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Electrical and Computer Engineering Faculty Research & Creative Works

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Dual Heuristic Dynamic Programing Control Of Grid-Connected Synchronverters, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi Oct 2019

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 …

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Neural Network Predictive Controller For Grid-Connected Virtual Synchronous Generator, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch Oct 2019

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 …

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Heuristic Dynamic Programming For Adaptive Virtual Synchronous Generators, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch Oct 2019

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. …

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Gan-Based Room Temperature Spintronics For Next Generation Low Power Consumption Electronic Devices, Vishal Saravade, Amirhossein Ghods, Andrew P. Woode, Chuanle Zhou, Ian Ferguson Oct 2019

Gan-Based Room Temperature Spintronics For Next Generation Low Power Consumption Electronic Devices, Vishal Saravade, Amirhossein Ghods, Andrew P. Woode, Chuanle Zhou, Ian Ferguson

Electrical and Computer Engineering Faculty Research & Creative Works

There has been an exponential growth in the microelectronics industry over the last 70 years with a consistent miniaturization of transistors' size and increase in the speed and on-chip transistors density with reasonable power consumption, as seen in Figure 1 [1]. This trend will saturate soon especially due to the unintended thermal noise that is dissipated, as the density of transistors on the chips increase and as the corresponding electronics approach their physical limits. There is a need to implement new processing and computing techniques [2] with more compact size, lower power consumption and enhanced performance. Neuromorphic computing mimics the …

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Stochastic Partial Inductance For The Power Integrity Analysis By Polynomial Chaos Expansion, Haimi Qiu, Lijun Jiang, Albert Ruehli Oct 2019

Stochastic Partial Inductance For The Power Integrity Analysis By Polynomial Chaos Expansion, Haimi Qiu, Lijun Jiang, Albert Ruehli

Electrical and Computer Engineering Faculty Research & Creative Works

Partial element equivalent circuit (PEEC) model has attracted great interests to overcome challenges in the power integrity analysis. However, the increasing miniaturization in electronics makes the uncertainty effect an ever more central issue to accurately estimate the inductance in the power distribution networks (PDNs) of high-speed PCBs. In this paper, we propose a stochastic partial inductance modeling approach to quantify the stochastic inductance. Specifically, the deterministic partial inductance is re-formulated using the polynomial chaos expansion. Following the Galerkin projection and polynomial orthogonality, stochastic partial inductance is represented by polynomial bases, which significantly reduces the complexity of the system and leads …

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Impulse Response For Full Wave Peec Models Avoiding Late Time Instability, Albert E. Ruehli, Luigi Lombardi, Giulio Antonini, Ye Tao, Michel S. Nakhla, Francesco Ferranti Oct 2019

Impulse Response For Full Wave Peec Models Avoiding Late Time Instability, Albert E. Ruehli, Luigi Lombardi, Giulio Antonini, Ye Tao, Michel S. Nakhla, Francesco Ferranti

Electrical and Computer Engineering Faculty Research & Creative Works

Late time instability is a key issue for obtaining time domain solutions using Computational Electromagnetic (CEM) full-wave techniques. However, impulse inputs lead to a convenient way to cascade time domain solutions for many problems. In this paper we show that stable impulse response solutions can be obtained for full-wave PEEC (Partial Element Equivalent Circuit) models which include retarded partial elements. The conventional time stepping technique is replaced by using the so called NILT (Numerical Inverse Laplace Transform) technique. We show that the late time unstable poles in the EM formulation are not excited by the NILT based solution.

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Analysis Of Electromagnetic Vortex Beams Using Modified Dynamic Mode Decomposition In Spatial Angular Domain, Yanming Zhang, Menglin L.N. Chen, Li (Lijun) Jun Jiang Sep 2019

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 …

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Iot-Based Cyber-Physical Communication Architecture: Challenges And Research Directions, Md Masud Rana, Rui Bo Sep 2019

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 …

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Editorial: Bio-Inspired Audio Processing, Models And Systems, Shih Chii Liu, John G. Harris, Mounya Elhilali, Malcolm Slaney Sep 2019

Editorial: Bio-Inspired Audio Processing, Models And Systems, Shih Chii Liu, John G. Harris, Mounya Elhilali, Malcolm Slaney

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

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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 Sep 2019

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, …

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Tvs Devices Transient Behavior Modeling Framework And Application To Seed, Li Shen, Shubhankar Marathe, Javad Meiguni, Guangxiao Luo, Jianchi Zhou, David Pommerenke Sep 2019

Tvs Devices Transient Behavior Modeling Framework And Application To Seed, Li Shen, Shubhankar Marathe, Javad Meiguni, Guangxiao Luo, Jianchi Zhou, David Pommerenke

Electrical and Computer Engineering Faculty Research & Creative Works

The transient behavior for four different types of TVS (non-snapback, snapback, spark gap, varistor) is modeled using the same modeling framework. By a 10 ns VF-TLP, the quasi-static I-V curve and the transient turn-on are captured and modeled in ADS. The models are applied in a SEED simulation to investigate the strengths and weaknesses of the modeling frame.

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Compact Endfire Coupled-Mode Patch Antenna With Vertical Polarization, Haozhan Tian, Lijun Jiang, Tatsuo Itoh Sep 2019

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 …

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Control Of A Three-Phase Grid-Connected Inverter Under Non-Ideal Grid Conditions With Online Parameter Update, Vikram Roy Chowdhury, Jonathan W. Kimball Sep 2019

Control Of A Three-Phase Grid-Connected Inverter Under Non-Ideal Grid Conditions With Online Parameter Update, Vikram Roy Chowdhury, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Three-phase grid-connected inverter modeling depends on the equivalent resistance and inductance between the inverter and the grid. However, these parameters are not fixed during the operation of the inverter and vary with the operating conditions. In this paper, a new globally stable adaptive controller is proposed to estimate these values online and update the controller during its operation. A model reference adaptive system (MRAS) based on two fictitious quantities with no physical significance is utilized, namely M = v · i∗ and N = v∗ x i, where v and i are the steady-state values of voltage and current in …

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Output-Constrained Control Of Nonaffine Multiagent Systems With Partially Unknown Control Directions, Bo Fan, Qinmin Yang, Sarangapani Jagannathan, Youxian Sun Sep 2019

Output-Constrained Control Of Nonaffine Multiagent Systems With Partially Unknown Control Directions, Bo Fan, Qinmin Yang, Sarangapani Jagannathan, Youxian Sun

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an output-constrained control algorithm is presented for the consensus control of a class of unknown nonaffine multiagent systems (MASs) with partially unknown control directions. Our contribution includes a step forward beyond the usual consensus stabilization result to show that the outputs of agents remain within user-defined time-varying constraints. To achieve the new results, an error transformation technique is established to generate an equivalent MAS from the original one. Stabilization and consensus of the transformed agent states ensure both the satisfaction of the time-varying constraints and the consensus of the original agent states. Based on the Nussbaum gain …

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Attack Detection In Linear Networked Control Systems By Using Learning Methodology, Haifeng Niu, A. Sahoo, C. Bhowmick, S. Jagannathan Aug 2019

Attack Detection In Linear Networked Control Systems By Using Learning Methodology, Haifeng Niu, A. Sahoo, C. Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel learning-based attack detection scheme for linear networked control systems (NCS) is introduced. The class of attacks considered here tends to increase network induced delays and packet losses which affects the physical system dynamics. For the network side, an adaptive observer is proposed to generate the attack detection residual, which in turn is utilized to determine the onset of an attack when it exceeds a predefined threshold. The uncertain stochastic physical system dynamics as a result of network-induced delays of packet losses require an optimal Q-learning based event-triggered controller that optimizes the control policy and the event-triggering instants simultaneously. …

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Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha Aug 2019

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 …

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Generalizing Multiple Access Wiretap And Wiretap Ii Channel Models: Achievable Rates And Cost Of Strong Secrecy, Mohamed Nafea, Aylin Yener Aug 2019

Generalizing Multiple Access Wiretap And Wiretap Ii Channel Models: Achievable Rates And Cost Of Strong Secrecy, Mohamed Nafea, Aylin Yener

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, new two-user multiple access wiretap channel models are studied. First, the multiple access wiretap channel II with a discrete memoryless main channel under different wiretapping scenarios is introduced. The wiretapper, as in the classical wiretap channel II model, chooses a fixed-size subset of the channel uses, in which it obtains noise-free observations of one of the codewords: a deterministic function, e.g., superposition, of the two codewords or each of the two codewords. A fourth wiretapping scenario is considered, in which the wiretapper, in each position it chooses, decides to observe either one of the codewords or both …

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Cascaded Fabry-Pérot Interferometers With Vernier Effect For Gas Pressure Measurement, Hongfeng Lin, Yanyan Xu, Farhan Mumtaz, Yutang Dai, Ai Zhou Aug 2019

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 …

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Detection Of Sensor Attacks In Uncertain Stochastic Linear Systems, Chandreyee Bhowmick, S. Jagannathan Aug 2019

Detection Of Sensor Attacks In Uncertain Stochastic Linear Systems, Chandreyee Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel attack detection scheme is developed for linear discrete-time systems with unknown dynamics that are subject to the additive process and output measurements noise. A novel stochastic adaptive observer is proposed to estimate the state vector in the presence of noisy sensor measurements and uncertain dynamics, and also to generate the innovation signal to detect attacks using a modified $\chi2} $ detector. It has been shown that the innovation signal, which is defined as the difference between the measured and the estimated output from the observer, has a Gaussian distribution with non-zero mean. The modified $\chi^ {2} $ detector …

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Microwave Reflectometry For Physical Inspections, Mohammad Tayeb Ahmad Ghasr, R. Zoughi, Satyajeet Shinde, Sasi Jothibasu Jul 2019

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.

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Switching Dynamics And Conductance Quantization Of Aloe Polysaccharides-Based Device, Z. X. Lim, I. A. Tayeb, Z. A.A. Hamid, M. F. Ain, A. M. Hashim, J. M. Abdullah, F. Zhao, K. Y. Cheong Jul 2019

Switching Dynamics And Conductance Quantization Of Aloe Polysaccharides-Based Device, Z. X. Lim, I. A. Tayeb, Z. A.A. Hamid, M. F. Ain, A. M. Hashim, J. M. Abdullah, F. Zhao, K. Y. Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

The switching behaviors of polysaccharides-based resistive random-access memories change substantially depending on the electrical inputs. Here, the switching dynamics of the device are presented by varying the applied current compliance (CC) and voltage sweeping rate (ν). The results show that the device resistance in the low-resistance state (RLRS) can be modulated over five orders of magnitude by varying (CC) and ν in the typical current-voltage measurements. The (RLRS) modulation is attributed to the variable tunneling gap between the filament tip and the top electrode (TE). Conductance quantization is observed once a single-atomic contact with resistance ≤12.9kΩ is formed. Depending on …

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Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta Jul 2019

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 …

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Analysis Of Sea Clutter Using Dynamic Mode Decomposition, Yanming Zhang, Lijun Jiang, Hong Tat Ewe Jul 2019

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 …

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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 Jul 2019

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 …

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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 Jul 2019

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 …

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Event-Triggered Adaptive Distributed State Estimation By Using Active-Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen Jul 2019

Event-Triggered Adaptive Distributed State Estimation By Using Active-Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel event-triggered adaptive observer for each node in the heterogeneous sensor networks (HSNs) in order to estimate state vector of an unknown target or process by using the sensed output when the input to the target/ process is unknown. A subset of nodes in the HSN referred to as active nodes, can sense the target periodically, estimate the target state vector by using their adaptive observer and can communicate the estimated state vector of the target with the neighboring nodes including passive nodes only at event triggered instants. The adaptive observer parameters of active nodes are …

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Recurrent Network And Multi-Arm Bandit Methods For Multi-Task Learning Without Task Specification, Thy Nguyen, Tayo Obafemi-Ajayi Jul 2019

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 …

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Stochastic Resonance Enables Bpp/Log∗ Complexity And Universal Approximation In Analog Recurrent Neural Networks, Emmett Redd, A. Steven Younger, Tayo Obafemi-Ajayi Jul 2019

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 …

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Multi-Objective Optimization Approach To Find Biclusters In Gene Expression Data, Jeffrey Dale, Junya Zhao, Tayo Obafemi-Ajayi Jul 2019

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.

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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 Jul 2019

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 …

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