Engineering Commons^™

Prediction Of Mechanical And Electrical Properties Of Carbon Fibre-Reinforced Self-Sensing Cementitious Composites, Zehao Kang, Farhad Aslani, Baoguo Han

Research outputs 2022 to 2026

The transmission of signal values in self-sensing concrete allows us to precisely locate damaged structures and prevent disasters. Currently, there are over ten functional materials used in self-sensing concrete applications. Carbon fibre (CF) is a well-known functional material that has been extensively studied for its reproducibility and accuracy in self-sensing concrete experiments. In contrast, this study is based on finite element modelling to rapidly predict the impact of the functional filler material, CF, on concrete performance. This paper simulates the mechanical and piezoresistive properties of concrete with unsized and desized short-cut CFs at lengths of 3, 6, and 12 mm. …

A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla

Electrical and Computer Engineering Faculty Research & Creative Works

Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new …

Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti

Electrical and Computer Engineering Faculty Publications

The exponential emergence of Field-Programmable Gate Arrays (FPGAs) has accelerated research on hardware implementation of Deep Neural Networks (DNNs). Among all DNN processors, domain-specific architectures such as Google’s Tensor Processor Unit (TPU) have outperformed conventional GPUs (Graphics Processing Units) and CPUs (Central Processing Units). However, implementing low-power TPUs in reconfigurable hardware remains a challenge in this field. Voltage scaling, a popular approach for energy savings, can be challenging in FPGAs, as it may lead to timing failures if not implemented appropriately. This work presents an ultra-low-power FPGA implementation of a TPU for edge applications. We divide the systolic array of …

Meso-Scale Seabed Quantification With Geoacoustic Inversion, Tim Sonnemann, Jan Dettmer, Charles W. Holland, Stan E. Dosso

Electrical and Computer Engineering Faculty Publications and Presentations

Abstract Knowledge of sub-seabed geoacoustic properties, for example depth dependent sound speed and porosity, is of importance for a variety of applications. Here, we present a semi-automated geoacoustic inversion method for autonomous underwater vehicle data that objectively adapts model inference to seabed structure. Through parallelized trans-dimensional Bayesian inference, we infer seabed properties along a 12 km survey track on the scale of about 10 cm and 50 m in the vertical and horizontal, respectively. The inferred seabed properties include sound speed, attenuation, density, and porosity as a function of depth from acoustic reflection coefficient data. Parameter uncertainties are quantified, and …

Rethinking Wind In Kentucky, Lawrence E. Holloway, Aron Patrick, Dan M. Ionel

Power and Energy Institute of Kentucky Faculty Publications

Recent analyses and developments suggest that wind energy could play a role in Kentucky's future power generation mix. This recent change in outlook for Kentucky wind has been driven by three factors: (1) improved wind turbine technologies, (2) improved economics, and (3) recent analyses showing improved grid reliability due to wind's complementarity to solar power generation.

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 …

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. …

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, …

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 …

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 …

Analysis Of Countermeasures Against Remote And Local Power Side Channel Attacks Using Correlation Power Analysis, Aurelien Tchoupou Mozipo, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

Countermeasures and deterrents to power side-channel attacks targeting the alteration or scrambling of the power delivery network have been shown to be effective against local attacks where the malicious agent has physical access to the target system. However, remote attacks that capture the leaked information from within the IC power grid are shown herein to be nonetheless effective at uncovering the secret key in the presence of these countermeasures/deterrents. Theoretical studies and experimental analysis are carried out to define and quantify the impact of integrated voltage regulators, voltage noise injection, and integration of on-package decoupling capacitors for both remote and …

Dynamic Model Of Ac-Ac Dual Active Bridge Converter Using The Extended Generalized Average Modeling Framework, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

The ac-ac dual active bridge (DAB) converter is an advanced bidirectional two-port grid interface converter that facilitates active and reactive power flow control between two grids without a dc-link capacitor. This article presents a novel modeling approach for the ac-ac DAB converter using the extended generalized average modeling (EGAM) technique. Unlike the conventional generalized average modeling (GAM) framework, the ac-ac DAB converter's dynamic state variables, including the leakage inductor current and ac grid side LC filters, exhibit grid and switching frequency components, making the standard GAM framework unsuitable for dynamic modeling involving two distinct excitation frequencies. Furthermore, the 2-D GAM …

Investigating Customer Churn In Banking: A Machine Learning Approach And Visualization App For Data Science And Management, Pahul Preet Singh, Fahim Islam Anik, Rahul Senapati, Arnav Sinha, Nazmus Sakib, Eklas Hossain

Electrical and Computer Engineering Faculty Publications and Presentations

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and, after that, end their connection with the bank. Therefore, customer retention is essential in today’s extremely competitive banking market. Additionally, having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele. These factors make reducing client attrition a crucial step that banks must pursue. In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. …

Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel optimal trajectory tracking scheme is introduced for nonlinear continuous-time systems in strict feedback form with uncertain dynamics by using neural networks (NNs). The method employs an actor-critic-based NN back-stepping technique for minimizing a discounted value function along with an identifier to approximate unknown system dynamics that are expressed in augmented form. Novel online weight update laws for the actor and critic NNs are derived by using both the NN identifier and Hamilton-Jacobi-Bellman residual error. A new continual lifelong learning technique utilizing the Fisher Information Matrix via Hamilton-Jacobi-Bellman residual error is introduced to obtain the significance of weights in …

An Impedance-Source-Based Soft-Switched High Step-Up Dc-Dc Converter With An Active Clamp, Saeed Habibi, Ramin Rahimi, Mehdi Ferdowsi, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes a high step-up dc-dc converter based on a trans-inverse impedance-source structure, in which the voltage gain of the converter is increased by using a lower number of turns ratio of the coupled inductors (CI) windings. The proposed converter achieves a very high voltage gain and a very low voltage stress on the switches. An active clamp is incorporated into the topology of the proposed converter, helping to absorb the energy of the leakage inductances of the CI, and to recycle that energy to the output of the converter to further increase the voltage gain. Furthermore, the active …

Toward Smart And Sustainable Cement Manufacturing Process: Analysis And Optimization Of Cement Clinker Quality Using Thermodynamic And Data-Informed Approaches, Jardel P. Gonçalves, Taihao Han, Gaurav Sant, Narayanan Neithalath, Jie Huang, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Cement manufacturing is widely recognized for its harmful impacts on the natural environment. In recent years, efforts have been made to improve the sustainability of cement manufacturing through the use of renewable energy, the capture of CO₂ emissions, and partial replacement of cement with supplementary cementitious materials. To further enhance sustainability, optimizing the cement manufacturing process is essential. This can be achieved through the prediction and optimization of clinker phases in relation to chemical compositions of raw materials and manufacturing conditions. Cement clinkers are produced by heating raw materials in kilns, where both raw material compositions and processing conditions …

Source Level Of Wind-Generated Ambient Sound In The Oceana, N. Ross Chapman, Michael Ainslie, Martin Siderius

Electrical and Computer Engineering Faculty Publications and Presentations

Inference of source levels for ambient ocean sound from local wind at the sea surface requires an assumption about the nature of the sound source. Depending upon the assumptions made about the nature of the sound source, whether monopole or dipole distributions, the estimated source levels from different research groups are different by several decibels over the frequency band 10–350 Hz. This paper revisits the research issues of source level of local wind-generated sound and shows that the differences in estimated source levels can be understood through a simple analysis of the source assumptions.

Advances In Growth, Doping, And Devices And Applications Of Zinc Oxide, Vishal Saravade, Zhe Chuan Feng, Manika Tun Nafisa, Chuanle Zhou, Na Lu, Benjamin Klein, Ian T. Ferguson

Electrical and Computer Engineering Faculty Research & Creative Works

Zinc oxide is a breakthrough multifunctional material of emerging interest applicable in the areas of electronics, computing, energy harvesting, sensing, optoelectronics, and biomedicine. Zno has a direct and wide bandgap and high exciton binding energy. It is nontoxic, earth-abundant, and biocompatible. However, the growth and characterization of high-quality zno has been a challenge and bottleneck in its development. Efforts have been made to synthesize device-quality zinc oxide and unleash its potential for multiple advanced applications. Zno could be grown as thin films, nanostructures, or bulk, and its properties could be optimized by tuning the growth techniques, conditions, and doping. Zinc …

Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool

Henry M. Rowan College of Engineering Faculty Scholarship

Artificial intelligence and neuroscience have a long and intertwined history. Advancements in neuroscience research have significantly influenced the development of artificial intelligence systems that have the potential to retain knowledge akin to humans. Building upon foundational insights from neuroscience and existing research in adversarial and continual learning fields, we introduce a novel framework that comprises two key concepts: feature distillation and re-consolidation. The framework distills continual learning (CL) robust features and rehearses them while learning the next task, aiming to replicate the mammalian brain's process of consolidating memories through rehearsing the distilled version of the waking experiences. Furthermore, the proposed …

A Study On The Incentives And Barriers For The Adoption Of Electric Vehicles In South Alabama, Marianne Loes, Daniela Wolter Ferreira Touma, Jennifer C. Zoghby

Association of Marketing Theory and Practice Proceedings 2024

ABSTRACT

Through a series of public policy incentives and consumer demand, more electric vehicles are being sold nationwide than ever before. Although electric vehicles (EVs) are being adopted nationwide by many consumers in recent years, car-buyers across South Alabama have not followed the trend. This research considers different stakeholders in the complex consumer buying process of car purchases, and it uses the extant technology adoption models to assess the public policy incentives, as well as statewide roadblocks, to EV adoption.

Wearable Mxene-Graphene Sensing Of Influenza And Sars-Cov-2 Virus In Air And Breath: From Lab To Clinic, Yanxiao Li, Zhekun Peng, Jiaoli Li, Congjie Wei, Shangbin Liu, Weixing Hao, Huanyu Cheng, Casey Burton, Yang Wang, Yue-Wern Huang, Chang Soo Kim, Fang Yao Stephen Hou, Donghyun (Bill) Kim, Chenglin Wu

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The rapidly expanding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants demand a continuous monitoring method through portable and wearable devices. Utilizing the rich surface chemistry and high chemical-to-electrical signal conversion of 2D MXene-graphene heterostructure thin films, a field-effect-transistor (FET) sensor, which has a flexible substrate to be assembled onto the mask and combines with a Bluetooth system for wireless transmission is developed, to detect the influenza and SARS-CoV-2 viruses in air and breath. At first, the developed sensors are examined in the laboratory through direct contact with sensing targets in solution form. The results show a low …

Understanding Roles And Evaluating Reactivity Of Fly Ashes In Calcium Aluminate Binders, Sai Akshay Ponduru, Taihao Han, Jie Huang, Narayanan Neithalath, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Calcium aluminate cement (CAC) is an alternative to Portland cement, valued for its superior early strength and thermal resistance. Partially replacing CAC with Fly ash (FA) can reduce carbon footprint and production costs of CAC, producing sustainable cementitious binders. This research investigates on various properties (i.e., hydration kinetics; phase assemblage evolution; compressive strength) of [CAC + FA] binders. Using 13 distinct FAs, up to 50% of CAC was substituted. The study measures hydration kinetics, compressive strength, and employs the number of constraints to estimate FA reactivity. Advanced quantitative analysis draws links between hydration kinetics and compressive strength and elucidate the …

A Time Synchronized Multi-Hop Mesh Network With Crystal-Free Nodes, Filip Maksimovic, Austin Patel, David C. Burnett, Thomas Watteyne, Kristofer S.J. Pister

Electrical and Computer Engineering Faculty Publications and Presentations

In this work we propose and demonstrate a protocol for a time synchronized channel hopping mesh network for wireless transceivers that use exclusively imprecise and inaccurate on-chip oscillators. This protocol is built on an IEEE 802.15.4 physical layer radio that enables interoperability with protocols such as 6TiSCH or Thread. A calibration-bootstrapped multi-hop mesh network is demonstrated with a single crystal-enabled node acting as the root. The protocol is designed to create a multi-hop mesh while compensating noisy and drifting oscillators and timers. With a 4 s synchronization period, an experimental implementation of the network maintains, in the worst case, 1.8 …

Miniaturized Fluorescence Ph Sensor With Assembly Free Ball Lens On A Tapered Multimode Optical Fiber, Bohong Zhang, Farhan Mumtaz, Muhammad Roman, Dinesh Reddy Alla, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In biochemistry, the absence of a compact, assembly-free pH sensor with high sensitivity and signal-to-noise ratio has been a persistent hurdle in achieving accurate pH measurements in real time, particularly in complex liquid environments. This manuscript introduces what we believe to be a novel solution in the form of a miniaturized pH sensor utilizing an assembly-free ball lens on a tapered multimode optical fiber (TMMF), offering the potential to revolutionize pH sensing in biochemical applications. A multimode optical fiber (MMF) was subjected to tapering processes, leading to the creation of an ultra-thin needle-like structure with a cross-sectional diameter of about …