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Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang Jan 2024

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Electrical & Computer Engineering Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …

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Generalized Functions In The Study Of Signals And Systems, Erik I. Verriest, Gunther Dirr, W. Steven Gray Jan 2024

Generalized Functions In The Study Of Signals And Systems, Erik I. Verriest, Gunther Dirr, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

We collect three instances where the theory of generalized functions may still make contributions to the study of signals and systems. In the first, a purely algebraic approach is presented for LTI-ODE's, in terms of two operators, D and T, respectively the differentiation operator and the multiplication-by-the-independent-variable operator. This formalism adds simplicity, a duality theory, and nicely generalizes to other classes of operator equations and their solutions. In the second part we extend the classical bilateral Laplace transform to include Bohl functions with support in ℝ by invoking Sato's hyperfunctions. Finally, in the third case we use the Colombeau algebra …

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An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen Jan 2024

An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …

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A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa Jan 2024

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, …

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Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan Jan 2024

Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Introduces a Novel Optimal Trajectory Tracking Control Scheme Designed for Uncertain Linear Discrete-Time (DT) Systems. in Contrast to Traditional Tracking Control Methods, Our Approach Removes the Requirement for the Reference Trajectory to Align with the Generator Dynamics of an Autonomous Dynamical System. Moreover, It Does Not Demand the Complete Desired Trajectory to Be Known in Advance, Whether through the Generator Model or Any Other Means. Instead, Our Approach Can Dynamically Incorporate Segments (Finite Horizons) of Reference Trajectories and Autonomously Learn an Optimal Control Policy to Track Them in Real Time. to Achieve This, We Address the Tracking Problem …

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Instantaneous Current And Average Power Flow Characterization Of A Dc-Dc-Dc Triple Active Bridge Converter, Jonathan Saelens, Lauryn Morris, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball Jan 2024

Instantaneous Current And Average Power Flow Characterization Of A Dc-Dc-Dc Triple Active Bridge Converter, Jonathan Saelens, Lauryn Morris, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

The Triple Active Bridge (TAB) is a Three-Port Power Converter that Facilitates Bi-Directional Power Flow and Provides Galvanic Isolation, Making It a Subject of Significant Research Attention. This is Attributed to its Diverse Applications in High-Frequency DC-DC Conversion, Electric Vehicles, Renewable Energy Integration, and Micro-Grids. Controlling the System at Run-Time Involves Modification of the Two Phase-Shift Parameters between Respective Bridges. by Analyzing the Fundamental Converter Operating Equations, Future Control Designers Can Use This Framework to Optimize Control Schemes to Mitigate the Under-Determined Nature of the TAB Converter. in This Paper, We Elucidate the Foundational Operational Principles of the TAB and …

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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 Jan 2024

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 …

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Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan Jan 2024

Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel …

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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 Jan 2024

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 …

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Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha Petrenko, Donald C. Wunsch Jan 2024

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

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Fabrication And Analysis Of A Gas Sensor Utilizing A Fabry-Perot Interferometer With Varied Thicknesses Of Zif-8 Coating On End-Faced Single-Mode Optical Fiber, Nahideh Salehifar, Rex E. Gerald, Jie Huang Jan 2024

Fabrication And Analysis Of A Gas Sensor Utilizing A Fabry-Perot Interferometer With Varied Thicknesses Of Zif-8 Coating On End-Faced Single-Mode Optical Fiber, Nahideh Salehifar, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the potential of integrating optical fiber and sensitive thin film coating materials to develop diverse chemical sensing platforms. We utilized a layer-by-layer synthesis technique, along with specific pre- post treatment methods, to create highly effective nonporous metal-organic frameworks (MOFs), specifically ZIF-8, ranging from the nanometer scale to the micrometer scale (800 nm to 110 μm). Additionally, we successfully cultivated ZIF-8 on both sides of a single-mode fiber (SMF) with thicknesses of 25 μm and 50 μm. In a separate experiment, we successfully grew ZIF-8 on one side of an optical fiber, achieving a thickness of 10 μm. …

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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 Jan 2024

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 …

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Multimode Fiber-Based Interferometric Sensors With Microwave Photonics, Chen Zhu, Shuaifei Tian, Lingmei Ma, Jie Huang Jan 2024

Multimode Fiber-Based Interferometric Sensors With Microwave Photonics, Chen Zhu, Shuaifei Tian, Lingmei Ma, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Interferometry is one of the most widely used investigative techniques in various fields. With the implementation of interferometry on optical fibers, fiber optic interferometers (FOIs) have gained tremendous growth and advancement in the past four decades and have been explored for measurements of a diverse array of physical, chemical, and biological parameters. FOIs are typically constructed using single-mode fibers (SMFs) and are interrogated in the optical domain using probing light with a tightly controlled state of polarization (SOP), to ensure high-quality interference signals that facilitate sensing applications. The stringent requirement on the single-mode operation, as well as SOP, has hindered …

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Time-Resolved Electromagnetic Near-Field Scanning: Dual Sparse Sampling In Time And Space, Yanming Zhang, Peifeng Ma, Lijun Jiang, Steven Gao Jan 2024

Time-Resolved Electromagnetic Near-Field Scanning: Dual Sparse Sampling In Time And Space, Yanming Zhang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

Time-resolved electromagnetic near-field scanning plays a pivotal role in antenna measurement and unraveling complex electromagnetic interference and compatibility issues. However, the rapid acquisition of high-resolution spatio–temporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This article presents a novel hybrid approach combining kriging for sparse spatial measurement, compressed sensing (cs) for sparse temporal sampling, and dynamic mode decomposition (dmd) for comprehensive analysis of the dual-sparse sampling electromagnetic near-field data. We leverage cs to optimize sparse sampling in the time domain and latin hypercube sampling to guide the probe position …

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An Equivalent Coil Model Of A Wireless Power Transfer System Including Eddy Loss, Hanyu Zhang, Daryl G. Beetner Jan 2024

An Equivalent Coil Model Of A Wireless Power Transfer System Including Eddy Loss, Hanyu Zhang, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

A wireless power transfer (WPT) system suffers from eddy loss if a conductive object is placed near the coupling coil. In this paper, a 3-coil equivalent circuit model for the coupling coil in a WPT system is proposed for analyzing the eddy loss due to nearby conductors. This model uses a third coil with inductive coupling to the original transmitting and receiving coils to model the eddy loss. The proposed model was validated by comparing the Z-parameters with a full-wave simulation and showing good correlation over the frequency of interest, where the traditional 2-coil model fails. The 3-coil model is …

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Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan Jan 2024

Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Realistic manipulation tasks involve a prolonged sequence of motor skills in varying control environments consisting of uncertain robot dynamic models and end-effector payloads. To address these challenges, this article proposes an adaptive critic (AC)-based basis function neural network (BFNN) optimal controller. Using a single neural network (NN) with a basis function, the proposed optimal controller simultaneously learns task-related optimal cost function, robot internal dynamics, and optimal control law. This is achieved through the development of a novel BFNN tuning law using closed-loop system stability. Therefore, the proposed optimal controller provides real-time, implementable, cost-effective control solutions for practical robotic tasks. The …

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Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi Jan 2024

Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. …

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Evaluating Electromagnetic Interference Effects On Gnss Receivers, Giorgi Tsintsadze, Haran Manoharan, Arushi Sahai, Daryl G. Beetner, Brian Booth Jan 2024

Evaluating Electromagnetic Interference Effects On Gnss Receivers, Giorgi Tsintsadze, Haran Manoharan, Arushi Sahai, Daryl G. Beetner, Brian Booth

Electrical and Computer Engineering Faculty Research & Creative Works

Electromagnetic interference can be highly disruptive to global navigation satellite system (GNSS) receivers. Interference can be intentional but can also occur from electronics modules placed within the same system, where these modules may create sufficient unintended radiated emissions to disrupt GNSS operation. In this paper, GNSS receiver performance is evaluated in the presence of multi-tone interference. An expression for the GNSS correlator output in the presence of continuous wave interference (CWI) is derived and is extended to predict the carrier to noise density ratio, C/N0, of the receiver in the presence of multi-tone interference. C/N0 is widely used for characterizing …

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A Method For Measuring The Transfer Function Inside A Compact Metallic Enclosure Using A Slot Antenna, Xiangrui Su, Wenchang Huang, Junghee Cho, Joonki Paek, Chulsoon Hwang Jan 2024

A Method For Measuring The Transfer Function Inside A Compact Metallic Enclosure Using A Slot Antenna, Xiangrui Su, Wenchang Huang, Junghee Cho, Joonki Paek, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Radio frequency desensitization issues comprise two components: noise radiation sources and the transfer function from noise sources to the victim antenna. For modern electronic products, noise sources are often located inside a compact metal enclosure, making it difficult to measure the transfer function using conventional methods. Moreover, opening the metallic enclosure would dramatically change the transfer function, and inserting a near-field probe into the enclosure is challenging and may not even be possible because of the limited space inside. In this article, a novel practical method for measuring the transfer function inside a compact metallic enclosure is proposed and experimentally …

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Virtual Inertia Scheduling (Vis) For Microgrids With Static And Dynamic Security Constraints, Buxin She, Fangxing Li, Jinning Wang, Hantao Cui, Xiaofei Wang, Rui Bo Jan 2024

Virtual Inertia Scheduling (Vis) For Microgrids With Static And Dynamic Security Constraints, Buxin She, Fangxing Li, Jinning Wang, Hantao Cui, Xiaofei Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Microgrids feature a high penetration of inverter-interfaced distributed energy resources (DERs). The low inertia characteristic and fast dynamics of DERs pose challenges to conventional decoupled static economic operation and dynamic control design within microgrids. Hence, this paper proposed virtual inertia scheduling (VIS) for microgrids, aiming to ensure both economy and security. First, a unified framework for device-level control and grid-level operation is introduced, with VIS serving as a key application to address low inertia issues. VIS actively harnesses the controllability and flexibility of DERs to effectively manage microgrid inertia. It updates the conventional economic operation framework by incorporating the virtual …

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Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao Jan 2024

Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the …

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A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao Jan 2024

A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. …

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Analytical Modeling Of Partially-Filled Tm010-Mode Dielectric Resonator For Accurate Dk And Df Extraction, Mehdi Mousavi, Chaofeng Li, Reza Asadi, Seyedmostafa Mousavi, Reza Vahdani, Xiaoning Ye, Mina Esmaeelpour, Donghyun Kim Jan 2024

Analytical Modeling Of Partially-Filled Tm010-Mode Dielectric Resonator For Accurate Dk And Df Extraction, Mehdi Mousavi, Chaofeng Li, Reza Asadi, Seyedmostafa Mousavi, Reza Vahdani, Xiaoning Ye, Mina Esmaeelpour, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

In high-speed printed circuit board (PCB) modeling, the accurate determination of the dielectric constant (DK) and dissipation factor (DF) is crucial for signal integrity analysis at design stage. This paper introduces an analytical approach using a partially filled TM010-mode dielectric resonator, an effective tool for DK and DF extraction of PCB material. We begin by discussing the theoretical underpinnings of TM010-mode dielectric resonators and their applicability in measuring the DK and DF of the material under test. A mathematical model is then presented, linking the resonant characteristics directly to the DK and DF of the dielectric material. The …

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Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan Jan 2024

Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a comprehensive approach for achieving multi-task safe optimal adaptive tracking (MSOAT) for a class of nonlinear discrete-time systems, particularly those in strict-feedback form, utilizing a multi-layer neural network (MNN)-based framework. To begin, a cost function with a novel Barrier function (BF) term is introduced for each subsystem to address the weak safely reachable problem, serving as a crucial tool for guiding the system's trajectory toward the safe set while avoiding unwanted sets. To deal with the tracking problem, the Hamilton-Jacobi-Bellman (HJB) framework is used through the actor-critic MNN-based backstepping technique to estimate the solution of the value …

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Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani Jan 2024

Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

While deep neural networks achieve remarkable visual perception capabilities for UAV position and orientation estimation, their resilience to different weather conditions still needs improvement. These models often suffer from catastrophic forgetting when adapted to new environments, losing previously acquired knowledge. Lifelong learning methods aim to balance learning flexibility and memory stability. In this paper, we present an image-based approach to estimate the relative altitude of a UAV using 2D images under varying weather conditions, including sunny, sunset, and foggy scenarios. Our experiments demonstrate significant performance degradation when the model is trained sequentially on different weather datasets, especially when new images …

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A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang Jan 2024

A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel hybrid approach for electromagnetic near-field scanning, combining model-based, i.e., Gaussian processes regression, and data-driven, i.e., dynamic mode decomposition, techniques. We first leverage the Latin hypercube sampling technique to achieve spatially sparse measurements. Subsequently, dynamic mode decomposition is applied to analyze the resulting spatiotemporal data with sparse spatial sampling, enabling the extraction of both frequency information and sparse dynamic modes. Finally, the Gaussian processes regression, also known as the Kriging method, is adopted for the full-state reconstruction. The proposed hybrid approach is benchmarked by an example of the crossed dipole antennas. The obtained results demonstrate that …

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Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang Jan 2024

Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

We consider the time-restricted double-spending attack (TR-DSA) on the Proof-of-Work-based blockchain, where an adversary conducts a DSA within a finite timeframe and simultaneously launches multiple types of attacks on the blockchain. To be specific, the adversary can conduct attacks to isolate some honest miners and cause block propagation delays among miners to enhance the success probability of the TR-DSA. We first develop the closed-form expression for the success probability of a TR-DSA with the aid of multiple types of attacks, which is leveraged to develop the closed-form expression for the expected profit of a TR-DSA. The numerical analysis reveals that …

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Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed Jan 2024

Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed

Electrical and Computer Engineering Faculty Research & Creative Works

Artificial intelligence (AI) offers significant benefits in search and rescue applications by enhancing the efficiency and effectiveness of the search. However, an over-reliance on AI can hinder the operation due to biases embedded in the underlying algorithms. This partiality, if left un-monitored, can pose a risk to the safety of those in need of disaster relief. Typically manifests into inaccuracies in the decision-making processes and if not carefully monitored can cause larger issues. This paper extends the knowledge presented in a previous work, which presents the design and modeling of search and rescue operations using unmanned aerial vehicle (UAV) swarms. …

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Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan Jan 2024

Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …

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Lidar From The Sky: Uav Integration And Fusion Techniques For Advanced Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa Jan 2024

Lidar From The Sky: Uav Integration And Fusion Techniques For Advanced Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct …

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