Image and Video Processing
Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation
Domen Preložnik and Žiga Špiclin
Inter-scanner variability of
magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or constraints on the latent space appears to be contingent upon the level and/or depth of supervision during model training. In this paper, we therefore propose an unsupervised domain adaptation technique based on self-supervised multi-stage unlearning (SSMSU). Building upon the state-of-the-art
segmentation framework nnU-Net, we employ deep supervision at deep encoder stages using domain classifier unlearning, applied sequentially across the deep stages to suppress domain-related latent features. Following self-configurable approach of the nnU-Net, the auxiliary feedback loop implements a self-supervised backpropagation schedule for the unlearning process, since continuous unlearning was found to have a detrimental effect on the main
segmentation task. Experiments were carried out on four public
datasets for benchmarking white-matter lesion
segmentation methods. Five
benchmark models and/or strategies, covering passive to active unsupervised domain adaptation, were tested. In comparison, the SSMSU demonstrated the advantage of unlearning by enhancing lesion sensitivity and limiting false detections, which resulted in higher overall
segmentation quality in terms of
segmentation overlap and relative lesion volume error. The proposed model inputs only the FLAIR modality, which simplifies preprocessing pipelines, eliminates the need for inter-modality registration errors and harmonization, which can introduce variability. Source code is available on https://github.com/Pubec/nnunetv2-unlearning.
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Audio and Speech Processing
BrainWhisperer: Leveraging Large-Scale ASR Models for Neural Speech Decoding
Tommaso Boccato, Michal Olak and Matteo Ferrante
Decoding continuous
speech from intracortical recordings is a central challenge for
brain-computer interfaces (BCIs), with transformative potential for individuals with conditions that impair their ability to speak. While recent microelectrode array (MEA) decoders achieve impressive accuracy, their performance is fundamentally limited by the small size of existing
datasets, they remain brittle to session-to-session variability, and their ability to generalize across participants remains unexplored. We introduce BrainWhisperer, a neural
speech decoder that integrates high-resolution MEA recordings with a large pretrained automatic
speech recognition (ASR) model. Building on interpretability findings showing that Whisper's encoder learns phoneme-selective representations with localized
attention, we train a customized version of Whisper, modified to process neural features, using a hybrid objective that combines CTC loss on phonemes–predicted from the third encoder layer–and cross-
entropy loss on word
tokens. We introduce domain-informed modifications including windowed self-
attention to capture articulatory continuity, hierarchical month/day-specific low-rank projections to address non-stationarity, and subject-specific embedders enabling cross-subject training. Evaluated on a publicly available MEA
dataset (Card et al.), BrainWhisperer matches or outperforms prior state-of-the-art decoders. Critically, cross-
dataset training improves performance even on individual
datasets without fine-tuning, demonstrating unprecedented generalization. The model supports dual decoding paths: a high-accuracy phoneme-based path with external
language model rescoring, and a fast direct text generation path enabling sub-100ms inference with minimal hardware requirements.
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Image and Video Processing · Cross-listed from q-bio.TO
Artificial intelligence applications in Parkinson's disease via retinal imaging
Ali Jafarizadeh, Hamidreza Ashayeri, Hadi Vahedi et al. (+6 more)
Parkinson's disease (PD) is projected to increase substantially due to population aging, making early diagnosis increasingly important, as timely detection may delay progression and reduce long-term complications. Retinal microvasculature has emerged as a promising anatomical biomarker of neurodegeneration, and when combined with artificial intelligence AI, retinal imaging may provide an advanced, noninvasive, and cost-effective screening strategy for PD. This study evaluated the evidence from the past 35 years regarding the capability of AI to detect early PD-related changes in retinal vascular structure. Five electronic databases including PubMed, Web of Science, Scopus, ScienceDirect, and ProQuest were systematically searched from January 1990 to January 2025. In addition, Annals of Neurology and Frontiers in Neuroscience were hand-searched, and the reference lists of included studies were screened for additional eligible publications. Nineteen studies met the inclusion criteria. Three principal diagnostic AI tasks were identified, including disease classification, retinal vessel
segmentation, and PD risk stratification. The best-performing models were ShAMBi-LSTM on the Drishti
dataset with 97.2 percent accuracy, 99.5 percent precision, 96.9 percent sensitivity, and an F1 score of 0.981 for classification, nnU-Net with 99.7 percent accuracy, 98.7 percent precision, 98.9 percent sensitivity, 99.8 percent specificity, and a Dice score of 98.9 percent for
segmentation, and AlexNet for risk prediction with area under the curve values of 0.77, 0.68, and 0.73 across
datasets. Overall, application of AI
algorithms to retinal vasculature for detecting early signs of PD and predicting disease severity suggests that integration of AI with retinal biomarkers holds substantial potential for earlier and more accurate detection compared with traditional clinical evaluation alone.
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All authors Ali Jafarizadeh, Hamidreza Ashayeri, Hadi Vahedi, Parsa Khalafi, Mirsaeed Abdollahi, Navid Sobhi, Ru-San Tan, Roohallah Alizadehsani, U. Rajendra Acharya
Image and Video Processing · Cross-listed from cs.CV
Three-dimensional reconstruction and segmentation of an aggregate stockpile for size and shape analyses
Erol Tutumluer, Haohang Huang, Jiayi Luo et al. (+2 more)
Aggregate size and shape are key properties for determining quality of aggregate materials used in road construction and transportation geotechnics applications. The composition and packing, layer stiffness, and load response are all influenced by these morphological characteristics of aggregates. Many aggregate imaging systems developed to date only focus on analyses of individual or manually separated aggregate particles. There is a need to develop a convenient and affordable system for acquiring 3D aggregate information from stockpiles in the field. This paper presents an innovative 3D imaging approach for potential field evaluation of large-sized aggregates, whereby engineers can perform inspection by taking
videos/images with mobile devices such as smartphone cameras. The approach leverages Structure-from-Motion (SfM) techniques to reconstruct the stockpile surface as 3D spatial data, i.e. point cloud, and uses a 3D
segmentation algorithm to separate and extract individual aggregates from the reconstructed stockpile. The preliminary results presented in this paper demonstrate the future potential of using 3D aggregate size and shape information for onsite Quality Assurance/Quality Control (QA/QC) tasks.
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All authors Erol Tutumluer, Haohang Huang, Jiayi Luo, Issam Qamhia, John M. Hart
Image and Video Processing · Cross-listed from q-bio.QM
Privacy-Preserving Collaborative Medical Image Segmentation Using Latent Transform Networks
Saheed Ademola Bello, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim et al. (+1 more)
Collaborative training across multiple institutions is becoming essential for building reliable medical image
segmentation models. However,
privacy regulations, data silos, and uneven data availability prevent hospitals from sharing raw scans or annotations, limiting the ability to train generalizable models. Latent-space collaboration frameworks such as
privacy-
segmentation framework (SF) offer a promising alternative, but such methods still face challenges in
segmentation accuracy and vulnerability to latent inversion and membership-inference attacks. This work introduces a
privacy-preserving collaborative medical image
segmentation framework (PPCMI-SF) designed for heterogeneous medical
datasets. The approach combines skip-connected autoencoders for images and masks with a keyed latent transform that applies client-specific orthogonal mixing and permutation to protect latent features before they are shared. A unified mapping network on the server-side performs multi-scale latent-to-latent translation, enabling
segmentation inference without exposing raw data. Experiments on four
datasets: PSFH ultrasound, ultrasound nerve
segmentation, FUMPE CTA, and cardiac MRI show that the proposed PPCMI-SF consistently achieves high Dice scores and improved boundary accuracy, as reflected by lower 95th percentile Hausdorff distance (HD95) and average symmetric surface distance (ASD) compared to the current state-of-the-art and performs competitively with
privacy-agnostic baselines.
Privacy tests confirm strong resistance to inversion and membership attacks, and the overall system achieves real-time inference with low communication overhead. These results demonstrate that accurate and efficient medical image
segmentation can be achieved without compromising data
privacy in multi-institution settings.
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All authors Saheed Ademola Bello, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan
Image and Video Processing · Cross-listed from cs.CV
Sketch It Out: Exploring Label-Free Structural Cues for Multimodal Gait Recognition
Chao Zhang, Zhuang Zheng, Ruixin Li et al. (+1 more)
Gait recognition is a non-intrusive biometric technique for security applications, yet existing studies are dominated by silhouette- and parsing-based representations. Silhouettes are sparse and miss internal structural details, limiting discriminability. Parsing enriches silhouettes with part-level structures, but relies heavily on upstream human parsers (e.g., label granularity and boundary precision), leading to unstable performance across
datasets and sometimes even inferior results to silhouettes. We revisit gait representations from a structural perspective and describe a design space defined by edge density and supervision form: silhouettes use sparse boundary edges with weak single-label supervision, while parsing uses denser cues with strong semantic priors. In this space, we identify an underexplored paradigm: dense part-level structure without explicit semantic labels, and introduce SKETCH as a new visual modality for gait recognition. Sketch extracts high-frequency structural cues (e.g., limb articulations and self-occlusion contours) directly from RGB images via edge-based detectors in a label-free manner. We further show that label-guided parsing and label-free sketch are semantically decoupled and structurally complementary. Based on this, we propose SKETCHGAIT, a hierarchically disentangled multi-modal framework with two independent streams for modality-specific learning and a lightweight early-stage fusion branch to capture structural complementarity. Extensive experiments on SUSTech1K and CCPG validate the proposed modality and framework: SketchGait achieves 92.9% Rank-1 on SUSTech1K and 93.1% mean Rank-1 on CCPG.
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All authors Chao Zhang, Zhuang Zheng, Ruixin Li, Zhanyong Mei
Image and Video Processing
Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction
Yuewen Huang, Zhitao Ye, Guangnan Feng et al. (+3 more)
Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus that affects pediatric patients with significantly greater severity and worse renal outcomes compared to adults. Despite the urgent clinical need, predicting pediatric LN prognosis remains unexplored in computational pathology. Furthermore, the only existing histopathology-based approach for LN relies on multiple costly staining protocols and fails to integrate complementary clinical data. To address these gaps, we propose the first
multimodal computational pathology framework for three-class treatment response prediction (complete remission, partial response, and no response) in pediatric LN, utilizing only routine PAS-stained biopsies and structured clinical data. Our framework introduces two key methodological innovations. First, a Clinical-Injection
Transformer (CIT) embeds clinical features as condition
tokens into patch-level self-
attention, facilitating implicit and bidirectional cross-modal interactions within a unified
attention space. Second, we design a decoupled representation-knowledge adaptation strategy using a domain-adapted Masked Autoencoder (MAE). This strategy explicitly separates self-supervised morphological feature learning from pathological knowledge extraction. Additionally, we introduce a multi-granularity morphological type injection mechanism to bridge distilled classification knowledge with downstream prognostic predictions at both the instance and patient levels. Evaluated on a cohort of 71 pediatric LN patients with KDIGO-standardized labels, our method achieves a three-class accuracy of 90.1% and an AUC of 89.4%, demonstrating its potential as a highly accurate and cost-effective prognostic tool.
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All authors Yuewen Huang, Zhitao Ye, Guangnan Feng, Fudan Zheng, Xia Gao, Yutong Lu
Signal Processing
Exploiting Segmented Waveguide-Enabled Pinching-Antenna Systems (SWANs) for Uplink Tri-Hybrid Beamforming
Hao Jiang, Chongjun Ouyang, Zhaolin Wang et al. (+4 more)
A segmented waveguide-enabled pinching-
antenna system (SWAN)-based tri-hybrid beamforming architecture is proposed for uplink multi-user
MIMO communications, which jointly optimizes digital, analog, and pinching beamforming. Both fully-connected (FC) and partially-connected (PC) structures between RF chains and segment feed points are considered. For the FC architecture, tri-hybrid beamforming is optimized using the weighted minimum mean-square error (WMMSE) and zero-forcing (ZF) approaches. Specifically, the digital, analog, and pinching beamforming components are optimized via a closed-form solution, Riemannian
manifold optimization, and a Gauss-Seidel search, respectively. For the PC architecture, an interleaved
topology tailored to the SWAN receiver is proposed, in which segments assigned to each RF chain (sub-array) are interleaved with those from other sub-arrays. Based on this structure, a WMMSE-based tri-hybrid design is developed, in which the Riemannian-
manifold update used for the FC structure is replaced by element-wise phase calibration to exploit sparsity in analog beamforming. To gain insight into the performance of the proposed system, the rate-scaling laws with respect to the number of segments are derived for both the FC and PC structures. Our results demonstrate that: i)~SWAN with the proposed tri-hybrid beamforming consistently outperforms conventional hybrid beamforming and conventional pinching-
antenna systems with pinching beamforming for both the FC and PC structures; and ii)~the PC structure can strike a good balance between sum rate and energy consumption when the number of segments is large; and iii) the achievable rate does not necessarily increase with the number of segments.
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All authors Hao Jiang, Chongjun Ouyang, Zhaolin Wang, Yuanwei Liu, Arumugam Nallanathan, Zhiguo Ding, Robert Schober
Systems and Control
On boundedness of solutions of three-state Moore-Greitzer compressor model with nonlinear proportional-integral controller for the surge subsystem
Anton S. Shiriaev, Leonid B. Freidovich, Alexander I. Shepeljavyi et al. (+2 more)
The work focuses on Lagrange stability of the origin for the three-state Moore-Greitzer compressor model in closed loop with a nonlinear PI controller, tuned only to stabilize a lower-dimensional invariant surge-dynamics subsystem.The linearization of the system is not stabilizable but the static nonlinearity satisfies a sector condition, and together with a structural property of the stall-dynamics subsystem, this plays an essential role in the analysis. The main contribution provides explicit conditions on the controller parameters together with analytical arguments that guarantee boundedness of all solutions of the closed-loop system. The analysis employs a non-standard application of circle-criterion-based arguments. Together with the additional arguments developed in the work, this stability test also shows that the closed-loop system is robust to certain perturbations and model uncertainties.
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All authors Anton S. Shiriaev, Leonid B. Freidovich, Alexander I. Shepeljavyi, Anders Robertsson, Rolf Johansson
Systems and Control
Joint Visible Light and RF Backscatter Communications for Ambient IoT Network: Fundamentals, Applications, and Opportunities
Boxuan Xie, Yifan Zhang, Kalle Koskinen et al. (+6 more)
The rapid growth of the Internet of Things (IoT) devices in the sixth generation (6G) wireless networks raises significant sustainability and scalability challenges due to energy consumption, deployment complexity, and environmental impact. Ambient IoT (A-IoT), leveraging ambient energy harvesting (EH) for batteryless device operation, has emerged as a promising solution to address these challenges. Among various EH and communication techniques, visible light communication (VLC) integrated with ambient backscatter communication (AmBC) offers remarkable advantages, including energy neutrality, high reliability, and enhanced security. In this article, we propose a joint VLC-AmBC architecture, emphasizing fundamental concepts, system designs, and practical implementations. We explore potential applications in environmental monitoring, healthcare, smart logistics, and secure communications. We present proof-of-concept demonstrations for three distinct types of ambient backscatter devices (AmBDs): EH-Only, VLC-Relay, and VLC-Control. Experimental results demonstrate the feasibility of implementing joint VLC-AmBC systems, highlighting their practical viability across various deployment scenarios. Finally, we outline future research directions, including integrated sensing and communication, as well as optimized energy-efficient deployment. Open issues, such as large-scale deployment challenges, are also discussed, thereby providing a clear roadmap for future developments in joint VLC-AmBC-enabled A-IoT ecosystems.
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All authors Boxuan Xie, Yifan Zhang, Kalle Koskinen, Alexis A. Dowhuszko, Jiacheng Wang, Ruichen Zhang, Zehui Xiong, Zhu Han, Riku Jäntti
Audio and Speech Processing
Temporal Pooling Strategies for Training-Free Anomalous Sound Detection with Self-Supervised Audio Embeddings
Kevin Wilkinghoff, Sarthak Yadav and Zheng-Hua Tan
Training-free anomalous sound detection (ASD) based on pre-trained audio
embedding models has recently garnered significant
attention, as it enables the detection of anomalous sounds using only normal reference data without task-specific model training or fine-tuning. However, existing
embedding-based approaches almost exclusively rely on temporal mean pooling, leaving temporal pooling in training-free ASD largely unexplored. In this paper, we present the first systematic evaluation of temporal pooling strategies for training-free ASD with pre-trained audio
embeddings. We propose relative deviation pooling (RDP), an adaptive pooling method that assigns larger weights to
embeddings with stronger temporal deviations, investigate feature-wise non-linear aggregation using generalized mean (GeM) pooling, and examine a hybrid combination of both strategies. Experiments on five
benchmark datasets demonstrate that the proposed pooling strategies consistently outperform mean pooling and achieve state-of-the-art performance for training-free ASD, including results that surpass previously reported trained systems and ensembles on the DCASE2025 ASD
dataset.
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Systems and Control
Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
Tichakorn Wongpiromsarn
We present a risk-aware formalism for evaluating system trajectories in the presence of uncertain interactions between the system and its environment. The proposed formalism supports
reasoning under uncertainty and systematically handles complex relationships among requirements and objectives, including hierarchical priorities and non-comparability. Rather than treating the environment as exogenous noise, we explicitly model how each system trajectory influences the environment and evaluate trajectories under the resulting distribution of environment responses. We prove that the formalism induces a preorder on the set of system trajectories, ensuring consistency and preventing cyclic preferences. Finally, we illustrate the approach with an autonomous driving example that demonstrates how the formalism enhances explainability by clarifying the rationale behind trajectory selection.
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Systems and Control
bayesgrid: An Open-Source Python Tool for Generating Probabilistic Synthetic Transmission-Distribution Grids Using Bayesian Hierarchical Models
Henrique O. Caetano, Rahul K. Gupta and Carlos D. Maciel
In this work, we present bayesgrid, an open-source python toolbox for generating synthetic power transmission-distribution systems for any geographical location worldwide, using the publicly available data from OpenStreetMap (OSM). The toolbox is based on
Bayesian Hierarchical Models (BHM) which is trained on existing distribution network databases to develop a probabilistic model and can be applied to any geographical location worldwide, leveraging transfer learning. Thanks to the BHM, the tool is capable of generating multiple instances of the distribution system for a same region. The generated networks contain three-phase phase-consistent unbalanced networks, radial
topology and information on the nodal demand distributions. The generated network also contain the critical reliability indices, specifically the interruption duration and frequency of failure for individual grid components, allowing its application in reliability-related studies. The tool is demonstrated for different case studies generating synthetic network
datasets for different geographical regions around the world. The framework allows saving the generated networks into open-source platforms: PandaPower and OpenDSS. We also present an application for computation of probabilistic hosting capacity using the synthetic networks.
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Signal Processing
Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling
Li Zeng, Jingyang Zhu, Zixin Wang et al. (+2 more)
Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliver global-scale computing services. However, the intensive computation within SCPN incurs a significant “unseen cost”: the frequent charge-discharge cycles accelerate the physical degradation of satellites' life-limiting and high-cost batteries, thereby threatening the long-term operational viability of such a system. Existing approaches, often relying on indirect metrics like Depth of Discharge (DoD) and neglecting the complex, nonlinear degradation process of battery aging, fail to accurately quantify this cost. To address this, we introduce a high-fidelity, physics-driven model that quantitatively links computational workload parameters to the nonlinear battery degradation. Building on this model, we formulate a degradation-aware scheduling problem and analyze heuristic policies across different energy regimes. Simulations reveal that the optimal strategy should be adaptive: in
solar-rich conditions, a myopic policy maximizing instantaneous
solar utilization is superior, whereas under energy scarcity, a reactive policy leveraging real-time battery state significantly extends lifetime.
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All authors Li Zeng, Jingyang Zhu, Zixin Wang, Yuanming Shi, Khaled B. Letaief
Signal Processing · Cross-listed from cs.NI
Service Function Chain Routing in LEO Networks Using Shortest-Path Delay Statistical Stability
Li Zeng, Zixin Wang, Yuanming Shi et al. (+1 more)
Low Earth orbit (LEO) satellite constellations have become a critical enabler for global coverage, utilizing numerous satellites orbiting Earth at high speeds. By decomposing complex network services into lightweight service functions, network function virtualization (NFV) transforms global network services into diverse service function chains (SFCs), coordinated by resource-constrained LEOs. However, the dynamic
topology of satellite networks, marked by highly variable inter-satellite link delays, poses significant challenges for designing efficient routing strategies that ensure reliable and low-latency communication. Many existing routing methods suffer from poor scalability and degraded performance, limiting their practical implementation. To address these challenges, this paper proposes a novel SFC routing approach that leverages the statistical properties of network link states to mitigate instability caused by instantaneous modeling in dynamic satellite networks. Through comprehensive simulations on end-to-end shortest-path propagation delays in LEO networks, we identify and validate the statistical stability of multi-hop routes. Building on this insight, we introduce the Stability-Aware Multi-Stage Graph Routing (SA-MSGR)
algorithm, which incorporates pre-computed average delays into a multi-stage graph optimization framework. Extensive simulations demonstrate the superior performance of SA-MSGR, achieving significantly lower and more predictable end-to-end SFC delays compared to representative baseline strategies.
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All authors Li Zeng, Zixin Wang, Yuanming Shi, Khaled B. Letaief
Signal Processing · Cross-listed from cs.LG
Robust Unscented Kalman Filtering via Recurrent Meta-Adaptation of Sigma-Point Weights
Kenan Majewski, Michał Modzelewski, Marcin Żugaj et al. (+1 more)
The Unscented Kalman Filter (UKF) is a ubiquitous tool for nonlinear state estimation; however, its performance is limited by the static parameterization of the Unscented Transform (UT). Conventional weighting schemes, governed by fixed scaling parameters, assume implicit Gaussianity and fail to adapt to time-varying dynamics or heavy-tailed measurement noise. This work introduces the Meta-Adaptive UKF (MA-UKF), a framework that reformulates sigma-point weight synthesis as a hyperparameter optimization problem addressed via
memory-augmented meta-learning. Unlike standard adaptive filters that rely on instantaneous heuristic corrections, our approach employs a Recurrent Context Encoder to compress the history of measurement innovations into a compact latent
embedding. This
embedding informs a policy network that dynamically synthesizes the mean and covariance weights of the sigma points at each time step, effectively governing the filter's trust in the prediction versus the measurement. By optimizing the system end-to-end through the filter's recursive logic, the MA-UKF learns to maximize tracking accuracy while maintaining estimation consistency. Numerical
benchmarks on maneuvering targets demonstrate that the MA-UKF significantly outperforms standard baselines, exhibiting superior robustness to non-Gaussian glint noise and effective generalization to out-of-distribution (OOD) dynamic regimes unseen during training.
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All authors Kenan Majewski, Michał Modzelewski, Marcin Żugaj, Piotr Lichota
Systems and Control
On Theoretical Stability Proof and Stability Margin Analysis of Enhanced Droop-Free Control Schemes for Islanded Microgrids
Weipeng Liu, Upendra Prasad, Yutian Liu et al. (+3 more)
This paper studies enhanced droop-free control strategies with sparse neighboring communication for achieving effective active power sharing of distributed energy resources (DERs) while maintaining the frequency stability of islanded microgrids. The normalized active power consensus (NAPC) based droop-free control can share the load among controllable DERs in proportion to their available capacities. However, existing literature exclusively takes the asymptotic stability of the NAPC based droop-free control for granted, lacking a comprehensive theoretical proof that is critical for ensuring its effective design and practical implementation. This paper, for the first time, provides a thorough theoretical proof of the asymptotic stability of two NAPC-based droop-free control schemes: ordinary NAPC (ONAPC) and amplifier-equipped NAPC (A-NAPC), by testifying that all effective
eigenvalues have negative real parts. The effect of various system settings on the stability margins is further analyzed with respect to the average admittance of the electrical network, the sparseness of the communication network, and the average available capacity of controllable DERs. Based on the sensitivity of
eigenvalues with respect to perturbations, a vulnerability analysis is conducted to identify the weaknesses in the microgrids. Case studies demonstrate that the available capacity of controllable DERs has the most decisive influence on the stability margin of NAPC-based droop-free control, while O-NAPC/ANAPC control scheme is more suitable for microgrids with DERs of larger/ smaller available capacities.
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All authors Weipeng Liu, Upendra Prasad, Yutian Liu, Yong Dong, Haoran Zhao, Lei Wu
Audio and Speech Processing
FlowW2N: Whispered-to-Normal Speech Conversion via Flow-Matching
Fabian Ritter-Gutierrez, Md Asif Jalal, Pablo Peso Parada et al. (+5 more)
Whispered-to-normal (W2N)
speech conversion aims to reconstruct missing phonation from whispered input while preserving content and speaker identity. This task is challenging due to temporal misalignment between whisper and voiced recordings and lack of paired data. We propose FlowW2N, a conditional flow matching approach that trains exclusively on synthetic, time-aligned whisper-normal pairs and conditions on domain-invariant features. We exploit high-level ASR
embeddings that exhibits strong invariance between synthetic and real whispered
speech, enabling generalization to real whispers despite never observing it during training. We verify this invariance across ASR layers and propose a selection criterion optimizing content informativeness and cross-domain invariance. Our method achieves SOTA intelligibility on the CHAINS and wTIMIT
datasets, reducing Word Error Rate by 26-46% relative to prior work while using only 10 steps at inference and requiring no real paired data, validated by a subjective listening study and F0-contour analysis.
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All authors Fabian Ritter-Gutierrez, Md Asif Jalal, Pablo Peso Parada, Karthikeyan Saravanan, Yusun Shul, Minseung Kim, Gun-Woo Lee, Han-Gil Moon
Audio and Speech Processing · Cross-listed from cs.SD
ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis
Youngwon Choi, Jinwoo Oh, Hwayeon Kim et al. (+1 more)
We investigate the use of zero-shot text-to-
speech (ZS-TTS) as a data augmentation source for low-resource personalized
speech synthesis. While synthetic augmentation can provide linguistically rich and phonetically diverse
speech, naively mixing large amounts of synthetic
speech with limited real recordings often leads to speaker similarity degradation during fine-tuning. To address this issue, we propose ZeSTA, a simple domain-conditioned training framework that distinguishes real and synthetic
speech via a lightweight domain
embedding, combined with real-data oversampling to stabilize adaptation under extremely limited target data, without modifying the base architecture. Experiments on LibriTTS and an in-house
dataset with two ZS-TTS sources demonstrate that our approach improves speaker similarity over naive synthetic augmentation while preserving intelligibility and perceptual quality. Audio samples are available on our web page.
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All authors Youngwon Choi, Jinwoo Oh, Hwayeon Kim, Hyeonyu Kim
Systems and Control
Security-Constrained Substation Reconfiguration Considering Busbar and Coupler Contingencies
Ali Rajaei and Jochen L. Cremer
Substation reconfiguration via busbar splitting can mitigate transmission grid congestion and reduce operational costs. However, existing approaches neglect the security of substation
topology, particularly for substations without busbar splitting (i.e., closed couplers), which can lead to severe consequences. Additionally, the computational complexity of optimizing substation
topology remains a challenge. This paper introduces a MILP formulation for security-constrained substation reconfiguration (SC-SR), considering N-1 line, coupler and busbar contingencies to ensure secure substation
topology. To efficiently solve this problem, we propose a heuristic approach with multiple master problems (HMMP). A central master problem optimizes dispatch, while independent substation master problems determine individual substation
topologies in parallel. Linear AC power flow equations ensure PF accuracy, while feasibility and optimality sub-problems evaluate contingency cases. The proposed HMMP significantly reduces computational complexity and enables scalability to large-scale power systems. Case studies on the IEEE 14-bus, 118-bus, and PEGASE 1354-bus system show the effectiveness of the approach in mitigating the impact of coupler and busbar tripping, balancing system security and cost, and computational efficiency.
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Systems and Control · Cross-listed from math.OC
Lyapunov characterization of boundedness of reachability sets for infinite-dimensional systems
Patrick Bachmann and Andrii Mironchenko
We prove a converse Lyapunov
theorem for boundedness of reachability sets for a general class of control systems whose flow is Lipschitz continuous on compact intervals with respect to trajectory-dominated inputs. We show that this condition is satisfied by broad classes of semilinear
evolution equations. For ordinary differential equations, as a consequence of our results, we obtain a converse Lyapunov
theorem for forward completeness, without a priori restrictions on the magnitude of inputs. As an example, we rederive Wintner's classical result using our Lyapunov approach.
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Systems and Control
Enhancing Power Systems Transmission Adequacy via Optimal BESS Siting and Sizing using Benders Decomposition with Feasibility Cuts
Ginevra Larroux, Matthieu Jacobs, Keyu Jia et al. (+2 more)
This work presents a general framework for the operationally driven optimal siting and sizing of battery energy storage systems in power transmission networks, aimed at enhancing their resource adequacy. The approach considers multi-period planning horizons, enforces network constraints at high temporal resolution, and targets large-scale meshed systems. The resulting computationally complex mixed-integer non-linear programming problem is reformulated as a mixed-integer second-order cone programming problem and solved via Generalized Benders Decomposition, with feasibility cuts enabling congestion management and voltage regulation under binding network limits. A tailored heuristic recovers an alternating-current power-flow-feasible operating point from the relaxed solution. The proposed formulation is parallelizable, yielding excellent computational performance, while featuring rigorous guarantees of convergence.
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All authors Ginevra Larroux, Matthieu Jacobs, Keyu Jia, Fabrizio Sossan, Mario Paolone
Signal Processing
A Digital Twin of the FPGA Digital Signal Processing Chain for MKIDs Readout: Root-Cause Analysis and Mitigation of Spurs
Mounir Abdkrimi, Olivier Rossetto, Olivier Bourrion et al. (+2 more)
The KID_READOUT board, developed for the CONCERTO millimeter-wave astronomy instrument, implements FPGA-based digital frequency multiplexing to read out large arrays of Microwave Kinetic Inductance Detectors (MKIDs). The complexity of the implemented multirate DSP chain, which combines tones synthesis, interpolation, digital frequency translation, polyphase filter-bank (PFB) channelization, and digital down-conversion (DDC), makes analytical performance optimization difficult. To address this, we developed a cycle-and bitaccurate, Python-based digital twin of the FPGA readout firmware DSP chain. Using this model, we identified the origin of previously measured and unexplained spurs in the readout channels, tracing them to periodicity mismatches between the excitation and analysis paths and to insufficient suppression of negative-frequency components by the DDC filters. Based on these insights, we implemented a mitigation strategy that aligns the periodicities and improves the DDC filter characteristics, effectively eliminating the spurs with a minor increase in FPGA resource usage.
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All authors Mounir Abdkrimi, Olivier Rossetto, Olivier Bourrion, Christophe Vescovi, Christophe Hoarau
Signal Processing
Physics Informed Deep Unfolded Full Waveform Inversion for Edema Detection
Ruizhi Zhang, Yhonatan Kvich, Rui Guo et al. (+2 more)
Edema is a potential indicator of underlying pathological changes. However, its low-contrast signature is often masked in conventional B-mode imaging by strong scatterers, making reliable detection challenging. Ultrasound (US) provides a non-invasive, non-ionizing, and cost-efficient imaging option that is widely used. Conventional techniques, which rely on beamforming, often lack sufficient physical interpretability. Quantitative US (QUS) can estimate physical properties such as the speed of sound (SoS) and density by solving a physics-based inverse problem directly on the measured US wavefields, i.e., the raw per-element channel data (CD), to recover their spatial distribution. However, state-of-the-art physics-based inversion methods, including full waveform inversion (FWI) and model-based quantitative radar and US (MB-QRUS), are computationally intensive and susceptible to local minima, which constrains their clinical utility. We introduce deep unfolded FWI (DUFWI), a physics-faithful unfolded iterative inversion method that exhibits FWI-like refinement behavior while learning the update rule from data, requiring only a small number of iterations for real-time SoS reconstruction. Across both simulated
datasets and hardware measurements acquired with a Verasonics US system, the DUFWI significantly outperforms classical FWI and MB-QRUS in reconstruction quality while maintaining high computational efficiency. These results demonstrate real-time edema diagnosis in both simulation and hardware experiments, with phantom-based validation using cylindrical rods, supporting practical deployment under typical US imaging setting.
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All authors Ruizhi Zhang, Yhonatan Kvich, Rui Guo, Oded Cohen, Yonina C. Eldar
Signal Processing · Cross-listed from cs.LG
FedCova: Robust Federated Covariance Learning Against Noisy Labels
Xiangyu Zhong, Xiaojun Yuan and Ying-Jun Angela Zhang
Noisy labels in distributed
datasets induce severe local overfitting and consequently compromise the global model in
federated learning (FL). Most existing solutions rely on selecting clean devices or aligning with public clean
datasets, rather than endowing the model itself with robustness. In this paper, we propose FedCova, a dependency-free
federated covariance learning framework that eliminates such external reliances by enhancing the model's intrinsic robustness via a new perspective on feature covariances. Specifically, FedCova encodes data into a discriminative but resilient feature space to tolerate label noise. Built on mutual information maximization, we design a novel objective for
federated lossy feature encoding that relies solely on class feature covariances with an error tolerance term. Leveraging feature subspaces characterized by covariances, we construct a subspace-augmented
federated classifier. FedCova unifies three key processes through the covariance: (1) training the network for feature encoding, (2) constructing a classifier directly from the learned features, and (3) correcting noisy labels based on feature subspaces. We implement FedCova across both symmetric and asymmetric noisy settings under heterogeneous data distribution. Experimental results on CIFAR-10/100 and real-world noisy
dataset Clothing1M demonstrate the superior robustness of FedCova compared with the state-of-the-art methods.
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Signal Processing
Low-Altitude Agentic Networks for Optical Wireless Communication and Sensing: An Oceanic Scenario
Tianqi Mao, Jiayue Liu, Zeping Sui et al. (+4 more)
The cross-domain oceanic connectivity ranging from
underwater to the sky has become increasingly indispensable for a plethora of data-consuming maritime applications, such as maritime meteorological monitoring and offshore exploration. However, broadband implementations can be severely hindered by the isolation from terrestrial networks, limited satellite resources, and the fundamental inability of
radio waves to bridge the water-air interface at high rates. To this end, this paper introduces an optical network bridging
underwater, air and near space, which features a number of cooperative low-altitude platforms (LAPs), serving as compute-capable, sensing-aware, and mission-adaptive
agents. The network architecture consists of three scenario-specific segments, i.e., water-air direct link, low-altitude mesh network, and the near-space access network. With coordinate sensing and intelligent control, the system tightly couples beam tracking and resource optimization, enabling resilient networking under high mobility and harsh maritime dynamics. Furthermore, we review enabling technologies spanning from water-air channel modeling, adaptive beam
alignment under sea-surface perturbations, to swarm-intelligence networking for decentralized control, integrated pose-
topology planning, and optical Integrated sensing and communication (
ISAC) for near-space target detection and beam
alignment. Finally, open issues are also highlighted, constituting a clear roadmap toward scalable, secure, and ultra-broadband oceanic optical networks.
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All authors Tianqi Mao, Jiayue Liu, Zeping Sui, Leyu Cao, Xiao Liang, Dezhi Zheng, Zhaocheng Wang
Audio and Speech Processing · Cross-listed from cs.SD
Multi-Stage Music Source Restoration with BandSplit-RoFormer Separation and HiFi++ GAN
Tobias Morocutti, Emmanouil Karystinaios, Jonathan Greif et al. (+1 more)
Music Source Restoration (MSR) targets recovery of original, unprocessed instrument stems from fully mixed and mastered audio, where production effects and distribution artifacts violate common linear-mixture assumptions. This technical report presents the CP-JKU team's system for the MSR ICASSP Challenge 2025. Our approach decomposes MSR into separation and restoration. First, a single BandSplit-RoFormer separator predicts eight stems plus an auxiliary other stem, and is trained with a three-stage curriculum that progresses from 4-stem warm-start fine-tuning (with LoRA) to 8-stem extension via head expansion. Second, we apply a HiFi++ GAN waveform restorer trained as a generalist and then specialized into eight instrument-specific experts.
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All authors Tobias Morocutti, Emmanouil Karystinaios, Jonathan Greif, Gerhard Widmer
Signal Processing · Cross-listed from physics.app-ph
Dielectric Barrier Corona Discharge Anomaly by Ionic Wind under Unipolar Voltage Excitation
Gan Fu
An anomalous back discharge movement phenomenon is induced by a set of dielectric barrier corona discharges (DBCD) at unipolar half-sine voltage waveforms, where the back discharge has a time delay that relates to the applied voltage level. An ionic wind model is employed to analyze the physical behavior. Theoretical explanation and quantitative analysis are presented in this study based on abundant experimental results of 5 typical insulating materials and a FEP insulating cable. A numerical model is derived, which indicates that the back discharge can be activated under a relatively low potential voltage level in this study. The results highlight that the back discharge movement phenomenon behaves distinctly under half-sine voltage with negative polarity, yielding a significantly different partial discharge (PD) pattern with positive polarity. Besides, PD amplitude dependent on dielectric thickness is demonstrated by plotting in phase resolved partial discharge (PRPD) pattern. Furthermore, comparative experiments are conducted with respect to the variation of air gap length and dielectric geometry, manifesting different influences on PD amplitude.
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Signal Processing
MLOps-Assisted Anomalous Reflector Metasurfaces Design Based on Red Hat OpenShift AI
Wael Elshennawy
The integration of artificial intelligence as a design tool for metasurfaces, and the implementation of a deep-learning model pose a challenge in the development of an automated solution due to high resources requirements. The presented work introduces a network-layer solution to configure such environment for end user objectives, and for an underlying physical-layer technology. An architecture is developed to design an anomalous reflector by employing the Redhat Openshift AI (RHOAI) technology to support an automated machine learning operations (MLOps) framework in smart
radio environments. This entails the design of lossless impenetrable metasurfaces characterized by a scalar surface impedance for an optimal anomalous reflection, achieved by optimizing the number of the Floquet modes through the utilization of a local power conservation constraint qualified as a fitness function. The metasurfaces design process is implemented by using a conditional generative
adversarial network (cGAN). An extended cGAN with a surrogate model assists in a high-quality freeform metasurfaces design, where it introduces a swift simulation tool for the metasurfaces design process and analysis of the far-field model. The paper focuses on the challenges of building such a system, and potential abstraction layers. The training accuracy value of the proposed model demonstrates the feasibility and benefits of deploying in containerized environment of Red Hat Openshift in comparison with other deployments of ResNet-50 reported in literature.
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Systems and Control
Adaptive Modular Geometric Control of Robotic Manipulators
Mahdi Hejrati, Amir Hossein Barjini, Gokhan Alcan et al. (+1 more)
This paper develops an adaptive modular
geometric control framework for robotic manipulators with uncertain inertial parameters. The manipulator is decomposed into rigid-body and joint modules, where each rigid-body module is represented by an Euler-Poincaré-type spatial dynamics on the Lie
algebra se(3), and configuration errors are defined intrinsically through logarithmic maps on SE(3). The joint modules impose local screw constraints that relate adjacent body twists, accelerations, and transmitted wrenches, yielding a recursive propagation structure for the interconnected multibody system. Within this formulation, local
geometric control laws are constructed at the module level, while the interconnection among modules is characterized by power-conjugate twist–wrench pairs induced by the natural duality pairing between the Lie
algebra se(3) and its dual space se(3)^*. For the nominal case, exponential tracking stability of the interconnected system is established using local configuration energy functions on SE(3) and the power-preserving structure of the modular interconnection. To address inertial parametric uncertainty, a
geometric adaptation law is introduced on the
manifold of symmetric positive-definite
matrices, ensuring physically consistent parameter estimates while retaining compatibility with the Lie-algebraic control formulation. Under the adaptive controller, semi-global uniform ultimate boundedness of the closed-loop tracking and parameter estimation errors is proven. Numerical simulations on a redundant high-inertia robotic manipulator demonstrate accurate pose tracking, smooth transient behavior, orientation regulation, and robustness under inertial perturbations. Comparative studies with state-of-the-art methods further illustrate the effectiveness of the proposed framework for complex robotic manipulation tasks.
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All authors Mahdi Hejrati, Amir Hossein Barjini, Gokhan Alcan, Jouni Mattila