Systems and Control
From Points to Sets: Set-Based Safety Verification in the Latent Space
Wenyuan Wu, Peng Xie, Zhen Zhang et al. (+3 more)
We extend latent representation methods for safety control design to set-valued states. Recent work has shown that barrier functions designed in a learned latent space can transfer safety guarantees back to the original system, but these methods evaluate certificates at single state points, ignoring state uncertainty. A fixed safety margin can partially address this but cannot adapt to the anisotropic and time-varying nature of the uncertainty gap across different safety constraints. We instead represent the system state as a zonotope, propagate it through the encoder to obtain a latent zonotope, and evaluate certificates over the worst case of the entire set. On a 16-dimensional quadrotor suspended-load gate passage task, set-valued evaluation achieves 5/5 collision-free passages, compared to 1/5 for point-based evaluation and 2/5 for a fixed-margin baseline. Set evaluation reports safety in 44.4% of per-head evaluations versus 48.5% for point-based, and this greater conservatism detects 4.1% blind spots where point evaluation falsely certifies safety, enabling earlier corrective control. The safety gap between point and set evaluation varies up to $12\times$ across certificate heads, explaining why no single fixed margin suffices and confirming the need for per-head, per-timestep adaptation, which set evaluation provides by construction.
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All authors Wenyuan Wu, Peng Xie, Zhen Zhang, Yanliang Huang, Karl H. Johansson, Amr Alanwar
Systems and Control
Robust Nonlinear System Identification in Reproducing Kernel Hilbert Spaces via Scenario Optimization
Jannis Lübsen and Annika Eichler
This paper proposes a method for constructing one-step prediction tubes for nonlinear systems using reproducing kernel
Hilbert spaces. We approximate a bounded reproducing kernel
Hilbert space (RKHS) hypothesis set by a finite-dimensional subspace using bounds based on n-widths and a greedy
algorithm for basis reduction. For kernels whose native spaces are norm-equivalent to Sobolev spaces, we derive how the required basis size scales with kernel smoothness and input dimension. This finite-dimensional representation enables the use of convex scenario optimization to obtain violation guarantees for the learned predictor without requiring an a priori bound on the true system's RKHS norm or Lipschitz constant. The method is demonstrated on an obstacle-avoidance task. We also discuss the main limitations of the current analysis, including dimensional scaling and dependence on i.i.d. data.
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Signal Processing · Cross-listed from cs.IT
Near-Field Integrated Sensing, Computing and Semantic Communication in Digital Twin-Assisted Vehicular Networks
Yinchao Yang, Yahao Ding, Jiaxiang Wang et al. (+5 more)
Digital twin (DT) technology offers transformative potential for vehicular networks, enabling high-fidelity virtual representations for enhanced safety and automation. However, seamless DT synchronization in dynamic environments faces challenges such as massive data transmission, precision sensing, and strict computational constraints. This paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for DT-assisted vehicular networks in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-
MIMO) configuration, each roadside unit (RSU) employs semantic communication to serve vehicles while simultaneously utilizing millimeter-wave (mmWave) radar for environmental mapping. We implement particle filtering at RSUs to achieve high-precision vehicle tracking. To optimize performance, we formulate a joint optimization problem balancing semantic communication rates and sensing accuracy under limited computational resources and power budget. Our solution includes a hybrid heuristic
algorithm for vehicle-to-RSU assignment and an alternating optimization approach for determining semantic extraction ratios and beamforming
matrices. Performance is extensively evaluated via the Cramér-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and resource utilization. Numerical results demonstrate that the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing integrated sensing and communication (
ISAC) schemes under constrained resource conditions.
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All authors Yinchao Yang, Yahao Ding, Jiaxiang Wang, Zhaohui Yang, Chen Zhu, Zhaoyang Zhang, Dusit Niyato, Mohammad Shikh-Bahaei
Signal Processing
Configuration Tuning for ISAC: Cost-Efficient Adaptation via RACE-CMA
Ashkan Jafari Fesharaki, Yasser Mestrah, Ibrahim Hemadeh et al. (+6 more)
This paper studies a feedback driven configuration tuning framework for adaptive sensing feedback in Integrated Sensing and Communication (
ISAC) systems. We propose a framework in which the User Equipment (UE) adapts sensing parameters under dynamic conditions while satisfying network defined constraints. The problem is formulated as a
stochastic constrained optimization problem, to improve sensing reliability and latency. We consider a bistatic
ISAC sensing feedback setup and instantiate the framework via threshold optimization as a representative case study, enabling benchmarking against baseline methods. To ensure efficiency under UE computational limits, we propose Ranking Aware, Constrained, and Efficient CMAES (RACE CMA), which integrates two stage racing, common random numbers, noise aware ranking, and feasible constraint handling. Results show that the proposed approach improves sensing reliability by about 35 percent while reducing computational cost by about 25 percent, yielding roughly a twofold gain in performance cost efficiency. This highlights that UE side configuration tuning is a promising mechanism for enhancing closed loop
ISAC performance under practical system constraints.
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All authors Ashkan Jafari Fesharaki, Yasser Mestrah, Ibrahim Hemadeh, Yi Ma, Mohammad Heggo, Arman Shojaeifard, Ahmet Serdar Tan, Rahim Tafazolli, Alain Mourad
Systems and Control
Physics-Informed Neural Optimal Control for Precision Immobilization Technique in Emergency Scenarios
Yangye Jiang, Jiachen Wang and Daofei Li
Precision Immobilization Technique (PIT) is a potentially effective intervention maneuver for emergency out-of-control vehicle, but its automation is challenged by highly nonlinear collision dynamics, strict safety constraints, and real-time computation requirements. This work presents a PIT-oriented neural optimal-control framework built around PicoPINN (Planning-Informed Compact Physics-Informed Neural Network), a compact physics-informed surrogate obtained through knowledge
distillation, hierarchical parameter clustering, and relation-
matrix-based parameter reconstruction. A hierarchical neural-OCP (Optimal Control Problem) architecture is then developed, in which an upper virtual decision layer generates PIT decision packages under scenario constraints and a lower coupled-MPC (Model Predictive Control) layer executes interaction-aware control. To evaluate the framework, we construct a PIT Scenario
Dataset and conduct surrogate-model comparison, planning-structure ablation, and multi-fidelity assessment from simulation to scaled by-wire vehicle tests. In simulation, adding the upper planning layer improves PIT success rate from 63.8% to 76.7%, and PicoPINN reduces the original PINN parameter count from 8965 to 812 and achieves the smallest average heading error among the learned surrogates (0.112 rad). Scaled vehicle experiments are further used as evidence of control feasibility, with 3 of 4 low-speed controllable-contact PIT trials achieving successful yaw reversal.
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Signal Processing
Brain-to-Speech: Prosody Feature Engineering and Transformer-Based Reconstruction
Mohammed Salah Al-Radhi, Géza Németh, Andon Tchechmedjiev et al. (+1 more)
This chapter presents a novel approach to
brain-to-
speech (BTS) synthesis from intracranial electroencephalography (iEEG) data, emphasizing prosody-aware feature engineering and advanced
transformer-based models for high-fidelity
speech reconstruction. Driven by the increasing interest in decoding
speech directly from
brain activity, this work integrates neuroscience, artificial intelligence, and signal processing to generate accurate and natural
speech. We introduce a novel pipeline for extracting key prosodic features directly from complex
brain iEEG signals, including intonation, pitch, and rhythm. To effectively utilize these crucial features for natural-sounding
speech, we employ advanced deep learning models. Furthermore, this chapter introduces a novel
transformer encoder architecture specifically designed for
brain-to-
speech tasks. Unlike conventional models, our architecture integrates the extracted prosodic features to significantly enhance
speech reconstruction, resulting in generated
speech with improved intelligibility and expressiveness. A detailed evaluation demonstrates superior performance over established baseline methods, such as traditional Griffin-Lim and CNN-based reconstruction, across both quantitative and perceptual metrics. By demonstrating these advancements in feature extraction and
transformer-based learning, this chapter contributes to the growing field of AI-driven neuroprosthetics, paving the way for assistive technologies that restore communication for individuals with
speech impairments. Finally, we discuss promising future research directions, including the integration of
diffusion models and real-time inference systems.
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All authors Mohammed Salah Al-Radhi, Géza Németh, Andon Tchechmedjiev, Binbin Xu
Systems and Control · Cross-listed from cs.RO
Hazard Management in Robot-Assisted Mammography Support
Ioannis Stefanakos, Roisin Bradley, Radu Calinescu et al. (+3 more)
Robotic and
embodied-AI systems have the potential to improve accessibility and quality of care in clinical settings, but their deployment in close physical contact with vulnerable patients introduces significant safety risks. This paper presents a hazard management methodology for MammoBot, an assistive robotic system designed to support patients during X-ray mammography. To ensure safety from early development stages, we combine stakeholder-guided process modelling with Software Hazard Analysis and Resolution in Design (SHARD) and System-Theoretic Process Analysis (STPA). The
robot-assisted workflow is defined collaboratively with clinicians, roboticists, and patient representatives to capture key human-
robot interactions. SHARD is applied to identify technical and procedural deviations, while STPA is used to analyse unsafe control actions arising from user interaction. The results show that many hazards arise not from component failures, but from timing mismatches, premature actions, and misinterpretation of system state. These hazards are translated into refined and additional safety requirements that constrain system behaviour and reduce reliance on correct human timing or interpretation alone. The work demonstrates a structured and traceable approach to safety-driven design with potential applicability to assistive robotic systems in clinical environments.
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All authors Ioannis Stefanakos, Roisin Bradley, Radu Calinescu, Beverley Townsend, Tianyuan Wang, Jihong Zhu
Systems and Control
Network Reconstruction in Consensus Algorithms with Hidden Agents
Melvyn Tyloo
Reconstructing the parameters that encode the influence between model variables based on time-series measurements represents an outstanding question in the theory of complex network-coupled systems. Here, we propose a solution to this problem for a class of noisy leader-follower consensus
algorithms, where one has access to measurements only from the followers but not from the leaders. Leveraging the directed
Laplacian coupling of such systems, we present an autoregressive expansion of the observed dynamics which can be truncated at different orders, depending on the
memory of the leaders. When their
memory is short, this allows one to correctly reconstruct the full dynamical
matrix with hidden leader
agents, provided some additional assumption on the system to lift the degeneracy in the reconstruction. We illustrate and check the theory using numerical simulations for the cases of both a single and multiple hidden leaders.
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Systems and Control
Quantifying Control Performance Loss for a Least Significant Bits Authentication Scheme
Bart Wolleswinkel and Riccardo Ferrari
Industrial control systems (ICSs) often consist of many legacy devices, which were designed without security requirements in mind. With the increase in cyberattacks targeting critical infrastructure, there is a growing urgency to develop legacy-compatible security solutions tailored to the specific needs and constraints of real-time control systems. We propose a least significant bits (LSBs) coding scheme providing message authentication and integrity, which is compatible with legacy devices and never compromises availability. The scheme comes with provable security guarantees, and we provide a simple yet effective method to deal with synchronization issues due to packet dropouts. Furthermore, we quantify the control performance loss for both a fixed-point and floating-point quantization architecture when using the proposed coding scheme. We demonstrate its effectiveness in detecting cyberattacks, as well as the impact on control performance, on a hydro power turbine control system.
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Systems and Control · Cross-listed from cs.RO
GraspSense: Physically Grounded Grasp and Grip Planning for a Dexterous Robotic Hand via Language-Guided Perception and Force Maps
Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina et al. (+3 more)
Dexterous robotic manipulation requires more than geometrically valid grasps: it demands physically grounded contact strategies that account for the spatially non-uniform mechanical properties of the object. However, existing grasp planners typically treat the surface as structurally homogeneous, even though contact in a weak region can damage the object despite a geometrically perfect grasp. We present a pipeline for grasp selection and force regulation in a five-fingered robotic hand, based on a map of locally admissible contact loads. From an operator command, the system identifies the target object, reconstructs its 3D geometry using SAM3D, and imports the model into Isaac Sim. A physics-informed
geometric analysis then computes a force map that encodes the maximum lateral contact force admissible at each surface location without deformation. Grasp candidates are filtered by
geometric validity and task-goal consistency. When multiple candidates are comparable under classical metrics, they are re-ranked using a force-map-aware criterion that favors grasps with contacts in mechanically admissible regions. An impedance controller scales the stiffness of each finger according to the locally admissible force at the contact point, enabling safe and reliable grasp execution. Validation on paper, plastic, and glass cups shows that the proposed approach consistently selects structurally stronger contact regions and keeps grip forces within safe bounds. In this way, the work reframes dexterous manipulation from a purely
geometric problem into a physically grounded joint planning problem of grasp selection and grip execution for future
humanoid systems.
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All authors Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina, Miguel Altamirano Cabrera, Safina Gulyamova, Dzmitry Tsetserukou
Signal Processing
A BEV-Fusion Based Framework for Sequential Multi-Modal Beam Prediction in mmWave Systems
Jiaming Zeng, Cunhua Pan, Haoyang Weng et al. (+3 more)
Beam prediction is critical for reducing beam-training overhead in millimeter-wave (mmWave) systems, especially in high-mobility vehicular scenarios. This paper presents a BEV-Fusion based framework that unifies camera,
LiDAR, radar, and GPS modalities in a shared bird's-eye-view (BEV) representation for spatially consistent multi-modal fusion. Unlike priorapproaches that fuse globally pooled one-dimensional features, the proposed method performs fusion in BEV space to preservecross-modal
geometric structure and visual semantic density. A learned camera-to-BEV module based on cross-
attention is adopted to generate BEV-aligned visual features without relying on precise camera calibration, and a temporal
transformer is used to aggregate five-step sequential observations for motion-aware beam prediction. Experiments on the DeepSense 6G
benchmark show that BEV-Fusion achieves approximately 87% distance- based accuracy (DBA) on scenarios 32, 33 and 34, outperforming the TransFuser baseline. These results indicate that BEV-space fusion provides an effective spatial abstraction for sensing-assisted beam prediction.
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All authors Jiaming Zeng, Cunhua Pan, Haoyang Weng, Ruijing Liu, Hong Ren, Jiangzhou Wang
Systems and Control
Predictor-Feedback CACC for Vehicular Platoons with Actuation and Communication Delays Based on a Multiple-Predecessor-Following CTH Nominal Strategy
Amirhossein Samii, Dimitrios Angelopoulos and Nikolaos Bekiaris-Liberis
We develop a predictor-feedback cooperative adaptive cruise control (CACC) design relying on a multiple-predecessor-following (MPF)
topology-based nominal delay-free CACC law. We consider vehicular platoons with heterogeneous vehicles, whose dynamics are described by a third-order linear system subject to actuation delay, along with vehicle-to-vehicle (V2V) communication delay. The design achieves individual vehicle stability, string stability, and zero, steady-state speed/spacing tracking errors, for any value of the actuation delay. The proofs of individual vehicle stability, string stability, and regulation rely on employment of an input-output approach on the frequency domain, capitalizing on the delay-compensating property of the design, which enables as to derive explicit string stability conditions on control and vehicle models parameters. The theoretical guarantees of string stability and the respective conditions on parameters are illustrated also numerically. We present consistent simulation results, for a ten-vehicle platoon, illustrating the potential of the design in traffic throughput improvement, as compared with a predictor-feedback CACC design in which, each ego vehicle's controller utilizes information only from a single preceding vehicle. We also present simulation results in a realistic scenario in which the leading vehicle's trajectory is obtained from NGSIM data.
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Systems and Control · Cross-listed from cs.RO
Leaderless Collective Motion in Affine Formation Control over the Complex Plane
Jesus Bautista, Enric Morella, Lili Wang et al. (+1 more)
We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the
Laplacian matrix used to achieve static formations of
robot swarms. Specifically, the resulting collective motion is characterized as a time-varying affine transformation of a reference configuration, or shape. Unlike the traditional leader-follower strategy, our leaderless scheme allows
agents to maintain distinct and possibly time-varying velocities, enabling a broader range of collective motions, including all the linear combinations of translations, rotations, scaling and shearing of a reference shape. Our analysis provides the analytic solution governing the resulting collective motion, explicitly designing the eigenvectors and
eigenvalues that define this motion as a function of the modified weights in the new
Laplacian matrix. To facilitate a more tractable analysis and design of affine formations in 2D, we propose the use of complex numbers to represent all relevant information. Simulations with up to 20
agents validate the theoretical results.
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All authors Jesus Bautista, Enric Morella, Lili Wang, Hector Garcia de Marina
Systems and Control · Cross-listed from math.OC
Parametric Nonconvex Optimization via Convex Surrogates
Renzi Wang, Panagiotis Patrinos and Alberto Bemporad
This paper presents a novel learning-based approach to construct a surrogate problem that approximates a given parametric nonconvex optimization problem. The surrogate function is designed to be the minimum of a finite set of functions, given by the composition of convex and monotonic terms, so that the surrogate problem can be solved directly through parallel convex optimization. As a proof of concept, numerical experiments on a nonconvex path tracking problem confirm the approximation quality of the proposed method.
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Systems and Control
Optimality Robustness in Koopman-Based Control
Yicheng Lin, Bingxian Wu, Nan Bai et al. (+3 more)
The Koopman operator enables simplified representations for nonlinear systems in data-driven optimal control, but the accompanying uncertainties inevitably induce deviations in the optimal controller and associated value function. This naturally raises the question of how such uncertainty-induced optimality deviation can be quantified and mitigated. To address this problem, we adopt a unified analysis-to-design perspective that connects the characterization of optimality robustness with its improvement through controller design. At the analysis level, we establish a unified treatment of multiple uncertainty sources in Koopman-based control, where approximation error and noisy data are incorporated into a common robustness analysis through a norm-bounded representation. At the design level, we develop a robustness-aware optimal control methodology that provably reduces such optimality deviations, thereby enhancing robustness while explicitly revealing a quantitative trade-off between nominal optimality and robustness. As for practical implementation aspect, we further propose a tractable policy iteration
algorithm, whose well-posedness and convergence are established via vanishing viscosity regularization and
elliptic partial differential equation (PDE) techniques. Numerical examples validate the theoretical findings and demonstrate the effectiveness of proposed methodology.
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All authors Yicheng Lin, Bingxian Wu, Nan Bai, Yunxiao Ren, Zhongkui Li, Zhisheng Duan
Systems and Control
An Additional Resonance Damping Control for Grey-Box D-PMSG Wind Farm Integrated Weak Grid
Tao Zhang, Songhao Yang, Zhiguo Hao et al. (+2 more)
Considerable efforts have been made to address the resonance issue of the Direct-drive Permanent Magnet Synchronous Generator (D-PMSG) wind farm integrated power systems. However, the D-PMSG controller structure and parameters are concealed because of commercial secrecy, thus the target system exhibits grey-box characteristics. The existing resonance damping methods are either unavailable for grey-box systems or economically infeasible, which makes resonance damping of grey-box systems extremely challenging. To address this issue, this paper proposes an Additional Resonance Damping Control (ARDC) specfically for the grey-box D-PMSG system. This strategy is achieved by incorporating an additional control loop outside the D-PMSG controller. Firstly, the external impedance characteristics are obtained by the frequency sweeping technique ofline and then the key parameter of the additional control loop is determined by the Bode-diagram-based method under the worst stability scenario. Once the resonance occurs, the external impedance of the black-box D-PMSG is reshaped online to increase the magnitude stability margin of the system, thus providing effective resonance damping. The ARDC's effectiveness is finally verfied in the simulation and controller-hardware-in-the-loop experiment under various operating conditions.
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All authors Tao Zhang, Songhao Yang, Zhiguo Hao, Hongyue Ma, Baohui Zhang
Systems and Control · Cross-listed from math.OC
Scaled Graph Containment for Feedback Stability: Soft-Hard Equivalence and Conic Regions
Eder Baron-Prada, Julius P. J. Krebbekx, Adolfo Anta et al. (+1 more)
Scaled graphs (SGs) offer a
geometric framework for feedback stability analysis. This paper develops containment conditions for SGs within multiplier-defined regions, addressing both circular and conic geometries. For circular regions, we show that soft and hard SG containment are equivalent whenever the associated multiplier is positive-negative. This enables hard stability certification from soft computations alone, bypassing both the positive semidefinite storage constraint and the homotopy condition of existing methods. Numerical experiments on systems with up to 300 states demonstrate computational savings of 15-44 % for the circular containment framework. We further characterize which conic regions are hyperbolically convex, a condition our frequency-domain certificate requires, and demonstrate that such regions provide tighter SG bounds than circles whenever the operator SG is nonsymmetric.
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All authors Eder Baron-Prada, Julius P. J. Krebbekx, Adolfo Anta, Florian Dörfler
Signal Processing
Rotatable Antenna Enabled Multi-Cell Mixed Near-Field and Far-Field Communications
Yunpu Zhang, Changsheng You, Ruichen Zhang et al. (+4 more)
Prior studies on mixed near-field and far-field communications have focused exclusively on single-
cell scenarios, where both near-field and far-field users are served by the same base station (BS), leading to intra-
cell mixed-field interference. In this paper, we consider a more general and practical multi-
cell mixed-field scenario consisting of multiple
cells, each serving multiple users, thus resulting in more complex inter-
cell mixed-field interference. To address this new challenge, we propose leveraging rotatable
antenna (RA) technology to enhance multi-
cell mixed-field communication performance by exploiting the additional spatial degree-of-freedom introduced by RA rotation to mitigate interference in an efficient way. Specifically, we study an RA-enabled multi-
cell mixed-field communication system in which each BS is equipped with an RA array to serve its associated users. We formulate a network-wide sum-rate maximization problem that jointly optimizes the transmit beamforming and the rotation angles of the RA arrays, subject to per-BS power constraints and admissible array rotation limits. To gain useful insights into the role of RAs in multi-
cell mixed-field communications, we first analyze a special case with a single user per
cell. For this case, we obtain a closed-form expression for the rotation-aware inter-
cell mixed-field interference using the Fresnel integrals and analytically show that RA rotation can effectively mitigate such interference, thereby substantially improving system performance. For the general case with multiple users per
cell, we develop an efficient double-layer
algorithm: the inner layer optimizes the transmit beamforming at each BS via semidefinite relaxation and successive convex approximation; while the outer layer determines the rotation angles of the RA arrays using particle swarm optimization.
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All authors Yunpu Zhang, Changsheng You, Ruichen Zhang, Beixiong Zheng, Hing Cheung So, Dusit Niyato, Tony Q. S. Quek
Audio and Speech Processing
Multimodal Deep Learning Method for Real-Time Spatial Room Impulse Response Computing
Zhiyu Li, Xinwen Yue, Shenghui Zhao et al. (+1 more)
We propose a
multimodal deep learning model for VR auralization that generates spatial room impulse responses (SRIRs) in real time to reconstruct scene-specific auditory perception. Employing SRIRs as the output reduces computational complexity and facilitates integration with personalized head-related transfer functions. The model takes two modalities as input: scene information and waveforms, where the waveform corresponds to the low-order reflections (LoR). LoR can be efficiently computed using geometrical acoustics (GA) but remains difficult for deep learning models to predict accurately. Scene geometry, acoustic properties, source coordinates, and listener coordinates are first used to compute LoR in real time via GA, and both LoR and these features are subsequently provided as inputs to the model. A new
dataset was constructed, consisting of multiple scenes and their corresponding SRIRs. The
dataset exhibits greater diversity. Experimental results demonstrate the superior performance of the proposed model.
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All authors Zhiyu Li, Xinwen Yue, Shenghui Zhao, Jing Wang
Signal Processing
Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
Ljupcho Milosheski, Fedja Močnik, Mihael Mohorčič et al. (+1 more)
Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate
Radio Environment Map (REM) estimation is therefore increasingly important for effective network planning and operation. Existing methods, from ray-tracing simulators to deep learning generative models, achieve promising results but require detailed 3D environment data such as
LiDAR-derived point clouds, which are costly to acquire, several gigabytes per km2 in size, and quickly outdated in dynamic environments. We propose a two-stage framework that eliminates the need for 3D data at inference time: in the first stage, a learned
estimator predicts elevation maps directly from satellite RGB imagery, which are then fed alongside
antenna parameters into the REM
estimator in the second stage. Across existing CNN- based REM estimation architectures, the proposed approach improves RMSE by up to 7.8% over image-only baselines, while operating on the same input feature space and requiring no 3D data during inference, offering a practical alternative for scalable
radio environment modelling.
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All authors Ljupcho Milosheski, Fedja Močnik, Mihael Mohorčič, Carolina Fortuna
Audio and Speech Processing
Active noise cancellation on open-ear smart glasses
Kuang Yuan, Freddy Yifei Liu, Tong Xiao et al. (+5 more)
Active noise cancellation (ANC) is widely deployed on consumer headphones and earbuds to suppress environmental noise. However, existing ANC systems require an error microphone at the user's ear canal to measure residual sound, preventing deployment on emerging open-ear wearable devices such as smart glasses and VR headsets, which leave the ear unoccluded. Here we present an ANC system for open-ear wearables that suppresses environmental noise using only microphones and miniaturized open-ear speakers embedded within the frame of the wearables, removing the need for an in-ear error microphone. Our low-latency computational pipeline uses a neural network to estimate the noise at the ear from an array of eight microphones distributed around the wearable's frame and generates an anti-noise signal in real-time. This mapping generalizes to unseen users and acoustic environments without prior acoustic measurement. We develop a custom glasses prototype and evaluate across eleven unseen users and eight unseen environments under mobility in the 100 to 1000 Hz frequency range, where environmental noise is concentrated. We achieve a mean noise reduction of 9.6 dB without any calibration, and 11.2 dB with a brief user-specific calibration. Further, we demonstrate that our approach extends to the broader class of open-ear wearables including VR headsets and headbands.
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All authors Kuang Yuan, Freddy Yifei Liu, Tong Xiao, Yiwen Song, Chengyi Shen, Saksham Bhutani, Justin Chan, Swarun Kumar
Systems and Control · Cross-listed from math.OC
Optimal Centered Active Excitation in Linear System Identification
Kaito Ito and Alexandre Proutiere
We propose an active learning
algorithm for linear system identification with optimal centered noise excitation. Notably, our
algorithm, based on ordinary least squares and semidefinite programming, attains the minimal sample complexity while allowing for efficient computation of an estimate of a system
matrix. More specifically, we first establish lower bounds of the sample complexity for any active learning
algorithm to attain the prescribed accuracy and confidence levels. Next, we derive a sample complexity upper bound of the proposed
algorithm, which matches the lower bound for any
algorithm up to universal factors. Our tight bounds are easy to interpret and explicitly show their dependence on the system parameters such as the state dimension.
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Signal Processing
Semantic Communication with an LLM-enabled Knowledge Base
Wuxia Hu, Caili Guo, Yang Yang et al. (+3 more)
Semantic communication (SC) can achieve superior coding and transmission performance based on the knowledge contained in the semantic knowledge base (KB). However, conventional KBs consist of source KBs and channel KBs, which are often costly to obtain data and limited in data scale. Fortunately, large
language models (LLMs) have recently emerged with extensive knowledge and generative capabilities. Therefore, this paper proposes an SC system with LLM-enabled knowledge base (SC-LMKB), which utilizes the generation ability of LLMs to significantly enrich the KB of SC systems. In particular, we first design an LLM-enabled generation mechanism with a
prompt engineering strategy for source data generation (SDG) and a cross-
attention alignment method for channel data generation (CDG). However,
hallucinations from LLMs may cause semantic noise, thus degrading SC performance. To mitigate the
hallucination issue, a cross-domain fusion codec (CDFC) framework with a
hallucination filtering phase and a cross-domain fusion phase is then proposed for SDG. In particular, the first phase filters out new data generated by the LMKB irrelevant to the original data based on semantic similarity. Then, a cross-domain fusion phase is proposed, which fuses source data with LLM-generated data based on their semantic importance, thereby enhancing task performance. Besides, a joint training objective that combines cross-
entropy loss and reconstruction loss is proposed to reduce the impact of
hallucination on CDG. Experiment results on three cross-modality
retrieval tasks demonstrate that the proposed SC-LMKB can achieve up to 72.6% and 90.7% performance gains compared to conventional SC systems and LLM-enabled SC systems, respectively.
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All authors Wuxia Hu, Caili Guo, Yang Yang, Chunyan Feng, Kuiyuan Ding, Shiwen Mao
Systems and Control · Cross-listed from cs.RO
MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems
Rui Huang, Zhiqian Cai, Siyu Tang et al. (+6 more)
Modular Aerial
Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations, providing high adaptability to diverse mission scenarios, fault conditions, and payload capacities. However, existing control
algorithms for MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands. This leads to attitude error accumulation as the number of drone units scales, ultimately causing severe oscillations during docking, separation, and waypoint tracking. To address these limitations, we first design a compact mechanical system that enables passive docking, detection-free passive locking, and
magnetic-assisted separation using a single micro servo. Second, we introduce a force-torque-equivalent and polytope-constraint virtual quadrotor that explicitly models feasible wrench sets. Together, these abstractions capture the full MARS dynamics and enable existing quadrotor controllers to be applied across different configurations. We further optimize the yaw angle that maximizes control authority to enhance agility. Third, building on this abstraction, we design a two-stage predictive-allocation pipeline: a constrained predictive tracker computes virtual inputs while respecting force/torque bounds, and a dynamic allocator maps these inputs to individual modules with balanced objectives to produce smooth, trackable motor commands. Simulations across over 10 configurations and real-world experiments demonstrate stable docking, locking, and separation, as well as effective control performance. To our knowledge, this is the first real-world demonstration of MARS achieving agile flight and transport with 40 deg peak pitch while maintaining an average position error of 0.0896 m. The
video is available at: https://youtu.be/yqjccrIpz5o
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All authors Rui Huang, Zhiqian Cai, Siyu Tang, Pengxuan Wei, Lidong Li, Xin Chen, Wenhan Cao, Zhenyu Zhang, Lin Zhao
Signal Processing
Waveguide to Meaning: Semantic-Aware NOMA for Pinching-Antenna Systems
Ishtiaque Ahmed, Haris Parvaiz and Leila Musavian
We investigate the performance of the pinching-
antenna systems (PASS) for semantic communication (SC) in both single-waveguide and multi-waveguide scenarios, under the constraints of bit-user quality of service (QoS) and bit-to-semantic decoding order in a heterogeneous users downlink non-orthogonal multiple access (NOMA). Multiple pinching
antennas in the single-waveguide scenario are at a minimum adjacent spacing required to prevent mutual coupling. An alternating optimization (AO)-based
algorithm optimizes users power allocation coefficients and position of pinching
antennas in the single-waveguide NOMA framework. For the multi-waveguide scenario, assuming adjacent waveguides at a sufficient lateral distance apart, the waveguides power allocation subproblem is solved using monotonic optimization and minorization-maximization (MM) approach. Specifically, a lower bound surrogate is iteratively maximized under the feasibility constraints such that a non-decreasing sequence of objective is obtained. Numerical results demonstrate that the NOMA based PASS exploiting SC offers higher semantic spectral efficiency (SE) while fulfilling the bit-user QoS requirement when compared to the considered conventional fixed
antenna system. Notably, the multi-waveguide scenario becomes more beneficial for creating adjustable wireless channels in stringentconditions with higher bit-user QoS and wider coverage area requirements.
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Systems and Control
Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset
Tinko Sebastian Bartels, Ruixiang Wu, Xinyu Lu et al. (+5 more)
Addressing the critical need for intelligent, context-aware energy management in renewable systems, we introduce the OpenCEM Simulator and
Dataset: the first open-source digital twin explicitly designed to integrate rich, unstructured contextual information with quantitative renewable energy dynamics. Traditional energy management relies heavily on numerical time series, thereby neglecting the significant predictive power embedded in human-generated context (e.g., event schedules, system logs, user intentions). OpenCEM bridges this gap by offering a unique platform comprising both a meticulously aligned,
language-rich
dataset from a real-world PV-and-battery microgrid installation and a modular simulator capable of natively processing this multi-modal context. The OpenCEM Simulator provides a high-fidelity environment for developing and validating novel control
algorithms and prediction models, particularly those leveraging Large
Language Models. We detail its component-based architecture, hybrid data-driven and physics-based modelling capabilities, and demonstrate its utility through practical examples, including context-aware load
forecasting and the implementation of online optimal battery charging control strategies. By making this platform publicly available, OpenCEM aims to accelerate research into the next generation of intelligent, sustainable, and truly context-aware energy systems.
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All authors Tinko Sebastian Bartels, Ruixiang Wu, Xinyu Lu, Yikai Lu, Fanzeng Xia, Haoxiang Yang, Yue Chen, Tongxin Li
Signal Processing
Operator-Theoretic Energy Functionals for Impulse-Excited Nonstationary Signal Analysis
Tahir Cetin Akinci
This study presents an operator theoretic framework for defect detection in impulse excited nonstationary systems. Measured responses are modeled as finite energy impulse responses perturbed by
stochastic disturbances and represented in the
Hilbert space L2(R). Time frequency representations are formulated as bounded linear analysis operators associated with continuous frames, enabling a consistent description of how structural perturbations redistribute transient signal energy. Within this formulation, a nonlinear Energy Concentration Index ECI is introduced to quantify localized transform domain energy over selected regions of the time frequency plane. The boundedness and continuity of the functional ensure that small physical variations in system parameters produce measurable changes in localized energy distribution. This property enables the construction of a statistical separability functional that links multi resolution energy geometry to classification performance. Based on these results, a compact Impulse Based Multi Resolution Energy Detector IMRED is derived. The analysis shows that variations in damping and resonant frequency produce systematic changes in time frequency coefficients and localized energy concentration. Experimental validation using impulse excited ceramic measurements demonstrates that the proposed descriptor captures defect induced structural differences with strong discriminative capability. The resulting IMRED statistic achieves an AUC of 0.908 and provides clearer class separation than global
Fourier band energy measures and non optimized wavelet band aggregation. These results establish a direct relationship between impulse response modeling, localized energy geometry, and statistical decision mechanisms, providing a mathematically grounded basis for energy driven defect detection in structural monitoring applications.
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Signal Processing
DDA-Net: Accurate TDD Channel Estimation via Deep Unfolding the Doppler-Delay-Angle Representation of Channel Signals
Yufei Ma, Xu Zhu and Tiejun Li
In TDD massive
MIMO systems, channel estimation under sparse frequency-hopping pilots is challenging: each snapshot captures only one narrow pilot block that hops across frequency, with tens of milliseconds between adjacent snapshots. Finite-window leakage and off-grid effects weaken the ideal Doppler-delay-angle (DDA) sparsity, limiting both classical sparse recovery and purely data-driven approaches lacking an explicit structured transform-domain model. We propose DDA-Net, a model-driven 3D deep unfolding network for joint multi-snapshot channel state reconstruction. DDA-Net integrates an ADMM-based formulation with a closed-form data-consistency update that avoids tensor inversion, a lightweight Doppler-domain learned prior, and delay oversampling to mitigate basis mismatch. It consistently outperforms strong baselines across three channel settings from 3GPP TR 38.901: UMa-NLOS, UMi-NLOS, and CDL-B. Ablations confirm that window-level 3D processing and explicit Doppler modeling yield target-dependent improvements. With minimal target-domain fine-tuning, the UMa-pretrained model surpasses both its zero-shot version and counterparts trained from scratch with the same number of target-domain samples. The Doppler-domain design proves consistently superior to its time-domain equivalent, with a wider margin after fine-tuning. These results demonstrate that combining exact physical data consistency with a learned DDA-domain prior is an effective and sample-efficient approach to channel state acquisition under sparse frequency-hopping pilots.
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Systems and Control
To Defer or To Shift? The Role of AI Data Center Flexibility on Grid Interconnection
Yize Chen and Xiaogui Zheng
The integration of AI data centers into power grid represents one of the most emerging and complex challenges for the energy systems. As computational demand scales at an unprecedented rate, the traditional grid planning study's paradigm of treating data centers as rigid, inflexible loads is becoming economically, mathematically and operationally untenable. This work tries to understand and address the large load interconnection bottleneck by modeling and evaluating AI load flexibility. By examining data center's temporal and spatial shifting capabilities within a grid capacity expansion framework, we build a quantitative grid planning model, and evaluate their impacts on additional generation, operational costs, and network congestion. Numerical study reveals interesting observations, as AI data center flexibility are not felt consistently, and increasing flexibility does not necessarily translate to less generation capacity required. Depending on data center's locations, flexibility range, and grid load conditions, flexible AI load can help reduce grid investment and operational costs by 3-21%. Our work also indicate that longer deferral time of AI compute has diminishing returns for offloading grid electricity dispatch pressure.
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Signal Processing
Quasi-stationary Slice Detection-Based Robust Respiration Rate Estimation under Large-scale Random Body Movement
Chendong Xu, Shuai Yao, Haoying Bao et al. (+2 more)
Radar-based non-contact respiration rate (RR) measurement has become increasingly popular due to its convenience, non-intrusiveness, and low cost. However, it is still quite challenging to accurately acquire vital signs estimation in complex measurement scenarios with large-scale random body movements (RBM), particularly for RR estimation due to strong low-frequency interferences. To cope with the RBM challenge in RR estimation, we propose a novel two-stage RR estimation scheme involving detecting the portion of signals, called as quasi-stationary slices, exhibiting the quasi-stationary pattern. At the detection stage, an enhanced deep neural network framework incorporating the dynamic snake convolution is exploited to detect the quasi-stationary slices in the micro-Doppler spectra. At the estimation stage, we mitigate RBM interferences and achieve accurate RR estimation by only using the portion of ridges consistent with the location of detected quasi-stationary slice. Extensive experimental results demonstrate that our proposed scheme can accurately detect quasi-stationary slices under normal scenarios with large-scale RBM, thereby reducing the error of subsequent RR estimation.
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All authors Chendong Xu, Shuai Yao, Haoying Bao, Chiyuan Ma, Qisong Wu