Papers

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Electrical Engineering and Systems Science

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Sunday 2 November

Image and Video Processing

Evaluating Video Quality Metrics for Neural and Traditional Codecs using 4K/UHD-1 Videos

Benjamin Herb, Rakesh Rao Ramachandra Rao, Steve Göring et al. (+1 more)

With neural video codecs (NVCs) emerging as promising alternatives for traditional compression methods, it is increasingly important to determine whether existing quality metrics remain valid for evaluating their performance. However, few studies have systematically investigated this using well-designed subjective tests. To address this gap, this paper presents a subjective quality assessment study using two traditional (AV1 and VVC) and two variants of a neural video codec (DCVC-FM and DCVC-RT). Six source videos (8-10 seconds each, 4K/UHD-1, 60 fps) were encoded at four resolutions (360p to 2160p) using nine different QP values, resulting in 216 sequences that were rated in a controlled environment by 30 participants. These results were used to evaluate a range of full-reference, hybrid, and no-reference quality metrics to assess their applicability to the induced quality degradations. The objective quality assessment results show that VMAF and AVQBits|H0|f demonstrate strong Pearson correlation, while FasterVQA performed best among the tested no-reference metrics. Furthermore, PSNR shows the highest Spearman rank order correlation for within-sequence comparisons across the different codecs. Importantly, no significant performance differences in metric reliability are observed between traditional and neural video codecs across the tested metrics. The dataset, consisting of source videos, encoded videos, and both subjective and quality metric scores will be made publicly available following an open-science approach (https://github.com/Telecommunication-Telemedia-Assessment/AVT-VQDB-UHD-1-NVC).

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All authors Benjamin Herb, Rakesh Rao Ramachandra Rao, Steve Göring, Alexander Raake

Signal Processing

Optimizing Uncertainty-Aware Deep Learning for On-the-Edge Murmur Detection in Low-Resource Settings

Andrea De Simone, Noemi Giordano, Silvia Seoni et al. (+2 more)

Early and reliable detection of heart murmurs is essential for the timely diagnosis of cardiovascular diseases, yet traditional auscultation remains subjective and dependent on expert interpretation. This work investigates artificial intelligence (AI)-based murmur detection using the CirCor Heart Sound dataset, with a focus on enabling uncertainty-aware, resource-efficient deployment on edge devices. Three convolutional neural network (CNN) architectures of increasing complexity (Light, Baseline, and Heavy) were compared in terms of classification performance, computational cost, and suitability for on-device inference. Additionally, Monte Carlo Dropout was applied for uncertainty estimation, providing confidence measures to improve prediction sensitivity. Results show that lightweight models can achieve accuracy comparable to deeper networks (91%) while requiring two orders of magnitude fewer parameters. Incorporating uncertainty-based selective classification further improved sensitivity by 3%, enhancing robustness and clinical reliability. The findings highlight the feasibility of developing computationally efficient, uncertainty-aware AI systems for heart murmur screening in low-resource and remote healthcare settings.

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All authors Andrea De Simone, Noemi Giordano, Silvia Seoni, Kristen M. Meiburger, Fabrizio Riente

Systems and Control

Secure Distributed Consensus Estimation under False Data Injection Attacks: A Defense Strategy Based on Partial Channel Coding

Jiahao Huang, Marios M. Polycarpou, Wen Yang et al. (+2 more)

This article investigates the security issue caused by false data injection attacks in distributed estimation, wherein each sensor can construct two types of residues based on local estimates and neighbor information, respectively. The resource-constrained attacker can select partial channels from the sensor network and arbitrarily manipulate the transmitted data. We derive necessary and sufficient conditions to reveal system vulnerabilities, under which the attacker is able to diverge the estimation error while preserving the stealthiness of all residues. We propose two defense strategies with mechanisms of exploiting the Euclidean distance between local estimates to detect attacks, and adopting the coding scheme to protect the transmitted data, respectively. It is proven that the former has the capability to address the majority of security loopholes, while the latter can serve as an additional enhancement to the former. By employing the time-varying coding matrix to mitigate the risk of being cracked, we demonstrate that the latter can safeguard against adversaries injecting stealthy sequences into the encoded channels. Hence, drawing upon the security analysis, we further provide a procedure to select security-critical channels that need to be encoded, thereby achieving a trade-off between security and coding costs. Finally, some numerical simulations are conducted to demonstrate the theoretical results.

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All authors Jiahao Huang, Marios M. Polycarpou, Wen Yang, Fangfei Li, Yang Tang

Systems and Control · Cross-listed from math.OC

Parallel KKT Solver in PIQP for Multistage Optimization

Fenglong Song, Roland Schwan, Yuwen Chen et al. (+1 more)

This paper presents an efficient parallel Cholesky factorization and triangular solve algorithm for the Karush-Kuhn-Tucker (KKT) systems arising in multistage optimization problems, with a focus on model predictive control and trajectory optimization for racing. The proposed approach directly parallelizes solving the KKT systems with block-tridiagonal-arrow KKT matrices on the linear algebra level arising in interior-point methods. The algorithm is implemented as a new backend of the PIQP solver and released as open source. Numerical experiments on the chain-of-masses benchmarks and a minimum curvature race line optimization problem demonstrate substantial performance gains compared to other state-of-the-art solvers.

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All authors Fenglong Song, Roland Schwan, Yuwen Chen, Colin N. Jones

Signal Processing

Lightweight ResNet-Based Deep Learning for Photoplethysmography Signal Quality Assessment

Yangyang Zhao, Matti Kaisti, Olli Lahdenoja et al. (+10 more)

With the growing application of deep learning in wearable devices, lightweight and efficient models are critical to address the computational constraints in resource-limited platforms. The performance of these approaches can be potentially improved by using various preprocessing methods. This study proposes a lightweight ResNet-based deep learning framework with Squeeze-and-Excitation (SE) modules for photoplethysmography (PPG) signal quality assessment (SQA) and compares different input configurations, including the PPG signal alone, its first derivative (FDP), its second derivative (SDP), the autocorrelation of PPG (ATC), and various combinations of these channels. Experimental evaluations on the Moore4Medical (M4M) and MIMIC-IV datasets demonstrate the model's performance, achieving up to 96.52% AUC on the M4M test dataset and up to 84.43% AUC on the MIMIC-IV dataset. The novel M4M dataset was collected to explore PPG-based monitoring for detecting atrial fibrillation (AF) and AF burden in high-risk patients. Compared to the five reproduced existing studies, our models achieves over 99% reduction in parameters and more than 60% reduction in floating-point operations (FLOPs).

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All authors Yangyang Zhao, Matti Kaisti, Olli Lahdenoja, Jonas Sandelin, Arman Anzanpour, Joonas Lehto, Joel Nuotio, Jussi Jaakkola, Arto Relander, Tuija Vasankari, Juhani Airaksinen, Tuomas Kiviniemi, Tero Koivisto

Systems and Control

Traffic-Aware Microgrid Planning for Dynamic Wireless Electric Vehicle Charging Roadways

Dipanjan Ghose, Junjie Qin and S Sivaranjani

Dynamic wireless charging (DWC) is an emerging technology that has the potential to reduce charging downtime and on-board battery size, particularly in heavy-duty electric vehicles (EVs). However, its spatiotemporal, dynamic, high-power demands pose challenges for power system operations. Since DWC demand depends on traffic characteristics such as speed, density, and dwell time, effective infrastructure planning must account for the coupling between traffic behavior and EV energy consumption. In this paper, we propose a novel traffic-aware microgrid planning framework for DWC. First, we use the macroscopic cell transmission model to estimate spatio-temporal EV charging demand along DWC corridors and integrate this demand into an AC optimal power flow formulation to design a supporting microgrid. Our framework explicitly links traffic patterns with energy demand and demonstrates that traffic-aware microgrid planning yields significantly lower system costs than worst-case traffic-based approaches. We demonstrate the performance of our model on a segment of I-210W in California under a wide range of traffic conditions.

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Systems and Control · Cross-listed from physics.soc-ph

Low-Cost Carriers in Aviation: Significance and Developments

Bruno Felipe de Oliveira and Alessandro V. M. Oliveira

This paper aims to discuss the impacts of low-cost airlines on the air transport market and, in particular, to present the most recent findings from the specialized literature in this field. To this end, several papers published on the topic since 2015 were selected and analyzed. Based on this analysis, the main subjects addressed in the studies were categorized into five groups: (i) impacts of low-cost airlines on competing carriers; (ii) impacts on airports; (iii) general effects on air transport demand; (iv) effects on passengers' choice processes; and (v) broader effects on geographical regions.

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Signal Processing

Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces

Mahdi Maleki, Reza Agahzadeh Ayoubi, Marouan Mizmizi et al. (+1 more)

We investigate how fully-passive electromagnetic skins (EMSs) can be engineered to enhance channel charting (CC) in dense urban environments. We employ two complementary state-of-the-art CC techniques, semi-supervised t-distributed stochastic neighbor embedding (t-SNE) and a semi-supervised Autoencoder (AE), to verify the consistency of results across nonparametric and parametric mappings. We show that the accuracy of CC hinges on a balance between signal-to-noise ratio (SNR) and spatial dissimilarity: EMS codebooks that only maximize gain, as in conventional Reconfigurable Intelligent Surface (RIS) optimization, suppress location fingerprints and degrade CC, while randomized phases increase diversity but reduce SNR. To address this trade-off, we design static EMS phase profiles via a quantile-driven criterion that targets worst-case users and improves both trustworthiness and continuity. In a 3D ray-traced city at 30 GHz, the proposed EMS reduces the 90th-percentile localization error from > 50 m to < 25 m for both t-SNE and AE-based CC, and decreases severe trajectory dropouts by over 4x under 15% supervision. The improvements hold consistently across the evaluated configurations, establishing static, pre-configured EMS as a practical enabler of CC without reconfiguration overheads.

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All authors Mahdi Maleki, Reza Agahzadeh Ayoubi, Marouan Mizmizi, Umberto Spagnolini

Image and Video Processing

Deep Generative Models for Enhanced Vitreous OCT Imaging

Simone Sarrocco, Philippe C. Cattin, Peter M. Maloca et al. (+2 more)

Purpose: To evaluate deep learning (DL) models for enhancing vitreous optical coherence tomography (OCT) image quality and reducing acquisition time. Methods: Conditional Denoising Diffusion Probabilistic Models (cDDPMs), Brownian Bridge Diffusion Models (BBDMs), U-Net, Pix2Pix, and Vector-Quantised Generative Adversarial Network (VQ-GAN) were used to generate high-quality spectral-domain (SD) vitreous OCT images. Inputs were SD ART10 images, and outputs were compared to pseudoART100 images obtained by averaging ten ART10 images per eye location. Model performance was assessed using image quality metrics and Visual Turing Tests, where ophthalmologists ranked generated images and evaluated anatomical fidelity. The best model's performance was further tested within the manually segmented vitreous on newly acquired data. Results: U-Net achieved the highest Peak Signal-to-Noise Ratio (PSNR: 30.230) and Structural Similarity Index Measure (SSIM: 0.820), followed by cDDPM. For Learned Perceptual Image Patch Similarity (LPIPS), Pix2Pix (0.697) and cDDPM (0.753) performed best. In the first Visual Turing Test, cDDPM ranked highest (3.07); in the second (best model only), cDDPM achieved a 32.9% fool rate and 85.7% anatomical preservation. On newly acquired data, cDDPM generated vitreous regions more similar in PSNR to the ART100 reference than true ART1 or ART10 B-scans and achieved higher PSNR on whole images when conditioned on ART1 than ART10. Conclusions: Results reveal discrepancies between quantitative metrics and clinical evaluation, highlighting the need for combined assessment. cDDPM showed strong potential for generating clinically meaningful vitreous OCT images while reducing acquisition time fourfold. Translational Relevance: cDDPMs show promise for clinical integration, supporting faster, higher-quality vitreous imaging. Dataset and code will be made publicly available.

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All authors Simone Sarrocco, Philippe C. Cattin, Peter M. Maloca, Paul Friedrich, Philippe Valmaggia

Signal Processing

Stacked Flexible Intelligent Metasurface Design for Multi-User Wireless Communications

Ahmed Magbool, Vaibhav Kumar, Marco Di Renzo et al. (+1 more)

Stacked intelligent metasurfaces (SIMs) have recently emerged as an effective solution for next-generation wireless networks. A SIM comprises multiple metasurface layers that enable signal processing directly in the wave domain. Moreover, recent advances in flexible metamaterials have highlighted the potential of flexible intelligent metasurfaces (FIMs), which can be physically morphed to enhance communication performance. In this paper, we propose a stacked flexible intelligent metasurface (SFIM)-based communication system for the first time, where each metasurface layer is deformable to improve the system's performance. We first present the system model, including the transmit and receive signal models as well as the channel model, and then formulate an optimization problem to maximize the system sum rate under constraints on the transmit power budget, morphing distance, and the unit-modulus condition of the meta-atom responses. To solve this problem, we develop an alternating optimization framework based on the gradient projection method. Simulation results demonstrate that the proposed SFIM-based system achieves significant performance gains compared to its rigid SIM counterpart.

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All authors Ahmed Magbool, Vaibhav Kumar, Marco Di Renzo, Mark F. Flanagan

Signal Processing · Cross-listed from stat.ME

A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems

Maxime Bouton, Pierre-Antoine Thouvenin, Audrey Repetti et al. (+1 more)

Markov Chain Monte Carlo (MCMC) algorithms are standard approaches to solve imaging inverse problems and quantify estimation uncertainties, a key requirement in absence of ground-truth data. To improve estimation quality, Plug-and-Play MCMC algorithms, such as PnP-ULA, have been recently developed to accommodate priors encoded by a denoising neural network. Designing scalable samplers for high-dimensional imaging inverse problems remains a challenge: drawing and storing high-dimensional samples can be prohibitive, especially for high-resolution images. To address this issue, this work proposes a distributed sampler based on approximate data augmentation and PnP-ULA to solve very large problems. The proposed sampler uses lightweight denoising convolutional neural network, to efficiently exploit multiple GPUs on a Single Program Multiple Data architecture. Reconstruction performance and scalability are evaluated on several imaging problems. Communication and computation overheads due to the denoiser are carefully discussed. The proposed distributed approach noticeably combines three very precious qualities: it is scalable, enables uncertainty quantification, for a reconstruction performance comparable to other PnP methods.

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All authors Maxime Bouton, Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais

Audio and Speech Processing

MULTI-Bench: A Multi-Turn Interactive Benchmark for Assessing Emotional Intelligence ability of Spoken Dialogue Models

Yayue Deng, Guoqiang Hu, Haiyang Sun et al. (+6 more)

Spoken Dialogue Models (SDMs) have advanced rapidly, yet their ability to sustain genuinely interactive multi-turn conversations remains underexplored, as most benchmarks focus on single-turn exchanges. We introduce Multi-Bench, the first benchmark explicitly designed to evaluate SDMs in multi-turn interactive dialogue with an emphasis on emotional intelligence. Multi-Bench employs a hierarchical structure with a basic track for emotion understanding and reasoning and an advanced track for emotion support and application. It comprises five carefully designed tasks and about 3.2K samples, ranging from emotion recognition to complex reasoning and interactive dialogue, supported by a reproducible evaluation framework. We evaluate six representative SDMs on eight subsets of Multi-Bench. Results show that while current SDMs achieve good performance on basic understanding tasks, they still have room for improvement in advanced multi-turn interactive dialogue and reasoning-related tasks, particularly in emotion awareness and application.

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All authors Yayue Deng, Guoqiang Hu, Haiyang Sun, Xiangyu Zhang, Haoyang Zhang, Fei Tian, Xuerui Yang, Gang Yu, Eng Siong Chng

Systems and Control

Minimizing Maximum Latency of Task Offloading for Multi-UAV-assisted Maritime Search and Rescue

Shuang Qi, Bin Lin, Yiqin Deng et al. (+2 more)

Unmanned Aerial Vehicles (UAVs) play a crucial role in Maritime Search and Rescue (MSAR), contributing to the improvement of rescue efficiency and reduction of casualties. Typically, UAVs equipped with cameras collect data from disaster areas and transmit it to the shore-based rescue command centers. By deploying Mobile Edge Computing (MEC) servers, UAVs can pre-process video footage to reduce data transmission volume, thus reducing transmission delays. However, the limited computational capacity and energy of UAVs pose significant challenges to the efficiency of UAV-assisted MSAR systems. To address these problems, in this paper, we investigate a multi-UAV assisted MSAR system consisting of multiple Surveillance UAVs (S-UAVs) and a Relay UAV (R-UAV). Then, we formulate a joint optimization problem to minimize the maximum total latency among all S-UAVs via jointly making the computing offloading decisions, R-UAV deployment, and the association between a S-UAV and rescue targets while ensuring that all targets are monitored by S-UAVs. Since the formulated optimization problem is typically hard to solve due to its non-convexity, we propose an effective iterative algorithm by breaking it into three sub-problems. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

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All authors Shuang Qi, Bin Lin, Yiqin Deng, Xianhao Chen, Yuguang Fang

Systems and Control · Cross-listed from cs.RO

Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning

Stella Kombo, Masih Haseli, Skylar X. Wei et al. (+1 more)

Autonomous systems often must predict the motions of nearby agents from partial and noisy data. This paper asks and answers the question: "can we learn, in real-time, a nonlinear predictive model of another agent's motions?" Our online framework denoises and forecasts such dynamics using a modified sliding-window Hankel Dynamic Mode Decomposition (Hankel-DMD). Partial noisy measurements are embedded into a Hankel matrix, while an associated Page matrix enables singular-value hard thresholding (SVHT) to estimate the effective rank. A Cadzow projection enforces structured low-rank consistency, yielding a denoised trajectory and local noise variance estimates. From this representation, a time-varying Hankel-DMD lifted linear predictor is constructed for multi-step forecasts. The residual analysis provides variance-tracking signals that can support downstream estimators and risk-aware planning. We validate the approach in simulation under Gaussian and heavy-tailed noise, and experimentally on a dynamic crane testbed. Results show that the method achieves stable variance-aware denoising and short-horizon prediction suitable for integration into real-time control frameworks.

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All authors Stella Kombo, Masih Haseli, Skylar X. Wei, Joel W. Burdick

Systems and Control · Cross-listed from cs.RO

When Semantics Connect the Swarm: LLM-Driven Fuzzy Control for Cooperative Multi-Robot Underwater Coverage

Jingzehua Xu, Weihang Zhang, Yangyang Li et al. (+5 more)

Underwater multi-robot cooperative coverage remains challenging due to partial observability, limited communication, environmental uncertainty, and the lack of access to global localization. To address these issues, this paper presents a semantics-guided fuzzy control framework that couples Large Language Models (LLMs) with interpretable control and lightweight coordination. Raw multimodal observations are compressed by the LLM into compact, human-interpretable semantic tokens that summarize obstacles, unexplored regions, and Objects Of Interest (OOIs) under uncertain perception. A fuzzy inference system with pre-defined membership functions then maps these tokens into smooth and stable steering and gait commands, enabling reliable navigation without relying on global positioning. Then, we further coordinate multiple robots by introducing semantic communication that shares intent and local context in linguistic form, enabling agreement on who explores where while avoiding redundant revisits. Extensive simulations in unknown reef-like environments show that, under limited sensing and communication, the proposed framework achieves robust OOI-oriented navigation and cooperative coverage with improved efficiency and adaptability, narrowing the gap between semantic cognition and distributed underwater control in GPS-denied, map-free conditions.

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All authors Jingzehua Xu, Weihang Zhang, Yangyang Li, Hongmiaoyi Zhang, Guanwen Xie, Jiwei Tang, Shuai Zhang, Yi Li

Signal Processing

Target Detection with Tightly-coupled Antennas: Analysis for Unknown Wideband Signals

Erfan Khordad, Peter J. Smith, Sachitha C. Bandara et al. (+2 more)

This paper presents analysis for target detection using tightly-coupled antenna (TCA) arrays with high mutual coupling (MC). We show that the wide operational bandwidth of TCAs is advantageous for target detection. We assume a sensing receiver equipped with a TCA array that collects joint time and frequency samples of the target's echo signals. Echoes are assumed to be unknown wideband signals, and noise at the TCA array follows a frequency-varying correlation model due to MC. We also assume that the echo signals are time varying, with no assumption on the temporal variation. We consider three regimes in frequency as constant, slowly or rapidly varying, to capture all possible spectral dynamics of the echoes. We propose a novel detector for the slowly-varying regime, and derive detectors based on maximum likelihood estimation (MLE) for the other regimes. For the rapidly-varying regime, we derive an extended energy detector for correlated noise with frequency and time samples. We analyze the performance of all the detectors. We also derive and analyze an ideal detector giving an upper bound on performance. We validate our analysis with simulations and demonstrate that our proposed detector outperforms the MLE-based detectors in terms of robustness to frequency variation. Also, we highlight that TCA arrays offer clear advantages over weakly-coupled antenna arrays in target detection.

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All authors Erfan Khordad, Peter J. Smith, Sachitha C. Bandara, Rajitha Senanayake, Robert W. Heath Jr

Systems and Control

Deep Q-Network for Optimizing NOMA-Aided Resource Allocation in Smart Factories with URLLC Constraints

Shi Gengtian, Jiang Liu and Shigeru Shimamoto

This paper presents a Deep Q-Network (DQN)- based algorithm for NOMA-aided resource allocation in smart factories, addressing the stringent requirements of Ultra-Reliable Low-Latency Communication (URLLC). The proposed algorithm dynamically allocates sub-channels and optimizes power levels to maximize throughput while meeting strict latency constraints. By incorporating a tunable parameter {\lambda}, the algorithm balances the trade-off between throughput and latency, making it suitable for various devices, including robots, sensors, and controllers, each with distinct communication needs. Simulation results show that robots achieve higher throughput, while sensors and controllers meet the low-latency requirements of URLLC, ensuring reliable communication for real-time industrial applications.

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Systems and Control

High-Power Dual-Channel Field Chamber for High-Frequency Magnetic Neuromodulation

Xiaoyang Tian, Hui Wang, Boshuo Wang et al. (+10 more)

Several novel methods, including magnetogenetics and magnetoelectric stimulation, use high frequency alternating magnetic fields to precisely manipulate neural activity. To quantify the behavioral effects of such interventions in a freely moving mouse, we developed a dual-channel magnetic chamber, specifically designed for rate-sensitive magnetothermal-genetic stimulation, and adaptable for other uses of alternating magnetic fields. Through an optimized coil design, the system allows independent control of two spatially orthogonal uniform magnetic fields delivered at different frequencies within a 10 cm x 10 cm x 6 cm chamber. The two channels have nominal frequencies of 50 and 550 kHz with peak magnetic field strengths of 88 and 12.5 mT, achieved with resonant coil drives having peak voltages of 1.6 and 1.8 kV and currents of 1.0 and 0.26 kA, respectively. Additionally, a liquid cooling system enables magnetic field generation for second-level duration, and an observation port and camera allow video capture of the animal's behavior within the chamber. The system generates high-amplitude magnetic fields across two widely separated frequency channels with negligible interference (< 1%). Relatively uniform magnetic field distribution (+/-10% across 94% of the chamber volume) is maintained throughout the chamber, and temperature increase of the inner side of the coil enclosure during the operation is limited to < 0.35 {\deg}C/s to ensure in vivo safety. Using cobalt-doped and undoped iron oxide nanoparticles, we demonstrate channel-specific heating rates of 3.5 {\deg}C/s and 1.5 {\deg}C/s, respectively, validating frequency-selectivity. Both channels can run continuously for four seconds stably.

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All authors Xiaoyang Tian, Hui Wang, Boshuo Wang, Jinshui Zhang, Dong Yan, Jeannette Ingabire, Samantha Coffler, Guillaume Duret, Quoc-Khanh Pham, Gang Bao, Jacob T. Robinson, Stefan M. Goetz, Angel V. Peterchev

Systems and Control · Cross-listed from physics.med-ph

Magnetic Materials for Transcranial Magnetic Stimulation (TMS)

Max Koehler, Akshata Sangle and Stefan M. Goetz

Various coils for transcranial magnetic stimulation (TMS) are widely available for clinical and research use. These coils are almost all designed as air coils, which require large levels of energy to achieve a given magnetic flux density and in turn electric field strength, whereas in other sectors, such as power electronics or electrical machines, magnetic materials have been used for a long time to achieve higher efficiencies. We tested the impact on the electric and magnetic properties of different soft magnetic materials, including various ferrite cores, laminated sheet materials of nonisotropic corn-oriented silicon-steel, non-oriented silicon-steel, as well as cobalt-iron, and soft magnetic compound powder cores with insulated particles. Every material led to a reduction in coil current and voltage for the same target electric field strength. For the same field energy, every material yielded lower losses. Most common materials saturated already at very low currents. More material in thicker layers could shift the saturation point but at the cost of high weight. Due to their low saturation flux density, ferrites appear unsuitable for the high amplitude requirements of TMS. Laminated sheet materials and powder cores reduce the pulse energy, but the laminated sheet material adds more weight for the same effect than powder cores. Thus, appropriate magnetic materials can reduce the required pulse energy. Saturation flux density is the most relevant parameter, whereas the permeability beyond a certain base level is practically irrelevant. Most importantly, the weight of a magnetic-core coil may always be increased compared to an air coil for the same target field.

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Saturday 1 November

Image and Video Processing · Cross-listed from cs.CV

Challenging DINOv3 Foundation Model under Low Inter-Class Variability: A Case Study on Fetal Brain Ultrasound

Edoardo Conti, Riccardo Rosati, Lorenzo Federici et al. (+2 more)

Purpose: This study provides the first comprehensive evaluation of foundation models in fetal ultrasound (US) imaging under low inter-class variability conditions. While recent vision foundation models such as DINOv3 have shown remarkable transferability across medical domains, their ability to discriminate anatomically similar structures has not been systematically investigated. We address this gap by focusing on fetal brain standard planes–transthalamic (TT), transventricular (TV), and transcerebellar (TC)–which exhibit highly overlapping anatomical features and pose a critical challenge for reliable biometric assessment. Methods: To ensure a fair and reproducible evaluation, all publicly available fetal ultrasound datasets were curated and aggregated into a unified multicenter benchmark, FetalUS-188K, comprising more than 188,000 annotated images from heterogeneous acquisition settings. DINOv3 was pretrained in a self-supervised manner to learn ultrasound-aware representations. The learned features were then evaluated through standardized adaptation protocols, including linear probing with frozen backbone and full fine-tuning, under two initialization schemes: (i) pretraining on FetalUS-188K and (ii) initialization from natural-image DINOv3 weights. Results: Models pretrained on fetal ultrasound data consistently outperformed those initialized on natural images, with weighted F1-score improvements of up to 20 percent. Domain-adaptive pretraining enabled the network to preserve subtle echogenic and structural cues crucial for distinguishing intermediate planes such as TV. Conclusion: Results demonstrate that generic foundation models fail to generalize under low inter-class variability, whereas domain-specific pretraining is essential to achieve robust and clinically reliable representations in fetal brain ultrasound imaging.

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All authors Edoardo Conti, Riccardo Rosati, Lorenzo Federici, Adriano Mancini, Maria Chiara Fiorentin

Systems and Control

Quantum Computing for EVs to Enhance Grid Resilience and Disaster Relief: Challenges and Opportunities

Tyler Christeson, Amin Khodaei and Rui Fan

The power grid is the foundation of modern society, however extreme weather events have increasingly caused widespread outages. Enhancing grid resilience is therefore critical to maintaining secure and reliable operations. In disaster relief and restoration, vehicle-to-grid (V2G) technology allows electric vehicles (EVs) to serve as mobile energy resources by discharging to support critical loads or regulating grid frequency as needed. Effective V2G operation requires coordinated charging and discharging of many EVs through optimization. Similarly, in grid restoration, EVs must be strategically routed to affected areas, forming the mobile charging station placement (CSP) problem, which presents another complex optimization challenge. This work reviews state-of-the-art optimization methods for V2G and mobile CSP applications, outlines their limitations, and explores how quantum computing (QC) could overcome current computational bottlenecks. A QC-focused perspective is presented on enhancing grid resilience and accelerating restoration as extreme weather events grow more frequent and severe.

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Systems and Control

Hybrid Quantum-Classical Optimization of the Resource Scheduling Problem

Tyler Christeson, Md Habib Ullah, Ali Arabnya et al. (+2 more)

Resource scheduling is critical in many industries, especially in power systems. The Unit Commitment problem determines the on/off status and output levels of generators under many constraints. Traditional exact methods, such as mathematical programming methods or dynamic programming, remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to initial conditions, and can become prohibitively time-consuming for large-scale systems. In this paper, we introduce a quantum-classical hybrid algorithm for UC and, by extension, other resource scheduling problems, that leverages Benders decomposition to decouple binary commitment decisions from continuous economic dispatch. The binary master problem is formulated as a quadratic unconstrained binary optimization model and solved on a quantum annealer. The continuous subproblem, which minimizes generation costs, with Lagrangian cuts feeding back to the master until convergence. We evaluate our hybrid framework on systems scaled from 10 to 1,000 generation units. Compared against a classical mixed-integer nonlinear programming baseline, the hybrid algorithm achieves a consistently lower computation-time growth rate and maintains an absolute optimality gap below 1.63%. These results demonstrate that integrating quantum annealing within a hybrid quantum-classical Benders decomposition loop can significantly accelerate large-scale resource scheduling without sacrificing solution quality, pointing toward a viable path for addressing the escalating complexity of modern power grids.

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All authors Tyler Christeson, Md Habib Ullah, Ali Arabnya, Amin Khodaei, Rui Fan

Signal Processing

Fairness-Aware Secure Communication in ISAC Systems with STAR-RIS and RSMA

Thanh Nha To, Hoang Lai Pham, Quynh Nguyen Thi et al. (+2 more)

In this paper, we investigate the integration of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) with rate-splitting multiple access (RSMA) for improving physical layer security (PLS) in integrated sensing and communication (ISAC) systems. Specifically, we consider a multi-user, multi-sensing target scenario, where each sensing target is treated as a potential eavesdropper, reflecting realistic deployment conditions. To enable fairness-aware secure communication among users while maintaining sensing performance, we formulate a joint optimization problem that designs the base station beamforming vectors and STAR-RIS coefficients, aiming to maximize the minimum secrecy rate under a minimum beampattern gain constraint. To solve the resulting non-convex problem, we propose an efficient algorithm based on alternating optimization (AO) and the majorization-minimization (MM) method. Simulation results verify the fast convergence of the proposed algorithm and demonstrate significant improvements in secure communication performance.

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All authors Thanh Nha To, Hoang Lai Pham, Quynh Nguyen Thi, Tuan Anh Pham, Le Thanh Bang

Systems and Control

Unveiling Uniform Shifted Power Law in Stochastic Human and Autonomous Driving Behavior

Wang Chen, Heye Huang, Ke Ma et al. (+4 more)

Accurately simulating rare but safety-critical driving behaviors is essential for the evaluation and certification of autonomous vehicles (AVs). However, current models often fail to reproduce realistic collision rates when calibrated on real-world data, largely due to inadequate representation of long-tailed behavioral distributions. Here, we uncover a simple yet unifying shifted power law that robustly characterizes the stochasticity of both human-driven vehicle (HV) and AV behaviors, especially in the long-tail regime. The model adopts a parsimonious analytical form with only one or two parameters, enabling efficient calibration even under data sparsity. Analyzing large-scale, micro-level trajectory data from global HV and AV datasets, the shifted power law achieves an average R2 of 0.97 and a nearly identical tail distribution, uniformly fits both frequent behaviors and rare safety-critical deviations, significantly outperforming existing Gaussian-based baselines. When integrated into an agent-based traffic simulator, it enables forward-rolling simulations that reproduce realistic crash patterns for both HVs and AVs, achieving rates consistent with real-world statistics and improving the fidelity of safety assessment without post hoc correction. This discovery offers a unified and data-efficient foundation for modeling high-risk behavior and improves the fidelity of simulation-based safety assessments for mixed AV/HV traffic. The shifted power law provides a promising path toward simulation-driven validation and global certification of AV technologies.

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All authors Wang Chen, Heye Huang, Ke Ma, Hangyu Li, Shixiao Liang, Hang Zhou, Xiaopeng Li

Image and Video Processing

Been There, Scanned That: Nostalgia-Driven LiDAR Compression for Self-Driving Cars

Ali Khalid, Jaiaid Mobin, Sumanth Rao Appala et al. (+4 more)

An autonomous vehicle can generate several terabytes of sensor data per day. A significant portion of this data consists of 3D point clouds produced by depth sensors such as LiDARs. This data must be transferred to cloud storage, where it is utilized for training machine learning models or conducting analyses, such as forensic investigations in the event of an accident. To reduce network and storage costs, this paper introduces DejaView. Although prior work uses interframe redundancies to compress data, DejaView searches for and uses redundancies on larger temporal scales (days and months) for more effective compression. We designed DejaView with the insight that the operating area of autonomous vehicles is limited and that vehicles mostly traverse the same routes daily. Consequently, the 3D data they collect daily is likely similar to the data they have captured in the past. To capture this, the core of DejaView is a diff operation that compactly represents point clouds as delta w.r.t. 3D data from the past. Using two months of LiDAR data, an end-to-end implementation of DejaView can compress point clouds by a factor of 210 at a reconstruction error of only 15 cm.

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All authors Ali Khalid, Jaiaid Mobin, Sumanth Rao Appala, Avinash Maurya, Stephany Berrio Perez, M. Mustafa Rafique, Fawad Ahmad

Systems and Control

Frequency Quality Assessment of GFM and GFL Converters and Synchronous Condensers

Taulant Kerci and Federico Milano

This paper compares the impact of different conventional and emerging technologies and control strategies on frequency quality. We study, in particular, the long-term dynamic performance of grid-forming (GFM) and grid-following (GFL) inverter-based resources (IBRs) as well as conventional synchronous machines. Extensive simulations and several realistic scenarios consider both short-term and long-term aspects of frequency quality. It is shown that, while overall GFM IBRs significantly improve frequency quality, a combination of GFL IBRs providing frequency support such as wind and batteries, and synchronous condensers, might be enough to meet similar frequency quality standards. Another result of the paper is that the need for automatic generation control (AGC) becomes less clear in GFM IBR-dominated grids from a frequency quality perspective.

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Systems and Control

Adaptive Federated Learning to Optimize Integrated Flows in Cyber-Physical Data Centers

Junhong Liu, Lanxin Du, Yujia Li et al. (+4 more)

Data centers play an increasingly critical role in societal digitalization, yet their rapidly growing energy demand poses significant challenges for sustainable operation. To enhance the energy efficiency of geographically distributed data centers, this paper formulates a multi-period optimization model that captures the interdependence of electricity, heat, and data flows. The optimization of such integrated multi-domain flows inherently involves mixed-integer formulations and the access to proprietary or sensitive datasets, which correspondingly exacerbate computational complexity and raise data-privacy concerns. To address these challenges, an adaptive federated learning-to-optimization approach is proposed, accounting for the heterogeneity of datasets across distributed data centers. To safeguard privacy, cryptography techniques are leveraged in both the learning and optimization processes. A model acceptance criterion with convergence guarantee is developed to improve learning performance and filter out potentially contaminated data, while a verifiable double aggregation mechanism is further proposed to simultaneously ensure privacy and integrity of shared data during optimization. Theoretical analysis and numerical simulations demonstrate that the proposed approach preserves the privacy and integrity of shared data, achieves near-optimal performance, and exhibits high computational efficiency, making it suitable for large-scale data center optimization under privacy constraints.

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All authors Junhong Liu, Lanxin Du, Yujia Li, Rong-Peng Liu, Yunfeng Li, Fei Teng, Francis Yunhe Hou

Signal Processing

Fast Time-Varying mmWave Channel Estimation: A Rank-Aware Matrix Completion Approach

Tianyu Jiang, Yan Yang, Hongjin Liu et al. (+3 more)

We consider the problem of high-dimensional channel estimation in fast time-varying millimeter-wave MIMO systems with a hybrid architecture. By exploiting the low-rank and sparsity properties of the channel matrix, we propose a two-phase compressed sensing framework consisting of observation matrix completion and channel matrix sparse recovery, respectively. First, we formulate the observation matrix completion problem as a low-rank matrix completion (LRMC) problem and develop a robust rank-one matrix completion (R1MC) algorithm that enables the matrix and its rank to iteratively update. This approach achieves high-precision completion of the observation matrix and explicit rank estimation without prior knowledge. Second, we devise a rank-aware batch orthogonal matching pursuit (OMP) method for achieving low-latency sparse channel recovery. To handle abrupt rank changes caused by user mobility, we establish a discrete-time autoregressive (AR) model that leverages the temporal rank correlation between continuous-time instances to obtain a complete observation matrix capable of perceiving rank changes for more accurate channel estimates. Simulation results confirm the effectiveness of the proposed channel estimation frame and demonstrate that our algorithms achieve state-of-the-art performance in low-rank matrix recovery with theoretical guarantees.

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All authors Tianyu Jiang, Yan Yang, Hongjin Liu, Runyu Han, Bo Ai, Mohsen Guizani

Image and Video Processing

GDROS: A Geometry-Guided Dense Registration Framework for Optical-SAR Images under Large Geometric Transformations

Zixuan Sun, Shuaifeng Zhi, Ruize Li et al. (+3 more)

Registration of optical and synthetic aperture radar (SAR) remote sensing images serves as a critical foundation for image fusion and visual navigation tasks. This task is particularly challenging because of their modal discrepancy, primarily manifested as severe nonlinear radiometric differences (NRD), geometric distortions, and noise variations. Under large geometric transformations, existing classical template-based and sparse keypoint-based strategies struggle to achieve reliable registration results for optical-SAR image pairs. To address these limitations, we propose GDROS, a geometry-guided dense registration framework leveraging global cross-modal image interactions. First, we extract cross-modal deep features from optical and SAR images through a CNN-Transformer hybrid feature extraction module, upon which a multi-scale 4D correlation volume is constructed and iteratively refined to establish pixel-wise dense correspondences. Subsequently, we implement a least squares regression (LSR) module to geometrically constrain the predicted dense optical flow field. Such geometry guidance mitigates prediction divergence by directly imposing an estimated affine transformation on the final flow predictions. Extensive experiments have been conducted on three representative datasets WHU-Opt-SAR dataset, OS dataset, and UBCv2 dataset with different spatial resolutions, demonstrating robust performance of our proposed method across different imaging resolutions. Qualitative and quantitative results show that GDROS significantly outperforms current state-of-the-art methods in all metrics. Our source code will be released at: https://github.com/Zi-Xuan-Sun/GDROS.

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All authors Zixuan Sun, Shuaifeng Zhi, Ruize Li, Jingyuan Xia, Yongxiang Liu, Weidong Jiang

Systems and Control

Efficiency and Optimality in Electrochemical Battery Model Parameter Identification: A Comparative Study of Estimation Techniques

Feng Guo, Luis D. Couto and Guillaume Thenaisie

Parameter identification for electrochemical battery models has always been challenging due to the multitude of parameters involved, most of which cannot be directly measured. This paper evaluates the efficiency and optimality of three widely-used parameter identification methods for electrochemical battery models: Least Squares Method (LS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Therefore, a Single Particle Model (SPM) of a battery was developed and discretized. Battery parameter grouping was then performed to reduce the number of parameters required. Using a set of parameters previously identified from a real battery as a benchmark, we generated fitting and validation datasets to assess the methods' runtime and accuracy. The comparative analysis reveals that PSO outperforms the other methods in terms of accuracy and stability, making it highly effective for parameter identification when there is no prior knowledge of the battery's internal parameters. In contrast, LS is better suited for minor adjustments in parameters, particularly for aging batteries, whereas GA lags behind in both computational efficiency and optimality with respect to PSO.

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