Signal Processing · Cross-listed from cs.LG
Decentralized Parameter-Free Online Learning
Tomas Ortega and Hamid Jafarkhani
We propose the first parameter-free decentralized online learning
algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning. This family of
algorithms connects multi-
agent coin-betting and decentralized online learning via gossip steps. To enable our decentralized analysis, we introduce a novel "betting function" formulation for coin-betting that simplifies the multi-
agent regret analysis. Our analysis shows sublinear network regret bounds and is validated through experiments on synthetic and real
datasets. This family of
algorithms is applicable to distributed sensing, decentralized optimization, and collaborative ML applications.
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Systems and Control · Cross-listed from cs.RO
Adaptive Legged Locomotion via Online Learning for Model Predictive Control
Hongyu Zhou, Xiaoyu Zhang and Vasileios Tzoumas
We provide an
algorithm for adaptive legged locomotion via online learning and model predictive control. The
algorithm is composed of two interacting modules: model predictive control (MPC) and online learning of residual dynamics. The residual dynamics can represent modeling errors and external disturbances. We are motivated by the future of autonomy where quadrupeds will autonomously perform complex tasks despite real-world unknown uncertainty, such as unknown payload and uneven terrains. The
algorithm uses random
Fourier features to approximate the residual dynamics in reproducing kernel
Hilbert spaces. Then, it employs MPC based on the current learned model of the residual dynamics. The model is updated online in a self-supervised manner using least squares based on the data collected while controlling the quadruped. The
algorithm enjoys sublinear dynamic regret, defined as the suboptimality against an optimal clairvoyant controller that knows how the residual dynamics. We validate our
algorithm in Gazebo and MuJoCo simulations, where the quadruped aims to track reference trajectories. The Gazebo simulations include constant unknown external forces up to $12\boldsymbol{g}$, where $\boldsymbol{g}$ is the
gravity vector, in flat terrain, slope terrain with $20\degree$ inclination, and rough terrain with $0.25m$ height variation. The MuJoCo simulations include time-varying unknown disturbances with payload up to $8~kg$ and time-varying ground friction coefficients in flat terrain.
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Systems and Control
A Predictive Flexibility Aggregation Method for Low Voltage Distribution System Control
Clément Moureau, Thomas Stegen, Mevludin Glavic et al. (+1 more)
This paper presents a method for predictive aggregation of the available flexibility at the residential unit level into a flexibility chart that represents the admissible active and reactive powers, along with the associated flexibility value. The method is also combined with centralized optimization to design a predictive
privacy-preserving control scheme to manage low-voltage distribution systems in real-time. Similarly to hierarchical control strategies, this approach divides the optimization horizon into a real-time stage, responsible for decisions in the current
market period, and an operational planning stage, which deals with decisions outside of this interval. First, a multiparametric optimization problem is solved offline at the residential unit level. Then, an operational planning problem, also formulated as a parametric optimization problem, is solved to account for the forecasts. The method generates the desired flexibility chart by combining the results of these two problems with measurements. The resulting approach is compatible with real-time control requirements, as heavy computations are performed offline in a decentralized manner. By linking real-time flexibility assessment with energy scheduling, our approach enables efficient and cost-effective management of low-voltage distribution systems. We validate this method on a low-voltage network of 43 buses by comparing it with a fully centralized optimization formulation with perfect foresight and a future-agnostic aggregation method.
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All authors Clément Moureau, Thomas Stegen, Mevludin Glavic, Bertrand Cornélusse
Systems and Control
Observer Design over Hypercomplex Quaternions
Michael Sebek
We develop observer design over hypercomplex quaternions in a characteristic-
polynomial-free framework. Using the standard right-module convention, we derive a right observable companion form and companion
polynomial that encode error dynamics through right-
eigenvalue similarity classes. We also give an Ackermann-type formula for real-coefficient target
polynomials, where
polynomial evaluation is similarity-equivariant. The resulting recipes place observer poles directly over quaternions and clarify when companion-coordinate updates and one-shot Ackermann formulas remain valid.
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Systems and Control · Cross-listed from cs.GT
Active Inverse Methods in Stackelberg Games with Bounded Rationality
Jianguo Chen, Jinlong Lei, Biqiang Mu et al. (+2 more)
Inverse game theory is utilized to infer the cost functions of all players based on game outcomes. However, existing inverse game theory methods do not consider the learner as an active participant in the game, which could significantly enhance the learning process. In this paper, we extend inverse game theory to active inverse methods. For Stackelberg games with bounded rationality, the leader, acting as a learner, actively chooses actions to better understand the follower's cost functions. First, we develop a method of active learning by leveraging Fisher information to maximize information gain about the unknown parameters and prove the consistency and asymptotic normality. Additionally, when leaders consider its cost, we develop a method of active inverse game to balance exploration and exploitation, and prove the consistency and asymptotic Stackelberg
equilibrium with quadratic cost functions. Finally, we verify the properties of these methods through simulations in the quadratic case and demonstrate that the active inverse game method can achieve Stackelberg
equilibrium more quickly through active exploration.
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All authors Jianguo Chen, Jinlong Lei, Biqiang Mu, Yiguang Hong, Hongsheng Qi
Signal Processing
Pseudo-Random TDM-MIMO FMCW Based Millimeter-Wave Sensing and Communication Integration for UAV Swarm
Yi Tao, Zhen Gao, Zhuoran Li et al. (+7 more)
The integrated sensing and communications (
ISAC) can achieve the sharing of hardware and spectrum resources, enabling efficient data transmission and environmental sensing. This fusion is particularly important for unmanned aerial vehicle (
UAV) swarms, as it enhances the overall performance, flexibility, and efficiency of such systems. To facilitate the collaborative operations among
UAVs, this paper proposes an
ISAC solution based on the pseudo-random time-division multiplexing (TDM)-multiple input multiple output (
MIMO) millimeter-wave (mmWave) frequency modulated continuous wave (FMCW). Specifically, a novel
ISAC chirp waveform is proposed to modulate data in both the delay domain and complex amplitude, while also possessing high-precision sensing capabilities. To address challenges in the TDM-
MIMO, we utilize the pseudo-random
antenna selection and compressed sensing
algorithms, ensuring that the maximum unambiguous velocity is not compromised. Moreover, by employing a chirp-division multiple access scheme, we propose an interference-free multiple
antenna transmission scheme to achieve dynamic allocation of time-frequency resources and multi-user transmission. Finally, we propose a communication and sensing fusion-based dynamic iterative computation scheme, simultaneously achieving data demodulation and sensing parameter estimation. Simulation results show that the proposed scheme can achieve
ISAC under the dynamic flight scenarios of
UAVs. Meanwhile, the scheme outperforms the mmWave-LoRadar in communication and sensing performance, yet its sensing performance is slightly lower than that of the traditional FMCW. Under the urban clutter modeling, the scheme still maintains favorable robustness despite a certain degree of performance degradation.
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All authors Yi Tao, Zhen Gao, Zhuoran Li, Ziwei Wan, Tuan Li, Chunli Zhu, Lei Chen, Guanghui Wen, Dezhi Zheng, Dusit Niyato
Systems and Control
Hypergame-based Cognition Modeling and Intention Interpretation for Human-Driven Vehicles in Connected Mixed Traffic
Jianguo Chen, Zhengqin Liu, Jinlong Lei et al. (+3 more)
With the practical implementation of connected and autonomous vehicles (CAVs), the traffic system is expected to remain a mix of CAVs and human-driven vehicles (HVs) for the foreseeable future. To enhance safety and traffic efficiency, the trajectory planning strategies of CAVs must account for the influence of HVs, necessitating accurate HV trajectory prediction. Current research often assumes that human drivers have perfect knowledge of all vehicles' objectives, an unrealistic premise. This paper bridges the gap by leveraging hypergame theory to account for cognitive and perception limitations in HVs. We model human bounded rationality without assuming them to be merely passive followers and propose a hierarchical cognition modeling framework that captures cognitive relationships among vehicles. We further analyze the cognitive stability of the system, proving that the strategy profile where all vehicles adopt cognitively
equilibrium strategies constitutes a hyper Nash
equilibrium when CAVs accurately learn HV parameters. To achieve this, we develop an inverse learning
algorithm for distributed intention interpretation via vehicle-to-everything (V2X) communication, which extends the framework to both offline and online scenarios. Additionally, we introduce a distributed trajectory prediction and planning approach for CAVs, leveraging the learned parameters in real time. Simulations in highway lane-changing scenarios demonstrate the proposed method's accuracy in parameter learning, robustness to noisy trajectory observations, and safety in HV trajectory prediction. The results validate the effectiveness of our method in both offline and online implementations.
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All authors Jianguo Chen, Zhengqin Liu, Jinlong Lei, Peng Yi, Yiguang Hong, Hong Chen
Image and Video Processing · Cross-listed from cs.CV
ClapperText: A Benchmark for Text Recognition in Low-Resource Archival Documents
Tingyu Lin, Marco Peer, Florian Kleber et al. (+1 more)
This paper presents ClapperText, a
benchmark dataset for handwritten and printed text recognition in visually degraded and low-resource settings. The
dataset is derived from 127 World War II-era archival
video segments containing clapperboards that record structured production metadata such as date, location, and camera-operator identity. ClapperText includes 9,813 annotated frames and 94,573 word-level text instances, 67% of which are handwritten and 1,566 are partially occluded. Each instance includes transcription, semantic category, text type, and occlusion status, with annotations available as rotated bounding boxes represented as 4-point polygons to support spatially precise OCR applications. Recognizing clapperboard text poses significant challenges, including motion blur, handwriting variation, exposure fluctuations, and cluttered backgrounds, mirroring broader challenges in historical document analysis where structured content appears in degraded, non-standard forms. We provide both full-frame annotations and cropped word images to support downstream tasks. Using a consistent per-
video evaluation protocol, we
benchmark six representative recognition and seven detection models under zero-shot and fine-tuned conditions. Despite the small training set (18
videos), fine-tuning leads to substantial performance gains, highlighting ClapperText's suitability for few-shot learning scenarios. The
dataset offers a realistic and culturally grounded resource for advancing robust OCR and document understanding in low-resource archival contexts. The
dataset and evaluation code are available at https://github.com/linty5/ClapperText.
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All authors Tingyu Lin, Marco Peer, Florian Kleber, Robert Sablatnig
Systems and Control · Cross-listed from cs.AI
Hypergraph Contrastive Sensor Fusion for Multimodal Fault Diagnosis in Induction Motors
Usman Ali, Ali Zia, Waqas Ali et al. (+4 more)
Reliable induction motor (IM) fault diagnosis is vital for industrial safety and operational continuity, mitigating costly unplanned downtime. Conventional approaches often struggle to capture complex
multimodal signal relationships, are constrained to unimodal data or single fault types, and exhibit performance degradation under noisy or cross-domain conditions. This paper proposes the
Multimodal Hypergraph Contrastive
Attention Network (MM-HCAN), a unified framework for robust fault diagnosis. To the best of our knowledge, MM-HCAN is the first to integrate contrastive learning within a hypergraph
topology specifically designed for
multimodal sensor fusion, enabling the joint modelling of intra- and inter-modal dependencies and enhancing generalisation beyond Euclidean
embedding spaces. The model facilitates simultaneous diagnosis of bearing, stator, and rotor faults, addressing the engineering need for consolidated di- agnostic capabilities. Evaluated on three real-world
benchmarks, MM-HCAN achieves up to 99.82% accuracy with strong cross-domain generalisation and resilience to noise, demonstrating its suitability for real-world deployment. An ablation study validates the contribution of each component. MM-HCAN provides a scalable and robust solution for comprehensive multi-fault diagnosis, supporting predictive maintenance and extended asset longevity in industrial environments.
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All authors Usman Ali, Ali Zia, Waqas Ali, Umer Ramzan, Abdul Rehman, Muhammad Tayyab Chaudhry, Wei Xiang
Image and Video Processing · Cross-listed from cs.LG
An Empirical Study on Variance-based MC Dropout Uncertainty-Error Correlation in 2D Brain Tumor Segmentation
Saumya B
Accurate
brain tumor
segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, the effectiveness of variance-based uncertainty - computed as pixel-wise variance across
stochastic forward passes - in identifying
segmentation errors, particularly near tumor boundaries, remains insufficiently studied. This study empirically examines the relationship between variance-based MC Dropout uncertainty and
segmentation error in 2D
brain tumor MRI
segmentation using a U-Net trained under four augmentation settings: none, horizontal flip, rotation, and scaling. Uncertainty was estimated as the pixel-wise variance across 50
stochastic forward passes and correlated with pixel-wise errors using Pearson and Spearman coefficients. Results show weak global correlations (r ~ 0.30-0.38) and negligible boundary correlations (|r| < 0.05). Although differences across augmentations were statistically significant (p < 0.001), they lacked practical relevance. These findings suggest that variance-based MC Dropout uncertainty provides limited cues for global and boundary error localization, and that the choice of uncertainty representation critically affects the utility of MC Dropout in medical image
segmentation. Alternative representations such as predictive
entropy or mutual information may better capture
segmentation errors, particularly at boundaries.
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Systems and Control
A Tsetlin Machine Image Classification Accelerator on a Flexible Substrate
Yushu Qin, Marcos L. L. Sartori, Shengyu Duan et al. (+3 more)
This paper introduces the first implementation of digital Tsetlin Machines (TMs) on flexible integrated
circuit (FlexIC) using Pragmatic's 600nm IGZO-based FlexIC technology. TMs, known for their energy efficiency, interpretability, and suitability for edge computing, have previously been limited by the
rigidity of conventional silicon-based chips. We develop two TM inference models as FlexICs: one achieving 98.5% accuracy using 6800 NAND2 equivalent logic gates with an area of 8X8 mm2, and a second more compact version achieving slightly lower prediction accuracy of 93% but using only 1420 NAND2 equivalent gates with an area of 4X4 mm2, both of which are custom-designed for an 8X8-pixel handwritten digit recognition
dataset. The paper demonstrates the feasibility of deploying flexible TM inference engines into wearable healthcare and edge computing applications.
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All authors Yushu Qin, Marcos L. L. Sartori, Shengyu Duan, Emre Ozer, Rishad Shafik, Alex Yakovlev
Systems and Control · Cross-listed from physics.plasm-ph
Modelling-driven requirements for Error Field Control Coil application to initial JT-60SA plasmas
L. Pigatto, G. Frello, Y.Q. Liu et al. (+4 more)
JT-60SA is a large superconducting tokamak built in Naka, Japan. After the successful achievement of its first MA-class
plasma, the installation of several additional sub-systems, including a set of non-axisymmetric Error Field Correction Coils (EFCC), is ongoing. Optimization of future JT-60SA
plasma scenarios will critically depend on the correct use of EFCC, including careful fulfillment of system specifications. In addition to that, preparation and risk mitigation of early ITER operations will greatly benefit from the experience gained by early EFCC application to JT-60SA experiments, in particular to optimize error field detection and control strategies. In this work, EFCC application in JT-60SA Initial Research Phase I perspective scenarios is modeled including
plasma response. Impact of (Resonant)
Magnetic Perturbations on the different
plasma scenarios is assessed for both core and pedestal regions by the linear resistive MHD code MARS-F. The dominant core response to EFs is discussed case by case and compared to mode locking thresholds from literature. Typical current/voltage amplitudes and wave-forms are then compared to EFCC specifications in order to assess a safe operational space.
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All authors L. Pigatto, G. Frello, Y.Q. Liu, L. Novello, M. Takechi, E. Tomasina, T. Bolzonella
Systems and Control · Cross-listed from cs.DC
Balancing Fairness and Performance in Multi-User Spark Workloads with Dynamic Scheduling (extended version)
Dāvis Kažemaks, Laurens Versluis, Burcu Kulahcioglu Ozkan et al. (+1 more)
Apache Spark is a widely adopted framework for large-scale data processing. However, in industrial analytics environments, Spark's built-in schedulers, such as FIFO and fair scheduling, struggle to maintain both user-level
fairness and low mean response time, particularly in long-running shared applications. Existing solutions typically focus on job-level
fairness which unintentionally favors users who submit more jobs. Although Spark offers a built-in fair scheduler, it lacks adaptability to dynamic user workloads and may degrade overall job performance. We present the User Weighted Fair Queuing (UWFQ) scheduler, designed to minimize job response times while ensuring equitable resource distribution across users and their respective jobs. UWFQ simulates a virtual fair queuing system and schedules jobs based on their estimated finish times under a bounded
fairness model. To further address task skew and reduce priority inversions, which are common in Spark workloads, we introduce runtime partitioning, a method that dynamically refines task granularity based on expected runtime. We implement UWFQ within the Spark framework and evaluate its performance using multi-user synthetic workloads and Google cluster traces. We show that UWFQ reduces the average response time of small jobs by up to 74% compared to existing built-in Spark schedulers and to state-of-the-art fair scheduling
algorithms.
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All authors Dāvis Kažemaks, Laurens Versluis, Burcu Kulahcioglu Ozkan, Jérémie Decouchant
Signal Processing
Multi-Target Flexible Angular Emulation for ISAC Base Station Testing Using a Conductive Amplitude and Phase Matrix Setup: Framework and Experimental Validation
Chunhui Li, Chengrui Wang, Zhiqiang Yuan et al. (+1 more)
Comprehensive evaluation of the functionalities,
algorithms, hardware components, and performance characteristics of future integrated sensing and communication (
ISAC) base stations (BSs) under realistic deployment scenarios in controlled laboratory environments represents a critical requirement for
ISAC technology advancement. A primary challenge in achieving this objective involves the emulation of multiple targets with arbitrary radar cross-section (RCS), range, angle, and Doppler profiles for
ISAC BS equipped with large-scale
antenna arrays using radar target simulator (RTS) with limited interface ports. In this work, we introduce a simple yet highly effective and practical conductive amplitude and phase
matrix framework to address this fundamental challenge. The core concept involves introducing a tunable conductive amplitude and phase modulation network in the test configuration between the
ISAC BS under test and a RTS. Based on this structure, we subsequently investigate the corresponding configurations for different sensing operational modes of
ISAC BSs, specifically the array duplex transmission and reception (ADTR) mode and the split-array transmission and reception (SATR) mode. For experimental validation, we design two distinct monostatic sensing scenarios to demonstrate the framework capabilities across both operational modes. The first scenario involves dynamic multi-drone sensing validation for ADTR mode operation, while the second scenario addresses static single-drone sensing for SATR mode validation. The experimental results demonstrate that the proposed framework can accurately emulate the joint RCS, range, velocity, and angular characteristics of multiple sensing targets within the conductive test environment, highlighting its significant potential for testing applications in sub-6 GHz
ISAC BS development and validation.
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All authors Chunhui Li, Chengrui Wang, Zhiqiang Yuan, Wei Fan
Audio and Speech Processing
MC-LExt: Multi-Channel Target Speaker Extraction with Onset-Prompted Speaker Conditioning Mechanism
Tongtao Ling, Shulin He, Pengjie Shen et al. (+1 more)
Multi-channel target speaker extraction (MC-TSE) aims to extract a target speaker's voice from multi-speaker signals captured by multiple microphones. Existing methods often rely on auxiliary clues such as direction-of-arrival (DOA) or speaker
embeddings. However, DOA-based approaches depend on explicit direction estimation and are sensitive to microphone array geometry, while methods based on speaker
embeddings model speaker identity in an implicit manner and may degrade in noisy-reverberant conditions. To address these limitations, we propose multi-channel listen to extract (MC-LExt), a simple but highly-effective framework for MC-TSE. Our key idea is to prepend a short enrollment utterance of the target speaker to each channel of the multi-channel mixture, providing an onset-prompted conditioning signal that can guide TSE. This design allows the deep neural network (DNN) to learn spatial and speaker identity cues jointly in a fully end-to-end manner. Experiments on noisy-reverberant
benchmarks, including WHAMR! and MC-Libri2Mix, demonstrate the effectiveness of MC-TSE.
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All authors Tongtao Ling, Shulin He, Pengjie Shen, Zhong-Qiu Wang
Audio and Speech Processing
Quantization-Based Score Calibration for Few-Shot Keyword Spotting with Dynamic Time Warping in Noisy Environments
Kevin Wilkinghoff, Alessia Cornaggia-Urrigshardt and Zheng-Hua Tan
Detecting occurrences of keywords with keyword spotting (KWS) systems requires thresholding continuous detection scores. Selecting appropriate thresholds is a non-trivial task, typically relying on optimizing performance on a validation
dataset. However, such greedy threshold selection often leads to suboptimal performance on unseen data, particularly in varying or noisy acoustic environments or few-shot settings. In this work, we investigate detection threshold estimation for template-based open-set few-shot KWS using dynamic time warping on noisy
speech data. To mitigate the performance degradation caused by suboptimal thresholds, we propose a score calibration approach that operates at the
embedding level by quantizing learned representations and applying quantization error-based normalization prior to DTW-based scoring and thresholding. Experiments on KWS-DailyTalk with simulated high frequency
radio channels show that the proposed calibration approach simplifies the selection of robust detection thresholds and significantly improves the resulting performance.
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Image and Video Processing
A Cross-Framework Study of Temporal Information Buffering Strategies for Learned Video Compression
Kuan-Wei Ho, Yi-Hsin Chen, Martin Benjak et al. (+2 more)
Recent advances in learned
video codecs have demonstrated remarkable compression efficiency. Two fundamental design aspects are critical: the choice of inter-frame coding framework and the temporal information propagation strategy. Inter-frame coding frameworks include residual coding, conditional coding, conditional residual coding, and masked conditional residual coding, each with distinct mechanisms for utilizing temporal predictions. Temporal propagation methods can be categorized as explicit, implicit, or hybrid buffering, differing in how past decoded information is stored and used. However, a comprehensive study covering all possible combinations is still lacking. This work systematically evaluates the impact of explicit, implicit, and hybrid buffering on coding performance across four inter-frame coding frameworks under a unified experimental setup, providing a thorough understanding of their effectiveness.
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All authors Kuan-Wei Ho, Yi-Hsin Chen, Martin Benjak, Jörn Ostermann, Wen-Hsiao Peng
Audio and Speech Processing
Towards Blind Data Cleaning: A Case Study in Music Source Separation
Azalea Gui, Woosung Choi, Junghyun Koo et al. (+5 more)
The performance of deep learning models for music source separation heavily depends on training data quality. However,
datasets are often corrupted by difficult-to-detect artifacts such as audio bleeding and label noise. Since the type and extent of contamination are typically unknown, cleaning methods targeting specific corruptions are often impractical. This paper proposes and evaluates two distinct, noise-agnostic data cleaning methods to address this challenge. The first approach uses data attribution via unlearning to identify and filter out training samples that contribute the least to producing clean outputs. The second leverages the Fréchet Audio Distance to measure and remove samples that are perceptually dissimilar to a small and trusted clean reference set. On a
dataset contaminated with a simulated distribution of real-world noise, our unlearning-based methods produced a cleaned
dataset and a corresponding model that outperforms both the original contaminated data and the small clean reference set used for cleaning. This result closes approximately 66.7% of the performance gap between the contaminated baseline and a model trained on the same
dataset without any contamination. Unlike methods tailored for specific artifacts, our noise-agnostic approaches offer a more generic and broadly applicable solution for curating high-quality training data.
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All authors Azalea Gui, Woosung Choi, Junghyun Koo, Kazuki Shimada, Takashi Shibuya, Joan Serrà, Wei-Hsiang Liao, Yuki Mitsufuji
Systems and Control · Cross-listed from stat.ML
Recursive Inference for Heterogeneous Multi-Output GP State-Space Models with Arbitrary Moment Matching
Tengjie Zheng, Jilan Mei, Di Wu et al. (+2 more)
Accurate learning of system dynamics is becoming increasingly crucial for advanced control and decision-making in engineering. However, real-world systems often exhibit multiple channels and highly nonlinear transition dynamics, challenging traditional modeling methods. To enable online learning for these systems, this paper formulates the system as Gaussian process state-space models (GPSSMs) and develops a recursive learning method. The main contributions are threefold. First, a heterogeneous multi-output kernel is designed, allowing each output dimension to adopt distinct kernel types, hyperparameters, and input variables, improving expressiveness in multi-dimensional dynamics learning. Second, an inducing-point management
algorithm enhances computational efficiency through independent selection and
pruning for each output dimension. Third, a unified recursive inference framework for GPSSMs is derived, supporting general moment matching approaches, including the extended Kalman filter (EKF), unscented Kalman filter (UKF), and assumed density filtering (ADF), enabling accurate learning under strong nonlinearity and significant noise. Experiments on synthetic and real-world
datasets show that the proposed method matches the accuracy of SOTA offline GPSSMs with only 1/100 of the runtime, and surpasses SOTA online GPSSMs by around 70% in accuracy under heavy noise while using only 1/20 of the runtime.
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All authors Tengjie Zheng, Jilan Mei, Di Wu, Lin Cheng, Shengping Gong
Audio and Speech Processing
DroneAudioset: An Audio Dataset for Drone-based Search and Rescue
Chitralekha Gupta, Soundarya Ramesh, Praveen Sasikumar et al. (+2 more)
Unmanned Aerial Vehicles (
UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage
vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing
datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The
dataset is publicly available at https://huggingface.co/
datasets/ahlab-drone-project/DroneAudioSet/ under the MIT license), a comprehensive drone audition
dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The
dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This
dataset is an important step towards enabling design and deployment of drone-audition systems.
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All authors Chitralekha Gupta, Soundarya Ramesh, Praveen Sasikumar, Kian Peen Yeo, Suranga Nanayakkara
Signal Processing · Cross-listed from cs.CR
Bilinear Compressive Security
Axel Flinth, Hubert Orlicki, Semira Einsele et al. (+1 more)
Beyond its widespread application in signal and image processing, compressed sensing principles have been greatly applied to secure information transmission (often termed 'compressive security'). In this scenario, the measurement
matrix $Q$ acts as a one time pad encryption key (in complex number domain) which can achieve perfect information-theoretic security together with other benefits such as reduced complexity and energy efficiency particularly useful in IoT. However, unless the
matrix is changed for every message it is vulnerable towards known plain text attacks: only $n$ observations suffices to recover a key $Q$ with $n$ columns. In this paper, we invent and analyze a new method (termed 'Bilinear Compressive Security (BCS)') addressing these shortcomings: In addition to the linear encoding of the message $x$ with a
matrix $Q$, the sender convolves the resulting vector with a randomly generated filter $h$. Assuming that $h$ and $x$ are sparse, the receiver can then recover $x$ without knowledge of $h$ from $y=h*Qx$ through blind deconvolution. We study a rather idealized known plaintext attack for recovering $Q$ from repeated observations of $y$'s for different, known $x_k$, with varying and unknown $h$ ,giving Eve a number of advantages not present in practice. Our main result for BCS states that under a weak
symmetry condition on the filter $h$, recovering $Q$ will require extensive sampling from transmissions of $\Omega\left(\max\left(n,(n/s)^2\right)\right)$ messages $x_k$ if they are $s$-sparse. Remarkably, with $s=1$ it is impossible to recover the key. In this way, the scheme is much safer than standard compressed sensing even though our assumptions are much in favor towards a potential attacker.
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All authors Axel Flinth, Hubert Orlicki, Semira Einsele, Gerhard Wunder
Systems and Control
TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making
Maonan Wang, Yirong Chen, Yuxin Cai et al. (+12 more)
Air-ground collaborative intelligence is becoming a key approach for next-generation urban intelligent transportation management, where aerial and ground systems work together on perception, communication, and decision-making. However, the lack of a unified multi-modal simulation environment has limited progress in studying cross-domain perception, coordination under communication constraints, and joint decision optimization. To address this gap, we present TranSimHub, a unified simulation platform for air-ground collaborative intelligence. TranSimHub offers synchronized multi-view rendering across RGB, depth, and semantic
segmentation modalities, ensuring consistent perception between aerial and ground viewpoints. It also supports information exchange between the two domains and includes a causal scene editor that enables controllable scenario creation and counterfactual analysis under diverse conditions such as different weather, emergency events, and dynamic obstacles. We release TranSimHub as an open-source platform that supports end-to-end research on perception, fusion, and control across realistic air and ground traffic scenes. Our code is available at https://github.com/Traffic-Alpha/TransSimHub.
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All authors Maonan Wang, Yirong Chen, Yuxin Cai, Aoyu Pang, Yuejiao Xie, Zian Ma, Chengcheng Xu, Kemou Jiang, Ding Wang, Laurent Roullet, Chung Shue Chen, Zhiyong Cui, Yuheng Kan, Michael Lepech, Man-On Pun
Audio and Speech Processing
LDCodec: A high quality neural audio codec with low-complexity decoder
Jiawei Jiang, Linping Xu, Dejun Zhang et al. (+3 more)
Neural audio coding has been shown to outperform classical audio coding at extremely low bitrates. However, the practical application of neural audio codecs is still limited by their elevated complexity. To address this challenge, we have developed a high-quality neural audio codec with a low-complexity decoder, named LDCodec (Low-complexity Decoder Neural Audio Codec), specifically designed for on-demand streaming media clients, such as smartphones. Specifically, we introduced a novel residual unit combined with Long-term and Short-term Residual Vector Quantization (LSRVQ), subband-fullband frequency discriminators, and perceptual loss functions. This combination results in high-quality audio reconstruction with lower complexity. Both our subjective and objective tests demonstrated that our proposed LDCodec at 6kbps outperforms Opus at 12kbps.
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All authors Jiawei Jiang, Linping Xu, Dejun Zhang, Qingbo Huang, Xianjun Xia, Yijian Xiao
Image and Video Processing
Confidence-Weighted Semi-Supervised Learning for Skin Lesion Segmentation Using Hybrid CNN-Transformer Networks
Saqib Qamar
Automated skin lesion
segmentation through dermoscopic analysis is essential for early skin cancer detection, yet remains challenging due to limited annotated training data. We present MIRA-U, a semi-supervised framework that combines uncertainty-aware teacher-student pseudo-labeling with a hybrid CNN-
Transformer architecture. Our approach employs a teacher network pre-trained via masked image modeling to generate confidence-weighted soft pseudo-labels, which guide a U-shaped CNN-
Transformer student network featuring cross-
attention skip connections. This design enhances pseudo-label quality and boundary delineation, surpassing reconstruction-based and CNN-only baselines, particularly in low-annotation regimes. Extensive evaluation on ISIC-2016 and PH2
datasets demonstrates superior performance, achieving a Dice Similarity Coefficient (DSC) of 0.9153 and Intersection over Union (IoU) of 0.8552 using only 50% labeled data. Code is publicly available on GitHub.
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Image and Video Processing
Symmetric Entropy-Constrained Video Coding for Machines
Yuxiao Sun, Meiqin Liu, Chao Yao et al. (+5 more)
As
video transmission increasingly serves machine
vision systems (MVS) instead of human
vision systems (HVS),
video coding for machines (VCM) has become a critical research topic. Existing VCM methods often bind codecs to specific downstream models, requiring retraining or supervised data, thus limiting generalization in multi-task scenarios. Recently, unified VCM frameworks have employed visual backbones (VB) and visual foundation models (VFM) to support multiple
video understanding tasks with a single codec. They mainly utilize VB/VFM to maintain semantic consistency or suppress non-semantic information, but seldom explore how to directly link
video coding with understanding under VB/VFM guidance. Hence, we propose a Symmetric
Entropy-Constrained
Video Coding framework for Machines (SEC-VCM). It establishes a symmetric
alignment between the
video codec and VB, allowing the codec to leverage VB's representation capabilities to preserve semantics and discard MVS-irrelevant information. Specifically, a bi-directional
entropy-constraint (BiEC) mechanism ensures
symmetry between the process of
video decoding and VB encoding by suppressing conditional
entropy. This helps the codec to explicitly handle semantic information beneficial to MVS while squeezing useless information. Furthermore, a semantic-pixel dual-path fusion (SPDF) module injects pixel-level priors into the final reconstruction. Through semantic-pixel fusion, it suppresses artifacts harmful to MVS and improves machine-oriented reconstruction quality. Experimental results on classical
video understanding tasks and MLLM-based tasks show SOTA rate-task performance. It achieves significant bitrate savings over H.266/VVC reference software VTM on
video instance
segmentation (37.4%),
video object
segmentation (29.8%), object detection (46.2%), multiple object tracking (44.9%), and MLLM-based
video grounding (97.6%).
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All authors Yuxiao Sun, Meiqin Liu, Chao Yao, Qi Tang, Jian Jin, Weisi Lin, Frederic Dufaux, Yao Zhao
Systems and Control · Cross-listed from quant-ph
Singularity-free dynamical invariants-based quantum control
Ritik Sareen, Akram Youssry and Alberto Peruzzo
State preparation is a cornerstone of
quantum technologies, underpinning applications in computation, communication, and sensing. Its importance becomes even more pronounced in non-Markovian open
quantum systems, where environmental
memory and model uncertainties pose significant challenges to achieving high-fidelity control. Invariant-based inverse engineering provides a principled framework for synthesizing analytic control fields, yet existing parameterizations often lead to experimentally infeasible, singular pulses and are limited to simplified noise models such as those of Lindblad form. Here, we introduce a generalized invariant-based protocol for finite-dimensional state preparation under arbitrary noise conditions. We transform the finite-dimensional control problem into the equivalent problem for a single-
qubit, by restricting the dynamics to a designed SU(2) subspace. The control protocol then proceeds in two-stages: first, we construct a family of bounded pulses that achieve perfect state preparation in a closed system; second, we identify the optimal member of this family that minimizes the effect of noise. The framework accommodates both (i) characterized noise, enabling noise-aware control synthesis, and (ii) uncharacterized noise, where a noise-agnostic variant preserves robustness without requiring a master-equation description. Numerical simulations demonstrate high-fidelity state preparation across diverse targets while producing smooth, hardware-feasible control fields. This singularity-free framework extends invariant-based control to realistic open-system regimes, providing a versatile route toward robust
quantum state engineering on NISQ hardware and other platforms exhibiting non-Markovian dynamics.
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Systems and Control · Cross-listed from cs.RO
Adaptive Cost-Map-based Path Planning in Partially Unknown Environments with Movable Obstacles
Liviu-Mihai Stan, Ranulfo Bezerra, Shotaro Kojima et al. (+4 more)
Reliable
navigation in disaster-response and other unstructured indoor settings requires
robots not only to avoid obstacles but also to recognise when those obstacles can be pushed aside. We present an adaptive,
LiDAR and odometry-based path-planning framework that embeds this capability into the ROS2 Nav2 stack. A new Movable Obstacles Layer labels all
LiDAR returns missing from a prior static map as tentatively movable and assigns a reduced traversal cost. A companion Slow-Pose Progress Checker monitors the ratio of commanded to actual velocity; when the
robot slows appreciably, the local cost is raised from light to heavy, and on a stall to lethal, prompting the global planner to back out and re-route. Gazebo evaluations on a Scout Mini, spanning isolated objects and cluttered corridors, show higher goal-reach rates and fewer deadlocks than a no-layer baseline, with traversal times broadly comparable. Because the method relies only on planar scans and CPU-level computation, it suits resource-constrained search and rescue
robots and integrates into heterogeneous platforms with minimal engineering. Overall, the results indicate that interaction-aware cost maps are a lightweight, ROS2-native extension for navigating among potentially movable obstacles in unstructured settings. The full implementation will be released as open source athttps://costmap-namo.github.io.
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All authors Liviu-Mihai Stan, Ranulfo Bezerra, Shotaro Kojima, Tsige Tadesse Alemayoh, Satoshi Tadokoro, Masashi Konyo, Kazunori Ohno
Systems and Control
Modeling and Dynamic Simulation of a Hybrid Wind-Wave System on a Hexagonal Semi-Submersible Platform
Saeid Bayat, Jerry Zuo and Jing Sun
Offshore renewable energy systems offer promising solutions for sustainable power generation, yet most existing platforms harvest either wind or wave energy in isolation. This study presents a hybrid floating offshore platform that integrates a wind turbine with three oscillating surge wave energy converters (WECs) into a hexagonal semi-submersible structure. In this configuration, the flaps are integrated with the platform geometry to provide both energy extraction and hydrodynamic stability. A modeling and simulation framework was developed using WEC-Sim and benchmarked against the NREL 5 MW semisubmersible reference. Metacentric height analysis confirmed hydrostatic stability across a range of prescribed flap angles. Sensitivity analysis of twelve
geometric variables identified flap dimensions and tower length as dominant drivers of stability, energy capture, and tower stress. Time-domain simulations revealed dependence on wave incidence angle, with variations in flap power sharing, capture width ratio (CWR), and platform response. The feasibility of using flap sweeps to modulate pitch motion was also demonstrated. Annual energy production (AEP) estimates based on site-specific data indicate 16.86 GWh from wind and 3.65 GWh from wave energy, with WECs contributing about 18% of the total. These results highlight the potential of integrated wind-wave platforms and point toward future studies on structural modeling and advanced control.
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Signal Processing
Multidimensional Physiology-Inspired Enhanced Vital Sign Monitoring Using MIMO mmWave Bio-radar
Heyao Zhu, Yimeng Zhao, Zirui Zhang et al. (+5 more)
With the intensiffcation of population aging and increasing burden of chronic diseases, the demand for vital signs monitoring is becoming increasingly urgent. A key challenge facing current non-contact detection technologies using millimeter wave (mmWave) radar is the low efffciency of multi-channel signal fusion in array radar systems based on equal weighting. To address this challenge, this paper proposes a vital sign enhancement detection method for multiple input and multiple output (
MIMO) bio-radar, driven by multidimensional physiological characteristics, which overcomes traditional limitations through a two-stage fusion strategy. Stage 1: Enhanced Vital Sign Detection Using Single-Channel Signals Based on Physiological Characteristics. First, a chest wall multi-scattering point model is constructed. For single channel time-distance two-dimensional echo signals, effective range bins are selected based on the respiratory/cardiac physiological frequency band energy ratio, and the signal-to-noise ratio (SNR) of respiration/heart signals is enhanced using phase-aligned maximal ratio combining (MRC). Stage 2: Multi-Channel Fusion Based on Organ Radiation Spatial Distribution Characteristics. The spatial radiation characteristics of cardiopulmonary organs are introduced for the ffrst time as the theoretical foundation for SNR-based channel screening, channel attribute identiffcation, and multi-channel weighted fusion. Then, we propose a template matching method to extract respiratory rate (RR) and heart rate (HR) by adopting physical models of respiration and cardiac activities. The experimental results demonstrate the existence of the spatial distribution characteristics of organ radiation. In addition, we analyzed the impact of distance and state on the
algorithm from these two aspects.
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All authors Heyao Zhu, Yimeng Zhao, Zirui Zhang, Huansheng Yi, Chenbin Gao, Canhua Xu, Jianqi Wang, Fugui Qi
Systems and Control · Cross-listed from math.OC
An Iterative Problem-Driven Scenario Reduction Framework for Stochastic Optimization with Conditional Value-at-Risk
Yingrui Zhuang, Lin Cheng, Ning Qi et al. (+2 more)
Scenario reduction (SR) alleviates the computational complexity of scenario-based
stochastic optimization with conditional value-at-risk (SBSO-CVaR) by identifying representative scenarios to depict the underlying uncertainty and tail risks. Existing distribution-driven SR methods emphasize statistical similarity but often exclude extreme scenarios, leading to weak tail-risk awareness and insufficient problem-specific representativeness. Instead, this paper proposes an iterative problem-driven scenario reduction framework. Specifically, we integrate the SBSO-CVaR problem structure into SR process and project the original scenario set from the distribution space onto the problem space. Subsequently, to minimize the SR optimality gap with acceptable computation complexity, we propose a tractable iterative problem-driven scenario reduction (IPDSR) method that selects representative scenarios that best approximate the optimality distribution of the original scenario set while preserving tail risks. Furthermore, the iteration process is rendered as a mixed-integer program to enable scenario partitioning and representative scenarios selection. And ex-post problem-driven evaluation indices are proposed to evaluate the SR performance. Numerical experiments show IPDSR significantly outperforms existing SR methods by achieving an optimality gap of less than 1% within an acceptable computation time.
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All authors Yingrui Zhuang, Lin Cheng, Ning Qi, Mads R. Almassalkhi, Feng Liu