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Articles 901 - 930 of 13207
Full-Text Articles in Entire DC Network
Freyr⁺: Harvesting Idle Resources In Serverless Computing Via Deep Reinforcement Learning, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Freyr⁺: Harvesting Idle Resources In Serverless Computing Via Deep Reinforcement Learning, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Computer Science Faculty Research & Creative Works
Serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions - the actual resource consumption may vary due to function types, dependencies, and input data sizes, thus mismatching the static resource configuration by users. Dynamic resource consumption against static configuration may lead to either poor function execution performance or low utilization. This paper proposes Freyr+, a novel resource manager (RM) that dynamically harvests idle resources from over-provisioned functions to accelerate under-provisioned functions for serverless platforms. Freyr+ monitors each function's resource utilization in real-time …
Smart Connected Farms And Networked Farmers To Improve Crop Production, Sustainability And Profitability, Asheesh K. Singh, Behzad J. Balabaygloo, Barituka Bekee, Samuel W. Blair, Suzanne Fey, Fateme Fotouhi, Ashish Gupta, Amit Jha, Jorge C. Martinez-Palomares, Kevin Menke, Aaron Prestholt, Vishesh K. Tanwar, Xu Tao, Anusha Vangala, Matthew E. Carroll, Sajal K. Das, Guilherme Depaula
Smart Connected Farms And Networked Farmers To Improve Crop Production, Sustainability And Profitability, Asheesh K. Singh, Behzad J. Balabaygloo, Barituka Bekee, Samuel W. Blair, Suzanne Fey, Fateme Fotouhi, Ashish Gupta, Amit Jha, Jorge C. Martinez-Palomares, Kevin Menke, Aaron Prestholt, Vishesh K. Tanwar, Xu Tao, Anusha Vangala, Matthew E. Carroll, Sajal K. Das, Guilherme Depaula
Computer Science Faculty Research & Creative Works
To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. a network and coordinated farmer network provide unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. the aim of this article is to provide a comprehensive overview of the state of the art in SCF including the …
Achieving Efficient And Privacy-Preserving Reverse Skyline Query Over Single Cloud, Yubo Peng, Xiong Li, Ke Gu, Jinjun Chen, Sajal K. Das, Xiaosong Zhang
Achieving Efficient And Privacy-Preserving Reverse Skyline Query Over Single Cloud, Yubo Peng, Xiong Li, Ke Gu, Jinjun Chen, Sajal K. Das, Xiaosong Zhang
Computer Science Faculty Research & Creative Works
Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine …
The Perils Of Wi-Fi Spoofing Attack Via Geolocation Api And Its Defense, Xiao Han, Junjie Xiong, Wenbo Shen, Mingkui Wei, Shangqing Zhao, Zhuo Lu, Yao Liu
The Perils Of Wi-Fi Spoofing Attack Via Geolocation Api And Its Defense, Xiao Han, Junjie Xiong, Wenbo Shen, Mingkui Wei, Shangqing Zhao, Zhuo Lu, Yao Liu
Computer Science Faculty Research & Creative Works
Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in …
Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria
Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria
Computer Science Faculty Research & Creative Works
Localization in a battlefield environment is increasingly challenging as GPS connectivity is often denied or unreliable, and physical deployment of anchor nodes across wireless networks for localization can be difficult in hostile battlefield terrain. This paper proposes a novel framework for the localization of moving objects in non-GPS battlefield environments using stereo vision and a deep learning model by recognizing naturally existing or artificial landmarks as anchors. The proposed method utilizes a custom-calibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark anchor recognition. The depth images are …
Mild Cognitive Impairment Classification Using A Novel Finer-Scale Brain Connectome, Yanjun Lyu, Lu Zhang, Xiaowei Yu, Chao Cao, Tianming Liu, Dajiang Zhu
Mild Cognitive Impairment Classification Using A Novel Finer-Scale Brain Connectome, Yanjun Lyu, Lu Zhang, Xiaowei Yu, Chao Cao, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Mild cognitive impairment (MCI) is recognized as a precursor to Alzheimer's disease (AD), a progressive and irreversible neurodegenerative disorder of the brain. The neurodegeneration of brain connectivity networks plays a pivotal role in the development and progression of MCI. Traditionally, brain networks are generated using coarse-grained brain regions, where the regions serve as nodes and their functional or structural connections are used as edges. Recently, a novel finer scale brain folding patterns named 3hinge gyrus (3HG) was identified, which is defined as the conjunctions coming from three directions on gyral crests. 3HGs have been shown playing an important role in …
Cav-Ad: A Robust Framework For Detection Of Anomalous Data And Malicious Sensors In Cav Networks, Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Cav-Ad: A Robust Framework For Detection Of Anomalous Data And Malicious Sensors In Cav Networks, Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Computer Science Faculty Research & Creative Works
The adoption of connected and automated vehicles (CAVs) has sparked considerable interest across diverse industries, including public transportation, underground mining, and agriculture sectors. However, CAVs' reliance on sensor readings makes them vulnerable to significant threats. Manipulating these readings can compromise CAV network security, posing serious risks for malicious activities. Although several anomaly detection (AD) approaches for CAV networks are proposed, they often fail to: i) detect multiple anomalies in specific sensor(s) with high accuracy or F1 score, and ii) identify the specific sensor being attacked. In response, this paper proposes a novel framework tailored to CAV networks, called CAV-AD, for …
Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch
Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch
Economics Faculty Research & Creative Works
The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO2 emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were …
Circular Economy Strategies For Reducing Embodied Carbon In Us Commercial Building Stocks: A System Dynamics Modeling Approach, Radwa Eissa, Islam H. El-Adaway
Circular Economy Strategies For Reducing Embodied Carbon In Us Commercial Building Stocks: A System Dynamics Modeling Approach, Radwa Eissa, Islam H. El-Adaway
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Environmental concerns over embodied carbon - which is generated during the extraction, transportation, manufacturing, construction, and disposal of building materials - have been increasing as the industry shifts to renewable energy and grid decarbonization efforts prevail. Commercial buildings, a rapidly growing sector and a major source of embodied carbon, can contribute immensely to the national climate goals by transitioning into a circular economy (CE). Nevertheless, embodied carbon research is rather dispersed, with sparse data on the actual impact of different CE strategies and how they scale on nationwide commercial building stocks. To address this research need, the goal of this …
Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee
Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee
Computer Science Faculty Research & Creative Works
No abstract provided.
A New Proper Orthogonal Decomposition Method With Second Difference Quotients For The Wave Equation, Andrew Calvin Janes
A New Proper Orthogonal Decomposition Method With Second Difference Quotients For The Wave Equation, Andrew Calvin Janes
Masters Theses
"Recently, researchers have investigated the relationship between proper orthogonal decomposition (POD), difference quotients (DQs), and pointwise in time error bounds for POD reduced order models of partial differential equations. In \cite {Sarahs}, a new approach to POD with DQs was developed that is more computationally efficient than the standard DQ POD approach and it also retains the guaranteed pointwise in time error bounds of the standard method. In this thesis, we extend the new DQ POD approach from \cite {Sarahs} to the case of second difference quotients (DDQs). Specifically, a new POD method utilizing DDQs and only one snapshot and …
The Geodetic Strain Rates From Gnss In The North American-Carribean-Cocos Triple Junction In Guatemala And Implications For Seismic Hazard, Tanaya Kashyap
The Geodetic Strain Rates From Gnss In The North American-Carribean-Cocos Triple Junction In Guatemala And Implications For Seismic Hazard, Tanaya Kashyap
Masters Theses
"Geodetic velocity data are pivotal for deciphering seismic risks, providing initial constraints for crustal surface strain rates. Gaining an understanding of the 2D surface strain tensor across an entire area necessitates knowledge of the surface velocity field, yet geodetic data such as measurements from the Global Navigation Satellite System (GNSS) are dispersed across the Earth’s surface. In the current scope of study, we have employed various methodologies to estimate strain rates in Guatemala, utilizing GNSS velocity data and leveraging the open-source Python tool Strain_2D. Strain_2D facilitates the computation of strain rates through diverse methods applied to the same dataset and …
Branching Fractional Brownian Motion, Reece Beattie-Hauser
Branching Fractional Brownian Motion, Reece Beattie-Hauser
Masters Theses
"Fractional Brownian Motion (FBM) is a Gaussian process whose increments are correlated over long times. FBM is an example of anomalous diffusion, and recently it has been used to model the distribution of serotonergic fibers in the brain [1, 2]. To better represent these fibers, branching FBM (bFBM), where FBM trajectories may randomly split into two, is introduced. One-dimensional bFBM is studied in both sub diffusive and super diffusive regimes, examining three potential behaviors of the correlations (memory) in a branching event: both trajectories retain the memory of previous steps, only one keeps the memory, and neither keeps the memory. …
Human Arm Stiffness Estimation Method Developed For Overground Physical Interaction Experiments: Experimental Validation And Perspectives For Future Applications, Tarani Kanth Kamma
Human Arm Stiffness Estimation Method Developed For Overground Physical Interaction Experiments: Experimental Validation And Perspectives For Future Applications, Tarani Kanth Kamma
Masters Theses
"To build a physically interactive robot for overground applications, it is crucial to first understand the biomechanics of humans underlying overground physical human-robot interaction (pHRI) tasks. Estimating human arm stiffness during overground interactive tasks is a promising first step toward this goal. For this, an arm stiffness estimation technique was developed in our previous works that consider the unique challenges involving overground pHRI, such as the need to estimate the arm stiffness from a short duration of data with fewer repetitions. In this work, our stiffness estimation method is further validated with a passive spring setup with known stiffness values, …
Pseudo-Three-Dimensional Back-Analysis Of Rainfall-Induced Landslides In Utuado, Puerto Rico, Mirna Kassem, Weibing Gong, Dimitrios Zekkos, Marin Clark, Stephen Hughes
Pseudo-Three-Dimensional Back-Analysis Of Rainfall-Induced Landslides In Utuado, Puerto Rico, Mirna Kassem, Weibing Gong, Dimitrios Zekkos, Marin Clark, Stephen Hughes
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Regional slope stability analyses can provide a system-level assessment of landslide susceptibility. This study aims to develop a regional back-analysis model for rainfall-induced landslides to compute ground shear strength that can be used for landslide prediction. Most commonly, mechanistic-based models for regional slope stability consist of simplified one-dimensional (1D) analyses compared to more realistic three-dimensional (3D) procedures. A pseudo-3D methodology is applied on 788 landslides over two 2.5 km2 areas in the plutonic rocks of the Utuado Batholith. First, an infinite slope stability triggering model is applied on a 1 m-resolution digital elevation model (DEM) to identify the triggering grid …
Incivility In Open Source Projects: A Comprehensive Annotated Dataset Of Locked Github Issue Threads, Ramtin Ehsani, Mia Mohammad Imran, Robert Zita, Kostadin Damevski, Preetha Chatterjee
Incivility In Open Source Projects: A Comprehensive Annotated Dataset Of Locked Github Issue Threads, Ramtin Ehsani, Mia Mohammad Imran, Robert Zita, Kostadin Damevski, Preetha Chatterjee
Computer Science Faculty Research & Creative Works
In the dynamic landscape of open-source software (OSS) development, understanding and addressing incivility within issue discussions is crucial for fostering healthy and productive collaborations. This paper presents a curated dataset of 404 locked GitHub issue discussion threads and 5961 individual comments, collected from 213 OSS projects. We annotated the comments with various categories of incivility using Tone Bearing Discussion Features (TBDFs), and, for each issue thread, we annotated the triggers, targets, and consequences of incivility. We observed that Bitter frustration, Impatience, and Mocking are the most prevalent TBDFs exhibited in our dataset. The most common triggers, targets, and consequences of …
System Dynamics Modeling For Investigating The Retention Of Skilled Labor In The Construction Market, Tamima Elbashbishy, Islam H. El-Adaway
System Dynamics Modeling For Investigating The Retention Of Skilled Labor In The Construction Market, Tamima Elbashbishy, Islam H. El-Adaway
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Limited skilled labor has been one of the greatest challenges facing the construction industry, especially after COVID. In 2019, around 80% of contractors in the US reported problems in hiring enough skilled workers. Labor retention has been one of the major contributors to skilled labor shortages in construction. The goal of this paper is to study the impact of key construction industry characteristics and economic conditions on skilled labor shortages. This goal is achieved by simulating the flow of skilled workers throughout the construction industry. To this end, a system dynamics (SD) model was developed to represent skilled labor and …
Investigating Price Fluctuation Transmission Among Construction Materials, Mohamad Abdul Nabi, Islam H. El-Adaway, Rayan H. Assaad
Investigating Price Fluctuation Transmission Among Construction Materials, Mohamad Abdul Nabi, Islam H. El-Adaway, Rayan H. Assaad
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Generally, Fluctuation In One Material's Price Can Cause A Series Of Chain Reactions In The Supply Chain System, Known As Price Fluctuation Transmission, As All Materials Are Interconnected And Interrelated. None Of The Previous Studies Have Investigated Price Fluctuation Transmission Among All Construction Materials. This Paper Aims To Fill This Gap In Knowledge. The Authors Collected Producer Price Index (PPI) Data For 16 Construction Materials, Modeled The Relationship Between Each Pair Of Materials Using Vector Autoregression Technique, And Validated The Causality Using Granger Causality Test. Network Analysis Was Performed To Identify The Price Fluctuation Transmission Capacity, Susceptibility, And Intermediatory Capacity …
Selected Trace Element Uptake By Rice Grain As Affected By Soil Arsenic, Water Management And Cultivar -A Field Investigation, Eric M. Farrow, Jianmin Wang, Honglan Shi, John Yang, Bin Hua, Baolin Deng
Selected Trace Element Uptake By Rice Grain As Affected By Soil Arsenic, Water Management And Cultivar -A Field Investigation, Eric M. Farrow, Jianmin Wang, Honglan Shi, John Yang, Bin Hua, Baolin Deng
Chemistry Faculty Research & Creative Works
Accumulation of arsenic (As) in rice grain was reported in many regions of the world, including the United States, which has been a threat to human health. This field research investigated the grain As accumulation and its relationship with the uptake of selenium (Se), molybdenum (Mo), and cadmium (Cd) in soils with and without monosodium methane arsonate (MSMA) amended, as effects of selected rice cultivars and water management. Results indicated that MSMA increased the accumulation of As and Se but decreased Mo for all six cultivars under four irrigation management. MSMA also increased grain-Cd in some cultivars. In no MSMA-amended …
Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi
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. …
Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan
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 …
Cryptographic Algorithms, Cryptocurrencies, And A Predictive Model Of Bitcoin Value By Pls Regression, Paul Kenneth O'Connor
Cryptographic Algorithms, Cryptocurrencies, And A Predictive Model Of Bitcoin Value By Pls Regression, Paul Kenneth O'Connor
Masters Theses
"With the invention of Bitcoin in 2009, as a seemingly timed response to the ongoing financial crisis, the popularity of the cryptocurrency has since continued to grow. Just this year, the Security Exchange Commission approved Bitcoin for exchange traded funds, allowing major investment firms to begin product trading. With this approval, and during this very moment of writing, Bitcoin has entered a bull market and reached a record value of over 72,000 USD. In addition, the Bitcoin halving event in April of 2024 is expected to increase demand even further. It has been anticipated that Bitcoin and other cryptocurrencies will …
Theoretical Modeling And Experimental Investigation Of Phase Selection During Rapid Solidification Of Alloys, Azeez Aremu Akinbo
Theoretical Modeling And Experimental Investigation Of Phase Selection During Rapid Solidification Of Alloys, Azeez Aremu Akinbo
Masters Theses
"A novel model, rooted in time-dependent nucleation theory, has been created to explore how rapid solidification affects the extended solubility in metal alloys. This model was used to forecast solubility concerning undercooling in multiple binary aluminum (Al) alloys, and its predictions for both eutectic and peritectic systems closely match experimental data. It was demonstrated that this developed model surpasses the T0 line method, which does not consider the kinetic aspects of nucleation. Furthermore, the model can be extended to ternary and multicomponent phases by assuming that the scarcest element or the slowest diffusing component restricts nucleation. Al-Cu and Al-Cr, vital …
Learning Social Fairness Preferences From Non-Expert Stakeholder Opinions In Kidney Placement, Mukund Telukunta, Sukruth Rao, Gabriella Stickney, Venkata Sriram Siddardh Nadendla, Casey I. Canfield
Learning Social Fairness Preferences From Non-Expert Stakeholder Opinions In Kidney Placement, Mukund Telukunta, Sukruth Rao, Gabriella Stickney, Venkata Sriram Siddardh Nadendla, Casey I. Canfield
Computer Science Faculty Research & Creative Works
Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial attempts were made in the literature to study algorithmic fairness in kidney placement, these methods replace true outcomes with surgeons' decisions due to the long delays involved in recording such outcomes reliably. However, the replacement of true outcomes with surgeons' decisions disregards expert stakeholders' biases as well as social opinions of other stakeholders who do not possess medical expertise. This paper alleviates the latter concern and designs a novel fairness feedback survey to evaluate an acceptance rate predictor (ARP) …
A Hybrid Algorithm To Dual Sparse Sampling Measurement In Time-Resolved Electromagnetic Near-Field Scanning, Yanming Zhang, Steven Gao, Lijun Jiang
A Hybrid Algorithm To Dual Sparse Sampling Measurement In Time-Resolved Electromagnetic Near-Field Scanning, Yanming Zhang, Steven Gao, Lijun Jiang
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation …
An Unsupervised Learning Framework For Determining The Excitation Coefficients Using Near-Field Antenna Measurements, Yanming Zhang, Peifeng Ma, Steven Gao, Lijun Jiang
An Unsupervised Learning Framework For Determining The Excitation Coefficients Using Near-Field Antenna Measurements, Yanming Zhang, Peifeng Ma, Steven Gao, Lijun Jiang
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
This article presents a novel unsupervised learning framework based on multiscale dynamic mode decomposition for determining the excitation coefficients of antennas using time-domain near-field measurements. The proposed framework integrates temporal multiscale analysis to extract a joint distribution of frequency, damping factors, and spatial modes, enabling precise extraction of excitation frequencies, rising/falling edges, and phases without labeled data. We validate the effectiveness of the proposed approach through two examples involving on-off keying modulation and a phase-shift dipole antenna. It is found that the proposed method performs well in handling nonstationary excitation signals and proves particularly advantageous for calibrating tunable antenna systems. …
A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang
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 …
Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang
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 …
Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan
Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This paper addresses a constrained neural network (NN)-based optimal tracking scheme for a class of uncertain nonlinear discrete-time systems in strict-feedback form by using a control barrier function (CBF). First, a modified barrier-type cost function is introduced for each subsystem, guiding the actual system trajectory toward the safe set or desired trajectory while avoiding unwanted sets. To address the tracking problem, an augmented system is employed to convert the time-varying optimal tracking to a time-invariant optimal regulation. Then, an actor-critic framework is employed with the backstepping technique to obtain both virtual and actual optimal control policies for each subsystem to …
Survey Of Hidden Markov Models (Hmms) For Sign Language Recognition (Slr), Iwan Sandjaja, Ahmad Alsharoa, Donald Wunsch, Jian Liu
Survey Of Hidden Markov Models (Hmms) For Sign Language Recognition (Slr), Iwan Sandjaja, Ahmad Alsharoa, Donald Wunsch, Jian Liu
Electrical and Computer Engineering Faculty Research & Creative Works
This paper surveyed several significant papers on specific topics applying the Hidden Markov Models (HMMs) for Sign Language Recognition (SLR), divided into five main episodes: Classical HMMs, Extended HMMs, HMMs and Machine Learning, HMMs and Sensor Fusion, and HMMs and Big Data. This stringent survey would contribute significantly to advanced research on unification brain models such as neural networks, adaptive resonance theory, and confabulation theory. First, the HMM was introduced as one of the popular methods of performing SLR, and each episode of its development was expounded. In each episode, a main paper and several supporting papers were summarized. Next, …