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Electrical and Computer Engineering
Electrical and Computer Engineering Faculty Research & Creative Works
- Keyword
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- Machine learning (6)
- Convolutional neural network (3)
- Isolation (3)
- Characteristic impedance (2)
- Distributed sensing (2)
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- Dynamic Mode Decomposition (DMD) (2)
- Feature selection (2)
- Ferrimagnetic (2)
- Mutual coupling (2)
- Neural Network (2)
- Notch filter (2)
- Optical frequency domain reflectometry (2)
- Power threshold (2)
- Source reconstruction method (2)
- Stripline (2)
- Subwavelength imaging (2)
- Virtual Synchronous Generator (2)
- AMC (1)
- ANN (1)
- Agricultural Field (1)
- Ancestry Classification (1)
- Antenna (1)
- Applied pressure (1)
- Autism spectrum disorder (1)
- Automation Of Manufacturing And Assembling Process (1)
- Backscattering (1)
- Band notch dipole (1)
- Bandit algorithm (1)
- Bandwidth (1)
- Battery Technology (1)
Articles 1 - 30 of 53
Full-Text Articles in Engineering
Energy-Performance Scalability Analysis Of A Novel Quasi-Stochastic Computing Approach, Prashanthi Metku, Ramu Seva, Minsu Choi
Energy-Performance Scalability Analysis Of A Novel Quasi-Stochastic Computing Approach, Prashanthi Metku, Ramu Seva, Minsu Choi
Electrical and Computer Engineering Faculty Research & Creative Works
Stochastic computing (SC) is an emerging low-cost computation paradigm for efficient approximation. It processes data in forms of probabilities and offers excellent progressive accuracy. Since SC's accuracy heavily depends on the stochastic bitstream length, generating acceptable approximate results while minimizing the bitstream length is one of the major challenges in SC, as energy consumption tends to linearly increase with bitstream length. To address this issue, a novel energy-performance scalable approach based on quasi-stochastic number generators is proposed and validated in this work. Compared to conventional approaches, the proposed methodology utilizes a novel algorithm to estimate the computation time based on …
Structured Iterative Hard Thresholding For Categorical And Mixed Data Types, Thy Nguyen, Tayo Obafemi-Ajayi
Structured Iterative Hard Thresholding For Categorical And Mixed Data Types, Thy Nguyen, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
In many applications, data exists in a mixed data type format, i.e. a combination of nominal (categorical) and numerical features. A common practice for working with categorical features is to use an encoding method to transform the discrete values into numeric representation. However, numeric representation often neglects the innate structures in categorical features, potentially degrading the performance of learning algorithms. Utilizing the numeric representation could also limit interpretation of the learned model, such as finding the most discriminative categorical features or filtering irrelevant attributes. In this work, we extend the iterative hard thresholding (IHT) algorithm to quantify the structure of …
Design And Sensitivity Analysis Of Ebg Stripline Common-Mode Filters, Marina Y. Koledintseva, Sergiu Radu, Joseph Nuebel
Design And Sensitivity Analysis Of Ebg Stripline Common-Mode Filters, Marina Y. Koledintseva, Sergiu Radu, Joseph Nuebel
Electrical and Computer Engineering Faculty Research & Creative Works
Workflow of electromagnetic bandgap (EBG) common-mode (CM) filter design of edge-coupled differential pairs on a printed circuit board (PCB) and sensitivity of its characteristics to variations of geometrical and material parameters are discussed. A number of simple 20-GHz EBG CM notch filters for differential strip line pairs are designed using full-wave numerical electromagnetic modeling, fabricating, and testing. The cases of one and two strip line differential pairs crossing the EBG patches are considered. The modeled and measured mixed-mode S-parameters are analyzed as functions of geometrical parameters, including size and number of EBG patches, gaps between them, geometry and position of …
Analysis And Introduction Of Effective Permeability With Additional Air-Gaps On Wireless Power Transfer Coils For Electric Vehicle Based On Sae J2954 Recommended Practice, Dongwook Kim, Hongseok Kim, Anfeng Huang, Qiusen He, Hanyu Zhang, Seungyoung Ahn, Yuyu Zhu, Jun Fan
Analysis And Introduction Of Effective Permeability With Additional Air-Gaps On Wireless Power Transfer Coils For Electric Vehicle Based On Sae J2954 Recommended Practice, Dongwook Kim, Hongseok Kim, Anfeng Huang, Qiusen He, Hanyu Zhang, Seungyoung Ahn, Yuyu Zhu, Jun Fan
Electrical and Computer Engineering Faculty Research & Creative Works
The wireless power transfer (WPT) method for electric vehicles (EVs) is becoming more popular, and to ensure the interoperability of WPT systems, the Society of Automotive Engineers (SAE) established the J2954 recommended practice (RP). It includes powering frequency, electrical parameters, specifications, testing procedures, and other contents for EV WPT. Specifically, it describes the ranges of self-inductances of the transmitting coil, the receiving coil, and coupling coefficient (k), as well as the impedance matching values of the WPT system. Following the electrical parameters listed in SAE J2954 RP is crucial to ensure the EV wireless charging system is interoperable. This paper …
Nonlinear Loss Model In Absorptive-Type Ferrite Frequency-Selective Limiters, Anatoliy O. Boryssenko, Scott M. Gillette, Marina Y. Koledintseva
Nonlinear Loss Model In Absorptive-Type Ferrite Frequency-Selective Limiters, Anatoliy O. Boryssenko, Scott M. Gillette, Marina Y. Koledintseva
Electrical and Computer Engineering Faculty Research & Creative Works
Absorptive-type ferrite-based frequency-selective limiters (FSLs) utilize nonlinear (NL) phenomena in magnetized ferrites to provide real-time analog signal processing of RF/microwave electromagnetic (EM) signals. There are no commercially available modeling tools that simulate these interactions, and the development and optimization of FSLs are largely done experimentally. FSL modeling and design is complicated by NL, multiscale, and Multiphysics nature of operation. In this article, an NL loss model in a ferrite is proposed and implemented in an efficient numerical algorithm. The equivalent linear magnetic loss tangent is represented in a closed form. A full-wave numerical EM model with high-fidelity meshing is set …
E-Mobility -- Advancements And Challenges, Aswad Adib, Khurram K. Afridi, Mahshid Amirabadi, Fariba Fateh, Mehdi Ferdowsi, Brad Lehman, Laura H. Lewis, Behrooz Mirafzal, Maryam Saeedifard, Mohammad B. Shadmand, Pourya Shamsi
E-Mobility -- Advancements And Challenges, Aswad Adib, Khurram K. Afridi, Mahshid Amirabadi, Fariba Fateh, Mehdi Ferdowsi, Brad Lehman, Laura H. Lewis, Behrooz Mirafzal, Maryam Saeedifard, Mohammad B. Shadmand, Pourya Shamsi
Electrical and Computer Engineering Faculty Research & Creative Works
Mobile platforms cover a broad range of applications from small portable electric devices, drones, and robots to electric transportation, which influence the quality of modern life. The end-to-end energy systems of these platforms are moving toward more electrification. Despite their wide range of power ratings and diverse applications, the electrification of these systems shares several technical requirements. Electrified mobile energy systems have minimal or no access to the power grid, and thus, to achieve long operating time, ultrafast charging or charging during motion as well as advanced battery technologies are needed. Mobile platforms are space-, shape-, and weight-constrained, and therefore, …
Two-Step Enhanced Deep Learning Approach For Electromagnetic Inverse Scattering Problems, He Ming Yao, Wei E.I. Sha, Lijun Jiang
Two-Step Enhanced Deep Learning Approach For Electromagnetic Inverse Scattering Problems, He Ming Yao, Wei E.I. Sha, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
In this letter, a new deep learning (DL) approach is proposed to solve the electromagnetic inverse scattering (EMIS) problems. The conventional methods for solving inverse problems face various challenges including strong ill-conditions, high contrast, expensive computation cost, and unavoidable intrinsic nonlinearity. To overcome these issues, we propose a new two-step machine learning based approach. In the first step, a complex-valued deep convolutional neural network is employed to retrieve initial contrasts (permittivity's) of dielectric scatterers from measured scattering data. In the second step, the previously obtained contrasts are input into a complex-valued deep residual convolutional neural network to refine the reconstruction …
Biomarker Discovery In Inflammatory Bowel Diseases Using Network-Based Feature Selection, Mostafa Abbas, John Matta, Thanh Le, Halima Bensmail, Tayo Obafemi-Ajayi, Vasant Honavar, Yasser El-Manzalawy
Biomarker Discovery In Inflammatory Bowel Diseases Using Network-Based Feature Selection, Mostafa Abbas, John Matta, Thanh Le, Halima Bensmail, Tayo Obafemi-Ajayi, Vasant Honavar, Yasser El-Manzalawy
Electrical and Computer Engineering Faculty Research & Creative Works
Reliable identification of Inflammatory biomarkers from metagenomics data is a promising direction for developing non-invasive, cost-effective, and rapid clinical tests for early diagnosis of IBD. We present an integrative approach to Network-Based Biomarker Discovery (NBBD) which integrates network analyses methods for prioritizing potential biomarkers and machine learning techniques for assessing the discriminative power of the prioritized biomarkers. Using a large dataset of new-onset pediatric IBD metagenomics biopsy samples, we compare the performance of Random Forest (RF) classifiers trained on features selected using a representative set of traditional feature selection methods against NBBD framework, configured using five different tools for inferring …
Data Collection And Analysis Techniques For Solar Car Telemetry Data, Michael Rouse, Miranda Sauer, Kurt Louis Kosbar
Data Collection And Analysis Techniques For Solar Car Telemetry Data, Michael Rouse, Miranda Sauer, Kurt Louis Kosbar
Electrical and Computer Engineering Faculty Research & Creative Works
Data collected from a solar car is monitored in real-time, which allows for intelligent decision making, efficient debugging, and high-quality testing for solar car teams. This paper compares three databases (MySQL, PostgreSQL, and MongoDB) to determine the optimal database system that should be used at solar car competitions. Each database system was tested using simulated solar car data to measure read and write speeds, and quality of performance on a low-power computer. Data were analyzed and displayed with custom interfaces to improve the user experience at solar car competitions.
Dual Heuristic Dynamic Programing Control Of Grid-Connected Synchronverters, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi
Dual Heuristic Dynamic Programing Control Of Grid-Connected Synchronverters, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi
Electrical and Computer Engineering Faculty Research & Creative Works
A new approach to control a grid-connected synchronverter by using a dual heuristic dynamic programing (DHP) design is presented. The disadvantages of conventional synchronverter controller such as the challenges to cope with nonlinearity, uncertainties, and non-inductive grids are discussed. To deal with the aforementioned challenges a neural network–based adaptive critic design is introduced to optimize the associated cost function. The characteristic of the neural networks facilitates the performance under uncertainties and unknown parameters (e.g. different power angles). The proposed DHP design includes three neural networks: system NN, action NN, and critic NN. The simulation results compare the performance of the …
Neural Network Predictive Controller For Grid-Connected Virtual Synchronous Generator, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch
Neural Network Predictive Controller For Grid-Connected Virtual Synchronous Generator, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a neural network predictive controller is proposed to regulate the active and the reactive power delivered to the grid generated by a three-phase virtual inertia-based inverter. The concept of the conventional virtual synchronous generator (VSG) is discussed, and it is shown that when the inverter is connected to non-inductive grids, the conventional PI-based VSGs are unable to perform acceptable tracking. The concept of the neural network predictive controller is also discussed to replace the traditional VSGs. This replacement enables inverters to perform in both inductive and non-inductive grids. The simulation results confirm that a well-trained neural network …
Heuristic Dynamic Programming For Adaptive Virtual Synchronous Generators, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch
Heuristic Dynamic Programming For Adaptive Virtual Synchronous Generators, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. …
Analysis Of Electromagnetic Vortex Beams Using Modified Dynamic Mode Decomposition In Spatial Angular Domain, Yanming Zhang, Menglin L.N. Chen, Li (Lijun) Jun Jiang
Analysis Of Electromagnetic Vortex Beams Using Modified Dynamic Mode Decomposition In Spatial Angular Domain, Yanming Zhang, Menglin L.N. Chen, Li (Lijun) Jun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
The orbital angular momentum (OAM) modes of electromagnetic (EM) beams are utilized for multiplexing in communication systems, where each OAM mode is encoded with data. The OAM index, or the so-called topological charge, identifies each OAM mode. Recently, the amplitude of OAM mode has also been used as another modulation format. Therefore, accurate extraction of not only the OAM index but also the corresponding amplitude is required. In this paper, a modified dynamic mode decomposition (DMD) algorithm is proposed to analyze the OAM modes. We show that accurate topological charges and high-resolution amplitude patterns of both single OAM mode and …
Iot-Based Cyber-Physical Communication Architecture: Challenges And Research Directions, Md Masud Rana, Rui Bo
Iot-Based Cyber-Physical Communication Architecture: Challenges And Research Directions, Md Masud Rana, Rui Bo
Electrical and Computer Engineering Faculty Research & Creative Works
In order to provide intelligent services, the Internet of Things (IoT) facilitates millions of smart cyber-physical devices to be enabled with network connectivity to sense, collect, process, and exchange information. Unfortunately, the traditional communication infrastructure is vulnerable to cyber attacks and link failures, so it is a challenging task for the IoT to explore these applications. In order to begin research and contribute into the IoT-based cyber-physical digital world, one will need to know the technical challenges and research opportunities. In this study, several key technical challenges and requirements for the IoT communication systems are identified. Basically, privacy, security, intelligent …
Discontinuous Galerkin Vs. Ie Method For Electromagnetic Scattering From Composite Metallic And Dielectric Structures, Y.-Y. Zhu, Q.-M. Cai, R. Zhang, X. Cao, Y.-W. Zhao, B. Gao, Jun Fan
Discontinuous Galerkin Vs. Ie Method For Electromagnetic Scattering From Composite Metallic And Dielectric Structures, Y.-Y. Zhu, Q.-M. Cai, R. Zhang, X. Cao, Y.-W. Zhao, B. Gao, Jun Fan
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, an efficient volume surface integral equation (VSIE) method with nonconformal discretization is developed for the analysis of electromagnetic scattering from composite metallic and dielectric (CMD) structures. This VSIE scheme utilizes curved tetrahedral (triangular) elements for volume (surface) modeling and the associated CRWG (CSWG) basis functions for volume current (surface) current modeling. Further, a discontinuous Galerkin (DG) volume integral equation (VIE) method and a DG surface integral equation (SIE) approach are adopted for dielectric and metallic parts, respectively, which allow both conformal and nonconformal volume/surface discretization improving meshing flexibility considerably. Numerical results are provided to demonstrate the accuracy, …
Compact Endfire Coupled-Mode Patch Antenna With Vertical Polarization, Haozhan Tian, Lijun Jiang, Tatsuo Itoh
Compact Endfire Coupled-Mode Patch Antenna With Vertical Polarization, Haozhan Tian, Lijun Jiang, Tatsuo Itoh
Electrical and Computer Engineering Faculty Research & Creative Works
Coupled-mode patch antenna (CMPA) is able to reform the beam by manipulating the phase of the fringing fields at the edges. In this paper, a method is proposed to realize endfire radiation with vertical polarization based on the concept of CMPA. Besides the phase controlled by the coupling, the asymmetric feeding introduces additional phase shift to one of the radiation slots, which makes the beam pointing to the forward endfire direction within the whole band. The ground of the antenna is truncated to be the same size of the top patch, which eliminates the undesired effect of the ground edge …
Cascaded Fabry-Pérot Interferometers With Vernier Effect For Gas Pressure Measurement, Hongfeng Lin, Yanyan Xu, Farhan Mumtaz, Yutang Dai, Ai Zhou
Cascaded Fabry-Pérot Interferometers With Vernier Effect For Gas Pressure Measurement, Hongfeng Lin, Yanyan Xu, Farhan Mumtaz, Yutang Dai, Ai Zhou
Electrical and Computer Engineering Faculty Research & Creative Works
A sensitivity enhanced gas pressure sensor with Vernier effect is proposed in this paper. The sensor is a cascade configuration which includes two Fabry-Pérot interferometers (FPIs) with different free spectrum range (FSR). Each Fabry-Pérot interferometer is fabricated by inserting a piece of hollow core fiber (HCF) in between two sections of single mode fiber (SMF). Femtosecond laser is applied for drilling an opening on the HCF of sensing FPI to make the air hole interact with outside environment, while the air hole of the reference FPI is kept closed. Gas pressure response is measured by monitoring the wavelength shift of …
Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha
Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha
Electrical and Computer Engineering Faculty Research & Creative Works
While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of …
Microwave Reflectometry For Physical Inspections, Mohammad Tayeb Ahmad Ghasr, R. Zoughi, Satyajeet Shinde, Sasi Jothibasu
Microwave Reflectometry For Physical Inspections, Mohammad Tayeb Ahmad Ghasr, R. Zoughi, Satyajeet Shinde, Sasi Jothibasu
Electrical and Computer Engineering Faculty Research & Creative Works
Utilizing microwave reflections to compare a reference device with counterfeit and/or aging devices under test. The reflection from the device under test varies based on certain properties, which results in each device having a unique and intrinsic electromagnetic signature. Comparisons of the electromagnetic signature of the device under test to the electromagnetic signature of a reference device enable evaluating the acceptability of the device under test.
Recurrent Network And Multi-Arm Bandit Methods For Multi-Task Learning Without Task Specification, Thy Nguyen, Tayo Obafemi-Ajayi
Recurrent Network And Multi-Arm Bandit Methods For Multi-Task Learning Without Task Specification, Thy Nguyen, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
This paper addresses the problem of multi-task learning (MTL) in settings where the task assignment is not known. We propose two mechanisms for the problem of inference of task's parameter without task specification: parameter adaptation and parameter selection methods. In parameter adaptation, the model's parameter is iteratively updated using a recurrent neural network (RNN) learner as the mechanism to adapt to different tasks. For the parameter selection model, a parameter matrix is learned beforehand with the task known apriori. During testing, a bandit algorithm is utilized to determine the appropriate parameter vector for the model on the fly. We explored …
Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta
Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta
Electrical and Computer Engineering Faculty Research & Creative Works
This paper applies machine learning feature selection techniques to the REGARDS stroke-related dataset to identify health-related biomarkers. A data-driven methodological framework is presented to evaluate multiple feature selection methods. In applying the framework, three classifiers are chosen in conjunction with two wrappers, and their performance with diverse classification targets such as Current Smoker, Current Alcohol Use, and Deceased is evaluated. The performance across logistic regression, random forest and naïve Bayes classifier methods, as quantified by the ROC Area Under Curve metric and selected features, was similar. However, significant differences were observed in running time. Performance of the selected features was …
Multi-Objective Optimization Approach To Find Biclusters In Gene Expression Data, Jeffrey Dale, Junya Zhao, Tayo Obafemi-Ajayi
Multi-Objective Optimization Approach To Find Biclusters In Gene Expression Data, Jeffrey Dale, Junya Zhao, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
Gene expression levels of organisms are measured by DNA microarrays. Finding biclusters in gene expression matrices provides invaluable information about effects of disease at the genetic level. These biclusters could identify which genes are up-regulated/down-regulated under certain conditions. This paper investigates a methodology for evolutionary-based biclustering using the NSGA-II algorithm. It also presents an improvement to the recovery and relevance external validation metrics as well as a new method for synthetic data generation for biclustering. Results obtained demonstrate its effectiveness in discovering useful biclusters on varied synthetic data when applied with the average Spearman's rho measure as the fitness function.
Distributed Fiber-Optic Pressure Sensor Based On Bourdon Tubes Metered By Optical Frequency-Domain Reflectometry, Chen Zhu, Yiyang Zhuang, Yizhen Chen, Rex E. Gerald Ii, Jie Huang
Distributed Fiber-Optic Pressure Sensor Based On Bourdon Tubes Metered By Optical Frequency-Domain Reflectometry, Chen Zhu, Yiyang Zhuang, Yizhen Chen, Rex E. Gerald Ii, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
We report a distributed fiber-optic pressure sensor based on Bourdon tubes using Rayleigh backscattering metered by optical frequency-domain reflectometry (OFDR). In the proposed sensor, a piece of single-mode fiber (SMF) is attached to the concave surfaces of Bourdon tubes using a thin layer of epoxy. The strain profiles along the concave surface of the Bourdon tube vary with applied pressure, and the strain variations are transferred to the attached SMF through the epoxy layer, resulting in spectral shifts in the local Rayleigh backscattering signals. By monitoring the local spectral shifts of the OFDR system, the pressure applied to the Bourdon …
A Novel Data-Driven Analysis Method For Nonlinear Electromagnetic Radiations Based On Dynamic Mode Decomposition, Yanming Zhang, Lijun Jiang
A Novel Data-Driven Analysis Method For Nonlinear Electromagnetic Radiations Based On Dynamic Mode Decomposition, Yanming Zhang, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
Nonlinear effects generated in complex electronic systems such as cell phones and computers cause broadband electromagnetic radiations. They are very difficult to model but could be key contributors to the radiated spurious emission (RSE) and radio frequency interference (RFI). In this paper, a novel data-driven characterization method is proposed to analyze the transient responses of the nonlinear circuits and their nonlinear electromagnetic radiations. It employs the dynamic mode decomposition (DMD) to simultaneously extract the temporal patterns and their corresponding dynamic modes. The temporal patterns show high order harmonics generated by the nonlinearity. Then these temporal spatial coherent patterns could provide …
Spatially Continuous Strain Monitoring Using Distributed Fiber Optic Sensors Embedded In Carbon Fiber Composites, Sasi Jothibasu, Yang Du, Sudharshan Anandan, Gurjot S. Dhaliwal, Rex E. Gerald Ii, Steve Eugene Watkins, K. Chandrashekhara, Jie Huang
Spatially Continuous Strain Monitoring Using Distributed Fiber Optic Sensors Embedded In Carbon Fiber Composites, Sasi Jothibasu, Yang Du, Sudharshan Anandan, Gurjot S. Dhaliwal, Rex E. Gerald Ii, Steve Eugene Watkins, K. Chandrashekhara, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
A distributed fiber optic strain sensor based on Rayleigh backscattering, embedded in a fiber-reinforced polymer composite, has been demonstrated. The optical frequency domain reflectometry technique is used to analyze the backscattered signal. The shift in the Rayleigh backscattered spectrum is observed to be linearly related to the change in strain of the composite material. The sensor (standard single-mode fiber) is embedded between the layers of the composite laminate. A series of tensile loads is applied to the laminate using an Instron testing machine, and the corresponding strain distribution of the laminate is measured. The results show a linear response indicating …
Stochastic Resonance Enables Bpp/Log∗ Complexity And Universal Approximation In Analog Recurrent Neural Networks, Emmett Redd, A. Steven Younger, Tayo Obafemi-Ajayi
Stochastic Resonance Enables Bpp/Log∗ Complexity And Universal Approximation In Analog Recurrent Neural Networks, Emmett Redd, A. Steven Younger, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
Stochastic resonance (SR) is a natural process that without limit increases the precision of signal measurements in biological and physical sciences. Most artificial neural networks (NNs) are implemented on digital computers of fixed precision. A NN accessing universal approximation and a computational complexity class more powerful that of a Turing machine needs analog signals utilizing SR's limitless precision increase. This paper links an analog recurrent (AR) NN theorem, SR, BPP/log∗ (a physically realizable, super-Turing computation class), and universal approximation so NNs following them can be made computationally more powerful. An optical neural network mimicking chaos indicates super-Turing computation has been …
Impedance Mismatch Effects In Microstrip And Stripline Ebg Common-Mode Filters, Marina Y. Koledintseva, Sergiu Radu, Joe Nuebel
Impedance Mismatch Effects In Microstrip And Stripline Ebg Common-Mode Filters, Marina Y. Koledintseva, Sergiu Radu, Joe Nuebel
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, impedance mismatch effects on the characteristics of common-mode (CM) electromagnetic bandgap (EBG) filters are studied using 3D full-wave numerical simulations. Herein, the terminations are fixed at 50 Ohms, and the effect of the differential line impedance variations are studied. Two types of CM EBG filters are considered in this work, both are designed using standard printed circuit board technology. The first group contains microstrip (MS) differential pairs running above the EBG plane, and the second group contains strip line (SL) differential pairs running on one of the layers next to the EBG plane. It is shown that …
Analysis Of Sea Clutter Using Dynamic Mode Decomposition, Yanming Zhang, Lijun Jiang, Hong Tat Ewe
Analysis Of Sea Clutter Using Dynamic Mode Decomposition, Yanming Zhang, Lijun Jiang, Hong Tat Ewe
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel method based on a dynamic mode decomposition (DMD) for sea clutter analysis is proposed. It extracts the temporal patterns and corresponding dynamic modes from the sea clutter simultaneously. Moreover, the temporal patterns display similar properties with traditional analysis using Doppler spectrum. The corresponding dynamic modes represent the cardinal feature within the sea clutter. To demonstrate the effectiveness of the proposed method, the measured sea clutter data collected by IPIX radar is analyzed. It is shown that DMD spectrum has the same frequency-shift and similar amplitude with the Doppler Spectrum. In addition, the Probability Density Function …
Genotype Combinations Linked To Phenotype Subgroups In Autism Spectrum Disorders, Junya Zhao, Thy Nguyen, Jonathan Kopel, Perry B. Koob, Donald A. Adieroh, Tayo Obafemi-Ajayi
Genotype Combinations Linked To Phenotype Subgroups In Autism Spectrum Disorders, Junya Zhao, Thy Nguyen, Jonathan Kopel, Perry B. Koob, Donald A. Adieroh, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
This paper investigates a computational model that allows for systematic comparison of phenotype data with genotype (Single Nucleotide Polymorphisms (SNPs)) data based on machine learning techniques to identify discriminant genotype markers associated with the phenotypic subgroups. The proposed discriminant SNP identifier model is empirically evaluated using Autism Spectrum Disorder (ASD) simplex sample. Six phenotype markers were selected to cluster the sample in a hexagonal lattice format yielding five multidimensional subgroups based on extremities of the phenotype markers. The SNP selection model includes random subspace selection of SNPs in conjunction with feature selection algorithms to determine which set of SNPs were …
Real‐Time Overhead Power Line Sag Monitoring, Jie Huang, Rui Bo
Real‐Time Overhead Power Line Sag Monitoring, Jie Huang, Rui Bo
Electrical and Computer Engineering Faculty Research & Creative Works
System and method for determining real-time sag and shape information of an electrical power line based on strain distribution along a length of an optical fiber associated with the power line. An embedded fiber coupled to an overhead transmission line measures strain using the backscatter of an optical signal, the optical signal is then interrogated using an interferometer.