Audio and Speech Processing
Phonemes vs. Projectors: An Investigation of Speech-Language Interfaces for LLM-based ASR
Ziwei Li, Lukuang Dong, Saierdaer Yusuyin et al. (+2 more)
Integrating pretrained
speech encoders with large
language models (LLMs) is promising for ASR, but performance and data efficiency depend on the
speech-
language interface. A common choice is a learned projector that maps encoder features into the LLM
embedding space, whereas an alternative is to expose discrete phoneme sequences to the LLM. Using the same encoder and LLM backbones, we compare phoneme-based and vanilla projector-based interfaces in high-resource English and low-resource Tatar. We also propose a BPE-phoneme interface that groups frequent local phoneme patterns while preserving explicit word-boundary cues for phoneme-to-grapheme generation. On LibriSpeech, the phoneme-based interface is competitive with the vanilla projector, and the BPE-phoneme interface yields further gains. On Tatar, the phoneme-based interface substantially outperforms the vanilla projector. We further find that phoneme supervision yields a phoneme-informed hybrid interface that is stronger than the vanilla projector.
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All authors Ziwei Li, Lukuang Dong, Saierdaer Yusuyin, Xianyu Zhao, Zhijian Ou
Systems and Control · Cross-listed from cs.LG
Stability Enhanced Gaussian Process Variational Autoencoders
Carl R. Richardson, Jichen Zhang, Ethan King et al. (+1 more)
A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invariant (LTI) system, using high-dimensional
video data. The mean and covariance function of the novel SEGP prior are derived from the definition of an LTI system, enabling the SEGP to capture the indirectly observed latent process using a combined probabilistic and interpretable physical model. The search space of LTI parameters is restricted to the set of semi-contracting systems via a complete and unconstrained parametrisation. As a result, the SEGP-VAE can be trained using unconstrained optimisation
algorithms. Furthermore, this parametrisation prevents numerical issues caused by the presence of a non-Hurwitz state
matrix. A case study applies SEGP-VAE to a
dataset containing
videos of spiralling particles. This highlights the benefits of the approach and the application-specific design choices that enabled accurate latent state predictions.
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All authors Carl R. Richardson, Jichen Zhang, Ethan King, Ján Drgoňa
Image and Video Processing
UHD Low-Light Image Enhancement via Real-Time Enhancement Methods with Clifford Information Fusion
Xiaohan Wang, Chen Wu, Dawei Zhao et al. (+8 more)
Considering efficiency, ultra-high-definition (UHD) low-light image restoration is extremely challenging. Existing methods based on
Transformer architectures or high-dimensional complex convolutional neural networks often suffer from the "
memory wall" bottleneck, failing to achieve millisecond-level inference on edge devices. To address this issue, we propose a novel real-time UHD low-light enhancement network based on
geometric feature fusion using Clifford
algebra in 2D Euclidean space. First, we construct a four-layer feature pyramid with gradually increasing resolution, which decomposes input images into low-frequency and high-frequency structural components via a Gaussian blur kernel, and adopts a lightweight U-Net based on depthwise separable convolution for dual-branch feature extraction. Second, to resolve structural information loss and artifacts from traditional high-low frequency feature fusion, we introduce spatially aware Clifford
algebra, which maps feature tensors to a multivector space (scalars, vectors, bivectors) and uses Clifford similarity to aggregate features while suppressing noise and preserving textures. In the reconstruction stage, the network outputs adaptive Gamma and Gain maps, which perform physically constrained non-linear brightness adjustment via Retinex theory. Integrated with FP16 mixed-precision computation and dynamic operator fusion, our method achieves millisecond-level inference for 4K/8K images on a single consumer-grade device, while outperforming state-of-the-art (SOTA) models on several restoration metrics.
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All authors Xiaohan Wang, Chen Wu, Dawei Zhao, Guangwei Gao, Dianjie Lu, Guijuan Zhang, Linwei Fan, Xu Lu, Shuai Wu, Hang Wei, Zhuoran Zheng
Image and Video Processing
Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark
Jinquan Yan, Zhicheng Zhao, Zhengzheng Tu et al. (+3 more)
UAV images are critical for applications such as large-area mapping, infrastructure inspection, and emergency response. However, in real-world flight environments, a single image is often affected by multiple degradation factors, including rain, haze, and noise, undermining downstream task performance. Current unified restoration approaches typically rely on implicit degradation representations that entangle multiple factors into a single condition, causing mutual interference among heterogeneous corrections. To this end, we propose DAME-Net, a Degradation-Aware Mixture-of-Experts Network that decouples explicit degradation perception from degradation-conditioned reconstruction for compositional
UAV image restoration. Specifically, we design a Factor-wise Degradation Perception module(FDPM) to provide explicit per-factor degradation cues for the restoration stage through multi-label prediction with label-similarity-guided soft
alignment, replacing implicit entangled conditions with interpretable and generalizable degradation descriptions. Moreover, we develop a Conditioned Decoupled MoE module(CDMM) that leverages these cues for stage-wise conditioning, spatial-frequency hybrid processing, and mask-constrained decoupled expert routing, enabling selective factor-specific correction while suppressing irrelevant interference. In addition, we construct the Multi-Degradation
UAV Restoration
benchmark (MDUR), the first large-scale
UAV benchmark for compositional
UAV image restoration, with 43 degradation configurations from single degradations to four-factor composites and standardized seen/unseen splits.Extensive experiments on MDUR demonstrate consistent improvements over representative unified restoration methods, with greater gains on unseen and higher-order composite degradations. Downstream experiments further validate benefits for
UAV object detection.
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All authors Jinquan Yan, Zhicheng Zhao, Zhengzheng Tu, Chenglong Li, Jin Tang, Bin Luo
Systems and Control · Cross-listed from cs.RO
Online Intention Prediction via Control-Informed Learning
Tianyu Zhou, Zihao Liang, Zehui Lu et al. (+1 more)
This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynamics or objectives include unknown parameters. The problem is formulated as an inverse optimal control / inverse reinforcement learning task, with the intention treated as a parameter in the objective. A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates. Simulations under varying noise levels and hardware experiments on a quadrotor drone demonstrate that the proposed approach achieves accurate, adaptive intention prediction in complex environments.
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All authors Tianyu Zhou, Zihao Liang, Zehui Lu, Shaoshuai Mou
Signal Processing · Cross-listed from quant-ph
Dyadic-Order Quantum Fractional Transforms: Circuit Constructions and Applications to Hartley and Cosine Transform Families
Matheus J. A. Oliveira, Israel F. Araujo, José R. de Oliveira Neto et al. (+1 more)
This paper presents a generalized
circuit framework for constructing Shih-type fractionalizations of
unitary operators of dyadic order, i.e., operators $U$ satisfying $U^{2^n}=I$. Building upon the architecture of the
quantum fractional
Fourier transform (QFrFT), we show that fractionalization can be implemented coherently as a weighted superposition of integer powers, $\sum_k c_k(\alpha)U^k$, where the coefficients are generated through an ancilla-domain
quantum Fourier transform and a diagonal phase modulation. Under the assumption that controlled implementations of the required powers of $U$ are available, the resulting
circuit yields a parameterized family of operators that interpolates the integer powers of $U$ and satisfies the additive property of fractional transforms. As concrete applications, we derive explicit
quantum circuit realizations of the
quantum fractional Hartley transform (QFrHT) and of the fractional cosine-transform families associated with Types~I and~IV. These constructions demonstrate the versatility of the proposed dyadic-order fractionalization framework for structured operators arising in
quantum signal processing.
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All authors Matheus J. A. Oliveira, Israel F. Araujo, José R. de Oliveira Neto, Juliano B. Lima
Image and Video Processing
AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer
Gautier Hénique, William Le, Gabriel Dayan et al. (+8 more)
Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is currently omitted as a clinical staging criteria. Recent works have advocated for the inclusion of iENE as a prognostic marker in HPV-positive OPC staging. However, several practical limitations continue to hinder its clinical integration, including inconsistencies in
segmentation, low contrast in the periphery of metastatic lymph nodes on CT imaging, and laborious manual annotations. To address these limitations, we propose a fully automated end-to-end pipeline that uses computed
tomography (CT) images with clinical data to assess the status of nodal ENE and predict treatment outcomes. Our approach includes a hierarchical 3D semi-supervised
segmentation model designed to detect and delineate relevant iENE from radiotherapy planning CT scans. From these
segmentations, a set of radiomics and deep features are extracted to train an imaging-detected ENE grading classifier. The predicted ENE status is then evaluated for its prognostic value and compared with existing staging criteria. Furthermore, we integrate these nodal features with primary tumor characteristics in a
multimodal,
attention-based outcome prediction model, providing a dynamic framework for outcome prediction. Our method is validated in an internal cohort of 397 HPV-positive OPC patients treated with radiation therapy or chemoradiotherapy between 2009 and 2020. For outcome prediction at the 2-year mark, our pipeline surpassed baseline models with 88.2% (4.8) in AUC for metastatic recurrence, 79.2% (7.4) for overall survival, and 78.1% (8.6) for disease-free survival. We also obtain a concordance index of 83.3% (6.5) for metastatic recurrence, 71.3% (8.9) for overall survival, and 70.0% (8.1) for disease-free survival, making it feasible for clinical decision making.
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All authors Gautier Hénique, William Le, Gabriel Dayan, Coralie Brodeur, Kristoff Nelson, Apostolos Christopoulos, Edith Filion, Phuc-Felix Nguyen-Tan, Laurent Letourneau-Guillon, Houda Bahig, Samuel Kadoury
Systems and Control
On the Existence of Quadratic Control Lyapunov Functions for Koopman-Operator based Bilinear Systems
Sami Leon Noel Aziz Hanna, Nicolas Hoischen, Sandra Hirche et al. (+1 more)
Koopman operator-based methods enable data-driven bilinear representations of unknown nonlinear control systems. Accurate representations often demand significantly higher dimensions than the original system, making control design challenging. Control Lyapunov Functions (CLFs) are widely used for controller synthesis, with quadratic CLF candidates being the most common due to their simplicity. Yet, we show that this class is highly restrictive, especially when the state dimension is large: under mild conditions, their existence implies stabilizability of the bilinear system by a constant input – that is, the control remains fixed over time. We establish this result by formulating a quadratically constrained quadratic program (QCQP) that exactly characterizes valid CLFs. Since QCQPs are NP-hard, we propose a convex semidefinite relaxation that offers a sufficient validity condition. For single-input systems, we prove that a quadratic CLF requires constant control stabilizability, and empirically demonstrate that this extends to high-dimensional multi-input systems in many cases.
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All authors Sami Leon Noel Aziz Hanna, Nicolas Hoischen, Sandra Hirche, Armin Lederer
Signal Processing
Joint Device Pairing and Bandwidth Allocation Optimisation for Semantic Feature Multiple Access Networks
Jiaxiang Wang, Zhaohui Yang, Mingzhe Chen et al. (+1 more)
This paper presents a Semantic Feature Multiple Access (SFMA) framework for multi-user semantic communication in downlink wireless systems. By extending SwinJSCC to a two-user superimposition paradigm, SFMA enables simultaneous semantic transmission to multiple users over shared time-frequency resources. A key innovation is the Cross-User
Attention (CUA) module, which facilitates controlled semantic feature exchange between paired users by leveraging inter-image similarity while mitigating interference. We formulate a joint user pairing and resource allocation problem to minimize global semantic distortion under constraints on bandwidth, end-to-end latency, and energy. This mixed-integer non-convex problem is decomposed into a Minimum-Weight Perfect Matching (MWPM) sub-problem and a convex bandwidth allocation feasibility check, with semi-closed-form bandwidth bounds derived from a strictly concave rate expression. A
polynomial-time
algorithm based on Blossom matching and bisection search is proposed. Extensive simulations on ImageNet-100 show that SFMA significantly improves reconstruction quality across pairing modes, and the proposed optimization effectively reduces overall distortion while satisfying physical-layer constraints.
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All authors Jiaxiang Wang, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei
Signal Processing
Semantic Feature Multiple Access Empowered Integrated Learning and Communication Networks
Jiaxiang Wang, Zhouxiang Zhao, Yahao Ding et al. (+4 more)
Integrated learning and communication (ILAC) unifies learned transceivers with
radio resource management, where semantic feature multiple access (SFMA) enables paired users to superpose their learned representations over shared time-frequency resources. Unlike conventional multiple access schemes, SFMA interference arises in the learned feature space and depends jointly on the user pair, the transmit power, and the compression ratio. This coupling ties binary pairing decisions to continuous resource variables, yielding a mixed-integer non-convex optimization problem. To address this problem, we first propose similarity-conditioned SFMA (SC-SFMA), a Swin
Transformer-based transceiver whose dual-conditioned similarity modulator (DC-SimM) gates cross-user feature fusion according to the inter-user semantic similarity. We then characterize the resulting pair-dependent interference by a bivariate logistic function parameterized by transmit power and compression ratio, thereby bridging the learned transceiver with network-level optimization. On this basis, we formulate a sum-rate maximization problem subject to per-user distortion, latency, energy, power, and bandwidth constraints. To solve this problem, we develop a three-block alternating optimization
algorithm that integrates dual-decomposition-assisted compression ratio allocation, trust-region successive convex approximation (SCA) for joint power-bandwidth optimization, and dynamic feasible graph-based user pairing. Simulation results show that SC-SFMA achieves considerable peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index measure (MS-SSIM) gains over deep joint source-channel coding (JSCC) and separation-based baselines. The proposed optimization framework attains significant sum rate improvements over conventional multiple access baselines.
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All authors Jiaxiang Wang, Zhouxiang Zhao, Yahao Ding, Zhijin Qin, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei
Systems and Control · Cross-listed from math.OC
A Unified Control-Theoretic Framework for Saddle-Point Dynamics in Constrained Optimization
Veronica Centorrino, Rawan Hoteit, Efe C. Balta et al. (+1 more)
This paper studies equality-constrained minimization problems through the lens of feedback control. We introduce a unified control-theoretic framework by showing that a PID feedback law acting on the dual variable induces the PID saddle-point flow (PID-SPF), a broad class of saddle-point dynamics associated with the augmented Lagrangian. This framework recovers several classical primal-dual flows as special cases. We prove that the
equilibria of the proposed flow coincide with the stationary points of the original problem. Our analysis reveals how the feedback gains affect the optimization: integral action enforces constraint satisfaction, proportional action introduces the augmented Lagrangian structure, and derivative action modifies the geometry of the primal dynamics by inducing a state-dependent Riemannian metric. Moreover, for convex problems with affine constraints, we establish global exponential convergence by leveraging contraction theory for all admissible PID gains, providing in the process explicit bounds on the convergence rate. Finally, we validate our theoretical results on numerical examples including an application to bilevel optimization.
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All authors Veronica Centorrino, Rawan Hoteit, Efe C. Balta, John Lygeros
Image and Video Processing
A GPU-enhanced workflow for non-Fourier SENSE reconstruction
Samuel Bianchi and Klaas P. Pruessmann
Purpose: Image reconstruction in challenging scenarios requires accurate characterisations of coil sensitivity profiles, local off-resonances (B0) and effective encoding fields. Reconstruction methods utilising all of this information rely on signal models that are not compatible with the classical
Fourier/k-space interpretation of the coil data. Hence, the FFT and related techniques are no more applicable, rendering image reconstruction computationally demanding. Methods: This article contains a workflow for accurate sensitivity and B0 mapping as well as other required processing steps. An implementation of non-
Fourier SENSE reconstruction is provide that is well suited for execution on a GPU using the FFT. Important practical aspects like stopping criteria and sources of image artifacts are analyzed and documented. Results: Highly performant image reconstruction could be demonstrated on a 2D and 3D spiral
dataset. These
datasets contain trajectories featuring readout durations up to 71.5ms and undersampling factors up to R = 7. Running the reconstruction on a GPU greatly boosts reconstruction speed. Stopping the reconstruction at the right moment is crucial for image quality. All methods included in this article are available in a public code repository. Conclusion: The provided implementation of non-
Fourier SENSE reconstruction is highly performant. When it is executed on GPU, runtimes reach a duration feasible in practice. The presented workflow ensures robust and accurate computation of coil sensitive profiles and off-resonance maps.
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Image and Video Processing
Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models
Wongi Jeong, Hoigi Seo and Se Young Chun
Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to professional designers. However, a desired outcome often requires generating a large number of HR images with different
prompts and seeds, resulting in high computational cost for both users and service providers. Generating low-resolution (LR) images first could alleviate computational burden, but it is not straightforward how to generate LR images that are perceptually consistent with their HR counterparts. Here, we consider the task of generating high-fidelity LR images, called Previews, that preserve perceptual similarity of their HR counterparts for an efficient workflow, allowing users to identify promising candidates before generating the final HR image. We propose the commutator-zero condition to ensure the LR-HR perceptual consistency for flow matching models, leading to the proposed training-free solution with downsampling
matrix selection and commutator-zero guidance. Extensive experiments show that our method can generate LR images with up to 33% computation reduction while maintaining HR perceptual consistency. When combined with existing acceleration techniques, our method achieves up to 3$\times$ speedup. Moreover, our formulation can be extended to image manipulations, such as warping and translation, demonstrating its generalizability.
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Signal Processing
Wideband Illumination with Liquid Crystal Reconfigurable Intelligent Surfaces: Modeling, Design, and Experimental Tests
Mohamadreza Delbari, Robin Neuder, Alejandro Jiménez-Sáez et al. (+2 more)
Liquid crystal (LC) is a promising hardware solution for implementing large RISs, as it is cost-effective, energy efficient, scalable, and capable of providing continuous phase shifts with low power consumption. However, the phase shift response of LC-based RISs is inherently frequency dependent. If unaddressed, this characteristic leads to performance degradation, particularly in wideband scenarios. This issue is especially critical in secure communication applications, where minor phase shift variations across elements can result in considerable information leakage. This paper addresses these frequency-induced variations by developing a physics-based model for an LC unit
cell across varying frequencies and proposing a novel phase shift design framework that maximizes secure communication across all subcarriers. Given the large number of elements in millimeter wave (mmWave) LC-RISs, acquiring full channel state information (CSI) is often impractical. Therefore, we optimize the phase shifts based solely on the locations of the legitimate mobile users (MUs) and potential eavesdroppers. Rather than targeting a single user point, the RIS is designed to illuminate a broader area. This approach enhances communication reliability for the MUs and mitigates performance degradation caused by location estimation errors. To solve the problem, we introduce both a semi-definite programming (SDP)-based solution and a low complexity heuristic method. While the SDP-based approach yields superior performance, it incurs higher computational complexity. Conversely, the scalable method exhibits a much slower scaling of complexity, which makes it highly suitable for extremely large RISs. Simulation results demonstrate that both
algorithms improve the secrecy rate compared to baseline methods. Finally, the proposed design is validated through experimental evaluations on an LC RIS setup.
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All authors Mohamadreza Delbari, Robin Neuder, Alejandro Jiménez-Sáez, Qikai Zhou, Vahid Jamali
Systems and Control
Discrete-Time Model of a Two-Speed PowerShift suitable for Real-Time Control and Simulation
Riccardo Morselli, Davide Tebaldi and Roberto Zanasi
In this paper, a new discrete-time approach to model the clutches engagement/disengagement in a two-speed powershift is proposed. The core idea is the development of a model for the computation of the exact torque needed to achieve the clutches engagement, including both the cases of single clutch engagement and of simultaneous clutch engagement (full lock condition). Based on this, the control logic for the clutches engagement and disengagement phases is also developed. The advantages in terms of real-time applicability with respect to the continuous-time version are shown through extensive simulation results.
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Systems and Control
Frequency Quality Metrics based on Second-Order Derivative and Autocorrelation
Taulant Kerci and Federico Milano
This industry-oriented paper originates from the observation that current frequency quality metrics utilized by transmission system operators (TSOs) fail to fully capture the dynamic behavior of the grid frequency. Motivated by this gap, the paper proposes novel frequency quality metrics based on second-order dynamics and
stochastic autocorrelation. Using real-world data with 0.1 s and 1 s resolution from the Irish, Great Britain and Nordic systems and running dynamic
stochastic simulations, the paper shows that the proposed metrics bring new and counterintuitive insights in terms of how good or poor the frequency quality of power grids is beyond current well-known metrics. In particular, the paper shows that a power system may show good frequency quality using standard metrics and poor frequency quality using the proposed metrics. Overall, the paper contributes to improve the understanding of frequency quality.
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Signal Processing
Flexible Cylindrical Array-Aided Secure Wireless Communications
Xiangyu Dong, Ran Yang, Songjie Yang et al. (+4 more)
Flexible-geometry arrays based on movable
antennas have shown considerable potential for improving wireless communication performance. In this letter, we investigate a multiuser multiple-input single-output (MU-MISO) downlink secure communication system aided by a flexible cylindrical array (FCLA) and artificial noise (AN), where each
antenna element rotates along circular tracks while the circular slices move along a vertical axis. To guarantee transmission security, we aim to maximize the achievable sum rate at multiple legitimate information receivers by jointly optimizing transmit beamforming, AN covariance
matrix, and
antenna placement under secrecy constraints for an eavesdropper. While the resulting problem is intractable to solve, we develop a block coordinate descent (BCD)-based framework that combines the Lagrangian dual transform, tight semidefinite relaxation (SDR), and Nesterov-accelerated projected gradient descent (PGD). Numerical results show that the proposed
algorithm converges rapidly and achieves significant sum-rate gains over
benchmark schemes by exploiting the geometry flexibility of the array.
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All authors Xiangyu Dong, Ran Yang, Songjie Yang, Weidong Mei, Lipeng Zhu, Yue Xiu, Zhongpei Zhang
Systems and Control
Efficient Uniform Feasible-Set Sampling for Approximate Linear MPC
Elias Milios, Felix Berkel, Felix Gruber et al. (+2 more)
Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate policy, typically by training a neural network on state-MPC input pairs. Generating training data is a major bottleneck, requiring solving the MPC for numerous states sampled from its feasible set. Since this feasible set is implicitly defined and unknown, efficient sampling is nontrivial but crucial. We propose the linear MPC Hit-and-Run (LMPC-HR) sampler for linear MPC with polyhedral constraints. We identify the feasible set boundaries along search directions, a crucial step within HR, by formulating the problem as a convex linear program, replacing expensive iterative searches with a single optimization step. A numerical study demonstrates that LMPC-HR reduces the computational cost of generating uniformly distributed samples from the feasible set by an order of magnitude compared to standard baselines.
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All authors Elias Milios, Felix Berkel, Felix Gruber, Melanie N. Zeilinger, Kim P. Wabersich
Audio and Speech Processing
PS-TTS: Phonetic Synchronization in Text-to-Speech for Achieving Natural Automated Dubbing
Changi Hong, Yoonah Song, Hwayoung Park et al. (+4 more)
Recently, artificial intelligence-based dubbing technology has advanced, enabling automated dubbing (AD) to convert the source
speech of a
video into target
speech in different
languages. However, natural AD still faces synchronization challenges such as duration and lip-synchronization (lip-sync), which are crucial for preserving the viewer experience. Therefore, this paper proposes a synchronization method for AD processes that paraphrases translated text, comprising two steps: isochrony for timing constraints and phonetic synchronization (PS) to preserve lip-sync. First, we achieve isochrony by paraphrasing the translated text with a
language model, ensuring the target
speech duration matches that of the source
speech. Second, we introduce PS, which employs dynamic time warping (DTW) with local costs of vowel distances measured from training data so that the target text composes vowels with pronunciations similar to source vowels. Third, we extend this approach to PSComet, which jointly considers semantic and phonetic similarity to preserve meaning better. The proposed methods are incorporated into text-to-
speech systems, PS-TTS and PS-Comet TTS. The performance evaluation using Korean and English lip-reading
datasets and a voice-actor dubbing
dataset demonstrates that both systems outperform TTS without PS on several objective metrics and outperform voice actors in Korean-to-English and English-to-Korean dubbing. We extend the experiments to French, testing all pairs among these
languages to evaluate cross-linguistic applicability. Across all
language pairs, PS-Comet performed best, balancing lip-sync accuracy with semantic preservation, confirming that PS-Comet achieves more accurate lip-sync with semantic preservation than PS alone.
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All authors Changi Hong, Yoonah Song, Hwayoung Park, Chaewoon Bang, Dayeon Ku, Do Hyun Lee, Hong Kook Kim
Systems and Control
Scheduling Cause-Effect Chains without Timing Anomalies in End-to-End Latency
Yixuan Zhu, Bo Zhang, Yinkang Gao et al. (+7 more)
In real-time systems, both individual task execution and data propagation must meet strict timing constraints. Cause-effect (CE) chains are widely used to analyze such behaviors by end-to-end latency. However, timing anomalies (TAs) can distort it, where a local reduction in execution times leads to an increase in the overall end-to-end latency. As a result, precisely analyzing the upper bounds of the latency becomes challenging, and such systems typically exhibit larger upper bounds than TA-eliminated systems. Existing studies either eliminate TAs by completely sacrificing average latency to simplify analysis or, despite adopting complex safe analysis methods, do not eliminate TAs effectively, still having high latencies. To address this issue, we identify two basic causes of TAs in end-to-end latency. Based on these causes, we propose the first treatment that eliminates TAs in the latency with negligible average latency loss using Deterministic Data Flow (DDF). We further formally prove its TA-free property. Therefore, we can get a precise upper bound for latency when all jobs execute with their worst-case execution times. Experimental results show that it effectively reduces the maximum end-to-end latency, the average latency, and latency jitter compared with the state-of-the-art (SOTA) method.
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All authors Yixuan Zhu, Bo Zhang, Yinkang Gao, Haoyuan Ren, Cheng Tang, Caixu Zhao, Lei Gong, Teng Wang, Wenqi Lou, Xi Li
Image and Video Processing · Cross-listed from cs.CV
Off-the-shelf Vision Models Benefit Image Manipulation Localization
Zhengxuan Zhang, Keji Song, Junmin Hu et al. (+2 more)
Image manipulation localization (IML) and general
vision tasks are typically treated as two separate research directions due to the fundamental differences between manipulation-specific and semantic features. In this paper, however, we bridge this gap by introducing a fresh perspective: these two directions are intrinsically connected, and general semantic priors can benefit IML. Building on this insight, we propose a novel trainable adapter (named ReVi) that repurposes existing off-the-shelf general-purpose
vision models (e.g., image generation and
segmentation networks) for IML. Inspired by robust principal component analysis, the adapter disentangles semantic redundancy from manipulation-specific information embedded in these models and selectively enhances the latter. Unlike existing IML methods that require extensive model redesign and full retraining, our method relies on the off-the-shelf
vision models with frozen parameters and only fine-tunes the proposed adapter. The experimental results demonstrate the superiority of our method, showing the potential for scalable IML frameworks.
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All authors Zhengxuan Zhang, Keji Song, Junmin Hu, Ao Luo, Yuezun Li
Systems and Control
Topology Identification of Dynamical Signed Graphs
Pelin Sekercioglu, Nana Wang, Angela Fontan et al. (+1 more)
We propose an adaptive control protocol for identifying the
topology of dynamical networks interconnected over undirected graphs with cooperative and antagonistic interactions. The signed network is modeled using a repelling
Laplacian.
Topology identification relies on an edge-based formulation of the network and adaptive control protocols through the design of a persistently excited auxiliary network. Our approach guarantees the simultaneous identification and synchronization of the unknown signed network and establishes uniform semiglobal practical asymptotic stability of the estimation errors. Numerical simulations validate our theoretical results.
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All authors Pelin Sekercioglu, Nana Wang, Angela Fontan, Dimos V. Dimarogonas
Systems and Control · Cross-listed from math.OC
A Bayesian Perspective on the Data-Driven LQR
Thierry Schwaller, Feiran Zhao and Florian Dörfler
The data-driven linear quadratic regulator (ddLQR) is a widely studied control method for unknown dynamical systems with disturbance. Existing approaches, both indirect, i.e., those that identify a model followed by model-based design, and direct, which bypasses the identification step, often rely on the certainty-equivalence principle and therefore do not explicitly account for model uncertainty. In this paper, we propose a
Bayesian formulation for both indirect and direct ddLQR that incorporates posterior uncertainty into the control design. The resulting expected cost decomposes into a certainty-equivalence term and a variance-dependent term, providing a principled interpretation of regularization. We further show that the indirect and direct formulations are equivalent under this perspective. The resulting direct method admits a tractable semidefinite program whose size is independent of the data length. Numerical simulations demonstrate improved optimality gap and closed-loop stability, particularly in low-data regimes.
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Systems and Control
A Study of the Circular Pursuit Dynamics using Bifurcation Theoretic Computational Approach
Kavita Shekhawat and Nandan K Sinha
A circular pursuit guidance problem involving pursuer-target engagement is studied in this paper using a bifurcation theory based numerical approach. While target is modeled as a point mass moving around in a circle with certain velocity, pursuer dynamics is driven by the relative position and orientation with respect to the target. A planar case is currently considered. A mathematical model representing the engagement scenario is derived and two cases are presented, one without and the other with a basic model for pursuer speed dynamics accounting for limitations imposed by available force. Analytical and simulation results are presented to elucidate the novel approach. Advantages of using this approach for arriving at laws for pursuer-target engagement are highlighted.
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Signal Processing
Experimental Study of Interference Suppression for Backscatter Communication in Distributed MIMO
Ahmet Kaplan, Gilles Callebaut, Jarne Van Mulders et al. (+1 more)
Bistatic backscatter communication requires strong illumination of a backscatter device (BD), while a spatially separated reader detects the weak modulated reflection. In practice, the resulting direct link interference (DLI) at the reader can dominate the received backscattered signal and limit detection performance. This paper experimentally investigates transmit beamforming that jointly maximizes BD illumination and suppresses DLI at the reader in a distributed multiple-input multiple-output setup. We compare phase-only maximum ratio transmission (PO-MRT) with the proposed direct-link suppression (DLS) scheme, which enforces a spatial null at the reader under per-
antenna power constraints. Measurements using a phase-coherent 42-element ceiling array at 920 MHz show that DLS reduces the DLI at the target reader and improves the signal-to-interference ratio by up to 31 dB compared to PO-MRT.
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All authors Ahmet Kaplan, Gilles Callebaut, Jarne Van Mulders, Erik G. Larsson
Signal Processing
Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations
Weijie Zhou, Zhaoyang Zhang, Yuzhi Yang et al. (+3 more)
Acquiring the channel state information from limited and noisy observations at pilot positions is critical for wireless multiple-input multiple-output (
MIMO)-orthogonal frequency division multiplexing (OFDM) systems. In this paper, we view this process as a conditional generative task in which the partial noisy channel estimates at the pilots are utilized as a “
prompt” to guide the
diffusion “inpainting” of the underlying channel. To this end, we resort to a general Conditional
Diffusion Transformer (CDiT) framework with a well-designed network architecture and update rule. In particular, we design a dedicated
embedding strategy to encode and adapt to different pilot patterns and noise levels, and utilize a special cross-
attention mechanism to align the partial raw channel observations with the denoised channel at each time step of the generation process. This architecture effectively anchors the
diffusion process, enabling the model to accurately recover full channel details from limited noisy observations. Comprehensive experimental results show that, the proposed approach achieves a performance gain of over 5 dB compared to the baselines under varying noise conditions, and provides robust channel acquisition even under a sparse pilot density of 1/32 without significant performance loss compared to the denser pilot cases. Moreover, it is capable of generating high-quality channel
matrices within just 10 inference steps, effectively balancing estimation accuracy with computational efficiency and inference speed. Ablation studies demonstrate the rationality of the model design and the necessity of its modules.
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All authors Weijie Zhou, Zhaoyang Zhang, Yuzhi Yang, Sen Yan, Zhixian Kong, Merouane Debbah
Systems and Control
Synthesizing Safety in Infinite-Horizon Optimal Control for Disturbed High-Relative-Degree Systems via Barrier-Regulating Auxiliary Variables
Zhanglin Shangguan, Wei Xiao, Qi Li et al. (+2 more)
Optimal stabilization of safety-critical nonlinear systems requires balancing long-term performance and strict safety constraints. Existing quadratic-programming-based control barrier function (CBF) safety filters are point-wise and may exhibit myopic behavior and local trapping when the safeguarding action conflicts with the nominal optimal control. This paper develops a safety-aware infinite-horizon optimal control framework by
embedding a barrier-Lyapunov function (BLF)-based safeguarding action into the system dynamics and introducing a barrier-regulating auxiliary variable, thereby reformulating the original constrained problem as an unconstrained one on an extended state space. To mitigate local trapping, we introduce an adaptive
alignment-conditioned tangential excitation orthogonal to the safety direction, with activation adaptively modulated by the degree of directional
alignment between the nominal and safeguarding controllers, and incorporate it as an admissible $\mathcal{L}2$ disturbance in an $H\infty$ formulation. For high-relative-degree systems under disturbances, we further augment the recursive high-order safe-set construction with barrier compensation terms to obtain a high-order BLF and formulate an
adversarial disturbance attenuation problem, which is approximately solved via safe-exploration-enhanced online critic learning. Simulations demonstrate reduced local trapping, improved safety–performance trade-offs, and safe operation under disturbances.
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All authors Zhanglin Shangguan, Wei Xiao, Qi Li, Bo Yang, Xinping Guan
Signal Processing
Radio Stripe-Based Distributed ISAC System with Dynamic Sensing-Communication Reconfiguration
Osmel Martínez Rosabal and Onel L. Alcaraz López
Integrated sensing and communications (
ISAC) has emerged as an intrinsic service of upcoming 6G wireless systems, enabling the reuse of communication signals for environmental sensing and supporting context-aware network functionalities. Meanwhile, the
evolution of the wireless infrastructure toward distributed systems creates new opportunities for collaborative sensing from spatially separated nodes. Motivated by this trend, this work investigates a
radio stripe aided
ISAC system as a low-complexity implementation of a distributed system. We study the trade-off between achievable sum rate and sensing precision when downlink signals are used for target localization within the service area. By exploiting the architectural homogeneity of the
radio stripes transceivers, each unit can be dynamically configured to operate in either communication or sensing mode. We formulate a targets localization problem considering the measurements of multiple sensing-communication configurations. Due to the large number of measurements and the continuity of the search space, we propose discretizing the service are and then solve the estimation problem in batches. The targets are finally estimated using a fusion strategy. Our results show that increasing the number devices and sensing APUs boosts sensing precision at the expense of degrading the sum rate. The latter remains constant for a given number of communication APUs regardless of their positions. Moreover, changing the number of
antennas reveals a non-monotonic impact on sensing performance due to the trade-off between array gain and illumination uniformity.
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Systems and Control
Linear Systems as Representations of Time Groups
Subhrajit Sinha
In this paper, we develop a representation-theoretic formulation of discrete-time linear systems. We show that such systems are naturally viewed as representations of time groups acting on vector spaces, thereby endowing the state space with a canonical algebraic structure. This perspective provides a unified framework for linear systems over different fields, in which familiar structural properties arise from the underlying representation. In particular, invariant decompositions of the state space correspond to invariant subrepresentations, while the distinctions between real, complex, and finite-field systems emerge from the algebraic properties of the base field and the time group. We further show that linear systems over finite fields naturally correspond to representations of finite cyclic time groups, leading to module structures over
polynomial quotient rings. This provides a systematic alternative to spectral analysis in settings where
eigenvalue-based methods are not the most natural organizing
language.
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Signal Processing
Robust Multi-Stream Massive MIMO Satellite Systems Based on Statistical CSI
Hangsong Yan, Alexei Ashikhmin, Hong Yang et al. (+2 more)
This paper investigates multi-stream downlink precoding for massive multiple-input multiple-output low-Earthorbit satellite (SAT) communication systems. We adopt a delay and Doppler precompensation approach to achieve coherent transmission. Under this setting, we formulate a signal transmission model that incorporates the near-independent properties of inter-SAT interference and compensation errors. We then demonstrate that moving beyond single-stream transmission requires both multi-SAT cooperation and multi-
antenna UTs. Based on this configuration and the established signal transmission model, we derive the first- and second-order statistical channel characteristics and utilize them to design locally optimal precoding
algorithms for both total power constraint (TPC) and per-
antenna power constraint (PAPC) conditions, which rely only on statistical channel state information (sCSI). In particular, the designed PAPC
algorithm achieves linear complexity with respect to the number of
antennas on the cooperative SATs. To reduce the computational complexity of the locally optimal precoder under TPC, we propose a low-complexity and robust precoding scheme optimized for both minimum mean squared error and sum-rate maximization objectives. Using majorization theory, we also provide a rigorous theoretical analysis of the optimal precoding structure under TPC. Moreover, the Lanczos
algorithm is adopted to further reduce the complexity of the proposed robust designs. Simulation results show that when each SAT is equipped with a sufficiently large number of
antennas, the proposed sCSI-based designs achieve performance comparable to that of instantaneous CSI-based designs.
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All authors Hangsong Yan, Alexei Ashikhmin, Hong Yang, Bin Song, Shu Sun