Systems and Control
Nonlinear System Identification Nano-drone Benchmark
Riccardo Busetto, Elia Cereda, Marco Forgione et al. (+3 more)
We introduce a
benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robotics research. The platform presents a challenging testbed due to its multi-input, multi-output nature, open-loop instability, and nonlinear dynamics under agile maneuvers. The
dataset comprises four aggressive trajectories with synchronized 4-dimensional motor inputs and 13-dimensional output measurements. To enable fair comparison of identification methods, the
benchmark includes a suite of multi-horizon prediction metrics for evaluating both one-step and multi-step error propagation. In addition to the data, we provide a detailed description of the platform and experimental setup, as well as baseline models highlighting the challenge of accurate prediction under real-world noise and actuation nonlinearities. All data, scripts, and reference implementations are released as open-source at https://github.com/idsia-robotics/nanodrone-sysid-
benchmark to facilitate transparent comparison of
algorithms and support research on agile, miniaturized aerial robotics.
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All authors Riccardo Busetto, Elia Cereda, Marco Forgione, Gabriele Maroni, Dario Piga, Daniele Palossi
Signal Processing
Relaying Signal When Monitoring Traffic: Double Use of Aerial Vehicles Towards Intelligent Low-Altitude Networking
Jiahui Liang, Wenlihan Lu, Tianyi Liu et al. (+5 more)
In intelligent low-altitude networks, integrating monitoring tasks into communication unmanned aerial vehicles (
UAVs) can consume resources and increase handoff latency for communication links. To address this challenge, we propose a strategy that enables a "double use" of
UAVs, unifying the monitoring and relay handoff functions into a single, efficient process. Our scheme, guided by an integrated sensing and communication framework, coordinates these multi-role
UAVs through a proactive handoff network that fuses multi-view sensory data from aerial and ground vehicles. A lightweight vehicle inspection module and a two-stage training procedure are developed to ensure monitoring accuracy and collaborative efficiency. Simulation results demonstrate the effectiveness of this integrated approach: it reduces communication outage probability by nearly 10% at a 200 Mbps requirement without compromising monitoring performance and maintains high resilience (86% achievable rate) even in the absence of multiple
UAVs, outperforming traditional ground-based handoff schemes. Our code is available at the https://github.com/Jiahui-L/UAP.
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All authors Jiahui Liang, Wenlihan Lu, Tianyi Liu, Kang Kang, Guixin Pan, Liuqing Yang, Xinhu Zheng, Shijian Gao
Signal Processing
Chirp Delay-Doppler Domain Modulation Based Joint Communication and Radar for Autonomous Vehicles
Zhuoran Li, Zhen Gao, Sheng Chen et al. (+3 more)
This paper introduces a sensing-centric joint communication and millimeter-wave radar paradigm to facilitate collaboration among intelligent vehicles. We first propose a chirp waveform-based delay-Doppler quadrature amplitude modulation (DD-QAM) that modulates data across delay, Doppler, and amplitude dimensions. Building upon this modulation scheme, we derive its achievable rate to quantify the communication performance. We then introduce an extended Kalman filter-based scheme for four-dimensional (4D) parameter estimation in dynamic environments, enabling the active vehicles to accurately estimate orientation and tangential-velocity beyond traditional 4D radar systems. Furthermore, in terms of communication, we propose a dual-compensation-based demodulation and tracking scheme that allows the passive vehicles to effectively demodulate data without compromising their sensing functions. Simulation results underscore the feasibility and superior performance of our proposed methods, marking a significant advancement in the field of autonomous vehicles. Simulation codes are provided to reproduce the results in this paper: \href{https://github.com/LiZhuoRan0/2026-IEEE-TWC-ChirpDelayDopplerModulationISAC}{https://github.com/LiZhuoRan0}.
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All authors Zhuoran Li, Zhen Gao, Sheng Chen, Dusit Niyato, Zhaocheng Wang, George K. Karagiannidis
Signal Processing
Decoupled Quadratic Kalman Filter for Elliptical Extended Object Tracking with Log-normal Axis Modeling
Simon Steuernagel and Marcus Baum
Extended object tracking involves estimating both the physical extent and kinematic parameters of a target object, where typically multiple measurements are observed per time step. In this article, we propose a deterministic closed-form elliptical extended object tracker, based on decoupling of the kinematics, orientation, and axis lengths. By disregarding potential correlations between these state components, fewer approximations are required for the individual
estimators than for an overall joint solution. This also enables a log-normal representation of the semi-axis lengths, i.e., opposed to related approaches, only positive axis lengths have support. The resulting
algorithm outperforms existing
algorithms, reaching the accuracy of sampling-based procedures. Additionally, a batch-based variant is introduced, yielding highly efficient computation while outperforming all comparable state-of-the-art
algorithms. This is validated both by a simulation study using common models from literature, as well as an extensive quantitative evaluation on real automotive radar data.
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Signal Processing · Cross-listed from cs.IT
Agile Affine Frequency Division Multiplexing
Yewen Cao and Yulin Shao
The advancement to 6G calls for waveforms that transcend static robustness to achieve intelligent adaptability. Affine Frequency Division Multiplexing (AFDM), despite its strength in doubly-dispersive channels, has been confined by chirp parameters optimized for worst-case scenarios. This paper shatters this limitation with Agile-AFDM, a novel framework that endows AFDM with dynamic, data-aware intelligence. By redefining chirp parameters as optimizable variables for each transmission block based on real-time channel and data information, Agile-AFDM transforms into an adaptive platform. It can actively reconfigure its waveform to minimize peak-to-average power ratio (PAPR) for power efficiency, suppress inter-carrier interference (ICI) for communication reliability, or reduce Cramer-Rao bound (CRLB) for sensing accuracy. This paradigm shift from a static, one-size-fits-all waveform to a context-aware signal designer is made practical by efficient, tailored optimization
algorithms. Comprehensive simulations demonstrate that this capability delivers significant performance gains across all metrics, surpassing conventional OFDM and static AFDM. Agile-AFDM, therefore, offers a crucial step forward in the design of agile waveforms for 6G and beyond.
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Systems and Control
Equivariant Filter Cascade for Relative Attitude, Target's Angular Velocity, and Gyroscope Bias Estimation
Gil Serrano, Pedro Lourenço, Bruno J. Guerreiro et al. (+1 more)
Rendezvous and docking between a chaser spacecraft and an uncooperative target, such as an inoperative satellite, require synchronization between the chaser spacecraft and the target. In these scenarios, the chaser must estimate the relative attitude and angular velocity of the target using onboard sensors, in the presence of gyroscope bias. In this work, we propose a cascade of Equivariant Filters (EqF) to address this problem. The first stage of the cascade estimates the chaser's attitude and the bias, using measurements from a
star tracker, while the second stage of the cascade estimates the relative attitude and the target's angular velocity, using observations of two known, non-collinear vectors fixed in the target frame. The stability of the EqF cascade is theoretically analyzed and simulation results demonstrate the filter cascade's performance.
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All authors Gil Serrano, Pedro Lourenço, Bruno J. Guerreiro, Rita Cunha
Signal Processing
Terahertz Signal Coverage Enhancement in Hall Scenarios Based on Single-Hop and Dual-Hop Reconfigurable Intelligent Surfaces
Ben Chen, Zhangdui Zhong, Ke Guan et al. (+4 more)
Terahertz (THz) communication offers ultra-high data rates and has emerged as a promising technology for future wireless networks. However, the inherently high free-space path loss of THz waves significantly limits the coverage range of THz communication systems. Therefore, extending the effective coverage area is a key challenge for the practical deployment of THz networks. Reconfigurable intelligent surfaces (RIS), which can dynamically manipulate electromagnetic wave propagation, provide a solution to enhance THz coverage. To investigate multi-RIS deployment scenarios, this work integrates an
antenna array-based RIS model into the ray-tracing simulation platform. Using an indoor
hall as a representative case study, the enhancement effects of single-hop and dual-hop RIS configurations on indoor signal coverage are evaluated under various deployment schemes. The developed framework offers valuable insights and design references for optimizing RIS-assisted indoor THz communication and coverage estimation.
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All authors Ben Chen, Zhangdui Zhong, Ke Guan, Danping He, Yiran Wang, Jianwen Ding, Qi Luo
Signal Processing
Pragmatic Earth-Fixed Beam Management for 3GPP NTN Common Signaling in LEO Satellites
Xavier Artiga, Màrius Caus and Ana Pérez-Neira
This work proposes a pragmatic method for the design of beam footprint layouts and beam hopping illumination patterns to efficiently broadcast 3GPP NTN common signaling to large coverage areas using EIRP-limited LEO satellites. This method minimizes the time resources required to sweep over the whole coverage while ensuring that the signal-to-interference-plus-noise ratio received by users is above a given threshold. It discusses the design of: (i) an Earth-fixed grid of beam layouts; (ii) beamforming vectors and beam power allocation; (iii) beam hopping patterns and (iv) space, time and frequency resource allocation of 3GPP common signaling. Two main beam layout solutions are proposed to significantly reduce the number of beams required to illuminate the coverage area: one based on phased array beams with low beam crossover levels and the other on widened beams. A numerical evaluation using practical system parameters showed that both solutions perform similarly, but that the best result is obtained with phased arrays beams with optimized beam cross over levels. Indeed, for the system evaluated, they allowed reducing the total number of beams from 1723 to 451, which combined with a proper beam hopping pattern and scheduling scheme allowed obtaining a coverage ratio of 100% and a common signaling efficiency (i.e. number of slots carrying common signaling over total number of slots) up to 80.6% for the most stringent common signaling periodicity of 20 ms considered by 3GPP.
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Systems and Control · Cross-listed from q-bio.NC
Temporal interference stimulation for deep brain neuromodulation in humans
Pierre Vassiliadis, Elena Beanato, Maximilian J. Wessel et al. (+1 more)
For decades, focal non-invasive neuromodulation of deep
brain regions has not been possible because of the steep depth-focality trade-off of conventional non-invasive
brain stimulation (NIBS) techniques, such as transcranial
magnetic stimulation (TMS) or classical transcranial electric stimulation (tES). Deep
brain stimulation has therefore largely relied on invasive approaches in clinical populations, requiring surgery. Transcranial Temporal Interference Stimulation (tTIS) has recently emerged as a promising method to overcome this challenge and allows for the first time focal non-invasive electrical deep
brain stimulation. The method, which was first validated through computational modeling and rodent work, has now been successfully translated to humans to target deep
brain regions such as the hippocampus or striatum. In this Perspective, we present current evidence for tTIS-based neuromodulation, underlying mechanisms and discuss future developments of this promising technology. More specifically, we highlight key opportunities and challenges for fundamental neuroscience as well as for the design of new interventions in neuropsychiatric disorders. We also discuss the status of understanding and challenges regarding the basic mechanisms of action of tTIS and possible lines of technological innovation to optimize stimulation, in particular in terms of intensity and focality. Overall, we suggest that following the first proof-of-concepts, an important multidisciplinary research effort is now required to further validate the use of tTIS in multiple applications, understand its underlying principles and optimize the technology in the view of a wider scientific and clinical deployment.
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All authors Pierre Vassiliadis, Elena Beanato, Maximilian J. Wessel, Friedhelm C. Hummel
Signal Processing
Sparse OFDM Design for Interference and Ambiguity Mitigation in Multi-Static ISAC
Navid Amani, Priyanka Maity, Musa Furkan Keskin et al. (+1 more)
The sixth-generation (6G) wireless networks promises the integration of radar-like sensing capabilities into communication infrastructure. In this paper, we investigate a multi-static sensing framework where half-duplex base stations (BSs) are assigned as either transmitter or sensing receiver nodes. We propose a randomized sparse resource allocation scheme based on orthogonal frequency division multiplexing (OFDM) waveform design tailored for the multi-static scenario to simultaneously mitigate inter-BS interference (IBI) and sensing ambiguities. The waveform design also ensures robustness against inter-symbol interference (ISI) and intercarrier interference (ICI) via a judicious choice of subcarrier spacing according to the deployment of BSs. The potential ambiguity caused by sparse signaling is addressed through controlled irregularity in both time and frequency domains, with a negligible noise floor elevation. Simulation results demonstrate the effectiveness and resilience of the proposed design in the presence of multiple targets and clutter.
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All authors Navid Amani, Priyanka Maity, Musa Furkan Keskin, Henk Wymeersch
Signal Processing
User Localization and Channel Estimation for Pinching-Antenna Systems (PASS)
Xiaoxia Xu, Xidong Mu, Yuanwei Liu et al. (+2 more)
This letter proposes a novel user localization and channel estimation framework for pinching-
antenna systems (PASS), where pinching
antennas are grouped into subarrays on each waveguide to cooperatively estimate user/scatterer locations, thus reconstructing channels. Both single-waveguide (SW) and multi-waveguide (MW) structures are considered. SW consists of multiple alternatingly activated subarrays, while MW deploys one subarray on each waveguide to enable concurrent subarray measurements. For the 2D scenarios with a fixed user/scatter height, an orthogonal matching pursuit-based geometry-consistent localization (OMP-GCL)
algorithm is proposed, which leverages inter-subarray
geometric relationships and compressed sensing for precise estimation. Theoretical analysis on Cramér-Rao lower bound (CRLB) demonstrates that: 1) The estimation accuracy can be improved by increasing the
geometric diversity through multi-subarray deployment; and 2) SW provides a limited
geometric diversity within a $180^\circ$ half space and leads to angle ambiguity, while MW enables full-space observations and reduces overheads. The OMP-GCL
algorithm is further extended to 3D scenarios, where user and scatter heights are also estimated. Numerical results validate the theoretical analysis, and verify that MW achieves centimeter- and decimeter-level localization accuracy in 2D and 3D scenarios with only three waveguides.
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All authors Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Hong Xing, Arumugam Nallanathan
Systems and Control · Cross-listed from cs.RO
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr et al. (+3 more)
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. However, during deployment, the parameters of the underlying MPC must usually be fine-tuned. This often renders AMPC impractical as it requires repeatedly generating a new
dataset and retraining the neural network. Recent work addresses this problem by adapting AMPC without retraining using approximated sensitivities of the MPC's optimization problem. Currently, this adaption must be done by hand, which is labor-intensive and can be unintuitive for high-dimensional systems. To solve this issue, we propose using
Bayesian optimization to tune the parameters of AMPC policies based on experimental data. By combining model-based control with direct and local learning, our approach achieves superior performance to nominal AMPC on hardware, with minimal experimentation. This allows automatic and data-efficient adaptation of AMPC to new system instances and fine-tuning to cost functions that are difficult to directly implement in MPC. We demonstrate the proposed method in hardware experiments for the swing-up maneuver on an inverted cartpole and yaw control of an under-actuated balancing unicycle
robot, a challenging control problem.
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All authors Henrik Hose, Paul Brunzema, Alexander von Rohr, Alexander Gräfe, Angela P. Schoellig, Sebastian Trimpe
Systems and Control
A Geometric Task-Space Port-Hamiltonian Formulation for Redundant Manipulators
Federico Califano, Camilla Rota, Riccardo Zanella et al. (+1 more)
We present a novel
geometric port-
Hamiltonian formulation of redundant manipulators performing a differential kinematic task $\eta=J(q)\dot{q}$, where $q$ is a point on the configuration
manifold, $\eta$ is a velocity-like task space variable, and $J(q)$ is a linear map representing the task, for example the classical analytic or
geometric manipulator Jacobian
matrix. The proposed model emerges from a change of coordinates from canonical
Hamiltonian dynamics, and splits the standard
Hamiltonian momentum variable into a task-space momentum variable and a null-space momentum variable. Properties of this model and relation to Lagrangian formulations present in the literature are highlighted. Finally, we apply the proposed model in an Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) design to stabilize and shape the impedance of a 7-DOF Emika Panda
robot in simulation.
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All authors Federico Califano, Camilla Rota, Riccardo Zanella, Antonio Franchi
Systems and Control
A Data-Driven Approach for Electric Vehicle Powertrain Modeling
Eymen Ipek and Mario Hirz
Electrification in the automotive industry and increasing powertrain complexity demand accelerated, cost-effective development cycles. While data-driven models are recently investigated at component level, a gap exists in systematically integrating them into cohesive, system-level simulations for virtual validation. This paper addresses this gap by presenting a modular framework for developing powertrain simulations. By defining standardized interfaces for key components-the battery, inverter, and electric motor-our methodology enables independently developed models, whether data-driven, physics-based, or empirical, to be easily integrated. This approach facilitates scalable system-level modeling, aims to shorten development timelines and to meet the agile demands of the modern automotive industry.
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Signal Processing · Cross-listed from cs.AR
PADE: A Predictor-Free Sparse Attention Accelerator via Unified Execution and Stage Fusion
Huizheng Wang, Hongbin Wang, Zichuan Wang et al. (+5 more)
Attention-based models have revolutionized AI, but the quadratic cost of self-
attention incurs severe computational and
memory overhead. Sparse
attention methods alleviate this by skipping low-relevance
token pairs. However, current approaches lack practicality due to the heavy expense of added sparsity predictor, which severely drops their hardware efficiency. This paper advances the state-of-the-art (SOTA) by proposing a bit-serial enable stage-fusion (BSF) mechanism, which eliminates the need for a separate predictor. However, it faces key challenges: 1) Inaccurate bit-sliced sparsity speculation leads to incorrect
pruning; 2) Hardware under-utilization due to fine-grained and imbalanced bit-level workloads. 3) Tiling difficulty caused by the row-wise dependency in sparsity
pruning criteria. We propose PADE, a predictor-free
algorithm-hardware co-design for dynamic sparse
attention acceleration. PADE features three key innovations: 1) Bit-wise uncertainty interval-enabled guard filtering (BUI-GF) strategy to accurately identify trivial
tokens during each bit round; 2) Bidirectional sparsity-based out-of-order execution (BS-OOE) to improve hardware utilization; 3) Interleaving-based sparsity-tiled
attention (ISTA) to reduce both I/O and computational complexity. These techniques, combined with custom accelerator designs, enable practical sparsity acceleration without relying on an added sparsity predictor. Extensive experiments on 22
benchmarks show that PADE achieves 7.43x speed up and 31.1x higher energy efficiency than Nvidia H100 GPU. Compared to SOTA accelerators, PADE achieves 5.1x, 4.3x and 3.4x energy saving than Sanger, DOTA and SOFA.
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All authors Huizheng Wang, Hongbin Wang, Zichuan Wang, Zhiheng Yue, Yang Wang, Chao Li, Yang Hu, Shouyi Yin
Systems and Control · Cross-listed from cond-mat.mtrl-sci
Estimating Reaction Rate Constants from Impedance Spectra: Simulating the Multistep Oxygen Evolution Reaction
Freja Vandeputte, Bart van den Boorn, Matthijs van Berkel et al. (+3 more)
The efficiency of water electrolysis in a photoelectrochemical
cell is largely limited by the oxygen
evolution reaction (OER) at its
semiconductor photoanode. Reaction rate constants are key to investigating the slow kinetics of the multistep OER, as they indicate the rate-determining step. While these rate constants are usually calculated based on first-principles simulations, this research aims to estimate them from experimental electrochemical impedance spectroscopy (EIS) data. Starting from a microkinetic model for charge transfer at the
semiconductor-electrolyte interface, an expression for the impedance as a function of the rate constants is derived. At lower potentials, the order of this impedance model is reduced, thus eliminating the rate constants corresponding to the last reaction steps. Moreover, it is shown that EIS data from at least two potentials needs to be combined in order to uniquely identify the rate constants of a particular reduced order model. Therefore, this work details a sample maximum likelihood
estimator that integrates not only multiple frequencies, but also multiple potentials simultaneously. Measuring multiple periods of the current density and potential signals, allows this frequency domain
estimator to take measurement uncertainty into account. In addition, due to the large numerical range of the rate constants, various scaling methods are implemented to achieve numerical stability. To find suitable initial values for the highly nonlinear optimization problem, different global estimation methods are compared. The complete estimation procedure of the rate constants is illustrated on simulated EIS data of a hematite photoanode.
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All authors Freja Vandeputte, Bart van den Boorn, Matthijs van Berkel, Anja Bieberle-Hütter, Gerd Vandersteen, John Lataire
Signal Processing
Robust Design for Multi-Antenna LEO Satellite Communications with Fractional Delay and Doppler Shifts: An RSMA-OTFS Approach
Yunnuo Xu, Yumeng Zhang, Yijie Mao et al. (+3 more)
Low-Earth-orbit (LEO) satellite communication systems face challenges due to high satellite mobility, which hinders the reliable acquisition of instantaneous channel state information at the transmitter (CSIT) and subsequently degrades multi-user transmission performance. This paper investigates a downlink multi-user multi-
antenna system, and tackles the above challenges by introducing orthogonal time frequency space (OTFS) modulation and rate-splitting multiple access (RSMA) transmission. Specifically, OTFS enables stable characterization of time-varying channels by representing them in the delay-Doppler domain. However, realistic propagation introduces various inter-symbol and inter-user interference due to non-orthogonal yet practical rectangular pulse shaping, fractional delays, Doppler shifts, and imperfect (statistical) CSIT. In this context, RSMA offers promising robustness for interference mitigation and CSIT imperfections, and hence is integrated with OTFS to provide a comprehensive solution. A compact cross-domain input-output relationship for RSMA-OTFS is established, and an ergodic sum-rate maximization problem is formulated and solved using a weighted minimum mean-square-error based alternating optimization
algorithm that does not depend on channel sparsity. Simulation results reveal that the considered practical propagation effects significantly degrade performance if unaddressed. Furthermore, the RSMA-OTFS scheme demonstrates improved ergodic sum-rate and robustness against CSIT uncertainty across various user deployments and CSIT qualities.
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All authors Yunnuo Xu, Yumeng Zhang, Yijie Mao, Bruno Clerck, Yun Hee Kim, Yujun Li
Audio and Speech Processing
Investigating the impact of stereo processing – a study for extending the Open Dataset of Audio Quality (ODAQ)
Sascha Dick, Christoph Thompson, Chih-Wei Wu et al. (+3 more)
In this paper, we present an initial study for extending Open
Dataset of Audio Quality (ODAQ) towards the impact of stereo processing. Monaural artifacts from ODAQ were adapted in combinations with left-right (LR) and mid-side (MS) stereo processing, across stimuli including solo instruments, typical wide stereo mixes and and hard-panned mixes. Listening tests in different presentation context – with and without direct comparison of MS and LR conditions – were conducted to collect subjective data beyond monaural artifacts while also scrutinizing the listening test methodology. The ODAQ
dataset is extended with new material along with subjective scores from 16 expert listeners. The listening test results show substantial influences of the stimuli's spatial characteristics as well as the presentation context. Notably, several significant disparities between LR and MS only occur when presented in direct comparison. The findings suggest that listeners primarily assess timbral impairments when spatial characteristics are consistent and focus on stereo image only when timbral quality is similar. The rating of an additional mono anchor was overall consistent across different stereo characteristics, averaging at 65 on the MUSHRA scale, further corroborating that listeners prioritize timbral over spatial impressions.
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All authors Sascha Dick, Christoph Thompson, Chih-Wei Wu, Pablo Delgado, Phillip A. Williams, Matteo Torcoli
Systems and Control · Cross-listed from cs.CR
LegionITS: A Federated Intrusion-Tolerant System Architecture
Tadeu Freitas, Carlos Novo, Manuel E. Correia et al. (+1 more)
The growing sophistication, frequency, and diversity of cyberattacks increasingly exceed the capacity of individual entities to fully understand and counter them. While existing solutions, such as Security Information and Event Management (SIEM) systems, Security Orchestration, Automation, and Response (SOAR) platforms, and Security Operation Center (SOC), play a vital role in mitigating known threats, they often struggle to effectively address emerging and unforeseen attacks. To increase the effectiveness of cyber defense, it is essential to foster greater information sharing between entities; however, this requires addressing the challenge of exchanging sensitive data without compromising confidentiality or operational security. To address the challenges of secure and confidential Cyber Threat Intelligence (CTI) sharing, we propose a novel architecture that federates Intrusion Tolerant Systems (ITSs) and leverages concepts from Malware Information Sharing Platform (MISP) to empower SOCs. This framework enables controlled collaboration and data
privacy while enhancing collective defenses. As a proof of concept, we evaluate one module by applying Differential
Privacy (DP) to
Federated Learning (FL), observing a manageable accuracy drop from 98.42% to 85.98% (average loss 12.44%) while maintaining reliable detection of compromised messages. These results highlight the viability of secure data sharing and establishes a foundation for the future full-scale implementation of LegionITS.
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All authors Tadeu Freitas, Carlos Novo, Manuel E. Correia, Rolando Martins
Signal Processing
Graph Signal Denoising Using Regularization by Denoising and Its Parameter Estimation
Hayate Kojima, Hiroshi Higashi and Yuichi Tanaka
In this paper, we propose an interpretable denoising method for graph signals using regularization by denoising (RED). RED is a technique developed for image restoration that uses an efficient (and sometimes black-box) denoiser in the regularization term of the optimization problem. By using RED, optimization problems can be designed with the explicit use of the denoiser, and the gradient of the regularization term can be easily computed under mild conditions. We adapt RED for denoising of graph signals beyond image processing. We show that many graph signal denoisers, including graph neural networks, theoretically or practically satisfy the conditions for RED. We also study the effectiveness of RED from a graph filter perspective. Furthermore, we propose supervised and unsupervised parameter estimation methods based on deep
algorithm unrolling. These methods aim to enhance the
algorithm applicability, particularly in the unsupervised setting. Denoising experiments for synthetic and real-world
datasets show that our proposed method improves signal denoising accuracy in mean squared error compared to existing graph signal denoising methods.
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Systems and Control · Cross-listed from cs.RO
Trajectory Tracking for Multi-Manipulator Systems in Constrained Environments
Mayank Sewlia, Christos K. Verginis and Dimos V. Dimarogonas
We consider the problem of cooperative manipulation by a mobile multi-manipulator system operating in obstacle-cluttered and highly constrained environments under spatio-temporal task specifications. The task requires transporting a grasped object while respecting both continuous
robot dynamics and discrete
geometric constraints arising from obstacles and narrow passages. To address this hybrid structure, we propose a multi-rate planning and control framework that combines offline generation of an STL-satisfying object trajectory and collision-free base footprints with online constrained inverse kinematics and continuous-time feedback control. The resulting closed-loop system enables coordinated reconfiguration of multiple manipulators while tracking the desired object motion. The approach is evaluated in high-fidelity physics simulations using three Franka Emika Panda mobile manipulators rigidly grasping an object.
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Signal Processing
Rethinking Gaussian-Windowed Wavelets for Damping Identification
Hadi M. Daniali and Martin v. Mohrenschildt
In modal analysis, the prevalent use of Gaussian-based wavelets (such as Morlet and Gabor) for damping estimation is rarely questioned. In this study, we challenge this conventional approach by systematically exploring envelope-based damping
estimators and proposing a data-driven framework that optimizes the shape and parameters of the envelope utilizing synthetic impulse responses with known ground-truth envelopes. The performance of the resulting
estimators is benchmarked across a range of scenarios and compared against frequency-domain damping estimation methods, including Least Squares Rational Function (LSRF), poly-reference Least Squares Complex Frequency-Domain (pLSCF), peak picking (PP), and the Yoshida method. Our findings indicate that Triangle and Welch windows consistently outperform or are on par with Gaussian wavelet methods in contexts of moderate to high signal-to-noise ratios (SNR). In contrast, Blackman filtering demonstrates superior robustness under low SNR conditions and scenarios involving closely spaced modes. Among the frequency-domain methods assessed, LSRF shows the most reliability at very low SNR; however, the non-Gaussian optimized envelope
estimators perform exceptionally well as the SNR improves.
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Systems and Control
KalMRACO: Unifying Kalman Filtering and Model Reference Adaptive Control for Robust Control and Estimation
Lauritz Rismark Fosso, Christian Holden and Sveinung Johan Ohrem
A common assumption when applying the Kalman filter is a priori knowledge of the system parameters. These parameters are not necessarily known, and this may limit the real-world applicability of the Kalman filter. The well-established Model Reference Adaptive Controller (MRAC) utilizes a known reference model and ensures that the input-output behavior of a potentially unknown system converges to that of the reference model. We present KalMRACO, a unification of Kalman filtering and MRAC leveraging the reference model of MRAC as the Kalman filter system model, thus eliminating, to a large degree, the need for knowledge of the underlying system parameters in the application of the Kalman filter. We also introduce the concept of blending estimated states and measurements in the feedback law to ensure stability during the initial transient. KalMRACO is validated through simulations and lab trials on an
underwater vehicle. Results show superior tracking of the reference model state, observer state convergence, and noise mitigation properties.
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Signal Processing · Cross-listed from cs.IT
Robust Beamforming for Multiuser MIMO Systems with Unknown Channel Statistics: A Hybrid Offline-Online Framework
Wenzhuo Zou, Ming-Min Zhao, An Liu et al. (+1 more)
Robust beamforming design under imperfect channel state information (CSI) is a fundamental challenge in multiuser multiple-input multiple-output (MU-
MIMO) systems, particularly when the channel estimation error statistics are unknown. Conventional model-driven methods usually rely on prior knowledge of the error covariance
matrix and data-driven deep learning approaches suffer from poor generalization capability to unseen channel conditions. To address these limitations, this paper proposes a hybrid offline-online framework that achieves effective offline learning and rapid online adaptation. In the offline phase, we propose a shared (among users) deep neural network (DNN) that is able to learn the channel estimation error covariance from observed samples, thus enabling robust beamforming without statistical priors. Meanwhile, to facilitate real-time deployment, we propose a sparse augmented low-rank (SALR) method to reduce complexity while maintaining comparable performance. In the online phase, we show that the proposed network can be rapidly fine-tuned with minimal gradient steps. Furthermore, a multiple basis model-agnostic meta-learning (MB-MAML) strategy is further proposed to maintain multiple meta-initializations and by dynamically selecting the best one online, we can improve the adaptation and generalization capability of the proposed framework under unseen or non-stationary channels. Simulation results demonstrate that the proposed offline-online framework exhibits strong robustness across diverse channel conditions and it is able to significantly outperform state-of-the-art (SOTA) baselines.
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All authors Wenzhuo Zou, Ming-Min Zhao, An Liu, Min-Jian Zhao
Systems and Control
Coordinated Fast Frequency Response from Electric Vehicles, Data Centers, and Battery Energy Storage Systems
Xiaojie Tao and Rajit Gadh
High renewable penetration has significantly reduced system inertia in modern power grids, increasing the need for fast frequency response (FFR) from distributed and non-traditional resources. While electric vehicles (EVs), data centers, and battery energy storage systems (BESS) have each demonstrated the capability to provide sub-second active power support, their combined frequency response potential has not been systematically evaluated. This paper proposes a coordinated control framework that aggregates these heterogeneous resources to provide fast, stable, and reliable FFR. Dynamic models for EV fleets, data center UPS and workload modulation, and BESS are developed, explicitly capturing their response times, power limits, and operational constraints. A hierarchical control architecture is introduced, where an upper-level coordinator dynamically allocates FFR among resources based on response speed and available capacity, and lower-level controllers implement the actual power response. Case studies based on the IEEE 39-bus test system demonstrate that the coordinated EV-DC-BESS framework improves frequency nadir by up to 0.2 Hz, reduces RoCoF, and accelerates frequency recovery compared with single-resource FFR. Results confirm that synergistic coordination significantly enhances grid stability, especially in low-inertia scenarios. This work highlights the value of multi-resource aggregation for future frequency regulation
markets in renewable-dominated grids.
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Signal Processing
Antenna Coding Optimization Based on Pixel Antennas for MIMO Wireless Power Transfer with DC Combining
Yijun Chen, Shanpu Shen, Tianrui Qiao et al. (+3 more)
This paper investigates
antenna coding based on pixel
antennas as a new degree of freedom for enhancing multiple-input multiple-output (
MIMO) wireless power transfer (WPT) systems.
Antenna coding is closely related to the Fluid
Antenna System (FAS) concept and further generalizes the radiation pattern reconfigurability. We first introduce a beamspace channel model to demonstrate reconfigurable radiation patterns enabled by
antenna coders. By jointly optimizing the
antenna coding and transmit beamforming with perfect channel state information (CSI), we exploit gains from
antenna coding, transmit beamforming, and rectenna nonlinearity to maximize the output DC power. We adopt an alternating optimization approach with the quasi-Newton method and Successive Exhaustive Boolean Optimization (SEBO) method with warm-start to handle the transmit beamforming design and
antenna coding design respectively. Finally, simulation results show that the proposed
MIMO WPT system with pixel
antennas achieves up to 15 dB gain in average output DC power compared with a conventional system with fixed
antenna configuration, highlighting the potential of pixel
antennas for boosting the WPT efficiency.
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All authors Yijun Chen, Shanpu Shen, Tianrui Qiao, Hongyu Li, Jun Qian, Ross Murch
Systems and Control
Fast Frequency Response Potential of Data Centers through Workload Modulation and UPS Coordination
Xiaojie Tao and Rajit Gadh
The rapid growth of renewable energy sources has significantly reduced system inertia and increased the need for fast frequency response (FFR) in modern power systems. Data centers, as large and flexible electrical consumers, hold great potential to contribute to frequency stabilization due to their controllable IT workloads and on-site uninterruptible power supply (UPS) systems. This paper investigates the feasibility of leveraging data centers for providing fast frequency response through real-time workload modulation and UPS coordination. A dynamic model combining data center power consumption and grid frequency dynamics is developed, capturing the interactions between IT servers, cooling systems, and energy storage. Control strategies based on frequency deviation are implemented to adjust server power and discharge UPS batteries during frequency events. Case studies on a modified IEEE 39-bus system demonstrate that the proposed strategy can effectively reduce frequency nadir and shorten recovery time without compromising service quality. The results highlight the promising role of data centers as grid-supporting resources in future low-inertia systems.
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Signal Processing
Hybrid Iterative Detection for OTFS: Interplay between Local L-MMSE and Global Message Passing
Ruohai Yang, Shuangyang Li, Han Yu et al. (+3 more)
Orthogonal time frequency space (OTFS) modulation has emerged as a robust solution for high-mobility wireless communications. However, conventional detection
algorithms, such as linear equalizers and message passing (MP) methods, either suffer from noise enhancement or fail under complex doubly-selective channels, especially in the presence of fractional delay and Doppler shifts. In this paper, we propose a hybrid low-complexity iterative detection framework that combines linear minimum mean square error (L-MMSE) estimation with MP-based probabilistic inference. The key idea is to apply a new delay-Doppler (DD) commutation precoder (DDCP) to the DD domain signal vector, such that the resulting effective channel
matrix exhibits a structured form with several locally dense blocks that are sparsely inter-connected. This precoding structure enables a hybrid iterative detection strategy, where a low-dimensional L-MMSE estimation is applied to the dense blocks, while MP is utilized to exploit the sparse inter-block connections. Furthermore, we provide a detailed complexity analysis, which shows that the proposed scheme incurs lower computational cost compared to the full-size L-MMSE detection. The simulation results of convergence performance confirm that the proposed hybrid MP detection achieves fast and reliable convergence with controlled complexity. In terms of error performance, simulation results demonstrate that our scheme achieves significantly better bit error rate (BER) under various channel conditions. Particularly in multipath scenarios, the BER performance of the proposed method closely approaches the matched filter bound (MFB), indicating its near-optimal error performance.
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All authors Ruohai Yang, Shuangyang Li, Han Yu, Zhiqiang Wei, Kai Wan, Giuseppe Caire
Image and Video Processing
Synthetic Aperture for High Spatial Resolution Acoustoelectric Imaging
Wei Yi Oon, Yuchen Tang, Baiqian Qi et al. (+1 more)
Acoustoelectric (AE) imaging provides electro-anatomical contrast by mapping the distribution of electric fields in biological tissues, by delivering ultrasound waves which spatially modulate the medium resistivity via the AE effect. The conventional method in AE imaging is to transmit focused ultrasound (FUS) beams; however, the depth-of-field (DOF) of FUS-AE is limited to the size of the focal spot, which does not span across the centimeter-scale of organs. Instead of fixing the focal depth on transmission, we propose to dynamically synthesize the AE modulation regions via a Synthetic Aperture approach (SA-AE). SA-AE involves a straightforward pixel-based delay-and-sum reconstruction of AE images from unfocused AE signals. In saline and ex vivo lobster nerve experiments, FUS-AE was shown to perform well only at the focal depth, with poor spatial resolution for out-of-focus electric sources. Meanwhile, SA-AE generally improved spatial resolution throughout the DOF, but introduced strong background noise. The flexibility of uncoupled, single-element induced AE signals in SA-AE was further leveraged to quantify their spatial coherence across the transmit aperture, obtaining maps of the coherence factor (CF) and pulse-length coherence factor (CFPL). Weighting SA-AE images with their derived CF and CFPL maps resulted in further improvement in image resolution and contrast, and notably, boosted the image SNR beyond that of FUS-AE. CFPL exhibited stronger noise suppression over CF. Using unfocused wave transmissions, the proposed coherence-weighted SA-AE strategy offers a high resolution yet noise-robust solution towards the practical imaging of fast biological currents.
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All authors Wei Yi Oon, Yuchen Tang, Baiqian Qi, Wei-Ning Lee
Signal Processing · Cross-listed from cs.CV
Quality-Aware Framework for Video-Derived Respiratory Signals
Nhi Nguyen, Constantino Álvarez Casado, Le Nguyen et al. (+2 more)
Video-based respiratory rate (RR) estimation is often unreliable due to inconsistent signal quality across extraction methods. We present a predictive, quality-aware framework that integrates heterogeneous signal sources with dynamic assessment of reliability. Ten signals are extracted from facial remote photoplethysmography (rPPG), upper-body motion, and deep learning pipelines, and analyzed using four spectral
estimators: Welch's method, Multiple Signal Classification (MUSIC), Fast
Fourier Transform (FFT), and peak detection. Segment-level quality indices are then used to train machine learning models that predict accuracy or select the most reliable signal. This enables adaptive signal fusion and quality-based segment filtering. Experiments on three public
datasets (OMuSense-23, COHFACE, MAHNOB-HCI) show that the proposed framework achieves lower RR estimation errors than individual methods in most cases, with performance gains depending on
dataset characteristics. These findings highlight the potential of quality-driven predictive modeling to deliver scalable and generalizable
video-based respiratory monitoring solutions.
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All authors Nhi Nguyen, Constantino Álvarez Casado, Le Nguyen, Manuel Lage Cañellas, Miguel Bordallo López