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Articles 991 - 1020 of 13207
Full-Text Articles in Entire DC Network
In-Plane And Out-Of-Plane 2-D Microdisplacement Sensor Based On A Single Microwave Resonator With Machine Learning, Shiyu Li, Osamah Alsalman, Jie Huang, Chen Zhu
In-Plane And Out-Of-Plane 2-D Microdisplacement Sensor Based On A Single Microwave Resonator With Machine Learning, Shiyu Li, Osamah Alsalman, Jie Huang, Chen Zhu
Electrical and Computer Engineering Faculty Research & Creative Works
Microwave displacement sensors have garnered significant research interest in recent years and have found successful applications in industrial automation and aerospace engineering. However, most microwave sensors are limited to measuring in-plane displacement, with the assumption that out-of-plane displacement remains constant during operation. In this work, we propose and experimentally demonstrate a novel concept for 2-D micro displacement sensing that simultaneously measures both in-plane and out-of-plane displacement. We developed a proof-of-concept sensor system based on a custom-made coaxial cable resonator (CCR) serving as the stator and a rubber-metal heterogeneous plate as the movable part. The in-plane and out-of-plane micromovements of the …
Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch
Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper explores the pivotal role of trust in the widespread application of Artificial Intelligence (AI) across various domains. We review AI applications in sectors like energy, healthcare, and autonomous vehicles and discuss the crisis of human trust they face. This paper introduces a novel framework that delineates the relationship between AI transparency and user trust, highlighting specific industry applications. Through a systematic review of recent literature, we first delve into factors such as emotional response, acceptance, transparency, accuracy, and interpretability that shape human trust in AI. We then underscore the necessity of ethical AI practices and highlight the importance …
Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria
Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or …
Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan
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 …
Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan
Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
In this work, a leader-follower tracking and formation control strategy for mobile robots (MRs) with uncertain dynamics is proposed. This strategy utilizes a continual lifelong safe reinforcement learning (CLSRL) framework based on multilayer neural networks (MNNs). The proposed design employs actor-critic MNNs, incorporating a barrier function. This function is derived from the Bellman optimality principle. It addresses the state constraints throughout the control design process. A novel online continual lifelong learning (CLL) method is introduced for MR formation. This method leverages the Bellman residual error for weight significance in MNNs. It addresses catastrophic forgetting and interlayer dependence through layer-specific regularizers. …
Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed
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. …
Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson
Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson
Electrical and Computer Engineering Faculty Research & Creative Works
This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped …
Tightening Qc Relaxations Of Ac Optimal Power Flow Through Improved Linear Convex Envelopes, Mohammad Rasoul Narimani, Daniel K. Molzahn, Katherine R. Davis, Mariesa L. Crow
Tightening Qc Relaxations Of Ac Optimal Power Flow Through Improved Linear Convex Envelopes, Mohammad Rasoul Narimani, Daniel K. Molzahn, Katherine R. Davis, Mariesa L. Crow
Electrical and Computer Engineering Faculty Research & Creative Works
AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear …
Simultaneous Frequency Regulation And Active Power Sharing In Islanded Microgrid Using Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Lauryn Morris, Praneeth Uddarraju, Jonathan W. Kimball
Simultaneous Frequency Regulation And Active Power Sharing In Islanded Microgrid Using Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Lauryn Morris, Praneeth Uddarraju, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a novel approach that integrates deep reinforcement learning (DRL) with the conventional virtual synchronous generator (VSG) to address dual objectives of microgrid (MG) control, frequency regulation and precise active power sharing. MGs typically consist of multiple Inverter-Based-Distributed-Generators (IBDGs) connected in parallel through different line impedances. The conventional active power loop (APL) of the VSG encounters significant steady-state frequency errors as load increases/decreases during islanded operation. To mitigate this issue, secondary-level controllers like proportional-integral (PI) control are added to the APL to regulate the frequency of IBDGs. However, PI control compromises power-sharing capabilities when the impedance values of …
Sensor Tasking For Low Earth Orbit Objects: Leveraging Space Sensor Data For Ground-Based Optical Observations, Smriti Nandan Paul, Hang Woon Lee
Sensor Tasking For Low Earth Orbit Objects: Leveraging Space Sensor Data For Ground-Based Optical Observations, Smriti Nandan Paul, Hang Woon Lee
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Because of recent advancements in space technologies, easier and more economical access to space, and an increase in commercial interests, the near-Earth space environment has witnessed an exploding number of objects being put into orbit. In particular, the low Earth orbit (LEO) region is at an increased risk of orbital collisions from large satellite constellation projects. Thus, monitoring LEO objects for space situational awareness and space traffic management has become increasingly imperative. In this paper, we use the concept of limited-CDF (cumulative distribution function) surface and mutual information for designing sensor tasking algorithms focusing on regular observation of known catalog …
Damage Initiation Of 15v38 Steel Bar During Square-To-Round Hot Rolling Process, Siva Sai Krishna Dasari, Henry Adekola Haffner, K. Chandrashekhara, Mario F. Buchley, Simon N. Lekakh, Ronald J. O'Malley
Damage Initiation Of 15v38 Steel Bar During Square-To-Round Hot Rolling Process, Siva Sai Krishna Dasari, Henry Adekola Haffner, K. Chandrashekhara, Mario F. Buchley, Simon N. Lekakh, Ronald J. O'Malley
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Hot rolling processes have been extensively used to produce round bars by reducing the cross-sectional area of continuously cast steel. The current trend toward increasing productivity often requires a more aggressive reduction per pass. Establishing safe and optimized hot rolling parameters must be determined to avoid damage while deforming the specific steel composition. Understanding the damage mechanism during the metal forming process is vital for product quality. Herein, a combined experimental and simulation approach is developed to track the evolution potential damage during hot bar rolling. Hot tension tests are conducted on as-cast vanadium micro alloyed 15V38 steel at different …
L∞ Bounds For Transient Growth In Repetitive And Iterative Learning Control Systems, Douglas A. Bristow, John R. Singler
L∞ Bounds For Transient Growth In Repetitive And Iterative Learning Control Systems, Douglas A. Bristow, John R. Singler
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This paper revisits the problem of large transient growth in Iterative Learning Control (ILC) and Repetitive Process Control (RPC) systems. In ILC and RPC problems a process is repeated iteratively, with new control calculations occurring in between each iteration. Large transient growth refers to the propensity of some control algorithms to grow error exponentially before eventually converging. While robust monotonic convergence algorithms (in which monotonic convergence is guaranteed usually in exchange for a small loss in performance) have largely eliminated the concern for large transient growth in ILC, similar results cannot always be obtained in RPC. The emergence of additive …
Optimal Tilt-Wing Evtol Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks, Shuan Tai Yeh, Xiaosong Du
Optimal Tilt-Wing Evtol Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks, Shuan Tai Yeh, Xiaosong Du
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of …
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
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 …
Tailored (La0.2pr0.2nd0.2tb0.2dy0.2)2ce2o7 As A Highly Active And Stable Nanocatalyst For The Oxygen Evolution Reaction, Sreya Paladugu, Ibrahim Munkaila Abdullahi, Palani Raja Jothi, Bo Jiang, Manashi Nath, Katharine Page
Tailored (La0.2pr0.2nd0.2tb0.2dy0.2)2ce2o7 As A Highly Active And Stable Nanocatalyst For The Oxygen Evolution Reaction, Sreya Paladugu, Ibrahim Munkaila Abdullahi, Palani Raja Jothi, Bo Jiang, Manashi Nath, Katharine Page
Chemistry Faculty Research & Creative Works
Designing highly active and robust catalysts for the oxygen evolution reaction is key to improving the overall efficiency of the water splitting reaction. It has been previously demonstrated that evaporation induced self-assembly (EISA) can be used to synthesize highly porous and high surface area cerate-based fluorite nano catalysts, and that substitution of Ce with 50% rare earth (RE) cations significantly improves electrocatalyst activity. Herein, the defect structure of the best performing nano catalyst in the series are further explored, Nd2Ce2O7, with a combination of neutron diffraction and neutron pair distribution function analysis. It is …
Perfect Polar Alignment Of Parallel Beloamphiphile Layers: Improved Structural Design Bias Realized In Ferroelectric Crystals Of The Novel “Methoxyphenyl Series Of Acetophenone Azines”, Harmeet Bhoday, Nathan Knotts, Rainer Glaser
Perfect Polar Alignment Of Parallel Beloamphiphile Layers: Improved Structural Design Bias Realized In Ferroelectric Crystals Of The Novel “Methoxyphenyl Series Of Acetophenone Azines”, Harmeet Bhoday, Nathan Knotts, Rainer Glaser
Chemistry Faculty Research & Creative Works
An Improved Design Is Described For Ferroelectric Crystals And Implemented With The "Methoxyphenyl Series" Of Acetophenone Azines, (MeO−Ph, Y)-Azines With Y=F (1), Cl (2), Br (3), Or I (4). The Crystal Structures Of These Azines Exhibit Polar Stacking Of Parallel Beloamphiphile Monolayers (PBAMs). Azines 1, 3, And 4 Form True Racemates Whereas Chloroazine 2 Crystallizes As A Kryptoracemate. Azines 1–4 Are Helical Because Of The N−N Bond Conformation. In True Racemates The Molecules Of Opposite Helicity (M And P) Are Enantiomers A(M) And A*(P) While In Kryptoracemates They Are Diastereomers A(M) And B*(P). The Stacking Mode Of PBAMs Is Influenced …
Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test Using Ensemble Machine Learning Techniques, Alireza Roshan, Magdy Abdelrahman
Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test Using Ensemble Machine Learning Techniques, Alireza Roshan, Magdy Abdelrahman
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Aggregate is the most extracted material from the world's mines and widely used in civil and construction projects. The Micro-Deval abrasion test (MD) is one of the most important tests that provides characteristics of crushed aggregates that show their resistance against mechanical abrasive factors such as repeated impact loading. The impact of various factors on abrasive resistance properties of aggregates has led researchers to seek correlations, often focusing on limited data samples, leading to reduced accuracy. This study employs machine learning (ML) methods to predict MD abrasion values, considering diverse aggregate properties. Various ensemble ML methods were applied, revealing the …
Photogrammetry-Based Method For Measuring Volume Changes Of Soil Specimens During Critical Phases Of Triaxial Testing, Sara Fayek, Xiong Zhang, Pingxin Xia
Photogrammetry-Based Method For Measuring Volume Changes Of Soil Specimens During Critical Phases Of Triaxial Testing, Sara Fayek, Xiong Zhang, Pingxin Xia
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Triaxial tests has been routinely used to measure the stress–strain relationship for geomaterials. During triaxial testing, many sources of errors that cannot be completely avoided but are often ignored or approximated using empirical equations. This paper presents a systematic investigation of soil volume change, volume strain nonuniformity, and cross-sectional calculation along the specimen during triaxial testing using a photogrammetry-based method. Consolidated drained triaxial tests were performed in which two parallel measurements were taken: (1) relative volume using the conventional triaxial testing and (2) absolute volume using the photogrammetry-based method. The difference in the observed volume and void ratio measurements between …
Two-Center And Path Interference In Dissociative Capture In P+ H2 Collisions, S. Bastola, M. Dhital, B. Lamichhane, A. Silvus, R. Lomsadze, A. Hasan, A. Igarashi, Michael Schulz
Two-Center And Path Interference In Dissociative Capture In P+ H2 Collisions, S. Bastola, M. Dhital, B. Lamichhane, A. Silvus, R. Lomsadze, A. Hasan, A. Igarashi, Michael Schulz
Physics Faculty Research & Creative Works
We have measured and calculated fully differential cross sections (FDCS) for dissociative capture in 75-keV p+H2 collisions. FDCS were analyzed in the kinetic energy release (KER) ranges 0 to 2.1 eV and 4 to 7 eV for two different molecular orientations. In the latter range, dissociation is dominated by electronic excitation to the 2pπu state. Here, we observed two-center interference for an orientation in the plane perpendicular to the initial beam axis and parallel to the transverse momentum transfer. The interference pattern is afflicted with a constant phase shift of π. In the range KER=0 to 2.1 eV, dissociation is …
Spectrum And Quench-Induced Dynamics Of Spin-Orbit-Coupled Quantum Droplets, Sonali Gangwar, Rajamanickam Ravisankar, S. (Simeon) I. Mistakidis, Paulsamy Muruganandam, Pankaj Kumar Mishra
Spectrum And Quench-Induced Dynamics Of Spin-Orbit-Coupled Quantum Droplets, Sonali Gangwar, Rajamanickam Ravisankar, S. (Simeon) I. Mistakidis, Paulsamy Muruganandam, Pankaj Kumar Mishra
Physics Faculty Research & Creative Works
We investigate the ground state and dynamics of one-dimensional spin-orbit coupled (SOC) quantum droplets within the extended Gross-Pitaevskii approach. As the SOC wave number increases, stripe droplet patterns emerge, with a flat-top background, for larger particle numbers. The surface energy decays following a power-law with respect to the interactions. At small SOC wave numbers, a transition from Gaussian to flat-top droplets occurs for either a larger number of atoms or reduced intercomponent attraction. The excitation spectrum shows that droplets for relatively small SOC wave numbers are stable, otherwise stripe droplets feature instabilities as a function of the particle number or …
Disseminating Over-The-Air Updates Via Intelligent Labeling In Multi-Tier Networks, Atefeh Asayesh, Asad Waqar Malik, Sajal K. Das
Disseminating Over-The-Air Updates Via Intelligent Labeling In Multi-Tier Networks, Atefeh Asayesh, Asad Waqar Malik, Sajal K. Das
Computer Science Faculty Research & Creative Works
Connected Vehicles Rely on Sophisticated Software Systems for Diverse Features, Including Navigation, Entertainment, Communication, and Safety Functions. as Technology Continues to Advance, the Reliance on Software in Connected Vehicles Becomes Increasingly Integral to their overall Performance and the Delivery of Innovative Features. Therefore, in the Domain of Software-Enabled Automobiles, the Implementation of over-The-Air (OTA) Software Updates is Deemed Essential for the Dissemination of Software and Fixes in Connected Vehicles. the Conventional Method of Addressing This Matter Entailed Manufacturers Undertaking the Task of Recalling Outdated Vehicles; However, the Central Issue Lies in the Considerable Challenge of Effectively Notifying Owners through Recall …
A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi
A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi
Engineering Management and Systems Engineering Faculty Research & Creative Works
Reinforcement learning (RL) is an exciting area within the domain of Markov Decision Processes (MDPs) in which the underlying optimization problem is solved either in a simulator of the real-world system or via direct interaction with the real-world system, when its underlying transition probabilities are difficult to estimate. The latter is commonly true of large-scale, real-world MDPs with complex underlying transition dynamics. RL is currently being widely researched in the world of medicine/neuroscience after some spectacular success stories demonstrating super-human behavior in computer games. In this paper, we propose a new actor-critic-based RL algorithm for approximately solving continuous state/action MDPs …
Undeniable Authentication Of Digital Twin-Managed Smart Microfactory, Anusha Vangala, Ashok Kumar Das, Sajal K. Das
Undeniable Authentication Of Digital Twin-Managed Smart Microfactory, Anusha Vangala, Ashok Kumar Das, Sajal K. Das
Computer Science Faculty Research & Creative Works
Smart Microfactories Use Additive Manufacturing to Create Products with Mixed Materials and Variable Sizes. Digital Twin Technology Enhances Control of the Additive Manufacturing Equipment in These Factories, Increasing Productivity and Minimizing Errors. the Digital Twins Communicate with the Machines to Furnish Sensitive Data and Instructions, Which Must Be Protected from Tampering. Authentication Rescues the Digital and Physical Twins from Menacing Attacks Such as Privileged Insider, Impersonation, Ephemeral Secret Leakage (ESL) and Man-In-The-Middle (MiTM) Attacks. to This End, We Propose Lightweight Authentication among the Digital and Physical Twins with the Undeniability of Issued Commands and Deniable Key Agreement. It Achieves Perfect …
Lease: Leveraging Energy-Awareness In Serverless Edge For Latency-Sensitive Iot Services, Aastik Verma, Anurag Satpathy, Sajal K. Das, Sourav Kanti Addya
Lease: Leveraging Energy-Awareness In Serverless Edge For Latency-Sensitive Iot Services, Aastik Verma, Anurag Satpathy, Sajal K. Das, Sourav Kanti Addya
Computer Science Faculty Research & Creative Works
Resource Scheduling Catering to Real-Time IoT Services in a Serverless-Enabled Edge Network is Particularly Challenging Owing to the Workload Variability, Strict Constraints on Tolerable Latency, and Unpredictability in the Energy Sources Powering the Edge Devices. This Paper Proposes a Framework LEASE that Dynamically Schedules Resources in Serverless Functions Catering to Different Microservices and Adhering to their Deadline Constraint. to Assist the Scheduler in Making Effective Scheduling Decisions, We Introduce a Priority-Based Approach that Offloads Functions from over-Provisioned Edge Nodes to Under-Provisioned Peer Nodes, Considering the Expended Energy in the Process Without Compromising the Completion Time of Microservices. for Real-World Implementations, …
Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo
Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo
Computer Science Faculty Research & Creative Works
In the Ambitious Realm of Space AI, the Integration of Federated Learning (FL) with Low Earth Orbit (LEO) Satellite Constellations Holds Immense Promise. However, Many Challenges Persist in Terms of Feasibility, Learning Efficiency, and Convergence. These Hurdles Stem from the Bottleneck in Communication, Characterized by Sporadic and Irregular Connectivity between LEO Satellites and Ground Stations, Coupled with the Limited Computation Capability of Satellite Edge Computing (SEC). This Paper Proposes a Novel FL-SEC Framework that Empowers LEO Satellites to Execute Large-Scale Machine Learning (ML) Tasks Onboard Efficiently. its Key Components Include I) Personalized Learning Via Divide-And-Conquer, Which Identifies and Eliminates Redundant …
Development Of A Deep Neural Network And Empirical Model For Predicting Local Gas Holdup Profiles In Bubble Columns, Sebastián Uribe, Ahmed Alalou, Mario E. Cordero, Muthanna H. Al-Dahhan
Development Of A Deep Neural Network And Empirical Model For Predicting Local Gas Holdup Profiles In Bubble Columns, Sebastián Uribe, Ahmed Alalou, Mario E. Cordero, Muthanna H. Al-Dahhan
Chemical and Biochemical Engineering Faculty Research & Creative Works
Estimating local gas holdup profiles in bubble columns is key for their performance evaluation and optimization, as well as for design and scale-up tasks. Up to the current day, there are important limitations in the accuracy and range of applicability of the available models in literature. Two alternatives for the prediction of such local fields can be found in the application of empirical models and the development of deep neural networks (DNN). The main drawback preventing the application of these techniques in previous years was the availability of a large enough databank of local gas holdup experimental measurements. Advances over …
Communication-Efficient Federated Learning For Leo Constellations Integrated With Haps Using Hybrid Noma-Ofdm, Mohamed Elmahallawy, Tony T. Luo, Khaled Ramadan
Communication-Efficient Federated Learning For Leo Constellations Integrated With Haps Using Hybrid Noma-Ofdm, Mohamed Elmahallawy, Tony T. Luo, Khaled Ramadan
Computer Science Faculty Research & Creative Works
Space AI has become increasingly important and sometimes even necessary for government, businesses, and society. An active research topic under this mission is integrating federated learning (FL) with satellite communications (SatCom) so that numerous low Earth orbit (LEO) satellites can collaboratively train a machine learning model. However, the special communication environment of SatCom leads to a very slow FL training process up to days and weeks. This paper proposes NomaFedHAP, a novel FL-SatCom approach tailored to LEO satellites, that (1) utilizes high-altitude platforms (HAPs) as distributed parameter servers (PSs) to enhance satellite visibility, and (2) introduces non-orthogonal multiple access (NOMA) …
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4 Time-Slotted Channel Hopping (TSCH) protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop …
Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das
Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on …
Towards Fine-Gained Services: Nfv-Assisted Tracking And Positioning Using Micro-Services For Multi-Robot Cooperation, Bo Yi, Lin Qiu, Jianhui Lv, Yingpu Nian, Xingwei Wang, Sajal K. Das
Towards Fine-Gained Services: Nfv-Assisted Tracking And Positioning Using Micro-Services For Multi-Robot Cooperation, Bo Yi, Lin Qiu, Jianhui Lv, Yingpu Nian, Xingwei Wang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Robotics as a Service (RaaS) emerges as a new paradigm to motivate diversified potential of the "remote-controlled economy" for flexible and efficient service provision with the help of cloud computing. The multi-robot cooperation (MRC) technology has been widely used in various intelligent logistics scenarios, such as warehouses, factories, airports and subway stations, benefiting from the advantages of high operational efficiency and low labor cost. While promising, the corresponding challenge is that the service functions deployed on logistics robots (LRs) are more prone to failures such as resource exhaustion and error configuration in the multi-robot system (MRS). In this way, it …