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Correction: Modeling Phase Selection And Extended Solubility In Rapid Solidified Alloys (Metallurgical And Materials Transactions A, (2023), 10.1007/S11661-023-07221-7), Azeez Akinbo, Yijia Gu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In the original online version of this article the reference citation in Fig. 3b was incorrect. The original article was corrected.

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Thermal Diffusivity Materials Characterization Via Active Microwave Thermography, Logan M. Wilcox, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a relatively new nondestructive evaluation method which is proposed in this work for thermal materials characterization. Specifically, AMT is investigated as a single-sided measurement option for out-of-plane thermal diffusivity (a parameter traditionally measured using a two-sided technique). Simulation and measurement results support the use of AMT for such a characterization for materials backed by an electromagnetically absorptive material. Both lossless and lossy materials may be measured, with better accuracy for lossless materials. The effect of heating time was also considered. The results indicate that for the 50 W system used here, 100 seconds of electromagnetic …

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Observer-Based Model Predictive Control With Continuous Control Set For Single-Phase Rectifiers, Milad Dehghanzadeh, Rui Bo, Kamal Al-Haddad

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a model predictive controller (MPC) is designed for a single-phase rectifier. The proposed MPC works with a continuous control set (CCS) that addresses variable switching issues in finite control set (FCS) MPCs. The observe-ability of the rectifier enables the design of a full-state observer to measure only output voltage in which the AC current of the rectifier is estimated. Both the proposed controller and observer are assessed with simulation studies and the results show the acceptable performance of the observer and CCS-MPC as well as good disturbance rejection in load and network parameter variations.

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Dipole-Moment-Based Reciprocity For Practical Desensitization Identification And Mitigation, Shengxuan Xia, Hanfeng Wang, Yansheng Wang, Zhonghua Wu, Chulsoon Hwang, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

Radio frequency interference can degrade the receiving sensitivity of antennas. The interference is usually caused by certain coupling structures, such as layouts without adequate grounding for the radio frequency signal return path. Those structures can be modeled as a set of equivalent dipole moments when they are electrically small. Herein, the dipole moment model-based coupling framework is applied to a practical cellphone design case to devise an engineering solution. The coupling framework incorporates dipole moments as radiation sources and a coupling model based on the reciprocity theorem. Unfortunately, near-field scan probes often lack access to all locations, owing to the …

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Managing Reserve Deliverability Risk Of Integrated Electricity-Heat Systems In Day-Ahead Market: A Distributionally Robust Joint Chance Constrained Approach, Yang Chen, Jianxue Wang, Siyuan Wang, Rui Bo, Chenjia Gu, Qingtao Li

Electrical and Computer Engineering Faculty Research & Creative Works

The integrated electricity and heat system (IEHS) is an emerging demand-side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy-limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance-constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two-stage distributionally robust joint chance constrained program is then incorporated to effectively …

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A Methodology For Predicting Acoustic Noise From Singing Capacitors In Mobile Devices, Xin Yan, Jianmin Zhang, Songping Wu, Ming Feng Xue, Chi Kin Benjamin Leung, Eric A. Macintosh, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Multilayer ceramic capacitors (MLCCs) connected to a power distribution network (PDN) can create acoustic noise through a combination of the power rail noise at the MLCCs and the piezoelectric effect of the capacitor's ceramic material. The deformation of the MLCCs brought on by power supply noise creates vibrations which cause the printed circuit board (PCB) to vibrate and generate the audible acoustic noise. In the following paper, a simulation methodology is presented to analyze the acoustic noise created by MLCCs on a PCB. A simulation model for the PCB vibration modal response is built and the modal superposition method is …

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Multi-Point Optical Fiber Fabry-Perot Curvature Sensor Based On Microwave Photonics, Chen Zhu, Ruimin Jie, Osamah Alsalman, Hongkun Zheng, Wassana Naku, Bo Liu, Lingmei Ma

Electrical and Computer Engineering Faculty Research & Creative Works

This article reports a multi-point curvature sensor system based on multiplexed optical fiber Fabry-Perot interferometric (FPI) sensor devices and a microwave photonics interrogation technique. The FPI sensor is fabricated with the assistance of a capillary tube, where a short section of the capillary is sandwiched between two single-mode fibers, forming the airgap Fabry-Perot cavity. Bending of the FPI device leads to changes in the fringe contrast of its reflection spectrum. Based on the microwave photonics filtering technique, variations of the fringe contrast are encoded into the changes in the peak magnitude of the passband in the frequency response of the …

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Distributed Detection Over Blockchain-Aided Internet Of Things In The Presence Of Attacks, Yiming Jiang, Jiangfan Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

Distributed detection over a blockchain-aided Internet of Things (BIoT) network in the presence of attacks is considered, where the integrated blockchain is employed to secure data exchanges over the BIoT as well as data storage at the agents of the BIoT. We consider a general adversary model where attackers jointly exploit the vulnerability of IoT devices and that of the blockchain employed in the BIoT. The optimal attacking strategy which minimizes the Kullback-Leibler divergence is pursued. It can be shown that this optimization problem is nonconvex, and hence it is generally intractable to find the globally optimal solution to such …

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Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a novel reinforcement learning-based optimal tracking control of partially uncertain nonlinear discrete-time (DT) systems with state constraints using zero-sum game (ZSG) formulation. To address optimal tracking, a novel augmented system consisting of tracking error and its integral value, along with an uncertain desired trajectory, is constructed. A barrier function (BF) with a tradeoff factor is incorporated into the cost function to keep the state trajectories to remain within a compact set and to balance safety with optimality. Next, by using the modified value functional, the ZSG formulation is introduced wherein an actor–critic neural network (NN) framework is …

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Three-Terminal Noise Source Extraction From A Qi-Based Wireless Power Transfer System For Predicting Conducted Emissions, Srinath Penugonda, Siqi Bai, Varittha Sanphuang, Nevin Altunyurt, Hongseok Kim, Daryl G. Beetner, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

An equivalent method to extract a three-terminal noise source from a Qi-based wireless power transfer system is proposed in this article. This method is capable of extracting a source for both the positive (P) and negative (N) lines with respect to the third terminal (the ground chassis or reference plane). The extracted sources are independent of the setup configuration and can be used to predict currents or voltages on the P and N lines on a setup that is different from the extraction setup. In the following article, a three-terminal model independent of the setup configuration is first extracted. The …

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Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-Time Systems Using Multilayer Neural Networks, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a lifelong integral reinforcement learning (LIRL)-based optimal tracking scheme for uncertain nonlinear continuous-time (CT) systems using multilayer neural network (MNN). In this LIRL framework, the optimal control policies are generated by using both the critic neural network (NN) weights and single-layer NN identifier. The critic MNN weight tuning is accomplished using an improved singular value decomposition (SVD) of its activation function gradient. The NN identifier, on the other hand, provides the control coefficient matrix for computing the control policies. An online weight velocity attenuation (WVA)-based consolidation scheme is proposed wherein the significance of weights is derived by …

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Patients Arms Segmentation And Gesture Identification Using Standalone 3d Lidar Sensors, Omar Rinchi, Nathanael Nisbett, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

The intelligent and autonomous learning of patients' activities will lead to an incredible progression toward future smart e-health systems. With the recent advances in artificial intelligence, signal processing, and computational capabilities; light detection and ranging (LiDAR) technology can play a significant role in enhancing the current patients' activity recognition (PAR) systems. In this paper, we propose confidential and accurate patient arms behavior monitoring using a standalone three-dimensional (3D) LiDAR sensor. Due to the unavailability of LiDAR data, we use a computer-programmed 3D simulator to generate virtual-LiDAR (V-LiDAR) 3D point cloud data that simulates real patient movements. These virtual data are …

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Aerial Lidar-Based 3d Object Detection And Tracking For Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa, Hichem Besbes, Yehia Massoud

Electrical and Computer Engineering Faculty Research & Creative Works

The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for …

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Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student's interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

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A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning Considering Multiple Extreme Weather Events, Siyuan Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Climate Change May Increase the Risk of an Area Being Hit by Multiple Extreme Weather Events, Which Brings Significant Challenges for Distribution System Planners in an Increasing Renewable Penetration Era. There is an Urgent Need for Planning Approaches to Be More Flexible and Allow for Adaptive Adjustments in the Future to Hedge Against High Uncertainties in Extreme Weather Event Scenarios. in This Work, We Propose a Resilience-Oriented Distribution System Planning Approach that Considers Multiple Extreme Weather Events. a Multi-Stage Hybrid-Stochastic-And-Robust Formulation is Developed to Model Decisions Not Only for Initial Investments, But Also for Adaptive Investments and Emergent Operations in …

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Cooperative Deep $Q$ -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, We Address Two Key Challenges in Deep Reinforcement Learning (DRL) Setting, Sample Inefficiency and Slow Learning, with a Dual-Neural Network (NN)-Driven Learning Approach. in the Proposed Approach, We Use Two Deep NNs with Independent Initialization to Robustly Approximate the Action-Value Function in the Presence of Image Inputs. in Particular, We Develop a Temporal Difference (TD) Error-Driven Learning (EDL) Approach, Where We Introduce a Set of Linear Transformations of the TD Error to Directly Update the Parameters of Each Layer in the Deep NN. We Demonstrate Theoretically that the Cost Minimized by the EDL Regime is an Approximation …

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Advances In Fiber-Optic Extrinsic Fabry-Perot Interferometric Physical And Mechanical Sensors: A Review, Chen Zhu, Hongkun Zheng, Lingmei Ma, Zheyi Yao, Bo Liu, Jie Huang, Yunjiang Rao

Electrical and Computer Engineering Faculty Research & Creative Works

Fabry-Perot Interferometers Have Found a Multitude of Scientific and Industrial Applications Ranging from Gravitational Wave Detection, High-Resolution Spectroscopy, and Optical Filters to Quantum Optomechanics. Integrated with Optical Fiber Waveguide Technology, the Fiber-Optic Fabry-Perot Interferometers Have Emerged as a Unique Candidate for High-Sensitivity Sensing and Have Undergone Tremendous Growth and Advancement in the Past Two Decades with their Successful Applications in an Expansive Range of Fields. the Extrinsic Cavity-Based Devices, I.e., the Fiber-Optic Extrinsic Fabry-Perot Interferometers (EFPIs), Enable Great Flexibility in the Design of the Sensitive Fabry-Perot Cavity Combined with State-Of-The-Art Micromachining and Conventional Mechanical Fabrication, Leading to the Development of …

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Decoupling Optimization For Complex Pdn Structures Using Deep Reinforcement Learning, Ling Zhang, Li Jiang, Jack Juang, Zhiping Yang, Er Ping Li, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Presents a New Optimization Method for Complex Power Distribution Networks (PDNs) with Irregular Shapes and Multilayer Structures using Deep Reinforcement Learning (DRL), Which Has Not Been Considered Before. a Fast Boundary Integration Method is Applied to Compute the Impedance Matrix of a PDN Structure. Subsequently, a New DRL Algorithm based on Proximal Policy Optimization (PPO) is Proposed to Optimize the Decoupling Capacitor (Decap) Placement by Minimizing the Number of Decaps While Satisfying the Desired Target Impedance. in the Proposed Approach, the PDN Structure Information is Encoded into Matrices and Serves as the Input of the DRL Algorithm, Which …

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Decentralized And Coordinated V-F Control For Islanded Microgrids Considering Der Inadequacy And Demand Control, Buxin She, Fangxing Li, Hantao Cui, Jinning Wang, Liang Min, Oroghene Oboreh-Snapps, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Proposes a Decentralized and Coordinated Voltage and Frequency (V-F) Control Framework for Islanded Microgrids, with Full Consideration of the Limited Capacity of Distributed Energy Resources (DERs) and V-F Dependent Load. First, the Concept of DER Inadequacy is Illustrated with the Challenges It Poses. Then, a Decentralized and Coordinated Control Framework is Proposed to Regulate the Output of Inverter-Based Generations and Reallocate Limited DER Capacity for V-F Control. the Control Framework is Composed of a Power Regulator and a V-F Regulator, Which Generates the Supplementary Signals for the Primary Controller. the Power Regulator Regulates the Output of Grid-Forming Inverters …

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Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such …

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Methodology For Analyzing Coupling Mechanisms In Rfi Problems Based On Peec, Xu Wang, Anfeng Huang, Wei Zhang, Reza Yazdani, Donghyun Kim, Takashi Enomoto, Taketoshi Sekine, Kenji Araki, Jun Fan, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, a Method for Analyzing Coupling Mechanisms in Radio Frequency Interference (RFI) Problems is Proposed. the Partial Element Equivalent Circuit (PEEC) Method is First Used to Derive the Retarded Inductances and Capacitances between Different Mesh Cells. with the Introduction of a Novel Partitioning Algorithm, the Capacitive Coupling and Inductive Coupling between Arbitrary Layout Parts Can Be Quantified based on the Magnitude of the Displacement Current and Induced Voltage Drop. the Accuracy of the PEEC Models is Validated by Comparison with Different Commercial Tools. the Proposed Coupling Mechanism Analysis Flow Provides a Useful Prelayout Tool for RFI Risk Analysis.

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Electromagnetic Transmit Array With Optical Control For Beamforming, Wei Zhang, Javad Meiguni, Yin Sun, Muqi Ouyang, Xin Yan, Xu Wang, Reza Yazdani, Daryl G. Beetner, Donghyun Kim, David Pommerenke

Electrical and Computer Engineering Faculty Research & Creative Works

This Proof-Of-Concept Paper Demonstrates the Feasibility of using a Slide Projector to Steer the Beam of a Transmit Array by Adding Solar Cells and Varactor Diodes to Each Unit Cell. by Irradiating Each Solar Cell with the Light of Different Intensities from a Slide Projector, the Measured Phase of the Wave Transmitted by the 4x4 Transmit Array Shifts within 92° at 4.26 GHz, While the Variation in Magnitude is Measured within 4 DB. Different Light Configurations Are Identified Via a Searching Algorithm to Achieve Peak/null Beamforming in a Particular Direction. the Beam of the Prototypical 4x4 Transmit Array Can Be …

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Nanobubble-Induced Aggregation Of Ultrafine Particles: A Molecular Dynamics Study, Eric Bird, Zhi Liang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Nanobubble-induced aggregation (NBIA) of fine and ultrafine particles in liquid is a promising method for enhancing floatation rates in mineral processing, cleaning contaminants from water, and reviving marine ecosystems. Although the current experimental techniques can measure the nanobubble capillary force between two surfaces with controlled approach speed, they are not capable of imaging NBIA dynamics of fine/ultrafine particles by real-time observation with nanoscale spatial resolution. In this work, we use molecular dynamics (MD) simulations to study dynamics of NBIA of Ag particles in a Lennard-Jones fluid system. The molecular-level modeling allows us to study microscopic details of NBIA dynamics that …

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Fine-Grained Activity Classification In Assembly Based On Multi-Visual Modalities, Haodong Chen, Niloofar Zendehdel, Ming-Chuan Leu, Zhaozheng Yin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red, green blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information …

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Additively Manufactured Carbon Fiber- Reinforced Thermoplastic Composite Mold Plates For Injection Molding Process, C. Bivens, A. Wood, D. Ruble, M. Rangapuram, S. K. Dasari, K. Chandrashekhara, J. Degrange

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Polymer injection molding processes have been used to create high-volume parts quickly and efficiently. Injection molding uses mold plates that are traditionally made of very hard tool steels, such as P20 steel, which is extremely heavy and has very long lead times to build new molds. In this study, composite-based additive manufacturing (CBAM) was used to create mold plates using long-fiber carbon fiber and polyether ether ketone (PEEK). These mold plates were installed in an injection molding machine, and rectangular flat plates were produced using Lustran 348 acrylonitrile butadiene styrene (ABS). Tensile and flexural testing was performed on these parts …

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Inverter Pq Control With Trajectory Tracking Capability For Microgrids Based On Physics-Informed Reinforcement Learning, Buxin She, Fangxing Li, Hantao Cui, Hang Shuai, Oroghene Oboreh-Snapps, Rui Bo, Nattapat Praisuwanna, Jingxin Wang, Leon M. Tolbert

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposed an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep reinforcement learning approach …

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A Methodology For Predicting Improved Dipole Source Configurations From Near-Field Scan Data, Hossein Rezaei, Xin Yan, David J. Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Equivalent sources found from near-electric and magnetic field scans are often used to predict and solve interference problems. While there are many ways to represent the source, the user lacks the ability to determine which of the many possible source configurations is more likely to represent the "true" source, and thus accurately represent field data outside the measurement area and in the presence of typical measurement errors. A methodology is proposed for estimating which of many possible dipole source representations of near-field scan data are likely to give better estimates of fields outside the measurement scan plane when utilizing imperfect …

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Filter-Based Fault Detection And Isolation In Distributed Parameter Systems Modeled By Parabolic Partial Differential Equations, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault …

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Cannabis-Assisted Psychotherapy For Complex Dissociative Posttraumatic Stress Disorder: A Case Report, Anya Ragnhildstveit, Miriam Kaiyo, Matthew Brian Snyder, Laura Kate Jackson, Alex Lopez, Chasity Mayo, Alyssa Claire Miranda, River Jude August, Paul Seli, Reid Robison, Lynnette Astrid Averill

Center for Medical Ethics and Health Policy Staff Publications

Background: A dissociative subtype of posttraumatic stress disorder, known as "D-PTSD", has been included in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. In addition to meeting criteria for PTSD, patients endorse prominent dissociative symptoms, namely depersonalization and derealization, or detachment from one's self and surroundings. At present, this population is supported by a highly heterogeneous and undeveloped literature. Targeted interventions are therefore lacking, and those indicated for PTSD are limited by poor efficacy, delayed onset of action, and low patient engagement. Here, we introduce cannabis-assisted psychotherapy (CAP) as a novel treatment for D-PTSD, drawing parallels to psychedelic …

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5-Meo-Dmt For Post-Traumatic Stress Disorder: A Real-World Longitudinal Case Study, Anya Ragnhildstveit, Ryan Khan, Paul Seli, Lisa Claire Bass, River Jude August, Miriam Kaiyo, Nathaniel Barr, Laura Kate Jackson, Michael Santo Gaffrey, Joseph Peter Barsuglia, Lynnette Astrid Averill

Center for Medical Ethics and Health Policy Staff Publications

Psychedelic therapy is, arguably, the next frontier in psychiatry. It offers a radical alternative to longstanding, mainstays of treatment, while exciting a paradigm shift in translational science and drug discovery. There is particular interest in 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT)—a serotonergic psychedelic—as a novel, fast-acting therapeutic. Yet, few studies have directly examined 5-MeO-DMT for trauma- or stress-related psychopathology, including post-traumatic stress disorder (PTSD). Herein, we present the first longitudinal case study on 5-MeO-DMT for chronic refractory PTSD, in a 23-year-old female. A single dose of vaporized bufotoxin of the Sonoran Desert Toad (Incilius alvarius), containing an estimated 10−15 mg …

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Full-Text Articles in Entire DC Network

Correction: Modeling Phase Selection And Extended Solubility In Rapid Solidified Alloys (Metallurgical And Materials Transactions A, (2023), 10.1007/S11661-023-07221-7), Azeez Akinbo, Yijia Gu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Thermal Diffusivity Materials Characterization Via Active Microwave Thermography, Logan M. Wilcox, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Observer-Based Model Predictive Control With Continuous Control Set For Single-Phase Rectifiers, Milad Dehghanzadeh, Rui Bo, Kamal Al-Haddad

Electrical and Computer Engineering Faculty Research & Creative Works

Dipole-Moment-Based Reciprocity For Practical Desensitization Identification And Mitigation, Shengxuan Xia, Hanfeng Wang, Yansheng Wang, Zhonghua Wu, Chulsoon Hwang, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

Managing Reserve Deliverability Risk Of Integrated Electricity-Heat Systems In Day-Ahead Market: A Distributionally Robust Joint Chance Constrained Approach, Yang Chen, Jianxue Wang, Siyuan Wang, Rui Bo, Chenjia Gu, Qingtao Li

Electrical and Computer Engineering Faculty Research & Creative Works

A Methodology For Predicting Acoustic Noise From Singing Capacitors In Mobile Devices, Xin Yan, Jianmin Zhang, Songping Wu, Ming Feng Xue, Chi Kin Benjamin Leung, Eric A. Macintosh, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Multi-Point Optical Fiber Fabry-Perot Curvature Sensor Based On Microwave Photonics, Chen Zhu, Ruimin Jie, Osamah Alsalman, Hongkun Zheng, Wassana Naku, Bo Liu, Lingmei Ma

Electrical and Computer Engineering Faculty Research & Creative Works

Distributed Detection Over Blockchain-Aided Internet Of Things In The Presence Of Attacks, Yiming Jiang, Jiangfan Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Three-Terminal Noise Source Extraction From A Qi-Based Wireless Power Transfer System For Predicting Conducted Emissions, Srinath Penugonda, Siqi Bai, Varittha Sanphuang, Nevin Altunyurt, Hongseok Kim, Daryl G. Beetner, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-Time Systems Using Multilayer Neural Networks, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Patients Arms Segmentation And Gesture Identification Using Standalone 3d Lidar Sensors, Omar Rinchi, Nathanael Nisbett, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

Aerial Lidar-Based 3d Object Detection And Tracking For Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa, Hichem Besbes, Yehia Massoud

Electrical and Computer Engineering Faculty Research & Creative Works

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning Considering Multiple Extreme Weather Events, Siyuan Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Cooperative Deep $Q$ -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Advances In Fiber-Optic Extrinsic Fabry-Perot Interferometric Physical And Mechanical Sensors: A Review, Chen Zhu, Hongkun Zheng, Lingmei Ma, Zheyi Yao, Bo Liu, Jie Huang, Yunjiang Rao

Electrical and Computer Engineering Faculty Research & Creative Works

Decoupling Optimization For Complex Pdn Structures Using Deep Reinforcement Learning, Ling Zhang, Li Jiang, Jack Juang, Zhiping Yang, Er Ping Li, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Decentralized And Coordinated V-F Control For Islanded Microgrids Considering Der Inadequacy And Demand Control, Buxin She, Fangxing Li, Hantao Cui, Jinning Wang, Liang Min, Oroghene Oboreh-Snapps, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Methodology For Analyzing Coupling Mechanisms In Rfi Problems Based On Peec, Xu Wang, Anfeng Huang, Wei Zhang, Reza Yazdani, Donghyun Kim, Takashi Enomoto, Taketoshi Sekine, Kenji Araki, Jun Fan, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Electromagnetic Transmit Array With Optical Control For Beamforming, Wei Zhang, Javad Meiguni, Yin Sun, Muqi Ouyang, Xin Yan, Xu Wang, Reza Yazdani, Daryl G. Beetner, Donghyun Kim, David Pommerenke

Electrical and Computer Engineering Faculty Research & Creative Works

Nanobubble-Induced Aggregation Of Ultrafine Particles: A Molecular Dynamics Study, Eric Bird, Zhi Liang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Fine-Grained Activity Classification In Assembly Based On Multi-Visual Modalities, Haodong Chen, Niloofar Zendehdel, Ming-Chuan Leu, Zhaozheng Yin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additively Manufactured Carbon Fiber- Reinforced Thermoplastic Composite Mold Plates For Injection Molding Process, C. Bivens, A. Wood, D. Ruble, M. Rangapuram, S. K. Dasari, K. Chandrashekhara, J. Degrange

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Inverter Pq Control With Trajectory Tracking Capability For Microgrids Based On Physics-Informed Reinforcement Learning, Buxin She, Fangxing Li, Hantao Cui, Hang Shuai, Oroghene Oboreh-Snapps, Rui Bo, Nattapat Praisuwanna, Jingxin Wang, Leon M. Tolbert

Electrical and Computer Engineering Faculty Research & Creative Works

A Methodology For Predicting Improved Dipole Source Configurations From Near-Field Scan Data, Hossein Rezaei, Xin Yan, David J. Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Filter-Based Fault Detection And Isolation In Distributed Parameter Systems Modeled By Parabolic Partial Differential Equations, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Cannabis-Assisted Psychotherapy For Complex Dissociative Posttraumatic Stress Disorder: A Case Report, Anya Ragnhildstveit, Miriam Kaiyo, Matthew Brian Snyder, Laura Kate Jackson, Alex Lopez, Chasity Mayo, Alyssa Claire Miranda, River Jude August, Paul Seli, Reid Robison, Lynnette Astrid Averill

Center for Medical Ethics and Health Policy Staff Publications

5-Meo-Dmt For Post-Traumatic Stress Disorder: A Real-World Longitudinal Case Study, Anya Ragnhildstveit, Ryan Khan, Paul Seli, Lisa Claire Bass, River Jude August, Miriam Kaiyo, Nathaniel Barr, Laura Kate Jackson, Michael Santo Gaffrey, Joseph Peter Barsuglia, Lynnette Astrid Averill

Center for Medical Ethics and Health Policy Staff Publications