Electrical Engineering and Systems Science
New submissions
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New submissions for Fri, 10 May 24
- [1] arXiv:2405.05336 [pdf, other]
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Title: Joint semi-supervised and contrastive learning enables zero-shot domain-adaptation and multi-domain segmentationAuthors: Alvaro Gomariz, Yusuke Kikuchi, Yun Yvonna Li, Thomas Albrecht, Andreas Maunz, Daniela Ferrara, Huanxiang Lu, Orcun GokselSubjects: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Despite their effectiveness, current deep learning models face challenges with images coming from different domains with varying appearance and content. We introduce SegCLR, a versatile framework designed to segment volumetric images across different domains, employing supervised and contrastive learning simultaneously to effectively learn from both labeled and unlabeled data. We demonstrate the superior performance of SegCLR through a comprehensive evaluation involving three diverse clinical datasets of retinal fluid segmentation in 3D Optical Coherence Tomography (OCT), various network configurations, and verification across 10 different network initializations. In an unsupervised domain adaptation context, SegCLR achieves results on par with a supervised upper-bound model trained on the intended target domain. Notably, we discover that the segmentation performance of SegCLR framework is marginally impacted by the abundance of unlabeled data from the target domain, thereby we also propose an effective zero-shot domain adaptation extension of SegCLR, eliminating the need for any target domain information. This shows that our proposed addition of contrastive loss in standard supervised training for segmentation leads to superior models, inherently more generalizable to both in- and out-of-domain test data. We additionally propose a pragmatic solution for SegCLR deployment in realistic scenarios with multiple domains containing labeled data. Accordingly, our framework pushes the boundaries of deep-learning based segmentation in multi-domain applications, regardless of data availability - labeled, unlabeled, or nonexistent.
- [2] arXiv:2405.05353 [pdf, other]
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Title: Eco-driving Accounting for Interactive Cut-in VehiclesComments: Accepted at 2024 IEEE International Conference on Mobility: Operations, Services, and Technologies (MOST)Subjects: Systems and Control (eess.SY)
Automated vehicles can gather information about surrounding traffic and plan safe and energy-efficient driving behavior, which is known as eco-driving. Conventional eco-driving designs only consider preceding vehicles in the same lane as the ego vehicle. In heavy traffic, however, vehicles in adjacent lanes may cut into the ego vehicle's lane, influencing the ego vehicle's eco-driving behavior and compromising the energy-saving performance. Therefore, in this paper, we propose an eco-driving design that accounts for neighbor vehicles that have cut-in intentions. Specifically, we integrate a leader-follower game to predict the interaction between the ego and the cut-in vehicles and a model-predictive controller for planning energy-efficient behavior for the automated ego vehicle. We show that the leader-follower game model can reasonably represent the interactive motion between the ego vehicle and the cut-in vehicle. More importantly, we show that the proposed design can predict and react to neighbor vehicles' cut-in behaviors properly, leading to improved energy efficiency in cut-in scenarios compared to baseline designs that consider preceding vehicles only.
- [3] arXiv:2405.05365 [pdf, other]
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Title: Enhancing Holonic Architecture with Natural Language Processing for System of SystemsComments: Preprint accepted in ICSOFT'24Subjects: Systems and Control (eess.SY); Multiagent Systems (cs.MA); Software Engineering (cs.SE)
The complexity and dynamic nature of System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems, termed holons. This paper proposes an innovative approach to enhance holon communication within SoS through the integration of Conversational Generative Intelligence (CGI) techniques. Our approach leverages advancements in CGI, specifically Large Language Models (LLMs), to enable holons to understand and act on natural language instructions. This fosters more intuitive human-holon interactions, improving social intelligence and ultimately leading to better coordination among diverse systems. This position paper outlines a conceptual framework for CGI-enhanced holon interaction, discusses the potential impact on SoS adaptability, usability and efficiency, and sets the stage for future exploration and prototype implementation.
- [4] arXiv:2405.05399 [pdf, ps, other]
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Title: 3-way equal filtering power divider using compact folded-arms square open-Loop resonatorComments: 5 pages, 5 figures, 1 tableSubjects: Systems and Control (eess.SY); Applied Physics (physics.app-ph)
Microstrip three-way (that is, 4.8 dB) integrated filtering power divider (FPD) is presented in this paper. The proposed FPD evenly distributes an input power signal into three equal output signals. The design incorporates balanced signal power division, and filtering technology for the removal of unwanted frequency elements and aimed at enhancing signal quality and efficiency in the radiofrequency (RF) front-end of communication systems. Microstrip folded-arms square open-loop resonator (FASOLR) is employed in the design implementation to achieve compact size. The proposed FPD features a 2.6 GHz centre frequency, with a 0.03 fractional bandwidth. The implementation is carried out on Rogers RT/Duroid 6010LM substrate with a dielectric constant of 10.7, a thickness of 1.27 mm and a loss tangent of 0.0023. The good agreement between the theoretical and practical results verifies the effectiveness of the FPD in delivering equal power outputs at the three output ports, and at the same time filtering out unwanted frequencies. The practical results of the prototype FPD indicate a good return loss of better than 15.5 dB and an insertion loss of better than 4.77+0.34 dB. The design prototype achieved compact size of 0.31 {\lambda}g x 0.18 {\lambda}g. {\lambda}g is the guided wavelength for the microstrip line impedance at the centre frequency of the 3-way equal filtering power divider.
- [5] arXiv:2405.05426 [pdf, other]
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Title: ATLS: Automated Trailer Loading for Surface VesselsAuthors: Amer Abughaida, Meet Gandhi, Jun Heo, Vaishnav Tadiparthi, Yosuke Sakamoto, Joohyun Woo, Sangjae BaeComments: To be presented at IEEE Intelligent Vehicles Symposium (IV 2024)Subjects: Systems and Control (eess.SY)
Automated docking technologies of marine boats have been enlightened by an increasing number of literature. This paper contributes to the literature by proposing a mathematical framework that automates "trailer loading" in the presence of wind disturbances, which is unexplored despite its importance to boat owners. The comprehensive pipeline of localization, system identification, and trajectory optimization is structured, followed by several techniques to improve performance reliability. The performance of the proposed method was demonstrated with a commercial pontoon boat in Michigan, in 2023, securing a success rate of 80\% in the presence of perception errors and wind disturbance. This result indicates the strong potential of the proposed pipeline, effectively accommodating the wind effect.
- [6] arXiv:2405.05503 [pdf, other]
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Title: Communications under Bursty Mixed Gaussian-impulsive Noise: Demodulation and Performance AnalysisSubjects: Signal Processing (eess.SP)
This is the second part of the two-part paper considering the communications under the bursty mixed noise composed of white Gaussian noise and colored non-Gaussian impulsive noise. In the first part, based on Gaussian distribution and student distribution, we proposed a multivariate bursty mixed noise model and designed model parameter estimation algorithms. However, the performance of a communication system will significantly deteriorate under the bursty mixed noise if a conventional signal detection algorithm with respect to Gaussian noise is applied. To address this issue, in the second part, we leverage the probability density function (PDF) to derive the maximum likelihood (ML) demodulation methods for both linear and nonlinear modulations, including M-array PSK (M-PSK) and MSK modulation schemes. We analyze the theoretical bit error rate (BER) performance of M-PSK and present close-form BER expressions. For the MSK demodulation based on the Viterbi algorithm, we derive a lower and upper bound of BER. Simulation results showcase that the proposed demodulation methods outperform baselines by more than 2.5dB when the BER performance reaches the order of magnitude of $10^{-3}$, and the theoretical analysis matches the simulated results well.
- [7] arXiv:2405.05520 [pdf, other]
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Title: Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclesComments: ISBI 2024 Accepted paper for presentationSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Single-Photon Emission Computed Tomography (SPECT) left ventricular assessment protocols are important for detecting ischemia in high-risk patients. To quantitatively measure myocardial function, clinicians depend on commercially available solutions to segment and reorient the left ventricle (LV) for evaluation. Based on large normal datasets, the segmentation performance and the high price of these solutions can hinder the availability of reliable and precise localization of the LV delineation. To overcome the aforementioned shortcomings this paper aims to give a recipe for diagnostic centers as well as for clinics to automatically segment the myocardium based on small and low-quality labels on reconstructed SPECT, complete field-of-view (FOV) volumes. A combination of Continuous Max-Flow (CMF) with prior shape information is developed to augment the 3D U-Net self-supervised learning (SSL) approach on various geometries of SPECT apparatus. Experimental results on the acquired dataset have shown a 5-10\% increase in quantitative metrics based on the previous State-of-the-Art (SOTA) solutions, suggesting a good plausible way to tackle the few-shot SSL problem on high-noise SPECT cardiac datasets.
- [8] arXiv:2405.05522 [pdf, other]
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Title: Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning PerspectivesSubjects: Signal Processing (eess.SP)
The use of deep learning (DL) for channel state information (CSI) feedback has garnered widespread attention across academia and industry. The mainstream DL architectures, e.g., CsiNet, deploy DL models on the base station (BS) side and the user equipment (UE) side, which are highly coupled and need to be trained jointly. However, two-sided DL models require collaborations between different network vendors and UE vendors, which entails considerable challenges in order to achieve consensus, e.g., model maintenance and responsibility. Furthermore, DL-based CSI feedback design invokes DL to reduce only the CSI feedback error, whereas jointly optimizing several modules at the transceivers would provide more significant gains. This article presents DL-based CSI feedback from the perspectives of one-sided model and joint multi-module learning. We herein introduce various novel one-sided CSI feedback architectures. In particular, the recently proposed CSI-PPPNet provides a one-sided one-for-all framework, which allows a DL model to deal with arbitrary CSI compression ratios. We review different joint multi-module learning methods, where the CSI feedback module is learned jointly with other modules including channel coding, channel estimation, pilot design and precoding design. Finally, future directions and challenges for DL-based CSI feedback are discussed, from the perspectives of inherent limitations of artificial intelligence (AI) and practical deployment issues.
- [9] arXiv:2405.05547 [pdf, ps, other]
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Title: 2-16 GHz Multifrequency X-Cut Lithium Niobate NEMS Resonators on a Single ChipComments: 4 pages, 5 figures, 4 tables, to be presented at NEMS 2024 in Kyoto, JapanSubjects: Systems and Control (eess.SY)
This work presents the design, fabrication, and testing of X-Cut Lithium Niobate (LN) acoustic nanoelectromechanical (NEMS) Laterally Vibrating Resonators (LVRs) and Degenerate LVRs (d-LVRs) operating in the S0 (YZ30) and SH0 (YZ-10) modes between 2 to 16 GHz range, monolithically fabricated on a single chip. The NEMS topology is optimized to extend the aforementioned fundamental modes in the C-, X-, and Ku-bands while preserving performance and mass manufacturability. The devices present acoustic wavelengths ({\lambda}) varying between 1800 and 400 nm and are fabricated on a 100 nm ultra-thin LN film on high resistivity silicon with a 3-mask process. Experimental results highlighted quality factor at resonance (Qs) and mechanical quality factors (Qm) as high as 477 and 1750, respectively, and electromechanical coupling (kt2) as high as 32.7%. Large kt2 (>10%) are recorded over a broad range of frequencies (2 - 8 GHz), while Qm exceeding 100 are measured up to 15 GHz. Further enhancement to performance and range of operation on the same chip can be achieved by decreasing {\lambda}, refining the fabrication process, and optimizing device topology. These additional steps can help pave the way for manufacturing high-performance resonators on a single chip covering the entire 1 - 25 GHz spectrum.
- [10] arXiv:2405.05564 [pdf, other]
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Title: Joint Edge Optimization Deep Unfolding Network for Accelerated MRI ReconstructionSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement. In this paper, we build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them. Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process. Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information.Numerical experiments, consisting of multi-coil and single-coil MRI data with different sampling schemes at a variety of sampling factors, demonstrate that the proposed method outperforms other compared methods.
- [11] arXiv:2405.05565 [pdf, other]
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Title: Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed DataSubjects: Image and Video Processing (eess.IV); Signal Processing (eess.SP)
Array synthetic aperture radar (SAR) three-dimensional (3D) imaging can obtain 3D information of the target region, which is widely used in environmental monitoring and scattering information measurement. In recent years, with the development of compressed sensing (CS) theory, sparse signal processing is used in array SAR 3D imaging. Compared with matched filter (MF), sparse SAR imaging can effectively improve image quality. However, sparse imaging based on handcrafted regularization functions suffers from target information loss in few observed SAR data. Therefore, in this article, a general 3D sparse imaging framework based on Regulation by Denoising (RED) and proximal gradient descent type method for array SAR is presented. Firstly, we construct explicit prior terms via state-of-the-art denoising operators instead of regularization functions, which can improve the accuracy of sparse reconstruction and preserve the structure information of the target. Then, different proximal gradient descent type methods are presented, including a generalized alternating projection (GAP) and an alternating direction method of multiplier (ADMM), which is suitable for high-dimensional data processing. Additionally, the proposed method has robust convergence, which can achieve sparse reconstruction of 3D SAR in few observed SAR data. Extensive simulations and real data experiments are conducted to analyze the performance of the proposed method. The experimental results show that the proposed method has superior sparse reconstruction performance.
- [12] arXiv:2405.05641 [pdf, other]
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Title: Channel Estimation for Holographic MIMO: Wavenumber-Domain Sparsity Inspired ApproachesComments: This paper has been submitted to IEEE WCL, Major RevisionSubjects: Signal Processing (eess.SP); Information Theory (cs.IT)
This paper investigates the sparse channel estimation for holographic multiple-input multiple-output (HMIMO) systems. Given that the wavenumber-domain representation is based on a series of Fourier harmonics that are in essence a series of orthogonal basis functions, a novel wavenumber-domain sparsifying basis is designed to expose the sparsity inherent in HMIMO channels. Furthermore, by harnessing the beneficial sparsity in the wavenumber domain, the sparse estimation of HMIMO channels is structured as a compressed sensing problem, which can be efficiently solved by our proposed wavenumber-domain orthogonal matching pursuit (WD-OMP) algorithm. Finally, numerical results demonstrate that the proposed wavenumber-domain sparsifying basis maintains its detection accuracy regardless of the number of antenna elements and antenna spacing. Additionally, in the case of antenna spacing being much less than half a wavelength, the wavenumber-domain approach remains highly accurate in identifying the significant angular power of HMIMO channels.
- [13] arXiv:2405.05658 [pdf, ps, other]
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Title: Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysisAuthors: Siddharth Agarwal, David A. Wood, Mariusz Grzeda, Chandhini Suresh, Munaib Din, James Cole, Marc Modat, Thomas C BoothSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks.
Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line CT or MR neuroimaging. A bivariate random-effects model was used for meta-analysis where appropriate. PROSPERO: CRD42021269563.
Results: Only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies. 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial haemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% CI 0.85 - 0.94) and 0.90 (95% CI 0.83 - 0.95) respectively. Other AI studies using CT and MRI detected target conditions other than haemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers.
Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation. - [14] arXiv:2405.05659 [pdf, ps, other]
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Title: End-to-End Waveform and Beamforming Optimization for RF Wireless Power TransferAuthors: Abdul Basit Khattak, Onel L. A. López, Amirhossein Azarbahram, Deepak Kumar, Matti Latva-ahoComments: ConferenceSubjects: Signal Processing (eess.SP)
Radio frequency (RF) wireless power transfer (WPT) is a key technology for future low-power wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is challenging for practical applications. The main factors contributing to it are the channel losses, transceivers' power consumption, and losses related, e.g., to the digital-to-analog converter (DAC), high-power amplifier, and rectenna. Optimizing PTE requires careful consideration of these factors, motivating the current work. Herein, we consider an analog multi-antenna power transmitter that aims to charge a single energy harvester. We first provide a mathematical framework to calculate the harvested power from multi-tone signal transmissions and the system power consumption. Then, we formulate the joint waveform and analog beamforming design problem to minimize power consumption and meet the charging requirements. Finally, we propose an optimization approach relying on swarm intelligence to solve the specified problem. Simulation results quantify the power consumption reduction as the DAC, phase shifters resolution, and antenna length are increased, while it is seen that increasing system frequency results in higher power consumption.
- [15] arXiv:2405.05667 [pdf, other]
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Title: VM-DDPM: Vision Mamba Diffusion for Medical Image SynthesisSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
In the realm of smart healthcare, researchers enhance the scale and diversity of medical datasets through medical image synthesis. However, existing methods are limited by CNN local perception and Transformer quadratic complexity, making it difficult to balance structural texture consistency. To this end, we propose the Vision Mamba DDPM (VM-DDPM) based on State Space Model (SSM), fully combining CNN local perception and SSM global modeling capabilities, while maintaining linear computational complexity. Specifically, we designed a multi-level feature extraction module called Multi-level State Space Block (MSSBlock), and a basic unit of encoder-decoder structure called State Space Layer (SSLayer) for medical pathological images. Besides, we designed a simple, Plug-and-Play, zero-parameter Sequence Regeneration strategy for the Cross-Scan Module (CSM), which enabled the S6 module to fully perceive the spatial features of the 2D image and stimulate the generalization potential of the model. To our best knowledge, this is the first medical image synthesis model based on the SSM-CNN hybrid architecture. Our experimental evaluation on three datasets of different scales, i.e., ACDC, BraTS2018, and ChestXRay, as well as qualitative evaluation by radiologists, demonstrate that VM-DDPM achieves state-of-the-art performance.
- [16] arXiv:2405.05676 [pdf, other]
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Title: Maximum Correntropy Polynomial Chaos Kalman Filter for Underwater NavigationSubjects: Signal Processing (eess.SP)
This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, attitude meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. The simulation results demonstrate an improved accuracy compared to the conventional deterministic sample point filters.
- [17] arXiv:2405.05715 [pdf, other]
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Title: Shifting the ISAC Trade-Off with Fluid Antenna SystemsAuthors: Jiaqi Zou, Hao Xu, Chao Wang, Lvxin Xu, Songlin Sun, Kaitao Meng, Christos Masouros, Kai-Kit WongComments: 5 pages, 5 figuresSubjects: Signal Processing (eess.SP)
As an emerging antenna technology, a fluid antenna system (FAS) enhances spatial diversity to improve both sensing and communication performance by shifting the active antennas among available ports. In this letter, we study the potential of shifting the integrated sensing and communication (ISAC) trade- off with FAS. We propose the model for FAS-enabled ISAC and jointly optimize the transmit beamforming and port selection of FAS. In particular, we aim to minimize the transmit power, while satisfying both communication and sensing requirements. An efficient iterative algorithm based on sparse optimization, convex approximation, and a penalty approach is developed. The simulation results show that the proposed scheme can attain 33% reductions in transmit power with guaranteed sensing and communication performance, showing the great potential of the fluid antenna for striking a flexible tradeoff between sensing and communication in ISAC systems.
- [18] arXiv:2405.05748 [pdf, other]
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Title: Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual ApproachSubjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Network slicing is a key feature in 5G/NG cellular networks that creates customized slices for different service types with various quality-of-service (QoS) requirements, which can achieve service differentiation and guarantee service-level agreement (SLA) for each service type. In Wi-Fi networks, there is limited prior work on slicing, and a potential solution is based on a multi-tenant architecture on a single access point (AP) that dedicates different channels to different slices. In this paper, we define a flexible, constrained learning framework to enable slicing in Wi-Fi networks subject to QoS requirements. We specifically propose an unsupervised learning-based network slicing method that leverages a state-augmented primal-dual algorithm, where a neural network policy is trained offline to optimize a Lagrangian function and the dual variable dynamics are updated online in the execution phase. We show that state augmentation is crucial for generating slicing decisions that meet the ergodic QoS requirements.
- [19] arXiv:2405.05754 [pdf, other]
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Title: Achieving Precisely-Assigned Performance Requirements for Spacecraft Attitude ControlAuthors: Jiakun LeiSubjects: Systems and Control (eess.SY)
This paper investigates the attitude control problem of spacecraft, with the objective of achieving precise performance criteria including precise settling time, steady-state error, and overshoot elimination. To tackle this challenge, we propose the Precisely-Assigned Performance (PAP) control scheme. Firstly, we utilize a parameterized function to explicitly characterize a reference for the transient responses, termed the Reference Performance Function (RPF). Subsequently, leveraging the concept of the RPF, we define a performance-satisfied tube region and introduce the concept of control barrier functions to derive a sufficient condition for the state trajectory to converge and remain confined within this tube region. By introducing the concept of Sontag's universal formula for stabilization, a PAP controller, constructed based on the backstepping method, is then designed to guide the system to satisfy these affine constraint conditions, and a disturbance observer is further integrated to handle perturbations. Theoretical proofs are presented to demonstrate the controller's capability to establish the boundedness of the overall system and ensure that each state trajectory will converge into the performance-satisfied region within a finite time duration under any conditions. Finally, numerical simulation results are presented to validate the effectiveness of the proposed method.
- [20] arXiv:2405.05801 [pdf, other]
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Title: 3D Positioning using a New Diffraction Path ModelComments: Accepted for publication in ICC 2024Subjects: Signal Processing (eess.SP)
Enhancing 3D and Z-axis positioning accuracy is crucial for effective rescue in indoor emergencies, ensuring safety for emergency responders and at-risk individuals. Additionally, reducing the dependence of a positioning system on fixed infrastructure is crucial, given its vulnerability to power failures and damage during emergencies. Further challenges from a signal propagation perspective include poor indoor signal coverage, multipath effects and the problem of Non-Line-OfSight (NLOS) measurement bias. In this study, we utilize the mobility provided by a rapidly deployable Uncrewed Aerial Vehicle (UAV) based wireless network to address these challenges. We recognize diffraction from window edges as a crucial signal propagation mechanism and employ the Geometrical Theory of Diffraction (GTD) to introduce a novel NLOS path length model. Using this path length model, we propose two different techniques to improve the indoor positioning performance for emergency scenarios.
- [21] arXiv:2405.05814 [pdf, ps, other]
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Title: MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT ReconstructionSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction in projection angles. Therefore, we propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff), designed to concentrate on the global distribution of information and facilitate the reconstruction of sparse views with local image characteristics. Specifically, the proposed model ingeniously integrates information from both comprehensive sampling and selectively sparse sampling tech-niques. Through precise adjustments in diffusion model, it is capable of extracting diverse noise distribution, furthering the understanding of the overall structure of images, and aiding the fully sampled model in recovering image information more effec-tively. By leveraging the inherent correlations within the projec-tion data, we have designed an equidistant mask, enabling the model to focus its attention more effectively. Experimental re-sults demonstrated that the multi-scale model approach signifi-cantly improved the quality of image reconstruction under ultra-sparse angles, with good generalization across various datasets.
- [22] arXiv:2405.05815 [pdf, other]
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Title: Non-myopic GOSPA-driven Gaussian Bernoulli Sensor ManagementSubjects: Systems and Control (eess.SY)
In this paper, we propose an algorithm for non-myopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type minimisation problem, whose cost function is the mean square generalised optimal sub-pattern assignment (GOSPA) error, over a future time window. We also propose an implementation of the sensor management algorithm based on an upper bound of the mean square GOSPA error and a Gaussian single-target posterior. Finally, we develop a Monte Carlo tree search algorithm to find an approximate optimal action within a given computational budget. The benefits of the proposed approach are demonstrated via simulations.
- [23] arXiv:2405.05848 [pdf, other]
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Title: Distributed Estimation for a 3-D Moving Target in Quaternion Space with Unknown CorrelationSubjects: Systems and Control (eess.SY)
For distributed estimations in a sensor network, the consistency and accuracy of an estimator are greatly affected by the unknown correlations between individual estimates. An inconsistent or too conservative estimate may degrade the estimation performance and even cause divergence of the estimator. Cooperative estimation methods based on Inverse Covariance Intersection (ICI) can utilize a network of sensors to provide a consistent and tight estimate of a target. In this paper, unlike most existing ICI-based estimators that only consider two-dimensional (2-D) target state estimation in the vector space, we address this problem in a 3-D environment by extending the ICI algorithm to the augmented quaternion space. In addition, the proposed algorithm is fully distributed, as each agent only uses the local information from itself and its communication neighbors, which is also robust to a time-varying communication topology. To evaluate the performance, we test the proposed algorithm in a camera network to track the pose of a target. Extensive Monte Carlo simulations have been performed to show the effectiveness of our approach.
- [24] arXiv:2405.05883 [pdf, other]
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Title: supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal DecontaminationSubjects: Signal Processing (eess.SP)
The presence of muscles throughout the active parts of the body such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy. These noises can be regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which trims and delays the signal unnecessarily. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy. Accordingly, a reward is generated. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-to-noise ratio (SNR) is between -5 dB to +1 dB. It filters less desirably at high SNR (+5 dB). A normalized root mean square (nRMSE) is formulated to depict the difference of filtered signal from ground truth. This is used to compare supDQN and conventional methods including wavelet denoising with debauchies and symlet wavelet, high order low pass filter, notch filter, and high pass filter. The proposed filtering strategy gives an average value nRMSE of 1.1974, which is lower than the conventional filters.
- [25] arXiv:2405.05911 [pdf, other]
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Title: Small-Scale Testbed for Evaluating C-V2X Applications on 5G Cellular NetworksAuthors: Kaj Munhoz Arfvidsson, Kleio Fragkedaki, Frank J. Jiang, Vandana Narri, Hans-Cristian Lindh, Karl H. Johansson, Jonas MårtenssonSubjects: Systems and Control (eess.SY); Emerging Technologies (cs.ET); Networking and Internet Architecture (cs.NI)
In this work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed's capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.
- [26] arXiv:2405.05937 [pdf, other]
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Title: Dynamics of a Towed Cable with Sensor-Array for Underwater Target Motion AnalysisSubjects: Signal Processing (eess.SP); Systems and Control (eess.SY)
During a war situation, many times an underwater target motion analysis (TMA) is performed using bearing-only measurements, obtained from a sensor array, which is towed by an own-ship with the help of a connected cable. It is well known that the own-ship is required to perform a manoeuvre in order to make the system observable and localise the target successfully. During the maneuver, it is important to know the location of the sensor array with respect to the own-ship. This paper develops a dynamic model of a cable-sensor array system to localise the sensor array, which is towed behind a sea-surface vessel. We adopt a lumped-mass approach to represent the towed cable. The discretized cable elements are modelled as an interconnected rigid body, kinematically related to one another. The governing equations are derived by balancing the moments acting on each node. The derived dynamics are solved simultaneously for all the nodes to determine the orientation of the cable and sensor array. The position of the sensor array obtained from this proposed model will further be used by TMA algorithms to enhance the accuracy of the tracking system.
- [27] arXiv:2405.05944 [pdf, other]
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Title: MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRIAuthors: Yan Zhuang, Tejas Sudharshan Mathai, Pritam Mukherjee, Brandon Khoury, Boah Kim, Benjamin Hou, Nusrat Rabbee, Ronald M. SummersComments: 23 pages, 13 figuresSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy. Current approaches have focused on delineating a limited set of abdominal structures (13 types). To date, there is no publicly available abdominal MRI dataset with voxel-level annotations of multiple organs and structures. Consequently, a segmentation tool for multi-structure segmentation is also unavailable. Methods: We curated a T1-weighted abdominal MRI dataset consisting of 195 patients who underwent imaging at National Institutes of Health (NIH) Clinical Center. The dataset comprises of axial pre-contrast T1, arterial, venous, and delayed phases for each patient, thereby amounting to a total of 780 series (69,248 2D slices). Each series contains voxel-level annotations of 62 abdominal organs and structures. A 3D nnUNet model, dubbed as MRISegmentator-Abdomen (MRISegmentator in short), was trained on this dataset, and evaluation was conducted on an internal test set and two large external datasets: AMOS22 and Duke Liver. The predicted segmentations were compared against the ground-truth using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD). Findings: MRISegmentator achieved an average DSC of 0.861$\pm$0.170 and a NSD of 0.924$\pm$0.163 in the internal test set. On the AMOS22 dataset, MRISegmentator attained an average DSC of 0.829$\pm$0.133 and a NSD of 0.908$\pm$0.067. For the Duke Liver dataset, an average DSC of 0.933$\pm$0.015 and a NSD of 0.929$\pm$0.021 was obtained. Interpretation: The proposed MRISegmentator provides automatic, accurate, and robust segmentations of 62 organs and structures in T1-weighted abdominal MRI sequences. The tool has the potential to accelerate research on various clinical topics, such as abnormality detection, radiotherapy, disease classification among others.
Cross-lists for Fri, 10 May 24
- [28] arXiv:2405.05446 (cross-list from cs.CV) [pdf, other]
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Title: GDGS: Gradient Domain Gaussian Splatting for Sparse Representation of Radiance FieldsAuthors: Yuanhao GongComments: arXiv admin note: text overlap with arXiv:2404.09105Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
The 3D Gaussian splatting methods are getting popular. However, they work directly on the signal, leading to a dense representation of the signal. Even with some techniques such as pruning or distillation, the results are still dense. In this paper, we propose to model the gradient of the original signal. The gradients are much sparser than the original signal. Therefore, the gradients use much less Gaussian splats, leading to the more efficient storage and thus higher computational performance during both training and rendering. Thanks to the sparsity, during the view synthesis, only a small mount of pixels are needed, leading to much higher computational performance ($100\sim 1000\times$ faster). And the 2D image can be recovered from the gradients via solving a Poisson equation with linear computation complexity. Several experiments are performed to confirm the sparseness of the gradients and the computation performance of the proposed method. The method can be applied various applications, such as human body modeling and indoor environment modeling.
- [29] arXiv:2405.05447 (cross-list from cs.RO) [pdf, other]
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Title: Dynamic Posture Manipulation During Tumbling for Closed-Loop Heading Angle ControlSubjects: Robotics (cs.RO); Systems and Control (eess.SY)
Passive tumbling uses natural forces like gravity for efficient travel. But without an active means of control, passive tumblers must rely entirely on external forces. Northeastern University's COBRA is a snake robot that can morph into a ring, which employs passive tumbling to traverse down slopes. However, due to its articulated joints, it is also capable of dynamically altering its posture to manipulate the dynamics of the tumbling locomotion for active steering. This paper presents a modelling and control strategy based on collocation optimization for real-time steering of COBRA's tumbling locomotion. We validate our approach using Matlab simulations.
- [30] arXiv:2405.05462 (cross-list from q-bio.NC) [pdf, other]
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Title: Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease BiomarkersSubjects: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the application of generative models to disorder-specific datasets and their ability to preserve diagnostic patterns remain relatively unexplored. Hence, in this study, we investigated the use of a generative adversarial network (GAN) in the context of Alzheimer's disease (AD) to generate functional network connectivity (FNC) and T1-weighted structural magnetic resonance imaging data from each other. We employed a cycle-GAN to synthesize data in an unpaired data transition and enhanced the transition by integrating weak supervision in cases where paired data were available. Our findings revealed that our model could offer remarkable capability, achieving a structural similarity index measure (SSIM) of $0.89 \pm 0.003$ for T1s and a correlation of $0.71 \pm 0.004$ for FNCs. Moreover, our qualitative analysis revealed similar patterns between generated and actual data when comparing AD to cognitively normal (CN) individuals. In particular, we observed significantly increased functional connectivity in cerebellar-sensory motor and cerebellar-visual networks and reduced connectivity in cerebellar-subcortical, auditory-sensory motor, sensory motor-visual, and cerebellar-cognitive control networks. Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer's patients.
- [31] arXiv:2405.05467 (cross-list from cs.SD) [pdf, other]
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Title: AFEN: Respiratory Disease Classification using Ensemble LearningComments: Under Review Process for MLForHC 2024Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
We present AFEN (Audio Feature Ensemble Learning), a model that leverages Convolutional Neural Networks (CNN) and XGBoost in an ensemble learning fashion to perform state-of-the-art audio classification for a range of respiratory diseases. We use a meticulously selected mix of audio features which provide the salient attributes of the data and allow for accurate classification. The extracted features are then used as an input to two separate model classifiers 1) a multi-feature CNN classifier and 2) an XGBoost Classifier. The outputs of the two models are then fused with the use of soft voting. Thus, by exploiting ensemble learning, we achieve increased robustness and accuracy. We evaluate the performance of the model on a database of 920 respiratory sounds, which undergoes data augmentation techniques to increase the diversity of the data and generalizability of the model. We empirically verify that AFEN sets a new state-of-the-art using Precision and Recall as metrics, while decreasing training time by 60%.
- [32] arXiv:2405.05473 (cross-list from math.DS) [pdf, other]
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Title: Topological bifurcations in a mean-field gameComments: 32 pages, 16 figuresSubjects: Dynamical Systems (math.DS); Systems and Control (eess.SY); Analysis of PDEs (math.AP); Optimization and Control (math.OC); Adaptation and Self-Organizing Systems (nlin.AO)
Mean-field games (MFG) provide a statistical physics inspired modeling framework for decision making in large-populations of strategic, non-cooperative agents. Mathematically, these systems consist of a forward-backward in time system of two coupled nonlinear partial differential equations (PDEs), namely the Fokker-Plank and the Hamilton-Jacobi-Bellman equations, governing the agent state and control distribution, respectively. In this work, we study a finite-time MFG with a rich global bifurcation structure using a reduced-order model (ROM). The ROM is a 4D two-point boundary value problem obtained by restricting the controlled dynamics to first two moments of the agent state distribution, i.e., the mean and the variance. Phase space analysis of the ROM reveals that the invariant manifolds of periodic orbits around the so-called `ergodic MFG equilibrium' play a crucial role in determining the bifurcation diagram, and impart a topological signature to various solution branches. We show a qualitative agreement of these results with numerical solutions of the full-order MFG PDE system.
- [33] arXiv:2405.05490 (cross-list from cs.RO) [pdf, other]
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Title: Banking Turn of High-DOF Dynamic Morphing Wing Flight by Shifting Structure Response Using OptimizationSubjects: Robotics (cs.RO); Systems and Control (eess.SY)
The 3D flight control of a flapping wing robot is a very challenging problem. The robot stabilizes and controls its pose through the aerodynamic forces acting on the wing membrane which has complex dynamics and it is difficult to develop a control method to interact with such a complex system. Bats, in particular, are capable of performing highly agile aerial maneuvers such as tight banking and bounding flight solely using their highly flexible wings. In this work, we develop a control method for a bio-inspired bat robot, the Aerobat, using small low-powered actuators to manipulate the flapping gait and the resulting aerodynamic forces. We implemented a controller based on collocation approach to track a desired roll and perform a banking maneuver to be used in a trajectory tracking controller. This controller is implemented in a simulation to show its performance and feasibility.
- [34] arXiv:2405.05498 (cross-list from cs.SD) [pdf, other]
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Title: The RoyalFlush Automatic Speech Diarization and Recognition System for In-Car Multi-Channel Automatic Speech Recognition ChallengeAuthors: Jingguang Tian, Shuaishuai Ye, Shunfei Chen, Yang Xiang, Zhaohui Yin, Xinhui Hu, Xinkang XuSubjects: Sound (cs.SD); Audio and Speech Processing (eess.AS)
This paper presents our system submission for the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) Challenge, which focuses on speaker diarization and speech recognition in complex multi-speaker scenarios. To address these challenges, we develop end-to-end speaker diarization models that notably decrease the diarization error rate (DER) by 49.58\% compared to the official baseline on the development set. For speech recognition, we utilize self-supervised learning representations to train end-to-end ASR models. By integrating these models, we achieve a character error rate (CER) of 16.93\% on the track 1 evaluation set, and a concatenated minimum permutation character error rate (cpCER) of 25.88\% on the track 2 evaluation set.
- [35] arXiv:2405.05500 (cross-list from cs.RO) [pdf, ps, other]
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Title: Research on the Tender Leaf Identification and Mechanically Perceptible Plucking Finger for High-quality Green TeaSubjects: Robotics (cs.RO); Systems and Control (eess.SY)
BACKGROUND: Intelligent identification and precise plucking are the keys to intelligent tea harvesting robots, which are of increasing significance nowadays. Aiming at plucking tender leaves for high-quality green tea producing, in this paper, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed. RESULTS: Based on segmentation algorithm and color features, the tender leaf identification algorithm shows an average identification accuracy of over 92.8%. The mechanically perceptible plucking finger plucks tender leaves in a way that a human hand does so as to remain high quality of tea products. Though finite element analysis, we determine the ideal size of grippers and the location of strain gauge attachment on a gripper to enable the employment of feedback control of desired gripping force. Revealed from our experiments, the success rate of tender leaf plucking reaches 92.5%, demonstrating the effectiveness of our design. CONCLUSION: The results show that the tender leaf identification algorithm and the mechanically perceptible plucking finger are effective for tender leaves identification and plucking, providing a foundation for the development of an intelligent tender leaf plucking robot.
- [36] arXiv:2405.05518 (cross-list from cs.CV) [pdf, other]
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Title: DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map ConstructionComments: The source code will be made publicly available at this https URLSubjects: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV)
Temporal information plays a pivotal role in Bird's-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code will be made publicly available at https://github.com/lynn-yu/DTCLMapper.
- [37] arXiv:2405.05549 (cross-list from cs.IT) [pdf, other]
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Title: Intelligent Reflecting Surface Aided AirComp: Multi-Timescale Design and Performance AnalysisComments: submitted to IEEE Journal for possible publicationSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
The integration of intelligent reflecting surface (IRS) into over-the-air computation (AirComp) is an effective solution for reducing the computational mean squared error (MSE) via its high passive beamforming gain. Prior works on IRS aided AirComp generally rely on the full instantaneous channel state information (I-CSI), which is not applicable to large-scale systems due to its heavy signalling overhead. To address this issue, we propose a novel multi-timescale transmission protocol. In particular, the receive beamforming at the access point (AP) is pre-determined based on the static angle information and the IRS phase-shifts are optimized relying on the long-term statistical CSI. With the obtained AP receive beamforming and IRS phase-shifts, the effective low-dimensional I-CSI is exploited to determine devices' transmit power in each coherence block, thus substantially reducing the signalling overhead. Theoretical analysis unveils that the achievable MSE scales on the order of ${\cal O}\left( {K/\left( {{N^2}M} \right)} \right)$, where $M$, $N$, and $K$ are the number of AP antennas, IRS elements, and devices, respectively. We also prove that the channel-inversion power control is asymptotically optimal for large $N$, which reveals that the full power transmission policy is not needed for lowering the power consumption of energy-limited devices.
- [38] arXiv:2405.05558 (cross-list from math.OC) [pdf, ps, other]
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Title: From Road Congestion to Vehicle-Control Enabled Artificial Traffic FluidsComments: 53 pagesSubjects: Optimization and Control (math.OC); Systems and Control (eess.SY)
This article provides an overview of the design of nonlinear feedback Cruise Controllers (CCs) for automated vehicles on lane-free roads. The feedback design problem is particularly challenging because of the various state constraints (e.g., collision-free movement, road geometry, speed limits) as well as the nature of the control objective (globally stabilizing distributed controllers that require measurements from neighboring vehicles only). Therefore, the resulting nonlinear control system is defined on an open set (not necessarily diffeomorphic to a linear space) for which the set of desired equilibria is non-compact. The proposed design of the CCs is based on energy-like control Lyapunov functions which combine potential functions with kinetic energy terms and other appropriate penalty terms. The feedback design in the microscopic level is accompanied by the derivation of the corresponding macroscopic traffic flow models. Explicit relations are established between selectable CC features and the obtained macroscopic traffic flow characteristics. This facilitates the active design of efficient traffic flow with desired properties, i.e., the construction of artificial traffic fluids.
- [39] arXiv:2405.05579 (cross-list from cs.HC) [pdf, ps, other]
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Title: Intelligent EC Rearview Mirror: Enhancing Driver Safety with Dynamic Glare Mitigation via Cloud Edge CollaborationSubjects: Human-Computer Interaction (cs.HC); Systems and Control (eess.SY)
Sudden glare from trailing vehicles significantly increases driving safety risks. Existing anti-glare technologies such as electronic, manually-adjusted, and electrochromic rearview mirrors, are expensive and lack effective adaptability in different lighting conditions. To address these issues, our research introduces an intelligent rearview mirror system utilizing novel all-liquid electrochromic technology. This system integrates IoT with ensemble and federated learning within a cloud edge collaboration framework, dynamically controlling voltage to effectively eliminate glare and maintain clear visibility. Utilizing an ensemble learning model, it automatically adjusts mirror transmittance based on light intensity, achieving a low RMSE of 0.109 on the test set. Furthermore, the system leverages federated learning for distributed data training across devices, which enhances privacy and updates the cloud model continuously. Distinct from conventional methods, our experiment utilizes the Schmidt-Clausen and Bindels de Boer 9-point scale with TOPSIS for comprehensive evaluation of rearview mirror glare. Designed to be convenient and costeffective, this system demonstrates how IoT and AI can significantly enhance rearview mirror anti-glare performance.
- [40] arXiv:2405.05668 (cross-list from cs.RO) [pdf, other]
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Title: Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip EstimationAuthors: Alessandro Toschi, Nicola Musiu, Francesco Gatti, Ayoub Raji, Francesco Amerotti, Micaela Verucchi, Marko BertognaComments: Accepted to 35th IEEE Intelligent Vehicles Symposium - IEEE IV 2024Subjects: Robotics (cs.RO); Systems and Control (eess.SY)
The sideslip angle, crucial for vehicle safety and stability, is determined using both longitudinal and lateral velocities. However, measuring the lateral component often necessitates costly sensors, leading to its common estimation, a topic thoroughly explored in existing literature. This paper introduces LOP-UKF, a novel method for estimating vehicle lateral velocity by integrating Lidar Odometry with the Pacejka tire model predictions, resulting in a robust estimation via an Unscendent Kalman Filter (UKF). This combination represents a distinct alternative to more traditional methodologies, resulting in a reliable solution also in edge cases. We present experimental results obtained using the Dallara AV-21 across diverse circuits and track conditions, demonstrating the effectiveness of our method.
- [41] arXiv:2405.05669 (cross-list from cs.RO) [pdf, other]
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Title: Passive Obstacle Aware Control to Follow Desired VelocitiesSubjects: Robotics (cs.RO); Systems and Control (eess.SY)
Evaluating and updating the obstacle avoidance velocity for an autonomous robot in real-time ensures robust- ness against noise and disturbances. A passive damping con- troller can obtain the desired motion with a torque-controlled robot, which remains compliant and ensures a safe response to external perturbations. Here, we propose a novel approach for designing the passive control policy. Our algorithm com- plies with obstacle-free zones while transitioning to increased damping near obstacles to ensure collision avoidance. This approach ensures stability across diverse scenarios, effectively mitigating disturbances. Validation on a 7DoF robot arm demonstrates superior collision rejection capabilities compared to the baseline, underlining its practicality for real-world ap- plications. Our obstacle-aware damping controller represents a substantial advancement in secure robot control within complex and uncertain environments.
- [42] arXiv:2405.05709 (cross-list from cs.IT) [pdf, other]
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Title: On the Capacity of Correlated MIMO Phase-Noise Channels: An Electro-Optic Frequency Comb ExampleComments: 45 pages, 3 figures, submitted to TIT, single-columnSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
The capacity of a discrete-time multiple-input-multiple-output channel with correlated phase noises is investigated. In particular, the electro-optic frequency comb system is considered, where the phase noise of each channel is a combination of two independent Wiener phase-noise sources. Capacity upper and lower bounds are derived for this channel and are compared with lower bounds obtained by numerically evaluating the achievable information rates using quadrature amplitude modulation constellations. Capacity upper and lower bounds are provided for the high signal-to-noise ratio (SNR) regime. The multiplexing gain (pre-log) is shown to be $M-1$, where $M$ represents the number of channels. A constant gap between the asymptotic upper and lower bounds is observed, which depends on the number of channels $M$. For the specific case of $M=2$, capacity is characterized up to a term that vanishes as the SNR grows large.
- [43] arXiv:2405.05757 (cross-list from cs.ET) [pdf, other]
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Title: Design and Implementation of Energy-Efficient Wireless Tire Sensing System with Delay Analysis for Intelligent VehiclesSubjects: Emerging Technologies (cs.ET); Systems and Control (eess.SY)
The growing prevalence of Internet of Things (IoT) technologies has led to a rise in the popularity of intelligent vehicles that incorporate a range of sensors to monitor various aspects, such as driving speed, fuel usage, distance proximity and tire anomalies. Nowadays, real-time tire sensing systems play important roles for intelligent vehicles in increasing mileage, reducing fuel consumption, improving driving safety, and reducing the potential for traffic accidents. However, the current tire sensing system drains a significant vehicle' energy and lacks effective collection of sensing data, which may not guarantee the immediacy of driving safety. Thus, this paper designs an energy-efficient wireless tire sensing system (WTSS), which leverages energy-saving techniques to significantly reduce power consumption while ensuring data retrieval delays during real-time monitoring. Additionally, we mathematically analyze the worst-case transmission delay and sensor reception ratio of the system to ensure the immediacy based on the collision probabilities of sensor transmissions. This system has been implemented and verified by the simulation and field train experiments. These results show that the proposed scheme provides enhanced performance in energy efficiency up to 76.5% in average and identifies the worst transmission delay accurately.
- [44] arXiv:2405.05770 (cross-list from q-bio.NC) [pdf, other]
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Title: A minimal dynamical system and analog circuit for non-associative learningSubjects: Neurons and Cognition (q-bio.NC); Signal Processing (eess.SP)
Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.
- [45] arXiv:2405.05782 (cross-list from math.OC) [pdf, ps, other]
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Title: Minimax problems for ensembles of affine-control systemsAuthors: Alessandro ScagliottiComments: 17 pagesSubjects: Optimization and Control (math.OC); Systems and Control (eess.SY)
In this paper, we consider ensembles of affine-control systems in $\mathbb{R}^n$, and we study simultaneous optimal control problems related to the worst-case minimization. After proving that such problems admit solutions, denoting with $(\Theta^N)_N$ a sequence of compact sets that parametrize the ensembles of systems, we first show that the corresponding minimax optimal control problems are $\Gamma$-convergent whenever $(\Theta^N)_N$ has a limit with respect to the Hausdorff distance. Besides its independent interest, the previous result plays a crucial role for establishing the Pontryagin Maximum Principle (PMP) when the ensemble is parametrized by a set $\Theta$ consisting of infinitely many points. Namely, we first approximate $\Theta$ by finite and increasing-in-size sets $(\Theta^N)_N$ for which the PMP is known, and then we derive the PMP for the $\Gamma$-limiting problem. The same strategy can be pursued in applications where we can reduce infinite ensembles to finite ones to compute the minimizers numerically.
- [46] arXiv:2405.05787 (cross-list from cs.RO) [pdf, other]
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Title: Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom StudyAuthors: Tianpeng Zhang (1), Sekeun Kim (2), Jerome Charton (2), Haitong Ma (1), Kyungsang Kim (2), Na Li (1), Quanzheng Li (2) ((1) SEAS, Harvard University (2) CAMCA, Massachusetts General Hospital and Harvard Medical School)Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
The paper introduces a novel autonomous robot ultrasound (US) system targeting liver follow-up scans for outpatients in local communities. Given a computed tomography (CT) image with specific target regions of interest, the proposed system carries out the autonomous follow-up scan in three steps: (i) initial robot contact to surface, (ii) coordinate mapping between CT image and robot, and (iii) target US scan. Utilizing 3D US-CT registration and deep learning-based segmentation networks, we can achieve precise imaging of 3D hepatic veins, facilitating accurate coordinate mapping between CT and the robot. This enables the automatic localization of follow-up targets within the CT image, allowing the robot to navigate precisely to the target's surface. Evaluation of the ultrasound phantom confirms the quality of the US-CT registration and shows the robot reliably locates the targets in repeated trials. The proposed framework holds the potential to significantly reduce time and costs for healthcare providers, clinicians, and follow-up patients, thereby addressing the increasing healthcare burden associated with chronic disease in local communities.
- [47] arXiv:2405.05866 (cross-list from math.OC) [pdf, ps, other]
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Title: Parameter identification for an uncertain reaction-diffusion equation via setpoint regulationSubjects: Optimization and Control (math.OC); Systems and Control (eess.SY); Dynamical Systems (math.DS)
The problem of estimating the reaction coefficient of a system governed by a reaction-diffusion partial differential equation is tackled. An estimator relying on boundary measurements only is proposed. The estimator is based upon a setpoint regulation strategy and leads to an asymptotically converging estimate of the unknown reaction coefficient. The proposed estimator is combined with a state observer and shown to provide an asymptotic estimate of the actual system state. A numerical example supports and illustrates the theoretical results.
- [48] arXiv:2405.05951 (cross-list from math.NA) [pdf, ps, other]
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Title: $\mathcal{H}_2$ optimal model reduction of linear systems with multiple quadratic outputsComments: 18 pages, 4 figuresSubjects: Numerical Analysis (math.NA); Systems and Control (eess.SY); Dynamical Systems (math.DS); Optimization and Control (math.OC)
In this work, we consider the $\mathcal{H}_2$ optimal model reduction of dynamical systems that are linear in the state equation and up to quadratic nonlinearity in the output equation. As our primary theoretical contributions, we derive gradients of the squared $\mathcal{H}_2$ system error with respect to the reduced model quantities and, from the stationary points of these gradients, introduce Gramian-based first-order necessary conditions for the $\mathcal{H}_2$ optimal approximation of a linear quadratic output (LQO) system. The resulting $\mathcal{H}_2$ optimality framework neatly generalizes the analogous Gramian-based optimality framework for purely linear systems. Computationally, we show how to enforce the necessary optimality conditions using Petrov-Galerkin projection; the corresponding projection matrices are obtained from a pair of Sylvester equations. Based on this result, we propose an iteratively corrected algorithm for the $\mathcal{H}_2$ model reduction of LQO systems, which we refer to as LQO-TSIA (linear quadratic output two-sided iteration algorithm). Numerical examples are included to illustrate the effectiveness of the proposed computational method against other existing approaches.
Replacements for Fri, 10 May 24
- [49] arXiv:2208.09781 (replaced) [pdf, ps, other]
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Title: Co-Optimizing Distributed Energy Resources in Linear Complexity under Net Energy MeteringComments: 20 pages, 8 figures, 2 tablesSubjects: Systems and Control (eess.SY); Optimization and Control (math.OC)
- [50] arXiv:2301.06132 (replaced) [pdf, other]
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Title: Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesComments: 17 pages, 12 figures. Accepted in TPAMI 2024. arXiv admin note: substantial text overlap with arXiv:2207.04266Subjects: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
- [51] arXiv:2309.10674 (replaced) [pdf, other]
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Title: USED: Universal Speaker Extraction and DiarizationSubjects: Sound (cs.SD); Audio and Speech Processing (eess.AS)
- [52] arXiv:2312.10593 (replaced) [pdf, other]
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Title: A Novel RFID Authentication Protocol Based on A Block-Order-Modulus Variable Matrix Encryption AlgorithmSubjects: Cryptography and Security (cs.CR); Signal Processing (eess.SP)
- [53] arXiv:2312.10594 (replaced) [pdf, ps, other]
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Title: Physics-Informed Representation and Learning: Control and Risk QuantificationComments: Accepted at the AAAI 24 conferenceSubjects: Systems and Control (eess.SY)
- [54] arXiv:2401.14129 (replaced) [pdf, ps, other]
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Title: Performance Analysis of Holographic MIMO Based Integrated Sensing and CommunicationsSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
- [55] arXiv:2401.15990 (replaced) [pdf, other]
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Title: Gland Segmentation Via Dual Encoders and Boundary-Enhanced AttentionComments: Published in: ICASSP 2024Journal-ref: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 2345-2349,Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
- [56] arXiv:2403.00987 (replaced) [pdf, other]
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Title: Composite Distributed Learning and Synchronization of Nonlinear Multi-Agent Systems with Complete Uncertain DynamicsAuthors: Emadodin Jandaghi, Dalton L. Stein, Adam Hoburg, Paolo Stegagno, Mingxi Zhou, Chengzhi YuanSubjects: Multiagent Systems (cs.MA); Robotics (cs.RO); Systems and Control (eess.SY)
- [57] arXiv:2403.04900 (replaced) [pdf, other]
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Title: Almost Global Asymptotic Trajectory Tracking for Fully-Actuated Mechanical Systems on Homogeneous Riemannian ManifoldsComments: Preprint. To appear in IEEE Control Systems LettersSubjects: Systems and Control (eess.SY); Robotics (cs.RO); Optimization and Control (math.OC)
- [58] arXiv:2403.12575 (replaced) [pdf, ps, other]
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Title: Exact model reduction for discrete-time conditional quantum dynamicsSubjects: Quantum Physics (quant-ph); Systems and Control (eess.SY)
- [59] arXiv:2403.17913 (replaced) [pdf, ps, other]
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Title: Enhancing Indoor and Outdoor THz Communications with Beyond Diagonal-IRS: Optimization and Performance AnalysisSubjects: Signal Processing (eess.SP)
- [60] arXiv:2404.09131 (replaced) [pdf, other]
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Title: Design of Artificial Interference Signals for Covert Communication Aided by Multiple Friendly NodesSubjects: Signal Processing (eess.SP)
- [61] arXiv:2404.11881 (replaced) [pdf, other]
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Title: Joint Transmitter and Receiver Design for Movable Antenna Enhanced Multicast CommunicationsComments: 13 pages, 9 figures, submitted to IEEE journal for possible publicationSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
- [62] arXiv:2404.12178 (replaced) [pdf, other]
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Title: Designing a sector-coupled European energy system robust to 60 years of historical weather dataSubjects: Physics and Society (physics.soc-ph); Systems and Control (eess.SY)
- [63] arXiv:2405.02180 (replaced) [pdf, other]
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Title: A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and PredictionSubjects: Machine Learning (cs.LG); Systems and Control (eess.SY)
- [64] arXiv:2405.02504 (replaced) [pdf, other]
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Title: Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRISubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
- [65] arXiv:2405.02991 (replaced) [pdf, other]
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Title: Steered Response Power for Sound Source Localization: A Tutorial ReviewAuthors: Eric Grinstein, Elisa Tengan, Bilgesu Çakmak, Thomas Dietzen, Leonardo Nunes, Toon van Waterschoot, Mike Brookes, Patrick A. NaylorSubjects: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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