Engineering Commons^™

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 …

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, …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Unsupervised Blstm-Based Electricity Theft Detection With Training Data Contaminated, Qiushi Liang, Shengjie Zhao, Jiangfan Zhang, Hao Deng

Electrical and Computer Engineering Faculty Research & Creative Works

Electricity theft can cause economic damage and even increase the risk of outage. Recently, many methods have implemented electricity theft detection on smart meter data. However, how to conduct detection on the dataset without any label still remains challenging. In this article, we propose a novel unsupervised two-stage approach under the assumption that the training set is contaminated by attacks. Specifically, the method consists of two stages: (1) a Gaussian mixture model is employed to cluster consumption patterns with respect to different habits of electricity usage, and with the goal of improving the accuracy of the model in the posterior …

Enhanced Sensitivity And Robustness In An Embeddable Strain Sensor Using Microwave Resonators, Yan Tang, Yizheng Chen, Qi Zhang, Biyao Shi, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Introduces A Novel, Cost-Effective, And Durable Strain Sensor With Exceptional Sensitivity And Resolution, Utilizing An Open-Ended Hollow Coaxial Cable Resonator (OE-HCCR). The OE-HCCR Is Characterized By Two Reflective Elements: A Metal Post That Connects The Inner And Outer Conductors At The Signal's Entrance, And A Terminal Flange Near The Coaxial Line's End, Establishing A Variable Gap. The Sensor Employs A Paired Anchor Ring In Conjunction With The Terminal Flange To Transduce And Direct Strain. Variations In The Gap Alter The Resonant Frequency By Modulating The Phase Of The Reflection Coefficient At The Cable's Terminus. Initial Calibration Revealed A …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

A Novel Physics-Assisted Genetic Algorithm For Decoupling Capacitor Optimization, Li Jiang, Ling Zhang, Shurun Tan, Da Li, Chulsoon Hwang, Jun Fan, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes a new physics-assisted genetic algorithm (PAGA) for decoupling capacitor (decap) optimization in power distribution networks (PDNs), which is a highly efficient approach to minimizing the number of decaps within an enormous search space. In the proposed PAGA method, the priority of the decap ports is first determined based on their physical loop inductances. Then, an initial solution is quickly obtained by placing decaps sequentially on the port with the highest priority. Subsequently, a GA with prior physical knowledge is developed to find better decap solutions progressively. A port removal scheme that eliminates the low-priority ports and a …

Ultra-Fast Annealing Improves Snr And Long-Term Stability Of A Highly Multiplexed Line-By-Line Fbg Array Inscribed By Femtosecond Laser In A Coreless Fiber For Extreme-Temperature Applications, Farhan Mumtaz, Bohong Zhang, Jeffrey D. Smith, Ronald J. O'Malley, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This study reports the fabrication of an 4th-order line-by-line Fiber Bragg Gratings (FBG) array using femtosecond laser inscription within a highly multimode coreless optical fiber, with a particular focus on achieving substantial multiplexing capabilities. An ultra-fast annealing procedure is employed, resulting in an impressive enhancement of the FBG sensor's fringe visibility by approximately 13 dB, signifying a notable improvement of approximately ~4 dB. This substantial enhancement contributes to the long-term stability and performance of the multiplexed FBG array in extreme temperature conditions. The systematic fabrication approach employed for the multiplexed FBG array guarantees a high signal-to-noise ratio (SNR) for each …

A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, …

Statistical Eye Diagrams For High-Speed Interconnects Of Packages: A Review, Junyong Park, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

An eye diagram, a critical metric in signal integrity analysis for high-speed interconnects such as packages, interposer, and printed circuit boards (PCBs), is generated by superposition of the received waveform. Obtaining an eye diagram is time-consuming, thus signal integrity analysis is inefficient. This article reviews that have been proposed to overcome this limitation. The statistical eye diagram provides a probability distribution depending on a sampling time and voltage, therefore it can be expanded to other metrics, such as the bit-error rate and shmoo plot. This article introduces previous research on statistical eye diagrams applied to complementary metal-oxide-semiconductors (CMOSs), noise, and …

A Segmentation Approach For Predicting Plane Wave Coupling To Pcb Structures, Shengxuan Xia, James Hunter, Aaron Harmon, Ahmed M. Hassan, Victor Khilkevich, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Evaluating the far-field radio frequency (RF) susceptibility of electronic devices often depends on extensive testing or full wave simulations. These methods are effective when complete system information is available but require substantial time and resources to evaluate a large number of variations in system configurations, where trace routings, integrated circuit (IC) package styles, trace terminations, arrival angle, and polarization of incoming wave, etc., are varied from one configuration to another. The goal of the following article is to develop simulation techniques for studying the statistical characteristics of coupling to typical printed circuit board (PCB) structures. Simulation time can be reduced …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …

Cascaded Weak Reflector Coaxial Cable Structure For Point And Distributed Large-Strain Sensing, Chen Zhu, Osamah Alsalman, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we present a truly distributed sensing modality based on a cascaded weak reflector coaxial cable structure (CWR-CCS) for large strain measurements. Compared to an optical fiber, a coaxial cable is much more robust and has a larger strain capability to survive harsh conditions, which may enable important applications in structural health monitoring. By drilling serial shallow holes into a commercial flexible coaxial cable perturbing the local impedance along its axial direction, CWRs along the coaxial cable are introduced due to impedance mismatch, forming the CWR-CCS. Gating a certain number of sequential reflectors in the time-domain reflection signal …

Modeling Wideband Radiated Emissions From Pcbs In Shielding Enclosures Based On Single-Plane Phaseless Near-Field Scanning, Zhifei Xiao, Zi An Wang, Li Jun Jiang, Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a wideband phase less source reconstruction method (SRM) for the evaluation of the radiated emissions from printed circuit boards (PCBs) in shielding enclosures. The PCBs are modeled with equivalent dipoles, and the numerical Green's function (NGF) is deployed to establish the relationship between the equivalent source and the input near-field (NF) data, thus the electromagnetic influences of the surrounding environments comprehensively accounted. This method only requires magnitude-only NF scanning over a single plane, thus significantly decreasing the NF measurement difficulty. To remedy the lack of phase information of the NF data, the input NF data are equally …

Open-Ended Hollow Coaxial Cable Resonance Sensing Via Permittivity Fluctuations For Applications To Exhaled Breath Health Monitoring, Peter Henry Holtmann

Masters Theses

"We report a pre-diagnostics breath analyzer based on the open-ended hollow coaxial cable resonator (OE-HCCR) to achieve real-time differentiation of healthy and compromised lung function through electromagnetic analysis of exhaled breath matter. A two-phase amplification scheme of electromagnetic signatures metered via the S11 phase curve offers high sensitivity to permittivity fluctuations in exposed airflow. The mathematical model of the breath analyzer is explained, followed by experimental validation of numerical findings from previous work. The collection of breath transient signals is performed through monitoring the reflected power magnitude and phase of the scattering parameter (S11) at the resonance frequency. Material fluctuations …

In Situ High Temperature Fiber-Optic Raman Sensor For Industrial Applications, Bohong Zhang

Doctoral Dissertations

"Continuous casting in steel production uses specially developed oxyfluoride glasses (mold fluxes) to lubricate a mold and control the solidification of the steel in the mold. The composition of the flux impacts properties, including basicity, viscosity, and crystallization rate, all of which affect the stability of the casting process and the quality of the solidified steel. However, mold fluxes interact with steel during the casting process, resulting in flux chemistry changes that must be considered in the flux design. Currently, the chemical composition of mold flux must be determined by extracting flux samples from the mold during casting and then …

Modeling And Analysis Methods For Esd And Emi Problems, Xin Yan

Doctoral Dissertations

"Electrostatic discharge (ESD) failures and Electromagnetic interference (EMI) problems are becoming more critical in electronic devices and large systems. In this work, four studies are presented to model and analyze ESD and EMI problems.

First, a simplified physical-based model for deep-snapback transient voltage suppressors (TVS) is developed. While based on physics, the number of parameters and components is minimized. Results show that the proposed model captures the most important behaviors of the TVS response using a limited number of parameters, allowing the model to be tuned relatively easily using data obtained only from package-level transient and quasi-static measurements. Second, a …

Controller Area Network With Flexible Data Rate (Can Fd) Eye Diagram Prediction, Junyong Park, Manho Lee, Shinyoung Park, Jonghoon Kim, Joungho Kim, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

A method for predicting the eye diagram for a controller area network with a flexible data rate (CAN FD) is proposed in this article. A CAN FD changes a data rate according to the status to overcome the limitation of latency. In other words, when data to be transmitted are accumulated, the CAN FD increases the data rate up to 5 Mb/s. The CAN FD has a bus topology consisting of multiple electronic control units, which results in a significant amount of signal reflection. Thus, the above causes the signal integrity analysis uncertain. To avoid this, this article proposes a …