Systems and Control
Distributed Nash Equilibrium Seeking with Logarithmic Bit Rates over Digital Channels
Zihao Ren, Chengyang Jiang, Lei Wang et al. (+2 more)
This paper introduces quantization techniques to reduce the communication complexity in the distributed Nash
equilibrium (NE) seeking problem, achieving an exponential reduction in bit rates over digital channels. The goal of distributed NE seeking
algorithms is to coordinate
agents in a network game toward
equilibrium through iterative message exchanges among them via a communication network. The computational complexity of this distributed
algorithm critically depends on network communication overhead in the digital channel, motivating the development of communication reduction mechanism. Regarding this, we proposed some quantizers based on sparsification and uniform quantization through a general class of ultimate-boundedness-based quantizers. Based on this, we propose a Passivity-Based NE seeking
Algorithm with Time-varying scaling Error state Quantization (PBA-TEQ), and show that the linear convergence can be achieved under a sufficient condition. Moreover, when employing either the scalarization quantizer or the greedy quantizer, both belonging to the ultimate-boundedness-based quantizers, within the PBA-TEQ framework, we establish a lower bound on communication complexity of $\log_2(\mathcal{O}(nd))$ bit rates per transmission to achieve unbiased linear convergence, with $n$ being the number of
agents and $d$ being the dimension of the decision state of the network game. Numerical simulation examples are provided to validate our theoretical results.
Read the full abstract ▾
All authors Zihao Ren, Chengyang Jiang, Lei Wang, Yang Liu, Kemi Ding
Signal Processing · Cross-listed from q-bio.NC
Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition
Wiga Maulana Baihaqi, Indriana Hidayah, Sri Kusrohmaniah et al. (+1 more)
Identifying individual learning styles optimizes pedagogical efficacy. While traditional questionnaires are structured, behavioral tracking methods require prolonged interaction log accumulation. To overcome these temporal constraints, this paper proposes an objective Electroencephalography (
EEG) approach evaluating Phase Locking Value (PLV) connectivity against localized features across the Active-Reflective (AR) and Verbal-Visual (VV) Felder-Silverman dimensions.
EEG signals were recorded from 28 participants during Raven's Advanced Progressive
Matrices tasks. Support Vector Machine classification used Leave-One-Subject-Out Cross-Validation (LOSO-CV) alongside a 70:30 intra-subject split. The VV dimension achieved 70.00% subject-level accuracy driven by distinct fronto-occipital polarization. Conversely, the AR dimension yielded lower cross-subject generalizability (55.56%) due to overlapping executive networks and a "Systematic Neural Inversion" phenomenon, where stable individual connectivity signatures operated diametrically opposed to global boundaries (up to 20-0 voting margins). Ultimately, these outcomes demonstrate that rigid "one-size-fits-all" classifiers are bounded by biological diversity, emphasizing the need for future adaptive feature transformation techniques to bridge the cross-subject generalization gap.
Read the full abstract ▾
All authors Wiga Maulana Baihaqi, Indriana Hidayah, Sri Kusrohmaniah, Noor Akhmad Setiawan
Signal Processing
Latent variable models for simultaneous EOV identification and removal in population-based SHM
M. D. Champneys, M. R. Jones, A. J. Hughes et al. (+3 more)
The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded in the case that the EOV signals are unmeasured. A common approach in conventional SHM is to apply projection-based methods that discard subspaces of healthy feature data,
reasoning that the EOV signal dominates the variance of the measured features. However, a common pitfall of projection-based approaches is that when damage acts close to the same variance-dominant direction, damage sensitivity is removed along with the EOV. An alternative identifying assumption for the removal of particular unmeasured EOVs is slowness; the latent EOV process is characterised by its long temporal correlation. In this paper, the latent EOV is cast as a state-space Gaussian process, enabling tractable $\mathcal{O}(T)$ inference via a Kalman filter. A robust hierarchical
Bayesian identification framework is developed that enables population-level identification of latent EOVs and EOV-free residual features, using a Laplace approximation. The approach is first validated on a single laboratory-scale
benchmark structure from the literature, subject to thermal EOVs, demonstrating robust damage detection and EOV recovery. The method is then applied to a simulated nine-turbine offshore wind farm with staggered deployment and damage, where it delivers a substantial true-positive uplift over projection and cointegration-based baselines at matched false-positive rates.
Read the full abstract ▾
All authors M. D. Champneys, M. R. Jones, A. J. Hughes, T. J. Rogers, E.J. Cross, K. Worden
Systems and Control
Coordinated Dynamic Operation of Integrated Electrolyzer-Compressor Systems
Amin Salehi, Janne Seppanen and Mahdi Pourakbari-Kasmaei
The increasing interaction between power and hydrogen sectors highlights the importance of coordinated operation of electrolyzers and electric-driven compressor stations (EDCSs). This becomes particularly of higher importance under transient disturbances. However, coordinated dynamic interactions of these coupled subsystems remain largely unexplored. This article addresses such gap by developing a dynamic model for an integrated electrolyzer-EDCS system and designing appropriate PID control schemes to address the potential disturbances affecting either component. To this end, linearized models of the electrolyzer and EDCS are first derived to enable systematic controller design. Then, two PID controllers, representing conservative and fast-tracking designs, are developed to coordinate the system response. The developed coordinated model is examined and verified under four different cases. The results demonstrate the effectiveness of the proposed model under disturbances from the compressor driver or the electrolyzer. Controlling the electrolyzer flow in response to EDCS disturbances coordinates system dynamics and mitigates undesirable transient fluctuations. Conversely, under electrolyzer disturbances, regulating the EDCS torque eliminates inconsistent responses in pressure, flow, and rotational speed, while preventing hazardous transient undershoots and overshoots. Overall, the proposed framework guarantees transient stability and operational reliability of the integrated electrolyzer-EDCS system.
Read the full abstract ▾
Signal Processing
Disaster Information Reachability of Medium-Wave AM Broadcasting as a Wide-Area Disaster Information Infrastructure: Lessons from the 2024 Noto Peninsula Earthquake
Eiichi Shoji
The 2024 Noto Peninsula Earthquake caused widespread power outages, communication failures, and road disruptions, and a tsunami warning was issued immediately after the earthquake, highlighting the importance of reliable disaster information transmission. The Noto Peninsula is surrounded by the sea on three sides and has limited transportation networks, making community isolation and securing reliable information transmission critical challenges. This study defines "Disaster Information Reachability" as the capacity to deliver necessary information to those who need it when they need it, and examines the role of medium-wave AM broadcasting from this perspective. In September 2024, the author conducted a mobile reception survey from Kanazawa to Suzu City and a fixed-point reception survey in Suzu City using commercially available
radio receivers and passive
radio receivers, including HOOPRA. Medium-wave stations in Niigata, Toyama, Akita, and Tokyo were clearly received, although the local NHK Kanazawa stations were affected by noise. The nationwide high-power NHK
Radio 2 network, which was still operational at the time, was confirmed to provide a wide-area, multidirectional reception pathway through maritime propagation, demonstrating its critical significance as a redundant disaster information infrastructure. The fact that FM relay station damage was documented in subsequent policy materials, while medium-wave AM broadcasting received no explicit
attention, suggests that its disaster information reachability remains insufficiently recognized. Medium-wave AM broadcasting, with its wide-area simultaneous broadcast capability, high-power coverage, and maritime propagation characteristics, functions effectively as a disaster information infrastructure in peninsular regions and warrants reassessment in preparation for large-scale disasters such as a Nankai Trough megaquake.
Read the full abstract ▾
Signal Processing
End-to-End Modelling of Earthquake-Induced Polarisation Perturbations in Submarine Optical Fibres
Rick M. Butler, José Núñez-Kasaneva, Gabriele Liga et al. (+3 more)
We present an end-to-end model of earthquake-perturbed signal propagation in submarine fibres. Ground displacements are converted to fibre strain via ocean pressure, and injected into a split-step model. Our model shows good qualitative
alignment with measurements from the Curie cable during the 2020 Oaxaca earthquake.
Read the full abstract ▾
All authors Rick M. Butler, José Núñez-Kasaneva, Gabriele Liga, Magnus Karlsson, Alex Alvarado, Christian Häger
Audio and Speech Processing
On-Policy Self-Distillation for Multi-Dialect ASR: Mastering Dialects, Retaining Mandarin
Shuiyuan Wang, Bingshen Mu, Pengshen Zhang et al. (+6 more)
Recent large-scale ASR models already achieve strong Mandarin recognition accuracy and have some ability to recognize Chinese dialects. However, their dialect recognition accuracy is still limited in real-world
speech. Direct dialect adaptation can lower dialect CER, but it may also raise Mandarin CER. We therefore study how to adapt a capable ASR model to improve multi-dialect recognition without degrading Mandarin recognition. We adopt an adaptation pipeline where continual pre-training (CPT) and dialect supervised fine-tuning (SFT) provide a strong foundation, and On-Policy Self-
Distillation (OPSD) serves as the final refinement. OPSD addresses the train–test mismatch in autoregressive ASR by training the student model on its own decoded prefixes while a frozen teacher, conditioned on the reference transcript as privileged context, provides soft
token-level targets. This replaces hard cross-
entropy updates on dialect data with
distillation, preserving Mandarin ability while refining dialect recognition. We instantiate the framework with Qwen3-ASR-1.7B and evaluate it on public and internal Mandarin and dialect test sets. Under matched refinement data and schedule, OPSD improves dialect recognition without raising Mandarin CER, whereas continued teacher-forced fine-tuning increases Mandarin CER. We will release the model weights and evaluation scripts.
Read the full abstract ▾
All authors Shuiyuan Wang, Bingshen Mu, Pengshen Zhang, Chengyou Wang, Yujie Liao, Chengdong Liang, Binbin Zhang, Qiangze Feng, Lei Xie
Systems and Control · Cross-listed from cs.LG
Forward and Inverse Virtual Metrology for Phototransistor Gain: A Hierarchical, Uncertainty-Aware Approach for Small Production Datasets
Mahshid Amirabgir, Lorenza Ferrario, Paolo Conci et al. (+2 more)
The customization, optimization and stabilization of the process flow of a silicon bipolar phototransistor commits months of cleanroom time before a finished device can be measured, so a model that predicts device gain from process parameters before a run has value out of proportion to its accuracy. We study this problem on a real fabrication history, thirteen to fourteen process runs of a single device: a small-sample, hierarchically structured setting unlike the large-corpus regime of conventional virtual metrology. Decomposing the variance of device gain, we find that roughly half of it lies between process runs rather than within them, so recipe-only prediction is bounded by construction. Building on these findings we provide a forward gain predictor with a relative, uncertainty-aware signal, an inverse search that returns recipes for a target gain, and, as the foundation for all of it, a multi-level data-quality assessment tailored to the nested physical entities of fabrication (batch, wafer, die) with an explicit cross-level linkage score. The normalized
dataset and analysis code are released for full reproducibility.
Read the full abstract ▾
All authors Mahshid Amirabgir, Lorenza Ferrario, Paolo Conci, Mahdieh Amirabgir, Giancarlo Orengo
Systems and Control
Disturbance-Observer-Based Grid-Forming Control for Unbalanced Grids
Juho Määttä, Marko Hinkkanen, Tuure Nurminen et al. (+4 more)
This article proposes a grid-forming control method for operation under unbalanced grid-voltage conditions. The method regulates the positive-sequence active power delivered to the grid and actively suppresses the negative-sequence converter voltage, controlling the voltage magnitude to be constant also during unbalanced faults when within the physical limits of the converter. Only the converter current is measured on the AC side, and a disturbance observer is used for synchronization as well as providing integral and resonant action. Estimates for the positive- and negative-sequence grid voltage are obtained from the disturbance observer. A current-limitation scheme for both balanced and unbalanced faults is integrated. Comprehensive stability analysis and tuning guidelines are provided. Experimental results using a 12.5-kVA converter demonstrate that the proposed method can operate during severe balanced and unbalanced faults.
Read the full abstract ▾
All authors Juho Määttä, Marko Hinkkanen, Tuure Nurminen, Orcun Karaca, Rayane Mourouvin, Jarno Kukkola, Lennart Harnefors
Image and Video Processing
ResPCC: A Loss-Resilient Neural Point Cloud Codec over Lossy Networks
Xueqin Niu, Mufan Liu, Yifan Wang et al. (+3 more)
Point cloud compression (PCC) is critical for efficient storage and transmission of 3D data. While recent learning-based PCC methods achieve good rate-distortion (R-D) performance, they generally rely on ideal transmission conditions. In practice, packet loss is a common issue and can severely distort latent features, causing coordinate drift and
geometric degradation. To address this challenge, we present ResPCC, the first end-to-end neural point cloud codec designed to offer intrinsic resilience against data loss. Our framework is loss-rate-aware and adapts to diverse packet loss conditions. At the encoder, we introduce a Condition-Adaptive Latent Modulation (CALM) module to adjust latent feature distributions according to the perceived loss rate, as well as a Spatial-Channel Interleaving (SCI) mechanism that transforms channel-wise data extinction into spatially scattered element-wise missing patterns. At the decoder, we develop a Mask-Aware Graph-based Latent Restoration (MGLR) module, followed by a Dictionary-based Refinement (DBR) stage to recover corrupted features and align them with canonical priors. Evaluations on ShapeNet and SemanticKITTI under 5% to 30% packet loss rates show that ResPCC consistently delivers superior stability and R-D performance over baselines. Our framework maintains high reconstruction fidelity under lossy conditions, providing a reliable solution for 3D data transmission over practical networks. Code is available at https://github.com/starrynight314/ResPCC.
Read the full abstract ▾
All authors Xueqin Niu, Mufan Liu, Yifan Wang, Le Yang, Jun Sun, Yiling Xu
Signal Processing
A Universal Random Precoding Framework for MIMO Systems
Jiazhen Dong, Lei Liu, Xiaojun Yuan et al. (+1 more)
Current wireless systems combat inter-symbol interference (ISI) by diagonalizing or sparsifying the channel
matrix, yet they remain vulnerable to selective fading. To address this, we propose a universal random precoding (RP) transmission framework based on the universality class. RP leverages random transforms to statistically exploit all subchannels and construct an equivalent channel belonging to the universality class, thereby enhancing diversity gain while maintaining backward compatibility with existing waveforms. Low-complexity implementations include the randomly permuted fast transform (FT-RP) and the interleaved block-sparse fast transform (IBSFT-RP). A cross-domain OAMP/MAMP (CD-OAMP/MAMP) detector is designed for RP systems, which is replica maximum a posteriori (MAP)-optimal according to state
evolution (SE). Simulation results on
MIMO systems demonstrate that RP with CD-OAMP/MAMP achieves near-RM performance with much lower complexity, with additional benefits of flexible compression ratios for spectral efficiency.
Read the full abstract ▾
All authors Jiazhen Dong, Lei Liu, Xiaojun Yuan, Baoming Bai
Audio and Speech Processing
MiDashengLM-Gen: Unified Audio Scene Generation via LLM-Driven Autoregressive Flow Matching
Xingwei Sun, Heinrich Dinkel, Gang Li et al. (+7 more)
Generating coherent audio scenes that simultaneously blend
speech, music, and sound effects remains a significant challenge. Current approaches typically rely on a disjointed pipeline where a frozen, decoupled text encoder feeds a separate audio decoder, limiting cross-modal optimization and leading to poor
speech intelligibility. To overcome these limitations, we introduce MiDashengLM-Gen, an end-to-end framework that couples a pre-trained Large
Language Model (LLM) with per-
token conditional flow matching for autoregressive, variable-length mixed-audio scene generation. MiDashengLM-Gen represents a first approach for general text-to-audio generation with one end-to-end trained model. Empirical evaluations demonstrate that MiDashengLM-Gen drastically improves
speech intelligibility over existing unified models. On the Seed-TTS
benchmark, English Word Error Rate (WER) drops from 12.15% to 2.79%, approaching the performance of dedicated Text-to-
Speech (TTS) systems (1.24%). Furthermore, the framework extends effectively to multilingual settings, yielding highly competitive multilingual WERs compared to existing baselines. Lastly, the model maintains competitive mixed-audio generation quality on the MECAT
benchmark. Code and checkpoints are available at https://github.com/xiaomi-research/midashenglm-gen and https://huggingface.co/mispeech/midashenglm-gen, and the demo page is available at https://xingws.github.io/midashenglm-gen-demo/.
Read the full abstract ▾
All authors Xingwei Sun, Heinrich Dinkel, Gang Li, Jiahao Mei, Yadong Niu, Zerui Han, Yuepeng Jiang, Jiahao Zhou, Lichun Fan, Jian Luan
Signal Processing
TRACE: A Modular Framework for RIS-Assisted Channel Estimation and Differential Channel-Aware Reconfiguration
Smriti Kumar, Mansi Ambwani, Arzad Alam Kherani et al. (+1 more)
Reconfigurable Intelligent Surface (RIS) research tightly couples channel-estimation, control and communication, yet existing implementations often rely on fixed algorithmic pipelines, making it difficult to compare alternative estimation, tracking and communication strategies under identical conditions and to study low-overhead RIS adaptation under time-varying channels. This paper addresses both challenges through two complementary contributions. First, it presents TRACE (Toolkit for RIS-assisted channel-estimation, adaptive control and communication experimentation), a modular socket-based framework that decouples the transmitter,
radio environment, controller and receiver through separate control- and data-plane interfaces, enabling reproducible evaluation across substitutable modules. Second, it proposes the differential channel-aware RIS update (DCAR)
algorithm, which estimates channel perturbations from reduced probe observations using a regularized differential update to reduce retraining overhead. TRACE is validated through interchangeable minimum mean square error and orthogonal matching pursuit channel-estimation, Gaussian random walk and Gauss–Markov channel
evolution, BPSK, QPSK and 16-QAM modulation techniques and multiple RIS sizes, without modifying the underlying framework. Within TRACE, DCAR is observed to reduce pilot overhead and computational complexity while maintaining beamforming performance close to Kalman-filter-based tracking under time-varying channels.
Read the full abstract ▾
All authors Smriti Kumar, Mansi Ambwani, Arzad Alam Kherani, Vimal Bhatia
Signal Processing
RaStream: Edge-Deployable Streaming Human Mesh Recovery from mmWave Radar
Jiazhen Dong and Lei Liu
Millimeter-wave (mmWave) radar provides a
privacy-preserving sensing modality for human motion analysis, yet continuous SMPL-X recovery on edge devices remains challenging because accurate reconstruction benefits from dense volumetric radar observations that preserve weak articulated reflections, while repeatedly processing such high-dimensional data over time incurs substantial computational cost. Temporal context is also necessary to resolve frame-local ambiguity, but SMPL-X attributes evolve at different rates, with body morphology remaining relatively stable and articulated pose, root orientation, and global translation changing rapidly. We present RaStream, a causal streaming framework that separates rich volumetric spatial perception from lightweight temporal
reasoning. For each radar observation, a localization-conditioned spatial encoder exploits global scene context to estimate a coarse body location and focuses detailed reconstruction on a center-guided region of interest while preserving native 3D radar structure. Global and local features are then fused into a compact representation, allowing volumetric evidence to be extracted once per observation and temporal history to be propagated through compact representations and fixed-size persistent states. RaStream further maintains a slow morphology state for shape-related information and a fast motion state for pose, orientation, and translation. On the 661K-frame M4Human
benchmark, RaStream reduces MVE from 90.90 mm for RT-Mesh to 84.27 mm with spatial-only inference and to 72.05 mm with causal temporal refinement. The Base configuration requires 26.93 ms per FP32 invocation on a Jetson Orin Nano. These results demonstrate practical edge-streaming radar mesh recovery through one-time volumetric evidence extraction and compact causal temporal propagation.
Read the full abstract ▾
Image and Video Processing
A comparison of CNN architectures for Alzheimer's disease detection in single-view MRI scans
Hiram Zuniga, Ulises Orozco-Rosas and Kenia Picos
Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life. Diagnosis relies on medical history, cognitive tests, physical exams, and MRI
brain scans, making deep learning suitable for Alzheimer's classification. This work proposes a
benchmark that evaluates ten different convolutional neural network (CNN) architectures (including ResNet, DenseNet, MobileNet, EfficientNet, and VGG family models) under the same held-out test split protocol. A two-stage transfer learning and full fine-tuning pipeline is introduced to perform training using a class-balanced subset (3,900 images) derived from the OASIS medical imaging
dataset, comprising 86,437 single-view MRI
brain scans labeled into four classifications of Alzheimer's disease: Non-Demented, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The best results were achieved by VGG16, with a 0.9637 validation accuracy and a 0.9533 test accuracy score. A key finding documented in this work is the difficulty of classifying the transition from Non-Demented to Very Mild Demented stages, observed consistently across all ten architectures.
Read the full abstract ▾
Signal Processing
Characterizing Nonlinearities in IM-DD Links via the Best Linear Approximation: Distortion Analysis and Modulation Optimization
Sebastian Fraga Fernández, Leonardo Minelli, Fernando de Bernardinis et al. (+3 more)
We use the best linear approximation (BLA) to characterize nonlinearities in IM-DD links. The orthogonal nonlinear distortion is shown to be non-Gaussian. The BLA is used to approximate the optimum modulation depth for different equalizer structures.
Read the full abstract ▾
All authors Sebastian Fraga Fernández, Leonardo Minelli, Fernando de Bernardinis, Felipe Villenas, Yunus Can Gültekin, Alex Alvarado
Systems and Control
Reinforcement Learning-Based Output Feedback LQR for Continuous-Time MIMO Systems
Qihua Chen, Mingxiang Liu, Lili Wang et al. (+2 more)
This article studies model-free output feedback linear quadratic regulation (LQR) for continuous-time linear systems with an $n$-dimensional state, an $m$-dimensional input, and a $p$-dimensional output, using filtered input–output data. Since the system state is unavailable, existing methods rely on dynamic filters to parameterize the hidden state using measurable input–output signals. However, the intrinsic dimension of the resulting filter-based parametrization can be smaller than the dimension of the complete filtered vector, and this deterministic redundancy can make the Bellman regressions rank deficient. We characterize this intrinsic dimension and show that the conventional filtered vector contains only $2n$ independent components for single-input multi-output (SIMO) systems and $n(m+1)$ independent components for general multi-input multi-output (
MIMO) systems. Based on this characterization, a reduced filtered vector is extracted directly from data and used to develop reduced model-free output feedback policy iteration and value iteration equations, eliminating the redundant directions and decreasing the number of unknown parameters while retaining a fully input–output data-based implementation. A numerical example illustrates the rank reduction and the effectiveness of the learned controller.
Read the full abstract ▾
All authors Qihua Chen, Mingxiang Liu, Lili Wang, Zhiyun Lin, Minyue Fu
Systems and Control · Cross-listed from physics.app-ph
A Frequency-Space Terahertz Transceiver Chip for Multi-Agent Communications and Spatial Awareness
Xiaoyue Xia, Zhicheng Lin, Hao Guo et al. (+7 more)
Future indoor embodied-intelligence systems require scalable hardware platforms that support both high-capacity multi-
agent connectivity and mutual spatial awareness. The terahertz (THz) spectrum offers abundant bandwidth and inherent spatial selectivity for integrated sensing and communication (
ISAC); however, conventional phased arrays and programmable metasurfaces rely on dense beamforming networks, element-level control, or external THz illumination, making scalable multibeam operation challenging. Here, we report a fully integrated 208-258GHz 65-nm CMOS THz transceiver chip that monolithically integrates broadband front ends with heterogeneous leaky-wave metasurface (HLM) apertures within a 1.5mm by 4.9mm area. The HLM generates strongly dispersive leaky modes, enabling 75 degree frequency-controlled beam scanning with only four meta-atoms. Co-design of frequency-domain and spatial-domain mixing achieves spectrally clean frequency-to-space mapping for spatial-frequency division multiple access (SFDMA) communication. The THz chip demonstrates multi-
agent simultaneous transmission and reception, two-dimensional localization, and sensing-enhanced communication, providing a scalable hardware platform for future THz embodied-intelligence networks.
Read the full abstract ▾
All authors Xiaoyue Xia, Zhicheng Lin, Hao Guo, Siran Wang, Jingyuan Zhang, Xinyu Fang, Ka Fai Chan, Shum Kam Man, Geng-Bo Wu, Chi Hou Chan
Image and Video Processing · Cross-listed from cs.CV
Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Denoising
Zhilin Ai, Boyu Li, Sidi Yang et al. (+5 more)
Lookup table (LUT)-based image denoising methods have attracted increasing
attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approaches require three identical LUTs to process RGB channels in parallel, resulting in large on-chip SRAM consumption. A simple alternative is to apply LUT processing only to the luminance (Y) channel in the YUV color space to reduce
memory usage. However, this naive strategy leads to degraded restoration quality, since ignoring the chrominance (UV) channels introduces color distortion and residual artifacts. In this work, we propose Hybrid-LUT, a YUV-based asymmetric channel-processing framework that combines LUT and filtering in a unified design. Specifically, a multi-band LUT branch with pixel-level weight fusion is applied to the Y channel to recover fine textures, while lightweight filtering is used for the UV channels to maintain color consistency. This design reduces LUT storage by two-thirds compared with RGB-LUT methods while maintaining the same runtime throughput. Extensive experiments show that Hybrid-LUT achieves state-of-the-art (SOTA) performance across multiple
benchmarks with only 421 KB of storage. In particular, our method surpasses existing LUT-based denoising approaches by at least 0.63 dB CPSNR on real-world
datasets, demonstrating its effectiveness for image denoising on resource-constrained edge devices. The project is available at https://github.com/Ai-ZL/Hybrid-LUT .
Read the full abstract ▾
All authors Zhilin Ai, Boyu Li, Sidi Yang, Wenqing Shi, Wenyong Zhou, Binxiao Huang, Chenchen Ding, Ngai Wong
Systems and Control
Energy-Aware Wind-Resilient Routing for Truck-Assisted Multi-UAV Delivery under Wind Uncertainty
Tianshun Li, Yanggang Sheng, Hongliang Lu et al. (+3 more)
Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery. In truck-
UAV systems,
UAVs complete assigned deliveries and safely return to a mobile truck or depot, while wind-induced propulsion costs vary online and are only partially observable. Existing routing methods often rely on static or deterministic energy models, which may underestimate headwind, crosswind, battery-voltage, and return-feasibility risks. This paper proposes Energy-Aware Wind-Resilient Routing (EWR), an online risk-sensitive planning framework for wind-aware and energy-safe
UAV routing. The delivery environment is represented as a time-dependent directed energy graph whose edge costs are updated using delayed noisy wind estimates, payload states, and conservative uncertainty margins. Experiments using synthetic delivery graphs with replayed wind logs from a public truck-
UAV delivery
dataset show that EWR improves mission success rates and reduces wind-induced return failures.
Read the full abstract ▾
All authors Tianshun Li, Yanggang Sheng, Hongliang Lu, Zhongzhen Wang, Haoang Li, Xinhu Zheng
Audio and Speech Processing
Deep Learning Based Relative Transfer Matrix Estimation for Multiple Sources and Multiple Microphones
Oshan A. B. Yalegama and Wageesha N. Manamperi
The Relative Transfer
Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, shows promising performance when applied to
speech enhancement in noisy environments. Estimating the ReTM of sound sources by exploiting the covariance
matrices of multichannel recordings is highly beneficial for practical applications and, to date, remains the only proposed approach. This paper investigates deep learning-based ReTM estimation. We propose three novel supervised learning frameworks using time and short-time frequency transform domain convolutional networks, and a Long Short-Term
Memory-based recurrent neural network. Experimental results demonstrate that the proposed models achieve more accurate estimation of the ReTM using five objective metrics compared to the covariance-based method. We also show the effectiveness of the proposed frameworks for
speech enhancement, achieving performance on par with the baseline method.
Read the full abstract ▾
Signal Processing · Cross-listed from cs.LG
FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting
Rentao Gu, Yihang Ding, Junjie Li et al. (+5 more)
Recent advances in Large
Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual
prompts for modality
alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data. To enable
prompt-free, frequency-aware adaptation of frozen LLMs, we propose FM-LLM (Frequency-Enhanced Mixture-of-Experts for adapting LLMs to Time Series Forecasting), an autoregressive framework grounded in constrained asymmetric coupling. A
Fourier Analysis Network (FAN)-based spectral
token aligner injects structured harmonic representations directly into the frozen LLM with numerical compatibility. An asymmetric Mixture-of-Experts (MoE) decoder enforces role separation: shared experts with lightweight FAN layers reconstruct the global periodic backbone, while routed experts-restricted to standard FFNs-specialize in modeling non-periodic residual dynamics. A time-frequency hybrid loss function jointly optimizes temporal accuracy and spectral consistency, mitigating error accumulation during long-horizon autoregressive rollouts. Evaluated across eleven public
benchmarks, FM-LLM achieves state-of-the-art performance on 59 out of 78 evaluation metrics. Compared to the strongest autoregressive LLM-based baseline, it delivers average improvements of 5.3% in MSE and 5.6% in MAE, with maximum gains reaching 8.0% for MSE and 8.4% for MAE. FM-LLM also demonstrates robust transferability, maintaining superior performance in 10% few-shot and zero-shot forecasting scenarios.
Read the full abstract ▾
All authors Rentao Gu, Yihang Ding, Junjie Li, Yi Ding, Weijing Sang, Xiaoli Huo, Xin Qin, Yuefeng Ji
Image and Video Processing · Cross-listed from cs.CV
Topology-Aware Query Selection for Surgical Instrument Instance Segmentation
Ze Zhang and Yang Zhang
Accurate foreground masks can still form an incorrect surgical-instrument instance set: duplicate, fragmented, merged, missed, or empty-frame predictions may preserve favorable pixel overlap while violating object identity and count. Final query selection is therefore a relational, variable-cardinality problem rather than a collection of independent candidate decisions. We evaluate
topology-aware query selection, which represents the nonempty candidates of a fixed Mask2Former as a complete graph, learns relational candidate and pair representations, predicts set cardinality, and solves an exact structured subset problem. The formal comparison is the complete relational path versus a node-feature-matched path; it evaluates the combined effect of pairwise geometry, message passing, and the additional relational-path capacity, not an isolated component. On the sealed 22-case source test, all three discovery seeds supported instance-set performance improvement with segmentation fidelity and predefined technical-safety preservation: instance F1 increased by 0.0504–0.0612 and positive-frame set-failure rate decreased by 0.0848–0.1060. Direct ROBUST-MIPS transfer reproduced the complete result in all three seeds. Endoscapes supported only one of three seeds and therefore did not establish stable direct transfer. Taken together, the results support a bounded conclusion: the evaluated complete path improved coherent instance-set construction from fixed Mask2Former candidates in specified native-instance contracts, while stable cross-domain transfer and component-specific effects remain unestablished.
Read the full abstract ▾
Signal Processing
Small Language Model enabled Autonomous agent for Language-Conditioned Cognitive Radar
Minhaj Uddin Ahmad, Zakia Zaman, Shunqiao Sun et al. (+1 more)
Modern radar systems require adapting their processing strategies in response to changing interference, clutter, and data availability. This paper introduces a framework for a small
language model (SLM)-driven autonomous
agent designed for
language-conditioned cognitive radar, functioning as an intelligent controller for a suite of array signal processing tools. Given a natural-
language command, the
agent extracts radar-operation-related cues, selects an appropriate sequence of signal-processing methods, configures parameters, and invokes executable tools for numerical computation. Experiments with a synthetic uniform linear array (ULA) radar demonstrate that, given a natural-
language command, the
agent performs meaningful
algorithm selection across diverse scenarios for sidelobe control, jammer suppression, multiple-null beamforming, coherent-source handling, and low-snapshot direction-of-arrival (DOA) estimation. Ablation results show that radar-specific prompting and physics-grounded tool execution are both required for reliable decisions and
hallucination-free numerical results.
Read the full abstract ▾
All authors Minhaj Uddin Ahmad, Zakia Zaman, Shunqiao Sun, Mizanur Rahman
Audio and Speech Processing · Cross-listed from cs.SD
Luna-TTS Family Technical Report
Feng Yin, Shuai Shi, Junjie Zheng et al. (+19 more)
Modern text-to-
speech (TTS) is dominated by autoregressive (AR) codec
language models, whose left-to-right decoding brings latency that grows with utterance length, error accumulation along the committed prefix, and an artificial generation order imposed on the Residual Vector Quantization (RVQ)
token grid. We propose Luna-TTS Family,
diffusion-
language-model-based TTS systems pretrained on 1 million hours of
speech across Chinese, English, Japanese, and Korean. The family is built by progressive adaptation of a pretrained AR text LLM, from causal to bidirectional and finally to block-causal
attention, and comprises two variants sharing a single tokenizer, data pipeline, and 0.6B backbone lineage. Luna-TTS is fully non-autoregressive: it generates the entire RVQ
token grid in a fixed number of parallel refinement steps, with zero-shot voice cloning and
speech editing arising natively as infilling. Luna-TTS Realtime, derived by continual training, is autoregressive over blocks of 32 codec frames (1.28s) while denoising each block in parallel; it supports KV-cached blockwise generation and incremental audio delivery, achieving an end-to-end RTF of 0.0240 and 41.6 ms local first-block latency under the warmed serving protocol. An annealed fine-tuning stage adds explicit control over emotion and non-verbal vocalizations (NVVs), and a reinforcement-learning stage applies GRPO with policy ratios computed over the realized denoising trajectory. On Seed-TTS-Eval, Luna-TTS achieves the best results on all four metrics among compared open-source and commercial systems (0.73 CER / 79.7 SIM on test-zh, 1.49 WER / 76.8 SIM on test-en); on the harder in-the-wild CV3-Eval, it posts the lowest Mandarin and English error rates in our comparison. Against leading commercial systems, it achieves the best results on most objective, model-based, and human-rated metrics for NVV and emotion control.
Read the full abstract ▾
All authors Feng Yin, Shuai Shi, Junjie Zheng, Kechenying Zhou, Yiqiu Wang, Chenyang He, Qiuhua Jiang, Mengxiao Bi, Yanmin Qian, Mingxin Chen, Xun Gong, Tianteng Gu, Bing Han, Peng Jiang, Chenda Li, Haiyang Sun, Han Wang, Wei Wang, Yi Wang, Leying Zhang, Wangyou Zhang, Chushu Zhou
Audio and Speech Processing
Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning
Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan et al. (+4 more)
Recent advances in model design and self-supervised audio representations have improved
speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts. We present a family-conditioned, multi-tier audio tagger that combines a LoRA-finetuned Whisper encoder with a lightweight, target-speaker-aware
Transformer for long-context inference and framewise prediction across tiers. To improve temporal coherence, we incorporate a simple sequence-level smoothing loss, and to enhance robustness across households, we introduce a factorized speaker-
token design with a shared tier
token and a learned family-specific offset, reducing family bias and promoting generalizable representations. Together, these choices enable efficient and effective infant-centered audio tagging of daylong audio recordings in home environments.
Read the full abstract ▾
All authors Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan, Kexin Hu, Bashima Islam, Mark Hasegawa-Johnson, Nancy L. McElwain
Signal Processing · Cross-listed from cs.HC
Measuring Browser Webcam Gaze Honestly: A Capture-Clock Methodology and Open Reference Implementation
Chi-Sheng Chen and Gabriel A. Brat
Browser-based webcam gaze trackers are increasingly used for crowd-scale data collection and in clinical settings where lab eye trackers are impractical, but the reported latency numbers may not represent real world functionality. The common practice of timestamping each gaze sample when it is emitted, rather than when its source frame was captured, makes the measured inference latency read about $0\,$ms no matter how slow the engine really is. We show how to measure it honestly, recovering a per-frame capture clock from the browser's re\-quest\-
Video\-Frame\-Call\-back (rVFC) API (captureTime where the browser exposes it for local camera streams, else presentationTime, in which case every recovered latency is a verifiable lower bound): exact source-frame pairing through a per-frame queue for engines that expose their inference pipeline, and a further lower bound for engines that do not, such as WebGazer. We release an open TypeScript implementation and
benchmark harness, demonstrated on two interchangeable engines: WebGazer and a new FaceMesh+KRR pipeline.
Read the full abstract ▾
Image and Video Processing
Alignment of Similarity-Transformed Images Based on Fourier–Mellin Transform Using Auxiliary Function Method
Shinji Yamashita, Yuma Kinoshita and Hitoshi Kiya
This paper proposes an
algorithm for estimating the similarity transformation, namely translation, scale, and rotation, between two images with subpixel accuracy. Image registration is a fundamental technique for aligning images acquired under different viewpoints and imaging conditions, and a representative approach based on maximizing discrete cross-correlation is the
Fourier–Mellin registration. However, the
Fourier–Mellin approach often fails to achieve sufficient
alignment accuracy when subpixel-level estimation is required. The proposed method integrates (i) scale-and-rotation estimation from the
Fourier magnitude spectrum in a log-polar representation and (ii) maximization of phase-only correlation based on the auxiliary function method. This integration enables a two-stage estimation procedure: it first estimates scale and rotation without being affected by translation, and then estimates translation with subpixel precision in the spatial domain using the corrected image pair. A simulation experiment on image pairs subjected to random similarity transformations demonstrates that the proposed method reduces estimation errors in scale, rotation, and translation compared with
Fourier–Mellin-based registration methods using discrete cross-correlation.
Read the full abstract ▾
Image and Video Processing · Cross-listed from cs.CV
From Synthesis to Removal: Physics-Grounded Reflection Simulation and Diffusion-Based Video Dereflection
Zepeng Wang, Jiagao Hu, Fuhao Li et al. (+3 more)
Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream
vision tasks. Although single-image reflection removal has been extensively studied,
video reflection removal remains largely underexplored due to the lack of paired
video data, temporally coherent removal models, and dedicated evaluation
benchmarks. We present a closed-loop framework that unifies physics-grounded reflection simulation,
diffusion-based
video dereflection, and
benchmark evaluation. Our S2R-Synthesis pipeline generates paired reflected and reflection-free
videos by performing physics-grounded augmentation in the structure space and rendering realistic reflected
videos with a trained
video diffusion renderer; the augmentation models key glass-related effects including roughness-induced blur, thickness-induced ghosting, and reflectance variation. Based on the synthesized data, we introduce S2R-Removal, the first
diffusion-based
video reflection removal model, which adapts a pretrained
video diffusion prior through reflection-aware latent adaptation and one-step pixel-
geometric refinement, recovering the clean transmission in a single denoising step. We further build S2R-Bench, the first
benchmark for
video reflection removal, supporting both full-reference evaluation and real-world human perceptual assessment. Experiments on S2R-Bench and multiple public image
benchmarks demonstrate state-of-the-art performance and faster inference than even non-
diffusion baselines, and validate the effectiveness of S2R-Synthesis. Project page: https://codingwzp.github.io/VideoDereflection_S2R.
Read the full abstract ▾
All authors Zepeng Wang, Jiagao Hu, Fuhao Li, Yuxuan Chen, Fei Wang, Daiguo Zhou
Systems and Control
Poisson Tangent Limits and Critical Policy Switching for Sampled Bellman Operators
Ming-Zhe Dai and Chengxi Zhang
Consider a discounted Markov decision process with continuous action space in which, at each state visit, the controller draws a random pool of $N$ candidate actions and selects among them. When the optimal action set has zero mass under the sampling distribution, the value of this random-candidate model converges to the optimal value as $N$ grows, but the rate of convergence and the asymptotic selection rule are governed jointly by the geometry of the optimal set and by the transition kernels of the near-optimal candidates. This paper develops an exact first-order theory of both. The rescaled near-optimal candidates converge to a marked Poisson point process, and the leading term of the value gap is the fixed point of a nonlinear tangential Bellman operator, a
stochastic generalization of the classical resolvent that emerges when several optimal actions compete. The asymptotic selection at exact ties is genuinely dynamic, driven by the transition kernels through the fixed point, and admits an explicit Mecke integral formula at the limiting Poisson level; perturbing the tie at the critical rate yields a switching layer in which the value gap and the selection interpolate continuously between the branch regimes, and in the unique-optimum heterogeneous case the critical class is propagated at the slowest global scale through the discounted reachability of the limit policy. Numerical experiments confirm the predicted rates, constants, and selection probabilities.
Read the full abstract ▾