applications. Here, it is found that limiting unnecessary stretches of Watson-Crick base pairing, referred to as unnecessary duplexes, can yield exceptionally low kinetic dispersions. Hybridization kinetics can be affected by unnecessary intra-oligomer duplexes containing only 2 base-pairs, and such duplexes explain up to 94% of previously reported kinetic dispersion. As a general design rule, it is recommended that unnecessary …

Depth And Frequency Dependence Of Geoacoustic Properties On The New England Mud Patch From Reflection Coefficient Inversion, Jiang Yong-Min, Charles W. Holland, Stan E. Dosso, Jan Dettmer

Electrical and Computer Engineering Faculty Publications and Presentations

Muddy sediments cover significant portions of continental shelves, but their physical properties remain poorly understood compared to sandy sediments. This paper presents a generally applicable model for sediment-column structure and variability on the New England Mud Patch (NEMP), based on trans-dimensional Bayesian inversion of wide-angle, broadband reflection-coefficient data in this work and in two previously published reflection-coefficient inversions at different sites on the NEMP. The data considered here include higher frequencies and larger bandwidth and cover lower reflection grazing angles than the previous studies, hence, resulting in geoacoustic profiles with significantly better structural resolution and smaller uncertainties. The general sediment-column …

Biomembrane-Based Memcapacitive Reservoir Computing System For Energy-Efficient Temporal Data Processing, Md Razuan Hossain, Ahmed S. Mohamed, Nicholas X. Armendarez, Joseph S. Najem, Md Sakib Hasan

Faculty and Student Publications

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy-dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy-efficient approach. Herein, volatile biomembrane-based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken-digit classification and a normalized mean square error of 7.81×10−4$7.81 \times …

The Role Of Xanthan Gum In Predicting Durability Properties Of Self-Compacting Concrete (Scc) In Mix Designs, Alireza Masoumi, Reza Farokhzad, Seyed Hooman Ghasemi

Henry M. Rowan College of Engineering Departmental Research

This study comprehensively investigates the rheological properties of self-compacting concrete (SCC) and their impact on critical parameters, including the migration coefficient, penetration depth of chlorine ions, specific electrical resistance, and compressive strength. A total of 43 mix designs were meticulously examined to explore the relationships between these properties. Quantitative analysis employed a backpropagation neural network model with a single hidden layer to accurately predict the resistant and durable characteristics of self-compacting concrete. The optimal number of neurons in the hidden layer was determined using a fitting component selection method, implemented in MATLAB software(2021b). Additionally, qualitative analysis was conducted using sensitivity …

Lactation Physiokinetics—Using Advances In Technology For A Fresh Perspective On Human Milk Transfer, Jimi Francis, Paul Flynn, Maisha Naowar, Premananda Indic, Darby Dickton

Electrical Engineering Faculty Publications and Presentations

Introduction: Though the nature of breastfeeding is critical, scant information is available on how the action of the milk transfer from mother to infant is regulated in humans, where the points of dysfunction are, and what can be done to optimize breastfeeding outcomes. While better therapeutic strategies are needed, before they can be devised, a basic scientific understanding of the biomechanical mechanisms that regulate human milk transfer from breast to stomach must first be identified, defined, and understood. Methods: Combining systems biology and systems medicine into a conceptual framework, using engineering design principles, this work investigates the use of biosensors …

Study Of Improved Sorting Weighting Cfar Detectors For Gaussian Environment, Souad Chabbi, Khadidja Belhi, M'Hamed Hamadouche

Emirates Journal for Engineering Research

The goal of this paper is to improve the detection performance and the false alarm regulation of the conventional order statistics Constant False Alarm Rate (OS-CFAR) detectors in a non-homogeneous Gaussian environment. To this end, we design and study the New Sorting Weighting (NSW-) and the Modified Sorting Weighting (MSW-) CFAR detectors. We find closed forms of the detection ( ) and the false alarm ( ) probabilities for both detectors. Moreover, we identify the optimum pairs of weights that maximize the and ensure a constant . Finally, we prove through Monte Carlo simulations that these detectors provide better detection …

Fortifying Iot Against Crimpling Cyber-Attacks: A Systematic Review, Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir

Karbala International Journal of Modern Science

The rapid growth and increasing demand for Internet of Things (IoT) devices in our everyday lives create exciting opportunities for human involvement, data integration, and seamless automation. This fully interconnected ecosystem considerably impacts crucial aspects of our lives, such as transportation, healthcare, energy management, and urban infrastructure. However, alongside the immense benefits, the widespread adoption of IoT also brings a complex web of security threats that can influence society, policy, and infrastructure conditions. IoT devices are particularly vulnerable to security violations, and industrial routines face potentially damaging vulnerabilities. To ensure a trustworthy and robust security framework, it is crucial to …

Smart Service Function Chain System For Dynamic Traffic Steering Using Reinforcement Learning (Chrl), Ahmed Nadhum, Ahmed Al-Saadi

Karbala International Journal of Modern Science

The rapid development of the Internet and network services coupled with the growth of communication infrastructure necessitates the employment of intelligent systems. The complexity of the network is heightened by these systems, as they offer diverse services contingent on traffic type, user needs, and security considerations. In this context, a service function chain offers a toolkit to facilitate the management of intricate network systems. However, various traffic types require dynamic adaptation in the sets of function chains. The problem of optimizing the order of service functions in the chain must be solved using the proposed approach, along with balancing the …

Cyber-Physical Multi-Robot Systems In A Smart Factory: A Networked Ai Agents Approach, Zixiang Nie

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization—all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.

However, the implementation of MRS introduces new sets of challenges, including …

Furthering Development Of Smart Fabrics To Improve The Accessibility Of Music Therapy, Ellie Nguyen, Daisy Z. Fernandez-Reyes, Franceli L. Cibrian

Engineering Faculty Articles and Research

In this paper, we present the design and development of HarmonicThreads, a smart, cost-effective fabric augmented by generative machine learning algorithms to create music in real time according to the user's interaction. In this manner, we hypothesize that individuals with sensory differences could take advantage of the fabric's flexibility, the music will adapt according to users' interaction, and the affordable hardware we propose will make it more accessible. We follow a design thinking methodology using data from a multidisciplinary team in Mexico and the United States. Then we will close this paper by discussing challenges in developing accessible smart fabrics …

Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

Turkish Journal of Electrical Engineering and Computer Sciences

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to …

Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy

Turkish Journal of Electrical Engineering and Computer Sciences

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with …

A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant …

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli

Turkish Journal of Electrical Engineering and Computer Sciences

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …

Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth

Turkish Journal of Electrical Engineering and Computer Sciences

The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in …