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2024

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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Modeling Of Power Distrubition Network (Pdn) Noise Coupling Induced Clock Phase Noise, Zhekun Peng, Junyong Park, Chaofeng Li, Joey Stecher, Srinivas Venkataraman, Xu Wang, Granthana Rangaswamy, Donghyun Kim Jan 2024

Modeling Of Power Distrubition Network (Pdn) Noise Coupling Induced Clock Phase Noise, Zhekun Peng, Junyong Park, Chaofeng Li, Joey Stecher, Srinivas Venkataraman, Xu Wang, Granthana Rangaswamy, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

Phase noise analysis is important to clock design. Noise sources of spurs shown on the phase noise result are challenging to find out due to the unknown source locations and coupling mechanisms. Noise from power distribution network (PDN) is one of the most troublesome sources. A behavioral modeling methodology is proposed to simulate the clock phase noise induced by PDN noise coupling for two different mechanisms: PDN-to-clock additive coupling and PDN-to-PDN up-conversion modulation. The thermal noise and 1/f flicker noise are simplified to provide a straightforward view of spur level. The model can be applied to both single-ended clock and …

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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 Jan 2024

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 …

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Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch Jan 2024

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 …

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Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria Jan 2024

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 …

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan Jan 2024

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. …

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson Jan 2024

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 …

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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 Jan 2024

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 …

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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 Jan 2024

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 …

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Sensor Tasking For Low Earth Orbit Objects: Leveraging Space Sensor Data For Ground-Based Optical Observations, Smriti Nandan Paul, Hang Woon Lee Jan 2024

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 …

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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 Jan 2024

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 …

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L∞ Bounds For Transient Growth In Repetitive And Iterative Learning Control Systems, Douglas A. Bristow, John R. Singler Jan 2024

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 …

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Optimal Tilt-Wing Evtol Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks, Shuan Tai Yeh, Xiaosong Du Jan 2024

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 …

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng Jan 2024

A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

The rapid development of device-edge-cloud collaborative computing techniques has actively contributed to the popularization and application of intelligent service models. The intensity of knowledge transfer plays a vital role in enhancing the performance of intelligent services. However, the existing knowledge transfer methods are mainly implemented through data fine-tuning and model distillation, which may cause the leakage of data privacy or model copyright in intelligent collaborative systems. To address this issue, we propose a secure and robust knowledge transfer framework through stratified-causality distribution adjustment (SCDA) for device-edge-cloud collaborative services. Specifically, a simple yet effective density-based estimation is first employed to obtain …

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Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le Jan 2024

Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le

Research Collection School Of Computing and Information Systems

Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better …

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Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao Jan 2024

Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …

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Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan Jan 2024

Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …

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Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He Jan 2024

Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He

Research Collection School Of Computing and Information Systems

Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …

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Clearspeech: Improving Voice Quality Of Earbuds Using Both In-Ear And Out-Ear Microphones, Dong Ma, Ting Dang, Ming Ding, Rajesh Krishna Balan Jan 2024

Clearspeech: Improving Voice Quality Of Earbuds Using Both In-Ear And Out-Ear Microphones, Dong Ma, Ting Dang, Ming Ding, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly and requires speech enhancement (SE). In this paper, we present ClearSpeech, a novel deep-learningbased SE system designed for wireless earbuds. Specifically, by jointly using the earbud’s in-ear and out-ear microphones, we devised a suite of techniques to effectively fuse the two signals and enhance the magnitude and phase of the speech spectrogram. We built an earbud prototype to evaluate ClearSpeech under …

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Remote Multi-Person Heart Rate Monitoring With Smart Speakers: Overcoming Separation Constraint, Ngoc Doan Thu Tran, Dong Ma, Rajesh Krishna Balan Jan 2024

Remote Multi-Person Heart Rate Monitoring With Smart Speakers: Overcoming Separation Constraint, Ngoc Doan Thu Tran, Dong Ma, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Heart rate is a key vital sign that can be used to understand an individual’s health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present …

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Early-Career Music Teachers’ Perspectives Of Their Initial Teacher Education Program In China, Han Meng, Jason Goopy Jan 2024

Early-Career Music Teachers’ Perspectives Of Their Initial Teacher Education Program In China, Han Meng, Jason Goopy

Research outputs 2022 to 2026

Initial teacher education plays an important role in preparing music teachers for schools. There is a growing interest in Chinese music teacher education, though limited research currently exists. This study investigated early-career teachers’ perspectives concerning the efficacy of the initial music teacher education program at Yu Cai Normal University (pseudonym), China. This mixed-methods study used a sequential explanatory design where qualitative interview data were used to provide further explanation and detail regarding survey results. The perceptions of early-career music teachers were sought on the importance, effectiveness, and usefulness of their initial teacher education and the most rewarding and challenging aspects …

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Influences On Student Decisions To Enrol In Higher-Level Mathematics Courses, Gregory Hine, Chris Forlin, Paola Chivers Jan 2024

Influences On Student Decisions To Enrol In Higher-Level Mathematics Courses, Gregory Hine, Chris Forlin, Paola Chivers

Research outputs 2022 to 2026

Addressing the participation rates in senior secondary mathematics courses in Australian schools remains a critically important issue. In this paper, the authors report on quantitative findings from a study in which all year 11 and year 12 (aged 17–18 years) Australian Tertiary Admissions Ranking (ATAR) students in Western Australia were invited to participate. The aim was to explore the perceptions of these students regarding their enrolment in higher-level mathematics courses. Data from 1633 students were collected using a survey instrument comprised of 12, 5-point, Likert-scale items. Data were analysed by applying two statistical procedures: calculating frequencies of the 12 items …

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Association Of Abnormal Lung Function And Its Subtypes With Arterial Stiffness: A Longitudinal Cohort Study, Zhiyuan Wu, Haiping Zhang, Yue Jiang, Zhiwei Li, Yutao Wang, Yixing Tian, Zheng Guo, Yulu Zheng, Xia Li, Lixin Tao, Xiuhua Guo Jan 2024

Association Of Abnormal Lung Function And Its Subtypes With Arterial Stiffness: A Longitudinal Cohort Study, Zhiyuan Wu, Haiping Zhang, Yue Jiang, Zhiwei Li, Yutao Wang, Yixing Tian, Zheng Guo, Yulu Zheng, Xia Li, Lixin Tao, Xiuhua Guo

Research outputs 2022 to 2026

BACKGROUND: Prior studies have reported the cross-sectional relationship between lung function and arterial stiffness, while the longitudinal association remains unclear to date. This study aimed to investigate whether abnormal lung function and its subtypes at baseline are associated with increased arterial stiffness using a cohort. METHODS AND RESULTS: This was a secondary analysis extracting 2461 participants from Beijing Health Management Cohort as baseline and annually followed for development of arterial stiffness. Abnormal lung function was defined by forced expiratory volume in 1s < 80% of the predicted value, forced vital capacity of the predicted value, or forced expiratory volume in 1s/ forced vital capacity ratio < 70%. Increased arterial stiffness was determined by brachial-ankle pulse wave velocity ≥ 1400 cm/s. Cox proportional hazards model was used to calculate the hazard ratio and population attributable fraction. The mean age was 42.8±8.1 years, and 444 (18.0%) cases developed increased arterial stiffness during a median follow-up of 3.0 years. The adjusted hazard ratio (95% CI) of arterial stiffness was 1.47 (95% CI, 1.10–1.96) for abnormal lung function, with a population attributable fraction of 3.9% (95% CI, 0.8–7.1). Of subtypes, only obstructive ventilatory dysfunction was significantly associated with arterial stiffness (adjusted hazard ratio, 2.06 [95% CI, 1.27–3.36]), not restricted ventilatory dysfunction (adjusted hazard ratio, 0.95 [95% CI, 0.54–1.65]). Consistent results were observed on multiple sensitivity analyses. CONCLUSIONS: Our study indicated a longitudinal association of abnormal lung function with increased arterial stiffness using a large cohort, especially for the obstructive ventilatory dysfunction.

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