Signal Processing
Maritime Communication in Evaporation Duct Environment with Ship Trajectory Optimization
Ruifeng Gao, Hao Zhang, Jue Wang et al. (+5 more)
In maritime wireless networks, the evaporation duct effect has been known as a preferable condition for long-range transmissions. However, how to effectively utilize the duct effect for efficient communication design is still open for investigation. In this paper, we consider a typical scenario of ship-to-shore data transmission, where a ship collects data from multiple oceanographic buoys, sails from one to another, and transmits the collected data back to a terrestrial base station during its voyage. A novel framework, which exploits priori information of the channel gain map in the presence of evaporation duct, is proposed to minimize the data transmission time and the sailing time by optimizing the ship's trajectory. To this end, a multi-objective optimization problem is formulated and is further solved by a dynamic population PSO-integrated NSGA-II
algorithm. Through simulations, it is demonstrated that, compared to the
benchmark scheme which ignores useful information of the evaporation duct, the proposed scheme can effectively reduce both the data transmission time and the sailing time.
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All authors Ruifeng Gao, Hao Zhang, Jue Wang, Ye Li, Yingdong Hu, Qiuming Zhu, Shu Sun, Meixia Tao
Signal Processing
Optimal Real-time Communication in 6G Ultra-Massive V2X Mobile Networks
He Huang, Zilong Liu, Zeping Sui et al. (+4 more)
This paper introduces a novel cooperative vehicular communication
algorithm tailored for future 6G ultra-massive vehicle-to-everything (V2X) networks leveraging integrated space-air-ground communication systems. Specifically, we address the challenge of real-time information exchange among rapidly moving vehicles. We demonstrate the existence of an upper bound on channel capacity given a fixed number of relays, and propose a low-complexity relay selection heuristic
algorithm. Simulation results verify that our proposed
algorithm achieves superior channel capacities compared to existing cooperative vehicular communication approaches.
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All authors He Huang, Zilong Liu, Zeping Sui, Wei Huang, Md. Noor-A-Rahim, Haishi Wang, Zhiheng Hu
Signal Processing
Sensing Management for Pilot-Free Predictive Beamforming in Cell-Free Massive MIMO Systems
Eren Berk Kama, Murat Babek Salman, Isaac Skog et al. (+1 more)
This paper introduces a sensing management method for integrated sensing and communications (
ISAC) in
cell-free massive multiple-input multiple-output (
MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data. To address this challenge, we propose a state-based approach that leverages sensing capabilities to track the user when there is no communication request. Upon receiving a communication request, predictive beamforming is employed based on the tracked user position, thereby reducing the need for channel estimation. Our framework incorporates an extended Kalman filter (EKF) based tracking
algorithm with adaptive sensing management to perform sensing operations only when necessary to maintain high tracking accuracy. The simulation results demonstrate that our proposed sensing management approach provides uniform downlink communication rates that are higher than with existing methods by achieving overhead-free predictive beamforming.
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All authors Eren Berk Kama, Murat Babek Salman, Isaac Skog, Emil Björnson
Audio and Speech Processing
Position: Towards Responsible Evaluation for Text-to-Speech
Yifan Yang, Hui Wang, Bing Han et al. (+4 more)
Recent advances in text-to-
speech (TTS) technology have enabled systems to generate
speech that is often indistinguishable from human
speech, bringing benefits to accessibility, content creation, and human-computer interaction. However, current evaluation practices are increasingly inadequate for capturing the full range of capabilities, limitations, and societal impacts of modern TTS systems. This position paper introduces the concept of Responsible Evaluation and argues that it is essential and urgent for the next phase of TTS development, structured through three progressive levels: (1) ensuring the faithful and accurate reflection of a model's true capabilities and limitations, with more robust, discriminative, and comprehensive objective and subjective scoring methodologies; (2) enabling comparability, standardization, and transferability through standardized
benchmarks, transparent reporting, and transferable evaluation metrics; and (3) assessing governance,
fairness, and security concerns around data provenance, disparities, misuse, spoofing, and traceability. Through this concept, we critically examine current evaluation practices, identify systemic shortcomings, and propose actionable recommendations. We hope this concept will not only foster more reliable TTS technology but also guide its development toward ethically sound and societally beneficial applications.
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All authors Yifan Yang, Hui Wang, Bing Han, Shujie Liu, Jinyu Li, Yong Qin, Xie Chen
Audio and Speech Processing · Cross-listed from cs.CL
SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
Cheng-Han Chiang, Xiaofei Wang, Linjie Li et al. (+7 more)
Current large
language models (LLMs) and spoken
language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from interacting during the user's turn and can lead to high response latency while it waits to think. Consequently, thinking after receiving the full input is not suitable for
speech-to-
speech interaction, where real-time, low-latency exchange is important. We address this by noting that humans naturally "think while listening." In this paper, we propose SHANKS, a general inference framework that enables SLMs to generate unspoken chain-of-thought
reasoning while listening to the user input. SHANKS streams the input
speech in fixed-duration chunks and, as soon as a chunk is received, generates unspoken
reasoning based on all previous
speech and
reasoning, while the user continues speaking. SHANKS uses this unspoken
reasoning to decide whether to interrupt the user and to make tool calls to complete the task. We demonstrate that SHANKS enhances real-time user-SLM interaction in two scenarios: (1) when the user is presenting a step-by-step solution to a math problem, SHANKS can listen, reason, and interrupt when the user makes a mistake, achieving 37.1% higher interruption accuracy than a baseline that interrupts without thinking; and (2) in a tool-augmented dialogue, SHANKS can complete 56.9% of the tool calls before the user finishes their turn. Overall, SHANKS moves toward models that keep thinking throughout the conversation, not only after a turn ends. Animated illustrations of Shanks can be found at https://d223302.github.io/SHANKS/
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All authors Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin, Kevin Lin, Shujie Liu, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang
Signal Processing
Memory-Augmented Generative AI for Real-time Wireless Prediction in Dynamic Industrial Environments
Rahul Gulia, Amlan Ganguly, Michael E. Kuhl et al. (+2 more)
Accurate and real-time prediction of wireless channel conditions, particularly the Signal-to-Interference-plus-Noise Ratio (SINR), is a foundational requirement for enabling Ultra-Reliable Low-Latency Communication (URLLC) in highly dynamic Industry 4.0 environments. Traditional physics-based or statistical models fail to cope with the spatio-temporal complexities introduced by mobile obstacles and transient interference inherent to smart warehouses. To address this, we introduce Evo-WISVA (Evolutionary Wireless Infrastructure for Smart Warehouse using VAE), a novel synergistic deep learning architecture that functions as a lightweight 2D predictive digital twin of the
radio environment. Evo-WISVA integrates a
memory-augmented Variational Autoencoder (VAE) featuring an
Attention-driven Latent
Memory Module (LMM) for robust, context-aware spatial feature extraction, with a Convolutional Long Short-Term
Memory (ConvLSTM) network for precise temporal
forecasting and sequential refinement. The entire pipeline is optimized end-to-end via a joint loss function, ensuring optimal feature
alignment between the generative and predictive components. Rigorous experimental evaluation conducted on a high-fidelity ns-3-generated industrial warehouse
dataset demonstrates that Evo-WISVA significantly surpasses state-of-the-art baselines, achieving up to a 47.6% reduction in average reconstruction error. Crucially, the model exhibits exceptional generalization capacity to unseen environments with vastly increased dynamic complexity (up to ten simultaneously moving obstacles) while maintaining amortized computational efficiency essential for real-time deployment. Evo-WISVA establishes a foundational technology for proactive wireless resource management, enabling autonomous optimization and advancing the realization of predictive digital twins in industrial communication networks.
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All authors Rahul Gulia, Amlan Ganguly, Michael E. Kuhl, Ehsan Rashedi, Clark Hochgraf
Signal Processing
Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach
Abidemi Orimogunje, Kyeong-Ju Cha, Hyunwoo Park et al. (+3 more)
Precise user localization and tracking enhances energy-efficient and ultra-reliable low latency applications in the next generation wireless networks. In addition to computational complexity and data association challenges with Kalman-filter localization techniques, estimation errors tend to grow as the user's trajectory speed increases. By exploiting mmWave signals for joint sensing and communication, our approach dispenses with additional sensors adopted in most techniques while retaining high resolution spatial cues. We present a hybrid mobility-aware adaptive framework that selects the Extended Kalman filter at pedestrian speed and the Unscented Kalman filter at vehicular speed. The scheme mitigates data-association problem and estimation errors through adaptive noise scaling, chi-square gating, Rauch-Tung-Striebel smoothing. Evaluations using Absolute Trajectory Error, Relative Pose Error, Normalized Estimated Error Squared and Root Mean Square Error metrics demonstrate roughly 30-60% improvement in their respective regimes indicating a clear advantage over existing approaches tailored to either indoor or static settings.
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All authors Abidemi Orimogunje, Kyeong-Ju Cha, Hyunwoo Park, Abdulahi A. Badrudeen, Sunwoo Kim, Dejan Vukobratovic
Image and Video Processing · Cross-listed from cs.CV
Online Generic Event Boundary Detection
Hyungrok Jung, Daneul Kim, Seunggyun Lim et al. (+2 more)
Generic Event Boundary Detection (GEBD) aims to interpret long-form
videos through the lens of human perception. However, current GEBD methods require processing complete
video frames to make predictions, unlike humans processing data online and in real-time. To bridge this gap, we introduce a new task, Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries of generic events immediately in streaming
videos. This task faces unique challenges of identifying subtle, taxonomy-free event changes in real-time, without the access to future frames. To tackle these challenges, we propose a novel On-GEBD framework,
Estimator, inspired by Event
Segmentation Theory (EST) which explains how humans segment ongoing activity into events by leveraging the discrepancies between predicted and actual information. Our framework consists of two key components: the Consistent Event Anticipator (CEA), and the Online Boundary Discriminator (OBD). Specifically, the CEA generates a prediction of the future frame reflecting current event dynamics based solely on prior frames. Then, the OBD measures the prediction error and adaptively adjusts the threshold using statistical tests on past errors to capture diverse, subtle event transitions. Experimental results demonstrate that
Estimator outperforms all baselines adapted from recent online
video understanding models and achieves performance comparable to prior offline-GEBD methods on the Kinetics-GEBD and TAPOS
datasets.
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All authors Hyungrok Jung, Daneul Kim, Seunggyun Lim, Jeany Son, Jonghyun Choi
Systems and Control
Decentralized CBF-based Safety Filters for Collision Avoidance of Cooperative Missile Systems with Input Constraints
Johannes Autenrieb and Mark Spiller
This paper presents a decentralized safety filter for collision avoidance in multi-
agent aerospace interception scenarios. The approach leverages robust control barrier functions (RCBFs) to guarantee forward invariance of safe sets under bounded inputs and high-relative-degree dynamics. Each effector executes its nominal cooperative guidance command, while a local quadratic program (QP) modifies the input only when necessary. Event-triggered activation based on range and zero-effort miss (ZEM) criteria ensures scalability by restricting active constraints to relevant neighbors. To ensure feasibility under multiple simultaneously active constraints, a slack-variable relaxation scheme is introduced that prioritizes critical
agents in a Pareto-optimal manner. Simulation results in many-on-many interception scenarios demonstrate that the proposed framework maintains collision-free operation with minimal deviation from nominal guidance, providing a computationally efficient and scalable solution for safety-critical multi-
agent aerospace systems.
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Systems and Control
Resilient Multi-Dimensional Consensus and Distributed Optimization against Agent-Based and Denial-of-Service Attacks
Hongjian Chen, Changyun Wen and Xiaolei Li
In this paper, we consider the resilient multi-dimensional consensus and distributed optimization problems of multi-
agent systems (MASs) in the presence of both
agent-based and denial-of-service (DoS) attacks. The considered
agent-based attacks can cover malicious, Byzantine, and stubborn
agents. The links between
agents in the network can be blocked by DoS attacks, which may lead the digraph to be time-varying and even disconnected. The objective is to ensure that the remaining benign
agents achieve consensus. To this end, an "auxiliary point"-based resilient control
algorithm is proposed for MASs. Under the proposed
algorithm, each healthy
agent constructs a "safe kernel" utilizing the states of its in-neighbors and updates its state toward a specific point within this kernel at each iteration. If an
agent cannot receive its neighbors' states owing to DoS attacks, it will use the states received immediately before the DoS period. Moreover, a resilient multi-dimensional distributed optimization (RMDO)
algorithm is also proposed. Theoretical proofs and numerical examples are presented to demonstrate the effectiveness of the proposed
algorithms.
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Audio and Speech Processing
Moises-Light: Resource-efficient Band-split U-Net For Music Source Separation
Yun-Ning Hung, Igor Pereira and Filip Korzeniowski
In recent years, significant advances have been made in music source separation, with model architectures such as dual-path modeling, band-split modules, or
transformer layers achieving comparably good results. However, these models often contain a significant number of parameters, posing challenges to devices with limited computational resources in terms of training and practical application. While some lightweight models have been introduced, they generally perform worse compared to their larger counterparts. In this paper, we take inspiration from these recent advances to improve a lightweight model. We demonstrate that with careful design, a lightweight model can achieve comparable SDRs to models with up to 13 times more parameters. Our proposed model, Moises-Light, achieves competitive results in separating four musical stems on the MUSDB-HQ
benchmark dataset. The proposed model also demonstrates competitive scalability when using MoisesDB as additional training data.
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Signal Processing
Low Complexity Weight Flexible Decoding Schemes of Linear Block Code for 6G xURLLC
Di Zhang, Yinglei Yang, Zilong Liu et al. (+3 more)
Low complexity error correction code is a key enabler for next generation ultra-reliable low-latency communications (xURLLC) in six generation (6G). Against this background, this paper proposes a decoding scheme for linear block code by leveraging certain interesting properties of dual codewords. It is found that dual codewords with flexible weights can provide useful decoding information for the locations and magnitudes of error bits, which yielding higher reliability performance. In addition, two decoding schemes are proposed, in which one directly utilizes intrinsic information for iterative decoding, and the other combines prior channel information with intrinsic information for decoding. Both schemes are implemented using vector multiplication and real-number comparisons, making them easy to implement in hardware. Simulation results demonstrate the validness of our study.
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All authors Di Zhang, Yinglei Yang, Zilong Liu, Shaobo Jia, Kyungchun Lee, Zhirong Zhang
Signal Processing · Cross-listed from cs.IT
Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO
Fabian Göttsch, Max Franke, Arash Pourdamghani et al. (+2 more)
We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric
cell-free massive
MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the
radio units in the cluster must exchange data with the corresponding DU over the fronthaul. Because the fronthaul links have limited capacity, this data must be quantized before it is sent over the fronthaul. We consider a routed fronthaul network, where the cluster processor placement and fronthaul traffic routing are jointly optimized with a mixed-integer linear program. For different numbers of users in the network, we investigate the effect of fronthaul quantization rates, a system parameter computed based on rate-distortion theory. Our results show that with optimized quantization rates, the fronthaul load is quite stable for a wide range of user loads without significant PHY performance loss. This demonstrates that the
cell-free massive
MIMO PHY and fronthaul network are resilient to varying user densities.
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All authors Fabian Göttsch, Max Franke, Arash Pourdamghani, Giuseppe Caire, Stefan Schmid
Signal Processing
Personalized Federated Learning-Driven Beamforming Optimization for Integrated Sensing and Communication Systems
Zhou Ni, Sravan Reddy Chintareddy, Peiyuan Guan et al. (+1 more)
In this paper, we propose an Expectation-Maximization-based (EM) Personalized
Federated Learning (PFL) framework for multi-objective optimization (MOO) in Integrated Sensing and Communication (
ISAC) systems. In contrast to standard
federated learning (FL) methods that handle all clients uniformly, the proposed approach enables each base station (BS) to adaptively determine its aggregation weight with the EM
algorithm. Specifically, an EM posterior is computed at each BS to quantify the relative suitability between the global and each local model, based on the losses of models on their respective
datasets. The proposed method is especially valuable in scenarios with competing communication and sensing objectives, as it enables BSs to dynamically adapt to application-specific trade-offs. To assess the effectiveness of the proposed approach, we conduct simulation studies under both objective-wise homogeneous and heterogeneous conditions. The results demonstrate that our approach outperforms existing PFL baselines, such as FedPer and pFedMe, achieving faster convergence and better multi-objective performance.
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All authors Zhou Ni, Sravan Reddy Chintareddy, Peiyuan Guan, Morteza Hashemi
Audio and Speech Processing · Cross-listed from cs.SD
XLSR-Kanformer: A KAN-Intergrated model for Synthetic Speech Detection
Phuong Tuan Dat and Tran Huy Dat
Recent advancements in
speech synthesis technologies have led to increasingly sophisticated spoofing attacks, posing significant challenges for automatic speaker verification systems. While systems based on self-supervised learning (SSL) models, particularly the XLSR-Conformer architecture, have demonstrated remarkable performance in synthetic
speech detection, there remains room for architectural improvements. In this paper, we propose a novel approach that replaces the traditional Multi-Layer Perceptron (MLP) in the XLSR-Conformer model with a Kolmogorov-Arnold Network (KAN), a powerful universal approximator based on the Kolmogorov-Arnold representation
theorem. Our experimental results on ASVspoof2021 demonstrate that the integration of KAN to XLSR-Conformer model can improve the performance by 60.55% relatively in Equal Error Rate (EER) LA and DF sets, further achieving 0.70% EER on the 21LA set. Besides, the proposed replacement is also robust to various SSL architectures. These findings suggest that incorporating KAN into SSL-based models is a promising direction for advances in synthetic
speech detection.
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Audio and Speech Processing · Cross-listed from cs.CL
Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
Qinhao Zhou, Xiang Xiang, Kun He et al. (+1 more)
In recent years, the growing interest in Large
Language Models (LLMs) has significantly advanced
prompt engineering, transitioning from manual design to model-based optimization.
Prompts for LLMs generally comprise two components: the instruction, which defines the task or objective, and the input, which is tailored to the instruction type. In natural
language generation (NLG) tasks such as machine translation, the input component is particularly critical, while the instruction component tends to be concise. Existing
prompt engineering methods primarily focus on optimizing the instruction component for general tasks, often requiring large-parameter LLMs as auxiliary tools. However, these approaches exhibit limited applicability for tasks like machine translation, where the input component plays a more pivotal role. To address this limitation, this paper introduces a novel
prompt optimization method specifically designed for machine translation tasks. The proposed approach employs a small-parameter model trained using a back-translation-based strategy, significantly reducing training overhead for single-task optimization while delivering highly effective performance. With certain adaptations, this method can also be extended to other downstream tasks.
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All authors Qinhao Zhou, Xiang Xiang, Kun He, John E. Hopcroft
Audio and Speech Processing · Cross-listed from q-bio.NC
Utilizing Information Theoretic Approach to Study Cochlear Neural Degeneration
Ahsan J. Cheema and Sunil Puria
Hidden hearing loss, or cochlear neural degeneration (CND), disrupts suprathreshold auditory coding without affecting clinical thresholds, making it difficult to diagnose. We present an information-theoretic framework to evaluate
speech stimuli that maximally reveal CND by quantifying mutual information (MI) loss between inner hair
cell (IHC) receptor potentials and auditory nerve fiber (ANF) responses and acoustic input and ANF responses. Using a phenomenological auditory model, we simulated responses to 50 CVC words under clean, time-compressed, reverberant, and combined conditions across different presentation levels, with systematically varied survival of low-, medium-, and high-spontaneous-rate fibers. MI was computed channel-wise between IHC and ANF responses and integrated across characteristic frequencies. Information loss was defined relative to a normal-hearing baseline. Results demonstrate progressive MI loss with increasing CND, most pronounced for time-compressed
speech, while reverberation produced comparatively smaller effects. These findings identify rapid, temporally dense
speech as optimal probes for CND, informing the design of objective clinical diagnostics while revealing problems associated with reverberation as a probe.
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Systems and Control
Delay Independent Safe Control with Neural Networks: Positive Lur'e Certificates for Risk Aware Autonomy
Hamidreza Montazeri Hedesh and Milad Siami
We present a risk-aware safety certification method for autonomous, learning enabled control systems. Focusing on two realistic risks, state/input delays and interval
matrix uncertainty, we model the neural network (NN) controller with local sector bounds and exploit positivity structure to derive linear, delay-independent certificates that guarantee local exponential stability across admissible uncertainties. To
benchmark performance, we adopt and implement a state-of-the-art IQC NN verification pipeline. On representative cases, our positivity-based tests run orders of magnitude faster than SDP-based IQC while certifying regimes the latter cannot-providing scalable safety guarantees that complement risk-aware control.
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Image and Video Processing
Fitzpatrick Thresholding for Skin Image Segmentation
Duncan Stothers, Sophia Xu, Carlie Reeves et al. (+1 more)
Accurate estimation of the body surface area (BSA) involved by a rash, such as psoriasis, is critical for assessing rash severity, selecting an initial treatment regimen, and following clinical treatment response. Attempts at
segmentation of inflammatory skin disease such as psoriasis perform markedly worse on darker skin tones, potentially impeding equitable care. We assembled a psoriasis
dataset sourced from six public atlases, annotated for Fitzpatrick skin type, and added detailed
segmentation masks for every image. Reference models based on U-Net, ResU-Net, and SETR-small are trained without tone information. On the tuning split we sweep decision thresholds and select (i) global optima and (ii) per Fitzpatrick skin tone optima for Dice and binary IoU. Adapting Fitzpatrick specific thresholds lifted
segmentation performance for the darkest subgroup (Fitz VI) by up to +31 % bIoU and +24 % Dice on UNet, with consistent, though smaller, gains in the same direction for ResU-Net (+25 % bIoU, +18 % Dice) and SETR-small (+17 % bIoU, +11 % Dice). Because Fitzpatrick skin tone classifiers trained on Fitzpatrick-17k now exceed 95 % accuracy, the cost of skin tone labeling required for this technique has fallen dramatically. Fitzpatrick thresholding is simple, model-agnostic, requires no architectural changes, no re-training, and is virtually cost free. We demonstrate the inclusion of Fitzpatrick thresholding as a potential future
fairness baseline.
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All authors Duncan Stothers, Sophia Xu, Carlie Reeves, Lia Gracey
Signal Processing
Cooperative Multi-Static ISAC Networks: A Unified Design Framework for Active and Passive Sensing
Yan Yang, Zhendong Li, Jianwei Zhao et al. (+4 more)
Multi-static cooperative sensing emerges as a promising technology for advancing integrated sensing and communication (
ISAC), enhancing sensing accuracy and range. In this paper, we develop a unified design framework for joint active and passive sensing (JAPS). In particular, we consider a JAPSbased cooperative multi-static
ISAC system for coexisting downlink (DL) and uplink (UL) communications. An optimization problem is formulated for maximizing the sum rate of both the DL and UL transmissions via jointly optimizing beamforming, receive filters and power allocation, while guaranteeing the sensing requirements and transmission power constraints. However, the formulated problem is a non-convex optimization problem that is challenging to solve directly due to the tight coupling among optimization variables. To tackle this complicated issue, we employ an efficient
algorithm architecture leveraging alternating optimization (AO). Specifically, with the given receive filters and transmission power for UL communication, the transmit beamforming subproblem is addressed by successive convex approximation (SCA)-based and penalty-based
algorithms. A fractional programming (FP)-based
algorithm is developed to tackle the receive filters and transmission power for UL communication optimization subproblem. Extensive numerical results validate the performance improvement of our proposed JAPS scheme and demonstrate the effectiveness of our proposed
algorithms.
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All authors Yan Yang, Zhendong Li, Jianwei Zhao, Qingqing Wu, Zhiqing Wei, Wen Chen, Weimin Jia
Signal Processing · Cross-listed from cs.LG
Chem-NMF: Multi-layer $\alpha$-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis
Yasaman Torabi, Shahram Shirani and James P. Reilly
Non-Negative
Matrix Factorization (NMF) is an unsupervised learning method offering low-rank representations across various domains such as audio processing, biomedical signal analysis, and image recognition. The incorporation of $\alpha$-divergence in NMF formulations enhances flexibility in optimization, yet extending these methods to multi-layer architectures presents challenges in ensuring convergence. To address this, we introduce a novel approach inspired by the Boltzmann probability of the energy barriers in chemical reactions to theoretically perform convergence analysis. We introduce a novel method, called Chem-NMF, with a bounding factor which stabilizes convergence. To our knowledge, this is the first study to apply a physical chemistry perspective to rigorously analyze the convergence behaviour of the NMF
algorithm. We start from mathematically proven asymptotic convergence results and then show how they apply to real data. Experimental results demonstrate that the proposed
algorithm improves clustering accuracy by 5.6% $\pm$ 2.7% on biomedical signals and 11.1% $\pm$ 7.2% on face images (mean $\pm$ std).
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Audio and Speech Processing · Cross-listed from cs.SD
Pitch Estimation With Mean Averaging Smoothed Product Spectrum And Musical Consonance Evaluation Using MASP
Murat Yasar Baskin
This study introduces Mean Averaging Smoothed Product (MASP) Spectrum, which is a modified version of the Harmonic Product Spectrum, designed to enhance pitch estimation for many
algorithm-wise deceptive frequency spectra that still lead clear pitches, for both harmonic and inharmonic cases. By introducing a global mean based smoothing for spectrum, the MASP
algorithm diminishes the unwanted sensitivity of HPS for spectra with missing partials. The method exhibited robust pitch estimations consistent with perceptual expectations. Motivated upon the strong correlation between consonance and periodicity, the same
algorithm is extended and, with the proposition of a harmonicity measure (H), used to evaluate musical consonance for two and three tones; yielding consonance hierarchies that align with perception and practice of music theory. These findings suggest that perception of pitch and consonance may share a similar underlying mechanism that depend on spectrum.
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Image and Video Processing
FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images
Jiasong Chen, Linchen Qian, Ruonan Gong et al. (+8 more)
Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stress distribution can be obtained by computational biomechanical analyses, especially structural Finite Element Analysis. For risk assessment, probabilistic rupture risk of TAA can be calculated by comparing stress with material strength using a material failure model. Although these engineering tools are currently available for TAA rupture risk assessment on patient specific level, clinical adoption has been limited due to two major barriers: labor intensive 3D reconstruction current patient specific anatomical modeling still relies on manual
segmentation, making it time consuming and difficult to scale to a large patient population, and computational burden traditional FEA simulations are resource intensive and incompatible with time sensitive clinical workflows. The second barrier was successfully overcome by our team through the development of the PyTorch FEA library and the FEA DNN integration framework. By incorporating the FEA functionalities within PyTorch FEA and applying the principle of static determinacy, we reduced the FEA based stress computation time to approximately three minutes per case. Moreover, by integrating DNN and FEA through the PyTorch FEA library, our approach further decreases the computation time to only a few seconds per case. This work focuses on overcoming the first barrier through the development of an end to end deep neural network capable of generating patient specific finite element meshes of the aorta directly from 3D CT images.
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All authors Jiasong Chen, Linchen Qian, Ruonan Gong, Christina Sun, Tongran Qin, Thuy Pham, Caitlin Martin, Mohammad Zafar, John Elefteriades, Wei Sun, Liang Liang
Systems and Control
A Cascade of Systems and the Product of Their $\theta$-Symmetric Scaled Relative Graphs
Xiaokan Yang, Ding Zhang, Wei Chen et al. (+1 more)
In this paper, we utilize a variant of the scaled relative graph (SRG), referred to as the $\theta$-symmetric SRG, to develop a graphical stability criterion for the feedback interconnection of a cascade of systems. A crucial submultiplicative property of $\theta$-symmetric SRG is established, enabling it to handle cyclic interconnections for which conventional graph separation methods are not applicable. By integrating both gain and refined phase information, the $\theta$-symmetric SRG provides a unified graphical characterization of the system, which better captures system properties and yields less conservative results. In the scalar case, the $\theta$-symmetric SRG can be reduced exactly to the scalar itself, whereas the standard SRG appears to be a conjugate pair. Consequently, the frequency-wise $\theta$-symmetric SRG is more suitable than the standard SRG as a multi-input multi-output extension of the classical Nyquist plot. Illustrative examples are included to demonstrate the effectiveness of the $\theta$-symmetric SRG.
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All authors Xiaokan Yang, Ding Zhang, Wei Chen, Li Qiu
Systems and Control · Cross-listed from math.OC
Safe Stabilization of the Stefan Problem with a High-Order Moving Boundary Dynamics by PDE Backstepping
Shumon Koga and Miroslav Krstic
This paper presents a safe stabilization of the Stefan PDE model with a moving boundary governed by a high-order dynamics. We consider a
parabolic PDE with a time-varying domain governed by a second-order response with respect to the Neumann boundary value of the PDE state at the moving boundary. The objective is to design a boundary heat flux control to stabilize the moving boundary at a desired setpoint, with satisfying the required conditions of the model on PDE state and the moving boundary. We apply a PDE backstepping method for the control design with considering a constraint on the control law. The PDE and moving boundary constraints are shown to be satisfied by applying the maximum principle for
parabolic PDEs. Then the closed-loop system is shown to be globally exponentially stable by performing Lyapunov analysis. The proposed control is implemented in numerical simulation, which illustrates the desired performance in safety and stability. An outline of the extension to third-order moving boundary dynamics is also presented. Code is released at https://github.com/shumon0423/HighOrderStefan_CDC2025.git.
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Image and Video Processing · Cross-listed from cs.LG
The Framework That Survives Bad Models: Human-AI Collaboration For Clinical Trials
Yao Chen, David Ohlssen, Aimee Readie et al. (+3 more)
Artificial intelligence (AI) holds great promise for supporting clinical trials, from patient recruitment and endpoint assessment to treatment response prediction. However, deploying AI without safeguards poses significant risks, particularly when evaluating patient endpoints that directly impact trial conclusions. We compared two AI frameworks against human-only assessment for medical image-based disease evaluation, measuring cost, accuracy, robustness, and generalization ability. To stress-test these frameworks, we injected bad models, ranging from random guesses to naive predictions, to ensure that observed treatment effects remain valid even under severe model degradation. We evaluated the frameworks using two randomized controlled trials with endpoints derived from spinal X-ray images. Our findings indicate that using AI as a supporting reader (AI-SR) is the most suitable approach for clinical trials, as it meets all criteria across various model types, even with bad models. This method consistently provides reliable disease estimation, preserves clinical trial treatment effect estimates and conclusions, and retains these advantages when applied to different populations.
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All authors Yao Chen, David Ohlssen, Aimee Readie, Gregory Ligozio, Ruvie Martin, Thibaud Coroller
Systems and Control
Model Predictive Path Integral Control for Roll-to-Roll Manufacturing
Christopher Martin, Apurva Patil, Wei Li et al. (+2 more)
Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains challenging due to subsystem interactions, nonlinearities, and process disturbances. This paper proposes a Model Predictive Path Integral (MPPI) control formulation for R2R systems, leveraging a GPU-based Monte-Carlo sampling approach to efficiently approximate optimal controls online. Crucially, MPPI easily handles non-differentiable cost functions, enabling the incorporation of complex performance criteria relevant to advanced manufacturing processes. A case study is presented that demonstrates that MPPI significantly improves tension regulation performance compared to conventional model predictive control (MPC), highlighting its suitability for real-time control in advanced manufacturing.
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All authors Christopher Martin, Apurva Patil, Wei Li, Takashi Tanaka, Dongmei Chen
Audio and Speech Processing · Cross-listed from cs.SD
Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
Xutao Mao, Ke Li, Cameron Baird et al. (+2 more)
The rapid advancement of fake voice generation technology has ignited a race with detection systems, creating an urgent need to secure the audio ecosystem. However, existing
benchmarks suffer from a critical limitation: they typically aggregate diverse fake voice samples into a single
dataset for evaluation. This practice masks method-specific artifacts and obscures the varying performance of detectors against different generation paradigms, preventing a nuanced understanding of their true vulnerabilities. To address this gap, we introduce the first ecosystem-level
benchmark that systematically evaluates the interplay between 17 state-of-the-art fake voice generators and 8 leading detectors through a novel one-to-one evaluation protocol. This fine-grained analysis exposes previously hidden vulnerabilities and sensitivities that are missed by traditional aggregated testing. We also propose unified scoring systems to quantify both the evasiveness of generators and the robustness of detectors, enabling fair and direct comparisons. Our extensive cross-domain evaluation reveals that modern generators, particularly those based on neural audio codecs and flow matching, consistently evade top-tier detectors. We found that no single detector is universally robust; their effectiveness varies dramatically depending on the generator's architecture, highlighting a significant generalization gap in current defenses. This work provides a more realistic assessment of the threat landscape and offers actionable insights for building the next generation of detection systems.
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All authors Xutao Mao, Ke Li, Cameron Baird, Ezra Xuanru Tao, Dan Lin
Audio and Speech Processing · Cross-listed from cs.SD
BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music
Mingyang Yao, Ke Chen, Shlomo Dubnov et al. (+1 more)
Automatic chord recognition (ACR) via deep learning models has gradually achieved promising recognition accuracy, yet two key challenges remain. First, prior work has primarily focused on audio-domain ACR, while symbolic music (e.g., score) ACR has received limited
attention due to data scarcity. Second, existing methods still overlook strategies that are aligned with human music analytical practices. To address these challenges, we make two contributions: (1) we introduce POP909-CL, an enhanced version of POP909
dataset with tempo-aligned content and human-corrected labels of chords, beats, keys, and time signatures; and (2) We propose BACHI, a symbolic chord recognition model that decomposes the task into different decision steps, namely boundary detection and iterative ranking of chord root, quality, and bass (inversion). This mechanism mirrors the human ear-training practices. Experiments demonstrate that BACHI achieves state-of-the-art chord recognition performance on both classical and pop music
benchmarks, with ablation studies validating the effectiveness of each module.
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All authors Mingyang Yao, Ke Chen, Shlomo Dubnov, Taylor Berg-Kirkpatrick