Audio and Speech Processing · Cross-listed from cs.SD
Synthetic Audio Forensics Evaluation (SAFE) Challenge
Kirill Trapeznikov, Paul Cummer, Pranay Pherwani et al. (+6 more)
The increasing realism of synthetic
speech generated by advanced text-to-
speech (TTS) models, coupled with post-processing and laundering techniques, presents a significant challenge for audio forensic detection. In this paper, we introduce the SAFE (Synthetic Audio Forensics Evaluation) Challenge, a fully blind evaluation framework designed to
benchmark detection models across progressively harder scenarios: raw synthetic
speech, processed audio (e.g., compression, resampling), and laundered audio intended to evade forensic analysis. The SAFE challenge consisted of a total of 90 hours of audio and 21,000 audio samples split across 21 different real sources and 17 different TTS models and 3 tasks. We present the challenge, evaluation design and tasks,
dataset details, and initial insights into the strengths and limitations of current approaches, offering a foundation for advancing synthetic audio detection research. More information is available at \href{https://stresearch.github.io/SAFE/}{https://stresearch.github.io/SAFE/}.
Read the full abstract ▾
All authors Kirill Trapeznikov, Paul Cummer, Pranay Pherwani, Jai Aslam, Michael S. Davinroy, Peter Bautista, Laura Cassani, Matthew Stamm, Jill Crisman
Image and Video Processing · Cross-listed from cs.CV
Visual Language Model as a Judge for Object Detection in Industrial Diagrams
Sanjukta Ghosh
Industrial diagrams such as piping and instrumentation diagrams (P&IDs) are essential for the design, operation, and maintenance of industrial plants. Converting these diagrams into digital form is an important step toward building digital twins and enabling intelligent industrial automation. A central challenge in this digitalization process is accurate object detection. Although recent advances have significantly improved object detection
algorithms, there remains a lack of methods to automatically evaluate the quality of their outputs. This paper addresses this gap by introducing a framework that employs Visual
Language Models (VLMs) to assess object detection results and guide their refinement. The approach exploits the
multimodal capabilities of VLMs to identify missing or inconsistent detections, thereby enabling automated quality assessment and improving overall detection performance on complex industrial diagrams.
Read the full abstract ▾
Image and Video Processing
Real-time nonlinear inversion of magnetic resonance elastography with operator learning
Juampablo E. Heras Rivera, Caitlin M. Neher and Mehmet Kurt
$\textbf{Purpose:}$ To develop and evaluate an operator learning framework for nonlinear inversion (NLI) of
brain magnetic resonance elastography (MRE) data, which enables real-time inversion of elastograms with comparable spatial accuracy to NLI. $\textbf{Materials and Methods:}$ In this retrospective study, 3D MRE data from 61 individuals (mean age, 37.4 years; 34 female) were used for development of the framework. A predictive deep operator learning framework (oNLI) was trained using 10-fold cross-validation, with the complex curl of the measured displacement field as inputs and NLI-derived reference elastograms as outputs. A structural prior mechanism, analogous to Soft Prior Regularization in the MRE literature, was incorporated to improve spatial accuracy. Subject-level evaluation metrics included Pearson's correlation coefficient, absolute relative error, and structural similarity index measure between predicted and reference elastograms across
brain regions of different sizes to understand accuracy. Statistical analyses included paired t-tests comparing the proposed oNLI variants to the convolutional neural network baselines. $\textbf{Results:}$ Whole
brain absolute percent error was 8.4 $\pm$ 0.5 ($\mu'$) and 10.0 $\pm$ 0.7 ($\mu''$) for oNLI and 15.8 $\pm$ 0.8 ($\mu'$) and 26.1 $\pm$ 1.1 ($\mu''$) for CNNs. Additionally, oNLI outperformed convolutional architectures as per Pearson's correlation coefficient, $r$, in the whole
brain and across all subregions for both the storage modulus and loss modulus (p < 0.05). $\textbf{Conclusion:}$ The oNLI framework enables real-time MRE inversion (30,000x speedup), outperforming CNN-based approaches and maintaining the fine-grained spatial accuracy achievable with NLI in the
brain.
Read the full abstract ▾
Systems and Control
Viability-Preserving Passive Torque Control
Zizhe Zhang, Yicong Wang, Zhiquan Zhang et al. (+2 more)
Conventional passivity-based torque controllers for manipulators are typically unconstrained, which can lead to safety violations under external perturbations. In this paper, we employ viability theory to pre-compute safe sets in the state-space of joint positions and velocities. These viable sets, constructed via data-driven and analytical methods for self-collision avoidance, external object collision avoidance and joint-position and joint-velocity limits, provide constraints on joint accelerations and thus joint torques via the
robot dynamics. A quadratic programming-based control framework enforces these constraints on a passive controller tracking a dynamical system, ensuring the
robot states remain within the safe set in an infinite time horizon. We validate the proposed approach through simulations and hardware experiments on a 7-DoF Franka Emika manipulator. In comparison to a baseline constrained passive controller, our method operates at higher control-loop rates and yields smoother trajectories.
Read the full abstract ▾
All authors Zizhe Zhang, Yicong Wang, Zhiquan Zhang, Tianyu Li, Nadia Figueroa
Image and Video Processing · Cross-listed from cs.CV
Unified Unsupervised Anomaly Detection via Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Gaochang Wu et al. (+5 more)
Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and medical analysis, where anomalies are scarce due to
privacy concerns and cold-start constraints. Existing methods, whether reconstruction-based (restoring normal counterparts) or
embedding-based (pretrained representations), fundamentally conduct image- or feature-level matching to generate anomaly maps. Nonetheless, matching noise has been largely overlooked, limiting their detection ability. Beyond earlier focus on unimodal RGB-based UAD, recent advances expand to
multimodal scenarios, e.g., RGB-3D and RGB-Text, enabled by point cloud sensing and
vision-
language models. Despite shared challenges, these lines remain largely isolated, hindering a comprehensive understanding and knowledge transfer. In this paper, we advocate unified UAD for both unimodal and
multimodal settings in the matching perspective. Under this insight, we present Unified Cost Filtering (UCF), a generic post-hoc refinement framework for refining anomaly cost volume of any UAD model. The cost volume is constructed by matching a test sample against normal samples from the same or different modalities, followed by a learnable filtering module with multi-layer
attention guidance from the test sample, mitigating matching noise and highlighting subtle anomalies. Comprehensive experiments on 22 diverse
benchmarks demonstrate the efficacy of UCF in enhancing a variety of UAD methods, consistently achieving new state-of-the-art results in both unimodal (RGB) and
multimodal (RGB-3D, RGB-Text) UAD scenarios. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.
Read the full abstract ▾
All authors Zhe Zhang, Mingxiu Cai, Gaochang Wu, Jing Zhang, Lingqiao Liu, Dacheng Tao, Tianyou Chai, Xiatian Zhu
Image and Video Processing
Wave-GMS: Lightweight Multi-Scale Generative Model for Medical Image Segmentation
Talha Ahmed, Nehal Ahmed Shaikh and Hassan Mohy-ud-Din
For equitable deployment of AI tools in hospitals and healthcare facilities, we need Deep
Segmentation Networks that offer high performance and can be trained on cost-effective GPUs with limited
memory and large batch sizes. In this work, we propose Wave-GMS, a lightweight and efficient multi-scale generative model for medical image
segmentation. Wave-GMS has a substantially smaller number of trainable parameters, does not require loading
memory-intensive pretrained
vision foundation models, and supports training with large batch sizes on GPUs with limited
memory. We conducted extensive experiments on four publicly available
datasets (BUS, BUSI, Kvasir-Instrument, and HAM10000), demonstrating that Wave-GMS achieves state-of-the-art
segmentation performance with superior cross-domain generalizability, while requiring only ~2.6M trainable parameters. Code is available at https://github.com/ATPLab-LUMS/Wave-GMS.
Read the full abstract ▾
Audio and Speech Processing
Evaluation of preprocessing pipelines in the creation of in-the-wild TTS datasets
Matías Di Bernardo, Emmanuel Misley, Ignacio Correa et al. (+3 more)
This work introduces a reproducible, metric-driven methodology to evaluate preprocessing pipelines for in-the-wild TTS corpora generation. We apply a custom low-cost pipeline to the first in-the-wild Argentine Spanish collection and compare 24 pipeline configurations combining different denoising and quality filtering variants. Evaluation relies on complementary objective measures (PESQ, SI-SDR, SNR), acoustic descriptors (T30, C50), and
speech-preservation metrics (F0-STD, MCD). Results expose trade-offs between
dataset size, signal quality, and voice preservation; where denoising variants with permissive filtering provide the best overall compromise for our testbed. The proposed methodology allows selecting pipeline configurations without training TTS models for each subset, accelerating and reducing the cost of preprocessing development for low-resource settings.
Read the full abstract ▾
All authors Matías Di Bernardo, Emmanuel Misley, Ignacio Correa, Mateo García Iacovelli, Simón Mellino, Gala Lucía Gonzalez Barrios
Systems and Control
A Dimension-Decomposed Learning Framework for Online Disturbance Identification in Quadrotor SE(3) Control
Tianhua Gao
Quadrotor stability under complex dynamic disturbances and model uncertainties poses significant challenges. One of them remains the underfitting problem in high-dimensional features, which limits the identification capability of current learning-based methods. To address this, we introduce a new perspective: Dimension-Decomposed Learning (DiD-L), from which we develop the Sliced Adaptive-Neuro Mapping (SANM) approach for
geometric control. Specifically, the high-dimensional mapping for identification is axially “sliced" into multiple low-dimensional submappings (“slices"). In this way, the complex high-dimensional problem is decomposed into a set of simple low-dimensional tasks addressed by shallow neural networks and adaptive laws. These neural networks and adaptive laws are updated online via Lyapunov-based adaptation without any pre-training or persistent excitation (PE) condition. To enhance the interpretability of the proposed approach, we prove that the full-state closed-loop system exhibits arbitrarily close to exponential stability despite multi-dimensional time-varying disturbances and model uncertainties. This result is novel as it demonstrates exponential convergence without requiring pre-training for unknown disturbances and specific knowledge of the model.
Read the full abstract ▾
Systems and Control
Sparse Continuation-Based Eigenvalue Tracking for Power System DDAEs
Andreas Bouterakos and Georgios Tzounas
In this paper, we formulate a continuation method for tracking
eigenvalue trajectories in power system models with time-delayed measurement and control signals. Such delays are known to weaken damping and reduce stability margins if not properly accounted for in stability analysis and control design. The proposed formulation follows selected eigenpairs directly from sparse delay differential-algebraic equation (DDAE) models with one or multiple delayed variables, avoiding repeated eigensolutions as system conditions or parameters vary. The continuation parameter can represent system and control parameters, constant delay magnitudes, or parameters governing nonconstant wide-area measurement system (WAMS) delays. The proposed method is validated on a modified IEEE 39-bus system and on a real-world-scale dynamic model of the Irish transmission network, demonstrating accurate tracking of critical
eigenvalue trajectories and clear computational advantages over repeated eigensolution-based analysis.
Read the full abstract ▾
Signal Processing
A Study of Neural Polar Decoders for Communication
Rom Hirsch, Ziv Aharoni, Henry D. Pfister et al. (+1 more)
In this paper, we adapt and analyze Neural Polar Decoders (NPDs) for end-to-end communication systems. While prior work demonstrated the effectiveness of NPDs on synthetic channels, this study extends the NPD to real-world communication systems. The NPD was adapted to complete OFDM and single-carrier communication systems. To satisfy practical system requirements, the NPD is extended to support any code length via rate matching, higher-order modulations, and robustness across diverse channel conditions. The NPD operates directly on channels with
memory, exploiting their structure to achieve higher data rates without requiring pilots and a cyclic prefix. Although NPD entails higher computational complexity than the standard 5G polar decoder, its neural network architecture enables an efficient representation of channel statistics, resulting in manageable complexity suitable for practical systems. Experimental results over 5G channels demonstrate that the NPD consistently outperforms the 5G polar decoder in terms of BER, BLER, and throughput. These improvements are particularly significant for low-rate and short-block configurations, which are prevalent in 5G control channels. Furthermore, NPDs applied to single-carrier systems offer performance comparable to OFDM with lower PAPR, enabling effective single-carrier transmission over 5G channels. These results position the NPD as a high-performance, pilotless, and robust decoding solution.
Read the full abstract ▾
All authors Rom Hirsch, Ziv Aharoni, Henry D. Pfister, Haim H. Permuter
Signal Processing
Compressed Multiband Sensing in FR3 Using Alternating Direction Method of Multipliers
Dexin Wang, Isha Jariwala, Ahmad Bazzi et al. (+3 more)
Joint detection and localization of users and scatterers in multipath-rich channels on multiple bands is critical for integrated sensing and communication (
ISAC) in 6G. Existing multiband sensing methods are limited by classical beamforming or computationally expensive approaches. This paper introduces alternating direction method of multipliers (ADMM)-assisted compressed multiband sensing (CMS), hereafter referred to as ADMM-CMS, which is a novel framework for multiband sensing using uplink quadrature amplitude modulation-modulated pilot symbols. To solve the CMS problem, we develop an adaptive ADMM
algorithm that adjusts to noise and ensures automatic stopping if converged. ADMM combines the decomposability of dual ascent with the robustness of augmented Lagrangian methods, making it suitable for large-scale structured optimization. Simulations show that ADMM-CMS achieves higher spatial resolution and improved denoising compared to Bartlett-type beamforming, yielding a 34 dB gain in per-
antenna transmit power for achieving a 0.9 successful recovery probability (SRP). Moreover, compared to performing compressed sensing separately on the constituent 7 GHz and 10 GHz sub-bands, ADMM-CMS achieves reductions in delay root mean squared error of 34.46% and 40.76%, respectively, at -41 dBm per-
antenna transmit power, while also yielding improved SRP. Our findings demonstrate ADMM-CMS as an efficient enabler of
ISAC in frequency range 3 (FR3, 7-24 GHz) for 6G systems.
Read the full abstract ▾
All authors Dexin Wang, Isha Jariwala, Ahmad Bazzi, Sundeep Rangan, Theodore S. Rappaport, Marwa Chafii
Systems and Control
Economic zone data-enabled predictive control for connected open water systems
Xiaoqiao Chen, Xuewen Zhang, Minghao Han et al. (+2 more)
The real-time operation of open water systems is essential for ensuring operational safety, satisfying operational requirements, and optimizing energy usage. However, existing rule-based control strategies rely heavily on human experience, while model-based approaches depend on accurate hydrodynamic models, which limit their applicability to water systems with complex dynamics and uncertain disturbances. In this work, we develop a fully data-driven, zone-based control framework with adaptive control target zone selection for safe and energy-efficient operation of connected open water systems. Specifically, we propose a mixed-integer economic zone data-enabled predictive control (DeePC) approach that aims to maintain the water levels of the branches within the desired water-level zone while reducing real-time operational energy consumption. The DeePC-based approach enables direct use of input-output data for predictive control, eliminating the need for explicit dynamic modeling. To handle multiple control objectives with different priorities, we employ lexicographic optimization and reformulate the traditional DeePC cost function to incorporate zone tracking and energy consumption minimization objectives. Additionally,
Bayesian optimization is utilized to determine the control target zone, which enables an effective trade-off between zone tracking and energy consumption in the presence of external disturbances. Comprehensive simulations and comparative analyses demonstrate the effectiveness of the proposed method. The proposed method maintains water levels within the desired water-level zone for 97.04% of the operating time, with an average energy consumption of 33.5 kWh per 0.5 hour. Compared to rule-based control method, the proposed method lowers zone-violation frequency by 74.96% and the average energy consumption by 22.44%.
Read the full abstract ▾
All authors Xiaoqiao Chen, Xuewen Zhang, Minghao Han, Adrian Wing-Keung Law, Xunyuan Yin
Audio and Speech Processing
CVSM: Contrastive Vocal Similarity Modeling
Christos Garoufis, Athanasia Zlatintsi and Petros Maragos
The availability of large, unlabeled
datasets across various domains has contributed to the development of a plethora of methods that learn representations for multiple target (downstream) tasks through self-supervised pre-training. In this work, we introduce CVSM (Contrastive Vocal Similarity Modeling), a contrastive self-supervised procedure for music signal representation learning in the audio domain that can be utilized for musical and vocal similarity modeling. Our method operates under a contrastive framework, maximizing the similarity between vocal excerpts and musical mixtures containing the same vocals; we devise both a label-informed protocol, leveraging artist identity information to sample the contrastive pairs, and a label-agnostic scheme, involving artificial mixture creation from randomly sampled vocal and accompaniment excerpts, which are paired with vocals from the same audio segment. We evaluate our proposed method in measuring vocal similarity both objectively, through linear probing on a suite of appropriate downstream tasks, and subjectively, via conducting a user study consisting of pairwise comparisons between different models in a recommendation-by-query setting. Our results indicate that the representations learned through CVSM are effective in musical and vocal similarity modeling, outperforming numerous baselines across both isolated vocals and complete musical mixtures. Moreover, while the availability of artist identity labels during pre-training leads to overall more consistent performance both in the evaluated downstream tasks and the user study, a label-agnostic CVSM variant incorporating hybrid pre-training with real and artificial mixtures achieves comparable performance to the label-informed one in artist identification and perceived vocal similarity.
Read the full abstract ▾
Signal Processing
Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements
Yang Zhou, Haochang Wu, Yunxi Mu et al. (+3 more)
High-speed railway tunnel communication systems require reliable
radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to effectively process sparse measurement data collected during actual railway operations. This letter proposes an inception-enhanced generative
adversarial network (Inc-GAN) that can reconstruct complete electric field distributions across tunnel cross-sections using sparse value lines measured during actual train operations as input. This directly addresses practical railway measurement constraints. Through an inception-based generator architecture and progressive training strategy, the method achieves robust reconstruction from single measurement signal lines to complete field distributions. Numerical simulation validation demonstrates that Inc-GAN can accurately predict electric fields based on measured data collected during actual train operations, with significantly improved computational efficiency compared to traditional methods, providing a novel solution for railway communication system optimization based on real operational data.
Read the full abstract ▾
All authors Yang Zhou, Haochang Wu, Yunxi Mu, Hao Qin, Xinyue Zhang, Xingqi Zhang
Systems and Control
Real-Time Peer-to-Peer Energy Trading for Multi-Microgrids: Improved Double Auction Mechanism and Prediction-Free Online Trading Approach
Kaidi Huang, Lin Cheng, Yue Zhou et al. (+4 more)
Peer-to-peer energy trading offers a promising solution for enhancing renewable energy utilization and economic benefits within interconnected microgrids. However, existing real-time P2P
markets face two key challenges: high computational complexity in trading mechanisms, and suboptimal participant decision-making under diverse uncertainties. Existing prediction-based decision-making methods rely heavily on accurate forecasts, which are typically unavailable for microgrids, while prediction-free methods suffer from myopic behaviors. To address these challenges, this paper proposes an improved double auction mechanism combined with an adaptive step-size search
algorithm to reduce computational burden, and a data-driven dual-reference online optimization (DDOO) framework to enhance participant decision-making. The improved mechanism simplifies bidding procedures, significantly reducing computational burden and ensuring rapid convergence to the
market equilibrium. Additionally, the prediction-free DDOO framework mitigates myopic decision-making by introducing two informative reference signals. Case studies on a 20-microgrid system demonstrate the effectiveness and scalability of the proposed mechanism and approach. The improved mechanism significantly decreases the computational time while increasing local energy self-sufficiency periods from 0.01% to 29.86%, reducing reverse power flow periods from 24.51% to 3.96%, and lowering average operating costs by 19.20%. Compared with conventional approaches such as Lyapunov optimization and model predictive control, the DDOO framework achieves a 10%-13% reduction in operating costs with an optimality gap of only 5.76%.
Read the full abstract ▾
All authors Kaidi Huang, Lin Cheng, Yue Zhou, Fashun Shi, Yufei Xi, Yingrui Zhuang, Ning Qi
Signal Processing
Towards Electrophysiological and Histological Mapping of Upper Limb Nerves in Pigs Using Epineural Stimulation
Jonathan Baum, Chamot-Nonin Manon, Oppelt Vera et al. (+3 more)
Understanding the relationship between nerve anatomy and the functional outcomes of electrical stimulation is critical for optimizing neural interface design. In this study, we conducted acute experiments on four pigs in which epineural cuff electrodes with multiple contacts were placed around upper limb nerves. A subset of electrical stimulation configurations – previously identified via computational study – was applied, and the resulting evoked electromyographic (EMG) responses were recorded from target muscles. Muscle recruitment curves were extracted and analysed offline to quantify activation patterns. Following the electrophysiological experiments, the stimulated nerves were harvested and processed for histological analysis to visualize fascicular organization and distribution. This work presents preliminary results from the combined analysis of muscle activation profiles and fascicle anatomy in one animal. Our findings aim to inform the design of stimulation strategies by linking electrode configuration to selective muscle recruitment, ultimately contributing to more effective neuromodulation and neuroprosthetic applications.
Read the full abstract ▾
All authors Jonathan Baum, Chamot-Nonin Manon, Oppelt Vera, David Guiraud, Christine Azevedo Coste, Thomas Guiho
Systems and Control · Cross-listed from cs.CY
Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
Reginald Juan M. Mercado, Muhammad Kabeer, Haider Al-Obaidy et al. (+1 more)
Proactive preservation of steel structures at culturally significant heritage sites like the San Sebastian Basilica in the Philippines requires accurate corrosion
forecasting. This study developed an Internet of Things hardware system connected with LoRa wireless communications to monitor heritage buildings with steel structures. From a three year
dataset generated by the IoT system, we built a machine learning framework for predicting atmospheric corrosion rates using only temperature and relative humidity data. Deployed via a Streamlit dashboard with ngrok tunneling for public access, the framework provides real-time corrosion monitoring and actionable preservation recommendations. This minimal-data approach is scalable and cost effective for heritage sites with limited monitoring resources, showing that advanced regression can extract accurate corrosion predictions from basic meteorological data enabling proactive preservation of culturally significant structures worldwide without requiring extensive sensor networks
Read the full abstract ▾
All authors Reginald Juan M. Mercado, Muhammad Kabeer, Haider Al-Obaidy, Rosdiadee Nordin
Systems and Control · Cross-listed from cs.RO
Single-Rod Brachiation Robot: Mechatronic Control Design and Validation of Prejump Phases
Juraj Lieskovský, Hijiri Akahane, Aoto Osawa et al. (+3 more)
A complete mechatronic design of a minimal configuration brachiation
robot is presented. The
robot consists of a single rigid rod with gripper mechanisms attached to both ends. The grippers are used to hang the
robot on a horizontal bar on which it swings or rotates. The motion is imposed by repositioning the
robot's center of mass, which is performed using a crank-slide mechanism. Based on a non-linear model, an optimal control strategy is proposed, for repositioning the center of mass in a bang-bang manner. Consequently, utilizing the concept of input-output linearization, a continuous control strategy is proposed that takes into account the limited torque of the crank-slide mechanism and its geometry. An increased
attention is paid to energy accumulation towards the subsequent jump stage of the brachiation. These two strategies are validated and compared in simulations. The continuous control strategy is then also implemented within a low-cost STM32-based control system, and both the swing and rotation stages of the brachiation motion are experimentally validated.
Read the full abstract ▾
All authors Juraj Lieskovský, Hijiri Akahane, Aoto Osawa, Jaroslav Bušek, Ikuo Mizuuchi, Tomáš Vyhlídal
Signal Processing
Integrated Sensing, Communication, and Positioning in Cellular Vehicular Networks
Xin Tong, Zhaoyang Zhang, Yuzhi Yang et al. (+4 more)
In this correspondence, a novel integrated sensing and communication (
ISAC) framework is proposed to accomplish data communication, vehicle positioning, and environment sensing simultaneously in a cellular vehicular network. By incorporating the vehicle positioning problem with the existing computational-imaging-based
ISAC models, we formulate a special integrated sensing, communication, and positioning problem in which the unknowns are highly coupled. To mitigate the rank deficiency and make it solvable, we discretize the region of interest (ROI) into sensing and positioning pixels respectively, and exploit both the line-of-sight and non-line-of-sight propagation of the vehicles' uplink access signals. The resultant problem is shown to be a
polynomial bilinear compressed sensing (CS) reconstruction problem, which is then solved by the alternating optimization (AO)
algorithm to iteratively achieve symbol detection, vehicle positioning and environment sensing. Performance analysis and numerical results demonstrate the effectiveness of the proposed method.
Read the full abstract ▾
All authors Xin Tong, Zhaoyang Zhang, Yuzhi Yang, Yu Ge, Zhaohui Yang, Henk Wymeersch, Mérouane Debbah
Systems and Control
Incomplete Air Mixing Reduces the Efficiency of Commercial Buildings Behaving as Virtual Batteries
Austin J. Lin, Jacques A. de Chalendar and Johanna L. Mathieu
Commercial building Heating, Ventilation, and Air Conditioning (HVAC) systems can provide flexibility to the electricity grid. Some researchers have found it convenient to model HVAC systems as virtual batteries. These models also better align with models used by grid planners and operators. However, experiments have shown that HVAC load shifting can be inefficient, and virtual battery models do not capture this inefficiency well. While the models typically use the average room temperature as the system's “state of charge," they do not capture other factors that affect HVAC power/energy such as airflow and mixing. Here, we develop a new analytical building model to explore how incomplete mixing of supply air into a conditioned space leads to inefficiency in a virtual battery capturing the dynamics of HVAC fan power load shifting. The model qualitatively matches experimental results better than previous models, and shows that, as mixing becomes worse, the virtual battery becomes less efficient. Unfortunately, air mixing is unmeasured/unmeasurable. However, we show that, by closing the loop around measurements of fan power, we can improve the virtual battery's performance without the need for air mixing measurements. For example, in one case, we show a roundtrip efficiency improvement from 0.75 to 0.99.
Read the full abstract ▾
Audio and Speech Processing · Cross-listed from cs.SD
WavInWav: Time-domain Speech Hiding via Invertible Neural Network
Wei Fan, Kejiang Chen, Xiangkun Wang et al. (+2 more)
Data hiding is essential for secure communication across digital media, and recent advances in Deep Neural Networks (DNNs) provide enhanced methods for
embedding secret information effectively. However, previous audio hiding methods often result in unsatisfactory quality when recovering secret audio, due to their inherent limitations in the modeling of time-frequency relationships. In this paper, we explore these limitations and introduce a new DNN-based approach. We use a flow-based invertible neural network to establish a direct link between stego audio, cover audio, and secret audio, enhancing the reversibility of
embedding and extracting messages. To address common issues from time-frequency transformations that degrade secret audio quality during recovery, we implement a time-frequency loss on the time-domain signal. This approach not only retains the benefits of time-frequency constraints but also enhances the reversibility of message recovery, which is vital for practical applications. We also add an encryption technique to protect the hidden data from unauthorized access. Experimental results on the VCTK and LibriSpeech
datasets demonstrate that our method outperforms previous approaches in terms of subjective and objective metrics and exhibits robustness to various types of noise, suggesting its utility in targeted secure communication scenarios.
Read the full abstract ▾
All authors Wei Fan, Kejiang Chen, Xiangkun Wang, Weiming Zhang, Nenghai Yu
Systems and Control
Global Convergence of Policy Gradient for Entropy Regularized Linear-Quadratic Control with Multiplicative Noise
Gabriel Diaz, Lucky Li and Wenhao Zhang
Reinforcement Learning (RL) has emerged as a powerful framework for sequential decision-making in dynamic environments, particularly when system parameters are unknown. This paper investigates RL-based control for
entropy-regularized linear-quadratic (LQ) control problems with multiplicative noise over an infinite time horizon. First, we adapt the regularized policy gradient (RPG)
algorithm to
stochastic optimal control settings, proving that despite the non-convexity of the problem, RPG converges globally under conditions of gradient domination and almost-smoothness. Second, based on zero-order optimization approach, we introduce a novel model free RL
algorithm: Sample-based regularized policy gradient (SB-RPG). SB-RPG operates without knowledge of system parameters yet still retains strong theoretical guarantees of global convergence. Our model leverages
entropy regularization to address the exploration versus exploitation trade-off inherent in RL. Numerical simulations validate the theoretical results and demonstrate the efficiency of SB-RPG in unknown-parameters environments.
Read the full abstract ▾
Systems and Control
Delay-Tolerant Augmented-Consensus-based Distributed Directed Optimization
Mohammadreza Doostmohammadian, Narahari Kasagatta Ramesh and Alireza Aghasi
Distributed optimization finds applications in large-scale machine learning, data processing and classification over multi-
agent networks. In real-world scenarios, the communication network of
agents may encounter latency that may affect the convergence of the optimization protocol. This paper addresses the case where the information exchange among the
agents (computing nodes) over data-transmission channels (links) might be subject to communication time-delays, which is not well addressed in the existing literature. Our proposed
algorithm improves the state-of-the-art by handling heterogeneous and arbitrary but bounded and fixed (time-invariant) delays over general strongly-connected directed networks. Arguments from
matrix theory, algebraic graph theory, and augmented consensus formulation are applied to prove the convergence to the optimal value. Simulations are provided to verify the results and compare the performance with some existing delay-free
algorithms.
Read the full abstract ▾
Systems and Control
Life Estimation of HVDC Cable Insulation under Load Cycles: from Macroscopic to Microscopic Charge Conduction Modelling
Bassel Diban and Giovanni Mazzanti
This paper goes one step forward in the life estimation of HVDC cable insulation under load cycles by introducing for the first time a microscopic model of charge conduction and transport i.e., Bipolar Charge Transport BCT model for electric field calculation inside the insulation thickness. The paper firstly includes the development and the validation of BCT model with that found in literature. Then, the parameters of the developed BCT model are optimized using Pulsed Electro-Acoustic PEA space charge measurements. Followed by the integration of the developed, validated and optimized model into the electric field calculation for life estimation of a 500 kV DC-XLPE insulated cable subjected to Type Test load cycles according to Cigre Techical Brochure 852. The developed microscopic model is compared to the macroscopic models already found in the literature. The microscopic model shows a comparable electric field inversion similarly to macroscopic models. However, the behavior of the microscopic model is noticed to be different under heating and cooling load cycles. In hot cable, the maximum electric field stabilizes at different amplitude and position inside the insulation thickness in both models. This investigation has been carried out in the framework of the HEU-NEWGEN research project.
Read the full abstract ▾
Audio and Speech Processing
Enhancing Photogrammetry Reconstruction For HRTF Synthesis Via A Graph Neural Network
Ludovic Pirard, Katarina C. Poole and Lorenzo Picinali
Traditional Head-Related Transfer Functions (HRTFs) acquisition methods rely on specialised equipment and acoustic expertise, posing accessibility challenges. Alternatively, high-resolution 3D modelling offers a pathway to numerically synthesise HRTFs using Boundary Elements Methods and others. However, the high cost and limited availability of advanced 3D scanners restrict their applicability. Photogrammetry has been proposed as a solution for generating 3D head meshes, though its resolution limitations restrict its application for HRTF synthesis. To address these limitations, this study investigates the feasibility of using Graph Neural Networks (GNN) using neural subdivision techniques for upsampling low-resolution Photogrammetry-Reconstructed (PR) meshes into high-resolution meshes, which can then be employed to synthesise individual HRTFs. Photogrammetry data from the SONICOM
dataset are processed using Apple Photogrammetry API to reconstruct low-resolution head meshes. The
dataset of paired low- and high-resolution meshes is then used to train a GNN to upscale low-resolution inputs to high-resolution outputs, using a Hausdorff Distance-based loss function. The GNN's performance on unseen photogrammetry data is validated geometrically and through synthesised HRTFs generated via Mesh2HRTF. Synthesised HRTFs are evaluated against those computed from high-resolution 3D scans, to acoustically measured HRTFs, and to the KEMAR HRTF using perceptually-relevant numerical analyses as well as behavioural experiments, including localisation and Spatial Release from Masking (SRM) tasks.
Read the full abstract ▾
Systems and Control · Cross-listed from cs.RO
Assist-as-needed Control for FES in Foot Drop Management
Andreas Christou, Elliot Lister, Georgia Andreopoulou et al. (+2 more)
Foot drop is commonly managed using Functional Electrical Stimulation (FES), typically delivered via open-loop controllers with fixed stimulation intensities. While users may manually adjust the intensity through external controls, this approach risks overstimulation, leading to muscle fatigue and discomfort, or understimulation, which compromises dorsiflexion and increases fall risk. In this study, we propose a novel closed-loop FES controller that dynamically adjusts the stimulation intensity based on real-time toe clearance, providing "assistance as needed". We evaluate this system by inducing foot drop in healthy participants and comparing the effects of the closed-loop controller with a traditional open-loop controller across various walking conditions, including different speeds and surface inclinations. Kinematic data reveal that our closed-loop controller maintains adequate toe clearance without significantly affecting the joint angles of the hips, the knees, and the ankles, and while using significantly lower stimulation intensities compared to the open-loop controller. These findings suggest that the proposed method not only matches the effectiveness of existing systems but also offers the potential for reduced muscle fatigue and improved long-term user comfort and adherence.
Read the full abstract ▾
All authors Andreas Christou, Elliot Lister, Georgia Andreopoulou, Don Mahad, Sethu Vijayakumar
Signal Processing · Cross-listed from cs.NI
FSMA: Gateway-Controlled Access for Scalable LoRa Non-Terrestrial Networks
Rohith Reddy Vennam, Maiyun Zhang, Raghav Subbaraman et al. (+2 more)
LoRa-based non-terrestrial networks are rapidly becoming infrastructure for global IoT, with LEO satellites delivering direct-to-device connectivity and drone-mounted gateways serving precision agriculture, emergency response, and environmental monitoring. Both settings share a fundamental MAC-layer bottleneck: large coverage footprints cause thousands of devices to collide on the same channel, while gateway mobility induces rapid link variations, making link-unaware transmissions a persistent source of wasted airtime. These challenges demand random-access solutions, yet ALOHA provides no collision coordination, CSMA sensing fails beyond ~15 km, and BSMA's continuous busy tone consumes 80-90% of the channel capacity. We propose Free Signal Multiple Access (FSMA), a synchronization-free, gateway-controlled MAC protocol. Like a traffic signal, FSMA uses a single LoRa upchirp (FreeChirp) to indicate channel availability. By transmitting it at a lower spreading factor than the node uplink, FSMA implicitly filters weak-link nodes through channel reciprocity, reducing the collision window by 6-200x without synchronization, handshakes, or hardware modifications. Hardware experiments with 25 COTS LoRa nodes and a drone-mounted gateway demonstrate 2x higher throughput, 2.5-5x better packet reception ratio, and up to 5x improved energy efficiency. Large-scale simulations with real TLE-based satellite trajectories confirm scalability to 5000+ devices per satellite pass.
Read the full abstract ▾
All authors Rohith Reddy Vennam, Maiyun Zhang, Raghav Subbaraman, Deepak Vasisht, Dinesh Bharadia
Audio and Speech Processing
SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision
Chunbo Hao, Ruibin Yuan, Jixun Yao et al. (+5 more)
Music structure analysis (MSA) underpins music understanding and controllable generation, yet progress has been limited by small, inconsistent corpora. We present SongFormer, a scalable framework that learns from heterogeneous supervision. SongFormer (i) fuses short- and long-window self-supervised learning representations to capture both fine-grained and long-range dependencies, and (ii) introduces a learned source
embedding to enable training with partial, noisy, and schema-mismatched labels. To support scaling and fair evaluation, we release SongFormDB, the largest MSA corpus to date (over 14k songs spanning
languages and genres), and SongFormBench, a 300-song expert-verified
benchmark. On SongFormBench, SongFormer sets a new state of the art in strict boundary detection (HR.5F) and achieves the highest functional label accuracy, while remaining computationally efficient; it surpasses strong baselines and Gemini 2.5 Pro on these metrics and remains competitive under relaxed tolerance (HR3F). Code,
datasets, and model are open-sourced at https://github.com/ASLP-lab/SongFormer.
Read the full abstract ▾
All authors Chunbo Hao, Ruibin Yuan, Jixun Yao, Qixin Deng, Xinyi Bai, Yanbo Wang, Wei Xue, Lei Xie
Signal Processing
Pioneering Scalable Prototyping for Mid-Band XL-MIMO Systems: Design and Implementation
Jiachen Tian, Yu Han, Zhengtao Jin et al. (+6 more)
The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-
MIMO), is emerging as a key enabler for future communication systems. Thanks to the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-
MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-
MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-
MIMO prototype system are presented. Benefiting from the novel architecture, the proposed prototype system supports metrics aligned with standardization, including a bandwidth of 200 MHz, up to 1024
antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing (TDD) mode, the prototype enables multiuser communication with support for up to 12 users, while retaining standard communication procedures. Built on software-defined
radio (SDR) platforms, the system is programmable and allows for flexible deployment of advanced
algorithms. Moreover, the modular architecture ensures high scalability, making the system adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results with the proposed prototype system demonstrate real-time digital sample processing at 1167.85 Gbps, a peak data throughput of 15.81 Gbps for 12 users, and a maximal spectral efficiency approaching 80 bit/s/Hz.
Read the full abstract ▾
All authors Jiachen Tian, Yu Han, Zhengtao Jin, Xi Yang, Jie Yang, Wankai Tang, Xiao Li, Wenjin Wang, Shi Jin
Signal Processing
Neyman Pearson Detector for Multiple Ambient Backscatter Zero-Energy-Devices Beacons using Near-Perfect Code
Shanglin Yang, Jean-Marie Gorce, Muhammad Jehangir Khan et al. (+2 more)
Recently, a novel ultra-low-power indoor localization system based on Zero-Energy Devices (ZEDs) has shown promising results in ambient backscatter communication. In this paper, we study detection of multiple coexisting ZEDs in ambient backscatter systems under interference and synchronization uncertainty. Building on a Neyman-Pearson (NP) formulation previously applied to single-tag detection, we introduce a detector tailored to multi-tag scenarios. The core idea is to use a Near-Perfect Code (NPC) as the synchronization sequence, which substantially improves the peak-to-sidelobe (PSL) ratio and thus separability among concurrent tags. The proposed scheme replaces dual band-pass filtering with dual correlators, enabling an explicit
Bayesian detector and tight control of the false-alarm rate; we further incorporate a contrast metric and multi-frequency combining to reveal secondary tags. Experiments on the CorteXlab testbed (part of the SLICES-EU infrastructure) confirm robustness at low SNR, with observed PSL improvements from about 11 dB to about 22 dB. These results advance scalable, reliable ambient backscatter localization in practical multi-tag environments.
Read the full abstract ▾
All authors Shanglin Yang, Jean-Marie Gorce, Muhammad Jehangir Khan, Dinh-Thuy Phan-Huy, Guillaume Villemaud