Systems and Control · Cross-listed from cs.LG
Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling
Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks et al. (+4 more)
The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on embedded hardware, especially when models are complex or horizons are long. Learning-based NMPC approximations shift this computation offline but typically demand large expert
datasets and costly training. We propose Sequential-AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, we wrap the policy in a safety-augmented online evaluation and fallback mechanism, yielding Safe Sequential-AMPC. Compared to a naive feedforward policy baseline across several
benchmarks, Sequential-AMPC requires substantially fewer expert MPC rollouts and yields candidate sequences with higher feasibility rates and improved closed-loop safety. On high-dimensional systems, it also exhibits better learning dynamics and performance in fewer epochs while maintaining stable validation improvement where the feedforward baseline can stagnate.
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All authors Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks, Johannes Edelmann, Manfred Plöchl, Radu Grosu, Ezio Bartocci
Systems and Control · Cross-listed from math.OC
Model Predictive Path Integral Control as Preconditioned Gradient Descent
Mahyar Fazlyab, Sina Sharifi and Jiarui Wang
Model Predictive Path Integral (MPPI) control is a widely used sampling-based method for trajectory optimization, yet its convergence properties remain only partially understood. This paper provides a direct convergence analysis using variational optimization. By lifting constrained trajectory optimization to a Kullback-Leibler (KL) regularized problem over decision distributions, we derive a reduced free-energy objective defined over a parametric sampling family. For general parametric families, we derive gradient and Hessian representations of this reduced objective and analyze preconditioned gradient descent on the sampling-distribution parameters. In the fixed-covariance Gaussian case, the classical MPPI update is recovered exactly as a unit-step preconditioned gradient update. We prove descent and stationarity guarantees for the exact expectation-based iteration when the Hessian of the reduced objective is bounded in the metric induced by the preconditioner. For the Gaussian family, we further show that the preconditioned Hessian is governed by the covariance of the Gibbs-tilted distribution relative to the covariance of the sampling distribution, yielding a covariance-dependent sufficient condition for the descent of exact unit-step MPPI. Numerical experiments illustrate the theory and the effect of key hyperparameters.
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Systems and Control · Cross-listed from cs.LG
Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability
Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi et al. (+1 more)
Accurate
forecasting of state-of-health (SOH) is essential for ensuring safe and reliable operation of lithium-ion
cells. However, existing models calibrated on laboratory tests at specific conditions often fail to generalize to new
cells that differ due to small manufacturing variations or operate under different conditions. To address this challenge, an uncertainty-aware transfer learning framework is proposed, combining a Long Short-Term
Memory (LSTM) model with domain adaptation via Maximum Mean Discrepancy (MMD) and uncertainty quantification through Conformal Prediction (CP). The LSTM model is trained on a virtual battery
dataset designed to capture real-world variability in electrode manufacturing and operating conditions. MMD aligns latent feature distributions between simulated and target domains to mitigate domain shift, while CP provides calibrated, distribution-free prediction intervals. This framework improves both the generalization and trustworthiness of SOH forecasts across heterogeneous
cells.
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All authors Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Marcello Canova
Systems and Control
Robust Optimal Operation of Virtual Power Plants Under Decision-Dependent Uncertainty of Price Elasticity
Tao Tan, Rui Xie, Meng Yang et al. (+1 more)
The rapid deployment of distributed energy resources (DERs) is one of the essential efforts to mitigate global
climate change. However, a vast number of small-scale DERs are difficult to manage individually, motivating the introduction of virtual power plants (VPPs). A VPP operator coordinates a group of DERs by setting suitable prices, and aggregates them for interaction with the power grid. In this context, optimal pricing plays a critical role in VPP operation. This paper proposes a robust optimal operation model for VPPs that considers uncertainty in the price elasticity of demand. Specifically, the demand elasticity is found to be influenced by the pricing decision, giving rise to decision-dependent uncertainty (DDU). An improved column-and-constraint (C&CG)
algorithm, together with tailored transformation and reformulation techniques, is developed to solve the robust model with DDU efficiently. Case studies based on actual electricity consumption data of London households demonstrate the effectiveness of the proposed model and
algorithm.
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All authors Tao Tan, Rui Xie, Meng Yang, Yue Chen
Audio and Speech Processing
ArrayDPS-Refine: Generative Refinement of Discriminative Multi-Channel Speech Enhancement
Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta et al. (+3 more)
Multi-channel
speech enhancement aims to recover clean
speech from noisy multi-channel recordings. Most deep learning methods employ discriminative training, which can lead to non-linear distortions from regression-based objectives, especially under challenging environmental noise conditions. Inspired by ArrayDPS for unsupervised multi-channel source separation, we introduce ArrayDPS-Refine, a method designed to enhance the outputs of discriminative models using a clean
speech diffusion prior. ArrayDPS-Refine is training-free, generative, and array-agnostic. It first estimates the noise spatial covariance
matrix (SCM) from the enhanced
speech produced by a discriminative model, then uses this estimated noise SCM for
diffusion posterior sampling. This approach allows direct refinement of any discriminative model's output without retraining. Our results show that ArrayDPS-Refine consistently improves the performance of various discriminative models, including state-of-the-art waveform and STFT domain models. Audio demos are provided at https://xzwy.github.io/ArrayDPSRefineDemo/.
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All authors Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta, Zhaoheng Ni, Sanjeel Parekh, Buye Xu
Systems and Control · Cross-listed from physics.soc-ph
On a Co-evolving Opinion-Leadership Model in Social Networks
Martina Alutto, Lorenzo Zino, Karl H. Johansson et al. (+1 more)
Leadership in social groups is often a dynamic characteristic that emerges from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, at the same time maintaining
alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co-evolve within a social network. Our model extends the Friedkin-Johnsen framework by making susceptibility to peer influence time-dependent, turning it into the leadership variable. Leadership strengthens when an
agent holds strong yet socially aligned opinions, and declines when such
alignment is lost, capturing the trade-off between conviction and social acceptance. After illustrating the emergent behavior of this complex system, we formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non-trivial
equilibrium, and examining two time-scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.
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All authors Martina Alutto, Lorenzo Zino, Karl H. Johansson, Angela Fontan
Signal Processing
Spectral Impact of Mismatches in Interleaved ADCs
Jérémy Guichemerre, Robert Reutemann, Thomas Burger et al. (+1 more)
Interleaved ADCs are critical for applications requiring multi-gigasample per second (GS/s) rates, but their performance is often limited by offset, gain, and timing skew mismatches across the sub-ADCs. We propose exact but compact expressions that describe the impact of each of those non-idealities on the output spectrum. We derive the distribution of the power of the induced spurs and replicas, critical for yield-oriented derivation of sub-ADC specifications. Finally, we provide a practical example in which calibration step sizes are derived under the constraint of a target production yield.
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All authors Jérémy Guichemerre, Robert Reutemann, Thomas Burger, Christoph Studer
Signal Processing
Towards Semantic-based Agent Communication Networks: Vision, Technologies, and Challenges
Ping Zhang, Rui Meng, Xiaodong Xu et al. (+24 more)
The International Telecommunication Union (ITU) identifies "Artificial Intelligence (AI) and Communication" as one of six key usage scenarios for 6G. Agentic AI, characterized by its ca-pabilities in multi-modal environmental sensing, complex task coordination, and continuous self-optimization, is anticipated to drive the
evolution toward
agent-based communication net-works. Semantic communication (SemCom), in turn, has emerged as a transformative paradigm that offers task-oriented efficiency, enhanced reliability in complex environments, and dynamic adaptation in resource allocation. However, comprehensive reviews that trace their technologi-cal
evolution in the contexts of
agent communications remain scarce. Addressing this gap, this paper systematically explores the role of semantics in
agent communication networks. We first propose a novel architecture for semantic-based
agent communication networks, structured into three layers, four entities, and four stages. Three wireless
agent network layers define the logical structure and organization of entity interactions: the intention extraction and understanding layer, the semantic encoding and processing layer, and the distributed autonomy and collabora-tion layer. Across these layers, four AI
agent entities, namely
embodied agents, communication
agents, network
agents, and application
agents, coexist and perform distinct tasks. Furthermore, four operational stages of semantic-enhanced agentic AI systems, namely perception,
memory,
reasoning, and action, form a cognitive cycle guiding
agent behavior. Based on the proposed architecture, we provide a comprehensive review of the state-of-the-art on how semantics en-hance
agent communication networks. Finally, we identify key challenges and present potential solutions to offer directional guidance for future research in this emerging field.
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All authors Ping Zhang, Rui Meng, Xiaodong Xu, Yaheng Wang, Zixuan Huang, Yiming Liu, Ruichen Zhang, Yinqiu Liu, Haonan Tong, Huishi Song, Gang Wu, Zhaoming Lu, Jiawen Kang, Geng Sun, Qinghe Du, Zhaohui Yang, Jingxuan Zhang, Han Meng, Lexi Xu, Haitao Zhao, Zesong Fei, Yiqing Zhou, Pei Xiao, Meixia Tao, Qinyu Zhang, Shuguang Cui, Rahim Tafazolli
Systems and Control · Cross-listed from cs.LG
Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning
Dogan Urgun and Gokhan Gungor
Designing effective auxiliary rewards for cooperative multi-
agent systems remains challenging, as misaligned incentives can induce suboptimal coordination, particularly when sparse task rewards provide insufficient grounding for coordinated behavior. This study introduces an autonomous reward design framework that uses large
language models (LLMs) to synthesize executable reward programs from environment instrumentation. The procedure constrains candidate programs within a formal validity envelope and trains policies from scratch using Multi-
Agent Proximal Policy Optimization (MAPPO) under a fixed computational budget. The candidates are then evaluated on the basis of their performance, and selection across generations solely based on the sparse task returns. The framework is evaluated in four Overcooked-AI layouts characterized by varying levels of corridor congestion, handoff dependencies, and structural asymmetries. The proposed reward design approach consistently yields higher task returns and delivery counts, with the most pronounced gains observed in environments dominated by interaction bottlenecks. Diagnostic analysis of the synthesized shaping components reveals stronger interdependence in action selection and improved signal
alignment in coordination-intensive tasks. These results demonstrate that the proposed LLM-guided reward search framework mitigates the need for manual engineering while producing shaping signals compatible with cooperative learning under finite budgets.
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Systems and Control
Graph-Theoretic Analysis of Residual Generation Under Computational Constraints
Jan Åslund
A unified structural framework is presented for model-based fault diagnosis that explicitly incorporates both fault locations and constraints imposed by the residual generation methodology. Building on the concepts of proper and minimal structurally overdetermined (PSO/MSO) sets and Test Equation Supports (TES/MTES), the framework introduces testable PSO sets, Residual Generation (RG) sets, irreducible fault signatures (IFS), and Irreducible RG (IRG) sets to characterize which submodels are suitable for residual generation under given computational restrictions. An operator $M^*$ is defined to extract, from any model, the largest testable PSO subset consistent with a specified residual generation method. Using this operator, an
algorithm is developed to compute all RG sets, and it is shown that irreducible fault signature sets form the join-irreducible elements of a join-semilattice of sets and fully capture the multiple-fault isolability properties in the method-constrained setting. The approach is exemplified on a semi-explicit linear DAE model, where low structural differential index can be used to define $M^*$. The results demonstrate that the proposed framework generalizes MTES-based analysis to residual generation scenarios with explicit computational limitations.
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Signal Processing
Incremental Learning-Based Open-Set Classification of Unknown UAVs via RF Signal Semantics
Julie Liu, Irshad A. Meer, Cicek Cavdar et al. (+1 more)
The proliferation of civilian and commercial unmanned aerial vehicles (
UAVs) has heightened the demand for reliable
radio frequency (RF)-based drone identification systems that can operate under dynamic and uncertain airspace conditions. Most existing RF-based recognition methods adopt a closed-set assumption, where all
UAV types are known during training. Such an assumption becomes unrealistic in practical deployments, as new or unknown
UAVs frequently emerge, leading to overconfident misclassifications and inefficient retraining cycles. To address these challenges, this paper proposes a unified incremental open-set learning framework for RF-based
UAV recognition that enables both novel class discovery and incremental adaptation. The framework first performs open-set recognition to separate unknown signals from known classes in the semantic feature space, followed by an unsupervised clustering module that discovers new
UAV categories by selecting between K-Means and Gaussian Mixture Models (GMM) based on composite validity scores. Subsequently, a lightweight incremental learning module integrates the newly discovered classes through a
memory-bounded replay mechanism that mitigates catastrophic forgetting. Experiments on a real-world
UAV RF
dataset comprising 24 classes (18 known and 6 unknown) show effective open-set detection, promising clustering performance under the evaluated noise settings, and stable incremental adaptation with minimal storage cost, supporting the potential of the proposed framework for open-world
UAV recognition.
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All authors Julie Liu, Irshad A. Meer, Cicek Cavdar, Mustafa Ozger
Systems and Control
Spatial Correlation, Non-Stationarity, and Degrees of Freedom of Holographic Curvature-Reconfigurable Apertures
Liuxun Xue, Shu Sun, Ruifeng Gao et al. (+1 more)
Low-altitude wireless platforms increasingly require lightweight, conformal, and densely sampled
antenna array apertures with high array gain and spatial selectivity. However, when deployed on nonplanar surfaces,
curvature alters the array
manifold, local visibility, and propagation support, potentially invalidating spatial-stationarity assumptions. In this paper, we investigate a
holographic curvature-reconfigurable aperture (HoloCuRA), modeled as a
curvature-controllable
holographic surface, and develop a visibility-aware spatial characterization framework for its low-altitude applications. Specifically, the framework jointly quantifies array-domain spatial non-stationarity (SnS), and spatial degrees of freedom (DoF) in line-of-sight, 3GPP non-line-of-sight, and isotropic-scattering propagation environments. For SnS, a novel Power-balanced, Visibility-aware Correlation-
Matrix Distance (PoVi-CMD) and a two-stage subarray-screening procedure are introduced. For DoF, the Rényi-2 effective rank is adopted, and tractable spatial-correlation expressions under isotropic scattering are developed for efficient DoF analysis. Furthermore, a realizable
antenna port mode is introduced to connect SnS with DoF. Numerical results reveal that
curvature and propagation support are the primary determinants of both SnS and DoF in HoloCuRA: array domain SnS determines whether subarray statistics can be treated as locally consistent, whereas DoF limits the global spatial modes. The findings provide useful guidance for low-altitude
antenna-system design.
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All authors Liuxun Xue, Shu Sun, Ruifeng Gao, Xiaoqian Yi
Systems and Control
C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents
Guihlerme Daubt and Adrian Redder
Safe
navigation in complex environments remains a central challenge for reinforcement learning (RL) in robotics. This paper introduces Continuous Space-Time Empowerment for Physics-informed (C-STEP) safe RL, a novel measure of
agent-centric safety tailored to deterministic, continuous domains. This measure can be used to design physics-informed intrinsic rewards by augmenting positive
navigation reward functions. The reward incorporates the
agents internal states (e.g., initial velocity) and forward dynamics to differentiate safe from risky behavior. By integrating C-STEP with
navigation rewards, we obtain an intrinsic reward function that jointly optimizes task completion and collision avoidance. Numerical results demonstrate fewer collisions, reduced proximity to obstacles, and only marginal increases in travel time. Overall, C-STEP offers an interpretable, physics-informed approach to reward shaping in RL, contributing to safety for agentic mobile robotic systems.
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Signal Processing · Cross-listed from cs.IT
RIS-Assisted D-MIMO for Energy-Efficient 6G Indoor Networks
Akshay Vayal Parambath, Jose Flordelis, Venkatesh Tentu et al. (+4 more)
We propose an alternating optimization framework for maximizing energy efficiency (EE) in reconfigurable intelligent surface (RIS) assisted distributed
MIMO (D-
MIMO) systems under both coherent and non-coherent reception modes. The framework jointly optimizes access point (AP) power allocation and RIS phase configurations to improve EE under per-AP power and signal-to-interference-plus-noise ratio (SINR) constraints. Using majorization-minimization for power allocation together with per-element RIS adaptation, the framework achieves tractable optimization of this non-convex problem. Simulation results for indoor deployments with realistic power-consumption models show that the proposed scheme outperforms equal-power and random-scatterer baselines, with clear EE gains. We evaluate the performance of both reception modes and quantify the impact of RIS phase-shift optimization, RIS controller architectures (centralized vs. per-RIS control), and RIS size, providing design insights for practical RIS-assisted D-
MIMO deployments in future 6G networks.
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All authors Akshay Vayal Parambath, Jose Flordelis, Venkatesh Tentu, Charitha Madapatha, Fredrik Rusek, Erik Bengtsson, Tommy Svensson
Image and Video Processing
Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic
Wanying Qu, Jianxiong Gao, Wei Wang et al. (+1 more)
Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale
brain mechanisms. Functional
magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained
brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, therefore making high-quality fMRI reconstruction a crucial task. Electroencephalography (
EEG) offers millisecond-level temporal cues that complement fMRI. Leveraging this complementarity, we present an
EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and strong temporal coherence at the cortical-vertex level. To address sampling irregularities common in real fMRI acquisitions, we incorporate a null-space intermediate-frame reconstruction, enabling measurement-consistent completion of arbitrary intermediate frames and improving sequence continuity and practical applicability. Experiments on the CineBrain
dataset demonstrate superior voxel-wise reconstruction quality and robust temporal consistency across whole-
brain and functionally specific regions. The reconstructed fMRI also preserves essential functional information, supporting downstream visual decoding tasks. This work provides a new pathway for estimating high-resolution fMRI dynamics from
EEG and advances
multimodal neuroimaging toward more dynamic
brain activity modeling.
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All authors Wanying Qu, Jianxiong Gao, Wei Wang, Yanwei Fu
Audio and Speech Processing · Cross-listed from cs.SD
Semantic-Aware Interruption Detection in Spoken Dialogue Systems: Benchmark, Metric, and Model
Kangxiang Xia, Bingshen Mu, Xian Shi et al. (+2 more)
Achieving natural full-duplex interaction in spoken dialogue systems (SDS) remains a challenge due to the difficulty of accurately detecting user interruptions. Current solutions are polarized between "trigger-happy" VAD-based methods that misinterpret backchannels and robust end-to-end models that exhibit unacceptable response delays. Moreover, the absence of real-world
benchmarks and holistic metrics hinders progress in the field. This paper presents a comprehensive frame-work to overcome these limitations. We first introduce SID-Bench, the first
benchmark for semantic-aware interruption detection built entirely from real-world human dialogues. To provide a rigorous assessment of the responsiveness-robustness trade-off, we propose the Average Penalty Time (APT) metric, which assigns a temporal cost to both false alarms and late responses. Building on this framework, we design an LLM-based detection model optimized through a novel training paradigm to capture subtle semantic cues of intent. Experimental results show that our model significantly outperforms mainstream baselines, achieving a nearly threefold reduction in APT. By successfully resolving the long-standing tension between speed and stability, our work establishes a new state-of-the-art for intelligent interruption handling in SDS. To facilitate future research, SID-Bench and the associated code are available at: https://github.com/xkx-hub/SID-bench.
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All authors Kangxiang Xia, Bingshen Mu, Xian Shi, Jin Xu, Lei Xie
Systems and Control · Cross-listed from cs.LG
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao et al. (+2 more)
Tuning control policies manually to meet high-level objectives is often time-consuming.
Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function. However, many systems, particularly those involving humans, require optimization based on subjective criteria. Preferential
Bayesian optimization addresses this by learning from pairwise comparisons instead of quantitative measurements, but relying solely on preference data can be inefficient. We propose a multi-fidelity, multi-modal
Bayesian optimization framework that integrates low-fidelity numerical data with high-fidelity human preferences. Our approach employs Gaussian process surrogate models with both hierarchical, autoregressive and non-hierarchical, coregionalization-based structures, enabling efficient learning from mixed-modality data. We illustrate the framework by tuning an autonomous vehicle's trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments involving the human decision maker while effectively adapting driving style to individual preferences.
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All authors Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao, Sebastian Hirt, Rolf Findeisen
Systems and Control · Cross-listed from cs.RO
Equivariant Filter Transformations for Consistent and Efficient Visual–Inertial Navigation
Chungeng Tian, Fenghua He and Ning Hao
This paper presents an equivariant filter (EqF) transformation approach for visual–inertial
navigation. By establishing analytical links between EqFs with different
symmetries, the proposed approach enables systematic consistency design and efficient implementation. First, we formalize the mapping from the global system state to the local error-state and prove that it induces a nonsingular linear transformation between the error-states of any two EqFs. Second, we derive transformation laws for the associated linearized error-state systems and unobservable subspaces. These results yield a general consistency design principle: for any unobservable system, a consistent EqF with a state-independent unobservable subspace can be synthesized by transforming the local coordinate chart, thereby avoiding ad hoc
symmetry analysis. Third, to mitigate the computational burden arising from the non-block-diagonal Jacobians required for consistency, we propose two efficient implementation strategies. These strategies exploit the Jacobians of a simpler EqF with block-diagonal structure to accelerate covariance operations while preserving consistency. Extensive Monte Carlo simulations and real-world experiments validate the proposed approach in terms of both accuracy and runtime.
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Audio and Speech Processing
How Open is Open TTS? A Practical Evaluation of Open Source TTS Tools
Teodora Răgman, Adrian Bogdan Stânea, Horia Cucu et al. (+1 more)
Open-source text-to-
speech (TTS) frameworks have emerged as highly adaptable platforms for developing
speech synthesis systems across a wide range of
languages. However, their applicability is not uniform – particularly when the target
language is under-resourced or when computational resources are constrained. In this study, we systematically assess the feasibility of building novel TTS models using four widely adopted open-source architectures: FastPitch, VITS, Grad-TTS, and Matcha-TTS. Our evaluation spans multiple dimensions, including qualitative aspects such as ease of installation,
dataset preparation, and hardware requirements, as well as quantitative assessments of synthesis quality for Romanian. We employ both objective metrics and subjective listening tests to evaluate intelligibility, speaker similarity, and naturalness of the generated
speech. The results reveal significant challenges in tool chain setup, data preprocessing, and computational efficiency, which can hinder adoption in low-resource contexts. By grounding the analysis in reproducible protocols and accessible evaluation criteria, this work aims to inform best practices and promote more inclusive,
language-diverse TTS development. All information needed to reproduce this study (i.e. code and data) are available in our git repository: https://gitlab.com/opentts_ragman/OpenTTS
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All authors Teodora Răgman, Adrian Bogdan Stânea, Horia Cucu, Adriana Stan
Image and Video Processing
Comparative analysis of dual-form networks for live land monitoring using multi-modal satellite image time series
Iris Dumeur, Jérémy Anger and Gabriele Facciolo
Multi-modal Satellite Image Time Series (SITS) analysis faces significant computational challenges for live land monitoring applications. While
Transformer architectures excel at capturing temporal dependencies and fusing multi-modal data, their quadratic computational complexity and the need to reprocess entire sequences for each new acquisition limit their deployment for regular, large-area monitoring. This paper studies various dual-form
attention mechanisms for efficient multi-modal SITS analysis, that enable parallel training while supporting recurrent inference for incremental processing. We compare linear
attention and retention mechanisms within a multi-modal spectro-temporal encoder. To address SITS-specific challenges of temporal irregularity and unalignment, we develop temporal adaptations of dual-form mechanisms that compute
token distances based on actual acquisition dates rather than sequence indices. Our approach is evaluated on two tasks using Sentinel-1 and Sentinel-2 data: multi-modal SITS
forecasting as a proxy task, and real-world
solar panel construction monitoring. Experimental results demonstrate that dual-form mechanisms achieve performance comparable to standard
Transformers while enabling efficient recurrent inference. The
multimodal framework consistently outperforms mono-modal approaches across both tasks, demonstrating the effectiveness of dual mechanisms for sensor fusion. The results presented in this work open new opportunities for operational land monitoring systems requiring regular updates over large geographic areas.
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Signal Processing · Cross-listed from stat.ME
Aitchison Geometry on the Simplex for Uncertainty Quantification in Bayesian Hyperspectral Image Unmixing
Hector Blondel, Lucas Drumetz and Thierry Chonavel
Most
algorithms for hyperspectral image unmixing produce point estimates of fractional abundances of the materials to be separated. However, in the absence of reliable ground truth, the ability to perform abundance uncertainty quantification (UQ) should be an important feature of
algorithms, e.g. to evaluate how hard the unmixing problem is and how much the results should be trusted. The usual modeling assumptions in
Bayesian models for unmixing rely heavily on the Euclidean geometry of the simplex and typically disregard spatial information. In addition, to our knowledge, abundance UQ is close to nonexistent. In this paper, we propose to leverage Aitchinson geometry from the compositional data analysis literature to provide practitioners with alternative tools for modeling prior abundance distributions. In particular we show how to design simplex-valued Gaussian Process priors using this geometry. Then we link Aitchinson geometry to constrained sampling
algorithms in the literature, and propose UQ diagnostics that comply with the constraints on abundance vectors. We illustrate these concepts on real and simulated data.
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Audio and Speech Processing
Photogrammetry-Reconstructed 3D Head Meshes for Accessible Individual Head-Related Transfer Functions
Ludovic Pirard, Lorenzo Picinali and Katarina C. Poole
Individual head-related transfer functions (HRTFs) are essential for accurate spatial audio binaural rendering but remain difficult to obtain due to measurement complexity. This study investigates whether photogrammetry-reconstructed (PR) head and ear meshes, acquired with consumer hardware, can provide a practically useful baseline for individual HRTF synthesis. Using the SONICOM HRTF
dataset, 72-image photogrammetry captures per subject were processed with Apple's Object Capture API to generate PR meshes for 150 subjects. Mesh2HRTF was used to compute PR synthetic HRTFs, which were compared against measured HRTFs, high-resolution 3D scan-derived HRTFs, KEMAR, and random HRTFs through numerical evaluation, auditory models, and a behavioural sound localisation experiment (N = 27). PR synthetic HRTFs preserved ITD cues but exhibited increased ILD and spectral errors. Auditory-model predictions and behavioural data showed substantially higher quadrant error rates, reduced elevation accuracy, and greater front-back confusions than measured HRTFs, performing worse than random HRTFs on perceptual metrics. Current photogrammetry pipelines support individual HRTF synthesis but are limited by insufficient pinna morphology details and high-frequency spectral fidelity needed for accurate individual HRTFs containing monaural cues.
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Signal Processing
Gaussian Phase Noise Effects on Hybrid Precoding MIMO Systems for Sub-THz Transmission
Yaya Bello, Yahia Medjahdi, Laurent Clavier et al. (+1 more)
The sub-THz spectrum offers numerous advantages, including massive multiple-input multiple-output (
MIMO) technology with large
antenna arrays that enhance spectral efficiency (SE) of future systems. Hybrid precoding (HP) thus emerges as a cost-effective alternative to fully digital precoding regarding complexity and energy consumption. However, sub-THz frequencies introduce hardware challenges, particularly phase noise (PN) from local oscillators (LOs). We analyze PN impact on
MIMO systems using HP, leveraging singular value decomposition and common LO architecture. We adopt the Gaussian PN (GPN) model, recognized as accurate for describing PN behavior in sub-THz transmissions. We derive a lower bound on achievable SE and provide closed-form bit error rate expressions for quadrature amplitude modulation (QAM), specifically 4-QAM and 16-QAM, under high-SNR and strong GPN conditions. These analytical results are validated through Monte Carlo simulations. We show that GPN can be effectively counteracted with a single pilot symbol in single-user
MIMO systems, unlike single-input single-output systems where mitigation proves infeasible. Simulation results compare conventional QAM against polar-QAM tailored for GPN-impaired systems. Finally, we introduce perspectives for further improvements in performance and energy efficiency.
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All authors Yaya Bello, Yahia Medjahdi, Laurent Clavier, Arthur Louchart
Systems and Control
A Low Cost Discrete Digital Isolator Circuit
Thomas Conway
This work presents a fully discrete, low cost digital isolator requiring no specialized ICs and implemented entirely with general purpose transistors and a two layer PCB embedded air core
transformer. The design avoids vendor lock in and long term component obsolescence risks, while providing >1 kV isolation, ~200 ns propagation delay, and validated NRZ data rates of 1 Mbps. A modified dual oscillator architecture enables inherent hardware lockout suitable for half bridge gate driver applications. Measured performance and PCB layout guidelines are provided.
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Signal Processing · Cross-listed from cs.IT
Robust and Secure Near-Field Communication via Curved Caustic Beams
Shicong Liu, Xianghao Yu and Robert Schober
Near-field beamfocusing with extremely large aperture arrays can effectively enhance physical layer security. Nevertheless, even small estimation errors of the eavesdropper's location may cause a pronounced focal shift, resulting in a severe degradation of the secrecy rate. In this letter, we propose a physics-informed robust beamforming strategy that leverages the electromagnetic (EM) caustic effect for near-field physical layer security provisioning, which can be implemented via phase shifts only. Specifically, we partition the transmit array into caustic and focusing subarrays to simultaneously bypass the potential eavesdropping region and illuminate the legitimate user, thereby significantly improving the robustness against the localization error of eavesdroppers. Moreover, by leveraging the connection between the phase gradient and the EM wave departing angle, we derive the corresponding piece-wise closed-form array phase profile for the subarrays. Simulation results demonstrate that the proposed scheme achieves up to an 80% reduction of the worst-case eavesdropping rate for a localization error of 0.25 m, highlighting its superiority for providing robust and secure communication.
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Systems and Control · Cross-listed from cs.LG
The impact of sensor placement on graph-neural-network-based leakage detection
J.J.H. van Gemert, V. Breschi, D.R. Yntema et al. (+2 more)
Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN-based leakage detection. We propose a novel PageRank-Centrality-based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.
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All authors J.J.H. van Gemert, V. Breschi, D.R. Yntema, K.J. Keesman, M. Lazar
Audio and Speech Processing
ACAVCaps: Enabling large-scale training for fine-grained and diverse audio understanding
Yadong Niu, Tianzi Wang, Heinrich Dinkel et al. (+6 more)
General audio understanding is a fundamental goal for large audio-
language models, with audio captioning serving as a cornerstone task for their development. However, progress in this domain is hindered by existing
datasets, which lack the scale and descriptive granularity required to train truly versatile models. To address this gap, we introduce ACAVCaps, a new large-scale, fine-grained, and multi-faceted audio captioning
dataset. Derived from the ACAV100M collection, ACAVCaps is constructed using a multi-expert pipeline that analyzes audio from diverse perspectives-including
speech, music, and acoustic properties-which are then synthesized into rich, detailed descriptions by a large
language model. Experimental results demonstrate that models pre-trained on ACAVCaps exhibit substantially stronger generalization capabilities on various downstream tasks compared to those trained on other leading captioning
datasets. The
dataset is available at https://github.com/xiaomi-research/acavcaps.
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All authors Yadong Niu, Tianzi Wang, Heinrich Dinkel, Xingwei Sun, Jiahao Zhou, Gang Li, Jizhong Liu, Junbo Zhang, Jian Luan
Image and Video Processing
Blind Quality Enhancement for G-PCC Compressed Dynamic Point Clouds
Tian Guo, Hui Yuan, Chang Sun et al. (+3 more)
Point cloud compression often introduces noticeable reconstruction artifacts, which makes quality enhancement necessary. Existing approaches typically assume prior knowledge of the distortion level and train multiple models with identical architectures, each designed for a specific distortion setting. This significantly limits their practical applicability in scenarios where the distortion level is unknown and computational resources are limited. To overcome these limitations, we propose the first blind quality enhancement (BQE) model for compressed dynamic point clouds. BQE enhances compressed point clouds under unknown distortion levels by exploiting temporal dependencies and jointly modeling feature similarity and differences across multiple distortion levels. It consists of a joint progressive feature extraction branch and an adaptive feature fusion branch. In the joint progressive feature extraction branch, consecutive reconstructed frames are first fed into a recoloring-based motion compensation module to generate temporally aligned virtual reference frames. These frames are then fused by a temporal correlation-guided cross-
attention module and processed by a progressive feature extraction module to obtain hierarchical features at different distortion levels. In the adaptive feature fusion branch, the current reconstructed frame is input to a quality estimation module to predict a weighting distribution that guides the adaptive weighted fusion of these hierarchical features. When applied to the latest geometry-based point cloud compression (G-PCC) reference software, i.e., test model category13 version 28, BQE achieved average PSNR improvements of 0.535 dB, 0.403 dB, and 0.453 dB, with BD-rates of -17.4%, -20.5%, and -20.1% for the Luma, Cb, and Cr components, respectively.
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All authors Tian Guo, Hui Yuan, Chang Sun, Wei Zhang, Raouf Hamzaoui, Sam Kwong
Signal Processing
Sensing-Assisted Adaptive Beam Probing with Calibrated Multimodal Priors and Uncertainty-Aware Scheduling
Abidemi Orimogunje, Vukan Ninkovic, Ognjen Kundacina et al. (+5 more)
Highly directional mmWave/THz links require rapid beam
alignment, yet exhaustive codebook sweeps incur prohibitive training overhead. This letter proposes a sensing-assisted adaptive probing policy that maps
multimodal sensing (radar/
LiDAR/camera) to a calibrated prior over beams, predicts per-beam reward with a deep Q-ensemble whose disagreement serves as a practical epistemic-uncertainty proxy, and schedules a small probe set using a Prior-Q upper-confidence score. The probing budget is adapted from prior
entropy, explicitly coupling sensing confidence to communication overhead, while a margin-based safety rule prevents low signal-to-noise ratio (SNR) locks. Experiments on DeepSense-6G (train: scenarios 42 and 44; test:43) with a 21-beam discrete
Fourier transform (DFT) codebook achieve Top-1/Top-3 of 0.81/0.99 with expected beam probe of 2 per sweep and zero observed outages at {\theta} = 0 dB with margin {\Delta} = 3 dB. The results show that
multimodal priors with ensemble uncertainty match link quality and improve reliability compared to ablations while cutting overhead with better predictive model.
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All authors Abidemi Orimogunje, Vukan Ninkovic, Ognjen Kundacina, Hyunwoo Park, Sunwoo Kim, Dejan Vukobratovic, Evariste Twahirwa, Gaspard Gashema
Signal Processing · Cross-listed from cs.NI
6D Movable Antenna for Internet of Vehicles: CSI-Free Dynamic Antenna Configuration
Maoxin Ji, Qiong Wu, Pingyi Fan et al. (+3 more)
Deploying six-dimensional movable
antenna (6DMA) systems in Internet-of-Vehicles (IoV) scenarios can greatly enhance spectral efficiency. However, the high mobility of vehicles causes rapid spatio-temporal channel variations, posing a significant challenge to real-time 6DMA optimization. In this work, we pioneer the application of 6DMA in IoV and propose a low-complexity, instantaneous channel state information (CSI)-free dynamic configuration method. By integrating vehicle motion prediction with offline directional response priors, the proposed approach optimizes
antenna positions and orientations at each reconfiguration epoch to maximize the average sum rate over a future time window. Simulation results in a typical urban intersection scenario demonstrate that the proposed 6DMA scheme significantly outperforms conventional fixed
antenna arrays and simplified 6DMA baseline schemes in terms of total sum rate.
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All authors Maoxin Ji, Qiong Wu, Pingyi Fan, Kezhi Wang, Wen Chen, Khaled B. Letaief