Image and Video Processing · Cross-listed from physics.ao-ph
Deep Learning Denoising of Real SWOT Sea Surface Height Observations
Gaétan Meis, Anaëlle Tréboutte, Maxime Ballarotta et al. (+2 more)
The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealing ocean features at finer scales. Nevertheless, the two-dimensional observations of KaRIn altimeter of SWOT suffer from instrumental errors. This noise degradation is altering the high frequencies of SWOT signal, and the small to sub-mesoscale dynamics of interest for some oceanographers. For this reason, Tréboutte et al. (2023) have developed a convolutional neural network (CNN) based on U-Net architecture to separate the noise from the physical signals contained in the SSH. Their approach has demonstrated great potential on simulated SWOT measurements, and Dibarboure et al. (2024) report a positive influence on actual flight data from SWOT. However, degraded denoising performance, and occasional negative side-effects have been observed in atypical conditions (e.g. very high surface waves, internal tides
solitons). In this study, we illustrate some of these limitations and we present an improved approach of the CNN-based denoising: we modified the training procedure to obtain a more robust version of the
algorithm, to avoid biases and artifacts in the denoised SSH. This paper also presents a more complete validation process with a robust and standardized evaluation
benchmark: these metrics could be of interest to assess other SWOT filtering and denoising
algorithms.
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All authors Gaétan Meis, Anaëlle Tréboutte, Maxime Ballarotta, Marie-Isabelle Pujol, Gérald Dibarboure
Systems and Control
Cost-Informed Learning for Aggregating Building HVAC Flexibility
Jingguan Liu, Cong Chen, Xiaomeng Ai et al. (+3 more)
This paper develops a cost-informed aggregation framework that learns an aggregate flexibility set of building heating, ventilation, and air-conditioning (HVAC) loads to minimize the aggregator's dispatch cost. Existing aggregation methods mainly use volume-oriented objectives and treat flexibility aggregation and downstream utilization as separate stages. Consequently, the resulting aggregate set may fail to preserve the flexibility most valuable for reducing downstream dispatch costs. To address this limitation, we represent aggregate HVAC flexibility using a parameterized storage-form surrogate and jointly learn the surrogate parameters and the inner-approximation objective from downstream dispatch-cost feedback. This cost-informed feedback allocates the surrogate's limited representation capacity to cost-relevant regions of the aggregate flexibility set. To account for electricity-price uncertainty, we formulate a distributionally robust conditional value-at-risk (DR-CVaR) downstream problem that captures both distributional ambiguity and tail risk. For efficient learning, we reformulate the DR-CVaR problem as a convex second-order cone program. We estimate cost gradients using randomized smoothing and a score-function
estimator, avoiding differentiation through large-scale building-level optimization problems. Case studies using NYISO price data show that the proposed framework reduces dispatch costs relative to a volume-oriented aggregation
benchmark and that suitable risk and ambiguity settings can lower the out-of-sample CVaR of dispatch costs.
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All authors Jingguan Liu, Cong Chen, Xiaomeng Ai, Jiakun Fang, Jinsong Wang, Jinyu Wen
Signal Processing
A Tutorial on Movable Antenna-Enabled ISAC Systems: Fundamentals, Parameter Estimation, and Security Issues
Zhendong Li, Zhou Su, Jianle Ba et al. (+14 more)
Movable
antenna (MA) technology has recently emerged as a promising paradigm for enhancing the flexibility and performance of wireless systems by allowing
antennas to dynamically adjust their spatial positions within a confined region. Meanwhile, integrated sensing and communication (
ISAC) has attracted significant
attention as a key technology for next-generation wireless networks, aiming to integrate communication and sensing within a shared hardware and spectral framework. By leveraging the additional spatial degrees of freedom offered by MA, MA-enabled
ISAC systems provide new opportunities to enhance both communication performance and sensing accuracy. However, the integration of MA into
ISAC also poses new challenges in parameter estimation and security. In this tutorial, we provide a comprehensive overview of MA-enabled
ISAC systems, with a particular focus on their fundamentals, parameter estimation, and security issues. First, we introduce the basic principles of MA and
ISAC, and review representative MA-enabled
ISAC systems and their potential applications. Then, we establish unified mathematical models for communication and sensing, and systematically review representative channel and sensing parameter estimation methods tailored to MA-enabled
ISAC systems. Numerical comparisons are provided to illustrate the characteristics and performance of different estimation methods. Furthermore, we investigate the security issues of MA-enabled
ISAC systems, introduce the fundamentals of physical-layer security (PLS), and summarize how the spatial reconfigurability of MAs can be exploited to enhance the security of
ISAC systems. Representative case studies are presented to demonstrate the performance gains of MA-enabled secure
ISAC systems. Finally, we identify several open issues and future research directions toward practical MA-enabled
ISAC systems.
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All authors Zhendong Li, Zhou Su, Jianle Ba, Linchu Chen, Yan Yang, Weichun Zhao, Jinyuan Huang, Tom H. Luan, Wen Chen, Qingqing Wu, Lipeng Zhu, Zhenyu Xiao, Weidong Mei, Nan Cheng, Ruijin Sun, Lin Chen, Ying Wang
Signal Processing
RIS-Assisted Secure ISAC: Fundamentals, Applications, and Future Directions
Zhendong Li, Jinyuan Huang, Weichun Zhao et al. (+3 more)
Integrated sensing and communication (
ISAC) has emerged as a key enabler for sixth-generation (6G) networks, yet its broadcast nature at the physical layer leaves transmissions vulnerable to malicious eavesdropping, particularly when physical blockages degrade direct links. Reconfigurable intelligent surface (RIS) offers a promising defense paradigm at the physical layer to address these issues, by reconstructing virtual line-of-sight links and dynamically reshaping the spatial propagation environment. In this article, we provide a comprehensive overview of RIS-assisted secure
ISAC systems, covering its fundamentals, practical applications, and open future research directions. We first outline cooperative deployment
topologies and basic electromagnetic operating principles. We then analyze the technical advantages of passive RIS integration in mitigating bottlenecks in resource competition across communication secrecy, sensing precision, and hardware sustainability. We also present representative applications, including urban vehicular networks, monitoring with unmanned aerial vehicle support, industrial Internet-of-thing, and
privacy in smart homes. A case study quantifies gains in secrecy rate and spatial nulling capabilities achieved through joint active and passive beamforming under full blockage of direct links. Finally, we identify key open challenges and future research directions to enable practical, scalable, and intelligent deployment of RIS-assisted secure
ISAC systems.
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All authors Zhendong Li, Jinyuan Huang, Weichun Zhao, Zhou Su, Enyu Shi, Wen Chen
Audio and Speech Processing · Cross-listed from cs.CL
Automatic estimation of verbal fluency index in people with Motor Neuron Disease using ASR alignment and pause modelling
Bahman Mirheidari, Leslie Ing, Daniel Blackburn et al. (+3 more)
Monitoring cognitive impairment (CI) in motor
neuron disease (MND) is essential for timely treatment and care, yet challenging due to co-occurring
speech difficulties. The Edinburgh Cognitive and Behavioural ALS Screen (ECAS) provides a robust metric for CI assessment, with the Verbal Fluency Index (VFI) a central element. Building on recent advances in automated
speech analysis, this study proposes a system for estimating VFI. It leverages a unique MND
dataset and combines ASR (WhisperX) and VAD (Silero) with refined timestamping to predict the VFI and extract several clinically interpretable measures. Our approach outperformed systems based on traditional acoustic features and self-supervised
embeddings, evaluated using multiple regression
algorithms. Clinically inspired features consistently outperformed the other sets, with the best models achieving strong results (P-words: R2 0.9, NRMSE 0.05; S-words: R2 0.8, NRMSE 0.08), demonstrating the feasibility of automated VFI estimation.
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All authors Bahman Mirheidari, Leslie Ing, Daniel Blackburn, Sharon Abrahams, Christopher McDermott, Heidi Christensen
Signal Processing
Time Invariance, Circle Symmetry, and the Completeness of the Cardiff Behavioral Model
Nicholas B. Tufillaro
Frequency-domain behavioral models of nonlinear microwave devices (the Cardiff model, X-parameters, higher-order sinusoidal describing functions, and the baseband models of power-amplifier predistortion) share one functional form, and each modeling framework justifies the form by its own argument, time invariance among them. Here we give the mathematical argument by which time invariance alone determines the model form, and state the result as a
theorem for two-port devices, with its extension to any number of ports. Under a shift of the time origin each harmonic phasor rotates by a multiple of the fundamental's angle, so the spectral map of a time-invariant device is equivariant under a weighted circle action, and classical invariant theory gives its general form. The result is a completeness
theorem for the form already in use: every time-invariant two-port response in single-tone periodic steady state is a phase factor attached to the fundamental multiplying a function of the wave magnitudes and their relative phase. The Cardiff exponents become counters: $m$ is the order of the load-side nonlinearity, $n$ is set by the drive-side harmonic, and $r$ is the number of conjugate pairs. The bound $r_{\max}=\lfloor K/2\rfloor$ for a load-side nonlinearity of degree $K$ makes the familiar restriction $r\le1$ a hypothesis about the device, exact for a cubic nonlinearity; for a loaded device the bound becomes a measurable decay in $r$. The results are illustrated with a published measurement and two simulations.
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Image and Video Processing
Adaptive Tiling for Least-Squares Phase Unwrapping: Runtime and Accuracy
Antoine Moevus and Max Mignotte
Phase unwrapping estimates the missing multiples of $2\pi$ in measured phase images. For large images, tiling limits the size of local reconstruction problems and enables parallel processing. Adaptive tiling could further reduce the number of local problems and boundaries by retaining large tiles where little refinement is needed. We investigate whether this reduction makes reconstruction faster. We compare complete reconstruction time and accuracy for a regular grid, quadtree, and kd-tree partitions. We also evaluate nine criteria for deciding where quadtree tiles should be subdivided, including residue count, fringe density, and measures of phase variation, at different tile sizes and budgets. In single-threaded experiments on a heterogeneous image
dataset, optimized adaptive partitions use fewer tiles but remain slower than the optimized grid, and some reconstructions lose substantial accuracy. Stage measurements explain why: constructing the partition and solving larger retained tiles outweigh the savings at tile boundaries. The criterion comparison also shows that more refinement does not consistently improve accuracy. These results motivate evaluating adaptive partitions by the complete time needed to reach a chosen reconstruction accuracy, including whether limited refinement can provide a faster approximate result.
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Image and Video Processing
BRiDCT: Fast Two-Dimensional DCTs Using SIMD: SIMD Organization, Register Blocking, and Numerical Verification
Antoine Moevus and Max Mignotte
Repeated two-dimensional discrete cosine transforms (DCTs) require implementations that remain fast across several array sizes while preserving the mathematical transform. Fewer arithmetic operations do not guarantee a faster DCT: execution time also depends on how the computation uses the CPU's single-instruction, multiple-data (SIMD) units, registers and
memory hierarchy. We implement established DCT
algorithms in SIMD and compare execution strategies for forward, inverse, and normalized round-trip transforms on float32 arrays from $8\times8$ to $256\times256$. The resulting library, BRiDCT, combines the Shao–Johnson factorization with SIMD lane organization, banded intermediate storage, padding, and register blocking in C11/ARM NEON. On one Apple M3 Max, the final native library has lower execution times than the tested general-purpose routes—Apple's Accelerate framework (vDSP), FFTW and Ooura—across their available cases from $8\times8$ to $256\times256$. It also outperforms the tested libjxl and floating-point libjpeg-turbo routes. The compiled NumPy interface outperforms OpenCV in all 130 tested contracts per session, including squares up to $1024\times1024$ and two rectangles, with call and allocation costs included. Complementary comparisons evaluate SciPy, DUCC and cached/planned pyFFTW. The measured gains come from organizing SIMD execution and intermediate storage, rather than introducing a new arithmetic factorization. A reproducible 474-input verification corpus probes cancellation, neighboring floats, dynamic range, and extreme amplitudes: all 1 260 core checks of the final configuration pass, while boundary diagnostics reveal overflow and weak-component limitations. Larger native transforms show size-dependent limits. Code, versioned releases,
dataset and recorded measurements: https://github.com/antmoev/bridct.
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Signal Processing
AI-Enabled Wireless Propagation Modeling and Radio Environment Maps for 5G Aerial Wireless Networks
Gautham Reddy, Kürşat Tekb{\i}y{\i}k, Bryton Petersen et al. (+3 more)
With the gaining prominence of aerial mobility applications, their success depends on the seamless integration of terrestrial and non-terrestrial network connectivity. However, providing reliable connectivity from terrestrial telecommunication networks remains challenging due to multi-
cell interference from base stations (BSs) under line-of-sight (LoS) conditions to unmanned aerial vehicles (
UAVs), coverage holes caused by
antenna sidelobe degradation, localized multipath fading effects, and the high-speed dynamics of aerial users. To model such complexities, often exacerbated by sparse real-world data, this work proposes a dual-stage
radio environment map (REM) framework. Our approach physically decouples the channel modeling, where a spatial
Transformer first anchors the deterministic, large-scale path loss geometry, while a gated recurrent unit (GRU) subsequently extrapolates the
stochastic, localized fast- fading deviations. By reformulating 3D spatial interpolation as a 1D radial sequence prediction task, the framework inherently aligns with the physics of propagation. We evaluate the proposed framework against state-of-the-art baselines, including 3D Kriging, UNet, Mamba, and Inception, using empirical 5G
datasets. The results demonstrate improved intra-site generalization across diverse altitudes, user dynamics, and reference signal received power (RSRP)
datasets, achieving signal-strength predictions with errors near 3 dB and REM spatial-similarity indices exceeding 0.75. Finally, we examine the influence of REMs and channel rank conditions on
UAV channel quality, underscoring the necessity of reliable channel modeling for robust aerial connectivity.
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All authors Gautham Reddy, Kürşat Tekb{\i}y{\i}k, Bryton Petersen, Antoine Lesage-Landry, Gunes Karabulut Kurt, Ismail Güvenç
Signal Processing
Automated feature-region selection for soft-sensor development from spectral-like measurements
Sebastián Espinel-Ríos and Wenchao Duan
Sustainable production increasingly relies on process analytical chemistry and process analytical technology to support monitoring, control, and automation. Techniques used in these contexts, including Raman spectroscopy, infrared spectroscopy, and electrochemical voltammetry, generate high-dimensional signals ordered along physical measurement axes, referred to here as spectral-like measurements. These signals can contain redundant, weakly informative, and noisy regions, complicating the development of data-driven soft sensors to map them to process variables. Selecting informative regions is nontrivial, as visually prominent regions are not necessarily the most predictive, while synergistic effects among regions cannot be readily inferred. Here, we introduce an automated feature-region selection framework that identifies a parsimonious set of contiguous regions while preserving channel ordering. The framework combines channel-level target correlation and a signal-to-noise indicator into a latent information fingerprint. Variable-width candidate intervals are derived from peaks in this fingerprint, and their combinations are ranked using held-out validation data. Final selection favours models using fewer channels among candidates with comparable predictive performance. The framework is demonstrated using cyclic voltammetric measurements of glucose acquired with a gold electrode sensor across four progressively broader nominal concentration ranges up to 350 g/L. Gaussian process regression is used within the framework to accommodate possible nonlinear relationships, account for observation noise, and quantify epistemic uncertainty. The selected models retained only 11-42 of the original 400 channels and reduced held-out test root mean squared error by 88.3-94.6% relative to full-feature Gaussian process regression
benchmarks.
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Systems and Control · Cross-listed from cs.RO
Fast Direction-Conditioned Reachability for Motion Prediction Under Model Uncertainty
Hrishav Das and Melkior Ornik
To avoid collisions, a
robot must repeatedly predict where nearby
agents may move, usually with an imperfect model of their dynamics. Reachable sets provide such predictions, but computing them when the system
matrices themselves are uncertain can become computationally expensive and conservative for frequent replanning. Moreover, a planner often needs to know only how far an
agent can move in one particular direction, for example toward the
robot, rather than the complete reachable set. We propose a direction-conditioned reachability method for linear systems with uncertain state and input
matrices. Given a query direction $d$, the method selects one admissible model $(A^\star,B^\star)$ whose reachable set extends nearly as far along $d$ as the reachable set of the entire uncertain model family, and then computes the reachable set of only this model with a standard reachability solver. On an uncertain linearized bicycle model, the complete selection-and-computation pipeline is about three times faster than computing the reachable set of the full uncertain family in the CORA toolbox, while its extent along $d$ is within $5\%$ of the full family's in the reported directions. We also use the method in a closed-loop multi-vehicle simulation in which the
robot queries, at each replanning step, how far each nearby vehicle can move toward it, and replans to avoid the resulting sets.
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Systems and Control
Evaluating Grid Strength with Rising Penetration of Inverter Based Resources
Mohammed N. Khamees, Gad Ilunga, Zhongxia Zhang et al. (+1 more)
Over the years, Dominion Energy Virginia (DEV) has experienced recurring power quality disturbances, including voltage and power oscillations, particularly in areas with significant inverter-based generation. While these phenomena suggest a potential relationship to system strength, no definitive correlation has yet been established. This paper presents methodologies for assessing system strength across the DEV network and identifying regions that may be vulnerable to such disturbances. To this end, three grid-strength metrics are evaluated: the Simple Short-
Circuit Ratio (SSCR), the Weighted Short-
Circuit Ratio (WSCR), and the Composite Short-
Circuit Ratio (CSCR). Historical event data will be analyzed using steady-state snapshots from DEV's proprietary Analysis on Demand (ANODE) platform, which provides 10-minute interval load-flow data. These snapshots will be processed using a developed software tool to compute system strength metrics. The analysis will investigate potential correlations between system strength and observed disturbances. Where such correlations are identified, the study will further evaluate the impact of deploying synchronous condensers (SynCons) as a mitigation strategy.
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All authors Mohammed N. Khamees, Gad Ilunga, Zhongxia Zhang, Richard E. Tuck
Systems and Control
Merging Large Language Models and Battery Physics for User-Aware Electric Vehicle Driving Management
Yukta Pareek, Yasaman Masoudi and Satadru Dey
Electric vehicle (EV) battery performance is strongly coupled with driver behavior, yet human intent is typically expressed semantically rather than numerically. This paper proposes a hybrid physics-artificial intelligence framework that integrates a Large
Language Model (LLM) as a high-level behavioral
reasoning layer within a physics-driven supervisory architecture. The LLM interprets textual user intent and structured battery feedback to generate bounded behavioral parameters that shape a discharge current envelope. A physics-driven safety filter then enforces physical safety constraints before computing feasible velocity recommendations. Lyapunov-based analysis establishes bounded recommendation error under battery model and
prompt inaccuracies. Simulation results demonstrate adaptive, user-aware operation without compromising physical safety. The proposed
reasoning-enforcement architecture provides a principled pathway for safe AI integration in EV energy management.
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Audio and Speech Processing
Dual-Microphone Steerable High-Order Neural Differential Beamformer
Weilong Huang and Emanuël A. P. Habets
Linear arrays of omnidirectional microphones produce beampatterns that are symmetric about the array axis. For these arrays, a beamformer is considered steerable if its beampattern maintains the same shape in the semicircular plane across all look directions from 0{\deg} to 180{\deg}. For dual-microphone arrays, conventional differential beamformers are generally non-steerable and restricted to first-order, which significantly limits spatial selectivity. To address these limitations, this study presents a neural differential beamformer (NDBF) with a dual-microphone array. The contributions are as follows: (i) NDBF is steerable; (ii) NDBF achieves high-order frequency-invariant beampatterns; and (iii) NDBF enables stereo recording using only two closely spaced omnidirectional microphones. Experimental results demonstrate that NDBF outperforms existing methods while overcoming the limitations of classical differential beamforming.
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Signal Processing
Digital Twin Enhanced Channel Twin for AI-Native CSI Inference: Generalizability and Scalability
Majumder Haider, Imtiaz Ahmed, Zoheb Hassan et al. (+2 more)
Accurate channel state information (CSI) is critical for advanced multi-
antenna wireless networks. While high-fidelity and site-specific ray-tracing (RT) equipped wireless digital twins can overcome overhead for CSI acquisition. However, computing deterministic, calibrated RT based CSI from a wireless digital twin for every orthogonal frequency-division multiplexing (OFDM) symbol violates the strict microsecond latency budgets of the 5G NR numerology. To overcome this computational bottleneck, this paper investigates three approaches within a calibrated 3D digital twin framework, namely (i) the generalization of the channel twin, (ii) the performance enhancement using a neural receiver, and (iii) a data-driven interpolation framework for scalability. We compute high-precision CSI for a sparse subset of temporal anchors and employ an
attention-based
Transformer to predict the remaining symbols. Unlike
polynomial splines or sequential long short-term
memory (LSTM) networks, the
Transformer exploits a global receptive field to capture the non-linear multipath dynamics while enabling parallelizable, real-time inference. To achieve spatial scalability, we introduce channel twin generalization. By fine-tuning the 3D RT models alongside the
Transformer, the framework leverages the CSI
dataset of one location to infer the channel behavior of an unseen environment. Transfer learning further adapts the model to a new environment using only a small fraction of locally collected data. Simulation results demonstrate that the proposed architecture substantially outperforms the baseline interpolators, achieves robust spatial transferability, and lowers the bit error rate through the unified neural receiver. These results establish a scalable, environment-agnostic foundation of distributed channel twins for high-quality, low-overhead CSI acquisition in next-generation cellular networks.
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All authors Majumder Haider, Imtiaz Ahmed, Zoheb Hassan, Danda B. Rawat, Huaiyu Dai
Systems and Control
Three High Performance Global Tracking Composite Adaptive Controllers for Fully Actuated Euler-Lagrange Systems: Experimental Validation
Luis Cervantes-Pérez, Jose Guadalupe Romero, Romeo Ortega et al. (+2 more)
Three adaptive global tracking controllers for fully actuated Euler-Lagrange systems, with verifiable performance improvement over existing designs, are reported in this letter. Two of these controllers ensure global exponential convergence under a weak interval excitation condition. Besides, one of the proposed controllers features a simple adaptive PID-like structure that-unlike classical solutions-avoids the need for additional filtering. We adopt a composite adaptation architecture, invoke a novel parameterization of the system dynamics and use a high performance estimation scheme recently introduced in the literature. Real-time experiments and a comparative study with a learning-based adaptive controller on a two-degrees-of-freedom manipulator arm illustrate the effectiveness of the proposed controllers.
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All authors Luis Cervantes-Pérez, Jose Guadalupe Romero, Romeo Ortega, Víctor Santibáñez, Jesús Sandoval
Systems and Control · Cross-listed from cs.RO
Spiderbot: An Open-Source Energy-Efficient Hexapod with Passive Gravity Compensation
Ritwik Sharma, Vimarsh Shah and Saransh Agrawal
Hexapod
robots can achieve static stability with fewer actuated joints than bipeds or quadrupeds, yet many platforms still use 3-DOF legs, increasing weight and continuous torque requirement with limited gain in locomotion capability on flat, inclined and moderately rough terrains. We release Spiderbot, an open-source hexapod that uses a 4-bar linkage with a passive spring to mechanically support body weight, with a 2-DOF per-leg design that substantially reduces energy consumption. This mechanism substantially offloads gravitational torque during standing stance consuming only 1.5W (reduction of over 90% over the unsprung version and up to 96% over other similar hexapods). The passive spring compensation extends to payloads of up to 3.25kg with no additional torque requirements. The platform enables long-duration deployments on a modest battery budget and costs under \$400, making it suitable for large-scale multi-agent experiments. We validate the locomotion capabilities of the platform with an RL policy trained in mjlab, including successful sim-to-real transfer, despite the complexity of the mechanism. The platform is evaluated on flat and rough terrains, slope up to $15^\circ$ and step obstacles. We release all the CAD files, assembling instructions, and full training and deployment code along with the model checkpoints at https://erc-bpgc.github.io/SpiderBot/.
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Signal Processing · Cross-listed from cs.LG
Resource-Efficient Distributed Recursive Gaussian Processes
Josephine King, Ali Emre Balci and Raj Thilak Rajan
Gaussian processes (GPs) provide a flexible framework for learning unknown functions from noisy measurements while quantifying predictive uncertainty, making them well suited for estimation in multi-
agent systems. However, when measurements are collected by multiple
agents, maintaining a unified GP model without centralized processing requires efficient distributed
algorithms that can operate using local measurements and communication with neighboring
agents. In this work, we develop two distributed recursive GP (RGP)
algorithms for multi-output GP regression: ADMM-RGP and PDMM-RGP. We analyze the stability and convergence of both
algorithms and develop parameter selection strategies to accelerate convergence, thus reducing the communication burden. The proposed methods are validated on a real-world multi-output wind
dataset, and their convergence behavior is examined across communication graphs with varying connectivity. Numerical experiments demonstrate that ADMM-RGP and PDMM-RGP can significantly reduce communication relative to the state of the art, while maintaining comparable estimation accuracy and network-wide consensus.
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Signal Processing
Untangling the Geometry and Speed for RF Sensing Spectrograms
Mert Torun, Darius Cuenca and Yasamin Mostofi
A fundamental challenge in RF sensing is that Doppler signatures observed by a link entangle the target's motion with the sensing geometry, resulting in limited applicability to unconstrained real-world settings. In this paper, we establish a new foundation for physically interpretable RF sensing that disentangles reflector speed from geometry, jointly recovering the speed, geometry factor, relative amplitude, and width of each dominant Doppler ridge. More specifically, we first develop a compact parametric representation of WiFi spectrograms and establish its low-dimensional structure through a systematic computer-
vision analysis of a large and diverse human-activity
dataset, thereby providing a tractable foundation for learning. Building on this representation, we then design a physics-informed autoencoder whose structured bottleneck and differentiable RF forward model enforce physically meaningful estimates of reflector speed and geometry. We further introduce a synthetic-to-real training framework, eliminating the need for real WiFi training data. We extensively validate the proposed framework under both known and time-varying geometries, using both independently generated synthetic test sets and 31 real WiFi experiments. The results demonstrate the superior performance in speed and geometry extraction, robustly recovering the underlying geometry, speeds, Doppler-ridge amplitudes, and ridge widths across all settings, while substantially outperforming the strongest baselines.
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Audio and Speech Processing · Cross-listed from cs.SD
A Stem-Agnostic Approach to Hybrid AI Music Detection
Richa Namballa, François Rigaud and Romain Hennequin
The inclusion of generative audio in the music production process has led to an increase in hybrid music tracks that blend authentic human performances with AI-generated stems, challenging traditional AI music detectors which operate in a binary setting. In this work, we propose a stem-agnostic framework for identifying synthetic audio sources within hybrid musical mixtures. We introduce the inspectrogram, a novel time-frequency representation that maps localized probabilities of synthetic content across the audio spectrum. By combining the inspectrogram with a Wiener filter estimating target stem energy dominance, a single CNN model evaluates whether the specific stem is generated. Trained on rendered hybrid mixtures and evaluated across various stem classes, our model achieves strong performance on high-frequency sources such as vocals, drums, and guitar, but struggles on the low-frequency, narrow-band bass. We conclude that the quality of separation impacts the detection accuracy and identify source separation as a primary bottleneck and a crucial direction for future research.
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Audio and Speech Processing
A Temporal-Envelope Frontend with Learnable Per-Channel Energy Normalization for Whisper-Based Children's ASR
Edem Ahadzi, Ruchi Pandey and Tomi H. Kinnunen
Temporal envelopes carry cues critical to
speech intelligibility, yet ASR frontends based on log-mel spectrograms do not explicitly model continuous sub-band envelope structure. This limitation is particularly acute for children's
speech, where high acoustic variability demands robust feature representations. We propose a modular time-domain frontend that decomposes
speech into sub-band envelopes using mel-spaced windowed-sinc filters and the
Hilbert transform, with learnable per-channel energy normalization (PCEN) jointly optimized with the Whisper model. On the MyST children's
speech corpus, systematic ablations identify full-band windowed-sinc filters,
Hilbert envelopes, a 25 Hz smoothing cutoff, and learnable PCEN as the best configuration. Under the same Whisper-small fine-tuning setup, the frontend reduces WER from 13.16% to 11.08%, a 15.8% relative reduction over the log-mel baseline, and outperforms the evaluated Kid-Whisper checkpoint on the same cleaned test split. These results show that temporal-envelope representations and learnable frontend normalization are effective complements to backend adaptation for children's ASR.
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Image and Video Processing · Cross-listed from cs.CV
nnFoundation: 3D Foundation Models for Radiology
Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski et al. (+80 more)
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise more transferable and data-efficient solutions, but existing approaches are limited in scale, evaluated narrowly, and often assume that a single pretrained model can support diverse downstream tasks. Here we present nnFoundation, complementary convolutional and
transformer-based 3D radiological foundation models. Developed within the Human Radiome Project (THRP), nnFoundation is trained on 2.1 million CT, MRI, and PET image volumes from 125 institutional and public
datasets. We evaluate them across 108 tasks spanning segmentation, detection, classification, report generation, and image
retrieval, including evaluations under domain shift, by external partners and in low-data and low-compute regimes. Across all task types, our convolution- and
transformer-based nnFoundation models consistently outperform both prior 3D foundation models and training from scratch, establishing state-of-the-art performance for radiological imaging. However, performance follows a consistent task-dependent structure: the convolutional nnFoundation model dominates spatially localized tasks, whereas the
transformer-based nnFoundation model excels in tasks requiring global semantic
reasoning and in frozen-feature settings. Dynamically aligning the foundation model
topology with the
dataset characteristics post-hoc further improves transfer across heterogeneous 3D settings. These results show that transferable 3D radiological performance is governed not by a single universal model, but by the interplay of scalable pretraining, complementary architectures, and
dataset-aware adaptation. We release nnFoundation models integrated into nnU-Net and nnDetection, enabling immediate application across established radiology workflows.
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All authors Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski, Yannick Kirchhoff, Marcel Knopp, Maximilian Rokuss, Elisa Stegmeier, Philipp Schader, Dasha Trofimova, Raphael Stock, Kim-Celine Kahl, Stephen Schaumann, Selen Erkan, David Zimmerer, Stefan Denner, Moritz Langenberg, Sebastian Ziegler, Katharina Eckstein, Maximilian Fischer, Jonathan Suprijadi, Bálint Kovács, Benjamin Hamm, Anand Deshpande, Dimitrios Bounias, Nico Disch, Shuhan Xiao, Jessica Kächele, Jan Sellner, Rajesh Baidya, Jeremias Traub, Lars Krämer, Maximilian Zenk, Tim Rädsch, Stefan Dvoretskii, Robin Peretzke, Jonathan Deissler, Alexandra Ertl, Partha Ghosh, Kris Dreher, Stefan Dinkelacker, Annika Reinke, Evangelia Christodoulou, Numan Saeed, Yoland Savriama, Santiago Estrada, David Kügler, Laura Alexandra Daza Barragan, Cristina Isabel Gonzalez Osorio, Jan Peeken, Michael Baumgartner, Marvin Teichmann, Guillaume Chabin, Matthias Kirchler, Valentin Koch, for the ALFA study, Markus Hohenhaus, Dimitri Koslov, Nina Decker, Mohammad Yaqub, Arnd Heuser, Martin Reuter, Julia A. Schnabel, Tobias Heimann, Florin Ghesu, Paul Brachmann, Claus P. Heußel, Alexander Radbruch, Gianluca Brugnara, Aditya Rastogi, Martha Foltyn-Dumitru, Heinz-Peter Schlemmer, Ignaz Reicht, Julius C. Holzschuh, Michael Bach, Bram Stieltjes, Kai Schlamp, Lena Maier-Hein, Marco Nolden, Ralf Floca, Paul F. Jäger, Philipp Vollmuth, Fabian Isensee, Klaus H. Maier-Hein
Systems and Control
Impact-Time Guidance via Normal Contraction to a Time-to-Go Isochron
Shivam Bajpai and Abhinav Sinha
We develop a contraction-based perspective on impact-time guidance that augments a baseline homing command with a timing bias. The proposed perspective treats the prescribed schedule as a moving time-to-go isochron and regulates motion normal to that set through velocity-normal lateral acceleration while the interceptor's speed remains constant. We derive a transport equation that characterizes homing-compatible time-to-go coordinates and define a predictor defect that quantifies the mismatch of approximate maps. We show that the scalar timing channel induces a coordinate-invariant rank-one metric on the normal quotient. To account for bounded lateral acceleration, we formulate a robust scalar filter and derive a necessary and sufficient condition for pointwise feasibility. We then show that terminal calibration and funnel invariance establish first interception at the prescribed time under the stated assumptions. We also develop a preterminal
alignment and homing handover that avoids singular inversion as lateral timing authority vanishes near collision-course
alignment. The proposed perspective accommodates analytic, numerical, and learned time-to-go maps that satisfy the required calibration and regularity conditions.
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Systems and Control · Cross-listed from math.OC
A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
Xiaoxing Ren, Thomas Parisini and Andreas A. Malikopoulos
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prior knowledge of the transition model. Each team member makes decisions based on local private information and delayed common information shared across the team. Using only this available information, each member learns an approximate low-rank Markov decision process and applies least-squares value iteration to compute its policy. This yields a fully decentralized learning and planning
algorithm that requires neither a centralized coordinator nor centralized training. We show that the resulting member-side solutions approximate the centralized team solution: despite partial observability, unknown dynamics, and delayed common information, each member recovers the corresponding component of an approximate team-optimal policy. We further establish finite-sample performance guarantees and derive a corresponding sample-complexity bound for the proposed
algorithm.
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Signal Processing
Attention Guided Conditional Adversarial Learning for EMG Artifact Suppression in Single Channel EEG
Haoyi Wang, Haowei Wang, Yihang Li et al. (+2 more)
Electromyographic activity can dominate scalp electroencephalography across a broad frequency range and obscure neural structure that is relevant to analysis and
brain computer interfaces. This paper evaluates P2P Trans, a conditional
adversarial denoiser that combines a U shaped encoder decoder, multi head self
attention at the bottleneck, cross
attention guided skip fusion, and a PatchGAN discriminator. The reported experiment used EEGdenoiseNet segments represented as 1024 sample sequences, synthesized paired mixtures at ten signal to noise ratios from minus 7 to 2 dB, and evaluated 50000 training examples and 5980 test examples. Against five convolutional encoder decoder baselines, P2P Trans achieved the best mean correlation coefficient of 0.8645 and the lowest mean spectral RRMSE of 0.3596. Its mean temporal RRMSE was 0.5797 and did not lead the comparison, which indicates a tradeoff between spectral suppression and pointwise waveform fidelity. The advantage was strongest under severe contamination. The results support
attention guided
adversarial restoration as a useful low SNR approach, while also identifying the need for subject independent evaluation, direct one dimensional modeling, and downstream task validation.
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All authors Haoyi Wang, Haowei Wang, Yihang Li, Wenjie Zhang, Yibo Wang
Systems and Control
Incentive Design for Multi-Agent Systems: A Bilevel Optimization Framework for Coordinating Independent Agents and Convergence Analysis
Xinyi Wei, Shuo Han and Jie Fu
Incentive design aims to guide the performance of a system towards a human's intention or preference. We study this problem in a multi-
agent system with one leader and multiple followers. Each follower independently solves a mdp to maximize its own expected total return with the same state space and action space. However, the leader's objective depends on the collective best-response policies of all followers. To influence these policies of followers, the leader provides side payments as incentives to individual followers at a cost, aiming to align the collective behaviors of followers with its own goal while minimizing this cost of incentive. Such a leader-followers interaction is formulated as a bilevel optimization problem: the lower level consists of followers individually optimizing their MDPs given the side payments, and the upper level involves the leader optimizing its objective function given the followers' best responses. The main challenge to solve the incentive design is that the leader's objective is generally non-concave and the lower level optimization problems can have multiple local optima. To this end, we employ a constrained optimization reformation of this bi-level optimization problem and develop an
algorithm that provably converges to a stationary point of the original problem, by leveraging several smoothness properties of value functions in MDPs. We validate our
algorithm in a
stochastic gridworld by examining its convergence, verifying that the constraints are satisfied, and evaluating the improvement in the leader's performance.
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Audio and Speech Processing · Cross-listed from cs.MM
ROAM-ASD: Robust Open-World Active Speaker Detection with Flexible Multimodal Fusion
Pu Wang and Hugo Van hamme
Active speaker detection (ASD) requires reliable association between visible faces and acoustic
speech, yet existing systems often degrade under challenging domains or incomplete observations. We introduce ROAM-ASD, a robust audiovisual framework that jointly models audio, full-face, and fine-grained mouth representations. A unified joint self-
attention mechanism processes all input streams together with modality-agnostic query
tokens, enabling direct interaction among available modality inputs. Modality dropout further improves robustness when input streams are unavailable. ROAM-ASD achieves state-of-the-art performance across five ASD
benchmarks: 98.8% mAP on WASD, 87.9% on UniTalk, 96.5% on AVA, 99.3% on ASW, and 98.2% on Talkies, improving over previous best systems by 5.1, 4.7, 0.9, 1.0, and 2.1 mAP points, respectively. ROAM-ASD also substantially improves zero-shot cross-
dataset generalization and remains robust to missing observations.
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Image and Video Processing · Cross-listed from cs.CV
Laryngeal Structure Segmentation in High-Speed Videoendoscopy Using Deep Learning
Sardar Nafis Bin Ali, Mohsen Zayernouri, Dimitar D. Deliyski et al. (+1 more)
Laryngeal high-speed videoendoscopy (HSV) offers an effective means of observing the motion of different laryngeal structures along with vibratory behaviors of the vocal folds under various voicing conditions. Segmentation of laryngeal tissues enables analysis of different tissue structures and their dynamics, helping characterize the involvement of laryngeal muscles in voice production. Given the large number of HSV frames, automating this task is imperative. While deep learning-based methods have been implemented in previous studies to segment laryngeal structures, they have not been applied to HSV data during connected
speech, which poses significant challenges due to excessive tissue movements and image quality limitations associated with fiberoptic image acquisition. The application of deep learning to connected
speech data is critical for capturing nonstationary laryngeal behaviors and identifying anomalous patterns associated with voice disorders. The present study aims to address these gaps by training U-Net models to detect the aryepiglottic folds and arytenoid cartilages, vocal folds, epiglottis, and glottal area, using HSV data from both sustained vowel phonation and connected
speech obtained from normophonic and disordered voices. Image pre-processing techniques, including noise removal and histogram equalization, were applied to improve the quality of the training HSV images and enhance network performance. Finally, to evaluate the accuracy and reliability of the networks, quantitative performance metrics were used alongside qualitative visual inspection of the test images. The high performance of the developed networks, with overall accuracies exceeding 95%, establishes their potential as reliable tools for automated laryngeal image analysis, quantitative characterization of laryngeal dynamics, and future detection of anomalous laryngeal behaviors in clinical settings.
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All authors Sardar Nafis Bin Ali, Mohsen Zayernouri, Dimitar D. Deliyski, Maryam Naghibolhosseini
Signal Processing
Post-Hoc Conformal Prediction for Reliable Wireless Communications
Xin Su, Meiyi Zhu, Osvaldo Simeone et al. (+1 more)
Deploying artificial intelligence (AI) in high-stakes wireless applications such as autonomous transportation requires guarantees of reliable operation. Such guarantees can often be obtained by designing systems that act on a set of predictions via conservative policies catering to all possible outcomes within the set. For instance, in location-based beam selection, a base station may identify a set of plausible locations and select a beam that ensures high capacity over the set. The miscoverage probability of the set with respect to the true outcome (e.g., the true user location) then quantifies the outage probability, while the size of the set determines the final performance or resource budget (e.g., the transmission capacity). Conventional conformal prediction (CP) applies when the target miscoverage level is prescribed in advance. However, in practical wireless systems, prediction sets may instead be selected under prescribed operational constraints, requiring the resulting miscoverage probability to be quantified post hoc. This paper develops a formal statistical framework for the data-driven selection of prediction sets that provides a reliable estimate of the resulting miscoverage probability. The methodology builds on backward CP and probably approximately correct CP, yielding distribution-free reliability guarantees on the miscoverage probability. We apply the framework on three distinct wireless applications: narrowband interference detection, near-field localization, and codebook-based beam identification. Numerical results validate the reliability guarantees and show that the proposed post-hoc conformal methods achieve accuracy comparable to the naive probability-based approach across all considered applications.
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All authors Xin Su, Meiyi Zhu, Osvaldo Simeone, Carlo Fischione