Image and Video Processing · Cross-listed from cs.CV
SAW: Toward a Surgical Action World Model via Controllable and Scalable Video Generation
Sampath Rapuri, Lalithkumar Seenivasan, Dominik Schneider et al. (+9 more)
A
surgical world model capable of generating realistic
surgical action
videos with precise control over tool-tissue interactions can address fundamental challenges in
surgical AI and simulation – from data scarcity and rare event synthesis to bridging the sim-to-real gap for
surgical automation. However, current
video generation methods, the very core of such
surgical world models, require expensive annotations or complex structured intermediates as conditioning signals at inference, limiting their scalability. Other approaches exhibit limited temporal consistency across complex laparoscopic scenes and do not possess sufficient realism. We propose
Surgical Action World (SAW) – a step toward
surgical action world modeling through
video diffusion conditioned on four lightweight signals:
language prompts encoding tool-action context, a reference
surgical scene, tissue affordance mask, and 2D tool-tip trajectories. We design a conditional
video diffusion approach that reformulates
video-to-
video diffusion into trajectory-conditioned
surgical action synthesis. The backbone
diffusion model is fine-tuned on a custom-curated
dataset of 12,044 laparoscopic clips with lightweight spatiotemporal conditioning signals, leveraging a depth consistency loss to enforce
geometric plausibility without requiring depth at inference. SAW achieves state-of-the-art temporal consistency (CD-FVD: 199.19 vs. 546.82) and strong visual quality on held-out test data. Furthermore, we demonstrate its downstream utility for (a)
surgical AI, where augmenting rare actions with SAW-generated
videos improves action recognition (clipping F1-score: 20.93% to 43.14%; cutting: 0.00% to 8.33%) on real test data, and (b)
surgical simulation, where rendering tool-tissue interaction
videos from simulator-derived trajectory points toward a visually faithful simulation engine.
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All authors Sampath Rapuri, Lalithkumar Seenivasan, Dominik Schneider, Roger Soberanis-Mukul, Yufan He, Hao Ding, Jiru Xu, Chenhao Yu, Chenyan Jing, Pengfei Guo, Daguang Xu, Mathias Unberath
Image and Video Processing
Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning
Yamin Arefeen, Sidharth Kumar, Steven Warach et al. (+2 more)
Purpose: To develop a data-efficient strategy for accelerated MRI reconstruction with
Diffusion Probabilistic Generative Models (DPMs) that enables faster scan times in clinical stroke MRI when only limited fully-sampled data samples are available. Methods: Our simple training strategy, inspired by the foundation model paradigm, first trains a DPM on a large, diverse collection of publicly available
brain MRI data in fastMRI and then fine-tunes on a small
dataset from the target application using carefully selected learning rates and fine-tuning durations. The approach is evaluated on controlled fastMRI experiments and on clinical stroke MRI data with a blinded clinical reader study. Results: DPMs pre-trained on approximately 4000 subjects with non-FLAIR contrasts and fine-tuned on FLAIR data from only 20 target subjects achieve reconstruction performance comparable to models trained with substantially more target-domain FLAIR data across multiple acceleration factors. Experiments reveal that moderate fine-tuning with a reduced learning rate yields improved performance, while insufficient or excessive fine-tuning degrades reconstruction quality. When applied to clinical stroke MRI, a blinded reader study involving two neuroradiologists indicates that images reconstructed using the proposed approach from $2 \times$ accelerated data are non-inferior to standard-of-care in terms of image quality and structural delineation. Conclusion: Large-scale pre-training combined with targeted fine-tuning enables DPM-based MRI reconstruction in data-constrained, accelerated clinical stroke MRI. The proposed approach substantially reduces the need for large application-specific
datasets while maintaining clinically acceptable image quality, supporting the use of foundation-inspired
diffusion models for accelerated MRI in targeted applications.
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All authors Yamin Arefeen, Sidharth Kumar, Steven Warach, Hamidreza Saber, Jonathan Tamir
Systems and Control · Cross-listed from cs.RO
From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks
Gabriele Gualandi and Alessandro V. Papadopoulos
Cyber-physical robotic systems are vulnerable to false data injection attacks (FDIAs), in which an adversary corrupts sensor signals while evading residual-based passive anomaly detectors such as the chi-squared test. Such stealthy attacks can induce substantial end-effector deviations without triggering alarms. This paper studies the resilience of redundant manipulators to stealthy FDIAs and advances the architecture from passive monitoring to active defence. We formulate a closed-loop model comprising a feedback-linearized manipulator, a steady-state Kalman filter, and a chi-squared-based anomaly detector. Building on this passive monitoring layer, we propose an active control-level defence that attenuates the control input through a monotone function of an anomaly score generated by a novel actuation-projected, measurement-free state predictor. The proposed design provides probabilistic guarantees on nominal actuation loss and preserves closed-loop stability. From the attacker perspective, we derive a convex QCQP for computing one-step optimal stealthy attacks. Simulations on a 6-DOF planar manipulator show that the proposed defence significantly reduces attack-induced end-effector deviation while preserving nominal task performance in the absence of attacks.
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Image and Video Processing
Reinforcing the Weakest Links: Modernizing SIENA with Targeted Deep Learning Integration
Riccardo Raciti, Lemuel Puglisi, Francesco Guarnera et al. (+2 more)
Percentage
Brain Volume Change (PBVC) derived from
Magnetic Resonance Imaging (MRI) is a widely used biomarker of
brain atrophy, with SIENA among the most established methods for its estimation. However, SIENA relies on classical image processing steps, particularly skull stripping and tissue
segmentation, whose failures can propagate through the pipeline and bias atrophy estimates. In this work, we examine whether targeted deep learning substitutions can improve SIENA while preserving its established and interpretable framework. To this end, we integrate SynthStrip and SynthSeg into SIENA and evaluate three pipeline variants on the ADNI and PPMI longitudinal cohorts. Performance is assessed using three complementary criteria: correlation with longitudinal clinical and structural decline, scan-order consistency, and end-to-end runtime. Replacing the skull-stripping module yields the most consistent gains: in ADNI, it substantially strengthens associations between PBVC and multiple measures of disease progression relative to the standard SIENA pipeline, while across both
datasets it markedly improves robustness under scan reversal. The fully integrated pipeline achieves the strongest scan-order consistency, reducing the error by up to 99.1%. In addition, GPU-enabled variants reduce execution time by up to 46% while maintaining CPU runtimes comparable to standard SIENA. Overall, these findings show that deep learning can meaningfully strengthen established longitudinal atrophy pipelines when used to reinforce their weakest image processing steps. More broadly, this study highlights the value of modularly modernizing clinically trusted neuroimaging tools without sacrificing their interpretability. Code is publicly available at https://github.com/Raciti/Enhanced-SIENA.git.
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All authors Riccardo Raciti, Lemuel Puglisi, Francesco Guarnera, Daniele Ravì, Sebastiano Battiato
Image and Video Processing
Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarking
Qian Qi, Jiangyun Tang, Jim Lee et al. (+2 more)
Robust invisible watermarks are widely used to support copyright protection, content provenance, and accountability by
embedding hidden signals designed to survive common post-processing operations. However,
diffusion-based image editing introduces a fundamentally different class of transformations: it injects noise and reconstructs images through a powerful generative prior, often altering semantic content while preserving photorealism. In this paper, we provide a unified theoretical and empirical analysis showing that non-
adversarial diffusion editing can unintentionally degrade or remove robust watermarks. We model
diffusion editing as a
stochastic transformation that progressively contracts off-
manifold perturbations, causing the low-amplitude signals used by many watermarking schemes to decay. Our analysis derives bounds on watermark signal-to-noise ratio and mutual information along
diffusion trajectories, yielding conditions under which reliable recovery becomes information-theoretically impossible. We further evaluate representative watermarking systems under a range of
diffusion-based editing scenarios and strengths. The results indicate that even routine semantic edits can significantly reduce watermark recoverability. Finally, we discuss the implications for content provenance and outline principles for designing watermarking approaches that remain robust under generative image editing.
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All authors Qian Qi, Jiangyun Tang, Jim Lee, Emily Davis, Finn Carter
Signal Processing
Identification and Visualization of Correlation Structures in Large-Scale Power Quality Data
Max Domagk, Jan Meyer and Marco Lindner
Large-scale power quality (PQ) measurement campaigns generate vast amounts of multivariate data, in which systematic dependencies are difficult to identify using conventional analysis techniques. This paper presents a methodology for the automated analysis and visualization of correlation structures in large PQ
datasets. Building on an existing framework, the approach is adapted for shorter observation periods and enhanced with aggregation and distance-based visualization techniques. Daily Spearman correlation coefficients are averaged via Fishers z-transformation and aggregated across phases, parameters, and sites. The resulting correlation structures are visualized using hierarchical clustering and multidimensional scaling to reveal consistent and recurring relationships. The methodology is demonstrated using data from 85 measurement sites within the German transmission system.
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Signal Processing
Joint and Streamwise Distributed MIMO Satellite Communications with Multi-Antenna Ground Users
Parisa Ramezani and Emil Björnson
We consider a low Earth orbit downlink communication, where multiple satellites jointly serve multi-
antenna ground users, transmitting multiple spatial streams per user. Using a line-of-sight-dominant satellite channel model with statistical channel state information, including angular information and large-scale fading, we study two distributed transmission modes with different fronthaul requirements. First, for joint transmission, where all satellites transmit all user streams, we formulate a sum spectral efficiency (SE) maximization problem under general convex power constraints and address the intractability of the exact ergodic SE expression by adopting a tractable approximation. Exploiting the equivalence between sum SE maximization and weighted sum mean square error minimization, we derive a novel iterative transceiver design. Second, to reduce fronthaul load, we propose streamwise transmission, where each stream is sent by a single satellite, and develop an eigenmode-based stream-satellite association using participation factors and a maximum-weight bipartite matching problem solved by the Hungarian
algorithm. Numerical simulations evaluate the validity of the SE approximation, demonstrate conditions under which streamwise transmission performs nearly optimally or trades SE for lower overhead, highlight the impact of stream/user loading, and show substantial performance gains over conventional
benchmarks.
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Systems and Control · Cross-listed from cs.ET
A Physics-Based Digital Human Twin for Galvanic-Coupling Wearable Communication Links
Silvia Mura, Chiara Cavigliano, Anna Marcucci et al. (+3 more)
This paper presents a systematic characterization of wearable galvanic coupling (GC) channels under narrowband and wideband operation. A physics-consistent digital human twin maps anatomical properties, propagation geometry, and electrode-skin interfaces into complex transfer functions directly usable for communication analysis. Attenuation, phase delay, and group delay are evaluated for longitudinal and radial configurations, and dispersion-induced variability is quantified through attenuation ripple and delay standard deviation metrics versus bandwidth. Results confirm electro-quasistatic, weakly dispersive behavior over 10 kHz-1 MHz. Attenuation is primarily geometry-driven, whereas amplitude ripple and delay variability increase with bandwidth, tightening equalization and synchronization constraints. Interface conditioning (gel and foam) significantly improves amplitude and phase stability, while propagation geometry governs link budget and baseline delay. Overall, the framework quantitatively links tissue electromagnetics to waveform distortion, enabling informed trade-offs among bandwidth, interface design, and transceiver complexity in wearable GC systems.
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All authors Silvia Mura, Chiara Cavigliano, Anna Marcucci, Pietro Savazzi, Anna Vizziello, Maurizio Magarini
Signal Processing
Environment-aware Near-field UE Tracking under Partial Blockage and Reflection
Hyunwoo Park, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu et al. (+1 more)
This paper proposes an environment-aware near-field (NF) user equipment (UE) tracking method for extremely large aperture arrays. By integrating known surface geometries and tracking the line-of-sight (LOS) and non-line-of-sight (NLOS) indicators per
antenna element, the method captures partial blockages and reflections specific to the NF spherical-wavefront regime, which are unavailable under the conventional far-field (FF) assumption. The UE positions are tracked by maximizing the cosine similarity between the predicted and received channels, enabling tracking even under complete LOS obstruction. Simulation results confirm that increasing environment-awareness improves accuracy, and that NF consistently outperforms FF baselines, achieving a $0.22\,\mathrm{m}$ root-mean-square error with full environment-awareness.
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All authors Hyunwoo Park, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Sunwoo Kim
Signal Processing
Exploiting Near-Field Dynamics with Movable Antennas to Enhance Discrete Transmissive RIS
Marjan Boloori, Chu Li and Aydin Sezgin
The design of low-complexity transceivers is crucial for the deployment of next-generation wireless systems. In this work, we combine two emerging concepts, movable
antennas (MA) and transmissive reconfigurable intelligent surfaces (TRIS), which have recently attracted significant
attention for enhancing wireless communication performance. In particular, we propose a compact base station (BS) architecture that integrates a single MA with a TRIS operating in their near-field region. We address the joint optimization of the MA location and the quantized TRIS phase configuration. Due to the non-convex coupling between spatial positioning and discrete phase constraints, an alternating optimization (AO) framework is developed, where the MA position is updated via gradient ascent (GA) and the TRIS phases are optimized through quantized phase
alignment. Simulation results demonstrate that the proposed architecture significantly outperforms conventional BS designs equipped with fixed fully-active
antenna arrays under the same channel model and transmit power constraint. Moreover, MA repositioning effectively mitigates the performance degradation caused by discrete TRIS phase quantization in near-field propagation environments. This reveals a favorable trade-off between hardware complexity and spatial signal processing, where the spatial adaptability of the MA can compensate for low-resolution TRIS phase control.
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Signal Processing
Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
Maurice Kuschel, Solveig Vieluf, Claus Reinsberger et al. (+2 more)
The use of wearables in medicine and wellness, enabled by AI-based models, offers tremendous potential for real-time monitoring and interpretable event detection. Explainable AI (XAI) is required to assess what models have learned and build trust in model outputs, for patients, healthcare professionals, model developers, and domain experts alike. Explaining AI decisions made on time-series data recorded by wearables is especially challenging due to the data's complex nature and temporal dependencies. Too often, explainability using interpretable features leads to performance loss. We propose a novel XAI method that combines explanation spaces and concept-based explanations to explain AI predictions on time-series data. By using Inherently Interpretable Components (IICs), which encapsulate domain-specific, interpretable concepts within a custom explanation space, we preserve the performance of models trained on time series while achieving the interpretability of concept-based explanations based on extracted features. Furthermore, we define a domain-specific set of IICs for wearable-based health monitoring and demonstrate their usability in real applications, including state assessment and epileptic seizure detection.
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All authors Maurice Kuschel, Solveig Vieluf, Claus Reinsberger, Tobias Loddenkemper, Tanuj Hasija
Signal Processing
AoI-FusionNet: Age-Aware Tightly Coupled Fusion of UWB-IMU under Sparse Ranging Conditions
Tehmina Bibi, Anselm Köhler, Jan-Thomas Fischer et al. (+1 more)
Accurate motion tracking of snow particles in avalanche events requires robust localization in global
navigation satellite system (GNSS)-denied outdoor environments. This paper introduces AoI-FusionNet, a tightly coupled deep learning-based fusion framework that directly combines raw ultra-wideband (UWB) time-of-flight (ToF) measurements with inertial measurement unit (IMU) data for 3D trajectory estimation. Unlike loose-coupled pipelines based on intermediate trilateration, the proposed approach operates directly on heterogeneous sensor inputs, enabling localization even under insufficient ranging availability. The framework integrates an Age-of-Information (AoI)-aware decay module to reduce the influence of stale UWB ranging measurements and a learned
attention gating mechanism that adaptively balances the contribution of UWB and IMU modalities based on measurement availability and temporal freshness. To evaluate robustness under limited data and measurement variability, we apply a
diffusion-based residual augmentation strategy during training, producing an augmented variant termed AoI-FusionNet-DGAN. We assess the performance of the proposed model using offline post-processing of real-world measurement data collected in an alpine environment and
benchmark it against UWB multilateration and loose-coupled fusion baselines. The results demonstrate that AoI-FusionNet substantially reduces mean and tail localization errors under intermittent and degraded sensing conditions.
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All authors Tehmina Bibi, Anselm Köhler, Jan-Thomas Fischer, Falko Dressler
Signal Processing
BER Analysis and Optimization of Pinching-Antenna-Based NOMA Communications
Mahmoud AlaaEldin, Amy S. Inwood, Xidong Mu et al. (+1 more)
This paper presents the first bit error rate (BER) analysis of a pinching-
antenna (PA)-based non-orthogonal multiple access (NOMA) communication system. The PA is assumed to be able to be placed anywhere along the waveguide and serves two NOMA user equipment (UEs) in both uplink (UL) and downlink (DL) scenarios. Exact closed-form expressions for the average BER of each user are derived under practical imperfect successive interference cancellation (SIC). These expressions are then used to optimize the PA location for minimizing the overall average BER of both UEs. In the UL case, the interference between the users' channels introduces phase-dependent fluctuations in the BER cost function, making it highly non-convex with many local extrema. To address this challenge, a smoothing technique is applied to extract the lower envelope of the BER function, effectively suppressing ripples and enabling a reliable identification of the global minimum. In the DL case, a joint optimization of the PA location and NOMA power allocation coefficients is proposed to minimize the average BER. Simulation results verify the accuracy of the analytical derivations and the effectiveness of the proposed optimization methods. Notably, the UL results demonstrate that an optimally positioned PA can create the required received power difference between two equally powered UEs for reliable power-domain NOMA decoding under imperfect SIC.
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All authors Mahmoud AlaaEldin, Amy S. Inwood, Xidong Mu, Michail Matthaiou
Systems and Control
From AI Weather Prediction to Infrastructure Resilience: A Real-Time Correction-Downscaling Framework for Tropical Cyclone Impact Forecasting
You Wu, Zhenguo Wang and Naiyu Wang
This paper addresses a missing capability in infrastructure resilience: turning fast, global AI weather forecasts into asset-scale, actionable risk intelligence. We introduce the AI-based Correction-Downscaling Framework (ACDF), which combines real-time bias correction, terrain-informed downscaling, and fragility-based power transmission system risk assessment for tropical cyclone impacts. ACDF separates storm-scale bias correction from terrain-aware refinement, mitigating error propagation while restoring the sub-kilometer wind variability that governs structural loading. Tested on 11 typhoons affecting Zhejiang, China under leave-one-storm-out evaluation, ACDF produces 500 m wind fields over a province-scale domain, reduces station-scale wind-speed MAE by 38.8% relative to Pangu-Weather, and runs in approximately 25 s per 12-h cycle on a single GPU. In the Typhoon Hagupit case, ACDF reproduced observed high-wind tails, identified a coastal high-risk corridor, and flagged the transmission line that subsequently failed, demonstrating actionable guidance at tower and line scales. ACDF provides an end-to-end pathway from global AI weather forecasts to operational, impact-based early warning for critical infrastructure.
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Signal Processing · Cross-listed from cs.IT
Rethinking Mutual Coupling in Movable Antenna MIMO Systems
Tianyi Liao, Wei Guo, Jun Qian et al. (+3 more)
Movable
antenna (MA) systems have emerged as a promising technology for future wireless communication systems. The movement of
antennas gives rise to mutual coupling (MC) effects, which have been previously ignored and can be exploited to enhance the capacity of multiple-input multiple-output (
MIMO) systems. To this end, we first model an MA-enabled point-to-point
MIMO communication system with MC effects using a
circuit-theoretic framework. The capacity maximization problem is then formulated as a non-concave optimization problem and solved via a block coordinate ascent (BCA)-based
algorithm. The subproblem of optimizing MA positions is challenging due to the presence of the analytically intractable MC
matrices. To overcome this difficulty, we develop a trust region method (TRM)-based
algorithm to optimize MA positions, wherein Sylvester equations are employed to compute the derivatives of the inverse square roots of the MC
matrices. Simulation results show significant capacity gains from leveraging MC effects, primarily due to customizable MC
matrices and superdirectivity.
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All authors Tianyi Liao, Wei Guo, Jun Qian, Shenghui Song, Jun Zhang, Khaled B. Letaief
Systems and Control · Cross-listed from cs.RO
Reinforcement Learning for Elliptical Cylinder Motion Control Tasks
Pawel Marczewski, Paulina Superczynska, Jakub Bernat et al. (+1 more)
The control of devices with limited input always bring
attention to solve by research due to its difficulty and non-trival solution. For instance, the inverted pendulum is benchmarking problem in control theory and machine learning. In this work, we are focused on the elliptical cylinder and its motion under limited torque. The inspiration of the problem is from untethered
magnetic devices, which due to distance have to operate with limited input torque. In this work, the main goal is to define the control problem of
elliptic cylinder with limited input torque and solve it by Reinforcement Learning. As a classical baseline, we evaluate a two-stage controller composed of an energy-shaping swing-up law and a local Linear Quadratic Regulator (LQR) stabilizer around the target
equilibrium. The swing-up controller increases the system's mechanical energy to drive the state toward a neighborhood of the desired
equilibrium, a linearization of the nonlinear model yields an LQR that regulates the angle and angular-rate states to the target orientation with bounded input. This swing-up + LQR policy is a strong, interpretable reference for underactuated system and serves a point of comparison to the learned policy under identical limits and parameters. The solution shows that the learning is possible however, the different cases like stabilization in upward position or rotating of half turn are very difficult for increasing mass or ellipses with a strongly unequal perimeter ratio.
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All authors Pawel Marczewski, Paulina Superczynska, Jakub Bernat, Szymon Szczesny
Image and Video Processing
GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification
Jiao Wang, Chi Liu, Yiying Zhang et al. (+8 more)
We propose glaucoma lesion evaluation and analysis with
multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma
dataset comprising scanning
laser ophthalmoscopy fundus images, circumpapillary OCT images, and visual field pattern deviation maps, annotated with four disease stages, enabling effective exploitation of
multimodal complementary information and facilitating accurate diagnosis and treatment across disease stages. To effectively integrate cross-modal information, we propose hierarchical attentive masked modeling (HAMM) for
multimodal glaucoma classification. Our framework employs hierarchical attentive encoders and light decoders to focus cross-modal representation learning on the encoder.
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All authors Jiao Wang, Chi Liu, Yiying Zhang, Hongchen Luo, Zhifen Guo, Ying Hu, Ke Xu, Jing Zhou, Hongyan Xu, Ruiting Zhou, Man Tang
Signal Processing
Unified framework for outage-constrained rate maximization in secure ISAC under various sensing metrics
Hancheng Zhu, Zongze Li and Yik-Chung Wu
Integrated sensing and communication (
ISAC) is poised to redefine the landscape of wireless networks by seamlessly combining data transmission and environmental sensing. However,
ISAC systems remain susceptible to eavesdropping, especially under uncertainty in eavesdroppers' channel state information, which can lead to secrecy outages. On the other hand, diverse and complex sensing performance requirements further complicate resource optimization, often requiring custom solutions for each scenario. To this end, this paper introduces a unified optimization framework that holistically addresses both the worst-case user secrecy rate and the sum secrecy rate across multiple users. Besides putting the two commonly used objectives into a single but flexible objective function, the framework accurately controls secrecy outage probabilities while accommodating a broad spectrum of sensing constraints. To solve such a general problem, we integrate the sensing requirements into the objective function through an auxiliary variable. This enables efficient alternating optimization and the proposed approach is theoretically guaranteed to converge to at least a stationary point of the original problem. Extensive simulation results show that the proposed framework consistently achieves higher optimized secrecy rates under various sensing constraints compared to existing methods. These results underscore the proposed unified framework's superiority and versatility in secure
ISAC systems.
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Systems and Control
On the strict-feedback form of hyperbolic distributed-parameter systems
Nicole Gehring
The paper is concerned with the strict-feedback form of
hyperbolic distributed-parameter systems. Such a system structure is well known to be the basis for the recursive backstepping control design for nonlinear ODEs and is also reflected in the Volterra integral transformation used in the backstepping-based stabilization of
parabolic PDEs. Although such integral transformations also proved very helpful in deriving state feedback controllers for
hyperbolic PDEs, they are not necessarily related to a strict-feedback form. Therefore, the paper looks at structural properties of
hyperbolic systems in the context of controllability. By combining and extending existing backstepping results, exactly controllable heterodirectional
hyperbolic PDEs as well as PDE-ODE systems are mapped into strict-feedback form. While stabilization is not the objective in this paper, the obtained system structure is the basis for a recursive backstepping design and provides new insights into coupling structures of distributed-parameter systems that allow for a simple control design. In that sense, the paper aims to take backstepping for PDEs back to its ODE origin.
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Image and Video Processing · Cross-listed from cs.CV
Empowering Semantic-Sensitive Underwater Image Enhancement with VLM
Guodong Fan, Shengning Zhou, Genji Yuan et al. (+3 more)
In recent years, learning-based
underwater image enhancement (UIE) techniques have rapidly evolved. However, distribution shifts between high-quality enhanced outputs and natural images can hinder semantic cue extraction for downstream
vision tasks, thereby limiting the adaptability of existing enhancement models. To address this challenge, this work proposes a new learning mechanism that leverages
Vision-
Language Models (VLMs) to empower UIE models with semantic-sensitive capabilities. To be concrete, our strategy first generates textual descriptions of key objects from a degraded image via VLMs. Subsequently, a text-image
alignment model remaps these relevant descriptions back onto the image to produce a spatial semantic guidance map. This map then steers the UIE network through a dual-guidance mechanism, which combines cross-
attention and an explicit
alignment loss. This forces the network to focus its restorative power on semantic-sensitive regions during image reconstruction, rather than pursuing a globally uniform improvement, thereby ensuring the faithful restoration of key object features. Experiments confirm that when our strategy is applied to different UIE baselines, significantly boosts their performance on perceptual quality metrics as well as enhances their performance on detection and
segmentation tasks, validating its effectiveness and adaptability.
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All authors Guodong Fan, Shengning Zhou, Genji Yuan, Huiyu Li, Jingchun Zhou, Jinjiang Li
Signal Processing
Dual-Chirp AFDM for Joint Delay-Doppler Estimation with Rydberg Atomic Quantum Receivers
Hanvit Kim, Hyeon Seok Rou, Kihong Min et al. (+2 more)
In this paper, we propose a joint delay-Doppler estimation framework for Rydberg atomic
quantum receivers (RAQRs) leveraging affine frequency division multiplexing (AFDM), as a future enabler of hyper integrated sensing and communication (
ISAC) in 6G and beyond. The proposed approach preserves the extreme sensitivity of RAQRs, while offering a pioneering solution to the joint estimation of delay-Doppler parameters of mobile targets, which has yet to be addressed in the literature due to the inherent coupling of time-frequency parameters in the optical readout of RAQRs to the best of our knowledge. To overcome this unavoidable ambiguity, we propose a dual-chirp AFDM framework where the utilization of distinct chirp parameters effectively converts the otherwise ambiguous estimation problem into a full-rank system, enabling unique delay-Doppler parameter extraction from RAQRs. Numerical simulations verify that the proposed dual-chirp AFDM shows superior delay-Doppler estimation performance compared to the classical single-chirp AFDM over RAQRs.
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All authors Hanvit Kim, Hyeon Seok Rou, Kihong Min, Giuseppe Thadeu Freitas de Abreu, Sunwoo Kim
Image and Video Processing · Cross-listed from cs.CV
UNIStainNet: Foundation-Model-Guided Virtual Staining of H&E to IHC
Jillur Rahman Saurav, Thuong Le Hoai Pham, Pritam Mukherjee et al. (+3 more)
Virtual immunohistochemistry (IHC) staining from hematoxylin and eosin (H&E) images can accelerate diagnostics by providing preliminary molecular insight directly from routine sections, reducing the need for repeat sectioning when tissue is limited. Existing methods improve realism through contrastive objectives, prototype matching, or domain
alignment, yet the generator itself receives no direct guidance from pathology foundation models. We present UNIStainNet, a SPADE-UNet conditioned on dense spatial
tokens from a frozen pathology foundation model (UNI), providing tissue-level semantic guidance for stain translation. A misalignment-aware loss suite preserves stain quantification accuracy, and learned stain
embeddings enable a single model to serve multiple IHC markers simultaneously. On MIST, UNIStainNet achieves state-of-the-art distributional metrics on all four stains (HER2, Ki67, ER, PR) from a single unified model, where prior methods typically train separate per-stain models. On BCI, it also achieves the best distributional metrics. A tissue-type stratified failure analysis reveals that remaining errors are systematic, concentrating in non-tumor tissue. Code is available at https://github.com/facevoid/UNIStainNet.
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All authors Jillur Rahman Saurav, Thuong Le Hoai Pham, Pritam Mukherjee, Paul Yi, Brent A. Orr, Jacob M. Luber
Image and Video Processing
Deep Learning Based Estimation of Blood Glucose Levels from Multidirectional Scleral Blood Vessel Imaging
Muhammad Ahmed Khan, Manqiang Peng, Ding Lin et al. (+1 more)
Regular monitoring of glycemic status is essential for diabetes management, yet conventional blood-based testing can be burdensome for frequent assessment. The sclera contains superficial microvasculature that may exhibit diabetes related alterations and is readily visible on the ocular surface. We propose ScleraGluNet, a multiview deep-learning framework for three-class metabolic status classification (normal, controlled diabetes, and high-glucose diabetes) and continuous fasting
plasma glucose (FPG) estimation from multidirectional scleral vessel images. The
dataset comprised 445 participants (150/140/155) and 2,225 anterior-segment images acquired from five gaze directions per participant. After vascular enhancement, features were extracted using parallel convolutional branches, refined with Manta Ray Foraging Optimization (MRFO), and fused via
transformer-based cross-view
attention. Performance was evaluated using subject-wise five-fold cross-validation, with all images from each participant assigned to the same fold. ScleraGluNet achieved 93.8% overall accuracy, with one-vs-rest AUCs of 0.971,0.956, and 0.982 for normal, controlled diabetes, and high-glucose diabetes, respectively. For FPG estimation, the model achieved MAE = 6.42 mg/dL and RMSE = 7.91 mg/dL, with strong correlation to laboratory measurements (r = 0.983; R2 = 0.966). Bland Altman analysis showed a mean bias of +1.45 mg/dL with 95% limits of agreement from -8.33 to +11.23$ mg/dL. These results support multidirectional scleral vessel imaging with multiview learning as a promising noninvasive approach for glycemic assessment, warranting multicenter validation before clinical deployment.
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All authors Muhammad Ahmed Khan, Manqiang Peng, Ding Lin, Saif Ur Rehman Khan
Image and Video Processing · Cross-listed from cs.CV
Marker-Based 3D Reconstruction of Aggregates with a Comparative Analysis of 2D and 3D Morphologies
Haohang Huang, Jiayi Luo, Issam Qamhia et al. (+3 more)
Aggregates, serving as the main skeleton in assemblies of construction materials, are important functional components in various building and transportation infrastructures. They can be used in unbound layer applications, e.g. pavement base and railroad ballast, bound applications of cement concrete and asphalt concrete, and as riprap and large-sized primary crushed rocks. Information on the size and shape or morphology of aggregates can greatly facilitate the Quality Assurance/Quality Control (QA/QC) process by providing insights of aggregate behavior during composition and packing. A full 3D characterization of aggregate particle morphology is difficult both during production in a quarry and at a construction site. Many aggregate imaging approaches have been developed to quantify the particle morphology by computer
vision, including 2D image-based approaches that analyze particle silhouettes and 3D scanning-based methods that require expensive devices such as 3D
laser scanners or X-Ray Computed
Tomography (CT) equipment. This paper presents a flexible and cost-effective photogrammetry-based approach for the 3D reconstruction of aggregate particles. The proposed approach follows a marker-based design that enables background suppression, point cloud stitching, and scale referencing to obtain high-quality aggregate models. The accuracy of the reconstruction results was validated against ground-truth for selected aggregate samples. Comparative analyses were conducted on 2D and 3D morphological properties of the selected samples. Significant differences were found between the 2D and 3D statistics. Based on the presented approach, 3D shape information of aggregates can be obtained easily and at a low cost, thus allowing convenient aggregate inspection, data collection, and 3D morphological analysis.
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All authors Haohang Huang, Jiayi Luo, Issam Qamhia, Erol Tutumluer, John M. Hart, Andrew J. Stolba
Systems and Control · Cross-listed from q-bio.NC
Minimal Set of Questions for Theories of Consciousness: Toward a Unified Explanatory Framework
Yoshiyuki Ohmura and Yasuo Kuniyoshi
A central challenge in consciousness research is the lack of agreement on what a theory of consciousness should explain, which makes it difficult to compare existing theories. We propose a framework for organizing explanatory targets of theories based on a minimal set of seven questions designed to be theoretically neutral, causally and functionally relevant, and applicable across different systems. We focus particularly on the role of causation based on the argument that causal relations cannot be fully specified within standard physical descriptions alone. Introducing an asymmetric causal structure allows internal mechanisms to be represented explicitly and helps distinguish between variable- and structure-level causation. As an example, we apply the proposed framework to analyzing the Dual-Laws Model. The aim of the framework is not to propose a definitive theory but to provide a common basis for analyzing and developing theories of consciousness.
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Audio and Speech Processing
Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces
Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho et al. (+2 more)
Transformer-based self-supervised
speech models (S3Ms) are often described as contextualized, yet what this entails remains unclear. Here, we focus on how a single frame-level S3M representation can encode phones and their surrounding context. Prior work has shown that S3Ms represent phones compositionally; for example, phonological vectors such as voicing, bilabiality, and nasality vectors are superposed in the S3M representation of [m]. We extend this view by proposing that phonological information from a sequence of neighboring phones is also compositionally encoded in a single frame, such that vectors corresponding to previous, current, and next phones are superposed within a single frame-level representation. We show that this structure has several properties, including orthogonality between relative positions, and emergence of implicit phonetic boundaries. Together, our findings advance our understanding of context-dependent S3M representations.
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All authors Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho, David R. Mortensen, David Harwath
Signal Processing · Cross-listed from q-bio.NC
Towards unified brain-to-text decoding across speech production and perception
Zhizhang Yuan, Yang Yang, Gaorui Zhang et al. (+7 more)
Speech production and perception are the main ways humans communicate daily. Prior
brain-to-text decoding studies have largely focused on a single modality and alphabetic
languages. Here, we present a unified
brain-to-sentence decoding framework for both
speech production and perception in Mandarin Chinese. The framework exhibits strong generalization ability, enabling sentence-level decoding when trained only on single-character data and supporting characters and syllables unseen during training. In addition, it allows direct and controlled comparison of neural dynamics across modalities. Mandarin
speech is decoded by first classifying syllable components in Hanyu Pinyin, namely initials and finals, from neural signals, followed by a post-trained large
language model (LLM) that maps sequences of toneless Pinyin syllables to Chinese sentences. To enhance LLM decoding, we designed a three-stage post-training and two-stage inference framework based on a 7-billion-parameter LLM, achieving overall performance that exceeds larger commercial LLMs with hundreds of billions of parameters or more. In addition, several characteristics were observed in Mandarin
speech production and perception:
speech production involved neural responses across broader cortical regions than auditory perception; channels responsive to both modalities exhibited similar activity patterns, with
speech perception showing a temporal delay relative to production; and decoding performance was broadly comparable across hemispheres. Our work not only establishes the feasibility of a unified decoding framework but also provides insights into the neural characteristics of Mandarin
speech production and perception. These advances contribute to
brain-to-text decoding in logosyllabic
languages and pave the way toward neural
language decoding systems supporting multiple modalities.
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All authors Zhizhang Yuan, Yang Yang, Gaorui Zhang, Baowen Cheng, Zehan Wu, Yuhao Xu, Xiaoying Liu, Liang Chen, Ying Mao, Meng Li
Image and Video Processing · Cross-listed from cs.CV
Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains
Guodong Sun, Qihang Liang, Xingyu Pan et al. (+2 more)
Accurate visual fault detection in freight trains remains a critical challenge for intelligent transportation system maintenance, due to complex operational environments, structurally repetitive components, and frequent occlusions or contaminations in safety-critical regions. Conventional instance
segmentation methods based on convolutional neural networks and
Transformers often suffer from poor generalization and limited boundary accuracy under such conditions. To address these challenges, we propose a lightweight self-prompted instance
segmentation framework tailored for freight train fault detection. Our method leverages the Segment Anything Model by introducing a self-
prompt generation module that automatically produces task-specific
prompts, enabling effective knowledge transfer from foundation models to domain-specific inspection tasks. In addition, we adopt a Tiny
Vision Transformer backbone to reduce computational cost, making the framework suitable for real-time deployment on edge devices in railway monitoring systems. We construct a domain-specific
dataset collected from real-world freight inspection stations and conduct extensive evaluations. Experimental results show that our method achieves 74.6 $AP^{\text{box}}$ and 74.2 $AP^{\text{mask}}$ on the
dataset, outperforming existing state-of-the-art methods in both accuracy and robustness while maintaining low computational overhead. This work offers a deployable and efficient
vision solution for automated freight train inspection, demonstrating the potential of foundation model adaptation in industrial-scale fault diagnosis scenarios. Project page: https://github.com/MVME-HBUT/SAM_FTI-FDet.git
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All authors Guodong Sun, Qihang Liang, Xingyu Pan, Moyun Liu, Yang Zhang
Signal Processing · Cross-listed from cs.IT
Boosting Spectral Efficiency via Spatial Path Index Modulation in RIS-Aided mMIMO
Ahmet M. Elbir, Abdulkadir Celik, Asmaa Abdallah et al. (+1 more)
Next generation wireless networks focus on improving spectral efficiency (SE) while reducing power consumption and hardware cost. Reconfigurable intelligent surfaces (RISs) offer a viable solution to meet these requirements. In order to enhance the SE, index modulation (IM) has been regarded as one of the enabling technologies via the transmission of additional information bits over the transmission media such as subcarriers,
antennas and spatial paths. In this work, we explore the usage of spatial paths and introduce spatial path IM (SPIM) for RIS-aided massive multiple-input multiple-output (mMIMO) systems. Thus, the proposed framework improves the network efficiency and the coverage with the use of RIS while SPIM provides SE improvement. In order to perform SPIM, we exploit the spatial diversity of the millimeter wave channel and assign the index bits to the spatial patterns of the channel between the base station and the users through RIS. We introduce a low complexity approach for the design of hybrid beamformers, which are constructed by the steering vectors corresponding to the selected spatial path indices for SPIM-mMIMO. Furthermore, we conduct a theoretical analysis on the SE of the proposed SPIM approach, and derive the SE relationship between the SPIM-based hybrid beamforming and fully digital (FD) beamforming. Via numerical simulations, we validate our theoretical results and show that the proposed SPIM approach presents an improved SE performance, even higher than that of the use of FD beamformers while using a few RF chains.
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All authors Ahmet M. Elbir, Abdulkadir Celik, Asmaa Abdallah, Ahmed M. Eltawil
Systems and Control · Cross-listed from cs.RO
Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning
Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan et al. (+1 more)
In this work, we propose a data-driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the challenge of learning a novel motor task in a high-dimensional, redundant motor space. A nudge is a series of vibrotactile feedback delivered to the learner to encourage motor movements that aid in task completion. We first model the
stochastic dynamics of human motor learning under haptic nudges using an Input-Output Hidden Markov Model (IOHMM), which explicitly decouples latent skill
evolution from observable performance measures. Leveraging this predictive model, we formulate the haptic nudge feedback design problem as a Partially Observable Markov Decision Process (POMDP). This allows us to derive an optimal nudging policy that minimizes long-term performance cost and implicitly guides the learner toward superior skill states. We validate our approach through a human participant study (N=30) involving a high-dimensional motor task rendered through a hand exoskeleton. Results demonstrate that participants trained with the POMDP-derived policy exhibit significantly accelerated movement efficiency and endpoint accuracy compared to groups receiving heuristic-based feedback or no feedback. Furthermore, synergy analysis reveals that the POMDP group discovers efficient low-dimensional motor representations more rapidly.
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All authors Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava