Signal Processing
On LLR Calculations for Soft-decision Decoding in Next-generation IM-DD Systems with Laser RIN
Felipe Villenas, Yunus Can Gültekin and Alex Alvarado
We propose a low-complexity LLR approximation for SD decoding in IM-DD systems with relative intensity noise. Our approximation results in no BER performance penalty for a concatenated KP4+Hamming FEC scheme, and outperforms mismatched AWGN-based LLRs.
Signal Processing
Drone-Based Antenna Measurement System with Optimized Positioning and ASPIRE-Based NF-FF Transformation
Simranjit Singh, Abha Nilesh Jadav, Aarish Dharmesh Patel et al. (+2 more)
Unmanned Aerial Vehicle (
UAV)-based
antenna measurement systems provide a flexible and cost-effective alternative to conventional
antenna test ranges for characterizing large and installed
antennas. However, their accuracy depends on precise
UAV positioning and efficient flight-time utilization, both of which are strongly influenced by the selection of drone assemblies, including the airframe, flight controller, propulsion system, positioning modules, and onboard instrumentation. This paper presents a comprehensive study of
UAV-based
antenna measurements with emphasis on improving positioning accuracy and optimizing flight endurance through systematic drone assembly selection. The acquired near-field measurement data are susceptible to positioning errors, amplitude and phase inconsistencies, and irregular sampling, which degrade the reconstructed far-field pattern. To address these challenges, the recorded near-field data are processed using the Adaptive Sparse Inverse Radiation Estimation (ASPIRE)
algorithm. ASPIRE compensates for positioning inaccuracies and reconstructs the far-field pattern from irregularly sampled near-field data using sparse signal recovery, enabling accurate Near-Field to Far-Field (NF-FF) transformation. At 6.7125 GHz, ASPIRE achieves a residual of 1.94% and a beamwidth error of 0.4 degrees relative to a conventional facility measurement while using only 24% of the 17,298-element RWG mesh as active support. The results demonstrate that the combination of optimized drone assembly selection and ASPIRE-based NF-FF transformation significantly improves the accuracy of
UAV-based
antenna measurements and produces far-field patterns that closely agree with conventional
antenna test range measurements.
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All authors Simranjit Singh, Abha Nilesh Jadav, Aarish Dharmesh Patel, Jaswant, Jigar M. Pandya
Systems and Control
Sharing Coefficient-Based Price Signals for Demand Response in Renewable Energy Communities
Alireza Shooshtari, Antonio Pepiciello and Jose Luis Dominguez-Garcia
Renewable energy communities can increase local photovoltaic (PV) use, but feeder-level surplus can still cause reverse power flow in low-voltage networks. Existing sharing coefficient methods are mainly used ex-post for surplus allocation and billing, so they do not directly guide demand toward hours and feeders where shared PV can reduce export. This paper proposes a sharing coefficient-based demand response framework that converts dynamic proportional allocation outcomes into household specific day-ahead price signals. The feeder-aware design first shares surplus within each feeder, while the feeder-agnostic design shares surplus through a single community pool. The energy community manager iteratively computes the allocation from submitted demand and PV forecasts, decomposes purchased energy into same-feeder, inter-feeder, and grid-import components, and coordinates household load reshaping through a convex optimization model. Using measured profiles from 15 households and AC power flow analysis, the framework reduces feeder reverse energy by 45.0% and 44.6% on selected high reverse energy days, and by 69.0% and 66.3% over the annual window, for the feeder-aware and feeder-agnostic cases, respectively. These results show that sharing coefficients can be used not only for ex-post billing, but also as operational price signals for demand response, with feeder-aware allocation providing an additional network benefit by accounting for household location in the low-voltage network.
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Signal Processing · Cross-listed from hep-ex
Commissioning and Low Latency Operation of the Graph Neural Network Electromagnetic Calorimeter Trigger at the Belle II Experiment
M. Neu, F. Baptist, I. Haide et al. (+10 more)
We present the commissioning and operation of the Graph Neural Network Electromagnetic Calorimeter Trigger Module (GNN-ETM) of the Belle II experiment at the SuperKEKB
collider. The GNN-ETM processes calorimeter trigger
cells as graph nodes to perform clustering and feature extraction. We fully integrate the system with the successive stages of the first-level trigger, develop slow-control drivers, and add online monitoring capabilities. We optimise the existing FPGA-based architecture through hardware-
algorithm co-design, achieving an overall system latency of 1.053 us. Our hardware implementation is validated through register-transfer-level simulations, achieving bit-accurate agreement with the offline reference model. Online monitoring enables the measurement of instantaneous trigger rates, providing a quantitative basis for trigger-level performance studies. In summary, we report on the GNN-ETM as a fully operational, low-latency trigger module with online control and monitoring capabilities, compatible with the latency requirements of the Belle II first-level trigger system.
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All authors M. Neu, F. Baptist, I. Haide, Y. Unno, J. Becker, T. Ferber, K. Arai, Y.-T. Lai, T. Koga, M. Maushart, H. Nakazawa, V. Savinov, K. Unger
Signal Processing
Site Geometry and Calibration Uncertainties in Digital Twin-enabled Channel Estimation
Lorenzo Del Moro, Francesco Linsalata, Umberto Spagnolini et al. (+1 more)
Fast ray tracing (RT) has stimulated the Digital Twin (DT) as an emerging technology for environment-aware communications. Since wireless propagation is governed by the interaction between site geometry and electromagnetic (EM) properties of the environment, DT-based approaches can provide site-specific prior information for channel estimation. In this work, we investigate the robustness of DT to aid the channel estimation, where multipath features extracted via RT are used to construct the low-rank (LR) eigenstructure of the channel covariance
matrix. This LR structure is used in channel estimation. However, the digital representation of propagation model is inaccurate and thus it affects the LR. We explicitly analyze these model mismatches that arise from user positioning errors, which translate into
geometric inconsistencies in the site representation, and EM material calibration errors. We derive a first-order perturbative model that separates
geometric perturbations, affecting angles and delays, from EM perturbations, affecting path gains. Based on this perturbed model, we provide a normalized mean-square error (NMSE) analysis that reveals a fundamental difference between
geometric and EM perturbations. In particular, we show that LR estimation is inherently robust to EM calibration perturbations, while positioning errors, dominate performance degradation by altering the channel eigenstructure. Numerical results confirm that, in urban, suburban and rural scenarios, positioning errors are the primary limiting factor, whereas EM calibration errors have a comparatively limited impact. Despite these mismatches, DT-empowered
estimators provide up to 10dB NMSE improvement, over baseline methods, in the urban low signal-to-noise ratio (SNR) settings, while achieving performance comparable to baseline
estimators at high SNR for moderate (< 1 m) positioning errors.
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All authors Lorenzo Del Moro, Francesco Linsalata, Umberto Spagnolini, Maurizio Magarini
Signal Processing
Tensor-based Random Access for Ambient IoT Contention Resolution
Alberto Rech, Alexis Decurninge, Ala Baccar et al. (+1 more)
Ambient Internet of Things (A-IoT) deployments impose stringent hardware constraints, including low-order modulations and simple transceiver architectures. We propose a tensor-based grant-free random access scheme for the initial access message (Msg1) tailored to these limitations. Each device transmits replicas of its short payload structured as a rank-1 tensor over multiple access occasions. The A-IoT receiver performs joint activity detection, channel estimation, and decoding via tensor decomposition and successive interference cancellation. Results show significant throughput gains over conventional schemes while preserving low-complexity transmitter designs.
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All authors Alberto Rech, Alexis Decurninge, Ala Baccar, Sofiane Kharbech
Image and Video Processing · Cross-listed from cs.RO
Differential Analysis of Multispectral Images for Terrain Identification
Omar Kashmar, Hemendra Arya and Fulvio Mastrogiovanni
Reliable terrain understanding is a prerequisite for autonomous
robot navigation. Yet, the widespread RGB-based perception can fail under low illumination, shadows, and material ambiguities. In this work we propose DRIFT, a lightweight multispectral framework that combines raw spectral bands and illumination-tolerant band-ratio representations through a dual-stream residual architecture and a differential fusion branch. Band ratios attenuate multiplicative acquisition effects (illumination/sensor gains), while the differential fusion explicitly highlights discrepancies between absolute-band and ratio-derived cues, which improves the robustness to noisy or partially unreliable spectral measurements. In the paper (i) we evaluate DRIFT on a new oil-on-soil multispectral
dataset acquired using a MicaSense RedEdge-P camera mounted on an Unmanned Aerial Vehicle, and (ii) we provide an additional controlled study on water-on-grass under varying illumination and thermal perturbations (hot/cold water) to analyze NIR-sensitive effects. DRIFT consistently improves over strong baselines, while remaining compatible with edge deployment.
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Systems and Control
Inertia-Aware Optimal Power Flow Using PINN in IBR-Dominated Power Systems
Mahyar Tofighi-Milani, Sajjad Fattaheian-Dehkordi, Franz Martin Rohrhofer et al. (+1 more)
The problem of Optimal Power Flow (OPF) is central to the secure and economic operation of modern power systems. However, increasing renewable energy penetration, and decreasing system inertia pose significant challenges to conventional optimization-based OPF solvers. While machine learning approaches have demonstrated substantial computational speed-ups, purely data-driven methods often suffer from data dependency, limited generalization, and lack of guaranteed physical feasibility. This paper suggests a physics-informed neural network (PINN) framework for solving the OPF problem in renewable energy-dominated, low-inertia power systems. In contrast to conventional OPF formulations, the model explicitly incorporates a location-aware inertia constraint based on the concept of system inertia strength, which accounts for the electrical distance between generation units and disturbance locations. Simulation results on a 6 GW test system demonstrate high accuracy. The mean absolute error (MAE) for both the training and testing
datasets is approximately 0.045% of the total system capacity. The findings demonstrate that the proposed PINN framework is capable of producing highly accurate OPF solutions while ensuring compliance with both physical laws and inertia-related constraints. Overall, the findings highlight the potential of physics-informed learning to enable secure, efficient, and computationally scalable OPF for future low-inertia power systems.
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All authors Mahyar Tofighi-Milani, Sajjad Fattaheian-Dehkordi, Franz Martin Rohrhofer, Matti Lehtonen
Systems and Control
An Improved Deep Reinforcement Learning Control Strategy for Traction Dual Rectifiers in EMUs
Zhigang Liu, Mingwei Tang, Xiangyu Meng et al. (+4 more)
Due to the use of PI-based d q current decoupling in the pulse rectifier of CRH5 high-speed trains, the PI parameters directly affect the traction system's control performance. Linearized control may have issues with reference trajectory changes or model mismatches, leading to a decrease in system performance, while nonlinear control may have problems with jitter and poor steady-state accuracy. This paper proposes a new control strategy that replaces all PI in the d q current decoupling control with a single intelligent
agent. This method based on Deep Reinforcement Learning (DRL) can avoid various drawbacks of linearization and nonlinear control and ensure the stability of intermediate DC voltage. However, when EMUs are in different working conditions and switching, the Twin Delayed Deep Deterministic Policy Gradient (TD3)
algorithm used in traction dual rectifiers does not have a good control effect. Focusing on the issue, Reward Shaping (RS) is added to re-design a nonlinear reward function, which can be combined with Prioritized Experience Replay (PER) to increase the convergence speed of the episode reward. The simulation results show that the improved control strategy can be effectively applied to EMUs working in multiple conditions. Finally, the stability analysis is carried out using Lyapunov's second method and the verification results of the hardware-in-the-loop (HIL) simulation platform show that the DRL control has a good effect.
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All authors Zhigang Liu, Mingwei Tang, Xiangyu Meng, Hui Wang, Qiao Zhang, Haoyu Wang, Mengru Li
Systems and Control
A Multi-Frequency Input-Admittance Model of Locomotive Rectifier Considering PWM Sideband Harmonic Coupling in Electrical Railways
Xiangyu Meng, Zhigang Liu, Guorong Li et al. (+3 more)
Electrical railway harmonic instability issues are common in the high-frequency range. The effective frequency of the traditional converter's small-signal averaging model is below 1/2 switching frequency since the pulse width modulation (PWM) sideband harmonic components are ignored. In this article, the dynamic propagations of perturbation frequency and the generated PWM sideband components are constructed first. Then the locomotive rectifier's multi-frequency input-admittance model is derived appropriately. Afterward, an admittance conversion approach is used to convert the multi-frequency model into the single-input-single-output (SISO) model whereas retaining the sideband frequency couplings. The proposed SISO model is more accurate than the traditional small-signal averaging model in the frequency range higher than 1 / 2 switching frequency. It is found that PWM sideband harmonics dominate the locomotive rectifier's input-admittance characteristic higher than 1 / 2 switching frequency. Finally, based on the proposed model, the influence of different switching frequencies, control bandwidths, and traction network impedance on system harmonic stability is revealed by the hardware-in-the-loop (HIL) results.
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All authors Xiangyu Meng, Zhigang Liu, Guorong Li, Xunjun Chen, Siqi Wu, Keting Hu
Systems and Control
Dissipativity-Based Multiport Stability Root-Cause Identification and Mitigation for Solid-State Transformers
Xiangyu Meng, Dong Xie, Hongjian Lin et al. (+3 more)
For solid-state
transformers (SSTs) in high-power grid-connected applications, improperly designed control loops can excite strong inherent AC-DC port coupling, leading to low-frequency oscillation issues, especially under weak grid conditions. To address this problem, this article establishes a multiport admittance
matrix for the SST, encompassing its AC dq axes and primary DC port, to characterize its inherent dynamics. Subsequently, a multiport dissipativity analysis is conducted to evaluate the robust stability of the SST. By leveraging the decomposition of passivity conditions into distinct self- and coupling-dissipativity indices, the specific root causes of instability are diagnosed. This framework reveals that a severe coupling-dissipativity failure, induced by the internal dynamics of the synchronization loop, is the dominant instability mechanism rather than a localized self-dissipativity issue. Guided by this diagnosis, a stabilizing controller featuring dynamics-free orthogonal signal reconstruction is designed to reshape the admittance characteristics of the SST. This enhancement specifically targets the identified coupling-dissipativity deficiencies, thereby resolving the root cause of the instability. Finally, the stability analysis and the effectiveness of the enhancement strategy are validated on a down-scaled SST prototype. Experimental results demonstrate that the criterion accurately predicts the coupling-induced oscillations and that the enhanced controller guarantees stable operation under challenging weak-grid conditions.
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All authors Xiangyu Meng, Dong Xie, Hongjian Lin, Chunxu Lin, Xinglai Ge, Zhigang Liu
Signal Processing · Cross-listed from physics.optics
Co-design approach to aperture masking for imaging through atmospheric turbulence
Olivier Herscovici-Schiller, Laurent M. Mugnier, Alice Fontbonne et al. (+5 more)
Aperture masking interferometry is a technique originally designed to alleviate the influence of atmospheric
turbulence on images recorded on ground-based
telescopes. In this communication, we explore the optimization of the aperture mask by an optical/digital co-design approach in order to obtain diffraction-limited images of relatively bright objects imaged through
turbulence. We show that, with a few simplifying assumptions, it is possible to express the Mean Square Error of the restored image as a function of the chosen mask, of the spatial Power Spectral Density of the observed object and of the noise level, without actually computing any image. This allows us to optimize the aperture mask with a reduced computing cost. We also implement a multi-frame myopic
algorithm to estimate jointly the observed object, the piston and the tip-tilt in front of each sub-aperture, and check by simulations that the aperture masks obtained indeed allow a satisfactory image reconstruction.
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All authors Olivier Herscovici-Schiller, Laurent M. Mugnier, Alice Fontbonne, Pauline Trouvé-Peloux, Frédéric Champagnat, Yann Lucas, Yann Lai-Tim, Frédéric Cassaing
Systems and Control
Jacobian Voltage Stiffness Metric – A Measure of Grid-Forming Capability and System Strength in IBR-Dominated Grids
Ambuj Gupta, Balarko Chaudhuri and Mark O'Malley
As power systems transition toward inverter-based resource (IBR)-dominated grids, traditional system strength definitions and metrics are becoming increasingly inadequate to characterize upcoming stability challenges. Emerging definitions characterize system strength in terms of "voltage source behind impedance (VSBI)" characteristics. Similarly, Grid-ForMing (GFM) IBRs are expected to contribute voltage stiffness by exhibiting near-constant VSBI characteristics in the (sub-)transient time frame. To quantify VSBI characteristics as a measure of system strength or grid-forming capability, this paper proposes the Jacobian Voltage Stiffness Metric (JVSM), derived from the frequency-domain Jacobian. JVSM provides a measure of both small-signal voltage magnitude and phase-angle stiffness. JVSM is demonstrated to serve as a compliance criterion for evaluating the VSBI characteristics of GFM IBRs. When applied for grid strength assessment, it more effectively identifies small-signal stability problems than state-of-the-art strength metrics. The proposed JVSM is validated through electromagnetic transient simulation case studies using the National Laboratory of the Rockies (NLR, formerly NREL) and WECC-approved industry-standard GFM IBR models and on a modified IEEE 39-bus system.
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Signal Processing
Multi-Domain Iterative Detection for Massive Connectivity in LEO Satellite Networks
Xinhua Liu, Yueqing Wang, Keke Ying et al. (+7 more)
Grant-Free (GF) random access is promising for low Earth orbit satellite Internet due to its reduced access latency. However, existing schemes suffer from poor performance in massive connectivity scenarios. To address this challenge, we firstly propose an iterative residual feedback multi-measurement vector approximate message passing
algorithm. This
algorithm leverages multi-domain synergistic sparsity in the spatial-frequency and angular-delay domains to alternately perform active user terminal detection (AUD) and channel estimation (CE). Additionally, a residual feedback mechanism is incorporated to suppress error accumulation, thereby enhancing AUD performance. Furthermore, conventional data detection (DD) methods significantly degrade when active user terminals are spatially close or outnumber the satellite's receive
antennas, making the demodulation problem rank-deficient or underdetermined. To mitigate this, we design a data modulation scheme via joint spatial-frequency multi-domain spreading, which utilizes observations from both spatial and frequency domains to facilitate multi-domain DD. Simulation results demonstrate that the proposed scheme significantly outperforms existing GF methods in terms of AUD accuracy, CE precision, and bit error rate, especially under conditions of low effective pilot length and practical signal-to-noise ratios.
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All authors Xinhua Liu, Yueqing Wang, Keke Ying, Peihu Duan, Dapeng Li, Ziwei Wan, Zhongliang Zhao, Chabalala S. Chabalala, Andrey Ivanov, Zhen Gao
Image and Video Processing
Joint-Embedding Predictive Architecture for Solar PV Panel Fault Classification
Seyyedhamid Azimidokht, Mehdi Monemi, Abdelhak Kharbouch et al. (+4 more)
The rapid expansion of
solar photovoltaic (PV) systems has increased the need for reliable and scalable fault classification, as manual inspection is impractical at scale. Thermal infrared (IR) imaging provides a non-contact solution for identifying PV faults; however, accurate classification remains challenging due to class imbalance, limited texture information, and subtle thermal differences. In this work, we investigate the applicability of Joint-
Embedding Predictive Architecture (JEPA) for thermal IR PV fault classification across various scenarios and propose JEFFNet (JEPA-EFFicientNet), a multibranch architecture that combines JEPA-based self-supervised representation learning with EfficientNetV2-S-based supervised convolutional feature extraction. JEFFNet fuses semantic representations from a JEPA-pretrained
Vision Transformer with convolutional features from EfficientNetV2-S, enabling complementary feature learning. JEFFNet is evaluated on two public thermal IR
datasets, PVF-10 and InfraredSolarModules (ISM), for both multiclass and derived binary (healthy/faulty) classification. On PVF-10, JEFFNet achieves an F1-score of $93.21$ and an accuracy of $94.33$ in the 10-class task, and an F1-score of $97.53$ and an accuracy of $96.41$ in the derived 2-class task. On ISM, JEFFNet achieves an F1-score of $72.60$ and an accuracy of $83.88$ in the 12-class task, and an F1-score of $94.69$ and an accuracy of $94.78$ in the derived 2-class task. JEFFNet also uses only 108.6M parameters versus 205.91M for GEPFNet, a 47.2% reduction. These results demonstrate that combining self-supervised semantic and supervised convolutional features provides an effective, parameter-efficient solution for thermal IR PV fault classification. The source code is publicly available at https://github.com/Azimi2kht/JEFFNet
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All authors Seyyedhamid Azimidokht, Mehdi Monemi, Abdelhak Kharbouch, Farid Hamzehaghdam, Mehdi Rasti, Jamshid Aghaei, Emil Kurvinen
Systems and Control
Cyclic Reformulation-Based Identification and Polytopic Uncertainty Modeling for Multirate Systems
Hiroshi Okajima and Kakeru Ono
Modern control systems increasingly rely on heterogeneous sensors operating at different sampling rates, where intermittently missing outputs pose fundamental challenges for system identification. This paper proposes a non-iterative, control-oriented identification method for multirate systems based on cyclic reformulation. The method transforms multirate data into an expanded time-invariant representation and yields M parameter sets from a single input-output
dataset, where M is the least common multiple of the sensor sampling periods. These parameter sets, expressed in a common state coordinate of the original dimension, are used in two complementary ways: their centroid, in which each output row is averaged over the phases at which that output is observed, serves as a noise-reduced nominal model, while their convex hull gives a polytopic uncertainty model whose vertices can be used directly in vertex-based LMI robust control design. Building on the exact cyclic-structure recovery established in the authors' preceding work, the paper contributes the centroid and polytopic models and evaluates them under finite noise through output-level validation statistics. Numerical simulations support both models: an illustrative SISO example shows that the centroid attains higher validation FIT than the best individual vertex and substantially outperforms an interpolation-based baseline, while a
MIMO multirate sensing example confirms that the constructed polytope contains models whose validation FIT exceeds 95% on average even at the highest tested noise level. The resulting polytope is a data-derived uncertainty description obtained from a single experiment, so that the framework links multirate system identification with robust-control-oriented uncertainty modeling without iterative EM-type optimization.
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Systems and Control · Cross-listed from cs.RO
Empirical Pedestrian Safety Assessment in a Mobile Robot Using a Predictive Social Force Model
Alireza Jafari, Yun-Hao Tsai and Yen-Chen Liu
Mobile
robots are going to share the sidewalks with pedestrians. They must ensure their objective safety and respect the walkers' subjective safety/comfort. Computationally efficient Social Force Models (SFM) present interpretable solutions for real-time
robot navigation in dynamic crowds. Recent explorations of Projected Time-to-collision (PTTC) integration into SFM variants, for example, PTTC-based SFM (TSFM), improve safety metrics. But the effect of predictive variants is unclear. We introduce Predictive SFM (PSFM) and Predictive TSFM (PTSFM) by integrating predicted social force vectors over a finite time horizon. The paper implements SFM, TSFM, PSFM, and PTSFM on a nonholonomic mobile
robot and performs experimental trials with volunteers attending a facing scenario. We systematically study objective and subjective safety across the variants. Minimum PTTC, average speed, minimum distance, lateral distance, and the maximum trajectory
curvature benchmark the objective safety. Likert scale post-interaction surveys assess subjective safety by marking comfort, smoothness, distance appropriateness, and speed suitability. We confirm that PTTC integration improves safety metrics. The prediction contribution is limited and occasionally visible in some of the sub-metrics. Some participants perceive smoother movements and safer speed behavior with predictive methods, but Mann-Whitney tests reveal no significant differences in subjective ratings. Therefore, PTTC-based
navigation enhances safety, whereas the formulated prediction offers limited additional benefits in single-pedestrian scenarios.
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Systems and Control
Data-driven predictive control of nonlinear systems using weighted regularization
Fritz A. Engeln, Sebastian Zieglmeier, Marta Zagórowska et al. (+1 more)
Data-driven control methods, like Data-enabled Predictive Control (DeePC), are often formulated for linear systems, where the principle of superposition allows global system behavior to be inferred from locally collected data through Willems' fundamental lemma. This principle does not hold for nonlinear systems, whose dynamics may vary across operating regions. We propose a data-driven predictive control framework for nonlinear systems that incorporates data column preferences according to their proximity to the current operating point through a weighted norm regularization, thereby localizing the predictor without discarding any data. We show how the proposed weighting scheme induces operating point-dependent data prioritization and ensures a well-posed optimization problem. A numerical study on a nonlinear two-tank system demonstrates that the proposed method matches or outperforms hard data-selection schemes while retaining the full data
matrix and its rank, thereby guaranteeing feasibility.
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All authors Fritz A. Engeln, Sebastian Zieglmeier, Marta Zagórowska, Jan-Willem van Wingerden
Signal Processing
Inter-frame Channel Prediction for Zak-OTFS
Muhammad Ubadah and Saif Khan Mohammed
Zak-Orthogonal Time Frequency Space (OTFS) modulation is known to be robust to Doppler spread in high mobility scenarios when compared to Orthogonal Frequency Division Multiplexing (OFDM). This is due to the fact that the channel response to a Zak-OTFS carrier within a frame can be accurately estimated from the channel response to another carrier within the same frame. However, an important open problem and question is whether inter-frame channel prediction is possible with Zak-OTFS, i.e., is it possible to accurately predict the channel response to a Zak-OTFS carrier in a frame based on knowledge of the channel response to some Zak-OTFS carrier in another frame (i.e., not the same frame). In this paper we show that indeed inter-frame channel prediction is possible. We show that the effective DD domain channel filter coefficients vary in a deterministic manner as we move from current to future frames in time and frequency. We also show that the subspace spanned by channel filter coefficients of consecutive frames in time/frequency is invariant to discrete shifts in time and frequency. We exploit the deterministic variation and subspace invariance to propose a novel deterministic ESPIRIT-type method which uses the effective DD domain channel filter taps/coefficients estimated in training frames (i.e., current/past frames in time and frequency having both pilot and data carriers) to predict the effective DD domain channel filter for frames which are several tens of frames in future and several tens of frames away in frequency.
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Systems and Control · Cross-listed from math.OC
Control Laguerre Tessellation: Semi-discrete Optimal Transport Over Control Systems
Ripon C. Sarker and Abhishek Halder
We study the optimal transport of optimally controlled
agents from a compactly supported absolutely continuous source to a discrete target measure. The ground cost for the transport is induced by the optimal cost of the
agents' motion. When this ground cost satisfies the twist condition, the optimal transport map is given almost everywhere in terms of a Laguerre tessellation of the state space. We refer to this control-theoretic generalization of Laguerre tessellation as Control Laguerre Tessellation (CLT), and illustrate it for two ground costs induced by linear controlled
agents with minimum energy and minimum time objectives.
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Audio and Speech Processing · Cross-listed from cs.SD
ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
Sang-Hoon Lee and Ha-Yeong Choi
Representation
alignment (REPA) has been investigated to accelerate
diffusion training, but we observe that regularizing intermediate representations in
diffusion Transformers (DiT) may implicitly entangle latents and limit generative capacity. To address this issue, we propose ReGen, a hierarchical multi-
prompt representation generation framework that jointly estimates multiple vector fields for both representations and data within a single
diffusion model. We further introduce generalized flow matching (GFM) to improve the generalization of conditional flow matching (CFM). We validate ReGen on single-stage waveform
diffusion models including neural audio codec and Wave-VAE. ReGen significantly improves waveform generation quality from highly compressed latent representations at 12.5 Hz. We also present ReGenVoice, a latent
diffusion model (LDM)-based text-to-
speech model that achieves strong
speech intelligibility (WER) and speaker similarity (SIM) with a small
dataset. Moreover, operating the LDM at 6.25 Hz with rich semantic and acoustic latent representation enables efficient training and sampling, requiring only 1 day of training on 4 GPUs and fast inference with an RTF of 0.08. Audio samples are available at https://regenvoice.github.io/demo/.
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Systems and Control
On robustness, input-to-state stability and backstepping for stochastic differential equations
Robert H. Moldenhauer, Dragan Nešić, Mathieu Granzotto et al. (+2 more)
We study conditions under which stability of the origin of
stochastic differential equations is robust to small perturbations. We express robustness in two ways, firstly in the sense that
stochastic stability is maintained under small parametric perturbations not exceeding a state-dependent bound vanishing at the origin but positive elsewhere, and secondly via
stochastic input-to-state stability (ISS) which allows non-zero perturbations everywhere. We prove the former property assuming the existence of a Lyapunov function certifying
stochastic stability of the nominal system. Under the same assumption,
stochastic ISS holds under a suitable state-dependent perturbation scaling.
Stochastic exponential stability is maintained under proportionally bounded perturbations and implies exponential ISS even without perturbation scaling. Finally, we propose a novel approach to
stochastic integrator backstepping in pure-feedback form that uses the tools from our robustness analysis.
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All authors Robert H. Moldenhauer, Dragan Nešić, Mathieu Granzotto, Romain Postoyan, Andrew R. Teel
Systems and Control · Cross-listed from cs.LG
Power Flow Feasibility Assessment Using Variational Graph Autoencoders
Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo et al. (+2 more)
Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little
attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational Graph Autoencoder (VGAE) that detects the power flow solution feasibility, using the IEEE 118-bus case, to assess the validity of the solutions provided by AI-driven solvers.
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All authors Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo, Pere Barlet-Ros, Oriol Gomis-Bellmunt
Image and Video Processing
Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging
Yawen Li, Yan Li, Zhe Xue et al. (+3 more)
Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup
auditing. Once those metadata disappear, clinically critical failure modes can be masked by strong aggregate performance, and many robust-learning methods lose the group structure they rely on. We present CAPRA, a calibrated proxy-axis framework for hidden subgroup analysis under missing metadata. CAPRA predicts image-derived semantic axes, calibrates axis posteriors on a small metadata-labeled split via patient-level cross-fitting, and organizes those posteriors into a calibrated subgroup interface that supports both deployment-time failure analysis and downstream robust learning without requiring subgroup labels at deployment. Across fundus, dermoscopy, and chest radiography, CAPRA reveals disparity patterns missed by metadata-only slicing, remains informative under
dataset shift, and produces subgroup partitions that align more closely with explicit failure axes than image-only or latent-slice baselines. The same interface can also be reused by downstream robust learners, although those gains are domain-dependent. Overall, CAPRA turns hidden subgroup analysis under missing metadata into a calibrated, interpretable, and reusable subgroup interface for deployment-time analysis and robust transfer.
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All authors Yawen Li, Yan Li, Zhe Xue, Yingxia Shao, Meiyu Liang, Guanhua Ye
Systems and Control
Latency-Aware Digital Twin-Assisted Cooperative Perception for Autonomous Vehicles
Boniface Uwizeyimana, Manobendu Sarker and Abraham O. Fapojuwo
This paper introduces a digital-twin (DT)-assisted cooperative perception framework designed to improve perception accuracy under end-to-end (E2E) latency constraints and to balance perception accuracy and E2E latency under communication resource constraints in autonomous vehicles. We formulate an optimization problem that maximizes perception accuracy subject to latency and communication limitations, and solve it using a newly proposed coarse-to-fine search (CTFS)
algorithm. Simulation results show that the proposed CTFS
algorithm achieves 96.6% perception accuracy, close to exhaustive search, under latency constraints while reducing computational complexity by approximately 85.78%. The DT-assisted framework further achieves a 50% reduction in the non-DT communication cost through estimated, time-synchronized state updates.
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Systems and Control
Structural Decoupling and Current-Angle Steering for Post-Fault Recovery of Current-Limited Grid-Forming Inverters
Neethu Sajeev, Stephen Arinze Obi and Jae-Jung Jung
Reliable fault recovery of grid-forming (GFM) converters under current-limited conditions is increasingly important as inverter-based resources replace synchronous generation. Existing current-limiting strategies primarily focus on current-angle regulation and synchronization trajectory shaping, while the interaction between the current limiter and the voltage control structure remains insufficiently understood. Consequently, post-fault recovery may exhibit converter trapping in current-limited control (CLC) or oscillatory transitions between CLC and constant voltage control (CVC). This paper shows that, under conventional PI-based voltage control, the interaction between the voltage controller and the current limiter creates a moving recovery boundary that contributes to these recovery failures. To address this issue, a post-fault recovery framework is proposed that combines structurally decoupled virtual admittance voltage control with current-angle steering. The proposed framework simultaneously improves synchronization trajectory
evolution and stabilizes the recovery boundary during fault recovery. Experimental validation on a 3-kVA GFM inverter prototype confirms reliable post-fault synchronization recovery under both symmetrical and unsymmetrical voltage sag conditions, with trapping and oscillatory CLC-CVC transitions eliminated.
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Systems and Control
Can the Cloud Drive? Infrastructure Feasibility of Offloading Autonomous Driving Across 5G and 6G
Pouya Parsa, Kawon Han and Seongjin Choi
Frontier autonomous-driving models – especially
vision-
language-action (VLA) models, whose forward pass approaches $\sim$60~TFLOPs – are outgrowing economical onboard deployment, since peak hardware sits idle most of the day. Cloud inference can instead share GPUs across active vehicles, but the vehicle must upload through a capacity-limited uplink, reach a GPU without queueing, and return a decision within the closed-loop budget. This paper asks: can the cloud drive? We answer with an analytical framework coupling communication limits, a roofline GPU service model,
stochastic latency, and utilization-aware cost across three model classes, three offloading strategies, and three communication generations, applied to New York City. Separating a reactive 100~ms budget from a 300~ms deliberative tier (presuming an onboard reactive fallback), we find three nested binding regimes. Communication binds first in dense
cells: 5G fails early, 5G-Advanced is the practical threshold for feature-level offloading, and 6G adds headroom. Compute binds next under the reactive budget: near-term VLA is latency-infeasible regardless of bandwidth, because autoregressive FP16 decode is
memory-bandwidth-bound (~114 ms on 2025 hardware). Its floor clears 100 ms around 2027; 6G then admits feature-level VLA by ~2028, 5G-Advanced only at light loading and not the dense corridor, and the deliberative tier from 2026. Cost binds last: once admissible, utilization-pooled cloud GPUs undercut onboard hardware for VLA, whose baseline (up to \$8,500 per vehicle-year) is expensive and idle; feature-level offloading (S2) is where the VLA cost crossover concentrates. Latency decides which model is admissible in which year; cost decides whether it is economical.
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Audio and Speech Processing
Technical Report for MERL's Real-TSE Challenge Submission
Dominik Klement, Yoshiki Masuyama, Christoph Boeddeker et al. (+4 more)
Target
speech extraction (TSE) has largely been dominated by neural network-based approaches trained and evaluated on synthetic fully overlapped data. The Real-TSE Challenge aims to advance performance on real-world far-field noisy and reverberant recordings. This technical report describes MERL's submission to the Real-TSE Challenge. Rather than proposing a novel model architecture, we built upon the baseline model and focused primarily on data preparation and cleaning. Our system was trained in four stages, beginning with pre-training on fully overlapped mixtures and simulated multi-talker conversations with noise and reverberation applied to both the mixture and the enrollment utterances. We then adapted the model to real-world conditions using noisy far-field recordings with pseudo-targets derived from processed close-talk microphone signals. Our submission achieved first place in the second track, demonstrating the critical importance of high-quality data preparation. Furthermore, we observed that DNSMOS and speaker similarity are susceptible to over-optimization, motivating an investigation of their robustness using
adversarial attacks. The results show that both metrics can be driven to extreme values without degrading the
token error rate or the VAD-based F1 score.
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All authors Dominik Klement, Yoshiki Masuyama, Christoph Boeddeker, Kohei Saijo, Julius Richter, Gordon Wichern, Jonathan Le Roux
Audio and Speech Processing
Phone Segmentation and Recognition through Phonological Activation Mapping
Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh et al. (+8 more)
Phone
segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised
speech models (S3Ms), so one only needs to steer them to solve both tasks. We leverage S3M-based Phonological Activation Mapping (SPAM), which maps each S3M representation frame to a vector of phonological feature activations, such as voicing and nasality. On top of SPAM, we introduce two simple but effective lightweight, gradient-descent-free prediction heads: a recognition head and a
segmentation head. Our method requires less than a minute of phonetic transcriptions, and the recognition head generalizes to phones unseen during training. Across a diverse range of
datasets, our approach attains strong
segmentation performance.
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All authors Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh, Chin-Jou Li, Eunjung Yeo, Daisuke Saito, Nobuaki Minematsu, Shinji Watanabe, Jian Zhu, David Harwath, David R. Mortensen
Audio and Speech Processing · Cross-listed from cs.SD
Optimal Transport-based Semantic Alignment for LLM-based Audio-Visual Speech Recognition
Xugang Lu, Peng Shen, Yu Tsao et al. (+1 more)
Large
language model (LLM)-based audio-visual
speech recognition (LLM-AVSR) has recently demonstrated strong robustness in adverse acoustic environments by leveraging complementary audio and visual information. Existing approaches typically employ independently pretrained acoustic and visual encoders, whose outputs are projected and fused as soft
prompts to condition an LLM for
speech recognition. However, most methods perform
multimodal fusion without explicitly addressing the representational discrepancy between audio, visual and text modalities, potentially limiting the effectiveness of cross-modal integration. In this paper, we propose an optimal transport (OT)-based semantic
alignment framework for LLM-AVSR. The proposed method explicitly bridges the modality gap by aligning the acoustic and visual representations with reference to the linguistic
embedding space of the LLM before
multimodal fusion. Specifically, OT is used to estimate probabilistic coupling
matrices that characterize structured correspondences between modality-specific features and linguistic
embeddings. The resulting OT couplings are further utilized as soft pseudo-labels to supervise contrastive learning, encouraging the extraction of semantically coherent and cross-modal consistent audio-visual representations. By anchoring
multimodal features to the linguistic space of the LLM, the proposed framework facilitates more effective
multimodal fusion and decoding. We implement the proposed framework using a Whisper-based acoustic encoder, an AV-HuBERT-based visual encoder, and a LLaMA3.2-3B decoder. Experiments conducted on the LRS3-TED
benchmark demonstrate consistent improvements over strong baselines and achieve state-of-the-art performance under both clean and noisy evaluation conditions across a wide range of signal-to-noise ratios (SNRs).
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All authors Xugang Lu, Peng Shen, Yu Tsao, Hisashi Kawai