Systems and Control · Cross-listed from cs.LG
Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
Sinan Ibrahim, Grégoire Ouerdane, Hadi Salloum et al. (+3 more)
The objective comparison of Reinforcement Learning (RL)
algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sensitive to environmental design, reward structures, and stochasticity inherent in both algorithmic learning and environmental dynamics. To manage this complexity, we introduce a rigorous benchmarking framework by extending converse optimality to discrete-time, control-affine, nonlinear systems with noise. Our framework provides necessary and sufficient conditions, under which a prescribed value function and policy are optimal for constructed systems, enabling the systematic generation of
benchmark families via homotopy variations and randomized parameters. We validate it by automatically constructing diverse environments, demonstrating our framework's capacity for a controlled and comprehensive evaluation across
algorithms. By assessing standard methods against a ground-truth optimum, our work delivers a reproducible foundation for precise and rigorous RL benchmarking.
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All authors Sinan Ibrahim, Grégoire Ouerdane, Hadi Salloum, Henni Ouerdane, Stefan Streif, Pavel Osinenko
Signal Processing
Fast Beam-Brainstorm: Few-Step Generative Site-Specific Beamforming with Flexible Probing
Zihao Zhou, Zhaolin Wang and Yuanwei Liu
A novel generative site-specific beamforming (GenSSBF) approach, termed fast beam-brainstorm (F-BBS), is proposed to address the practical bottlenecks of slow beam generation and fixed channel probing lengths in existing GenSSBF. To accelerate beam generation, F-BBS utilizes a two-stage
distillation strategy that learns an average velocity field, instead of an instantaneous one, to guide the beam generative process. This strategy enables larger generation steps, realizing few-step or even one-step beam generation. Furthermore, to accommodate flexible channel probing lengths, a
stochastic masking mechanism and a beam index-aware masked condition encoder are proposed, enabling a single trained model to operate with variable-length channel probing observations without retraining. Therefore, FBBS achieves the fast generation of high-fidelity communication beams from coarse and variable-length channel probing feedback, i.e., reference signal received power (RSRP), from user equipments. Simulation results on accurate ray-tracing
datasets show that 1) F-BBS achieves comparable performance while reducing the beam generation cost by over 90% compared with
diffusion-based GenSSBF solutions, 2) F-BBS realizes robust performance across variable channel probing length, and 3) FBBS offers a desirable trade-off between beamforming gain and beam probing overhead.
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Signal Processing
3D Spherical Directly-Connected Antenna Array for Low-Altitude UAV Swarm ISAC
Haoyu Jiang, Zhenjun Dong, Zhiwen Zhou et al. (+1 more)
Recently a novel multi-
antenna architecture termed ray
antenna array (RAA) was proposed, where several simple uniform linear arrays (sULAs) are arranged in a ray-like structure to enhance communication and sensing performance. By eliminating the need for phase shifters, it also significantly reduces hardware costs. However, RAA is prone to signal blockage and has no elevation angle resolution capability in three-dimensional (3D) scenarios. To address such issues, in this paper we propose a novel spherical directly-connected
antenna array (DCAA), which composes of multiple simple uniform planar arrays (sUPAs) placed over a spherical surface. All elements within each sUPA are directly connected. Compared to conventional arrays with hybrid analog/digital beamforming (HBF), DCAA significantly reduces hardware cost, improves energy focusing, and provides superior and uniform angular res olution for 3D space. These advantages make DCAA particularly suitable for integrated sensing and communication (
ISAC) in low-altitude unmanned aerial vehicles (
UAV) swarm scenarios, where targets may frequently move away from the boresight of traditional arrays, degrading both communication and sensing performance. Simulation results demonstrate that the proposed spherical DCAA achieves significantly better angular resolution and higher spectral efficiency than conventional array with HBF, highlighting its strong potential for
UAV swarm
ISAC systems.
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All authors Haoyu Jiang, Zhenjun Dong, Zhiwen Zhou, Yong Zeng
Signal Processing
Programmable Unitary RF Networks for Narrowband Hybrid Beamforming
Nikola Zlatanov and Damir Salakhov
Conventional hybrid beamforming architectures are compared with one another and with the fully-digital architecture under the same radiated
antenna power. However, the physically relevant power budget is the power supplied by the RF chains to the passive analog beamforming network, which then transfers it to the
antenna ports. Some architectures, such as fully-connected architectures, can divert part of this power to terminated ports even under ideal passive-component assumptions, thereby delivering less power to the
antenna ports than that supplied by the RF chains. Other architectures, such as partially connected architecture, transfer all RF-chain power to the
antenna ports but have restricted beamforming flexibility and therefore do not attain the fully-digital sum-rate optimum in general. Motivated by this RF-chain injected-power and beamforming flexibility viewpoint, this paper proposes a hybrid beamforming architecture that, under ideal passive-component assumptions: i) transfers all RF-chain injected power to the
antenna ports without loss; and ii) with continuous phase control and sufficiently many programmable phase layers, attains the fully-digital sum-rate optimum under the same RF-chain injected-power constraint. The proposed hybrid beamforming architecture uses a programmable
unitary analog beamforming network realized by interlacing fixed mixing layers with programmable diagonal phase-shifting layers. We compare this architecture with fully-connected, partially connected, and Butler/DFT beam-selection baselines under ideal passive-component assumptions and show numerically that none of these baselines attains the fully-digital sum-rate optimum under the same RF-chain injected-power constraint. Finally, we compare the different architectures when component losses are taken into account and illustrate the balance between beamforming flexibility and accumulated losses.
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Systems and Control
An Extended T-A Formulation Based on Potential-Chain Recursion for Electromagnetic Modeling of Parallel-Wound No-Insulation HTS Coils
Zhe Pan, Qi Xu, Ruixiang Wang et al. (+2 more)
Parallel-wound no-insulation (PW-NI) high-temperature superconducting (HTS) coils significantly reduce charging delay while maintaining excellent self-protection capability, demonstrating great potential for high-field applications. Existing models that couple the T-A formulation with equivalent
circuits have demonstrated high accuracy in electromagnetic analysis of PW-NI coils. However, eliminating the computational overhead caused by frequent variable mapping and data exchange between electromagnetic and
circuit modules is important for improving computational efficiency, particularly in long-duration transient simulations of large-scale magnets. To address this issue, an extended T-A formulation based on potential-chain recursion, termed PCR-TA, is proposed. By directly
embedding inter-tape current sharing and radial current bypass behaviors into the finite-element framework, this method computes the transient electromagnetic response of PW-NI coils without requiring an explicit equivalent
circuit model. Building upon it, a multi-scale approach is further developed for large-scale PW-NI coils. The validity of the proposed method and its multi-scale extension is verified through comparisons with experimental measurements and field-
circuit coupled modeling results. Comparative analyses demonstrate that the PCR-TA method achieves a speedup of approximately 2.4 over the field-
circuit coupled method, whereas its multi-scale extension further increases this speedup to roughly 5.8. Furthermore, the PCR-TA method is extended to model the continuous transition of PW-NI coils from power-supply charging to closed-loop operation. This work provides an efficient method and tool for the electromagnetic modeling of PW-NI coils under both driven and closed-loop operating conditions.
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All authors Zhe Pan, Qi Xu, Ruixiang Wang, Zhenghao Jin, Jianzhao Geng
Systems and Control
Optimal Control for Steady Circulation of a Diffusion Process via Spectral Decomposition of Fokker-Planck Equation
Norihisa Namura and Hiroya Nakao
We present a formulation of an optimal control problem for a two-dimensional
diffusion process governed by a Fokker-Planck equation to achieve a nonequilibrium steady state with a desired circulation while accelerating convergence toward the stationary distribution. To achieve the control objective, we introduce costs for both the probability density function and flux rotation to the objective functional. We formulate the optimal control problem through dimensionality reduction of the Fokker-Planck equation via eigenfunction expansion, which requires a low-computational cost. We demonstrate that the proposed optimal control achieves the desired circulation while accelerating convergence to the stationary distribution through numerical simulations.
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Image and Video Processing
Deep Learning-Based Airway Segmentation in Systemic Lupus Erythematosus Patients with Interstitial Lung Disease (SLE-ILD): A Comparative High-Resolution CT Analysis
Sirong Piao, Ying Ming, Ruijie Zhao et al. (+11 more)
To characterize lobar and segmental airway volume differences between systemic lupus erythematosus (SLE) patients with interstitial lung disease (ILD) and those without ILD (non-ILD) using a deep learning-based approach on non-contrast chest high-resolution CT (HRCT). Methods: A retrospective analysis was conducted on 106 SLE patients (27 SLE-ILD, 79 SLE-non-ILD) who underwent HRCT. A customized deep learning framework based on the U-Net architecture was developed to automatically segment airway structures at the lobar and segmental levels via HRCT. Volumetric measurements of lung lobes and segments derived from the
segmentations were statistically compared between the two groups using two-sample t-tests (significance threshold: p < 0.05). Results: At lobar level, significant airway volume enlargement in SLE-ILD patients was observed in the right upper lobe (p=0.009) and left upper lobe (p=0.039) compared to SLE-non-ILD. At the segmental level, significant differences were found in segments including R1 (p=0.016), R3 (p<0.001), and L3 (p=0.038), with the most marked changes in the upper lung zones, while lower zones showed non-significant trends. Conclusion: Our study demonstrates that an automated deep learning-based approach can effectively quantify airway volumes on HRCT scans and reveal significant, region-specific airway dilation in patients with SLE-ILD compared to those without ILD. The pattern of involvement, predominantly affecting the upper lobes and specific segments, highlights a distinct topographic phenotype of SLE-ILD and implicates airway structural alterations as a potential biomarker for disease presence. This AI-powered quantitative imaging biomarker holds promise for enhancing the early detection and monitoring of ILD in the SLE population, ultimately contributing to more personalized patient management.
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All authors Sirong Piao, Ying Ming, Ruijie Zhao, Jiaru Wang, Ran Xiao, Rui Zhao, Zicheng Liao, Qiqi Xu, Shaoze Luo, Bing Li, Lin Li, Zhuangfei Ma, Fuling Zheng, Wei Song
Signal Processing
Fully 3D-Printed Wideband Metasurface Folded Reflectarray Antenna
Evangelos Vassos, Thomas Whittaker, Abdul Jabbar et al. (+2 more)
This article presents a fully 3D-printed wideband metasurface folded reflectarray
antenna (MFRA) operating in the millimeter-wave n257 band. The proposed MFRA integrates a novel polarization-rotating reflective metasurface (RMS), a compact embedded horn feed, and a polarization-selective metasurface polarization grid (MPG), all fabricated using a low-cost in-house 3D-printed method. Unlike conventional PCB-based FRAs constrained to planar unit-
cell geometries, the proposed anisotropic meta-element design exploits full three-dimensional dielectric control by tailoring varying unit-
cell heights. This volumetric tuning, combined with the spatial distribution of the meta-elements, enables phase compensation exceeding $400^{\circ}$ across the aperture, supporting robust wideband performance. An MFRA prototype is in-house fabricated and experimentally validated. Measured results agree well with simulations, achieving a $-10$ dB impedance bandwidth of 20.7% (26–32 GHz) and a peak realized gain of 31.1 dBi at 28.2 GHz. The
antenna exhibits sidelobe levels below $-20$ dB, cross-polarization below $-30$ dB, and a compact height-to-diameter ratio of 0.20. Stable pencil beams with an average HPBW of $3.7^{\circ}$ are maintained across the operating band. To further validate the robustness of the proposed in-house designed MFRA, a commercially manufactured RMS was also obtained, whose measured performance shows excellent agreement with the in-house 3D-printed version, confirming a cost-effective rapid-prototyping
antenna solution. The proposed MFRA is a cost-effective solution for beyond 5G and 6G high-gain point-to-point mmWave wireless applications, such as fixed wireless access, near field communication, and beam focusing.
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All authors Evangelos Vassos, Thomas Whittaker, Abdul Jabbar, Aakash Bansal, Will Whittow
Systems and Control
Distributed Adaptive Control for DC Power Distribution in Hybrid-Electric Aircraft: Design and Experimental Validation
Wasif H. Syed, Juan E. Machado, Hans Würfel et al. (+2 more)
To reduce CO2 emissions and tackle increasing fuel costs, the aviation industry is swiftly moving towards the electrification of aircraft. From the viewpoint of systems and control, a key challenge brought by this transition corresponds to the management and safe operation of the propulsion system's onboard electrical power distribution network. In this work, for a series-hybrid-electric propulsion system, we propose a distributed adaptive controller for regulating the voltage of a DC bus that energizes the electricity-based propulsion system. The proposed controller – whose design is based on principles of back-stepping, adaptive, and passivity-based control techniques – also enables the proportional sharing of the electric load among multiple converter-interfaced sources, which reduces the likelihood of over-stressing individual sources. Compared to existing control strategies, our method ensures stable, convergent, and accurate voltage regulation and load-sharing even if the effects of power lines of unknown resistances and inductances are considered. The performance of the proposed control scheme is experimentally validated and compared to state-of-the-art controllers in a power hardware-in-the-loop (PHIL) environment.
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All authors Wasif H. Syed, Juan E. Machado, Hans Würfel, Ekrem Hanli, Johannes Schiffer
Systems and Control
A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness
Xiucheng Wang, Yuhao Pan, Nan Cheng et al. (+9 more)
Radio maps (RMs) provide the digital representation of the wireless propagation environment, mapping complex geographical and topological boundary conditions to critical spatial-spectral metrics that range from received signal strength to full channel state information
matrices. The integration of artificial intelligence into next generation wireless networks further necessitates the accurate construction of RMs as a foundational prerequisite for electromagnetic digital twins. This paper presents a comprehensive survey of learning-based RM construction, systematically addressing three intertwined dimensions: data, paradigms, and physics-awareness. From the data perspective, we review physical measurement campaigns, ray tracing simulation engines, and publicly available
benchmark datasets, identifying their respective strengths and fundamental limitations. From the paradigm perspective, we establish a core taxonomy that categorizes RM construction into source-aware forward prediction and \added{source-agnostic} inverse reconstruction, and examine five principal neural architecture families spanning convolutional neural networks,
vision transformers, graph neural networks, generative
adversarial networks, and
diffusion models. We further survey optics-inspired methods adapted from neural radiance fields and 3D Gaussian splatting for continuous wireless radiation field modeling. From the physics-awareness perspective, we introduce a three-level integration framework encompassing data-level feature engineering, loss-level partial differential equation regularization, and \added{architecture-level} structural isomorphism. Open challenges including foundation model development, physical
hallucination detection, and amortized inference for real-time deployment are discussed to outline future research directions.
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All authors Xiucheng Wang, Yuhao Pan, Nan Cheng, Çağkan Yapar, Ruijin Sun, Zhisheng Yin, Conghao Zhou, Wenchao Xu, Yuxiang Zhang, Jianhua Zhang, Shuguang Cui, Xuemin Shen
Systems and Control · Cross-listed from cs.RO
From Optimizable to Interactable: Mixed Digital Twin-Empowered Testing of Vehicle-Infrastructure Cooperation Systems
Jianghong Dong, Chunying Yang, Mengchi Cai et al. (+4 more)
Sufficient testing under corner cases is critical for the long-term operation of vehicle-infrastructure cooperation systems (VICS). However, existing corner-case generation methods are primarily AI-driven, and VICS testing under corner cases is typically limited to simulation. In this paper, we introduce an L5 ”Interactable” level to the VICS digital twin (VICS-DT) taxonomy, extending beyond the conventional L4 ”Optimizable” level. We further propose an L5-level VICS testing framework, IMPACT (Interactive Mixed-digital-twin Paradigm for Advanced Cooperative vehicle-infrastructure Testing). By enabling direct human interactions with VICS entities, IMPACT incorporates highly uncertain and unpredictable human behaviors into the testing loop, naturally generating high-quality corner cases that complement AI-based methods. Furthermore, the mixedDT-enabled ”Physical-Virtual Action Interaction” facilitates safe VICS testing under corner cases, incorporating real-world environments and entities rather than purely in simulation. Finally, we implement IMPACT on the I-VIT (Interactive Vehicle-Infrastructure Testbed), and experiments demonstrate its effectiveness. The experimental
videos are available at our project website: https://dongjh20.github.io/IMPACT.
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All authors Jianghong Dong, Chunying Yang, Mengchi Cai, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li
Systems and Control
PowerDAG: Supervisory Agentic AI System for Automating Distribution Grid Analysis
Emmanuel O. Badmus and Amritanshu Pandey
Distribution grid analyses include tasks such as network information
retrieval, power-flow analysis, hosting-capacity assessment, DER planning, and state estimation. Completing these tasks often requires long-horizon, stateful workflows in which an engineer retrieves data, loads a feeder, runs simulations, evaluates results, and exports outputs. The growing volume of these analyses is outpacing the limited engineering workforce, causing suboptimal outcomes and delays. Large
Language Model (LLM)-orchestrated
agents can help, but they often struggle for two reasons: (i) they lack
algorithms to determine the right context for an unseen grid task, and (ii) they cannot verify proposed actions against the environment state beforehand and instead rely on feedback after execution. We propose PowerDAG, an agentic artificial intelligence (AI) system that formalizes workflows as directed acyclic graphs (DAGs) and addresses current gaps in this formalism through two mechanisms, adaptive
retrieval and Just-in-Time supervision. To dynamically retrieve relevant context, it curates and ranks expert exemplars using an adaptive score-decay cutoff that matches the query complexity. For supervision, it evaluates prerequisites before every tool call. If an
agent proposes an invalid action, the supervisor blocks execution, preserves the environment, and returns a corrective advisory. We evaluate PowerDAG on 150 held-out queries from a 200-record expert-verified
benchmark that covers 10 of the most commonly performed distribution-grid analyses, comparing 6 agentic systems across 10 LLMs for a total of 9,000 runs. PowerDAG reaches a success rate of 98.0% with GPT-5.5, 97.3% with Gemini 3.1 Pro, and 92.7% with Qwen3.6-27B, improving success rates by 6 to 50 percentage points over baselines.
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Systems and Control · Cross-listed from cs.RO
Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks
Jinyu Miao, Pu Zhang, Rujun Yan et al. (+8 more)
Advanced autonomous driving systems require accurate vehicle dynamics modeling. However, identifying a precise dynamics model remains challenging due to strong nonlinearities and the coupled longitudinal and lateral dynamic characteristics. Previous research has employed physics-based analytical models or neural networks to construct vehicle dynamics representations. Nevertheless, these approaches often struggle to simultaneously achieve satisfactory performance in terms of system identification efficiency, modeling accuracy, and compatibility with linear control strategies. In this paper, we propose a fully data-driven dynamics modeling method tailored for complex distributed electric-drive trucks (DETs), leveraging Koopman operator theory to represent highly nonlinear dynamics in a lifted linear
embedding space. To achieve high-precision modeling, we first propose a novel dual-branch encoder which encodes dynamic states and provides a powerful basis for the proposed Koopman-based methods entitled KODE. A physics-informed supervision mechanism, grounded in the
geometric consistency of temporal vehicle motion, is incorporated into the training process to facilitate effective learning of both the encoder and the Koopman operator. Furthermore, to accommodate the diverse driving patterns of DETs, we extend the vanilla Koopman operator to a mixture-of-Koopman operator framework, enhancing modeling capability. Simulations conducted in a high-fidelity TruckSim environment and real-world experiments demonstrate that the proposed approach achieves state-of-the-art performance in long-term dynamics state estimation.
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All authors Jinyu Miao, Pu Zhang, Rujun Yan, Yifei He, Bowei Zhang, Zheng Fu, Ke Wang, Qi Song, Kun Jiang, Mengmeng Yang, Diange Yang
Image and Video Processing
Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration
Ivor J. A. Simpson and Neill D. F. Campbell
Image registration is an ill-posed dense
vision task, where multiple solutions achieve similar loss values, motivating probabilistic inference. Variational inference has previously been employed to capture these distributions, however restrictive assumptions about the posterior form can lead to poor characterisation, overconfidence and low-quality samples. More flexible posteriors are typically bottlenecked by the complexity of high-dimensional covariance
matrices required for dense 3D image registration. In this work, we present a
memory and computationally efficient inference method, Structured SIR, that enables expressive, multi-modal, characterisation of uncertainty with high quality samples. We propose the use of a Sampled Importance Resampling (SIR)
algorithm with a novel
memory-efficient high-dimensional covariance parameterisation as the sum of a low-rank covariance and a sparse, spatially structured Cholesky precision factor. This structure enables capturing complex spatial correlations while remaining computationally tractable. We evaluate the efficacy of this approach in 3D dense image registration of
brain MRI data, which is a very high-dimensional problem. We demonstrate that our proposed method produces uncertainty estimates that are significantly better calibrated than those produced by variational methods, achieving equivalent or better accuracy. Crucially, we show that the model yields highly structured multi-modal posterior distributions, enable effective and efficient uncertainty quantification.
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Audio and Speech Processing
Robust Nasality Representation Learning for Cleft Palate-Related Velopharyngeal Dysfunction Screening in Real-World Settings
Weixin Liu, Bowen Qu, Amy Stone et al. (+8 more)
Velopharyngeal dysfunction (VPD) is characterized by inadequate velopharyngeal closure during
speech and often causes hypernasality and reduced intelligibility. Although
speech-based machine learning models can perform well under standardized clinical recording conditions, their performance often drops in real-world settings because of domain shift caused by differences in devices, channels, noise, and room acoustics. To improve robustness, we propose a two-stage framework for VPD screening. First, a nasality-focused
speech representation is learned by supervised contrastive pre-training on an auxiliary corpus with phoneme
alignments, using oral-context versus nasal-context supervision. Second, the encoder is frozen and used with lightweight classifiers on 0.5-second
speech chunks, whose probabilities are aggregated to produce recording-level decisions with a fixed threshold. On an in-domain clinical cohort of 82 subjects, the proposed method achieved perfect recording-level screening performance (macro-F1 = 1.000, accuracy = 1.000). On a separate out-of-domain set of 131 heterogeneous public Internet recordings, large pretrained
speech representations degraded substantially, while MFCC was the strongest baseline (macro-F1 = 0.612, accuracy = 0.641). The proposed method achieved the best out-of-domain performance (macro-F1 = 0.679, accuracy = 0.695), improving on the strongest baseline under the same evaluation protocol. These results suggest that learning a nasality-focused representation before clinical classification can reduce sensitivity to recording artifacts and improve robustness for deployable
speech-based VPD screening.
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All authors Weixin Liu, Bowen Qu, Amy Stone, Maria E. Powell, Shama Dufresne, Stephane Braun, Izabela Galdyn, Michael Golinko, Bradley Malin, Zhijun Yin, Matthew E. Pontell
Audio and Speech Processing
Uncertainty Quantification and Risk Control for Multiple Sound Source Localization
Vadim Rozenfeld and Bracha Laufer Goldshtein
Reliable Sound Source Localization (SSL) plays an essential role in many downstream tasks, where informed decision making depends not only on accurate localization but also on the confidence in each estimate. This need for reliability becomes even more pronounced in challenging conditions, such as reverberant environments and multi-source scenarios. However, existing SSL methods typically provide only point estimates, offering limited or no Uncertainty Quantification (UQ). We leverage the Conformal Prediction (CP) framework and its extensions for controlling general risk functions to develop two complementary UQ approaches for SSL. The first assumes that the number of active sources is known and constructs prediction regions that cover the true source locations. The second addresses the more challenging setting where the source count is unknown, first reliably estimating the number of active sources and then forming corresponding prediction regions. We evaluate the proposed methods on extensive simulations and real-world recordings across varying reverberation levels and source configurations. Results demonstrate reliable finite-sample guarantees and consistent performance for both known and unknown source-count scenarios, highlighting the practical utility of the proposed frameworks for uncertainty-aware SSL.
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Systems and Control · Cross-listed from math.OC
A Cycle-Based Solvability Condition for Real Power Flow Equations
Puskar Neupane and Bai Cui
Certifying power flow solvability is important for reliable power system operations under volatile operating conditions, but solving power flow equations repeatedly can be costly and may encounter convergence issues. In this paper, we develop an explicit cycle-based solvability condition for the lossless real power flow equations on meshed networks. We decompose every feasible nodal balance solution into a particular flow plus a cycle flow correction vector. The power flow problem is then reduced to enforcing edge-wise feasibility and cycle consistency. We show that the cycle consistency function is strongly monotone and is the gradient of a strongly convex energy function. By exploiting these properties, we derive an explicit condition for the existence and uniqueness of a power flow solution with bounded angle difference. The resulting condition is invariant under the choice of cycle basis and can be verified through simple algebraic computations. Numerical results on standard test systems show that the proposed condition is significantly less conservative than existing sufficient conditions and closely approximates true loading limits.
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Signal Processing
A Joint Graph-Cut Channel Estimation Method for Multi-user Holographic MIMO
Jiaxin Zhang, Wenqian Shen, Kai Yang et al. (+2 more)
To address the challenges of high-dimensional channel estimation and underutilized spatial correlations among users in
holographic MIMO (HMIMO) systems, this paper proposes a joint graph-cut
algorithm for multi-user channel estimation in the wavenumber domain. The size of the conventional angular domain channel
matrix increases with the number of
antennas in densely-spaced HMIMO. Therefore, user channels are projected into the wavenumber domain via a
Fourier harmonic transform, revealing their inherent clustered sparsity and exploiting common scatterer clusters among users. Subsequently, a joint graph-cut channel estimation (JGC-CE)
algorithm based on multi-user common supports is designed. In each iteration, the
algorithm first partitions user clusters to extract shared supports. Then for each user, it performs users' individual graph update and channel estimation to reconstruct the channel
matrix. Simulation results demonstrate that the proposed method outperforms independent estimation schemes for individual users in accuracy while reducing pilot length.
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All authors Jiaxin Zhang, Wenqian Shen, Kai Yang, Zhen Gao, Jianping An
Image and Video Processing · Cross-listed from cs.CV
A 3D Reconstruction Benchmark for Asset Inspection
James L. Gray, Nikolai Goncharov, Alexandre Cardaillac et al. (+3 more)
Asset management requires accurate 3D models to inform the maintenance, repair, and assessment of buildings, maritime vessels, and other key structures as they age. These downstream applications rely on high-fidelity models produced from aerial surveys in close proximity to the asset, enabling operators to locate and characterise deterioration or damage and plan repairs. Captured images typically have high overlap between adjacent camera poses, sufficient detail at millimetre scale, and challenging visual appearances such as reflections and transparency. However, existing 3D reconstruction
datasets lack examples of these conditions, making it difficult to
benchmark methods for this task. We present a new
dataset with ground truth depth maps, camera poses, and mesh models of three synthetic scenes with simulated inspection trajectories and varying levels of surface condition on non-Lambertian scene content. We evaluate state-of-the-art reconstruction methods on this
dataset. Our results demonstrate that current approaches struggle significantly with the dense capture trajectories and complex surface conditions inherent to this domain, exposing a critical scalability gap and pointing toward new research directions for deployable 3D reconstruction in asset inspection. Project page: https://roboticimaging.org/Projects/asset-inspection-
dataset/
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All authors James L. Gray, Nikolai Goncharov, Alexandre Cardaillac, Ryan Griffiths, Jack Naylor, Donald G. Dansereau
Systems and Control
Real-Time, Crowdsourcing-Enhanced Forecasting of Building Functionality During Urban Floods
Lei Xie, Peihui Lin, Naiyu Wang et al. (+1 more)
Urban flood emergency response increasingly relies on infrastructure impact forecasts rather than hazard variables alone. However, real-time predictions are unreliable due to biased rainfall, incomplete flood knowledge, and sparse observations. Conventional open-loop
forecasting propagates impacts without adjusting the system state, causing errors during critical decisions. This study presents CRAF (Crowdsourcing-Enhanced Real-Time Awareness and
Forecasting), a physics-informed, closed-loop framework that converts sparse human-sensed evidence into rolling, decision-grade impact forecasts. By coupling physics-based simulation learning with crowdsourced observations, CRAF infers system conditions from incomplete data and propagates them forward to produce multi-step, real-time predictions of zone-level building functionality loss without online retraining. This closed-loop design supports continuous state correction and forward prediction under weakly structured data with low-latency operation. Offline evaluation demonstrates stable generalization across diverse storm scenarios. In operational deployment during Typhoon Haikui (2023) in Fuzhou, China, CRAF reduces 1-3 hour-ahead forecast errors by 84-95% relative to fixed rainfall-driven
forecasting and by 73-80% relative to updated rainfall-driven
forecasting, while limiting computation to 10 minutes per update cycle. These results show that impact-state
alignment-rather than hazard refinement alone-is essential for reliable real-time decision support, providing a pathway toward operational digital twins for resilient urban infrastructure systems.
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All authors Lei Xie, Peihui Lin, Naiyu Wang, Paolo Gardoni
Systems and Control
Distributed Equilibrium-Seeking in Target Coverage Games via Self-Configurable Networks under Limited Communication
Jayanth Bhargav, Zirui Xu, Vasileios Tzoumas et al. (+2 more)
We study a target coverage problem in which a team of sensing
agents, operating under limited communication, must collaboratively monitor targets that may be adaptively repositioned by an attacker. We model this interaction as a zero-sum game between the sensing team (known as the defender) and the attacker. However, computing an exact Nash
equilibrium (NE) for this game is computationally prohibitive as the action space of the defender grows exponentially with the number of sensors and their possible orientations. Exploiting the submodularity property of the game's utility function, we propose a distributed framework that enables
agents to self-configure their communication neighborhoods under bandwidth constraints and collaboratively maximize the target coverage. We establish theoretical guarantees showing that the resulting sensing strategies converge to an approximate NE of the game. To our knowledge, this is the first distributed, communication-aware approach that scales effectively for games with combinatorial action spaces while explicitly incorporating communication constraints. To this end, we leverage the distributed bandit-submodular optimization framework and the notion of Value of Coordination that were introduced in [1]. Through simulations, we show that our approach attains near-optimal game value and higher target coverage compared to baselines.
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All authors Jayanth Bhargav, Zirui Xu, Vasileios Tzoumas, Mahsa Ghasemi, Shreyas Sundaram
Signal Processing · Cross-listed from cs.IT
Shannon meets Gödel-Tarski-Löb: Undecidability of Shannon Feedback Capacity for Finite-State Channels
Angshul Majumdar
We study the exact decision problem for feedback capacity of finite-state channels (FSCs). Given an encoding $e$ of a binary-input binary-output rational unifilar FSC with specified rational initial distribution, and a rational threshold $q$, we ask whether the feedback capacity satisfies $C_{fb}(W_e, \pi_{1,e}) \ge q$. We prove that this exact threshold problem is undecidable, even when restricted to a severely constrained class of rational unifilar FSCs with bounded state space. The reduction is effective and preserves rationality of all channel parameters. As a structural consequence, the exact threshold predicate does not lie in the existential theory of the reals ($\exists\mathbb{R}$), and therefore cannot admit a universal reduction to finite systems of
polynomial equalities and inequalities over the real numbers. In particular, there is no
algorithm deciding all instances of the exact feedback-capacity threshold problem within this class. These results do not preclude approximation schemes or solvability for special subclasses; rather, they establish a fundamental limitation for exact feedback-capacity
reasoning in general finite-state settings. At the metatheoretic level, the undecidability result entails corresponding Gödel-Tarski-Löb incompleteness phenomena for sufficiently expressive formal theories capable of representing the threshold predicate.
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Signal Processing · Cross-listed from physics.optics
Virtual Polarization Modulation: Enabling CSI-Free DCO-OFDM over Dynamic OWC Channels
Tian Cao, Ping Wang, Tianfeng Wu et al. (+2 more)
In dynamically varying optical wireless communication (OWC) links, conventional quadrature amplitude modulation (QAM) in optical orthogonal frequency-division multiplexing (OFDM) requires frequent channel estimation and equalization, incurring pilot overhead and processing latency. This paper proposes a virtual polarization modulation (VPM)-based direct-current-biased optical OFDM (DCO-OFDM) scheme that maps each data symbol onto the three-dimensional Stokes space and places its corresponding Jones vector across two adjacent OFDM subcarriers. Using a rotation-based analytical framework, closed-form symbol error rate (SER) expressions are derived for arbitrary spherical constellations, along with upper and lower bounds and high signal-to-noise ratio (SNR) approximations. The framework is further extended to practical OWC scenarios with frequency-selective channels and atmospheric
turbulence. Monte Carlo (MC) simulations validate the theoretical results. The results show that under practical OWC impairments, VPM outperforms QAM with least-squares (LS) channel estimation and minimum mean square error (MMSE) equalization. At a target SER of $10^{-5}$, 16-VPM achieves SNR gains of approximately 7.5 dB and 4 dB over equalized 16-QAM and 8-QAM, respectively, in frequency-selective channels, and a 6 dB advantage over equalized 16-QAM under atmospheric
turbulence. By eliminating the need for channel state information, the proposed VPM-based DCO-OFDM provides a robust and low-latency solution for dynamic OWC links.
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All authors Tian Cao, Ping Wang, Tianfeng Wu, Kaile Wang, Jian Song
Systems and Control · Cross-listed from cs.AR
ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization
Panuganti Chirag Sai, Gandholi Sarat, R. Raghunatha Sarma et al. (+2 more)
Reducing latency and energy consumption is critical to improving the efficiency of
memory systems in modern computing. This work introduces ReLMXEL (Reinforcement Learning for
Memory Controller with Explainable Energy and Latency Optimization), a explainable multi-
agent online reinforcement learning framework that dynamically optimizes
memory controller parameters using reward decomposition. ReLMXEL operates within the
memory controller, leveraging detailed
memory behavior metrics to guide decision-making. Experimental evaluations across diverse workloads demonstrate consistent performance gains over baseline configurations, with refinements driven by workload-specific
memory access behaviour. By incorporating explainability into the learning process, ReLMXEL not only enhances performance but also increases the transparency of control decisions, paving the way for more accountable and adaptive
memory system designs.
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All authors Panuganti Chirag Sai, Gandholi Sarat, R. Raghunatha Sarma, Venkata Kalyan Tavva, Naveen M
Signal Processing
Near-Field NLOS Localization via Position-Unknown HRIS:From Self-Localization to Target Positioning
Hua Chen, Linke Yu, Tuo Wu et al. (+4 more)
Current reconfigurable intelligent surface (RIS)-aided near-field (NF) localization methods assume the RIS position is known a priori, and it has limited their practical applicability. This paper applies a hybrid RIS (HRIS) at an unknown position to locate non-line-of-sight (NLOS) NF targets. To this end, we first propose a two-stage gridless localization framework for achieving HRIS self-localization, and then determine the positions of the NF targets. In the first stage, we use the NF Fresnel approximation to convert the signal model into a virtual far-field model through delay-based cross-correlation of centrally symmetric HRIS elements. Such a conversion will naturally extend the aperture of the virtual array. A single-snapshot decoupled atomic norm minimization (DANM)
algorithm is then proposed to locate an NF target relative to the HRIS, which includes a two-dimensional (2-D) direction of arrival (DOA) estimation with automatic pairing, the multiple signal classification (MUSIC) method for range estimation, and a total least squares (TLS) method to eliminate the Fresnel approximation error. In the second stage, we leverage the unique capability of HRIS in simultaneous sensing and reflection to estimate the HRIS-to-base station (BS) direction vectors using atomic norm minimization (ANM), and derive the three-dimensional (3-D) HRIS position with two BSs via the least squares (LS)-based
geometric triangulation. Furthermore, we propose a semidefinite relaxation (SDR)-based HRIS phase optimization method to enhance the received signal power at the BSs, thereby improving the HRIS localization accuracy, which, in turn, enhances NF target positionings. The Cramer-Rao bound (CRB) for the NF target parameters and the position error bound (PEB) for the HRIS coordinates are derived as performance
benchmarks.
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All authors Hua Chen, Linke Yu, Tuo Wu, Maged Elkashlan, Naofal Al-Dhahir, Merouane Debbah, K. C. Ho
Systems and Control
STLts-Div: Diversified Trace Synthesis from STL Specifications Using MILP (Extended Version)
Martin Jouve-Genty, Han Su, Sota Sato et al. (+3 more)
Modern cyber-physical systems are complex, and requirements are often written in Signal Temporal Logic (STL). Writing the right STL is difficult in practice; engineers benefit from concrete executions that illustrate what a specification actually admits. Trace synthesis addresses this need, but a single witness rarely suffices to understand intent or explore edge cases - diverse satisfying behaviors are far more informative. We introduce diversified trace synthesis: the automatic generation of sets of behaviorally diverse traces that satisfy a given STL formula. Building on a MILP encoding of STL and system model, we formalize three complementary diversification objectives - Boolean distance, random Boolean distance, and value distance - all captured by an objective function and solved iteratively. We implement these ideas in STLts-Div, a lightweight Python tool that integrates with Gurobi.
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All authors Martin Jouve-Genty, Han Su, Sota Sato, Jie An, Zhenya Zhang, Ichiro Hasuo
Signal Processing
WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems
Chunxi Chen, Jingwen Zhang, Chao Yu et al. (+2 more)
It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, and etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is designed to detect the Doppler frequencies of the downlink CSI at the stations, such that their locations and the trajectory of the target with respect to the AP can be inferred. The joint detection can be conducted by searching the optimal stations' locations and target's trajectory, such that their corresponding Doppler frequencies fit the observed ones best. Due to the tremendous non-convex search space, a low-complexity sub-optimal
algorithm integrating alternate optimization, extended Kalman filter and density-based clustering is proposed in WiSLAT. Experiments conducted in indoor environments demonstrate the effectiveness of WiSLAT, achieving a median trajectory-tracking error of 0.68 m.
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All authors Chunxi Chen, Jingwen Zhang, Chao Yu, Fan Liu, Rui Wang
Audio and Speech Processing · Cross-listed from cs.CL
Xiutian Zhao, Ismail Rasim Ulgen, Philipp Koehn et al. (+2 more)
Large audio-
language models (LALMs) can produce expressive
speech, yet reliable
emotion control remains elusive: conversions often miss the target affect and may degrade linguistic fidelity through refusals,
hallucinations, or paraphrase. We present, to our knowledge, the first
neuron-level study of
emotion control in
speech-generative LALMs and demonstrate that compact
emotion-sensitive
neurons (ESNs) are causally actionable, enabling training-free
emotion steering at inference time. ESNs are identified via success-filtered activation aggregation enforcing both
emotion realization and content preservation. Across three LALMs (Qwen2.5-Omni-7B, MiniCPM-o 4.5, Kimi-Audio), ESN interventions yield
emotion-specific gains that generalize to unseen speakers and are supported by automatic and human evaluation. Controllability depends on selector design, mask sparsity, filtering, and intervention strength. Our results establish a mechanistic framework for training-free
emotion control in
speech generation.
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All authors Xiutian Zhao, Ismail Rasim Ulgen, Philipp Koehn, Björn Schuller, Berrak Sisman
Systems and Control
The Geometry of Coordinated Trajectories for Non-stop Flying Carriers Holding a Cable-Suspended Load
Pieter van Goor, Chiara Gabellieri and Antonio Franchi
This work considers the problem of using multiple aerial carriers to hold a cable-suspended load while remaining in periodic motion at all times. Using a novel differential
geometric perspective, it is shown that the problem may be recast as that of finding an immersion of the unit circle into the smooth
manifold of admissible configurations. Additionally, this
manifold is shown to be path connected under a mild assumption on the attachment points of the carriers to the load. Based on these ideas, a family of simple linear solutions to the original problems is presented that overcomes the constraints of alternative solutions previously proposed in the literature. Simulation results demonstrate the flexibility of the theory in identifying suitable solutions.
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Signal Processing
Physical Layer Security for FAS-Aided Short-Packet Systems: A Variable Block-Correlation Approach
Jianchao Zheng, Tuo Wu, Kai-Kit Wong et al. (+5 more)
This paper presents a comprehensive physical layer security (PLS) framework for fluid
antenna system (FAS)-aided short-packet communications under the variable block-correlation model (VBCM). We consider a downlink wiretap scenario in which a base station transmits confidential short packets to a legitimate receiver user (RU) in the presence of an eavesdropper user (EU), where both the RU and EU are equipped with fluid
antennas. Unlike existing FAS security analyses that rely on constant block-correlation models or infinite-blocklength assumptions, we incorporate the VBCM to accurately capture the non-uniform spatial correlation structure inherent in practical FAS deployments. By employing a piecewise linear approximation of the decoding error probability and Gauss-Chebyshev quadrature, we derive closed-form and asymptotic expressions for the average achievable secrecy throughput (AAST). We further prove that the AAST is monotonically non-decreasing in the number of RU ports, which reduces the three-dimensional joint optimization of transmit power, blocklength, and port number to a two-dimensional grid search (GS). Numerical results demonstrate that the FAS-aided system achieves up to an order-of-magnitude secrecy throughput improvement over conventional fixed-position
antenna systems, and reveal that blocklength selection is the most critical design parameter in the joint optimization.
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All authors Jianchao Zheng, Tuo Wu, Kai-Kit Wong, Baiyang Liu, Runyu Pan, Maged Elkashlan, Kin-Fai Tong, Sumei Sun