Systems and Control · Cross-listed from cs.HC
Strategic Shaping of Human Prosociality: A Latent-State POMDP Framework
Zahra Zahedi, Xinyue Hu, Shashank Mehrotra et al. (+2 more)
We propose a decision-theoretic framework in which a
robot strategically can shape inferred human's prosocial state during repeated interactions. Modeling the human's prosociality as a latent state that evolves over time, the
robot learns to infer and influence this state through its own actions, including helping and signaling. We formalize this as a latent-state POMDP with limited observations and learn the transition and observation dynamics using expectation maximization. The resulting belief-based policy balances task and social objectives, selecting actions that maximize long-term cooperative outcomes. We evaluate the model using data from user studies and show that the learned policy outperforms baseline strategies in both team performance and increasing observed human cooperative behavior.
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All authors Zahra Zahedi, Xinyue Hu, Shashank Mehrotra, Mark Steyvers, Kumar Akash
Image and Video Processing · Cross-listed from cs.CR
Authenticated Contradictions from Desynchronized Provenance and Watermarking
Alexander Nemecek, Hengzhi He, Guang Cheng et al. (+1 more)
Cryptographic provenance standards such as C2PA and invisible watermarking are positioned as complementary defenses for content authentication, yet the two verification layers are technically independent: neither conditions on the output of the other. This work formalizes and empirically demonstrates the $\textit{Integrity Clash}$, a condition in which a digital asset carries a cryptographically valid C2PA manifest asserting human authorship while its pixels simultaneously carry a watermark identifying it as AI-generated, with both signals passing their respective verification checks in isolation. We construct metadata washing workflows that produce these authenticated fakes through standard editing pipelines, requiring no cryptographic compromise, only the semantic omission of a single assertion field permitted by the current C2PA specification. To close this gap, we propose a cross-layer audit protocol that jointly evaluates provenance metadata and watermark detection status, achieving 100% classification accuracy across 3,500 test images spanning four conflict-
matrix states and three realistic perturbation conditions. Our results demonstrate that the gap between these verification layers is unnecessary and technically straightforward to close.
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All authors Alexander Nemecek, Hengzhi He, Guang Cheng, Erman Ayday
Audio and Speech Processing · Cross-listed from cs.SD
When Spoof Detectors Travel: Evaluation Across 66 Languages in the Low-Resource Language Spoofing Corpus
Kirill Borodin, Vasiliy Kudryavtsev, Maxim Maslov et al. (+2 more)
We introduce LRLspoof, a large-scale
multilingual synthetic-
speech corpus for cross-lingual spoof detection, comprising 2,732 hours of audio generated with 24 open-source TTS systems across 66
languages, including 45 low-resource
languages under our operational definition. To evaluate robustness without requiring target-domain bonafide
speech, we
benchmark 11 publicly available countermeasures using threshold transfer: for each model we calibrate an EER operating point on pooled external
benchmarks and apply the resulting threshold, reporting spoof rejection rate (SRR). Results show model-dependent cross-lingual disparity, with spoof rejection varying markedly across
languages even under controlled conditions, highlighting
language as an independent source of domain shift in spoof detection. The
dataset is publicly available at \href{https://huggingface.co/
datasets/lab260/LRLspoof}{\textbf{\underline{HuggingFace}}} and \href{https://modelscope.cn/
datasets/lab260/LRLspoof}{\textbf{\underline{ModelScope}}}
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All authors Kirill Borodin, Vasiliy Kudryavtsev, Maxim Maslov, Mikhail Gorodnichev, Grach Mkrtchian
Systems and Control
A Passivity-Agnostic Framework for Distributed Adaptive Synchronization under Unknown Leader Dynamics
Moh Kamalul Wafi and Milad Siami
We present a passivity-agnostic framework for distributed adaptive synchronization under position-only communication, bounded disturbances, and unknown leader dynamics. By passivity-agnostic we mean the design does not require the closed loop system to be strictly positive real (SPR) a priori: it certifies SPR when present and recovers it by frequency shaping when absent. Followers are heterogeneous second-order systems with unknown (possibly unstable) dynamics. In the SPR regime, a structured reparameterization yields gradient-based adaptive error dynamics; Lyapunov analysis guarantees global asymptotic synchronization in the disturbance-free case, exact rejection of constant disturbances, and bounded responses to time-varying disturbances, with parameter convergence under persistent excitation. In the non-SPR regime, frequency shaping recovers effective passivity of the unshaped transfer function, enabling the same stability guarantees via standard passivity/Lyapunov arguments using Meyer-Kalman-Yakubovich (MKY) Lemma. Simulations across
star, cyclic, path, and arbitrary graphs demonstrate scalable synchronization, robust tracking, and parameter adaptation under multiple disturbance profiles, confirming that the frequency-shaped non-SPR designs match the performance of the SPR case.
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Image and Video Processing
Loss Design and Architecture Selection for Long-Tailed Multi-Label Chest X-Ray Classification
Nikhileswara Rao Sulake
Long-tailed class distributions pose a significant challenge for multi-label chest X-ray (CXR) classification, where rare but clinically important findings are severely underrepresented. In this work, we present a systematic empirical evaluation of loss functions, CNN backbone architectures and post-training strategies on the CXR-LT 2026
benchmark, comprising approximately 143K images with 30 disease labels from PadChest. Our experiments demonstrate that LDAM with deferred re-weighting (LDAM-DRW) consistently outperforms standard BCE and asymmetric losses for rare class recognition. Amongst the architectures evaluated, ConvNeXt-Large achieves the best single-model performance with 0.5220 mAP and 0.3765 F1 on our development set, whilst classifier re-training and test-time augmentation further improve ranking metrics. On the official test leaderboard, our submission achieved 0.3950 mAP, ranking 5th amongst all 68 participating teams with total of 1528 submissions. We provide a candid analysis of the development-to-test performance gap and discuss practical insights for handling class imbalance in clinical imaging settings. Code is available at https://github.com/Nikhil-Rao20/Long_Tail.
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Image and Video Processing · Cross-listed from cs.CV
AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning
Paul Friedrich, Florentin Bieder, Florian M. Thieringer et al. (+1 more)
Facial feminization surgery (FFS) is a key component of gender affirmation for transgender and gender diverse patients, aiming to reshape craniofacial structures toward a female morphology. Current
surgical planning procedures largely rely on subjective clinical assessment, lacking quantitative and reproducible anatomical guidance. We therefore propose AutoFFS, a novel data-driven framework that generates counterfactual skull morphologies through
adversarial free-form deformations. Our method performs a deformation-based targeted
adversarial attack on an ensemble of pre-trained binary sex classifiers that learned sexual dimorphism, effectively transforming individual skull shapes toward the target sex. The generated counterfactual skull morphologies provide a quantitative foundation for preoperative planning in FFS, driving advances in this largely overlooked patient group. We validate our approach through classifier-based evaluation, propose Morphological Fréchet Distance (MFD) and Morphological Kernel Distance (MKD) to evaluate distributional
alignment of generated and real populations, and perform a human perceptual study, confirming that the generated morphologies exhibit target sex characteristics.
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All authors Paul Friedrich, Florentin Bieder, Florian M. Thieringer, Philippe C. Cattin
Audio and Speech Processing · Cross-listed from cs.SD
Sequence-Level Unsupervised Training in Speech Recognition: A Theoretical Study
Zijian Yang, Jörg Barkoczi, Ralf Schlüter et al. (+1 more)
Unsupervised
speech recognition is a task of training a
speech recognition model with unpaired data. To determine when and how unsupervised
speech recognition can succeed, and how classification error relates to candidate training objectives, we develop a theoretical framework for unsupervised
speech recognition grounded in classification error bounds. We introduce two conditions under which unsupervised
speech recognition is possible. The necessity of these conditions are also discussed. Under these conditions, we derive a classification error bound for unsupervised
speech recognition and validate this bound in simulations. Motivated by this bound, we propose a single-stage sequence-level cross-
entropy loss for unsupervised
speech recognition.
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All authors Zijian Yang, Jörg Barkoczi, Ralf Schlüter, Hermann Ney
Systems and Control · Cross-listed from cs.IT
Characterizing Information Accuracy in Timeliness-Based Gossip Networks
Emirhan Tekez, Melih Bastopcu and Sinan Gezici
We investigate information accuracy in timeliness-based gossip networks where the source evolves according to a continuous-time Markov chain (CTMC) with $M$ states and disseminates status updates to a network of $n$ nodes. In addition to direct source updates, nodes exchange their locally stored packets via gossip and accept incoming packets solely based on whether the incoming packet is fresher than their local copy. As a result, a node can possess the freshest packet in the network while still not having the current source state. To quantify the amount of accurate information flowing in the network under such a gossiping scheme, we introduce two accuracy metrics, average accuracy, defined as the expected fraction of nodes carrying accurate information in any given subset, and freshness-based accuracy, defined as the accuracy of the freshest node in any given subset. Using a
stochastic hybrid systems (SHS) framework, we first derive steady-state balance equations and obtain
matrix-valued recursions that characterize these metrics in fully connected gossip networks under binary CTMCs. We then extend our analysis to the general multi-state information source using a joint CTMC approach. Finally, we quantify the fraction of nodes whose information is accurate due to direct source pushes versus gossip exchanges. We verify our findings with numerical analyses and provide asymptotic insights.
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Signal Processing
Improving the Estimation of Ship Length via ISAR
John R. Bennett
A method for estimating the aspect angle of ships at sea from an ISAR is developed. The ISAR AutoTrack (IAT)
algorithm uses the information from the adaptive motion compensation velocity to improve the tracker estimation of the ship aspect angle and thus to improve the estimation of ship length. The IAT is based on classical methods of autofocus for synthetic aperture radar. The average mocomp velocity yields the error in the in-range component of the ship velocity; the linear time trend of the velocity determines the cross-range component of the ship velocity. The IAT has two methods for implementing the
algorithm, the Search and Analytical methods. Both methods benefit from an intelligent smoothing process that removes system errors, random noise, and ocean waves. The goal of the IAT is to measure ship length to within 10 percent over all azimuth angles and ranges relative to the aircraft and for (unsigned) aspect angles from 5 to 85 degrees. Using the IAT allows a major reduction in the radar resources dedicated to tracking; and since the IAT creates its estimates during the ISAR time window it is unaffected by ship maneuvers. Recommendations for further development and testing of the IAT are presented.
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Signal Processing · Cross-listed from cs.CV
3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems
Narges Moeini, Namhoon Kim, Justin Romberg et al. (+1 more)
Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Inspired by the strong 2D image denoising properties of Field of Junctions (ICCV 2021), we propose a novel, fully volumetric 3D Field of Junctions (3D FoJ) representation that optimizes a junction of 3D wedges that best explain each 3D patch of a full volume, while encouraging consistency between overlapping patches. In addition to direct volume denoising, we leverage our 3D FoJ representation as a structural prior that: (i) requires no training data, and thus precludes the risk of
hallucination, (ii) preserves and enhances sharp edge and corner structures in 3D, even under low signal to noise ratio (SNR), and (iii) can be used as a drop-in denoising representation via projected or proximal gradient descent for any volumetric inverse problem with low SNR. We demonstrate successful volume reconstruction and denoising with 3D FoJ across three diverse 3D imaging tasks with low-SNR measurements: low-dose X-ray computed
tomography (CT), cryogenic electron
tomography (cryo-ET), and denoising point clouds such as those from
lidar in adverse weather. Across these challenging low-SNR volumetric imaging problems, 3D FoJ outperforms the evaluated classical denoisers, untrained neural denoisers, and denoisers trained only on noisy examples. Code is available at https://github.com/voilalab/3D-Field-of-Junctions.
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All authors Narges Moeini, Namhoon Kim, Justin Romberg, Sara Fridovich-Keil
Signal Processing
Orchestrating Multimodal DNN Workloads in Wireless Neural Processing
Sai Xu, Kai-Kit Wong, Yanan Du et al. (+1 more)
In edge inference, wireless resource allocation and accelerator-level deep neural network (DNN) scheduling have yet to be co-optimized in an end-to-end manner. The lack of coordination between wireless transmission and accelerator-level DNN execution prevents efficient overlap, leading to higher end-to-end inference latency. To address this issue, this paper investigates
multimodal DNN workload orchestration in wireless neural processing (WNP), a paradigm that integrates wireless transmission and multi-core accelerator execution into a unified end-to-end pipeline. First, we develop a unified communication-computation model for
multimodal DNN execution and formulate the corresponding optimization problem. Second, we propose O-WiN, a framework that orchestrates DNN workloads in WNP through two tightly coupled stages: simulation-based optimization and runtime execution. Third, we develop two
algorithms, RTFS and PACS. RTFS schedules communication and computation sequentially, whereas PACS interleaves them to enable pipeline parallelism by overlapping wireless data transfer with accelerator-level DNN execution. Simulation results demonstrate that PACS significantly outperforms RTFS under high modality heterogeneity by better masking wireless latency through communication-computation overlap, thereby highlighting the effectiveness of communication-computation pipelining in accelerating
multimodal DNN execution in WNP.
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All authors Sai Xu, Kai-Kit Wong, Yanan Du, Hyundong Shin
Systems and Control · Cross-listed from cs.NI
Resilient Chaotic Cross-Layer Routing for Smart Grid IoT Networks
Dhrumil Bhatt, Anakha Kurup and R.C. Mala
This paper presents the Distributed Adaptive Multi-
Radio Cross-Layer Routing (DAMCR) protocol, designed to enhance reliability, adaptability, and energy efficiency in smart grid and industrial Internet of Things (IoT) communication networks. DAMCR integrates Chaotic Frequency-Hopping Spread Spectrum (C-FHSS) to improve physical-layer security and jamming resilience with Link-Adaptive Quality Power Control (LAQPC) to dynamically regulate transmission power based on instantaneous link quality and residual node energy. To meet heterogeneous traffic requirements, the protocol incorporates priority-aware message classification that differentiates between periodic monitoring data and time-critical fault and protection messages. The proposed framework is implemented and evaluated in MATLAB using a heterogeneous network composed of LoRa, Wi-Fi, and dual-
radio nodes operating under AWGN, Rayleigh, and Rician fading environments. Extensive simulation results demonstrate that DAMCR consistently achieves a Packet Delivery Ratio (PDR) exceeding 95% across all evaluated scenarios, while maintaining end-to-end latency between 17 and 23 ms, even in the presence of controlled jamming attacks. These results confirm that the tight integration of chaos-based spectrum agility, cross-technology routing, and energy-aware cross-layer adaptation significantly improves communication reliability, latency stability, and resilience compared to conventional single-
radio and static-routing protocols.
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Audio and Speech Processing
TCG CREST System Description for the DISPLACE-M Challenge
Nikhil Raghav and Md Sahidullah
This report presents the TCG CREST system description for Track 1 (Speaker Diarization) of the DISPLACE-M challenge, focusing on naturalistic medical conversations in noisy rural-healthcare scenarios. Our study evaluates the impact of various voice activity detection (VAD) methods and advanced clustering
algorithms on overall speaker diarization (SD) performance. We compare and analyze two SD frameworks: a modular pipeline utilizing SpeechBrain with ECAPA-TDNN
embeddings, and a state-of-the-art (SOTA) hybrid end-to-end neural diarization system, Diarizen, built on top of a pre-trained WavLM. With these frameworks, we explore diverse clustering techniques, including agglomerative hierarchical clustering (AHC), and multiple novel variants of spectral clustering, such as SC-adapt, SC-PNA, and SC-MK. Experimental results demonstrate that the Diarizen system provides an approximate $39\%$ relative improvement in the diarization error rate (DER) on the post-evaluation analysis of Phase~I compared to the SpeechBrain baseline. Our best-performing submitted system employing the Diarizen baseline with AHC employing a median filtering with a larger context window of $29$ achieved a DER of 10.37% on the development and 9.21% on the evaluation sets, respectively. Our team ranked fifth out of the 11 participating teams after the Phase~I evaluation.
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Image and Video Processing · Cross-listed from cs.CV
Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering
Hari Prasanth S.M., Pejman Habibiroudkenar, Eerik Alamikkotervo et al. (+2 more)
Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone
forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED
dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s
forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory
forecasting without reliance on RGB imagery or training data.
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All authors Hari Prasanth S.M., Pejman Habibiroudkenar, Eerik Alamikkotervo, Dimitrios Bouzoulas, Risto Ojala
Systems and Control
A System-of-Systems Convergence Paradigm for Societal Challenges of the Anthropocene
Megan S. Harris, Mohammad Mahdi Naderi, Ehsanoddin Ghorbanichemazkati et al. (+11 more)
Modern societal challenges, such as
climate change, urbanization, and water resource management, demand integrated, multi-discipline, multi-problem approaches to frame and address their complexity. Unfortunately, current methodologies often operate within disciplinary silos, leading to fragmented insights and missed opportunities for convergence. A critical barrier to cross-disciplinary integration lies in the disparate ontologies that shape how different fields conceptualize and communicate knowledge. To address these limitations, this paper proposes a system-of-systems (SoS) convergence paradigm grounded in a meta-cognition map, a framework that integrates five complementary domains: real-world observations, systems thinking, visual modeling, mathematics, and computing. The paradigm is based on the Systems Modeling
Language (SysML), offering a standardized, domain-neutral approach for representing and analyzing complex systems. The proposed methodology is demonstrated through a case study of the Chesapeake Bay Watershed, a socio-environmental system requiring coordination across land use, hydrology, economic and policy domains. By modeling this system with SysML, the study illustrates practical strategies for navigating interdisciplinary challenges and highlights the potential of agile SoS modeling to support large-scale, multi-dimensional decision-making.
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All authors Megan S. Harris, Mohammad Mahdi Naderi, Ehsanoddin Ghorbanichemazkati, Sina Jangjoo, Emily Lapan, Seyed Amirreza Hosseini, Fabian Schipfer, Stephen Craig, Enayat Moallemi, Inas Khayal, Laura M. Arpan, Tian Tang, John C. Little, Amro M. Farid
Signal Processing
The Chebyshev Polynomial Series Frequency Modulation Model for Waveform Design and Analysis
Stephen P. Blackstock, Amaro Tuninetti, Dieter Vanderelst et al. (+2 more)
Polynomial phase signals (PPS) are a staple of waveform design and analysis in sonar, radar, and communications fields. They also find application in the modeling of bioacoustic emissions, especially those of echolocating animals such as bats and odontocetes. This work presents a novel PPS waveform formulation that exploits some special properties of Chebyshev
polynomials, such as orthogonality, recurrence relations, and equivalence to trigonometric functions. The result is the Chebyshev
Polynomial Frequency Modulation (CPSFM) family of waveforms, which prove useful in the modeling of bioacoustic signals and the approximation of non-
polynomial-phase signals such as
hyperbolic chirps. We demonstrate that the CPSFM model admits compact analytic expressions for fundamental continuous-time signal processing functions such as the
Fourier transform, the convolution and correlation operations, and the ambiguity function. Derivations for these expressions using CPSFM are presented, along with their application to the analysis of biosonar emissions of Mexican free-tailed bats.
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All authors Stephen P. Blackstock, Amaro Tuninetti, Dieter Vanderelst, Laura N. Kloepper, Michael R. Haberman
Systems and Control
A Hetero-functional Graph State Estimator for Watershed Systems: Application to the Chesapeake Bay
Megan S. Harris, John C. Little and Amro M. Farid
Regional watersheds are complex systems of systems encompassing hydrology, land-use decision-making, estuarine ecological feedbacks, and overlapping governance jurisdictions. Their effective management underlies many modern societal challenges and therefore requires models that capture interdependencies between natural and institutional systems. Regional-specific models such as the Chesapeake Assessment Scenario Tool, used in this paper's case study, provide valuable nutrient estimates but rely on structurally opaque watershed routing that limits integration into broader systems-level analyses. This paper introduces a modeling framework for watershed systems. First, a region-independent reference architecture is developed. Second, the Weighted Least Squares Error Hetero-functional Graph State
Estimator, an extension of Hetero-functional Graph Theory (HFGT), is adapted to estimate nutrient flows from uncertain data. The framework is demonstrated through instantiation in the Chesapeake Bay Watershed. By establishing a shared ontology grounded in Systems Modeling
Language and HFGT, the approach enables integration of economic and governance systems to support sustainable watershed management.
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Systems and Control
PAC Finite-Time Safety Guarantees for Stochastic Systems with Unknown Disturbance Distributions
Taoran Wu, Dominik Wagner, C.-H. Luke Ong et al. (+1 more)
We investigate the problem of establishing finite-time probabilistic safety guarantees for discrete-time
stochastic dynamical systems subject to unknown disturbance distributions, using barrier certificate methods. Our approach develops a data-driven safety certification framework that relies only on a finite collection of independent and identically distributed (i.i.d.) disturbance samples. Within this framework, we propose a certification procedure such that, with confidence at least $1-\delta$ over the sampled disturbances, if the output of the certification procedure is accepted, the probability that the system remains within a prescribed safe set over a finite horizon is at least $1-\epsilon$. A key challenge lies in formally characterizing the probably approximately correct (PAC) generalization behavior induced by finite samples. To address this, we derive PAC generalization bounds using tools from VC dimension, scenario optimization, and Rademacher complexity. These results illuminate the fundamental trade-offs between sample size, model complexity, and safety tolerance, providing both theoretical insight and practical guidance for designing reliable, data-driven safety certificates in discrete-time
stochastic systems.
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All authors Taoran Wu, Dominik Wagner, C.-H. Luke Ong, Bai Xue
Signal Processing
ScreenAnt: Transparent On-Screen Antennas for 6G
Shun Zhuge and Qing Wang
6G will require on-device
antenna systems to operate at ultra-high frequency bands, achieve robust beamforming on the compact user devices, and be blockage-robust. Conventional edge-mounted
antennas on devices have limited apertures, suffer from the 'death grip' caused by user-induced blockage, and have poor scalability at mmWave and sub-THz bands. To address these issues, motivated by the rapid
evolution of transparent materials and
antennas, we propose ScreenAnt in this work–which integrates a transparent
antenna array onto the screens of future mobile devices. Specifically, we propose using a transparent on-screen uniform planar array and develop a framework to model its electromagnetic property, spatial configuration, and blockage robustness under realistic user-induced blockage. We also design a gradient-ascent-based
algorithm to efficiently optimize power and phase control of on-screen
antennas to maximize ScreenAnt's spectral efficiency. Our thorough simulations show that the proposed ScreenAnt can increase the uplink spectral efficiency by over 50% compared to edge-mounted
antennas at 28 GHz, and by more than 150% at 300 GHz. ScreenAnt also demonstrates strong robustness against user-induced blockage, paving the way for practical and high-capacity 6G user device designs.
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Systems and Control
Dynamic Connectivity and Local Frequency Strength under Stochastic Variations
Bruno Pinheiro and Daniel Dotta
This paper introduces a novel metric, termed the Generalized Fiedler Vector (GFV), to evaluate the dynamic connectivity in power systems. The proposed metric leverages the network connectivity, represented by the system
Laplacian matrix, together with the nodal inertia distribution, following a formulation previously developed by the first author. By capturing the interplay between system
topology and dynamic properties, the GFV provides valuable insights for the optimal siting of
stochastic generation to mitigate its impact on local and system-wide frequency variability. The effectiveness of the proposed approach is demonstrated through Monte Carlo simulations performed on the IEEE 68-bus test system.
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Image and Video Processing
Guaranteed Image Classification via Goal-oriented Joint Semantic Source and Channel Coding
Wenchao Wu, Min Qiu, Yansha Deng et al. (+1 more)
To enable critical applications such as remote diagnostics, image classification must be guaranteed under bandwidth constraints and unreliable wireless channels through joint source and channel coding (JSCC) design. However, most existing JSCC methods focus on minimizing image distortion, implicitly assuming that all image regions contribute equally to classification performance, thereby overlooking their varying importance for the task. In this paper, we propose a goal-oriented joint semantic source and channel coding (G-JSSCC) framework that applies various levels of source coding compression and channel coding protection across image regions based on their semantic importance. Specifically, we design a semantic information extraction method that identifies and ranks various image regions based on their contributions to classification, where the contribution is measured by the shapely value from explainable artificial intelligence (AI). Based on that, we design a semantic source coding and a semantic channel coding method, which allocates higher-quality compression and stronger error protection to image regions of great semantic importance. In addition, we define a new metric, termed coding efficiency, to evaluate the effectiveness of the source and channel coding in the classification task. Simulations show that our proposed G-JSSCC framework improves classification probability by 2.70 times, reduces transmission cost by 38%, and enhances coding efficiency by 5.91 times, compared to the
benchmark scheme using uniform compression and an idealized channel code to uniformly protect the whole image.
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All authors Wenchao Wu, Min Qiu, Yansha Deng, Jinhong Yuan
Signal Processing
Plug-and-play forward backward algorithm to restore Landsat images: A preliminary step to uncover the history of surface waters
Pierre Audisio, Barbara Belletti and Nelly Pustelnik
The temporal and spatial analysis of river dynamics is a key factor for studying and understanding human impacts on floodplains. To assess the changes taking place, it is necessary to have high-resolution images with a large spatial coverage and a high temporal revisit frequency over the long term. Satellite imagery meets several of these criteria. For instance, Sentinel data provide high-resolution images but only after 2015. Therefore, to study water surface
evolution prior to this date, it is necessary to rely on other satellite images such as Landsat, which offers longer historical coverage, albeit with lower spatial resolution. In this study, we aim to increase the spatial resolution of Landsat data from 30 to 10 meters (resolution of Sentinel images). To achieve this goal, we develop an innovative single image super-resolution method based on a plug-and-play approach.
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Signal Processing
Multiresolution Adaptive Block-Coordinate Forward-Backward for Image Reconstruction
Edgar Desainte-Maréville, Marion Foare, Paulo Gonçalves et al. (+2 more)
Classical first-order optimization methods for imaging inverse problems scale poorly with image resolution. Wavelet based multilevel strategies can accelerate convergence under strong blur, but their fixed coarse-to-fine schedules lose effectiveness in moderate-blur or noise-dominated regimes. In this work, we propose an adaptive multiresolution block coordinate Forward-Backward
algorithm for image restoration. Multiresolution block selection is driven by the local magnitude of the proximal update via a
stochastic non-smooth Gauss-Southwell rule applied to the wavelet decomposition of the image. This adaptive selection strategy dynamically balances updates across scales, emphasizing coarse or fine blocks according to the degradation regime. As a result, the proposed method automatically adapts to varying blur and noise levels without relying on a predefined hierarchical update scheme.
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All authors Edgar Desainte-Maréville, Marion Foare, Paulo Gonçalves, Nelly Pustelnik, Elisa Riccietti
Signal Processing
Quantum-PROBE: Rydberg Atomic Receiver-Based Multi-AoA Estimation with RF Lens
Hong-Bae Jeon, Kaibin Huang and Chan-Byoung Chae
This paper presents the
Quantum-Power pROfile Based Estimation (PROBE) framework, a Rydberg Atomic Receiver (RARE)-based multi-user angle-of-arrival (AoA) estimation approach equipped with a
radio-frequency (RF) lens front end. We establish a physics-consistent analytical model showing that magnitude-only RARE measurements, processed via the beam-propagation method (BPM) and snapshot-wise power accumulation, can be rigorously characterized as a nonnegative superposition of AoA-dependent, lens-induced spatial power profiles. This formulation reveals a structured and interpretable power-domain dictionary that enables multi-user AoA recovery without explicit phase reconstruction. Building on this foundation, we develop two complementary recovery strategies: (i) a principled non-negative least absolute shrinkage and selection operator (NN-LASSO)-based solver that estimates a sparse nonnegative angular representation via an accelerated proximal-gradient method followed by cluster-based AoA decoding, and (ii) a low-complexity successive interference cancellation (SIC)
algorithm that iteratively identifies and removes dominant power-profile components through cosine-similarity matching. Simulation results demonstrate that the proposed
Quantum-PROBE framework consistently outperforms representative RARE- and RF-based
benchmarks across diverse system configurations, while offering a clear accuracy-complexity tradeoff between the NN-LASSO and SIC variants for practical
quantum sensing deployments.
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Systems and Control · Cross-listed from cs.RO
Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-drones
Ilenia Carboni, Elia Cereda, Lorenzo Lamberti et al. (+3 more)
Sub-30g nano-sized aerial
robots can leverage their agility and form factor to autonomously explore cluttered and narrow environments, like in industrial inspection and search and rescue missions. However, the price for their tiny size is a strong limit in their resources, i.e., sub-100 mW microcontroller units (MCUs) delivering $\sim$100 GOps/s at best, and
memory budgets well below 100 MB. Despite these strict constraints, we aim to enable complex
vision-based tasks aboard nano-drones, such as dense 3D scene reconstruction: a key robotic task underlying fundamental capabilities like spatial awareness and motion planning. Top-performing 3D reconstruction methods leverage neural radiance fields (NeRF) models, which require GBs of
memory and massive computation, usually delivered by high-end GPUs consuming 100s of Watts. Our work introduces Tiny-DroNeRF, a lightweight NeRF model, based on Instant-NGP, and optimized for running on a GAP9 ultra-low-power (ULP) MCU aboard our nano-drones. Then, we further empower our Tiny-DroNeRF by leveraging a collaborative
federated learning scheme, which distributes the model training among multiple nano-drones. Our experimental results show a 96% reduction in Tiny-DroNeRF's
memory footprint compared to Instant-NGP, with only a 5.7 dB drop in reconstruction accuracy. Finally, our
federated learning scheme allows Tiny-DroNeRF to train with an amount of data otherwise impossible to keep in a single drone's
memory, increasing the overall reconstruction accuracy. Ultimately, our work combines, for the first time, NeRF training on an ULP MCU with
federated learning on nano-drones.
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All authors Ilenia Carboni, Elia Cereda, Lorenzo Lamberti, Daniele Malpetti, Francesco Conti, Daniele Palossi
Image and Video Processing · Cross-listed from cs.CV
FireRed-OCR Technical Report
Hao Wu, Haoran Lou, Xinyue Li et al. (+19 more)
We present FireRed-OCR, a systematic framework to specialize general VLMs into high-performance OCR models. Large
Vision-
Language Models (VLMs) have demonstrated impressive general capabilities but frequently suffer from “structural
hallucination” when processing complex documents, limiting their utility in industrial OCR applications. In this paper, we introduce FireRed-OCR, a novel framework designed to transform general-purpose VLMs (based on Qwen3-VL) into pixel-precise structural document parsing experts. To address the scarcity of high-quality structured data, we construct a “Geometry + Semantics” Data Factory. Unlike traditional random sampling, our pipeline leverages
geometric feature clustering and multi-dimensional tagging to synthesize and curate a highly balanced
dataset, effectively handling long-tail layouts and rare document types. Furthermore, we propose a Three-Stage Progressive Training strategy that guides the model from pixel-level perception to logical structure generation. This curriculum includes: (1) Multi-task Pre-
alignment to ground the model's understanding of document structure; (2) Specialized SFT for standardizing full-image Markdown output; and (3) Format-Constrained Group Relative Policy Optimization (GRPO), which utilizes reinforcement learning to enforce strict syntactic validity and structural integrity (e.g., table closure, formula syntax). Extensive evaluations on OmniDocBench v1.5 demonstrate that FireRed-OCR achieves state-of-the-art performance with an overall score of 92.94%, significantly outperforming strong baselines such as DeepSeek-OCR 2 and OCRVerse across text, formula, table, and reading order metrics. We open-source our code and model weights to facilitate the “General VLM to Specialized Structural Expert” paradigm.
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All authors Hao Wu, Haoran Lou, Xinyue Li, Zuodong Zhong, Zhaojun Sun, Phellon Chen, Xuanhe Zhou, Kai Zuo, Yibo Chen, Xu Tang, Yao Hu, Boxiang Zhou, Jian Wu, Yongji Wu, Wenxin Yu, Yingmiao Liu, Yuhao Huang, Manjie Xu, Gang Liu, Yidong Ma, Zhichao Sun, Changhao Qiao
Systems and Control
Critical Clearing Time Enhancement of Droop-Controlled Grid-Forming Inverters with Adaptive Function-Based Parameters
Dewan Mahnaaz Mahmud, Vinu Thomas, Bogdan Marinescu et al. (+1 more)
With the increasing penetration of renewable energy sources, grid-forming (GFM) inverters are becoming essential for voltage and frequency regulation. However, the transient stability of GFM inverter is critically affected by the current limiters that are embedded with the standard control schemes. This paper proposes a novel adaptive function to enhance the transient stability of droop-controlled GFM inverters. The proposed method autonomously adjusts the active power reference and the droop gain based on the terminal voltage of the inverter. Also, the acceleration of the phase angle is prevented, leading to the maximization of critical clearing time (CCT). The proposed method is benchmarked against two state-of-the-art GFM inverter CCT enhancement methods. Effectiveness of the proposed method is validated through electromagnetic transient (EMT) simulations in MATLAB/Simulink\textsuperscript{\textregistered}.
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All authors Dewan Mahnaaz Mahmud, Vinu Thomas, Bogdan Marinescu, Mickaël Hilairet
Image and Video Processing
Near-Field Focusing Operators for Planar Multi-Static Microwave Imaging Using Back-Projection in the Spatial Domain
Matthias M. Saurer, Marius Brinkmann, Han Na et al. (+2 more)
Based on a plane-wave expansion of the observation data in quasi-planar multi-static scattering scenarios, an improved formalism for image creation utilizing back-projection in the spatial domain is derived. The underlying integral expressions for different focusing operators are derived analytically leading to magnitude correction factors, which are mostly relevant for reconstructing microwave images when the distance from the scattering object to the aperture plane is small. It is shown that the derived imaging procedure is superior to the traditional back-projection only compensating the phase delay of the measurement signals and validate our findings based on simulated as well as measured data. Since the derived focusing operators correspond to a low-pass filtering of the spatial images, the resulting modified multi-static back-projection
algorithms effectively suppress imaging artifacts as well.
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All authors Matthias M. Saurer, Marius Brinkmann, Han Na, Quanfeng Wang, Thomas Eibert
Signal Processing
Control Plane for Reconfigurable Intelligent Surfaces
Fabio Saggese, Victor Croisfelt, Kyriakos Stylianopoulos et al. (+2 more)
Research on reconfigurable intelligent surfaces (RISs) has predominantly focused on purely physical (PHY)-layer aspects, particularly, on how signals are dynamically shaped by a controllable wireless propagation environment. However, integrating RISs as system-level network elements requires the development of an RIS-compatible control plane. In this article, we explore design options for such a control plane across two key dimensions: i) the allocation of spectral resources for the control plane (in- or out-of-band), and ii) the rate selection for the data plane (multiplexing or diversity). While our analysis is necessarily simplified, it reveals the fundamental trade-offs inherent in these design choices, which are crucial for integrating RIS technology into future networks.
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All authors Fabio Saggese, Victor Croisfelt, Kyriakos Stylianopoulos, George C. Alexandropoulos, Petar Popovski
Signal Processing
Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins
Fabio Saggese, Federico Chiariotti, Shashi Raj Pandey et al. (+3 more)
Digital twins (DTs) of physical systems enable real-time remote tracking, control, and learning, but require to be updated with environmental sensory data to maintain
alignment with their physical counterparts. In a network context, integrated sensing and communication (
ISAC) capabilities can expand the DT's environmental awareness by linking received updates to the location where wireless sensors acquired them. Integrating localization services, however, increases the complexity of the communication system, and can only be supported through smart access optimization. To tackle this problem, we design a two-step goal-oriented approach: firstly, sensors with a high Value of Information (VoI) inform the network of their resource demands through a push-based random access; then, pull-based scheduled transmissions of the actual sensory data are optimized to satisfy
ISAC performance constraints. This design allows to maximize the VoI of the information delivered to the DT while locating the transmitting nodes, significantly outperforming existing schemes.
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All authors Fabio Saggese, Federico Chiariotti, Shashi Raj Pandey, Henk Wymeersch, Luca Sanguinetti, Petar Popovski