Signal Processing
Multi-Mode Pinching Antenna Systems Enabled Multi-User Communications
Xiaoxia Xu, Xidong Mu, Yuanwei Liu et al. (+1 more)
This paper proposes a novel multi-mode pinching-
antenna systems (PASS) framework. Multiple data streams can be transmitted within a single waveguide through multiple guided modes, thus facilitating efficient multi-user communications through the mode-domain multiplexing. A physic model is derived, which reveals the mode-selective power radiation feature of pinching
antennas (PAs). A two-mode PASS enabled two-user downlink communication system is investigated. Considering the mode selectivity of PA power radiation, a practical PA grouping scheme is proposed, where each PA group matches with one specific guided mode and mainly radiates its signal sequentially. Depending on whether the guided mode leaks power to unmatched PAs or not, the proposed PA grouping scheme operates in either the non-leakage or weak-leakage regime. Based on this, the baseband beamforming and PA locations are jointly optimized for sum rate maximization, subject to each user's minimum rate requirement. 1) A simple two-PA case in non-leakage regime is first considered. To solve the formulated problem, a channel orthogonality based solution is proposed. The channel orthogonality is ensured by large-scale and wavelength-scale equality constraints on PA locations. Thus, the optimal beamforming reduces to maximum-ratio transmission (MRT). Moreover, the optimal PA locations are obtained via a Newton-based one-dimension search
algorithm that enforces two-scale PA-location constraints by Newton's method. 2) A general multi-PA case in both non-leakage and weak-leakage regimes is further considered. A low-complexity particle-swarm optimization with zero-forcing beamforming (PSO-ZF)
algorithm is developed, thus effectively tackling the high-oscillatory and strong-coupled problem. Simulation results demonstrate the superiority of the proposed multi-mode PASS over conventional single-mode PASS and fixed-
antenna structures.
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All authors Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Arumugam Nallanathan
Image and Video Processing
Leveraging Second-Order Curvature for Efficient Learned Image Compression: Theory and Empirical Evidence
Yichi Zhang and Fengqing Zhu
Training learned image compression (LIC) models entails navigating a challenging optimization landscape defined by the fundamental trade-off between rate and distortion. Standard first-order optimizers, such as SGD and Adam, struggle with gradient conflicts arising from competing objectives, leading to slow convergence and suboptimal rate-distortion performance. In this work, we demonstrate that a simple utilization of a second-order quasi-Newton optimizer, SOAP, dramatically improves both training efficiency and final performance across diverse LICs. Our theoretical and empirical analyses reveal that Newton preconditioning inherently resolves the intra-step and inter-step update conflicts intrinsic to the R-D objective, facilitating faster, more stable convergence. Beyond acceleration, we uncover a critical deployability benefit: second-order trained models exhibit significantly fewer activation and latent outliers. This substantially enhances robustness to post-training quantization. Together, these results establish second-order optimization, achievable as a seamless drop-in replacement of the imported optimizer, as a powerful, practical tool for advancing the efficiency and real-world readiness of LICs.
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Signal Processing · Cross-listed from cs.LG
SA-PEF: Step-Ahead Partial Error Feedback for Efficient Federated Learning
Dawit Kiros Redie, Reza Arablouei and Stefan Werner
Biased gradient compression with error feedback (EF) reduces communication in
federated learning (FL), but under non-IID data, the residual error can decay slowly, causing gradient mismatch and stalled progress in the early rounds. We propose step-ahead partial error feedback (SA-PEF), which integrates step-ahead (SA) correction with partial error feedback (PEF). SA-PEF recovers EF when the step-ahead coefficient $\alpha=0$ and step-ahead EF (SAEF) when $\alpha=1$. For non-convex objectives and $\delta$-contractive compressors, we establish a second-moment bound and a residual recursion that guarantee convergence to stationarity under heterogeneous data and partial client participation. The resulting rates match standard non-convex Fed-SGD guarantees up to constant factors, achieving $O((\eta,\eta_0TR)^{-1})$ convergence to a variance/heterogeneity floor with a fixed inner step size. Our analysis reveals a step-ahead-controlled residual contraction $\rho_r$ that explains the observed acceleration in the early training phase. To balance SAEF's rapid warm-up with EF's long-term stability, we select $\alpha$ near its theory-predicted optimum. Experiments across diverse architectures and
datasets show that SA-PEF consistently reaches target accuracy faster than EF.
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Systems and Control
Distributed Learning over Noisy Communication Networks
Emrah Akyol and Marcos Vasconcelos
We study binary coordination games over graphs under log-linear learning when neighbor actions are conveyed through explicit noisy communication links. Each edge is modeled as either a binary symmetric channel (BSC) or a binary erasure channel (BEC). We analyze two operational regimes. For binary symmetric and binary erasure channels, we provide a structural characterization of the induced learning dynamics. In a fast-communication regime,
agents update using channel-averaged payoffs; the resulting learning dynamics coincide with a Gibbs sampler for a scaled coordination potential, where channel reliability enters only through a scalar attenuation coefficient. In a snapshot regime,
agents update from a single noisy realization and ignore channel statistics; the induced Markov chain is generally nonreversible, but admits a high-temperature expansion whose drift matches that of the fast Gibbs sampler with the same attenuation. We further formalize a finite-$K$ communication budget, which interpolates between snapshot and fast behavior as the number of channel uses per update grows. This viewpoint yields a communication-theoretic interpretation in terms of retransmissions and repetition coding, and extends naturally to heterogeneous link reliabilities via effective edge weights. Numerical experiments illustrate the theory and quantify the tradeoff between communication resources and steady-state coordination quality.
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Signal Processing
Sequential Processing Strategies in Fronthaul Constrained Cell-Free Massive MIMO Networks
Vida Ranjbar, Robbert Beerten, Marc Moonen et al. (+1 more)
In a
cell-free massive
MIMO (CFmMIMO) network with a daisy-chain fronthaul, the amount of information that each access point (AP) needs to communicate with the next AP in the chain is determined by the location of the AP in the sequential fronthaul. Therefore, we propose two sequential processing strategies to combat the adverse effect of fronthaul compression on the sum of users' spectral efficiency (SE): 1) linearly increasing fronthaul capacity allocation among APs and 2) Two-Path users' signal estimation. The two strategies show superior performance in terms of sum SE compared to the equal fronthaul capacity allocation and Single-Path sequential signal estimation.
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All authors Vida Ranjbar, Robbert Beerten, Marc Moonen, Sofie Pollin
Image and Video Processing
Task-Based Adaptive Transmit Beamforming for Efficient Ultrasound Quantification
Oisín Nolan, Wessel L. van Nierop, Louis D. van Harten et al. (+2 more)
Wireless and wearable ultrasound devices promise to enable continuous ultrasound monitoring, but power consumption and data throughput remain critical challenges. Reducing the number of transmit events per second directly impacts both. We propose a task-based adaptive transmit beamforming method, formulated as a
Bayesian active perception problem, that adaptively chooses where to scan in order to gain information about downstream quantitative measurements, avoiding redundant transmit events. Our proposed Task-Based Information Gain (TBIG) strategy applies to any differentiable downstream task function. When applied to recovering ventricular dimensions from echocardiograms, TBIG recovers accurate results using fewer than 2% of scan lines typically used, showing potential for large reductions in the power usage and data rates necessary for monitoring. Code is available at https://github.com/tue-bmd/task-based-ulsa.
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All authors Oisín Nolan, Wessel L. van Nierop, Louis D. van Harten, Tristan S.W. Stevens, Ruud J.G. van Sloun
Signal Processing
Grover's Search-Inspired Quantum Reinforcement Learning for Massive MIMO User Scheduling
Ruining Fan, Xingyu Huang, Mouli Chakraborty et al. (+2 more)
The efficient user scheduling policy in the massive Multiple Input Multiple Output (mMIMO) system remains a significant challenge in the field of 5G and Beyond 5G (B5G) due to its high computational complexity, scalability, and Channel State Information (CSI) overhead. This paper proposes a novel Grover's search-inspired
Quantum Reinforcement Learning (QRL) framework for mMIMO user scheduling. The QRL
agent can explore the exponentially large scheduling space effectively by applying Grover's search to the reinforcement learning process. The model is implemented using our designed
quantum-gate-based
circuit, which imitates the layered architecture of reinforcement learning, where
quantum operations act as policy updates and decision-making units. Moreover, the simulation results demonstrate that the proposed method achieves proper convergence and significantly outperforms classical Convolutional Neural Networks (CNN) and
Quantum Deep Learning (QDL)
benchmarks.
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All authors Ruining Fan, Xingyu Huang, Mouli Chakraborty, Avishek Nag, Anshu Mukherjee
Signal Processing
Deep Learning based Three-stage Solution for ISAC Beamforming Optimization
Qian Gao, Ruikang Zhong and Yuanwei Liu
In this paper, a general
ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning
algorithms to provide a three-stage solution for
ISAC beamforming. The three-stage beamforming optimization solution includes three modules: 1) an unsupervised learning based feature extraction
algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization
algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction
algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL
agent. Simulation results demonstrate that the proposed three-stage solution outperforms the baseline RL
algorithm by optimizing the intuitional beampattern rather than beamforming.
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Systems and Control · Cross-listed from cs.LG
Learning Contextual Runtime Monitors for Safe AI-Based Autonomy
Alejandro Luque-Cerpa, Mengyuan Wang, Emil Carlsson et al. (+3 more)
We introduce a novel framework for learning context-aware runtime monitors for AI-based control ensembles. Machine-learning (ML) controllers are increasingly deployed in (autonomous) cyber-physical systems because of their ability to solve complex decision-making tasks. However, their accuracy can degrade sharply in unfamiliar environments, creating significant safety concerns. Traditional ensemble methods aim to improve robustness by averaging or voting across multiple controllers, yet this often dilutes the specialized strengths that individual controllers exhibit in different operating contexts. We argue that, rather than blending controller outputs, a monitoring framework should identify and exploit these contextual strengths. In this paper, we reformulate the design of safe AI-based control ensembles as a contextual monitoring problem. A monitor continuously observes the system's context and selects the controller best suited to the current conditions. To achieve this, we cast monitor learning as a contextual learning task and draw on techniques from contextual multi-armed bandits. Our approach comes with two key benefits: (1) theoretical safety guarantees during controller selection, and (2) improved utilization of controller diversity. We validate our framework in two simulated autonomous driving scenarios, demonstrating significant improvements in both safety and performance compared to non-contextual baselines.
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All authors Alejandro Luque-Cerpa, Mengyuan Wang, Emil Carlsson, Sanjit A. Seshia, Devdatt Dubhashi, Hazem Torfah
Signal Processing
Integrated Sensing and Communication for Segmented Waveguide-Enabled Pinching Antenna Systems
Qian Gao, Ruikang Zhong, Hyundong Shin et al. (+1 more)
In this paper, an integrated sensing and communication (
ISAC) design for segmented waveguide-enabled pinching-
antenna array (SWAN) systems is proposed to improve the performance of systems by leveraging the low in-waveguide propagation loss of segmented waveguides. The hybrid segment selection and multiplexing (HSSM) protocol is implemented to provide favorable performance with less hardware cost. To achieve this, a joint transmit beamforming optimization, segment selection, and pinching
antenna positioning problem is formulated to maximize the sum communication rate with the constraints of sensing performance. To solve the maximization problem, we propose a segment hysteresis based reinforcement learning (SHRL)
algorithm to learn segment selection and pinching
antenna positions in different progress to explore better strategies. Simulation results demonstrate that 1) the proposed SWAN-
ISAC scheme outperforms the other baseline schemes, and 2) the proposed HARL
algorithm achieves better performance compared to conventional RL
algorithms.
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All authors Qian Gao, Ruikang Zhong, Hyundong Shin, Yuanwei Liu
Signal Processing
RL based Beamforming Optimization for 3D Pinching Antenna assisted ISAC Systems
Qian Gao, Ruikang Zhong, Yue Liu et al. (+2 more)
In this paper, a three-dimensional (3D) deployment scheme of pinching
antenna array is proposed, aiming to enhances the performance of integrated sensing and communication (
ISAC) systems. To fully realize the potential of 3D deployment, a joint
antenna positioning, time allocation and transmit power optimization problem is formulated to maximize the sum communication rate with the constraints of target sensing rates and system energy. To solve the sum rate maximization problem, we propose a heterogeneous graph neural network based reinforcement learning (HGRL)
algorithm. Simulation results prove that 3D deployment of pinching
antenna array outperforms 1D and 2D counterparts in
ISAC systems. Moreover, the proposed HGRL
algorithm surpasses other baselines in both performance and convergence speed due to the advanced observation construction of the environment.
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All authors Qian Gao, Ruikang Zhong, Yue Liu, Hyundong Shin, Yuanwei Liu
Signal Processing
Precoding Design for Multi-User MIMO Joint Communications and Sensing
Charlotte Muth, Shrinivas Chimmalgi and Laurent Schmalen
We investigate precoding for multi-user (MU) multiple-input multiple-output (
MIMO) joint communications and sensing (JCAS) systems, taking into account the potential interference between sensing and communication channels. We derive indicators for the sensing and communication performance, i.e., the detection probability and the communication signal-to-interference-and-noise ratio (SINR) for general input signals. Our results show that the use of the communication signal for sensing can prevent a loss in communication performance if channel interference occurs, while the kurtosis of the transmit alphabet of the communication signal limits the sensing performance. We present simulation results of example setups.
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Signal Processing · Cross-listed from math.NA
Quantitative synthetic aperture radar inversion
Liliana Borcea, Josselin Garnier, Alexander V. Mamonov et al. (+1 more)
We study an inverse scattering problem for monostatic synthetic aperture radar (SAR): Estimate the wave speed in a heterogeneous, isotropic and nonmagnetic medium probed by waves emitted and measured by a moving
antenna. The forward map, from the wave speed to the measurements, is derived from Maxwell's equations. It is a nonlinear map that accounts for multiple scattering and it is very oscillatory at high frequencies. This makes the standard, nonlinear least squares data fitting formulation of the inverse problem difficult to solve. We introduce an alternative, two-step approach: The first step computes the nonlinear map from the measurements to an approximation of the electric field inside the unknown medium aka, the internal wave. This is done for each
antenna location in a non-iterative manner. The internal wave fits the data by construction, but it does not solve Maxwell's equations. The second step uses optimization to minimize the discrepancy between the internal wave and the solution of Maxwell's equations, for all
antenna locations. The optimization is iterative. The first step defines an imaging function whose computational cost is comparable to that of standard SAR imaging, but it gives a better estimate of the support of targets. Further iterations improve the quantitative estimation of the wave speed. We assess the performance of the method with numerical simulations and compare the results with those of standard inversion.
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All authors Liliana Borcea, Josselin Garnier, Alexander V. Mamonov, Jörn Zimmerling
Image and Video Processing
SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Jia Fu, Litingyu Wang, He Li et al. (+27 more)
Accurate delineation of Gross Tumor Volume (GTV), Lymph Node Clinical Target Volume (LN CTV), and Organ-at-Risk (OAR) from Computed
Tomography (CT) scans is essential for precise radiotherapy planning in Nasopharyngeal Carcinoma (NPC). Building upon SegRap2023, which focused on OAR and GTV
segmentation using single-center paired non-contrast CT (ncCT) and contrast-enhanced CT (ceCT) scans, the SegRap2025 challenge aims to enhance the generalizability and robustness of
segmentation models across imaging centers and modalities. SegRap2025 comprises two tasks: Task01 addresses GTV
segmentation using paired CT from the SegRap2023
dataset, with an additional external testing set to evaluate cross-center generalization, and Task02 focuses on LN CTV
segmentation using multi-center training data and an unseen external testing set, where each case contains paired CT scans or a single modality, emphasizing both cross-center and cross-modality robustness. This paper presents the challenge setup and provides a comprehensive analysis of the solutions submitted by ten participating teams. For GTV
segmentation task, the top-performing models achieved average Dice Similarity Coefficient (DSC) of 74.61% and 56.79% on the internal and external testing cohorts, respectively. For LN CTV
segmentation task, the highest average DSC values reached 60.24%, 60.50%, and 57.23% on paired CT, ceCT-only, and ncCT-only subsets, respectively. SegRap2025 establishes a large-scale multi-center, multi-modality
benchmark for evaluating the generalization and robustness in radiotherapy target
segmentation, providing valuable insights toward clinically applicable automated radiotherapy planning systems. The
benchmark is available at: https://hilab-git.github.io/SegRap2025_Challenge.
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All authors Jia Fu, Litingyu Wang, He Li, Zihao Luo, Huamin Wang, Chenyuan Bian, Zijun Gao, Chunbin Gu, Xin Weng, Jianghao Wu, Yicheng Wu, Jin Ye, Linhao Li, Yiwen Ye, Yong Xia, Elias Tappeiner, Fei He, Abdul qayyum, Moona Mazher, Steven A Niederer, Junqiang Chen, Chuanyi Huang, Lisheng Wang, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Shichuan Zhang, Shaoting Zhang, Wenjun Liao, Guotai Wang
Systems and Control
Tilt-based Aberration Estimation in Transmission Electron Microscopy
Jilles S. van Hulst, Erik M. Franken, Bart J. Janssen et al. (+2 more)
Transmission electron microscopes (TEMs) enable atomic-scale imaging but suffer from aberrations caused by lens imperfections and environmental conditions, reducing image quality. These aberrations can be compensated by adjusting electromagnetic lenses, but this requires accurate estimates of the aberration coefficients, which can drift over time. This paper introduces a method for the estimation of aberrations in TEM by leveraging the relationship between an induced tilt of the electron beam and the resulting image shift. The method uses a Kalman filter (KF) to estimate the aberration coefficients from a sequence of image shifts, while accounting for the drift of the aberrations over time. The applied tilt sequence is optimized by minimizing the trace of the predicted error covariance in the KF, which corresponds to the A-optimality criterion in experimental design. We show that this optimization can be performed offline, as the cost criterion is independent of the actual measurements. The resulting non-convex optimization problem is solved using a gradient-based, receding-horizon approach with multi-starts. Additionally, we develop an approach to estimate specimen-dependent noise properties using expectation maximization (EM), which are then used to tailor the tilt pattern optimization to the specific specimen being imaged. The proposed method is validated on a real TEM set-up with several optimized tilt patterns. The results show that optimized patterns significantly outperform naive approaches and that the aberration and drift model accurately captures the underlying physical phenomena. A direct comparison with the widely used Zemlin tableau shows that the proposed method achieves comparable or higher image quality on amorphous specimens, while additionally extending to non-amorphous specimens where the Zemlin tableau cannot operate.
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All authors Jilles S. van Hulst, Erik M. Franken, Bart J. Janssen, W.P.M.H. Heemels, Duarte J. Antunes
Signal Processing · Cross-listed from cs.RO
Vibro-Sense: Robust Vibration-based Impulse Response Localization and Trajectory Tracking for Robotic Hands
Wadhah Zai El Amri and Nicolás Navarro-Guerrero
Rich contact perception is crucial for robotic manipulation, yet traditional tactile skins remain expensive and complex to integrate. This paper presents a scalable alternative: high-accuracy whole-body touch localization via vibro-acoustic sensing. By equipping a robotic hand with seven low-cost piezoelectric microphones and leveraging an Audio Spectrogram
Transformer, we decode the vibrational signatures generated during physical interaction. Extensive evaluation across stationary and dynamic tasks reveals a localization error of under 5 mm in static conditions. Furthermore, our analysis highlights the distinct influence of material properties: stiff materials (e.g., metal) excel in impulse response localization due to sharp, high-bandwidth responses, whereas textured materials (e.g., wood) provide superior friction-based features for trajectory tracking. The system demonstrates robustness to the
robot's own motion, maintaining effective tracking even during active operation. Our primary contribution is demonstrating that complex physical contact dynamics can be effectively decoded from simple vibrational signals, offering a viable pathway to widespread, affordable contact perception in robotics. To accelerate research, we provide our full
datasets, models, and experimental setups as open-source resources.
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Signal Processing
Vehicular Wireless Positioning – A Survey
Sharief Saleh, Satyam Dwivedi, Russ Whiton et al. (+9 more)
The rapid advancement of connected and autonomous vehicles has driven a growing demand for precise and reliable positioning systems capable of operating in complex environments. Meeting these demands requires an integrated approach that combines multiple positioning technologies, including wireless-based systems, perception-based technologies, and motion-based sensors. This paper presents a comprehensive survey of wireless-based positioning for vehicular applications, with a focus on satellite-based positioning (such as global
navigation satellite systems (GNSS) and low-Earth-orbit (LEO) satellites), cellular-based positioning (5G and beyond), and IEEE-based technologies (including Wi-Fi, ultrawideband (UWB), Bluetooth, and vehicle-to-vehicle (V2V) communications). First, the survey reviews a wide range of vehicular positioning use cases, outlining their specific performance requirements. Next, it explores the historical development, standardization, and
evolution of each wireless positioning technology, providing an in-depth categorization of existing positioning solutions and
algorithms, and identifying open challenges and contemporary trends. Finally, the paper examines sensor fusion techniques that integrate these wireless systems with onboard perception and motion sensors to enhance positioning accuracy and resilience in real-world conditions. This survey thus offers a holistic perspective on the historical foundations, current advancements, and future directions of wireless-based positioning for vehicular applications, addressing a critical gap in the literature.
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All authors Sharief Saleh, Satyam Dwivedi, Russ Whiton, Peter Hammarberg, Musa Furkan Keskin, Julia Equi, Hui Chen, Florent Munier, Olof Eriksson, Fredrik Gunnarsson, Fredrik Tufvesson, Henk Wymeersch
Audio and Speech Processing
Decoding Speech Envelopes from Electroencephalogram with a Contrastive Pearson Correlation Coefficient Loss
Yayun Liang, Yuanming Zhang, Fei Chen et al. (+2 more)
Recent advances in reconstructing
speech envelopes from Electroencephalogram (
EEG) signals have enabled continuous auditory
attention decoding (AAD) in multi-speaker environments. Most Deep Neural Network (DNN)-based envelope reconstruction models are trained to maximize the Pearson correlation coefficients (PCC) between the attended envelope and the reconstructed envelope (attended PCC). While the difference between the attended PCC and the unattended PCC plays an essential role in auditory
attention decoding, existing methods often focus on maximizing the attended PCC. We therefore propose a contrastive PCC loss which represents the difference between the attended PCC and the unattended PCC. The proposed approach is evaluated on three public
EEG AAD
datasets using four DNN architectures. Across many settings, the proposed objective improves envelope separability and AAD accuracy, while also revealing
dataset- and architecture-dependent failure cases.
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All authors Yayun Liang, Yuanming Zhang, Fei Chen, Jing Lu, Zhibin Lin
Audio and Speech Processing · Cross-listed from cs.SD
Audio Deepfake Detection in the Age of Advanced Text-to-Speech models
Robin Singh, Aditya Yogesh Nair, Fabio Palumbo et al. (+3 more)
Recent advances in Text-to-
Speech (TTS) systems have substantially increased the realism of synthetic
speech, raising new challenges for audio deepfake detection. This work presents a comparative evaluation of three state-of-the-art TTS models–Dia2, Maya1, and MeloTTS–representing streaming, LLM-based, and non-autoregressive architectures. A corpus of 12,000 synthetic audio samples was generated using the Daily-Dialog
dataset and evaluated against four detection frameworks, including semantic, structural, and signal-level approaches. The results reveal significant variability in detector performance across generative mechanisms: models effective against one TTS architecture may fail against others, particularly LLM-based synthesis. In contrast, a multi-view detection approach combining complementary analysis levels demonstrates robust performance across all evaluated models. These findings highlight the limitations of single-paradigm detectors and emphasize the necessity of integrated detection strategies to address the evolving landscape of audio deepfake threats.
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All authors Robin Singh, Aditya Yogesh Nair, Fabio Palumbo, Florian Barbaro, Anna Dyka, Lohith Rachakonda
Signal Processing
User Localization via Active Sensing with Electromagnetically Reconfigurable Antennas
Ruizhi Zhang, Yuchen Zhang and Ying Zhang
This paper presents an end-to-end deep learning framework for electromagnetically reconfigurable
antenna (ERA)-aided user localization with active sensing, where ERAs provide additional electromagnetic reconfigurability to diversify the received measurements and enhance localization informativeness. To balance sensing flexibility and overhead, we adopt a two-timescale design: the digital combiner is updated at each stage, while the ERA patterns are reconfigured at each substage via a spherical-harmonic representation. The proposed mechanism integrates
attention-based feature extraction and LSTM-based temporal learning, enabling the system to learn an optimized sensing strategy and progressively refine the UE position estimate from sequential observations. Simulation results show that the proposed approach consistently outperforms conventional digital beamforming-only and single-stage sensing baselines in terms of localization accuracy. These results highlight the effectiveness of ERA-enabled active sensing for user localization in future wireless systems.
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Audio and Speech Processing
Erasing Your Voice Before It's Heard: Training-free Speaker Unlearning for Zero-shot Text-to-Speech
Myungjin Lee, Eunji Shin and Jiyoung Lee
Modern zero-shot text-to-
speech (TTS) models offer unprecedented expressivity but also pose serious crime risks, as they can synthesize voices of individuals who never consented. In this context, speaker unlearning aims to prevent the generation of specific speaker identities upon request. Existing approaches, reliant on retraining, are costly and limited to speakers seen in the training set. We present TruS, a training-free speaker unlearning framework that shifts the paradigm from data deletion to inference-time control. TruS steers identity-specific hidden activations to suppress target speakers while preserving other attributes (e.g., prosody and
emotion). Experimental results show that TruS effectively prevents voice generation on both seen and unseen opt-out speakers, establishing a scalable safeguard for
speech synthesis. The demo and code are available on http://mmai.ewha.ac.kr/trus.
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Systems and Control
A Timing-Anomaly Free Dynamic Scheduling on Heterogeneous Systems
Yixuan Zhu, Yinkang Gao, Lei Gong et al. (+9 more)
Heterogeneous systems commonly adopt dynamic scheduling
algorithms to improve resource utilization and enhance scheduling flexibility. However, such flexibility may introduce timing anomalies, wherein locally reduced execution times can lead to an increase in the overall system execution time. This phenomenon significantly complicates the analysis of Worst-Case Response Time (WCRT), rendering conventional analysis either overly pessimistic or unsafe, and often necessitating exhaustive state-space exploration to ensure correctness. To address this challenge, this paper presents the first timing-anomaly-free dynamic scheduling
algorithm for heterogeneous systems, referred to as Deterministic Dynamic Execution. It achieves a safe and tight WCRT estimate through a single offline simulation execution. The core idea is to apply deterministic execution constraints, which partially restrict the resource allocation and execution order of tasks at runtime. Based on a formally defined execution progress model for heterogeneous system scheduling, we prove the correctness of the proposed execution constraints and their ability to eliminate timing anomalies. Furthermore, we propose two methods to generate execution constraints. The first method derives execution constraints directly from the execution traces produced by existing scheduling
algorithms. The second method is a heuristic-based approach that constructs execution constraints, enabling further reduction of the WCRT. Experimental results on synthetically generated DAG task sets under various system configurations demonstrate that, compared to traditional dynamic scheduling
algorithms, our approach not only eliminates timing anomalies but also effectively reduces both the WCRT and response time jitter.
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All authors Yixuan Zhu, Yinkang Gao, Lei Gong, Binze Jiang, Xiaohang Gong, Zihan Wang, Cheng Tang, Wenqi Lou, Teng Wang, Chao Wang, Xi Li, Xuehai Zhou
Systems and Control
Reducing End-to-End Latency of Cause-Effect Chains with Shared Cache Analysis
Yixuan Zhu, Yinkang Gao, Bo Zhang et al. (+8 more)
Cause-effect chains, as a widely used modeling method in real-time embedded systems, are extensively applied in various safety-critical domains. End-to-end latency, as a key real-time attribute of cause-effect chains, is crucial in many applications. But the analysis of end-to-end latency for cause-effect chains on multicore platforms with shared caches still presents an unresolved issue. Traditional methods typically assume that the worst-case execution time (WCET) of each task in the cause-effect chain is known. However, in the absence of scheduling information, these methods often assume that all shared cache accesses result in misses, leading to an overestimation of WCET and, consequently, affecting the accuracy of end-to-end latency. However, effectively integrating scheduling information into the WCET analysis process of the chains may introduce two challenges: first, how to leverage the structural characteristics of the chains to optimize shared cache analysis, and second, how to improve analysis accuracy while avoiding state space explosion. To address these issues, this paper proposes a novel end-to-end latency analysis framework designed for multi-chain systems on multicore platforms with shared caches. This framework extracts scheduling information and structural characteristics of cause-effect chains, constructing fine-grained and scalable inter-core
memory access contexts at the basic block level for time-sensitive shared cache analysis. This results in more accurate WCET (TSC-WCET) estimates, which are then used to derive the end-to-end latency. Finally, we conduct experiments on dual-core and quad-core systems with various cache configurations, which show that under certain settings, the average maximum end-to-end latency of cause-effect chains is reduced by up to 34% and 26%.
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All authors Yixuan Zhu, Yinkang Gao, Bo Zhang, Xiaohang Gong, Binze Jiang, Lei Gong, Wenqi Lou, Teng Wang, Chao Wang, Xi Li, Xuehai Zhou
Signal Processing · Cross-listed from cs.RO
RF-MatID: Dataset and Benchmark for Radio Frequency Material Identification
Xinyan Chen, Qinchun Li, Ruiqin Ma et al. (+3 more)
Accurate material identification plays a crucial role in
embodied AI systems, enabling a wide range of applications. However, current
vision-based solutions are limited by the inherent constraints of optical sensors, while
radio-frequency (RF) approaches, which can reveal intrinsic material properties, have received growing
attention. Despite this progress, RF-based material identification remains hindered by the lack of large-scale public
datasets and the limited benchmarking of learning-based approaches. In this work, we present RF-MatID, the first open-source, large-scale, wide-band, and geometry-diverse RF
dataset for fine-grained material identification. RF-MatID includes 16 fine-grained categories grouped into 5 superclasses, spanning a broad frequency range from 4 to 43.5 GHz, and comprises 142k samples in both frequency- and time-domain representations. The
dataset systematically incorporates controlled geometry perturbations, including variations in incidence angle and stand-off distance. We further establish a multi-setting, multi-protocol
benchmark by evaluating state-of-the-art deep learning models, assessing both in-distribution performance and out-of-distribution robustness under cross-angle and cross-distance shifts. The 5 frequency-allocation protocols enable systematic frequency- and region-level analysis, thereby facilitating real-world deployment. RF-MatID aims to enable reproducible research, accelerate algorithmic advancement, foster cross-domain robustness, and support the development of real-world application in RF-based material identification.
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All authors Xinyan Chen, Qinchun Li, Ruiqin Ma, Jiaqi Bai, Li Yi, Jianfei Yang
Systems and Control · Cross-listed from cs.LG
Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models
Qing Lyu, Zhe Fu and Alexandre Bayen
Identifying safety-critical scenarios is essential for autonomous driving, but the rarity of such events makes supervised labeling impractical. Traditional rule-based metrics like Time-to-Collision are too simplistic to capture complex interaction risks, and existing methods lack a systematic way to verify whether statistical anomalies truly reflect physical danger. To address this gap, we propose an unsupervised anomaly detection framework based on a multi-
agent Transformer that models normal driving and measures deviations through prediction residuals. A dual evaluation scheme has been proposed to assess both detection stability and physical
alignment: Stability is measured using standard ranking metrics in which Kendall Rank Correlation Coefficient captures rank agreement and Jaccard index captures the consistency of the top-K selected items; Physical
alignment is assessed through correlations with established Surrogate Safety Measures (SSM). Experiments on the NGSIM
dataset demonstrate our framework's effectiveness: We show that the maximum residual aggregator achieves the highest physical
alignment while maintaining stability. Furthermore, our framework identifies 388 unique anomalies missed by Time-to-Collision and statistical baselines, capturing subtle multi-
agent risks like reactive braking under lateral drift. The detected anomalies are further clustered into four interpretable risk types, offering actionable insights for simulation and testing.
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Signal Processing · Cross-listed from math.OC
Decentralized Stochastic Constrained Optimization via Prox-Linearization
Shivangi Dubey Sharma, Basil M. Idrees, Lavish Arora et al. (+1 more)
This paper studies consensus-based decentralized
stochastic optimization for minimizing possibly non-convex expected objectives with convex non-smooth regularizers and nonlinear functional inequality constraints. We reformulate the constrained problem using the exact-penalty model and develop two
algorithms that require only local
stochastic gradients and first-order constraint information. The first method, Decentralized
Stochastic Momentum-based Prox-Linear
Algorithm (D-SMPL), combines constraint linearization with a prox-linear step, resulting in a linearly constrained quadratic subproblem per iteration. Building on this approach, we propose a successive convex approximation (SCA) variant, Decentralized SCA Momentum-based Prox-Linear (D-SCAMPL), which handles additional objective structure through strongly convex surrogate subproblems while still allowing infeasible initialization. Both methods incorporate recursive momentum-based gradient
estimators and a consensus mechanism requiring only two communication rounds per iteration. Under standard smoothness and regularity assumptions, both
algorithms achieve an oracle complexity of $\mathcal{O}(\epsilon^{-3/2})$, matching the optimal rate known for unconstrained centralized
stochastic non-convex optimization. Numerical experiments on energy-optimal ocean trajectory planning corroborate the theory and demonstrate improved performance over existing decentralized baselines.
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All authors Shivangi Dubey Sharma, Basil M. Idrees, Lavish Arora, Ketan Rajawat
Systems and Control
Neural Cooperative Reach-While-Avoid Certificates for Interconnected Systems
Jingyuan Zhou, Haoze Wu and Kaidi Yang
Providing formal guarantees for neural network-based controllers in large-scale interconnected systems remains a fundamental challenge. In particular, using neural certificates to capture cooperative interactions and verifying these certificates at scale is crucial for the safe deployment of such controllers. However, existing approaches fall short on both fronts. To address these limitations, we propose neural cooperative reach-while-avoid certificates with Dynamic-Localized Vector Control Lyapunov and Barrier Functions, which capture cooperative dynamics through state-dependent neighborhood structures and provide decentralized certificates for global exponential stability and safety. Based on the certificates, we further develop a scalable training and verification framework that jointly synthesizes controllers and neural certificates via a constrained optimization objective, and leverages a sufficient condition to ensure formal guarantees considering modeling error. To improve scalability, we introduce a structural reuse mechanism to transfer controllers and certificates between substructure-isomorphic systems. The proposed methodology is validated with extensive experiments on multi-
robot coordination and vehicle platoons. Results demonstrate that our framework ensures certified cooperative reach-while-avoid while maintaining strong control performance.
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Audio and Speech Processing
ASR for Affective Speech: Investigating Impact of Emotion and Speech Generative Strategy
Ya-Tse Wu and Chi-Chun Lee
This work investigates how emotional
speech and generative strategies affect ASR performance. We analyze
speech synthesized from three emotional TTS models and find that substitution errors dominate, with emotional expressiveness varying across models. Based on these insights, we introduce two generative strategies: one using transcription correctness and another using emotional salience, to construct fine-tuning subsets. Results show consistent WER improvements on real emotional
datasets without noticeable degradation on clean LibriSpeech utterances. The combined strategy achieves the strongest gains, particularly for expressive
speech. These findings highlight the importance of targeted augmentation for building
emotion-aware ASR systems.
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Systems and Control
Efficient Trajectory Design and Communication Scheduling for Dual-UAV Jamming-Aided Secure Communication Networks
Xinran Wang, Peng Wu, Xiaopeng Yuan et al. (+2 more)
We study dual-unmanned aerial vehicle (
UAV) jamming-aided secure communication networks, in which one
UAV delivers confidential data to multiple ground users (GUs), while a cooperative
UAV provides protective interference against a ground eavesdropper. To enforce
fairness, we maximize the minimum secrecy throughput across GUs by jointly designing trajectories and communication scheduling. The key difficulty lies in the continuous-time nature of
UAV trajectories and the tight space-time coupling between the transmitter and the jammer, which jointly render the problem infinite-dimensional and nonconvex. To address these challenges, we characterize, for the first time, the structure of the optimal trajectories and rigorously prove that they follow a collaborative successive hover-and-fly (co-SHF) structure, where the two
UAVs visit a limited number of synchronized co-hovering point pairs, and during each flight segment at least one
UAV moves at maximum speed. Leveraging this structure, we reformulate the problem into a finite-dimensional form, without loss of optimality, over hovering and turning points, hovering durations, and scheduling. For tractability, we adopt a minimum-distance approximation of continuous anti-collision constraints and employ concave lower bounds on secrecy throughput within a successive convex approximation (SCA) method, which converges and, thanks to the co-SHF reduction in optimization variables and constraints, achieves low computational complexity. Numerical results show that, compared with time-discretization and no-jamming
benchmarks, the proposed co-SHF design improves the min-secrecy and user
fairness while requiring significantly less runtime.
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All authors Xinran Wang, Peng Wu, Xiaopeng Yuan, Yulin Hu, Anke Schmeink
Audio and Speech Processing · Cross-listed from cs.CL
MiLorE-SSL: Scaling Multilingual Capabilities in Self-Supervised Models without Forgetting
Jing Xu, Minglin Wu, Xueyuan Chen et al. (+2 more)
Self-supervised learning (SSL) has greatly advanced
speech representation learning, but
multilingual SSL models remain constrained to
languages encountered during pretraining. Retraining from scratch to incorporate new
languages is computationally expensive, while sequential training without migitation strategies often leads to catastrophic forgetting. To address this, we propose MiLorE-SSL, a lightweight framework that combines LoRA modules with a soft mixture-of-experts (MoE) mechanism for efficient continual
multilingual training. LoRA provides efficient low-rank adaptation, while soft MoE promotes flexible expert sharing across
languages, reducing cross-lingual interference. To further mitigate forgetting, we introduce limited replay data from existing
languages, avoiding reliance on large historical corpora. Experiments on ML-SUPERB demonstrate that MiLorE-SSL achieves strong performance in new
languages and improves the ability in existing ones with only 2.14% trainable parameters.
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All authors Jing Xu, Minglin Wu, Xueyuan Chen, Xixin Wu, Helen Meng