Image and Video Processing
Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?
Thierry Judge, Nicolas Duchateau, Andreas Østvik et al. (+13 more)
Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain remains limited, even though this biomarker is highly relevant for early diagnosis and the characterization of subtle abnormalities. from clinical data. Deep learning is a promising alternative, but its development is constrained by the lack of reliable motion references. Existing solutions rely either on STE-derived labels or on simulations generated by physics-based models, but these synthetic sequences still have limited realism compared with clinical data.In this paper, we propose a novel simulation strategy that incorporates speckle decorrelation measures from real
videos and uses an iterative refinement process to improve the motion realism in the simulations. We created an open-source photorealistic
dataset of 1,478
videos with reference motion, which was used to train an echocardiographic motion estimation
algorithm. The proposed method achieves unmatched performance on global and regional strain, notably reaching a GLS variability of 1.42% in an inter-expert setting compared to 1.78% for the clinical reference.
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All authors Thierry Judge, Nicolas Duchateau, Andreas Østvik, Khuram Faraz, Anders Austlid Taskén, Sigve Karlsen, Thor Edvardsen, Harald Brunvand, Md Abulkalam Azad, Havard Dalen, Bjørnar Grenne, Gabriel Kiss, Pierre-Yves Courand, Lasse Lovstakken, Pierre-Marc Jodoin, Olivier Bernard
Systems and Control · Cross-listed from math.OC
Disjunctive Sum of Squares
Amir Ali Ahmadi, Sanjeeb Dash, Yixuan Hua et al. (+1 more)
We introduce the concept of disjunctive sum of squares for certifying nonnegativity of
polynomials. Unlike the popular sum of squares approach where nonnegativity is certified by a single algebraic identity, the disjunctive sum of squares approach certifies nonnegativity with multiple algebraic identities which can be found in parallel. Our main result is a disjunctive Positivstellensatz proving that we can keep the degree of each algebraic identity as low as the degree of the
polynomial whose nonnegativity is in question. Based on this result, we construct a semidefinite programming based converging hierarchy of lower bounds for the problem of minimizing a
polynomial over a compact basic semialgebraic set, where the size of the largest semidefinite constraint is fixed throughout the hierarchy. We further prove a second disjunctive Positivstellensatz which leads to an optimization-free hierarchy for
polynomial optimization. We specialize this result to the problem of proving copositivity of
matrices. Finally, we describe how the disjunctive sum of squares approach can be combined with a branch-and-bound
algorithm and we present numerical experiments on
polynomial, copositive, and combinatorial optimization problems.
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All authors Amir Ali Ahmadi, Sanjeeb Dash, Yixuan Hua, Bartolomeo Stellato
Systems and Control
On the Solvability of Quasi-Regulator Equations in Non-smooth Output Regulation
Zirui Niu, Daniele Astolfi and Giordano Scarciotti
Motivated by the prevalence of non-smooth, possibly non-periodic signals in real-world applications, the output regulation of linear systems subject to non-smooth non-periodic exogenous signals has emerged as a challenging problem. A fundamental prerequisite for solving this problem is the existence of solutions to the so-called “quasi-regulator equations”. In this paper, we investigate the solvability of these equations. To this end, we reformulate the quasi-regulator equations as differential-algebraic equations and highlight the critical role played by the system's relative degree. We finally propose a “non-smooth non-resonance condition” that, under specific relative degree requirements, provides a necessary and sufficient characterization of the solvability of the quasi-regulator equations.
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Systems and Control · Cross-listed from cs.RO
Integrated Exploration-Aware UAV Route Optimization and Path Planning
Jimin Choi, Grant Stagg, Cameron K. Peterson et al. (+1 more)
Uncrewed aerial vehicles (
UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated sites, wildfire areas, and damaged infrastructure, where limited flight endurance must be allocated between visiting reported locations and gathering new information. In these settings, prior information regarding hazards is often incomplete, spatially imprecise, and subject to change during execution. For example, initial reports may identify a region where a hazard is likely to exist, but the actual hazard may be displaced, partially observed, or entirely unreported. We present an integrated exploration-aware
UAV route optimization and path planning framework for hazard monitoring under uncertain and evolving prior information. The environment is represented as a spatial risk map, where each location has an associated belief of hazardous conditions. Reported hazards are modeled as uncertain regions of interest (ROIs) rather than confirmed target locations, requiring the
UAV to inspect reported areas while also using its limited flight endurance to explore informative regions. The proposed method solves a vehicle routing problem over reported ROIs, augments the route with auxiliary pseudo-nodes to improve spatial coverage, allocates the remaining flight distance budget across route segments, and optimizes dynamically feasible B-spline trajectories for local exploration. During execution,
UAV measurements update a grid-based belief map, and the remaining trajectory is replanned when new information and the remaining budget justify adaptation. Across 48 scenario configurations, online replanning improves average KL reduction by 15.9% over the offline optimized planner and 48.6% over straight-line traversal.
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All authors Jimin Choi, Grant Stagg, Cameron K. Peterson, Max Z. Li
Audio and Speech Processing
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios
Changhao Pan, Rui Yang, Han Wang et al. (+12 more)
Recent advances in
speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A comprehensive evaluation
benchmark for long-form
speech is indispensable for two reasons: 1) existing test scenarios are often confined to limited domains, creating a significant gap with the diverse downstream applications; 2) existing metrics overlook critical long-text factors such as consistency and coherence, failing to generalize reliably. To this end, we propose Swanbench-
Speech, a comprehensive
benchmark that decomposes long-form
speech quality into specific, disentangled dimensions. SwanBench-
Speech has three key properties. 1) Rich
speech scenarios: Focusing on long-form
speech generation and dialog generation, SwanBench-
Speech covers acoustics, semantics, and expressiveness challenges, and consists of 1,101 samples spanning 17 common
speech scenarios; 2) Comprehensive evaluation dimensions: Along the acoustics, semantics, and expressiveness axes, SwanBench-
Speech defines an automated evaluation protocol with seven metrics to provide a comprehensive, accurate, and standardized assessment; 3) Valuable Insights: Through extensive experiments, we reveal that current models still struggle in highly expressive scenarios and exhibit a notable gap in consistency and hierarchy compared to real recordings.
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All authors Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
Systems and Control · Cross-listed from cs.RO
SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
Kangyu Wu, Peng Cui, Guoxi Chen et al. (+1 more)
Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large
Language Models (LLMs) demonstrate inherent latency in real-time inference operations. To address these limitations, this paper proposes SARAD, a novel safety-aware hybrid framework that synergizes LLMs and DRL for autonomous driving. SARAD substitutes the random exploration of DRL with
Retrieval-Augmented Generation (RAG)-enhanced, LLM-guided decisions sourced from a dynamic expert knowledge repository. An
attention discriminator is proposed to integrate the prior knowledge of LLMs into DRL policy optimization. A collision predictor module, fine-tuned with historical collision data, is further designed to improve vehicle safety. Extensive experiments show that SARAD achieves significant performance improvements in the Highway-Env simulator, validating the effectiveness of the proposed model in autonomous driving.
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All authors Kangyu Wu, Peng Cui, Guoxi Chen, Ya Zhang
Systems and Control
Actor-Identifier-Critic Reinforcement Learning for Adaptive Model-Free Optimal Control of Nonlinear Systems with Stochastic Packet Dropouts
Kianoush Aqabakee, Kosar Behnia, Amirhossein Heydarian Ardakani et al. (+2 more)
Packet dropouts in control systems poses a critical challenge, as it can significantly compromise system performance and stability. In these conditions, classical controllers often struggle to deliver effective control, as they rely on accurate system models, which may not always be available. This paper proposes a novel Actor-Identifier-Critic~(AIC) controller to address model-free tracking control of nonlinear systems in the presence of packet dropouts in both the controller-to-actuator and sensor-to-controller channels. Using an identifier to learn the system dynamics, the proposed controller is able to handle packet dropouts in the communication link and facilitate gradient propagation from the critic to the actor within a model-free control framework. The performance of the proposed method is demonstrated on two nonlinear SIMO and
MIMO systems and a case study on power system stability subject to
stochastic packet dropouts.
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All authors Kianoush Aqabakee, Kosar Behnia, Amirhossein Heydarian Ardakani, Farzaneh Abdollahi, Elham Shirazi
Signal Processing
Unified Analytical Framework for SPAD Array Receivers with Dead-Time-Induced Blocking Loss and Inter-Symbol Interference in PAM-OWC Systems
Chen Wang, Zhiyong Xu, Jingyuan Wang et al. (+3 more)
Optical wireless communication (OWC) leveraging single-
photon avalanche diode (SPAD) arrays offers exceptional sensitivity for
photon-starving links. However, the inherent dead time of SPADs critically limits achievable data rates by introducing non-linear
photon-counting distortions: blocking loss within a symbol duration and inter-symbol interference (ISI) across durations. This paper proposes a unified analytical framework capturing both distortions across all operational speed regimes for pulse-amplitude modulation (PAM), by establishing comprehensive statistical models for SPAD array receivers. For low and medium-speed systems (symbol duration longer than dead time), we derive exact closed-form expressions for the
photon counts probability distribution using renewal theory, explicitly incorporating blocking loss and ISI. For high-speed systems (symbol duration shorter than dead time), we develop a Markov chain model characterizing the steady-state operational states and integrate it with trigger probability to obtain the exact binomial
photon counts distribution. Furthermore, we propose low-complexity, near-optimal threshold detection schemes based on these models. This work provides essential theoretical tools for designing and optimizing high-performance SPAD-based OWC systems employing PAM.
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All authors Chen Wang, Zhiyong Xu, Jingyuan Wang, Jianhua Li, Weifeng Mou, Huatao Zhu
Systems and Control · Cross-listed from math.OC
Model Predictive Control for Constrained Linear Positive Systems on Graphs
Roland Schurig, David Ohlin, Anders Rantzer et al. (+2 more)
Positive systems describing networks with inherently non-negative states and inputs arise naturally in routing, logistics, and compartmental modelling. We consider problems modelled as positive linear systems in incidence form with linear cost. The addition of capacity constraints on states (storage) and inputs (flows between nodes) significantly increases the problem complexity. Leveraging the analytic structure of the unconstrained problem, an explicit suboptimal admissible controller is constructed. This yields graph-computable performance bounds and a minimum stabilising horizon length for a model predictive controller without terminal conditions. A convex program enables efficient computation of the optimal bound and horizon. These results highlight how system structure enables explicit MPC guarantees that are typically not available.
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All authors Roland Schurig, David Ohlin, Anders Rantzer, Emma Tegling, Rolf Findeisen
Signal Processing
On Unified CRLB Framework from Generic Signals to ISAC Waveforms with Virtual Array Sensing
Yanpeng Su, Norman Franchi and Maximilian Lübke
This paper presents a unified Cramér-Rao lower bound (CRLB) framework for signal-level parameters in integrated sensing and communications (
ISAC)-enabled radar systems. Starting from the generic signal model, we analyze the coupling between delay and Doppler in the Fisher information
matrix (FIM), which is unsolved and often overlooked in relevant studies. Addressing this issue, we derive the conditions under which the coupling terms can be eliminated and demonstrate that these conditions are typically satisfied for
ISAC-enabled waveforms. Afterward, the CRLBs of representative
ISAC waveforms are derived within the unified framework, enabling consistent and comparable analysis across the waveforms and avoiding model-dependent discrepancies. Further, the framework is extended to virtual array (VA) sensing systems, where the impact of different multiplexing schemes is analyzed. Simulation results demonstrate the consistency between the CRLBs derived from the proposed framework and those obtained from waveform-specific analyses. The proposed framework shows strong generality, waveform-compatibility, and flexibility, offering a versatile tool for the CRLB analysis of various waveforms, including those lacking existing analytical results.
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Signal Processing
Channel Measurements and Characterization with Phase Drift Compensation for Outdoor 330-360 GHz MIMO Communications
Tian Qiu, Taihao Zhang, Cunhua Pan et al. (+5 more)
In this paper, an outdoor channel measurement campaign at 330-360 GHz employing a 128 * 4 virtual
antenna array (VAA)-based multiple-input multiple-output (
MIMO) configuration is conducted. The transmitter (Tx) and receiver (Rx) location pairs are classified into line-of-sight (LoS) and obstructed-LoS (OLoS) scenarios to enable a detailed investigation of outdoor terahertz (THz) band channel characteristics. During the measurement process, the stationarity of the outdoor environment is carefully verified, and a linear phase drift (PD) effect is identified. Then, we propose a PD-aware Space-Alternating Generalized Expectation-Maximization (SAGE)
algorithm, which significantly improves both delay resolution and channel parameter estimation accuracy. Based on the processed measurement data, we characterize key channel properties, including the power delay profile, path loss, shadow fading, delay spread, angular spread, Rician K-factor, as well as their cumulative distribution functions and correlation characteristics. In addition, near-field effects and
MIMO-specific properties, including the spatial non-stationarity and the cluster birth-death property, are analyzed.
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All authors Tian Qiu, Taihao Zhang, Cunhua Pan, Hong Re, Yongchao He, Chenzhou Lin, Bingchang Hua, Jiangzhou Wang
Audio and Speech Processing
Audio-Mind: An Auditable Agentic Framework for Audio Understanding
Yucheng Wang, Jing Peng, Hanqi Li et al. (+6 more)
Audio
agents extend large audio-
language models (LALMs) by decomposing audio questions into tool calls, intermediate evidence, and iterative
reasoning steps. However, as LALMs become stronger, the key challenge shifts from enabling tool use to determining when agentic evidence acquisition genuinely benefits audio understanding. We propose Audio-Mind, an auditable and pluggable framework for conditional evidence acquisition in audio understanding. Audio-Mind dynamically combines a strong frontend with planner-guided tool use, preserving frontend judgment when initial evidence is sufficient while acquiring bounded external evidence for questions with unresolved evidence gaps. Experiments on MMAR and MSU-Bench show that Audio-Mind outperforms prior audio-
agent baselines, reaching 80.4% accuracy on MMAR and 82.8% accuracy on MSU-Bench. A matched-backbone comparison highlights why this design matters: under strong audio frontends, agentic decomposition can become an orchestration bottleneck when the workflow does not preserve the frontend's holistic audio-grounded judgment. Beyond accuracy, Audio-Mind produces higher-quality, auditable
reasoning traces that expose uncertainty, tool evidence, and answer rationales, offering a potential basis for more reliable audio-QA annotation and error analysis.
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All authors Yucheng Wang, Jing Peng, Hanqi Li, Chenghao Wang, Wenming Tu, Yu Xi, Zhaokai Sun, Kai Yu, Shuai Wang
Systems and Control
Towards Autonomous Commissioning of Industrial Drives via Multi-Objective Bayesian Optimization
David Petrovic, Gian Antonio Susto and Angelo Cenedese
The commissioning of industrial electric drives still relies heavily on manual tuning of cascaded control loops, requiring expert knowledge and significant time. In this paper, we propose a fully automated approach for tuning the current control loop of industrial drives using
Bayesian Optimization (BO) directly on real hardware, without requiring a system model or firmware modifications. The drive is treated as a black-box system, and the controller parameters are iteratively updated through closed-loop experiments. The tuning problem is formulated as a multi-objective optimization task that directly minimizes tracking error, time-weighted error, overshoot, and oscillatory behavior, enabling the identification of Pareto-optimal controller configurations. To address discrete parameters, noisy evaluations, and limited budgets, we adopt a multivariate Tree-structured Parzen
Estimator (TPE) as the underlying BO strategy. The proposed method operates under practical industrial constraints, including communication latency and limited evaluation budgets. The experimental validation on a real motor drive system under no-load conditions shows that the method achieves performance comparable to expert tuning within a few minutes and without human intervention. Results show that Gaussian Process (GP)-based BO can yield highly competitive final solutions, but TPE-based BO is better aligned with this setting due to faster convergence, richer Pareto-front approximation, and lower computational overhead.
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Audio and Speech Processing · Cross-listed from cs.AI
Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha et al. (+1 more)
Existing Visual
Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on visually ambiguous
tokens before sufficient context is available. We propose DLLM-VSR, to the best of our knowledge, the first
Diffusion Large
Language Model (DLLM)-based VSR framework, formulating transcription as iterative masked denoising with flexible-order decoding. With confidence-based unmasking, DLLM-VSR commits high-confidence positions early and uses the committed
tokens as bidirectional context to refine ambiguous ones. To adapt DLLMs to VSR, we introduce a two-stage masked-denoising training strategy that separates visual-to-text content
alignment from length modeling. We further observe a performance gap compared with an upper-bound setting where the ground-truth transcript length is provided at inference, allowing the model to focus on transcript content decoding. To reduce this gap, we develop length-guided candidate decoding, which uses
video duration to construct plausible transcript-length hypotheses and reranks the decoded candidates using length plausibility and decoding confidence. The proposed method achieves a 19.4% word error rate on LRS3, establishing state-of-the-art performance among methods using only LRS3 as labeled training data.
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All authors Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha, Yong Man Ro
Signal Processing
A Unified Framework for Unbiased Non-Coherent Over-the-Air Computation
Martin Dahl, Zheng Chen and Erik G. Larsson
Over-the-Air Computation (OAC) enables efficient data aggregation in large-scale distributed systems by exploiting the superposition property of wireless multiple-access channels. In contrast to most existing studies on OAC assuming exact channel state information, we consider non-coherent OAC (NC-OAC) where the channel phase is unknown at the transmitters. A three-step framework for NC-OAC with a mapping between source data and codewords is proposed: 1) Devices encode their data to non-negative codewords; 2) Devices transmit a sequence of symbols with amplitude proportional to their codewords, such that the receiver can estimate the codeword sum. Estimation of the codeword sum is studied under two scenarios of global channel amplitude knowledge: statistical or instantaneous; 3) The estimated codeword sum is decoded to the desired source data sum at the receiver. With the proposed framework, we first study prior work on NC-OAC and map these to the framework. Next, we define and compare the two most commonly (often implicitly) used mappings for NC-OAC: the Affine and the Augmented Affine mappings. Under the constraint of unbiased estimation, we show that with uniformly distributed data and standard channel assumptions, the Augmented Affine mapping exhibits an order of magnitude lower estimation variance than the Affine mapping with both statistical and instantaneous channel knowledge. This result is validated by extensive simulations. Finally, we propose and analyze a new mapping, which demonstrates superior performance over the previous two affine mappings.
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Signal Processing
Experimental Characterization of a Multifunction X-Band AESA Radar Demonstrator
Francesco Mancuso, Giulio Meucci, Matteo Pardi et al. (+2 more)
Modern naval surveillance demands multifunction radar systems capable of operating in cluttered and contested environments. This paper presents the experimental characterization of a compact, X-band Active Electronically Scanned Array (AESA) radar demonstrator. The system was evaluated in a realistic coastal field environment at Naval Support and Experimentation Centre (CSSN) and, specifically, its specialized institute, the G. Vallauri Institute, which has historical expertise in testing and evaluating the performance of operational sensors as well as those under development, using real maritime targets and an active noise jammer. The trials assessed three core functions: direction-of-arrival (DoA) estimation, adaptive jammer suppression using MVDR beamforming, and high-resolution Inverse Synthetic Aperture Radar (ISAR) imaging. The results confirm that the demonstrator successfully detects and localizes targets, effectively suppresses high-power interference, and generates clear ISAR images of non-cooperative vessels. These findings validate the multifunction performance of the AESA demonstrator, confirming its suitability for advanced naval surveillance applications.
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All authors Francesco Mancuso, Giulio Meucci, Matteo Pardi, Giulio Giovannetti, Alberto Lupidi
Signal Processing
Transformer-Based Heartbeat Monitoring with FMCW Radar Under Random Body Motion
Matteo Pardi, Amir Hosein Oveis, Saba Kharabadze et al. (+1 more)
Millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar enables contactless cardiac monitoring, but heartbeat estimation becomes challenging when respiration and random body motion (RBM) distort the radar signal. In this paper, we propose a hybrid framework for 77 GHz FMCW radar that combines model-based signal processing with a Convolutional Neural Network (CNN)-
Transformer network. The first block extracts chest displacement and constructs meaningful high-level motion features from raw radar data, while the second block reconstructs a photoplethysmography (PPG)-like signal from the extracted features. In this study, a synchronized PPG signal is used as the ground truth for heartbeat monitoring in supervised training. The method is evaluated following the IEEE AESS Radar Challenge Problem I protocol using the official
datasets and figures of merit across three motion scenarios: stationary, deep breathing, and RBM. Results show that the proposed architecture reliably reconstructs the PPG signal in all scenarios, achieving high fidelity in controlled conditions and maintaining robust performance under motion. This enables reliable average heart rate (AHR) and heart rate variability (HRV) estimation even where
benchmark methods fail, and leads to the highest total score among the compared approaches.
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All authors Matteo Pardi, Amir Hosein Oveis, Saba Kharabadze, Ajeet Kumar
Signal Processing
A Unified Maximum-Likelihood Framework for 3D InISAR Phase Unwrapping with Outlier Rejection
Matteo Pardi, Francesco Mancuso, Elisa Giusti et al. (+1 more)
This paper presents a novel mathematical framework for phase unwrapping in three-dimensional interferometric ISAR (3D InISAR) imaging. The approach works on a scatterer-by-scatterer basis and does not rely on any spatial continuity assumptions, making it suitable for sparse point clouds. The formulation is derived from the Mixed-Integer Least Squares (MILS) theory, an optimal maximum-likelihood framework for joint estimation of integer and real unknowns in the presence of Gaussian noise. This provides a unified way to handle generic sensor geometries, multi-baseline, multi-frequency, or hybrid setups. The method also produces a natural a posteriori quality metric for each unwrapped phase, which can be used to build a statistical test to reject outliers. The
algorithm is simple to implement and has a computational cost suitable for operational systems. This paper presents the theoretical foundations of the framework and a first validation study on a standard L-shaped dual-frequency setup using Monte Carlo simulations. Results show that the proposed framework enables reliable 3D reconstruction in challenging ambiguity conditions.
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All authors Matteo Pardi, Francesco Mancuso, Elisa Giusti, Marco Martorella
Signal Processing
A Gray-Box Approach for Decentralized Grid-Equivalent Model Identification
Sanjay Chandrasekaran, Florian Dörfler and Silvia Mastellone
We propose a decentralized, frequency-domain identification
algorithm that estimates the grid-equivalent model using local measurements from the perspective of each converter. Since local electric signals in a multi-converter setup are affected by voltage inputs from the grid, estimating a direct equivalent impedance yields biased and inaccurate results. To overcome this, we design a framework that decouples the effect of the equivalent impedance (passive) from that of the equivalent voltage (active). The parameters and equivalent grid voltages are then estimated using a least-squares
algorithm and a Kalman filter, respectively, applied across frequency samples, with additional pre-processing techniques to remove the influence of the grid on the locally estimated models. We then demonstrate the accuracy and performance of our
algorithm on an interconnected $5-$converter system in grid-forming mode.
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Systems and Control
Information Age-Controllability Trade-offs in Communication-Constrained Networks
Songita Das, Gourab Ghatak, Chen Quan et al. (+1 more)
We investigate the trade-off between controllability, channel access, and age-related performance in a wireless network of control systems. Controllers share a random-access channel to transmit control inputs to actuators over slotted blocks. We measure reliable control via block controllability, where a block is controllable if it contains a required number of consecutive successful transmissions. In parallel, we capture information freshness via the age of information. To enable efficient allocation of channel resources over time, we introduce adaptive access probabilities at the block level, prioritizing controllers that have not yet achieved controllability. We then derive closed-form expressions for block controllability probability, the peak latency between inter-block consecutive successes, and peak age of information. We further characterize the peak control latency, defined as the time between consecutive controllable blocks. Finally, we optimize access probabilities to jointly balance controllability and age-related metrics. Numerical results illustrate the effectiveness of the proposed adaptive access policies in managing this trade-off in interference-limited wireless control networks.
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All authors Songita Das, Gourab Ghatak, Chen Quan, Geethu Joseph
Systems and Control · Cross-listed from cs.RO
Safety-Critical Adaptive Impedance Control via Nonsmooth Control Barrier Functions under State and Input Constraints
Faisal Lawan, Xiaoran Han, Joaquin Carrasco et al. (+2 more)
Safe physical interaction is critical for deploying robotic manipulators in human-
robot interaction and contact-rich tasks, where uncertainty, external forces, and actuator limitations can compromise both performance and safety. We propose an online adaptive impedance control framework that enforces joint-state safety while achieving compliant interaction under uncertain dynamics. The approach combines a quadratic-program-based safety filter with a novel composed position-velocity non-smooth control barrier function (NCBF), enabling joint position and velocity constraints to be enforced through a unified relative-degree-one barrier. Unknown dynamics are compensated online using an interval type-2 fuzzy logic system, while actuator torque limits are handled through soft constraints with exact penalty recovery of feasible solutions. A disturbance-observer-enhanced safety mechanism improves robustness against modelling errors and external interaction forces. Using composite Lyapunov analysis, we prove forward invariance of the safe set and the uniform ultimately boundedness of the impedance-tracking error. Simulations on a 7-DOF manipulator with severe parametric uncertainty and external interaction wrenches demonstrate safe constraint satisfaction and robust impedance tracking.
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All authors Faisal Lawan, Xiaoran Han, Joaquin Carrasco, Barry Lennox, Xiaoxiao Cheng
Signal Processing
A Lightweight Method for Multiple Signal Direction Estimation with Adaptive Notch Filters
Burak Soner
For multi-signal detection and direction-of-arrival (DoA) estimation, conventional Capon beamforming degrades when there are more transmitters than receive
antennas minus one. This paper proposes a lightweight method using adaptive notch filters (ANFs) with only two receive
antennas for simultaneous DoA of two or more narrowband signals. Cascaded ANF stages form isolated channels per signal, and Capon estimates direction on each. The method has very low computational cost, and in simulation, for transmitters separable in time, frequency, and angle, performance approaches that of an oracle with prior signal knowledge. As with ANFs themselves, multiple components at closely spaced frequencies are poorly resolved, forming the main limitation of the proposed method. Simulations are complemented by an experimental implementation on a low-cost software-defined
radio (ADALM-Pluto).
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Signal Processing · Cross-listed from cs.AI
Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains
Lev Telyatnikov, Raffael Theiler, Leandro Von Krannichfeldt et al. (+1 more)
Progress in Prognostics and Health Management (PHM) is hindered by the lack of standardized and reusable evaluation practices across tasks,
datasets, and application domains. Reported results are often difficult to reproduce and compare, as key protocol choices, such as data splits, preprocessing, label
alignment, temporal windowing, and metrics, are often implicit or implemented ad hoc. We introduce \picid, a modular evaluation infrastructure that formalizes the PHM evaluation pipeline as an explicit, executable, and reproducible protocol. Through well-defined abstractions, \picid enforces deterministic, leakage-safe
dataset construction while remaining flexible across diverse PHM settings. The framework supports fault detection, diagnostics, and prognostics through a unified interface and can be extended to new
datasets and model classes without violating protocol invariants. By standardizing data contracts and evaluation boundaries, \picid also enables fair cross-task comparisons across diagnostics (classification) and prognostics (regression), allowing identical model families to be evaluated consistently across heterogeneous settings. We demonstrate \picid through an empirical evaluation of thirteen models on twelve
datasets spanning batteries, bearings, turbofan engines, hydraulics, filtration systems, and buildings. This work establishes a reusable foundation for standardized, fair and reproducible evaluation in PHM.
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All authors Lev Telyatnikov, Raffael Theiler, Leandro Von Krannichfeldt, Olga Fink
Systems and Control · Cross-listed from cs.IT
ISAC Privacy: Challenges and Solutions for 6G
Onur Günlü, Stefano Tomasin, João P. Vilela et al. (+4 more)
Integrated sensing and communication (
ISAC) is a promising feature of future communication networks. While spatial sensing can improve network performance and enable external services, it also creates
privacy challenges that go beyond the confidentiality of communication content. Future networks using millimeter-wave (mmWave) and sub-terahertz (THz) frequencies may collect or infer detailed information about people, devices, bystanders, passive objects, and environments in a sixth-generation (6G) deployment area. Such sensing can reveal location and environment data, support behavioral profiling such as movement or activity recognition, and, in advanced cases, expose physiological information such as breathing frequency or heart-rate-related data. Thus, the capabilities of spatial sensing must be controlled to satisfy
privacy requirements. In this work, we organize
privacy-sensitive
ISAC data into three sensing levels: location and environment data, behavioral data, and physiological data, and use this classification as the organizing principle throughout the paper. Based on this classification, we discuss internal and external
ISAC applications, identify
privacy challenges related to consent, transparency, data ownership, profiling, bystander exposure, and sensitive sensing data, review representative solution directions, and outline future research directions for
privacy-preserving
ISAC.
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All authors Onur Günlü, Stefano Tomasin, João P. Vilela, Francesco Chiti, Prajnamaya Dass, Angeliki Alexiou, Utz Roedig
Systems and Control · Cross-listed from cs.RO
Natural Locomotion: Principle and Method
Mirado Mortel, Luc Jaulin, Lionel Lapierre et al. (+1 more)
Robotic locomotion can become efficient when mechanisms exploit passive dynamics, compliance, and resonance rather than track prescribed trajectories. This paper formulates natural locomotion as an exchange principle for systems whose motion is mediated by environmental constraints or interactions. A motion is natural when an internal oscillator returns periodically, the body pose drifts, and the mean Propulsion–Oscillator Exchange power (POE power) vanishes over one cycle. The selected family is a Natural Locomotion
Manifold (NLM). We develop the conservative realization of this principle for continuous ideal environmental constraints: the constraints do no external work, total mechanical energy is conserved, and zero mean POE power is an internal exchange with the environment-mediated propulsive channel, not external energy input. The method is a closed/open construction. The propulsive channel is first closed to reveal an effective internal oscillator, organized by scalar action-angle structure in one effective degree of freedom or by nonlinear modal sectors in several degrees of freedom. The channel is then reopened, pose is reconstructed, and accepted cycles must preserve internal recurrence and zero mean POE power. We demonstrate the principle on two ideal nonholonomic no-slip systems: a Chaplygin-sleigh / pendulum-driven car and a three-body extension. In the scalar case, POE closure is equivalent to the missing internal return condition, giving a
theorem-backed computation of the NLM family. In the multi-degree case, POE closure remains necessary but must be completed by modal identity, internal return, dynamics consistency, same fixed passive architecture, and nonzero displacement. Natural locomotion becomes a design question: which passive architectures support no, one, or several certified NLM families?
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All authors Mirado Mortel, Luc Jaulin, Lionel Lapierre, Simon Rohou
Systems and Control
Digital-Based Potentiostat and Mesoporous Microelectrode Co-Design for Non-Enzymatic Glucose Detection at 0.3V-VDD and 1.65nW-Power
Andrea De Gregorio, Mara Serrapede, Danilo Kaddouri et al. (+6 more)
This paper presents a proof-of-concept ultra-low voltage and ultra-low power chronoamperometric electrochemical sensor for non-enzymatic glucose readout integrated
circuit (IC) in 130nm CMOS detection featuring a reconfigurable Digital-Based (DB) Potentiostat. The signal transfer and noise characteristics of the new digital-based architecture are analytically described in the frequency domain for the first time by an equivalent linearized model that is validated by simulations and experiments. Based on experiments, the proposed DB potentiostat enables the detection of a wide electrochemical current range, spanning from 600pA to 650nA, with R2=0.991 linearity and consumes only 1.65nW (53.5nW) at V dd = 300mV (V dd = 500mV). The proposed DB readout is tested in a proof of-concept platform for non-enzymatic glucose detection with nanostructured microelectrodes, demonstrating successful non enzymatic glucose detection at physiological levels at the lowest reported voltage and power, even in the presence of an interferent (ascorbic acid) and under aerobic conditions, thus revealing a strong potential for emerging Point of Care (PoC) diagnostics applications.
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All authors Andrea De Gregorio, Mara Serrapede, Danilo Kaddouri, Paolo Angelini, Giuseppe Bruno, Simone Luigi Marasso, Salvatore Guastella, Andrea Lamberti, Paolo Crovetti
Signal Processing
Spatiotemporal Tracking in Cooperative ISAC Networks: A Stochastic Geometry Framework
Bowen Wang, Nanxi Li, Jingzhou Wu et al. (+2 more)
We adopt a
stochastic-geometry framework to study continuous target tracking in integrated sensing and communication (
ISAC) networks, with base-station locations modelled as a Poisson point process. The single-BS analysis shows that the
antenna energy-conservation identity forces the mean inter-BS coupling gain to unity, making densification an
antenna-irreducible liability for monostatic sensing, while a first-passage-time analysis reveals a target-distance-dependent beamwidth trap. These findings rule out single-BS tracking under densification, motivating a multi-BS cooperative treatment. The static-cluster cooperative mean tracking lifetime is then shown to exhibit a sharp percolation phase transition, with the resulting sensing-capacity ceiling saturating above a critical macro density. Yet the static-cluster idealisation itself misrepresents modern network deployments, where the cooperating cluster is dynamically re-selected as the target drifts; we therefore lift this assumption with a dynamic clustering model that maps the $K$-nearest-neighbour handover onto a 2D Brownian motion with
stochastic resetting, and obtain a Bessel-function closed form for the dynamic mean tracking lifetime that dissolves the phase transition under any positive handover rate. With a per-link reliability floor, the dynamic clustering framework preserves classical linear density scaling throughout the realistic 6G regime and delivers an order-of-magnitude capacity lift at small-
cell densities. Monte-Carlo simulations corroborate all theoretical predictions.
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All authors Bowen Wang, Nanxi Li, Jingzhou Wu, Zheng Jiang, Jianchi Zhu
Signal Processing
Cross-Predictive Sparse Bayesian Learning with Application to XL-MIMO Channel Estimation
Arttu Arjas and Italo Atzeni
Accurate channel estimation is a key requirement in extremely large-scale multiple-input multiple-output (XL-
MIMO) systems. Sparse
Bayesian learning (SBL) is a well-established framework for exploiting channel sparsity, but its performance depends on parametric prior assumptions and hyperparameter optimization based on marginal likelihood, which may be sensitive to noise, limited pilot observations, and model mismatch. In this work, we propose cross-predictive SBL (CP-SBL), a data-driven variant of SBL in which the sparsity-inducing weights are learned by minimizing a randomized cross-predictive objective rather than through likelihood maximization. The proposed method preserves the hierarchical
Bayesian structure of SBL while replacing parametric prior learning with a predictive consistency criterion derived from random data splitting. Numerical results for near-field XL-
MIMO channel estimation show that CP-SBL consistently achieves lower normalized mean squared error than the baseline SBL across a wide range of signal-to-noise ratios, pilot lengths, numbers of
antennas, and numbers of propagation paths, with comparable complexity and without requiring manual hyperparameter tuning.
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Signal Processing
Tensor Train Decomposition Based Noise Reduction and Enhanced Parameter Estimation for FMCW MIMO Radar Systems
Luoyan Zhu, Sergiy A. Vorobyov, Jie Wang et al. (+2 more)
Frequency modulated continuous wave (FMCW) radar is widely used in autonomous driving and industrial inspection due to its high-resolution target location and velocity estimation capability. However, the plethora of connected devices in automotive applications introduces electromagnetic interference and brings challenges to location-aware services, primarily due to the issue of low signal-to-noise ratio (SNR) caused by mixed noise contamination. Conventional
matrix-based signal processing methods exhibit performance deterioration when handling higher-order signals under low SNR conditions. To address this challenge, this paper proposes a tensor decomposition-based framework that jointly performs noise reduction and parameter estimation for four-dimensional signals in FMCW multiple-input multiple-output (
MIMO) radar systems. Specifically, the framework exploits the inherent low-rank structure and multidimensional correlations of the received signals through tensor train decomposition to effectively separate noise subspace. A data smoothing processor then reconstructs an augmented signal tensor to resolve rank deficiency caused by coherent signals. Finally, an enhanced rotational subspace
algorithm is employed to jointly decouple the distance, velocity, and angle parameters by exploiting the structural fitting to the restored signal. Both simulation and field experiments under real-world noise demonstrate that our proposed framework achieves significant noise reduction while improving target SNR and parameter estimation accuracy. These advancements make the proposed framework a robust solution for high-precision
MIMO FMCW radar applications in dynamic, noise-polluted environments.
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All authors Luoyan Zhu, Sergiy A. Vorobyov, Jie Wang, Yinsheng Liu, Zhangdui Zhong
Signal Processing · Cross-listed from cs.LG
Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
Mohammad Taha Askari, Lutz Lampe and Amirhossein Ghazisaeidi
We present the first neural probabilistic amplitude shaping that outperforms existing methods while accounting for all implementation losses, using a block-less, easily implementable sequential autoregressive encoder compatible with arithmetic distribution matching, yielding reduced rate loss and higher achievable information rates.
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