Signal Processing
Degeneracy-Aware Resilient Resource Allocation in Cell-Free Cache-Aided MU-MIMO Networks
Sayanti Ghosh, Indrakshi Dey and Nicola Marchetti
Cell-free cache-aided multi-user multiple-input-multiple-output (
MIMO) (CF-CA-MU-
MIMO) networks improve spectral efficiency through coded multicast delivery and distributed spatial multiplexing, but their distributed architecture introduces vulnerabilities to jamming, cache-aware eavesdropping, Byzantine corruption, and pilot-contamination attacks. This paper develops a degeneracy-aware resilient framework based on four vulnerability-mode partitions (subfile, edge node, multicast stream, and user) and three attack-aware structural metrics: Degeneracy-Weighted Path Robustness (DWPR$^{\mathrm{att}}$), trust-aware Functional Substitution Score (FSS$^{\mathrm{trust}}$), and a robust degeneracy index ($D_k^{\mathrm{rob}}$). These metrics are incorporated into a fully decentralized consensus-based
agent framework (DC-ABM) using trust-weighted trimmed-mean aggregation and adaptive trust
evolution. Five theoretical results are established: (i) a tight top-mass concentration lemma, (ii) matching
memory–rate–resilience achievability and converse bounds, (iii) a robust-degeneracy bound with outage characterization, (iv) a secrecy–cache coupling
theorem, and (v) a Byzantine-robust mean-square convergence result with an explicit breakdown threshold $f_{\max}$. Simulations validate the analytical bounds and demonstrate $1.8\times$ to $3\times$ faster convergence than distributed alternating direction method of multipliers (ADMM), multi-
agent reinforcement learning (MARL)/graph neural network (GNN)-based control, and Su–Vaidya consensus while maintaining throughput up to the predicted threshold $f_{\max}\approx0.19$.
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Systems and Control
CONDUCTOR: An LLM-Orchestrated Digital Twin for Uncertainty-Aware Distribution Grid Operations
Antonio Alcántara, Aysegül Kahraman, Anosh Arshad Sundhu et al. (+1 more)
Large
language models (LLMs) are proposed as natural-
language interfaces to power system analysis, yet existing frameworks are validated almost exclusively on synthetic
benchmarks and support only deterministic studies. We present CONDUCTOR, an LLM-orchestrated digital twin for distribution grid operations. An open-weights LLM orchestrates power system analysis and optimization solvers and, unlike prior systems, also performs uncertainty-aware studies: probabilistic security assessment, robust corrective dispatch, and flexibility-envelope and hosting-capacity characterization. We test it on the Bornholm 60 kV distribution network - a real Danish island power system - using one year of smart-meter measurements. An operator case study spans deterministic assessment, probabilistic risk quantification, and robust dispatch. Across a 68-
prompt behavioral catalog scoring tool use, evidence consistency, state-mutation discipline, and refusal calibration, the orchestrator answers 98.5% of tasks correctly on the first attempt - the lone failure being a missing answer, not a wrong one. The full pipeline is released open source.
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All authors Antonio Alcántara, Aysegül Kahraman, Anosh Arshad Sundhu, Spyros Chatzivasileiadis
Systems and Control
Multiplayer Reach-Avoid Differential Games with Defender-Side Information Delay
Zehua Zhao, Rui Yan, Jianping He et al. (+1 more)
We consider a class of pursuit-evasion games in which multiple defenders and attackers move in the plane with bounded speeds, while each defender observes the states of other
agents with a constant time delay. For the one-attacker-one-defender case, we derive an explicit analytical characterization of the attacker's delayed attack region and prove its convexity under mild assumptions. When the defender can guarantee capture, we formulate a convex optimization problem to compute the capture point and derive optimal strategies for both players. These strategies are shown to constitute a subgame-perfect Nash
equilibrium by exploiting the sequential structure induced by the information delay. The analysis is further extended to the one-attacker-multiple-defender scenario and to the general multiplayer setting. In the latter case, delay-aware pairwise winning relations are incorporated into a maximum matching formulation to address the defender-attacker assignment. Numerical simulations for one-on-one, one-vs-multiple, and multi-
agent cases validate the theoretical results and illustrate the impact of information delay on game outcomes and optimal strategies.
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All authors Zehua Zhao, Rui Yan, Jianping He, Xiaoming Duan
Signal Processing · Cross-listed from quant-ph
Offline Channel-Independent QAOA Angles for RIS Power Aggregation: Unit-Circle Phase Dictionaries and Infinite-Size Spin-Glass Limits
Burhan Gulbahar
Reconfigurable intelligent surfaces (RIS) maximize received power by setting per-element phases. Discrete-phase optimization is NP-hard in the worst case, while the
quantum approximate optimization
algorithm (QAOA) applied to RIS faces limited phase alphabets, either per-problem angle optimization or uncharacterized training cost exposed to barren plateaus, and no scalable performance
benchmark. We introduce a $2^{M}$-phase $\theta$ dictionary for optimizing power $\|\mathbf{A} \, e^{j\theta}\|^{2}$ having $K \times N$ channel
matrix $\mathbf{A}$ and QAOA angle offline optimization with instance and size-independent infinite-size limit of the mixed-$q$ Gaussian ensemble of Basso et al. Our design bounds the
spin-
Hamiltonian interaction order to at most quartic for any $M$, and the deployed order-2 reduction lies below the even-$q\!\ge\!4$ regime in which constant-level QAOA limitations are proved. We perform analytical, state-vector,
matrix-product-state and Pauli-path-simulation numerical studies for $N=K \leq 100$ and QAOA depth $p=9$, verifying offline angle transfer to Rayleigh, Rician/line-of-sight, cascaded double-fading and spatially-correlated RIS channels at $N\!\in\!\{5,12\}$. We observe performance reaching a near-optimal multi-start single-flip local-search reference for $N\!\le\!16$ under order-2 modeling with $2^{5}{=}32$-phase dictionary while the order-4 model shows a performance ceiling below the classical reference. The approach suggests a route to near-optimal large-$N$ performance on future fault-tolerant (FTQ)
quantum computers, which enable the higher-depth QAOA
circuits.
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Audio and Speech Processing
SphereVBx: Spherical Variational Bayes Clustering for Simplified EEND-VC Diarization
Petr Pálka, Jiangyu Han, Prachi Singh et al. (+3 more)
We propose SphereVBx, a
Bayesian clustering framework for hyperspherical
embeddings based on Toroidal Probabilistic Spherical Discriminant Analysis (T-PSDA). The method follows the variational
Bayesian formulation of VBx while replacing the Gaussian Probabilistic Linear Discriminant Analysis (PLDA) backend with T-PSDA, resulting in variational inference in a mixture of von Mises-Fisher distributions. We apply SphereVBx to speaker diarization and in particular to the end-to-end neural diarization with vector clustering (EEND-VC) framework. A parameter-free variant, denoted SphereVBx-PF, corresponds to a spherical similarity model closely related to cosine scoring and does not require pretrained backend parameters. Experiments on multiple diarization
benchmarks show that SphereVBx improves clustering accuracy in cascaded diarization pipelines and achieves comparable or better performance in the EEND-VC framework while significantly simplifying its clustering stage.
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All authors Petr Pálka, Jiangyu Han, Prachi Singh, Marc Delcroix, Naohiro Tawara, Lukáš Burget
Audio and Speech Processing
A Multi-Stage Separation-and-Classification Framework Guided by Complementary Acoustic-to-Semantic Clues
Younghoo Kwon, Junwoo Park, Han Yin et al. (+1 more)
This report describes the system proposed for the DCASE 2026 Challenge Task 4: Spatial Semantic
Segmentation of Sound Scenes (S5). Specifically, we develop a multi-stage framework in which each stage couples a separation model with a classification model. The first stage performs source separation and classification directly on the multi-channel mixture. Its outputs are then propagated to the following stage as two complementary clues that progressively refine each target estimate: (i) an enrollment clue, the separated waveform itself, serving as a low-level acoustic reference; and (ii) a class clue, the predicted label encoded as a one-hot vector. The third stage reuses the second-stage outputs under the same scheme, forming an iterative self-guided refinement process. In addition, we use a fine-grained frame-level audio
embedding from an audio encoder pretrained on a large audio corpus as an additional clue to further improve the audio separation performance. On the test set, the proposed system achieves a CAPI-SDRi of 15.51 dB, a mixture accuracy of 71.09%, and a source accuracy of 78.62%; with an improvement of 7.02 dB, 10.38%p and 8.22%p compared with the challenge baseline, respectively.
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All authors Younghoo Kwon, Junwoo Park, Han Yin, Jung-Woo Choi
Systems and Control · Cross-listed from math.OC
Trade-off invariance for weighted scalarizations in multi-objective optimization
Jona Klemenc and Alessandro Scagliotti
We consider weighted-sum scalarizations for an abstract multi-objective minimization problem defined by the vector-valued map $U\ni u\mapsto \big( f_1(u),\ldots, f_N(u)\big)$, where $U$ is an arbitrary nonempty set and no
topology, convexity, compactness, or lower semicontinuity assumption is imposed. Using the open simplex as parameter space for positive weights, we show that the Trade-off Invariance Principle for scalarizations yields a generic uniqueness property in the objective space. Namely, for almost every weight vector, all minimizers of the corresponding weighted-sum scalarization have the same objective vector, although the minimizers themselves need not be unique. Moreover, excluding again a null-measure subset, all minimizing sequences determine the same limiting objective vector, independently of the chosen sequence. We also give a
geometric interpretation of these results in the attainable objective set: for almost every positive weight vector, the scalarization exposes at most one nondominated point. Furthermore, minimizing sequences determine at most one asymptotically exposed objective vector in the closure of the attainable set.
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Signal Processing
Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi et al. (+3 more)
The deployment of unmanned aerial vehicles (
UAV) as open
radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage. However, optimizing
UAV trajectories in dynamic and unfamiliar environments remains a critical challenge, particularly due to the need for extensive retraining in each new scenario. In this paper, we introduce a novel
UAV trajectory optimization framework that integrates enhanced continual transfer learning within the O-RAN architecture. The proposed system maintains a library of pre-trained models and employs a model selection mechanism to identify and transfer knowledge from the most relevant environments, minimizing adaptation time and improving efficiency. When no sufficiently similar model is available, a fallback model empowered by continuous refinements ensures baseline performance. The framework leverages real-world city maps and ray tracing techniques to enhance learning reliability and improve trajectory planning. Simulation results demonstrate that the proposed model selection-based transfer learning approach reduces convergence time by 44% to 56% compared to retraining from scratch, and up to 40% compared to traditional transfer learning without model selection.
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All authors Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi, Mahnaz Arvaneh, Walid Saad, Hamed Ahmadi
Signal Processing
WiWorld-RealData: A Real-World Multi-Modal Dataset for 6G Wireless World Models
Yinyin Jiao, Huixin Xu, Jianhua Zhang et al. (+7 more)
As sixth-generation wireless systems evolve from reliable connectivity toward environment intelligence, wireless world models aim to learn how physical environments and user states affect wireless propagation, requiring real-world data with explicit correspondences between channel responses and environment observations. However, existing channel-environment
datasets are predominantly simulation-based or designed for specific communication tasks, limiting their support for general environment-channel relationship learning. To address this gap, we construct WiWorld-RealData, a real-world multi-band channel and multi-modal environment sensing
dataset for 6G wireless world model research. It provides synchronized channel impulse responses measured at 3.7 and 6.775 GHz together with multi-view and panoramic images, light detection and ranging point clouds, millimeter-wave radar observations, and global
navigation satellite system trajectories. Unified timestamps, sample identifiers, and metadata establish sample-level correspondences across these heterogeneous modalities. The overall measurement campaign produced approximately 10 TB of data, while the current public release provides aligned channel-environment samples from a representative continuous outdoor route. A path-loss prediction case study further validates the
dataset using a continuous test route segment, achieving a mean absolute error of 2.02 dB and a root mean square error of 2.69 dB under few-shot adaptation. WiWorld-RealData supports cross-band propagation analysis, environment-aware channel modeling, wireless digital twins, and channel foundation model research. The
dataset is available at https://scc.bupt.edu.cn/
dataset-manage/
datasets/44 and https://doi.org/10.57760/sciencedb.40663.
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All authors Yinyin Jiao, Huixin Xu, Jianhua Zhang, Yuelong Qiu, Jingjing Wang, Shaoyi Liu, Yuxiang Zhang, Li Yu, Xuebin Sun, Guangyi Liu
Signal Processing
Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
Bingnan Xiao, Shuyan Hu, Xiaojing Chen et al. (+5 more)
Next-generation wireless systems are expected to be “AI-native," with neural networks (NNs) embedded throughout the physical (PHY) layer protocol stack to improve spectral efficiency, latency, and network autonomy. However, the opacity of deep learning (DL) models raises increasing concerns about system reliability, safety, and
privacy, especially under complex and time-varying network environments. This survey studies explainable AI (XAI) in wireless PHY layers from the explainability perspective. We first formalize a series of responsibility-oriented goals for wireless XAI. Then, we develop a systematic taxonomy of explainability approaches and distill practical criteria for deploying explanations in communication scenarios. We provide a comprehensive review of where and how XAI can be applied throughout the PHY layer, connecting representative learning paradigms to appropriate explanation techniques, evaluation metrics, and deployment considerations. Open challenges and future directions are discussed, including explainability-performance tradeoffs, explainability-aware data processing, customized XAI for communication-specific structures, cross-layer explanation consistency, and emerging needs for explaining LLM- and Agentic-AI-driven PHY layers.
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All authors Bingnan Xiao, Shuyan Hu, Xiaojing Chen, Zhiyuan Zhai, Bingcong Li, Wei Ni, Xin Wang, Ekram Hossain
Signal Processing · Cross-listed from q-bio.NC
EEG Interpretation Across Chant Listening: A Single-Subject Pilot Investigation Using Spectral and Functional Connectivity Analysis
Prerna Singh, Aishwarya Ghosh, Neelam Sinha et al. (+1 more)
This technical report presents an
EEG-based investigation of neural activity across five auditory conditions: Resting State (RS), Shiv Tandav Stotra (STS), Mahasudarshan Mantra (MM), Aum Chant, and Tanpura Listening.
EEG recordings acquired from a healthy 5-year-old participant were analyzed using spectral power estimation and functional connectivity measures based on the weighted Phase Lag Index (wPLI). Spectral analysis revealed condition-specific modulation of neural oscillatory activity, with STS listening producing the highest relative power across multiple frequency bands, particularly within the beta range. Functional connectivity analysis demonstrated distinct network organizations across conditions. STS listening exhibited the strongest and most widespread connectivity pattern, characterized by prominent long-range interactions among frontal, temporal, parietal, and occipital regions. Tanpura listening generated a dense yet balanced connectivity network, while Aum listening showed moderate distributed connectivity. In contrast, MM and resting-state conditions displayed comparatively weaker and more localized network organization. These preliminary findings suggest that different chant-listening conditions engage distinct neural mechanisms involving both cortical activation and large-scale neural synchronization. The study establishes a methodological framework for future investigations examining the role of culturally relevant auditory interventions in cognitive development, neuroeducation, and child-centered neuroscience research.
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All authors Prerna Singh, Aishwarya Ghosh, Neelam Sinha, Deepti Navaratna
Image and Video Processing
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
Deepak Bhatia, Saad Ahmad, Smiti Tripathy et al. (+7 more)
Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools. This work presents Female-RHINO: (R)eproductive (H)ealth (I)maging A(N)alysis T(O)ol, a real-time AI-assisted framework for automated quantitative uterine MRI analysis and structured reporting during image acquisition. We present an end-to-end system that integrates inline communication with the MRI scanner and deep learning-based analysis to derive quantitative uterine biomarkers from sagittal T2-weighted pelvic MRI. The framework combines
segmentation and anatomical landmark detection models trained and evaluated on more than 500 multi-center
datasets spanning diverse protocols, vendors, and patient populations. It performs volumetry, detects and quantifies common incidental findings such as fibroids and Nabothian cysts, and extracts six anatomical landmarks for biometric assessment. Results are compiled into a structured clinician-oriented report with integrated visualizations, without manual interaction. Evaluation on independent retrospective and prospective cohorts demonstrated robust performance across varying acquisition settings. Mean Dice similarity coefficients were 0.82 for the uterus and 0.80 for fibroids, with lower but consistent agreement for Nabothian cysts. Landmark detection achieved a mean radial error of 3.7 mm. End-to-end processing was completed in under 70 seconds, enabling availability of results during the ongoing scan. Prospective deployment yielded immediate, standardized, and reproducible analyses supported by inter-observer agreement. The proposed system enables real-time scanner-integrated AI for automated uterine MRI analysis and reporting, with potential to improve standardization, efficiency, and clinical workflow in pelvic imaging.
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All authors Deepak Bhatia, Saad Ahmad, Smiti Tripathy, Maria Camila Bustos Vivas, Lieselotte Kratzsch, Anika Knupfer, Jordina Aviles Verdera, Susanne Schulz-Heise, Matthias May, Jana Hutter
Image and Video Processing
High Resolution Sediment-Specific Surface Soil Moisture Retrieval Using Sentinel-1 Time Series and Auxiliary Data
Alireza Hamedianfar, Oleg Antropov, Matthieu Molinier et al. (+5 more)
In this study, we examine the potential of continuous ground moisture monitoring over a mining site using a combination of in-situ soil moisture sensors and multi-sensor SAR images. We focus on assessing and improving methodologies for
retrieval of surface soil moisture, i.e. ground moisture, from SAR measurements focusing on detailed in situ reference observations for several key geomaterials, i.e. sediments, typical in the study site. The mining site represents a limestone quarry locate in the southeastern Finland. Our hypothesis is that sediment-specific well-calibrated models can be instrumental in improving soil moisture
retrieval under different weather conditions to produce spatially explicit soil moisture estimates at high resolution compared to baseline approaches. Studied SAR data are represented by Copernicus Sentinel-1 C-band images, while auxiliary
datasets include optical Sentinel-2 data. Reference data were collected using IoT enabled capacitance sensors. The examined machine learning methods include Xgboost, LightGBM, RFs, linear regression and k-nearest neighbors regression. The best performance was achieved with the most comprehensive feature set which combines Sentinel-1 backscatter, time-series based soil moisture indices, Sentinel-2 optical, topographic, and temperature predictors. In the best sediment-area-level configurations, RMSE decreased to 0.037-0.050 m^3 m^(-3) (3.7-5.0 volumetric % points), with R^2 values reaching 0.90. Tree-based ensemble methods, especially LightGBM, RF, and XGBoost, provided the most accurate and stable predictions. Accuracy varied by sediment texture, with the lowest errors for clay and organic soil and higher errors for flotation sand and gravel. Adding sediment information improved Sentinel-1-only
retrievals by more than 2 vol-%, but provided little additional benefit when richer multi-source feature sets were used.
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All authors Alireza Hamedianfar, Oleg Antropov, Matthieu Molinier, Ulla Salmela, Hanna Kukkula, Lauri Seitsonen, Pauliina Liwata-Kenttälä, Maarit Middleton
Audio and Speech Processing
The effect of micro-changes in the pluck trajectory on the sound of an acoustic guitar
Marek Pluta, Jan Jasiński, Daniel Tokarczyk et al. (+1 more)
This study explores how micro-changes in the plucking trajectory of a guitar pick influence the sound of an acoustic guitar. Using a state-of-the-art robotic plucker, a series of measurements has been performed, where the plectrum was moved towards the instrument by a step of 192 micrometers, resulting in an increased attack depth. It has been analysed how the effect of these changes is reflected in loudness, timbre, harmonic content and how the sound progresses during decay. This methodology has been repeated for guitar plectra made from six different materials to investigate how the pick itself influences the effect of a change in the plucking trajectory. The results of the study show that at a low depth the string is not fully excited resulting in weak and markedly altered sound. The range of this effect changes with the mechanical properties of the plectrum material. After this range an increase in depth results in an increase in sound loudness, a decrease in inharmonicity and noisiness and a shift in timbre where the sound becomes fuller in low frequencies and rougher. Presented findings help to understand the nuanced relationship between plucking trajectory and acoustic output. They provide important insights regarding the importance of plucking in guitar testing methodologies, showing that the mech
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All authors Marek Pluta, Jan Jasiński, Daniel Tokarczyk, Julia Grygiel
Systems and Control
An Integer Linear Programming Approach for Maximum Power Extraction from Solar PV Plants under Partial Shading
Ushnik Chakrabarti and Binoy Kumar Karmakar
Partial shading in
solar photovoltaic (SPV) plants, particularly in urban environments, is a common challenge caused by nearby trees, buildings, or other fixed obstructions, leading to a significant reduction in overall system efficiency. Dynamic and static PV array reconfiguration strategies are widely regarded as effective approaches for mitigating the adverse effects of partial shading. However, Dynamic Array Reconfiguration (DAR) is rarely adopted in practical systems due to high switching complexity and substantial computational requirements. In contrast, Static Array Reconfiguration (SAR) does not require complex switching arrangements or additional computational resources, making it more suitable for real-world implementation. However, SAR is a one-time configuration and cannot adapt to dynamically changing shading conditions. Existing SAR techniques rearrange PV modules based on assumed shading regions rather than the actual shading pattern, which limits their effectiveness under practical, time-varying conditions. In this work, an SAR technique is proposed that explicitly considers the actual shading pattern on the PV array. The proposed approach accounts for shading caused by nearby fixed obstructions that varies throughout the day as well as across different seasons. The performance of the proposed technique was evaluated by comparing it with existing methods considering a PV array with a square
matrix, and a small-scale laboratory prototype of non-square
matrix was developed to demonstrate its practical applicability in real-world scenarios. It has been observed that the method consistently delivers an optimal power output for both software simulation and practical experiment compared to other available techniques.
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Systems and Control
Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning
Yuzhou Wei, Wenjie Liu, Yifan Xie et al. (+3 more)
Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing data-driven MPC methods focus on linear systems, and little
attention has been given to nonlinear dynamics under disturbances. To fill this gap, we propose a robust data-driven min-max MPC scheme for unknown nonlinear systems with process disturbances. We represent the unknown nonlinear dynamics using vector fields built from a dictionary of basis functions, yielding an equivalent linear form with unknown
matrices. These unknown
matrices are characterized by a set-membership representation derived from noisy input-state data. Using this uncertainty description, we formulate a min-max MPC problem. Two online scenarios are studied: i) when state measurements are noise-free, and, ii) when they are corrupted by process disturbance. For each case, we derive a Lyapunov-based semidefinite program (SDP) to compute a stabilizing state-feedback controller. The resulting schemes are shown to guarantee recursive feasibility and either exponential or robust stability of the closed-loop system depending on whether there is process disturbance. Simulation studies on
benchmark examples illustrate the effectiveness and competitive performance of the proposed approach compared to existing data-driven and model-based controllers.
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All authors Yuzhou Wei, Wenjie Liu, Yifan Xie, Frank Allgöwer, Jian Sun, Gang Wang
Signal Processing
PROTECT-90: A Fault Dataset for Power System Protection
Julian Oelhaf, Georg Kordowich, Christian Bergler et al. (+3 more)
The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform
datasets that enable transparent and reproducible evaluation. To address this gap, this paper introduces the PROTECT-90
dataset, an open electromagnetic transient (EMT)-simulated reference
benchmark for high-voltage fault studies with consistent digital-fault-recorder-like measurements, publicly released with this work. The
dataset comprises 9,022 physically consistent short-
circuit simulation episodes generated on a standardized 90 kV double-line
topology with systematically documented domain randomization of grid operating points, line parameters, and fault conditions. For each episode, synchronized three-phase voltage and current waveforms are recorded at eight measurement locations and released together with structured, machine-readable metadata describing fault type, fault location, inception time, and operating conditions. All modeling assumptions, parameter ranges, and data-generation procedures are explicitly documented to ensure transparency and cross-study comparability. By combining physically grounded EMT simulation, balanced scenario coverage, and open accessibility, PROTECT-90 establishes a standardized foundation for reproducible benchmarking of protection-oriented signal processing and learning-based methods.
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All authors Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann Jäger, Siming Bayer
Systems and Control
Control Based Enhanced Regenerative Modes for Hydraulic Multi-Actuator Systems
Bideaux Eric, Mohamed Smaoui, Tardy Grégory et al. (+1 more)
This paper focuses on a control-based approach for enhancing the regenerative capabilities of hydraulic multi-actuator systems using individual metering valves. Thanks to this architecture, pressure and displacement of each actuator can be controlled nearly independently. By determining online, the right pressure to be driven, it enables the optimization of regenerative control strategies for resistive or driving forces. Globally, this control strategy behaves such as a load sensing approach but each metering valve is piloted in order to activate regenerative mode when it is allowable. The main contribution relies on optimizing the pressure to be controlled in each actuator and the main pump in order to maximize the regenerative capacity of a hydraulic machine while following a displacement. The effectiveness of the proposed approach is proved in simulation. Only a single pump line regeneration is explored here but extensions to multi-pump or direct regeneration are also possible.
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All authors Bideaux Eric, Mohamed Smaoui, Tardy Grégory, Aurore Lanoix
Audio and Speech Processing
Digital Revival: Acoustic Documentation and Digital Reactivation of Historical Woodwind Instruments
Lior Arbel and Itai Weissman
Historical woodwind instruments exhibit complex acoustic behaviors that are central to their musical, organological, and cultural significance. However, due to material fragility, aging, and strict conservation requirements, many original instruments held in museum collections can no longer be played. As a result, their acoustic identity remains insufficiently documented, limiting both acoustical research and historically informed performance practice. Digital Revival is an ongoing research project developed in close collaboration with the Rijksmuseum (Amsterdam) and the Kunstmuseum Den Haag. The project investigates how controlled, non-invasive acoustic sampling and digital sound modeling can be used to document, preserve, and reactivate the sonic characteristics of historical woodwind instruments while fully respecting conservation constraints. Recording sessions are designed in consultation with conservators and instrument specialists and combine performance-informed excitation, high-resolution audio capture, and spectral analysis to document timbral, dynamic, and articulatory features. The resulting
datasets function both as analytical resources and as playable digital instruments, enabling comparative study of spectral envelopes, transient behavior, and response characteristics across registers and playing techniques. Performer interaction is explored through electronic wind instruments (EWI), allowing real-time control of historically derived sound material. By integrating musical acoustics, conservation science, and artistic research, Digital Revival proposes a sustainable framework for extending the acoustic presence of historical instruments beyond the museum context, supporting research, education, and contemporary performance without compromising the physical integrity of the original artifacts.
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Signal Processing
A Conditional Timing Protection Level: Holdover-Limited Undetected Time Error Under GNSS Spoofing
Chakshu Baweja
A GNSS timing receiver under spoofing has no nominal-geometry fault for position-domain RAIM to bound: the threat is a slow, common-mode pull of served clock time that the receiver's own time-accuracy flag need not reveal. We make three graded contributions. First, a field measurement: solving the receiver clock trajectory from raw L1 pseudoranges and broadcast ephemeris, we show a recorded over-the-air spoof from the public JammerTest 2024 campaign pulled a u-blox ZED-F9P by about 1.01 ms of served time while it reported at most 51 ns, a gap near 20,000x. Second, an impossibility: against an adversary free to choose the ramp rate, no finite unconditional bound on undetected time error exists under a single self-referential clock-aided monitor, because a ramp slow enough to keep the disciplined reference in lock-step is never alarmed while the error grows without limit, so any finite guarantee is conditional. Third, the conditional bound: the Timing Protection Level (TPL), a model-free monitor's static detectability floor plus the oscillator's coast over the detection latency, holds given detection by an independent cross-satellite consistency check a coherent spoofer does not drive in lock-step. Each term is a closed form over a primitive verified in the open Kshana simulator, so the sum is reproducible by hand. Calibrated on the recorded attack, the budget is 114 ns at one-second recovery and 458 ns at a 60-second coast, thousands of times below the 1.01 ms accepted; a clock-aided sequential test alone gives essentially no protection on this slow ramp (it alarms only near the ~1 ms capture), while the model-free monitor alarms during the ramp. We are explicit: the bound is calibrated, not field-validated; carries no integrity-risk budget; and is reported as a band at long coast. The simulator, bound, and calibration example are open source under AGPL-3.0.
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Systems and Control
From Stabilizing Regions to Certified Controllers: Closing the Selection Gap in Unified PID/PI Analysis for Time-Delay Plants
Senol Gulgonul
A recent unified treatment of PID tuning for time-delay plants (An, Tang, Sun, Zhang and Chen, Automatica, 2026) combines the D-partition method with a boundary gradient vector (BGV) to orient the boundaries of stabilizing, relative-stability and stability-margin regions. That method answers a feasibility question, namely where admissible gains lie, and it leaves a manual interior-point test to fix the unstable-pole count in each
cell, with the choice of a single controller left to the user. This note makes three contributions. First, the one operation the BGV leaves manual, the absolute unstable-pole count, is available analytically: exactly for delay-free designs through a companion-
matrix or Routh count, and through an argument-principle (Mikhailov) evaluation for retarded-type delay loops. Labelling every
cell with its analytic count removes the interior-point test and decides the whole partition. Second, we add the step the BGV framework cannot reach, a time-domain selection rule that returns one certified controller: among monotone step responses we choose the minimum-settling-time PI gains, characterized by a tangency condition, with monotonicity guaranteed by external positivity (a nonnegative closed-loop impulse response). Third, we flag a neutral-type pitfall that the unified analysis never delimits: an ideal PID with derivative action on a first-order-plus-dead-time (FOPTD) plant is of neutral type, with a root chain on the imaginary axis when k Kd = T. We reproduce the authors' delay-free
benchmark exactly, recovering both admissible Kp intervals, and demonstrate the full pipeline on a FOPTD plant, delivering a certified monotone, fast-settling PI controller that the region-only method can neither locate nor justify; the selected gains match an independent closed-form tangency rule to within one percent. All claims are validated numerically.
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Image and Video Processing
A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading
Inam Ullah, Imran Razzak and Shoaib Jameel
Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requires more than an image-level label. It requires understanding how lesion evidence is distributed around retinal vessels and how this evidence relates to quantitative vascular biomarkers. We present a dual-edge spatial-Jacobian image graph for interpretable DR grading. Each fundus image is represented as a graph node with four aligned evidence streams: AutoMorph vessel information ($X_1$), DR-XAI-style lesion evidence maps ($X_2$), a 128-dimensional lesion-based contrastive image
embedding ($X_3$), and AutoMorph morphometric biomarkers ($X_4$). The spatial edge branch ($X_{12}$) encodes vessel-lesion geometry, while the Jacobian branch ($X_{34}$) models
embedding-biomarker sensitivity. Lightweight two-
token attention fuses both edge families into a final image graph. On 2,910 matched non-augmented APTOS images, the full graph achieves 0.8076 accuracy, 0.8312 quadratic weighted kappa, 0.5915 macro-F1, and 0.9330 adjacent-grade accuracy; referable DR reaches 0.9055 accuracy and 0.9711 AUROC. The framework is positioned as an explainable representation-learning tool for lesion-biomarker hypothesis generation, rather than as a deployment-ready clinical classifier. The code is available at https://github.com/Inamullah-Colab/dual-edge-dr-graph-xai.
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Audio and Speech Processing
Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training
Wonchul Shin, Inyong Choi and Kyogu Lee
Recent end-to-end models for
EEG-guided target
speech extraction report impressive results, underscoring potential for neuro-steered hearing technologies. However, our analysis reveals that high within-trial performance can be driven by trial-specific
EEG structure that acts as shortcuts for target selection, leading to poor generalization on unseen trials. To overcome this gap, we propose TRUST-TSE, a two-stage framework to mitigate shortcut learning. By introducing contrastive pretraining with attended-speaker negative sampling, we encourage the
EEG encoder to capture fine-grained
EEG–
speech alignment while suppressing trial-identity cues. We also employ a confidence-weighted extraction objective based on
EEG–source similarity to guide extraction using the learned representations. Experiments on KUL and DTU
datasets show that TRUST-TSE outperforms end-to-end baselines under strict cross-trial protocols, addressing a key reliability bottleneck of existing approaches.
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Systems and Control
Safe Packetized Control for Stochastic Constrained Networked Systems
Omid Akbarzadeh, MohammadHossein Ashoori, Mohammad H. Mamduhi et al. (+1 more)
This work develops a formal framework for the synthesis of packetized safety controllers for discrete-time
polynomial stochastic networked control systems (dt-PSNCS) operating under communication constraints, including uplink delays (plant-to-controller) and downlink packet losses (controller-to-actuator). In this setting, the controller is deployed remotely and exchanges information with the plant over an imperfect wireless communication network. Our proposed approach treats the downlink channel as an erasure channel, with packet losses characterized by an independent Bernoulli process. To systematically manage both uplink delays and downlink packet loss, we first introduce a buffer collocated with the plant that accommodates the packetized safety control (PSC) mechanism. We augment the plant and buffer states into a unified augmented-state representation that accurately captures the system
evolution in the presence of communication imperfections. Our proposed framework synthesizes safety controllers based on control barrier certificates (CBCs), providing probabilistic safety guarantees that remain robust in the presence of both communication delays and packet losses. To achieve this, we reformulate the safety constraints as a sum-of-squares (SOS) optimization program, thereby facilitating the systematic construction of CBCs and their corresponding safety controllers. We validate the proposed framework through three (physical) case studies, demonstrating its effectiveness and practical applicability.
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All authors Omid Akbarzadeh, MohammadHossein Ashoori, Mohammad H. Mamduhi, Abolfazl Lavaei
Audio and Speech Processing
Progressive Alignment Objectives for Aligner-Encoder based ASR
Jaeyoung Lee, Masato Mimura and Takafumi Moriya
Aligner-Encoders are recently proposed seq2seq end-to-end ASR models that replace decoder
attention by predicting the uth
token directly from the u-th encoder position, so the encoder must learn the
alignment internally without cross-
attention or a transducer
lattice. In practice, this
alignment often forms abruptly in the upper layers, making training sensitive and brittle on long utterances. We propose InterAligner, which adds an intermediate Aligner objective so
alignment can form progressively across depth, together with an intermediate CTC loss (InterCTC) to stabilize optimization. On LibriSpeech with a 17-layer Conformer, a final-only Aligner reaches 5.0/7.8 WER (test-clean/other). InterCTC improves to 3.4/6.0, and InterAligner further reduces WER to 3.1/5.6 with the largest gains on long utterances.
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Audio and Speech Processing
Evaluation of Headrest-Integrated Loudspeakers for Enhanced Spatial Audio Immersion in Automotive Cabins
Martin Wolters, Jacobo Giralt, Harald Mundt et al. (+1 more)
Immersive object-based spatial audio is now firmly established in the music industry as the standard for production, distribution, and playback. The number of automobiles integrating such content to provide premium entertainment experiences is steadily increasing, driving the development of new audio rendering techniques. While loudspeakers integrated into automotive headrests have been around for more than 50 years, they have not yet achieved status as a standard feature in new cars. However, they represent a powerful tool for reproducing immersive audio by enabling the creation of personal sound zones with reduced passenger distraction while effectively complementing existing cabin speakers. We conducted subjective assessments using paired comparison experiments to measure preference and multiple spatial audio attributes. We modeled the resulting probability outcomes using a probabilistic choice model, the Bradley-Terry-Luce rank ordering. The results indicate that headrest-integrated speakers can improve the audio perception in immersive audio scenarios.
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All authors Martin Wolters, Jacobo Giralt, Harald Mundt, Arijit Biswas
Audio and Speech Processing
Joint Learning of Covariance Estimation and White Noise Gain for Robust MVDR Beamforming
Yongyi Deng, Hanchen Pei, Jianbo Ma et al. (+3 more)
The minimum variance distortionless response (MVDR) beamformer is widely used for multichannel
speech enhancement due to strong noise suppression while preserving target signals. In practice, its performance is sensitive to microphone self-noise and array mismatches. Existing approaches typically rely on fixed, manually tuned WNG thresholds or diagonal loading, leading to suboptimal performance under unknown or time-varying acoustic conditions. This paper proposes a data-driven MVDR framework that adaptively estimates the WNG constraint using a deep neural network. The network jointly predicts a time-frequency noise mask for covariance estimation and a frequency-dependent WNG threshold, enabling dynamic robustness-directivity control. A differentiable robust MVDR layer is integrated into the framework, allowing end-to-end optimization. Experiments demonstrate consistent improvements in
speech quality and intelligibility over conventional fixed-WNG MVDR methods.
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All authors Yongyi Deng, Hanchen Pei, Jianbo Ma, Gongping Huang, Jingdong Chen, Jacob Benesty
Audio and Speech Processing
DTT-BSR+: A Generative-Regression Cascade for Music Source Restoration
Youran Ni, Shihong Tan, Yuzhu Wang et al. (+1 more)
Music source restoration (MSR) requires jointly addressing source unmixing and the inversion of non-linear production effects. Current methods struggle to achieve accurate target signal reconstruction while maintaining semantic consistency. To address this limitation, we propose DTT-BSR+, a two-stage cascade MSR system that decouples distribution fitting from signal reconstruction into separate stages. A generative DTT-BSR separator in the first stage produces stems matching the prior of clean sources, and a modified Demucs network in the second stage enhances the first stage output using time-domain and multi-resolution spectral losses. DTT-BSR+ improves multi-mel signal-to-noise ratio (MMSNR) over the single-stage DTT-BSR across all stems, and surpasses the state-of-the-art X-LANCE MSR system on five stems. We also reveal through Fréchet Audio Distance (FAD) decomposition an implicit trade-off between signal reconstruction accuracy and semantic distribution fitting across stems.
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All authors Youran Ni, Shihong Tan, Yuzhu Wang, Gongping Huang
Image and Video Processing · Cross-listed from cs.CV
Flood Mapping from RGB imagery using a Vision Foundation Model
Vladyslav Polushko, Tilman Bucher, Ronald Rösch et al. (+3 more)
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost. To produce flood maps, deep learning models for water
segmentation are often used. CNN based and small
vision transformer models are used. However, they need much data for adaptation to a change of scenery, i.e., another flooding event.
Vision foundation models or large
vision transformers are known to generalize across domains. Recently, foundation models for Earth observation became available. They are pretrained on satellite data, whose spatial resolution, viewing geometry, and radiometry differ from nadir RGB imagery. Thus, adaptation is required. We investigate how a satellite-pretrained Earth observation foundation model can be adapted to centimeter-scale floodwater mapping from RGB imagery. Specifically, we fine-tune a model we call Prithvi-2.0-UPN consisting of the Prithvi-EO-2.0-600M
Vision Transformer combined with a UPerNet decoder for binary water
segmentation on two RGB
datasets (BlessemFlood21, NeuenahrFlood). In a first experiment we observe that Prithvi-2.0-UPN reaches state-of-the-art results on BlessemFlood21 and NeuenahrFlood, when trained on their
datasets. In a second experiment we show that Prithvi-2.0-UPN performs better than state-of-the-art baseline models for transfer to a new flood event (trained on BlessemFlood21, tested on NeuenahrFlood) in a zero-shot setting. However, the performance indicates room for improvement. In this respect, we investigate in a third experiment how performance improves when further fine-tuning the models with small shares of NeuenahrFlood training data: Prithvi-2.0-UPN improves the fastest and reaches almost the performance level when fully trained on NeuenahrFlood, indicating transfer capabilities.
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All authors Vladyslav Polushko, Tilman Bucher, Ronald Rösch, Thomas März, Markus Rauhut, Andreas Weinmann
Audio and Speech Processing
Autoencoder based optimized SSL representations: Complexity Minimization and improved Dysarthric ASR
Paban Sapkota, Hemant Kumar Kathania, Mikko Kurimo et al. (+2 more)
Self-supervised learning (SSL) models extract rich
speech representations but often come with high-dimensional features, increasing computational complexity. This work explores an SSL-AutoEncoder (SSL-AE) bottlenecking approach to efficiently reduce feature dimensions while maintaining dysarthric Automatic
Speech Recognition (ASR) performance. By leveraging an autoencoder, we transform high-dimensional SSL features into a compact space, reducing model complexity and training time. Our method preserves essential
speech information, achieving reduced Word Error Rates (WER) while significantly lowering computational costs. Experiments show SSL-AE bottlenecking reduces training time by 8x compared to the SSL baseline, demonstrating efficiency without sacrificing recognition performance. These results highlight AE as an effective solution for SSL feature compression in resource-constrained environments.
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All authors Paban Sapkota, Hemant Kumar Kathania, Mikko Kurimo, Shrikanth Narayanan, Sudarsana Reddy Kadiri