Papers

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Electrical Engineering and Systems Science

eess Watch Electrical Engineering and Systems Science

Sunday 19 October

Audio and Speech Processing

Towards Real-Time Generative Speech Restoration with Flow-Matching

Tsun-An Hsieh and Sebastian Braun

Generative models have shown robust performance on speech enhancement and restoration tasks, but most prior approaches operate offline with high latency, making them unsuitable for streaming applications. In this work, we investigate the feasibility of a low-latency, real-time generative speech restoration system based on flow-matching (FM). Our method tackles diverse real-world tasks, including denoising, dereverberation, and generative restoration. The proposed causal architecture without time-downsampling achieves introduces an total latency of only 20 ms, suitable for real-time communication. In addition, we explore a broad set of architectural variations and sampling strategies to ensure effective training and efficient inference. Notably, our flow-matching model maintains high enhancement quality with only 5 number of function evaluations (NFEs) during sampling, achieving similar performance as when using ~20 NFEs under the same conditions. Experimental results indicate that causal FM-based models favor few-step reverse sampling, and smaller backbones degrade with longer reverse trajectories. We further show a side-by-side comparison of FM to typical adversarial-loss-based training for the same model architecture.

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Audio and Speech Processing

Adaptive Deterministic Flow Matching for Target Speaker Extraction

Tsun-An Hsieh and Minje Kim

Generative target speaker extraction (TSE) methods often produce more natural outputs than predictive models. Recent work based on diffusion or flow matching (FM) typically relies on a small, fixed number of reverse steps with a fixed step size. We introduce Adaptive Discriminative Flow Matching TSE (AD-FlowTSE), which extracts the target speech using an adaptive step size. We formulate TSE within the FM paradigm but, unlike prior FM-based speech enhancement and TSE approaches that transport between the mixture (or a normal prior) and the clean-speech distribution, we define the flow between the background and the source, governed by the mixing ratio (MR) of the source and background that creates the mixture. This design enables MR-aware initialization, where the model starts at an adaptive point along the background-source trajectory rather than applying the same reverse schedule across all noise levels. Experiments show that AD-FlowTSE achieves strong TSE with as few as a single step, and that incorporating auxiliary MR estimation further improves target speech accuracy. Together, these results highlight that aligning the transport path with the mixture composition and adapting the step size to noise conditions yields efficient and accurate TSE.

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Signal Processing

Stochastic Geometry Analysis of Asymmetric Uplink Interference for Urban UAV-RC Networks

Donggu Lee, Sung Joon Maeng and Ismail Guvenc

Uncrewed aerial vehicles (UAVs) have emerged as a flexible platform for providing coverage over challenging environments, particularly for public safety and surveillance missions in urban areas. However, deploying the UAVs in dense urban areas introduces unique challenges, most notably asymmetric uplink (UL, remote controller to UAV) interference due to a higher chance of line-of-sight (LoS) interference at the UAV. In this letter, we propose a stochastic geometry framework to tractably analyze the large-scale asymmetric interference in urban areas. We incorporate a log-Gaussian Cox process (LGCP) model to capture the spatial correlation of the interference field in both UL and downlink (DL) as a function of the UAV altitude and the two-dimensional (2-D) distance between the remote controller and UAV. To quantify the UL and the DL interference asymmetry, we also define the interference asymmetry ratio characterizing the interference disparity between the UL and the DL. Our numerical results demonstrate that the interference asymmetry ratio increases as the UAV altitude and 2-D distance increase, highlighting that the UL interference worsens.

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Systems and Control

Safe Payload Transfer with Ship-Mounted Cranes: A Robust Model Predictive Control Approach

Ersin Das, William A. Welch, Patrick Spieler et al. (+14 more)

Ensuring safe real-time control of ship-mounted cranes in unstructured transportation environments requires handling multiple safety constraints while maintaining effective payload transfer performance. Unlike traditional crane systems, ship-mounted cranes are consistently subjected to significant external disturbances affecting underactuated crane dynamics due to the ship's dynamic motion response to harsh sea conditions, which can lead to robustness issues. To tackle these challenges, we propose a robust and safe model predictive control (MPC) framework and demonstrate it on a 5-DOF crane system, where a Stewart platform simulates the external disturbances that ocean surface motions would have on the supporting ship. The crane payload transfer operation must avoid obstacles and accurately place the payload within a designated target area. We use a robust zero-order control barrier function (R-ZOCBF)-based safety constraint in the nonlinear MPC to ensure safe payload positioning, while time-varying bounding boxes are utilized for collision avoidance. We introduce a new optimization-based online robustness parameter adaptation scheme to reduce the conservativeness of R-ZOCBFs. Experimental trials on a crane prototype demonstrate the overall performance of our safe control approach under significant perturbing motions of the crane base. While our focus is on crane-facilitated transfer, the methods more generally apply to safe robotically-assisted parts mating and parts insertion.

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All authors Ersin Das, William A. Welch, Patrick Spieler, Keenan Albee, Aurelio Noca, Jeffrey Edlund, Jonathan Becktor, Thomas Touma, Jessica Todd, Sriramya Bhamidipati, Stella Kombo, Maira Saboia, Anna Sabel, Grace Lim, Rohan Thakker, Amir Rahmani, Joel W. Burdick

Signal Processing · Cross-listed from cs.IT

Unlocking Off-the-Grid Sparse Recovery with Unlimited Sensing: Simultaneous Super-Resolution in Time and Amplitude

Ruiming Guo and Ayush Bhandari

The recovery of Dirac impulses, or spikes, from filtered measurements is a classical problem in signal processing. As the spikes lie in the continuous domain while measurements are discrete, this task is known as super-resolution or off-the-grid sparse recovery. Despite significant theoretical and algorithmic advances over the past decade, these developments often overlook critical challenges at the analog-digital interface. In particular, when spikes exhibit strong-weak amplitude disparity, conventional digital acquisition may result in clipping of strong components or loss of weak ones beneath the quantization noise floor. This motivates a broader perspective: super-resolution must simultaneously resolve both amplitude and temporal structure. Under a fixed bit budget, such information loss is unavoidable. In contrast, the emerging theory and practice of the Unlimited Sensing Framework (USF) demonstrate that these fundamental limitations can be overcome. Building on this foundation, we demonstrate that modulo encoding within USF enables digital super-resolution by enhancing measurement precision, thereby unlocking temporal super-resolution beyond conventional limits. We develop new theoretical results that extend to non-bandlimited kernels commonly encountered in practice and introduce a robust algorithm for off-the-grid sparse recovery. To demonstrate practical impact, we instantiate our framework in the context of time-of-flight imaging. Both numerical simulations and hardware experiments validate the effectiveness of our approach under low-bit quantization, enabling super-resolution in amplitude and time.

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Signal Processing · Cross-listed from cs.LG

A Primer on Kolmogorov-Arnold Networks (KANs) for Probabilistic Time Series Forecasting

Cristian J. Vaca-Rubio, Roberto Pereira, Luis Blanco et al. (+2 more)

This work introduces Probabilistic Kolmogorov-Arnold Network (P-KAN), a novel probabilistic extension of Kolmogorov-Arnold Networks (KANs) for time series forecasting. By replacing scalar weights with spline-based functional connections and directly parameterizing predictive distributions, P-KANs offer expressive yet parameter-efficient models capable of capturing nonlinear and heavy-tailed dynamics. We evaluate P-KANs on satellite traffic forecasting, where uncertainty-aware predictions enable dynamic thresholding for resource allocation. Results show that P-KANs consistently outperform Multi Layer Perceptron (MLP) baselines in both accuracy and calibration, achieving superior efficiency-risk trade-offs while using significantly fewer parameters. We build up P-KANs on two distributions, namely Gaussian and Student-t distributions. The Gaussian variant provides robust, conservative forecasts suitable for safety-critical scenarios, whereas the Student-t variant yields sharper distributions that improve efficiency under stable demand. These findings establish P-KANs as a powerful framework for probabilistic forecasting with direct applicability to satellite communications and other resource-constrained domains.

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All authors Cristian J. Vaca-Rubio, Roberto Pereira, Luis Blanco, Engin Zeydan, Màrius Caus

Systems and Control · Cross-listed from physics.optics

Ultra High Sensitivity Soil Moisture Detection Using Photonic Crystal Cavity with SIW Technology

Justin Jose and Nikhil Kumar

Soil nutrients and water content are two crucial factors that significantly affect agricultural production yields. Hence, monitoring and measuring the water content and soil type are critical requirements. This study proposes a two-dimensional structure of photonic crystals centered around a symmetrical cross-shaped slot. The cross-slots act as resonators, and the photonic crystals surrounding the slots tune the resonance frequency of the resonators to enhance mode confinement within the resonator. The various resonant modes are located in the 2.1 GHz, 5.2 GHz, and 8.1 GHz bands, which correspond to the S band, C band, and X band, respectively. These bands are used to compare the absorption, whereas the upper resonant mode is of the order of 20 GHz. Band structure analysis was performed using the Plane Wave Method (PWM). The resonant frequency is computed using a 3D electromagnetic (EM) simulation software that utilizes the Finite Element Method (FEM) and lies in the radiation mode region of the band structure of the photonic crystal. Varying the incident angle had a negligible effect on the absorption characteristics of the sensor, allowing it to produce accurate sensing results regardless of the incident angle. The sensor's sensitivity is maximized using this design, which results in a sensitivity of 85.4 % in the 2.1 GHz resonant frequency, which is much higher than that of a single column of photonic crystal-based SIW, resulting in 50.6 % of sensitivity at 2.1 GHz, at which there is a frequency shift of the order of GHz. In contrast, in the proposed design, the frequency shift is on the order of MHz, resulting in ultra-high sensitivity.

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Audio and Speech Processing · Cross-listed from cs.SD

SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models

Chih-Kai Yang, Yen-Ting Piao, Tzu-Wen Hsu et al. (+8 more)

Knowledge editing enables targeted updates without retraining, but prior work focuses on textual or visual facts, leaving abstract auditory perceptual knowledge underexplored. We introduce SAKE, the first benchmark for editing perceptual auditory attribute knowledge in large audio-language models (LALMs), which requires modifying acoustic generalization rather than isolated facts. We evaluate eight diverse editing methods on three LALMs across reliability, generality, locality, and portability, under single and sequential edits. Results show that most methods enforce edits reliably but struggle with auditory generalization, intra-attribute locality, and multimodal knowledge propagation, and often exhibit forgetting or degeneration in sequential editing. Additionally, fine-tuning the modality connector emerges as a more robust and balanced baseline compared with directly editing the LLM backbones. SAKE reveals key limitations of current methods and provides a foundation for developing auditory-specific LALM editing techniques.

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All authors Chih-Kai Yang, Yen-Ting Piao, Tzu-Wen Hsu, Szu-Wei Fu, Zhehuai Chen, Ke-Han Lu, Sung-Feng Huang, Chao-Han Huck Yang, Yu-Chiang Frank Wang, Yun-Nung Chen, Hung-yi Lee

Audio and Speech Processing · Cross-listed from cs.SD

Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations

Bo-Han Feng, Chien-Feng Liu, Yu-Hsuan Li Liang et al. (+9 more)

Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While their perception, reasoning, and task performance have been widely studied, their safety alignment under paralinguistic variation remains underexplored. This work systematically investigates the role of speaker emotion. We construct a dataset of malicious speech instructions expressed across multiple emotions and intensities, and evaluate several state-of-the-art LALMs. Our results reveal substantial safety inconsistencies: different emotions elicit varying levels of unsafe responses, and the effect of intensity is non-monotonic, with medium expressions often posing the greatest risk. These findings highlight an overlooked vulnerability in LALMs and call for alignment strategies explicitly designed to ensure robustness under emotional variation, a prerequisite for trustworthy deployment in real-world settings.

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All authors Bo-Han Feng, Chien-Feng Liu, Yu-Hsuan Li Liang, Chih-Kai Yang, Szu-Wei Fu, Zhehuai Chen, Ke-Han Lu, Sung-Feng Huang, Chao-Han Huck Yang, Yu-Chiang Frank Wang, Yun-Nung Chen, Hung-yi Lee

Audio and Speech Processing

SAC: Neural Speech Codec with Semantic-Acoustic Dual-Stream Quantization

Wenxi Chen, Xinsheng Wang, Ruiqi Yan et al. (+9 more)

Speech codecs that convert continuous speech signals into discrete tokens have become essential for speech language models. However, existing codecs struggle to balance high-quality reconstruction with semantically rich representations, limiting their effectiveness in both generative and understanding tasks. In this work, we propose SAC, a neural speech codec with semantic-acoustic dual-stream quantization. By disentangling semantic and acoustic modeling into two dedicated streams, SAC enables each to be optimized for its respective role. Comprehensive evaluations show that SAC achieves strong reconstruction performance across diverse bitrates under both clean and noisy conditions, with particularly high scores on UTMOS and WER, indicating superior naturalness and intelligibility. Moreover, SAC substantially surpasses prior codecs in semantic representation, approaching the level of continuous self-supervised embeddings. When used as a tokenizer for LLM-based text-to-speech, SAC enables a single-stage autoregressive (AR) TTS model that clearly outperforms state-of-the-art AR systems. Our disentanglement analysis further validates the effectiveness of the dual-stream design, offering new potential for controllable speech generation.

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All authors Wenxi Chen, Xinsheng Wang, Ruiqi Yan, Yushen Chen, Zhikang Niu, Ziyang Ma, Xiquan Li, Yuzhe Liang, Hanlin Wen, Shunshun Yin, Ming Tao, Xie Chen

Audio and Speech Processing · Cross-listed from cs.SD

Schrödinger Bridge Mamba for One-Step Speech Enhancement

Jing Yang, Sirui Wang, Chao Wu et al. (+2 more)

We present Schrödinger Bridge Mamba (SBM), a novel model for efficient speech enhancement by integrating the Schrödinger Bridge (SB) training paradigm and the Mamba architecture. Experiments of joint denoising and dereverberation tasks demonstrate SBM outperforms strong generative and discriminative methods on multiple metrics with only one step of inference while achieving a competitive real-time factor for streaming feasibility. Ablation studies reveal that the SB paradigm consistently yields improved performance across diverse architectures over conventional mapping. Furthermore, Mamba exhibits a stronger performance under the SB paradigm compared to Multi-Head Self-Attention (MHSA) and Long Short-Term Memory (LSTM) backbones. These findings highlight the synergy between the Mamba architecture and the SB trajectory-based training, providing a high-quality solution for real-world speech enhancement. Demo page: https://sbmse.github.io

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All authors Jing Yang, Sirui Wang, Chao Wu, Lei Guo, Fan Fan

Audio and Speech Processing

Audio dequantization using instantaneous frequency

Vojtěch Kovanda and Pavel Rajmic

We present a dequantization method that employs a phase-aware regularizer, originally successfully applied in an audio inpainting problem. The method promotes a temporal continuity of sinusoidal components in time-frequency representation of the audio signal, and avoids energy loss artifacts commonly encountered with l1-based regularization approaches. The proposed method is called the Phase-Aware Audio Dequantizer (PHADQ). The method is evaluated against the state-of-the-art using the SDR and PEMO-Q ODG objective metrics, and a~subjective MUSHRA-like test.

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Audio and Speech Processing · Cross-listed from cs.AI

End-to-end Listen, Look, Speak and Act

Siyin Wang, Wenyi Yu, Xianzhao Chen et al. (+4 more)

Human interaction is inherently multimodal and full-duplex: we listen while watching, speak while acting, and fluidly adapt to turn-taking and interruptions. Realizing these capabilities is essential for building models simulating humans. We present ELLSA (End-to-end Listen, Look, Speak and Act), which, to our knowledge, is the first full-duplex, end-to-end model that simultaneously perceives and generates across vision, text, speech, and action within a single architecture, enabling interaction patterns previously out of reach, yielding more natural, human-like behaviors. At its core is a novel SA-MoE architecture (Self-Attention Mixture-of-Experts) that routes each modality to specialized experts and fuses them through a unified attention backbone. This provides a generalizable solution for joint multimodal perception and concurrent generation, leveraging strong pre-trained components while enabling efficient modality integration and mitigating modality interference. On speech-interaction and robot-manipulation benchmarks, ELLSA matches modality-specific baselines, while uniquely supporting advanced multimodal and full-duplex behaviors such as dialogue and action turn-taking, defective instruction rejection, speaking-while-acting, context-grounded visual question answering, and action barge-ins. We contend that ELLSA represents a step toward more natural and general interactive intelligence, contributing to the broader pursuit of artificial general intelligence. All data, code and model checkpoints will be released at https://github.com/bytedance/SALMONN/tree/ELLSA.

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All authors Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Chao Zhang

Systems and Control · Cross-listed from cs.RO

Adaptive Invariant Extended Kalman Filter for Legged Robot State Estimation

Kyung-Hwan Kim, DongHyun Ahn, Dong-hyun Lee et al. (+2 more)

State estimation is crucial for legged robots as it directly affects control performance and locomotion stability. In this paper, we propose an Adaptive Invariant Extended Kalman Filter to improve proprioceptive state estimation for legged robots. The proposed method adaptively adjusts the noise level of the contact foot model based on online covariance estimation, leading to improved state estimation under varying contact conditions. It effectively handles small slips that traditional slip rejection fails to address, as overly sensitive slip rejection settings risk causing filter divergence. Our approach employs a contact detection algorithm instead of contact sensors, reducing the reliance on additional hardware. The proposed method is validated through real-world experiments on the quadruped robot LeoQuad, demonstrating enhanced state estimation performance in dynamic locomotion scenarios.

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All authors Kyung-Hwan Kim, DongHyun Ahn, Dong-hyun Lee, JuYoung Yoon, Dong Jin Hyun

Systems and Control

A Control-Theoretic Approach to Dynamic Payment Routing for Success Rate Optimization

Aniket Agrawal and Harsharanga Patil

This paper introduces a control-theoretic framework for dynamic payment routing, implemented within JUSPAY's Payment Orchestrator to maximize transaction success rate. The routing system is modeled as a closed-loop feedback controller continuously sensing gateway performance, computing corrective actions, and dynamically routes transactions across gateway to ensure operational resilience. The system leverages concepts from control theory, reinforcement learning, and multi-armed bandit optimization to achieve both short-term responsiveness and long-term stability. Rather than relying on explicit PID regulation, the framework applies generalized feedback-based adaptation, ensuring that corrective actions remain proportional to observed performance deviations and the computed gateway score gradually converges toward the success rate. This hybrid approach unifies control theory and adaptive decision systems, enabling self-regulating transaction routing that dampens instability, and improves reliability. Live production results show an improvement of up to 1.15% in success rate over traditional rule-based routing, demonstrating the effectiveness of feedback-based control in payment systems.

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Systems and Control · Cross-listed from math.OC

Local integral input-to-state stability for non-autonomous infinite-dimensional systems

Yongchun Bi, Panyu Deng, Jun Zheng et al. (+1 more)

In this paper, we prove comparison principles for nonlinear differential equations with time-varying coefficients and develop Lyapunov analytical tools for the integral input-to-state stability (iISS) analysis of nonlinear non-autonomous infinite-dimensional systems, which involve nonlinearities satisfying a superlinear growth, {bringing} difficulties to the iISS {analysis.} Specifically, our approach starts by establishing several forms of comparison principles for a wide range of ordinary differential equations having time-varying coefficients and superlinear terms, paving the way to conduct iISS assessment for general nonlinear non-autonomous infinite-dimensional systems within the Lyapunov stability framework. Then, by using the comparison principles, we prove a local {iISS} {(LiISS)} Lyapunov theorem for the nonlinear non-autonomous infinite-dimensional systems in the framework of Banach spaces. {Furthermore,} we provide sufficient conditions of the existence of a local iISS Lyapunonv functional (LiISS-LF) and construct LiISS-LFs for the systems in the framework of Hilbert spaces. Finally, we preset two examples to illustrate the proposed {Lyapunov} method for the LiISS analysis: one is to show how to obtain the LiISS of a nonlinear finite-dimensional system with time-varying coefficients and superlinear terms under linear state feedback control law while another one is to show how to employ the interpolation inequalities to handle superliner terms and establish the LiISS-LF for a class of multi-dimensional parabolic equations with space-time-varying coefficients. To demonstrate the validity of the results, numerical experiments are also conducted to verify the LiISS of these two classes of systems.

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All authors Yongchun Bi, Panyu Deng, Jun Zheng, Guchuan Zhu

Systems and Control

Linear State Estimation in Presence of Bounded Uncertainties: A Comparative Analysis

Ayan Das, Anushka Sharma and Anamitra Pal

A variety of algorithms have been proposed to address the power system state estimation problem in the presence of uncertainties in the data. However, less emphasis has been given to handling perturbations in the model. In the context of linear state estimation (LSE), which is the focus of this paper, perturbations in the model come from variations in the line parameters. Since the actual values of the line parameters can be different from the values stored in a power utility's database, we investigate three approaches in this paper to estimate the states in the presence of bounded uncertainties in the data and the model. The first approach is based on interval arithmetic, the second is based on convex optimization, and the third is based on generalized linear fractional programming. The three algorithms are applied to multiple IEEE test systems and compared in terms of their speed and accuracy. The results indicate that the first two algorithms are extremely fast and give expected results, while the third suffers from scalability issues and is unsuitable for LSE.

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Systems and Control · Cross-listed from math.OC

Geometric Control Theory Over Networks: Minimal Node Cardinality Disturbance Decoupling Problems

Luca Claude Gino Lebon and Claudio Altafini

In this paper we show how to formulate and solve disturbance decoupling problems over networks while choosing a minimal number of input and output nodes. Feedback laws that isolate and eliminate the impact of disturbance nodes on specific target nodes to be protected are provided using state, output, and dynamical feedback. For that, we leverage the fact that when reformulated in terms of sets of nodes rather than subspaces, the controlled and conditional invariance properties admit a simple graphical interpretation. For state and dynamical feedback, the minimal input and output cardinality solutions can be computed exactly in polynomial time, via min-cut/max-flow algorithms.

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Saturday 18 October

Systems and Control · Cross-listed from cs.DS

Robust Dynamic Staffing with Predictions

Yiding Feng, Vahideh Manshadi, Rad Niazadeh et al. (+1 more)

We consider a natural dynamic staffing problem in which a decision-maker sequentially hires workers over a finite horizon to meet an unknown demand revealed at the end. Predictions about demand arrive over time and become increasingly accurate, while worker availability decreases. This creates a fundamental trade-off between hiring early to avoid understaffing (when workers are more available but forecasts are less reliable) and hiring late to avoid overstaffing (when forecasts are more accurate but availability is lower). This problem is motivated by last-mile delivery operations, where companies such as Amazon rely on gig-economy workers whose availability declines closer to the operating day. To address practical limitations of Bayesian models (in particular, to remain agnostic to the underlying forecasting method), we study this problem under adversarial predictions. In this model, sequential predictions are adversarially chosen uncertainty intervals that (approximately) contain the true demand. The objective is to minimize worst-case staffing imbalance cost. Our main result is a simple and computationally efficient online algorithm that is minimax optimal. We first characterize the minimax cost against a restricted adversary via a polynomial-size linear program, then show how to emulate this solution in the general case. While our base model focuses on a single demand, we extend the framework to multiple demands (with egalitarian/utilitarian objectives), to settings with costly reversals of hiring decisions, and to inconsistent prediction intervals. We also introduce a practical "re-solving" variant of our algorithm, which we prove is also minimax optimal. Finally we conduct numerical experiments showing that our algorithms outperform Bayesian heuristics in both cost and speed, and are competitive with (approximate or exact) Bayesian-optimal policies when those can be computed.

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All authors Yiding Feng, Vahideh Manshadi, Rad Niazadeh, Saba Neyshabouri

Systems and Control · Cross-listed from math.OC

Adversarial Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Model Uncertainty

Dennis J. Marquis, Blake Wilhelm, Devaprakash Muniraj et al. (+1 more)

This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to uncertainties in the aerodynamic model of the sUAS. The controller is trained using the Robust Adversarial Reinforcement Learning framework, where an adversary perturbs the environment (aerodynamic model) to expose the agent (sUAS) to demanding scenarios. In our formulation, the adversary introduces rate-bounded perturbations to the aerodynamic model coefficients. We demonstrate that adversarial training improves robustness compared to controllers trained using stochastic model uncertainty. The learned controller is also benchmarked against a switched uncertain initial condition controller. The effectiveness of the approach is validated through high-fidelity simulations using a realistic six-degree-of-freedom fixed-wing aircraft model, showing accurate and robust path-following performance under a variety of uncertain aerodynamic conditions.

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All authors Dennis J. Marquis, Blake Wilhelm, Devaprakash Muniraj, Mazen Farhood

Image and Video Processing · Cross-listed from cs.CR

A Versatile Framework for Designing Group-Sparse Adversarial Attacks

Alireza Heshmati, Saman Soleimani Roudi, Sajjad Amini et al. (+2 more)

Existing adversarial attacks often neglect perturbation sparsity, limiting their ability to model structural changes and to explain how deep neural networks (DNNs) process meaningful input patterns. We propose ATOS (Attack Through Overlapping Sparsity), a differentiable optimization framework that generates structured, sparse adversarial perturbations in element-wise, pixel-wise, and group-wise forms. For white-box attacks on image classifiers, we introduce the Overlapping Smoothed L0 (OSL0) function, which promotes convergence to a stationary point while encouraging sparse, structured perturbations. By grouping channels and adjacent pixels, ATOS improves interpretability and helps identify robust versus non-robust features. We approximate the L-infinity gradient using the logarithm of the sum of exponential absolute values to tightly control perturbation magnitude. On CIFAR-10 and ImageNet, ATOS achieves a 100% attack success rate while producing significantly sparser and more structurally coherent perturbations than prior methods. The structured group-wise attack highlights critical regions from the network's perspective, providing counterfactual explanations by replacing class-defining regions with robust features from the target class.

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All authors Alireza Heshmati, Saman Soleimani Roudi, Sajjad Amini, Shahrokh Ghaemmaghami, Farokh Marvasti

Systems and Control · Cross-listed from quant-ph

QRTlib: A Library for Fast Quantum Real Transforms

Armin Ahmadkhaniha, Lu Chen, Jake Doliskani et al. (+1 more)

Real-valued transforms such as the discrete cosine, sine, and Hartley transforms play a central role in classical computing, complementing the Fourier transform in applications from signal and image processing to data compression. However, their quantum counterparts have not evolved in parallel, and no unified framework exists for implementing them efficiently on quantum hardware. This article addresses this gap by introducing QRTlib, a library for fast and practical implementations of quantum real transforms, including the quantum Hartley, cosine, and sine transforms of various types. We develop new algorithms and circuit optimizations that make these transforms efficient and suitable for near-term devices. In particular, we present a quantum Hartley transform based on the linear combination of unitaries (LCU) technique, achieving a $4\times$ reduction in circuit size compared to prior methods. We also implement an improved quantum sine transform of Type I that removes the need for large multi-controlled operations. QRTlib provides the first complete implementations of these quantum real transforms in Qiskit.

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All authors Armin Ahmadkhaniha, Lu Chen, Jake Doliskani, Zhifu Sun

Systems and Control · Cross-listed from cs.AR

Towards Intelligent Traffic Signaling in Dhaka City Based on Vehicle Detection and Congestion Optimization

Kazi Ababil Azam, Hasan Masum, Masfiqur Rahaman et al. (+1 more)

The vehicular density in urbanizing cities of developing countries such as Dhaka, Bangladesh result in a lot of traffic congestion, causing poor on-road experiences. Traffic signaling is a key component in effective traffic management for such situations, but the advancements in intelligent traffic signaling have been exclusive to developed countries with structured traffic. The non-lane-based, heterogeneous traffic of Dhaka City requires a contextual approach. This study focuses on the development of an intelligent traffic signaling system feasible in the context of developing countries such as Bangladesh. We propose a pipeline leveraging Real Time Streaming Protocol (RTSP) feeds, a low resources system Raspberry Pi 4B processing, and a state of the art YOLO-based object detection model trained on the Non-lane-based and Heterogeneous Traffic (NHT-1071) dataset to detect and classify heterogeneous traffic. A multi-objective optimization algorithm, NSGA-II, then generates optimized signal timings, minimizing waiting time while maximizing vehicle throughput. We test our implementation in a five-road intersection at Palashi, Dhaka, demonstrating the potential to significantly improve traffic management in similar situations. The developed testbed paves the way for more contextual and effective Intelligent Traffic Signaling (ITS) solutions for developing areas with complicated traffic dynamics such as Dhaka City.

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All authors Kazi Ababil Azam, Hasan Masum, Masfiqur Rahaman, A. B. M. Alim Al Islam

Signal Processing · Cross-listed from cs.IT

Feedback Lunch: Learned Feedback Codes for Secure Communications

Yingyao Zhou, Natasha Devroye and Onur Günlü

We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specifically, we focus on a seeded modular code design for the block-fading Gaussian wiretap channel with channel-output feedback, combining universal hash functions for security and learned feedback-based codes for reliability. The trade-off between communication reliability and information leakage is studied, illustrating that feedback enables agreeing on a secret key shared between legitimate parties, overcoming the security advantage of the eavesdropper. Our findings motivate code designs for sensing-assisted secure communications in the context of integrated sensing and communication (ISAC).

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Systems and Control · Cross-listed from cs.IT

Enhancing Channel Estimation in RIS-aided Systems via Observation Matrix Design

Zijian Zhang and Mingyao Cui

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for enhancing wireless communications through dense antenna arrays. Accurate channel estimation is critical to unlocking their full performance potential. To enhance RIS channel estimators, this paper proposes a novel observation matrix design scheme. Bayesian optimization framework is adopted to generate observation matrices that maximize the mutual information between received pilot signals and RIS channels. To solve the formulated problem efficiently, we develop an alternating Riemannian manifold optimization (ARMO) algorithm to alternately update the receiver combiners and RIS phase-shift matrices. An adaptive kernel training strategy is further introduced to iteratively refine the channel covariance matrix without requiring additional pilot resources. Simulation results demonstrate that the proposed ARMO-enhanced estimator achieves substantial gains in estimation accuracy over state-of-the-art methods.

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Audio and Speech Processing · Cross-listed from cs.CL

Hallucination Benchmark for Speech Foundation Models

Alkis Koudounas, Moreno La Quatra, Manuel Giollo et al. (+2 more)

Hallucinations in automatic speech recognition (ASR) systems refer to fluent and coherent transcriptions produced by neural ASR models that are completely unrelated to the underlying acoustic input (i.e., the speech signal). While similar to conventional decoding errors in potentially compromising the usability of transcriptions for downstream applications, hallucinations can be more detrimental due to their preservation of syntactically and semantically plausible structure. This apparent coherence can mislead subsequent processing stages and introduce serious risks, particularly in critical domains such as healthcare and law. Conventional evaluation metrics are primarily centered on error-based metrics and fail to distinguish between phonetic inaccuracies and hallucinations. Consequently, there is a critical need for new evaluation frameworks that can effectively identify and assess models with a heightened propensity for generating hallucinated content. To this end, we introduce SHALLOW, the first benchmark framework that systematically categorizes and quantifies hallucination phenomena in ASR along four complementary axes: lexical, phonetic, morphological, and semantic. We define targeted metrics within each category to produce interpretable profiles of model behavior. Through evaluation across various architectures and speech domains, we have found that SHALLOW metrics correlate strongly with word error rate (WER) when recognition quality is high (i.e., low WER). Still, this correlation weakens substantially as WER increases. SHALLOW, therefore, captures fine-grained error patterns that WER fails to distinguish under degraded and challenging conditions. Our framework supports specific diagnosis of model weaknesses and provides feedback for model improvement beyond what aggregate error rates can offer.

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All authors Alkis Koudounas, Moreno La Quatra, Manuel Giollo, Sabato Marco Siniscalchi, Elena Baralis

Signal Processing

Topology-Aware Hybrid Wi-Fi/BLE Fingerprinting via Evidence-Theoretic Fusion and Persistent Homology

Behrad Mousaei Shir-Mohammad, Behzad Moshiri and Abolfazl Yaghmaei

Indoor localization remains challenging in GNSS-denied environments due to multipath, device heterogeneity, and volatile radio conditions. We propose a topology-aware, hybrid Wi-Fi/BLE fingerprinting framework that (i) applies physically consistent RSS normalization (dBm z-scoring or dBm -> linear mW -> z-score), (ii) denoises streams with classical Bayesian filters (KF/UKF/PF), (iii) combines complementary regressors (Random Forest and weighted kNN with a diagonal Mahalanobis metric), (iv) performs evidence-theoretic fusion via Dempster-Shafer theory (DST), and (v) augments each sample with persistent-homology (PH) descriptors. The system outputs both (x, y) estimates and interpretable belief maps, and is engineered for microcontroller-class deployment with per-update cost O(T log M + log M + Mp + S). We evaluate on two heterogeneous datasets, including a new 1,200-sample ESP32 survey, and report ablations, robustness to test-only noise, and significance across 10 stratified splits. Under 10% synthetic RSS noise, the full pipeline attains 3.40 m (Dataset 1) and 2.45 m (Dataset 2) RMSE, improving a strong PF + RF baseline by about 37%. Averaged across splits, it yields 4.993 +/- 0.15 m versus 6.292 +/- 0.13 m (20.6% relative reduction; p < 0.001). In noise-free tests, accuracy tightens to 0.44 m and 0.32 m (up to 56% better). Compared with recent learning-heavy approaches that assume large site-specific datasets and GPU inference, our method delivers competitive accuracy with formal uncertainty quantification and low computational cost suitable for real-time deployment.

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Systems and Control

SMP-RCR: A Sparse Multipoint Moment Matching Method for RC Reduction

Siyuan Yin, Yuncheng Xu, Lin Liu et al. (+4 more)

In post–layout circuit simulation, efficient model order reduction (MOR) for many–port resistor–capacitor (RC) circuits remains a crucial issue. The current mainstream MOR methods for such circuits include high–order moment matching methods and elimination methods. High-order moment matching methods–characterized by high accuracy, such as PRIMA and TurboMOR–tend to generate large dense reduced-order systems when the number of ports is large, which impairs the efficiency of MOR. Another common type of MOR method for many–port circuits is based on Gaussian elimination, with the SIP method as a representative. The main limitation of this method lies in the inadequate matching of high–order moments. In this paper, we propose a sparse multipoint moment matching method and present comprehensive theoretical analysis results regarding the multi–frequency high–order moment matching property. Meanwhile, to enhance the algorithm's efficiency, sparse control and deflation techniques are introduced to further optimize the algorithm. Numerical experiments demonstrated that, compared to SIP, the accuracy is improved by more than two orders of magnitude at high frequency points without adding many extra linear components. Compared to TurboMOR methods, our method achieves a speed improvement of more than twice while maintaining the same level of precision.

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All authors Siyuan Yin, Yuncheng Xu, Lin Liu, Fan Yang, Xuan Zeng, Chengtao An, Yangfeng Su

Systems and Control

Small-Signal Stability Analysis of Power Systems by Implicit Multilinear Models

Christoph Kaufmann, Georg Pangalos, Gerwald Lichtenberg et al. (+1 more)

This paper proposes a new approach to perform small-signal stability analysis based on linearization of implicit multilinear models. Multilinear models describe the system dynamics by multilinear functions of state, input, and algebraic variables. Using suitable transformations of variables, they can also represent trigonometric functions, which often occur in power systems modeling. This allows tensor representations of grid-following and grid-forming power converters. This paper introduces small-signal stability analysis of equilibrium points based on implicit multilinear models using generalized eigenvalues. The generalized eigenvalues are computed from linear descriptor models of the linearized implicit multilinear model. The proposed approach is tested using a 3-bus network example, first by comparing time-domain simulations of the implicit multilinear model with those of the nonlinear model, and second by comparing the generalized eigenvalues with those of the linearized nonlinear model. The results show that the decomposed tensor representation of the implicit multilinear model allows for a faster linearization compared to conventional methods in MATLAB Simulink.

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All authors Christoph Kaufmann, Georg Pangalos, Gerwald Lichtenberg, Oriol Gomis-Bellmunt

Signal Processing

Performance Evaluation of High Power Microwave Systems Against UAVs A Probabilistic Antenna Propagation Framework with Sensitivity Analysis

Muhammad Khalil, Ke Wang and Jinho Choi

We present an uncertainty-aware probabilistic framework for high-power microwave (HPM) counter-UAV performance under stochastic target motion, beam-pointing uncertainty, atmospheric propagation, and uncertain target susceptibility. It couples stochastic UAV kinematics, a jitter-to-gain model, free-space spreading, gaseous absorption, and rain attenuation, and a logistic energy–response model to derive closed-form statistics of received pulse energy and per-pulse and cumulative effectiveness probabilities. Slant-range variability arises from integrated acceleration noise. Received pulse energy is treated as a target-level exposure metric rather than the exact energy absorbed by an internal component. Closed-form moments and a log-normal approximation yield the mean per-pulse probability through Gaussian–Hermite quadrature and a dwell-time expression under an independent-pulse assumption. Analytical predictions closely match Monte Carlo results under matched assumptions. For a vulnerable-target threshold of $E_{\mathrm{th}}=10^{-2}\,\mathrm{J}$, the model predicts $\bar{P}_{\mathrm{kill}}\gtrsim0.4$ per pulse and $P_{\mathrm{kill,tot}}>99\%$ within about $0.1\,\mathrm{s}$ at kilohertz PRF. For a hardened target with $E_{\mathrm{th}}=10^{-1}\,\mathrm{J}$, it predicts $\bar{P}_{\mathrm{kill}}\approx2.2\times10^{-4}$ ($\approx0.02\%$) and $P_{\mathrm{kill,tot}}\approx20\%$ after $1\,\mathrm{s}$ at $1\,\mathrm{kHz}$ under the i.i.d. pulse assumption. Elasticity analysis identifies slant range as dominant ($S_{\bar{R}}\approx-2$), followed by aperture diameter and transmit power; pointing jitter and atmospheric variability are less influential in the evaluated regimes. Within its assumptions, the framework supports system sizing, trade-off analysis, and risk-aware mission planning.

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