Signal Processing
WEKP-PLP: A Wireless Environment Knowledge Pool-Enhanced Path Loss Prediction Framework for Shore-to-Ship Communication
Jikun Du, Lei Tian, Jianhua Zhang et al. (+6 more)
Accurate shore-to-ship path loss prediction is essential for maritime mobile communication systems, but it remains challenging because dynamic sea surface conditions can alter reflection paths and multipath effects, introducing uncertainty into the observed path loss. This paper proposes a wireless environment knowledge pool-enhanced path loss prediction (WEKP-PLP) method for point prediction and interval characterization of shore-to-ship path loss. The proposed method first constructs a wireless environment knowledge pool (WEKP) from ray tracing (RT) simulations under different wind speeds, temperatures, salinities, frequencies,
antenna heights, and propagation distances. The WEKP learns the mapping from environmental and system parameters to path loss residual
quantiles and provides a prior that contains both the median prediction and the associated prediction interval. To adapt this prior to real scenarios, a small number of measurement samples are used to learn the residual between the WEKP median prediction and the measured path loss through Gaussian process regression (GPR). The experimental results show that the WEKP-PLP provides accurate point prediction and compact intervals with reliable coverage. The proposed method achieves an MAE of 1.03 dB and an RMSE of 1.54 dB. The ablation results further confirm that both the WEKP prior and the residual correction using a small number of measurement samples are necessary for accurate and reliable shore-to-ship path loss prediction.
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All authors Jikun Du, Lei Tian, Jianhua Zhang, Pan Tang, Zihang Ding, Bin Ao, Zhen Zhang, Jialin Wang, Yuanzhi He
Systems and Control · Cross-listed from math.OC
Optimizing Energy Efficiency and Grid Stability via Public EV Charging Flexibility
Marek Miltner, Artem Bryksa, Ondřej Štogl et al. (+4 more)
This study evaluates the potential of electric vehicle (EV) charging flexibility to enhance both energy efficiency and power grid stability. Using real-world data from public charging stations in Prague, we analyze individual and aggregated charging sessions to explore how optimizing charging times can reduce energy waste, minimize grid imbalances, and support the integration of renewable energy. By aligning EV charging with periods of lower grid demand and higher renewable generation, we demonstrate a significant improvement in energy efficiency, reduc ing the need for costly system support and ancillary services. Our findings suggest that cooperation between power distributors and transmission system operators can unlock new opportunities for maintaining grid stability while promoting sustainable energy use in an increasingly uncertain energy landscape.
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All authors Marek Miltner, Artem Bryksa, Ondřej Štogl, Daniel Vašata, Magda Friedjungová, Ram Rajagopal, Oldřich Starý
Systems and Control · Cross-listed from stat.AP
Transit Destination Inference from Tap-In-Only Bus Smart-Card Data: A Hierarchical Bayesian Approach
Gefei Zhao, Jiahe Ling and Yuelong Su
Entry-only automatic fare collection systems record boardings but not alightings, preventing direct construction of origin-destination (OD)
matrices. This study develops a Hierarchical
Bayesian Latent-Destination (HBLD) model that combines station-hour boarding and inferred alighting demand with passenger card histories. Trip-chain destinations are treated as noisy evidence with a reliability parameter, allowing destination uncertainty to propagate into OD flows. The model was applied to 838,305 bus tap-ins collected in Changzhou in May 2025 and linked to stop-network and hourly weather data. It estimates destination distributions over feasible downstream and reverse-direction through-terminal stops using network, time-of-day, weather, and smoothed historical demand effects. A
Bayesian personalization layer uses prior card trips and reverts to the shared trip-level distribution when history is unavailable. Fitted by
stochastic variational inference and evaluated on the final week, HBLD outperformed the strongest baseline. Observed boarding patterns consistently improved prediction, especially without card history, while inferred alighting patterns helped only when trip-chain evidence was strongly trusted. The model captured travel consistent with through-terminal riding and bus-assisted road crossing and estimated destinations for trips unresolved by deterministic chaining. Because true alightings were unavailable, scores measure agreement with trip-chain outputs rather than actual destination accuracy. HBLD provides uncertainty-aware destination predictions and OD
matrices for service management, planning, scheduling, and resource allocation.
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Signal Processing
Efficient Automatic Modulation Classification for Next-Generation Wireless Networks
To Truong An, Antonios Argyriou, Annisa Anggun Puspitasari et al. (+2 more)
With the imminent development of sixth-generation (6G) networks, there will be a demand for high-accuracy, computationally-efficient, and low-inference time automatic modulation classification (AMC)
algorithms. To address this need, we propose a new deep-learning based model for AMC that is called the threshold denoise recurrent neural network (TDRNN). The TDRNN combines an adaptive threshold denoising (TD)
algorithm and a recurrent neural network (RNN) that together achieve high accuracy and fast inference. The TD module adaptively reduces the noise level of the received signal, while the RNN module performs the modulation classification on the denoised result. The two subsystems are jointly optimized to reach the optimal architecture. The proposed TDRNN is evaluated for various modulation schemes and signal-to-noise ratios (SNR). The experimental results demonstrate that the TDRNN outperforms existing methods in terms of accuracy, speed, and computational complexity making it an ideal solution for 6G wireless communication systems.
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All authors To Truong An, Antonios Argyriou, Annisa Anggun Puspitasari, Simon L. Cotton, Byung Moo Lee
Image and Video Processing
pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting
Zixiang Tong and Jin Yang
pyALDIC is an open-source Python implementation of augmented Lagrangian digital image correlation (AL-DIC) for full-field displacement and strain measurement. The software combines a graphical user interface with a scriptable Python API and supports adaptive quadtree meshing, mask-aware subset splitting near cracks and holes, and selectable Local DIC and AL-DIC solver modes. Numba acceleration enables efficient analysis, while automated tests, documentation, and reproducible examples support reliable use acrossWindows, macOS, and Linux. Verification cases include synthetic displacement fields, rigid-body motion, Mode-I cracking, adaptive refinement, and experimental uniaxial tension. pyALDIC is distributed through PyPI, GitHub, and Zenodo under a BSD-3-Clause license for reproducibility. pyALDIC is openly available at https://github.com/zachtong/pyALDIC.
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Image and Video Processing · Cross-listed from cs.CV
Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge
Hongruixuan Chen, He Huang, Haifeng Wang et al. (+19 more)
Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, may be unavailable because of cloud, smoke, or darkness. The Bright Challenge evaluated all-weather building damage mapping from a submeter-resolution pre-event optical image and a post-event SAR image. Participants were required to detect and delineate each building and assign exactly one of three mutually exclusive damage labels. The challenge extended the globally distributed Bright
dataset with instance-level annotations for about 291,000 buildings across 16 disaster events spanning seven disaster types. The final phase was evaluated exclusively on two 2025 events absent from training: a wildfire event in California and a hurricane in Jamaica. A total of 157 participants made 1,289 submissions, and 46 teams entered the final phase. The two winning solutions achieved test mAPs of 0.182 and 0.181, approximately 8.7 times the public baseline of 0.021, but remained far below the best in-domain holdout score of 0.513. Across teams ranked in both phases, performance declined sharply and the rank order changed substantially. The two leading solutions independently favored modality-specific encoding, staged or late optical–SAR fusion, and an optical-dominant separation of building localization from damage recognition. The winning method additionally used scene-aware threshold adjustment and pseudo-label adaptation. These results identify cross-event generalization and stable severity discrimination as the principal remaining challenges. All data, annotations, baseline code, and winning solutions are publicly available at https://github.com/ChenHongruixuan/BRIGHT.
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All authors Hongruixuan Chen, He Huang, Haifeng Wang, Jian Song, Junjue Wang, Weihao Xuan, Hamish Mitchell, Jiepan Li, Wei He, Liangpei Zhang, Zijie Wang, Chen Zhong, Jiazhen Zhao, Lei Hu, Ting Hu, Hongyan Zhang, Gregory Angelides, Miriam Cha, Clifford Broni-Bediako, Junshi Xia, Taylor Perron, Naoto Yokoya
Systems and Control · Cross-listed from cs.AI
When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies
Zongtan Li
Adding a learned adapter to a frozen, command-conditioned locomotion policy is worthwhile only if the interface exposes improvements that are both real and recoverable from deployment-time observations. We introduce an adapter necessity audit that separates global operating-point gain,same-state counterfactual headroom, deployment gain over a cross-fitted fixed action, and state-allocation gain over a frequency-matched randomized policy. Source-cluster learner refits map these quantities and constraint violations to a GO/NO-GO/ABSTAIN decision. Closed-loop command- response identification provides optional decision features. On Go2, an archived scale-prefix diagnostic finds 5.2% same-state headroom but only 0.55% recovered allocation gain. Our confirmatory audit evaluates direct, scale, heading, and yaw interventions on twenty independent clusters for each of three query distributions induced by direct control, VGCC, and MPC, using 200 full learner refits. At 1% deployment and allocation thresholds and a 5% violation tolerance, direct queries return NO-GO, while VGCC and MPC queries ABSTAIN. VGCC has the largest mean deployment gain (1.34%), but its allocation lower bound is 0.09% and its violation upper bound is 6.25%. A deployment-representative twenty-cluster H1 audit also returns NO-GO, whereas a learner-level synthetic control returns GO. The audit therefore tests whether observable signal justifies state-dependent adaptation rather than presuming that an adapter is valuable.
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Image and Video Processing · Cross-listed from cs.CV
Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet
Geran Zhao, Xiaotian Li, Poorya Chavoshnejad et al. (+4 more)
Learning high-fidelity point-cloud features in the 3D space poses significant challenges, including permutation invariance, lack of local context, difficulty in fine-grained surface reconstruction, and high computational cost. In this article, we propose Trans-Unet, a novel framework that addresses these issues by first tansforming 3D point-cloud data into a 2D grid domain and then employing a U-shaped hybrid model that integrates Convolutional Neural Networks, and self-
attention mechanisms. The proposed Trans-Unet effectively learns and reconstructs precise features from high-resolution 3D point-cloud data (with 40,401 points in surface and 2,382 points in fiber) derived from a predefined finite element
brain patch growth model, enabling accurate prediction of
brain folding patterns. By combining multiple techniques, Trans-Unet leverages the complementary strengths: the 3D-to-2D transformation preserves fine-grained structural information while significantly reducing computational cost and the curse of dimensionality; convolutional blocks capture hierarchical, low-level local representations; and the self-
attention mechanism models global, high-level semantics and long-range dependencies. The
dataset consists of 3D point-clouds containing both
brain surface patches and fiber information generated by a large-scale finite element model. Trans-Unet is applied to predict
brain surface folding from the initial state (state 0 or states 0-2) to the final state (state 3). Experimental results demonstrate that Trans-Unet achieves high-resolution predictions of
brain patch growth, surpassing existing methods in both fidelity and accuracy.
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All authors Geran Zhao, Xiaotian Li, Poorya Chavoshnejad, Mir Jalil Razavi, Akbar Solhtalab, Lijun Yin, Guifang Fu
Signal Processing
Noncoherent Detection and Interference Nulling for Terrestrial-Satellite Downlink Coexistence in the Upper Mid-Band
Shizhen Jia, C. Nicolas Barati, Marco Mezzavilla et al. (+1 more)
Terrestrial–satellite coexistence in the upper mid-band is challenging when a terrestrial base station has limited prior information about non-terrestrial receivers and their uplink transmissions. This paper studies noncoherent victim sensing and interference nulling, where uplink sensing snapshots are observed while the transmitted waveform is treated as unknown. For a single victim, we show that the generalized likelihood-ratio test reduces to a principal-eigenvector
estimator of the sample covariance. For multiple victims, we combine MDL-based model-order selection with MUSIC to recover anonymous direction and power information that is sufficient for beam design without pilot knowledge or user identities. These estimates are then used in a nulling beamformer that preserves the intended terrestrial link while reducing leakage toward detected victims. We further analyze the single-victim
estimator in the large-
matrix regime and show that estimation accuracy improves with sensing SNR, which reveals the observed interplay between path loss and estimation quality. Site-specific ray-tracing results show significant reduction of NTN INR with only modest degradation of TN SINR.
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All authors Shizhen Jia, C. Nicolas Barati, Marco Mezzavilla, Sundeep Rangan
Systems and Control · Cross-listed from cs.LG
A Graph-Based Control Interface for Traffic Signals on Heterogeneous Road Networks
Bertil Braun
We present a traffic-signal control interface in which a shared graph neural network assigns scores to individual traffic movements. Each junction converts these scores into its own variable-sized set of legal signal phases using a deterministic incidence
matrix. Directed corridor nodes provide traffic context, while movement nodes represent controlled input-to-output paths through junctions. Typed mean aggregation produces one scalar per movement; phase definitions and signal timing remain outside the learned network. This makes graph size and junction-specific action count independent of the learned parameter shapes. PPO experiments evaluate the interface on unseen synthetic grid geometries, altered signal coverage, and five heterogeneous city graphs. The policies retained performance across unseen geometries within the synthetic grid family, while changes in signal coverage exposed sensitivity to a signal-coverage distribution shift. A single trained city-policy instance executed across all five city graphs, with heterogeneous outcomes. These results provide feasibility evidence rather than a general estimate of transfer to arbitrary road networks.
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Signal Processing
Advances in Wavelet Denoising for Communication Signals: From Parameter Selection Toward Data-Driven Optimization
Priyalakshmi Sheela and Indrakshi Dey
Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but efficacy depends on coupled choices of transform, mother wavelet, decomposition level, thresholding rule, and shrinkage function. Pairing communication experiments with a PRISMA-aligned synthesis of 2020-2025 studies from ten sources, coded by parameter focus and domain (biomedical, industrial, geophysical, communication), this review traces a shift from defaults to similarity-, sparsity-,
entropy-, energy-, sub-band-SNR-, and task-loss-driven selection, but finds communication evidence scarce. We
benchmark DWT, SWT, and WPT on an impulsive-noise OFDM channel under SNR gain, MSE, BER, EVM, and real-time feasibility, with Friedman and Wilcoxon tests, efficiency-index ranking, and assessment of embedded DSP/FPGA constraints. As waveform-fidelity gains need not improve hard-decision performance, the Unified Decision Framework prioritises receiver-level validation over fidelity surrogates, validated on synthetic pilot-aided OFDM and measured IEEE 802.11n channels. On the synthetic link, the configuration attains the lowest channel-estimation NMSE, significantly outperforms a fixed-parameter wavelet $(p < 10^{-11})$, and is statistically indistinguishable from the strongest classical baseline in BER; on measured channels it attains the lowest BER and EVM. The supported claim is the lowest estimation error at statistically equivalent bit-error performance, and it is conditional on the disturbance being structured or impulsive, since under AWGN alone wavelet thresholding yields no systematic BER improvement. Within these limits the framework generalises to held-out data and adapts to the observed channel, recasting wavelet denoising into a reproducible, communication-aware design methodology extensible to deep-unfolding architectures.
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Systems and Control · Cross-listed from cs.HC
Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch
Kumar Akash, Zhaobo Zheng, Teruhisa Misu et al. (+4 more)
Driving style is a key factor in the comfort and acceptance of automated vehicle (AV) features. In SAE Level-2 automation, where the driver must supervise the system and remain ready to intervene, mismatches between the automation's driving style and the driver's preference can reduce trust and trigger takeovers. This paper proposes an adaptive driving-style control framework that minimizes such preference mismatch. In a driving-simulator study, we compare fixed, trust-based, and preference-based adaptation heuristics and analyze their effects on preference mismatch and trust. We then train a driving-preference prediction model and use it in an implicit adaptation policy that selects among bounded driving styles for upcoming events. A validation study shows that the predictive policy achieves equal or lower preference mismatch than comparison baselines, particularly when starting from a less defensive style, while also yielding higher average trust. The results provide a step toward developing human-aware driving automation that can implicitly adapt its driving style to the driver's preferences.
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All authors Kumar Akash, Zhaobo Zheng, Teruhisa Misu, Vidya Krishnamoorthy, Mia Dong, Yuni Lee, Gaojian Huang
Signal Processing
Leveraging Agonistic-Antagonistic Coactivation in Single-Grid HDsEMG for Hand Gesture Recognition
Firas Darwish, Dhiyaa Al Jorf, Costanza Armanini et al. (+2 more)
Surface Electromyography (sEMG) is critical for intention prediction in human-computer interfaces, such as for prosthetics control. Although deep learning models for Hand Gesture Recognition (HGR) yield excellent results, they impose high computational and hardware demands. This paper addresses this bottleneck by exploiting redundancies in agonist-antagonist muscle activity, hypothesizing that coactivations present in the sEMG signals from the extensor or flexor groups alone are sufficient for accurate HGR. We evaluate this by comparing convolutional neural networks (CNNs) trained on one muscle grid against CNN architectures trained jointly on both grids. Experiments were conducted using 16 gestures from a
dataset of high-density sEMG signals from 20 subjects. The results demonstrate that the extensor grid alone achieves performance (89.5% balanced accuracy, 0.99 AUROC) comparable to the dual-grid system (94.6% balanced accuracy, 1.00 AUROC). Notably, even when applying slow joint fusion to capture spatial features across grids, model performance did not improve. GradCAM visualizations and anatomical analysis further indicate that the extensor region provides superior signal quality compared to the flexors. Our findings suggest that for a base set of DoF gestures, HGR hardware requirements and computational complexity can be halved without a prohibitive loss in accuracy.
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All authors Firas Darwish, Dhiyaa Al Jorf, Costanza Armanini, Eion Tyacke, Farah E. Shamout
Systems and Control
Allostatic Control Systems: Goal Governance in Changing Environments
Thomson D. Nguy
Allostatic control systems govern not only how a system pursues a goal, but whether the goal itself remains appropriate as the environment changes. This matters whenever a controller can continue to regulate successfully against a reference that no longer serves the system. We develop this as a two-timescale problem: a fast loop regulates under the current reference, while a slow loop governs whether that reference should move. We then test one candidate mechanism in which slow-loop movement waits for mature outcome evidence. In a preregistered synthetic experiment, that mechanism increased decision cost by 1.51% relative to otherwise identical bounded adaptation. The central failure was timing: the evidence-to-effect pathway often could not make correction effective before the environment changed again. The result motivates a broader design principle: an allostatic controller must be able to revise an inappropriate goal faster than serviceability is lost by continuing to defend it. We therefore treat allostatic control systems as a general engineering problem of goal governance under changing environments, and the negative result as one step toward making that problem operational and falsifiable.
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Signal Processing
Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
Khalid El-Darymli, Christoph H. Gierull, Katerina Biron et al. (+1 more)
Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep sigma-point process (DSPP) model for predicting RCS in synthetic aperture radar (SAR) imagery using a RADARSAT-2
dataset containing 208,191 verified ships. The DSPP model not only strives for predictive accuracy but also characterizes the uncertainty inherent in the intricate relationships among radar signals, ship parameters, and environmental conditions. Unlike traditional approaches that rely on deterministic equations with static parameters, the DSPP uses a hierarchical Gaussian process framework with
Bayesian inference to capture variability and uncertainty in RCS predictions. By generating predictive distributions rather than single estimates, the model accounts for the complex dynamics governing radar returns. Using a Matern kernel with automatic relevance determination, the DSPP identifies and ranks critical features across radar, operational, and environmental domains, thereby supporting transparency and interpretability. Performance evaluations demonstrate the model's superiority over linear regression baselines, with a 20.83 percent reduction in root mean squared error, a 25.89 percent increase in R-squared, and a 44.4 percent reduction in both the residual interquartile range and median absolute deviation on the test data. By providing calibrated uncertainty bounds, the DSPP enhances prediction reliability and supports robust decision-making. This work represents a shift toward probabilistic models that incorporate the inherent uncertainty of complex phenomena. By transitioning from fixed equations to distributions over outcomes, the DSPP fosters a deeper understanding of RCS behavior and enables systems to operate effectively in dynamic environments.
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All authors Khalid El-Darymli, Christoph H. Gierull, Katerina Biron, Weimin Huang
Image and Video Processing · Cross-listed from cs.ET
Quantum Adaptive Sensing for Accelerated MRI
Asmit Ganguly, Suprajit Dewanji, Chenyang Zhao et al. (+1 more)
Compressed sensing accelerates MRI by reconstructing images from undersampled k-space, but performance depends strongly on sampling distribution. We propose an adaptive framework that selects Cartesian phase-encode lines sequentially using a fixed-cardinality quadratic unconstrained binary optimization (QUBO) formulation. The objective combines a preference for central k-space, signal-energy information from previously acquired measurements, and pairwise terms that encourage spatially dispersed sampling. The formulation is compatible with classical annealing and
quantum-annealing hardware. Retrospective experiments used simulated eight-coil 3D MRI data; QUBO problems were solved with parallel tempering, and images were reconstructed with SENSE and total-variation regularization. At 20% and 10% sampling, the proposed method improved PSNR, SSIM, NMSE, and HFEN compared with the evaluated static Cartesian strategies, including variable-density Poisson-disc sampling, although gains varied with resolution, acceleration, and noise level. In a reduced-pool experiment, a D-Wave
quantum-classical hybrid solver achieved reconstruction quality comparable to variable-density Poisson-disc sampling, demonstrating feasibility on current
quantum optimization infrastructure. While these results do not establish
quantum computational advantage, the direct QUBO representation provides a practical framework for adaptive MRI sampling and may benefit from future advances in
quantum-annealing hardware. Prospective scanner validation and systematic
quantum-classical benchmarking remain necessary.
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All authors Asmit Ganguly, Suprajit Dewanji, Chenyang Zhao, Danny J. J. Wang
Systems and Control · Cross-listed from stat.ME
Equilibrium Causal Digital Twins: Validation, Transport, and Identification Limits
Faraz Dadgostari and Neda Nazemi
Digital twins are often used to predict how a system would respond to an intervention. In systems with feedback, a twin must reproduce an
equilibrium counterfactual, and a twin developed in one domain may fail after mechanisms change. We study when these predictions can be validated and transported. For
equilibrium causal games, we give conditions on the mechanisms,
equilibrium selection, and intervention design under which agreement with experimental distributions identifies the counterfactual of interest. We show why agreement of means and covariances is insufficient for distributional queries. We then introduce cyclic selection diagrams and derive criteria for direct reuse and for hybrid models that combine invariant source mechanisms with target information. An impossibility result constructs systems that agree under every experiment in a finite design but disagree on the target counterfactual, showing that validation requires structural assumptions. For linear models, we derive intervention requirements that depend on the mechanisms that changed, the observation model, and graph support. When point identification fails, we characterize the remaining range of query values. We also provide statistical tests for reconstructed means and covariances and illustrate the theory in synthetic feedback systems.
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Systems and Control · Cross-listed from cs.LG
Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction
Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez et al. (+1 more)
Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur. The prediction model must be auditable by operators and must train across base stations without pooling per-slice KPIs, which are commercially sensitive because slices are leased to individual tenants. Neural additive models (NAMs) offer auditability because each KPI contributes through a visible shape function. However, visibility alone does not guarantee physical validity. On the ColO-RAN testbed
dataset, unconstrained NAMs learn effects that contradict wireless physics, for example predicting higher risk when channel quality improves. This failure appears under both local and centralized training, and non-IID
federated averaging worsens it. We present Monotone FedNAM, a
federated additive model in which KPIs with unambiguous physical direction are represented as monotone splines whose constraints survive FedAvg aggregation by construction, while contestable KPIs remain unconstrained. The model trains and operates as a Non-RT RIC rApp and is compact enough for deployment as a Near-RT RIC xApp. Monotone FedNAM eliminates all monotonicity violations, raises constrained shape consistency from 0.71 to 1.00, generalizes to an unseen scheduling policy, and reduces uplink traffic by 65%, at a cost of 0.04 to 0.07 AUC. These results show that physically constrained
federated additive models can support auditable SLA risk inference for multi-tenant O-RAN service assurance
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All authors Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez, Syed A. Zaidi
Image and Video Processing · Cross-listed from cs.CV
Synthetic data generation framework for quality control automation in gravure printing
Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali et al. (+1 more)
Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in rotogravure printing. Deep learning models give prospects for automation. However, training robust deep learning models, such as YOLO or
Vision Transformers, is heavily hindered by the extreme scarcity of real-world industrial defects images. To overcome this limitation, this paper introduces a novel synthetic data generation framework tailored for rotogravure printing quality control. The proposed pipeline automatically generates high-fidelity images of specific printing defects (creases, streaks, misregistration, etc.) and outputs corresponding bounding boxes and annotations. To validate the framework, a synthetic
dataset of 7533 images was generated and used to train the state-of-the-art object-detection model RFDETR. Experimental results demonstrate that the model trained on our synthetic data achieves a Mean Average Precision (mAP) of 80.9% on real industrial testing samples. This framework provides a zero-cost, rapid-deployment solution for automating defect inspection in printing lines without requiring massive manual data collection.
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All authors Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali, Andrea Trombin
Signal Processing
Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models
Yingchao Huang, Xin Wang, Yuhan Su et al. (+1 more)
Cognitive impairment (CI) is a growing public health concern. Early and accurate diagnosis is critical for enabling timely intervention and improving patient outcomes.
Speech-based CI detection has emerged as a promising non-invasive approach, as
speech signals encode both linguistic and acoustic markers associated with cognitive decline. Recent advances in large
language models (LLMs) further strengthen the potential of
speech-based assessment by enabling more expressive representation learning and improved generalization across diverse speakers, recording devices, and clinical environments. Moreover,
multimodal learning by jointly modeling linguistic and acoustic features allows for a more comprehensive characterization of cognitive and behavioral changes related to CI, leading to more reliable detection. In this work, we propose a
multimodal CI detection framework based on open-source LLMs that integrates
speech audio and corresponding transcripts while preserving patient
privacy. Acoustic
embeddings are extracted directly from
speech signals, while textual
embeddings are generated from automatically transcribed
speech. These modality-specific
embeddings are then concatenated to create a combined feature vector and used for downstream classification, without requiring access to raw or sensitive patient data. The proposed approach is evaluated on the ADReSS20 and ADReSSo21
benchmark datasets. Experimental results show that the proposed
multimodal framework achieves an CI classification accuracy of 92.4% and consistently outperforms single-modality baselines. Our work establishes a new state-of-the-art for CI identification, with the proposed method demonstrating superior cross-
dataset generalization. This advance highlights the power of an LLM-based
multimodal framework that fuses linguistic and acoustic data to enable robust, scalable, and non-invasive screening.
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All authors Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao
Systems and Control
Interoperability of Electric Drive Models using HELICS
Ajay Pratap Yadav
Accurate modeling and simulation are essential for the effective design, testing, and evaluation of electric machine systems. However, existing models often face interoperability challenges due to differences in programming
languages (e.g., C, MATLAB, Python) and the separation of components such as inverters and controllers across diverse environments. These challenges are amplified by the growing use of advanced simulation platforms like Hardware-in-the-Loop (HIL) and Controller-HIL, which require repeated adaptations for compatibility. This paper presents a HELICS-based co-simulation framework that enables seamless coordination among heterogeneous tools and models, providing a unified platform for integrating and testing electric drive models regardless of their origin. The approach is demonstrated through the co-simulation of an inverter-fed permanent magnet synchronous machine (PMSM) drive under speed control, showcasing reduced development time, flexible reuse of existing models, and efficient integration into both software and HIL environments offering a scalable, modular solution for collaborative and repeatable electric drive system testing and development.
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Signal Processing · Cross-listed from cs.LG
Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking
Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck et al. (+2 more)
Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.
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All authors Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy
Signal Processing
Constellation Selection and Power Allocation for Multi-Cell OFDM-ISAC: Managing Inter-Cell Interference and Sensing Sidelobes
Kaitao Meng, Kawon Han, Christos Masouros et al. (+1 more)
Future integrated sensing and communication (
ISAC) networks are expected to operate in dense multi-
cell environments, where multiple base stations (BSs) share their time-frequency resources for communication and sensing. In such scenarios, the delay–Doppler (DD) sensing performance is strongly affected by random finite-alphabet orthogonal frequency-division multiplexing (OFDM) symbols, power allocation, receive filtering, and interference. This paper develops a modulation- and receive-filter-aware framework for the sensing-interference management in multi-
cell OFDM-
ISAC systems. Starting from a discrete-time OFDM sensing model, we derive closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range–Doppler bin under matched filtering (MF) and reciprocal filtering (RF). The analysis reveals distinct interference structures: MF depends on fourth-order constellation moments and power-overlap terms, whereas RF is governed by inverse-symbol-power and ratio-type interference terms. Based on these expressions, we obtain sensing-oriented power allocation structures, including a ramped water-filling solution for MF and a square-root allocation rule for RF. Furthermore, we jointly optimize the finite-alphabet constellation selection and power allocation under realistic communication and power constraints, and obtain tractable mixed-integer convex formulations for both MF and RF. Additionally, we study spectrum-overlap coordination in multi-
cell scenarios and reveal the distinct MF/RF preferences for shared and orthogonalized tones. Furthermore, we extend the interference model to inter-
cell propagation delays exceeding the cyclic prefix (CP), and show how the resultant delay violation redistributes the nominal interference spectrum into a delay-distorted effective spectrum...
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All authors Kaitao Meng, Kawon Han, Christos Masouros, Lajos Hanzo
Audio and Speech Processing
From Read Speech to Spoken Digits: A Task-Specific Evaluation of Speech Privacy With Informed Attackers
Jule Pohlhausen, Anjana Rajasekhar, Anna Leschanowsky et al. (+1 more)
Protecting
speech privacy in real-life audio recordings is a growing concern. This contribution evaluates the effectiveness of three obfuscation techniques in protecting linguistic
speech content, using digit recognition as a task-specific and practically motivated evaluation scenario. As a first baseline, a general-purpose
speech recognition model and a digit-specific classifier were applied as informed attackers to recognise both single digits and concatenated digit sequences. Our experimental results demonstrate significant differences in recognition performance across digit modality,
speech rate, and attack model. These findings emphasize the need for more comprehensive and application-oriented evaluation methods to ensure
speech privacy.
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All authors Jule Pohlhausen, Anjana Rajasekhar, Anna Leschanowsky, Joerg Bitzer
Systems and Control · Cross-listed from cs.AI
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
Ari Luna Rueda, Eike Cramer, Klaus Hellgardt et al. (+1 more)
We present a structured large-
language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-interface construction, normalization, manipulated-variable controlled-variable (MV-CV) pairing, controller specification, closed loop simulation, scenario generation, performance evaluation and
Bayesian-optimization (BO) based tuning. Generated artifacts are executed and validated before downstream tasks proceed, and failed artifacts are repaired using validation feedback. The proposed approach is demonstrated on a nonlinear gas-preheater
benchmark with coupled pressure and temperature dynamics. The generated workflow produces a physically consistent decentralized PI (proportional-integral) feedback-feedforward control structure and an executable tuning environment.
Bayesian optimization reduces the closed loop performance objective, which aggregates set-point tracking and disturbance-rejection errors for the controlled variables, by approximately 26.5% relative to the initial controller generated by the workflow, mainly through improved pressure-loop transient performance. This figure quantifies the automated tuning stage rather than a comparison against a manually designed controller. The results demonstrate the feasibility of using structured large-
language-model-based code generation to construct executable control-design workflows, while also highlighting the need for broader validation on larger plantwide-control
benchmarks.
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All authors Ari Luna Rueda, Eike Cramer, Klaus Hellgardt, Mehmet Mercangöz
Image and Video Processing · Cross-listed from cs.CV
HGeo-TopoMap: Boosting Topological Mapping with Hierarchical Geometric Priors
Siyu Li, Kunyu Peng, Di Wen et al. (+3 more)
Topological maps are key outputs of autonomous driving perception systems, delivering essential road information for path planning. They identify instances such as centerlines and traffic signs, along with their connectivity relationships. Due to the lack of explicit markings for centerlines in real-world environments, the detection of centerline instances remains a significant challenge. To tackle this problem, we propose HGeo-TopoMap, which leverages an explicit prior map and implicit spatial relations to hierarchically boost topological mapping. First, a
geometric adaptive learning module is designed for the road structure map obtained via inverse perspective mapping. This module discretely encodes semantic and spatial features from the map, followed by a prior-mask
attention mechanism that selectively focuses on informative regions. Then, a
geometric consistency learning module is devised, which leverages the
geometric properties and spatial relationships of centerlines. Built on the geometry-aware decoder, it enforces spatial consistency by aligning features of centerline instances with identical
geometric orientations. The proposed method is evaluated on the OpenLane-V2
dataset across the centerline, lane segment, and robustness
benchmarks. Beyond substantial improvements in topological mapping accuracy, the proposed method offers the benefit of enhanced robustness, consistently outperforming baselines under both standard and challenging conditions. The source code and model weights will be made publicly available at https://github.com/lynn-yu/HGeo-TopoMap.
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All authors Siyu Li, Kunyu Peng, Di Wen, Beiping Hou, Zhiyong Li, Kailun Yang
Systems and Control
When Persistency is not Exciting in Data-Driven Predictive Control
Gianluca Giacomelli, Manuel Klädtke, Chuyu Lu et al. (+3 more)
Understanding how to collect data that is "meaningful" for control purposes is of paramount importance in data-driven control. While existing approaches have primarily relied on the satisfaction of a rank condition to assess the quality of an experiment, we show that satisfying it is not always sufficient to achieve satisfactory closed-loop performance. Focusing on scenarios where white-noise-like excitation cannot be used for data collection, we examine the frequency-domain implications of linear behavioral representation. This analysis demonstrates that leakage effects are the main driver for data to represent the dynamics of the system. These findings are reflected in our numerical results. Data-enabled predictive controllers built on
datasets with insufficient bandwidth, despite fulfilling standard rank conditions, suffer from severe ill-conditioning and fail to achieve reference tracking.
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All authors Gianluca Giacomelli, Manuel Klädtke, Chuyu Lu, Siep Weiland, Moritz Schulze Darup, Valentina Breschi