Systems and Control
Inertia Partitioning Modular Robust Control Framework for Reconfigurable Multibody Systems
Mohammad Dastranj and Jouni Mattila
A novel modular modeling and control framework based on Lagrangian mechanics is proposed for multibody systems, motivated by the challenges of modular control of systems with closed kinematic chains and by the need for a modeling framework that remains locally updatable under reconfiguration of body-level
geometric and inertial properties. In the framework, modularity is defined with respect to the degrees of freedom of the multibody system, represented in the model by the minimal generalized coordinates, and the inertial properties of each body are partitioned with respect to how they are reflected in the kinetic energy of the system through the motion induced by each degree of freedom. By expressing body contributions through body-fixed-frame Jacobians and spatial inertia
matrices, the dynamic model remains locally updatable under changes in
geometric and inertial parameters, which is advantageous for reconfigurable multibody systems. For multibody systems in which a mapping between the auxiliary and minimal generalized coordinates is available, the approach accommodates closed kinematic chains in a minimal-coordinate ordinary-differential-equation form without explicit constraint-force calculation or differential-algebraic-equation formulation. Based on the resulting modular equations of motion, a robust model-based controller is designed for trajectory tracking, and practical boundedness of the tracking error is analyzed under bounded uncertainty and external disturbance. The proposed framework is implemented in simulation on a three-degree-of-freedom series-parallel manipulator, where uncertainties and disturbances are introduced to assess robustness. The results are consistent with the expected stability and tracking performance, indicating the potential of the framework for trajectory-tracking control of reconfigurable multibody systems with closed kinematic chains.
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Systems and Control
Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training
Bastien Giraud, Rahul Nellikkath, Johanna Vorwerk et al. (+2 more)
The AC Optimal Power Flow (AC-OPF) problem is central to power system operations but remains computationally challenging to solve due to its non-convex, nonlinear nature. Neural networks (NNs) offer rapid surrogates; however, their black-box behavior introduces severe risk of operational constraint violations that compromise grid safety. This paper introduces a verification-informed NN training framework that embeds global worst-case violations directly into the training objective, allowing to train models with rigorous safety guarantees. We evaluate our method across two established NN architectures for AC-OPF predictions, benchmarking both their performance and safety margins. Through rigorous post-hoc verification, we achieve substantial reductions in worst-case violations and, for the first time, successfully verify all operational constraints of large-scale AC-OPF proxies. Experiments on systems ranging from 57 to 793 buses demonstrate scalability, speed, and reliability, bridging the gap between machine learning (ML) acceleration and verified, real-time deployment of AC-OPF solutions, paving the way toward safe data-driven optimal control.
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All authors Bastien Giraud, Rahul Nellikkath, Johanna Vorwerk, Maad Alowaifeer, Spyros Chatzivasileiadis
Systems and Control
Embroidery Actuator Utilizing Embroidery Patterns to Generate Diverse Fabric Deformations
Yuki Ota and Yuki Funabora
This paper presents a novel Embroidery Actuator, a fabric-integrated pneumatic actuator that enables diverse and controllable deformations through embroidery pattern design. Unlike conventional fabric actuators that rely on fiber- or thread-shaped actuators, the proposed actuator is fabricated by directly stitching an inflatable tube onto the fabric using a cord-embroidery technique. The embroidered thread and the fabric jointly form a sleeve that constrains the expansion of the inflatable tube, converting internal pressure into targeted bending or stretching deformations. By varying the embroidery pattern, such as zigzag or cross configurations, different
geometric constraints can be realized, allowing for flexible control of deformation direction and magnitude. Analytical deformation models based on the Neo-Hookean model and Lagrange's equations were developed to predict the relationship between pneumatic pressure and bending angle, and were experimentally validated using motion-capture measurements. The results demonstrated that the actuator achieves strong agreement with the analytical deformation model.
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Signal Processing
Approaching Domain Generalization with Embeddings for Robust Discrimination and Recognition of RF Communication Signals
Lukas Henneke and Frank Kurth
Radio frequency (RF) signal recognition plays a critical role in modern wireless communication and security applications. Deep learning-based approaches have achieved strong performance but typically rely heavily on extensive training data and often fail to generalize to unseen signals. In this paper, we propose a method to learn discriminative
embeddings without relying on real-world RF signal recordings by training on signals of synthetic wireless protocols. We validate the approach on a
dataset of real RF signals and show that the learned
embeddings capture features enabling accurate discrimination of previously unseen real-world signals, highlighting its potential for robust RF signal classification and anomaly detection.
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Audio and Speech Processing
Matching Reverberant Speech Through Learned Acoustic Embeddings and Feedback Delay Networks
Philipp Götz, Gloria Dal Santo, Sebastian J. Schlecht et al. (+2 more)
Reverberation conveys critical acoustic cues about the environment, supporting spatial awareness and immersion. For auditory augmented reality (AAR) systems, generating perceptually plausible reverberation in real time remains a key challenge, especially when explicit acoustic measurements are unavailable. We address this by formulating blind estimation of artificial reverberation parameters as a reverberant signal matching task, leveraging a learned room-acoustic prior. Furthermore, we propose a feedback delay network (FDN) structure that reproduces both frequency-dependent decay times and the direct-to-reverberation ratio of a target space. Experimental evaluation against a leading automatic FDN tuning method demonstrates improvements in estimated room-acoustic parameters and perceptual plausibility of artificial reverberant
speech. These results highlight the potential of our approach for efficient, perceptually consistent reverberation rendering in AAR applications.
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All authors Philipp Götz, Gloria Dal Santo, Sebastian J. Schlecht, Vesa Välimäki, Emanuël A.P. Habets
Image and Video Processing · Cross-listed from cs.LG
Adapting Interleaved Encoders with PPO for Language-Guided Reinforcement Learning in BabyAI
Aryan Mathur and Asaduddin Ahmed
Deep reinforcement learning
agents often struggle when tasks require understanding both
vision and
language. Conventional architectures typically isolate perception (for example, CNN-based visual encoders) from decision-making (policy networks). This separation can be inefficient, since the policy's failures do not directly help the perception module learn what is important. To address this, we implement the Perception-Decision Interleaving
Transformer (PDiT) architecture introduced by Mao et al. (2023), a model that alternates between perception and decision layers within a single
transformer. This interleaving allows feedback from decision-making to refine perceptual features dynamically. In addition, we integrate a contrastive loss inspired by CLIP to align textual mission
embeddings with visual scene features. We evaluate the PDiT encoders on the BabyAI GoToLocal environment and find that the approach achieves more stable rewards and stronger
alignment compared to a standard PPO baseline. The results suggest that interleaved
transformer encoders are a promising direction for developing more integrated autonomous
agents.
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Signal Processing
HAPS-ISAC for 6G: Architecture, Design Trade-offs, and a Practical Roadmap
Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi et al. (+1 more)
To meet the ambitious goals of next-generation 6G networks, including ultra-high data rates and ubiquitous coverage, we propose a novel high-altitude platform station (HAPS)-based integrated sensing and communication (
ISAC) architecture. Operating in the stratosphere, the HAPS functions as both a powerful communication hub and an advanced environmental sensor. Combined with a fleet of cooperative uncrewed aerial vehicles (
UAVs), this dual-purpose system forms a scalable and intelligent 3D network. Simulation results indicate that this approach significantly boosts network performance, improves sensing accuracy, and ensures a fairer service distribution across users, outperforming conventional
UAV-only baselines. We conclude by outlining the prospective applications and a deployment roadmap for this technology for smart cities and other large-scale environments.
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All authors Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi, Halim Yanikomeroglu
Audio and Speech Processing
Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement
Sarabeth S. Mullins, Georg Götz, Eric Bezzam et al. (+2 more)
Accurate far-field
speech datasets are critical for tasks such as automatic
speech recognition (ASR), dereverberation,
speech enhancement, and source separation. However, current
datasets are limited by the trade-off between acoustic realism and scalability. Measured corpora provide faithful physics but are expensive, low-coverage, and rarely include paired clean and reverberant data. In contrast, most simulation-based
datasets rely on simplified geometrical acoustics, thus failing to reproduce key physical phenomena like diffraction, scattering, and interference that govern sound propagation in complex environments. We introduce Treble10, a large-scale, physically accurate room-acoustic
dataset. Treble10 contains over 3000 broadband room impulse responses (RIRs) simulated in 10 fully furnished real-world rooms, using a hybrid simulation paradigm implemented in the Treble SDK that combines a wave-based and geometrical acoustics solver. The
dataset provides six complementary subsets, spanning mono, 8th-order Ambisonics, and 6-channel device RIRs, as well as pre-convolved reverberant
speech scenes paired with LibriSpeech utterances. All signals are simulated at 32 kHz, accurately modelling low-frequency wave effects and high-frequency reflections. Treble10 bridges the realism gap between measurement and simulation, enabling reproducible, physically grounded evaluation and large-scale data augmentation for far-field
speech tasks. The
dataset is openly available via the Hugging Face Hub, and is intended as both a
benchmark and a template for next-generation simulation-driven audio research.
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All authors Sarabeth S. Mullins, Georg Götz, Eric Bezzam, Steven Zheng, Daniel Gert Nielsen
Systems and Control · Cross-listed from cs.RO
Combining High Level Scheduling and Low Level Control to Manage Fleets of Mobile Robots
Sabino Francesco Roselli, Ze Zhang and Knut Åkesson
The deployment of mobile
robots for material handling in industrial environments requires scalable coordination of large fleets in dynamic settings. This paper presents a two-layer framework that combines high-level scheduling with low-level control. Tasks are assigned and scheduled using the compositional
algorithm ComSat, which generates time-parameterized routes for each
robot. These schedules are then used by a distributed Model Predictive Control (MPC) system in real time to compute local reference trajectories, accounting for static and dynamic obstacles. The approach ensures safe, collision-free operation, and supports rapid rescheduling in response to disruptions such as
robot failures or environmental changes. We evaluate the method in simulated 2D environments with varying road capacities and traffic conditions, demonstrating high task completion rates and robust behavior even under congestion. The modular structure of the framework allows for computational tractability and flexibility, making it suitable for deployment in complex, real-world industrial scenarios.
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Systems and Control
Context-awareness for Dependable Low-Power IoT
David E. Ruiz-Guirola, Prasoon Raghuwanshi, Gabriel M. de Jesus et al. (+2 more)
Dependability is the ability to consistently deliver trusted and uninterrupted service in the face of operational uncertainties. Ensuring dependable operation in large-scale, energy-constrained Internet of Things (IoT) deployments is as crucial as challenging, and calls for context-aware protocols where context refers to situational or state information. In this paper, we identify four critical context dimensions for IoT networks, namely energy status, information freshness, task relevance, and physical/medium conditions, and show how each one underpins core dependability attributes. Building on these insights, we propose a two-step protocol design framework that incorporates operation-specific context fields. Through three representative use cases, we demonstrate how context awareness can significantly enhance system dependability while imposing only minimal control-plane overhead.
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All authors David E. Ruiz-Guirola, Prasoon Raghuwanshi, Gabriel M. de Jesus, Mateen Ashraf, Onel L. A. López
Signal Processing · Cross-listed from math.NA
Numerical Spectrum Linking: Identification of Governing PDE via Koopman-Chebyshev Approximation
Phonepaserth Sisaykeo and Shogo Muramatsu
A numerical framework is proposed for identifying partial differential equations (PDEs) governing dynamical systems directly from their observation data using Chebyshev
polynomial approximation. In contrast to data-driven approaches such as dynamic mode decomposition (DMD), which approximate the Koopman operator without a clear connection to differential operators, the proposed method constructs finite-dimensional Koopman
matrices by projecting the dynamics onto a Chebyshev basis, thereby capturing both differential and nonlinear terms. This establishes a numerical link between the Koopman and differential operators. Numerical experiments on
benchmark dynamical systems confirm the accuracy and efficiency of the approach, underscoring its potential for interpretable operator learning. The framework also lays a foundation for future integration with symbolic regression, enabling the construction of explicit mathematical models directly from data.
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Systems and Control
NeuroDOB: A Deep Neural Observer-Based Controller for Vehicle Lateral Dynamics
Sangmin Kim, Taehun Kim, Guntae Kim et al. (+1 more)
This paper proposes NeuroDOB, a deep neural network based observer controller for vehicle lateral dynamics, which replaces the conventional disturbance observer (DOB) with a deep neural network (DNN) to enhance personalized lateral control. Unlike conventional DOBs that compensate for general disturbances such as road friction variation and crosswind, NeuroDOB explicitly addresses unmodeled vehicle dynamics and driver-specific behaviors by learning the steering compensation signal from driver-in-the-loop simulations using CarSim's embedded controller as a surrogate driver. The proposed architecture integrates NeuroDOB with a linear quadratic regulator (LQR), where the DNN outputs a delta error correction added to the baseline LQR steering input to produce the final control command. Input features to the DNN include lateral position and yaw angle errors, and the LQR control input. Experimental validation using a lateral dynamic bicycle model within CarSim demonstrates that NeuroDOB effectively adapts to individual driving habits, improving lateral control performance beyond what conventional LQR controllers achieve. The results indicate the potential of deep neural network based observer to enable personalized and adaptive autonomous vehicle control. In cognitive terms, the proposed architecture can be viewed as a dual-system control structure. The baseline LQR corresponds to System 1, a model-based, fast, and analytic
reasoning layer ensuring stability. The NeuroDOB acts as System 2, a reflective, data-driven layer that learns compensation from experience and corrects the analytical bias of System 1. Together, they form an integrated decision process analogous to human intuition-reflection interaction, enabling both stability and adaptability in lateral control.
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All authors Sangmin Kim, Taehun Kim, Guntae Kim, Chang Mook Kang
Systems and Control · Cross-listed from cs.CR
zkSTAR: A zero knowledge system for time series attack detection enforcing regulatory compliance in critical infrastructure networks
Paritosh Ramanan, Sathwik Yamana, H.M. Mohaimanul Islam et al. (+1 more)
Industrial control systems (ICS) form the operational backbone of critical infrastructure networks (CIN) such as power grids, water supply systems, and gas pipelines. As cyber threats to these systems escalate, regulatory agencies are imposing stricter compliance requirements to ensure system-wide security and reliability. A central challenge, however, is enabling regulators to verify the effectiveness of detection mechanisms without requiring utilities to disclose sensitive operational data. In this paper, we introduce zkSTAR, a zero-knowledge based cyberattack detection framework that leverages zk-SNARKs to enable regulatory compliance while delivering provable detection guarantees with complete data
privacy. Our approach builds on established residual-based statistical hypothesis testing methods applied to state-space detection models. Specifically, we design a two-pronged zk-SNARK architecture that enforces (i) temporal consistency of the state-space dynamics and (ii) statistical consistency of the detection tests, enabling regulators to verify correctness and prevent suppression of alarms without visibility into utility-level data. We formally analyze the soundness and zero-knowledge properties of our framework and validate its practical feasibility through computational experiments on real-world ICS
datasets. Our work demonstrates that zkSNARKs can provide a compliant, scalable,
privacy-preserving alternative for detecting data-driven cyberattacks on ICS driven critical infrastructure networks.
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All authors Paritosh Ramanan, Sathwik Yamana, H.M. Mohaimanul Islam, Abhiram Reddy Alugula
Systems and Control · Cross-listed from cs.RO
Seq-DeepIPC: Sequential Sensing for End-to-End Control in Legged Robot Navigation
Oskar Natan and Jun Miura
We present Seq-DeepIPC, a sequential end-to-end perception-to-control model for legged
robot navigation in real-world environments. Seq-DeepIPC advances intelligent sensing for autonomous legged
navigation by tightly integrating multi-modal perception (RGB-D + GNSS) with temporal fusion and control. The model jointly predicts semantic
segmentation and depth estimation, giving richer spatial features for planning and control. For efficient deployment on edge devices, we use a lightweight model as the encoder, reducing computation while maintaining accuracy. Heading estimation is simplified by removing the noisy IMU and instead deriving global heading via differential analysis of sequential GNSS coordinates. We collected a larger and more diverse
dataset that includes both road and grass terrains, and validated Seq-DeepIPC on a
robot dog. Comparative and ablation studies show that sequential inputs improve perception and control in our models, while other baselines do not benefit. Seq-DeepIPC achieves competitive or better results with reasonable model size; although GNSS-only heading is less reliable near tall buildings, it is robust in open areas. Overall, Seq-DeepIPC extends end-to-end
navigation beyond wheeled
robots to more versatile and temporally-aware systems. To support future research, we will release the codes to our GitHub repo at https://github.com/oskarnatan/Seq-DeepIPC.
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Signal Processing
Planning Oriented Integrated Sensing and Communication
Xibin Jin, Guoliang Li, Shuai Wang et al. (+5 more)
Integrated sensing and communication (
ISAC) enables simultaneous localization, environment perception, and data exchange for connected autonomous vehicles. However, most existing
ISAC designs prioritize sensing accuracy and communication throughput, treating all targets uniformly and overlooking the impact of critical obstacles on motion efficiency. To overcome this limitation, we propose a planning-oriented
ISAC (PISAC) framework that reduces the sensing uncertainty of planning-bottleneck obstacles and expands the safe navigable path for the ego-vehicle, thereby bridging the gap between physical-layer optimization and motion-level planning. The core of PISAC lies in deriving a closed-form safety bound that explicitly links
ISAC transmit power to sensing uncertainty, based on the Cramér-Rao Bound and occupancy inflation principles. Using this model, we formulate a bilevel power allocation and motion planning (PAMP) problem, where the inner layer optimizes the
ISAC beam power distribution and the outer layer computes a collision-free trajectory under uncertainty-aware safety constraints. Comprehensive simulations in high-fidelity urban driving environments demonstrate that PISAC achieves up to 40% higher success rates and over 5% shorter traversal times than existing
ISAC-based and communication-oriented
benchmarks, validating its effectiveness in enhancing both safety and efficiency.
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All authors Xibin Jin, Guoliang Li, Shuai Wang, Fan Liu, Miaowen Wen, Huseyin Arslan, Derrick Wing Kwan Ng, Chengzhong Xu
Systems and Control · Cross-listed from cs.RO
An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control
ZhengKai Huang, YiKun Wang, ChenYu Hui et al. (+1 more)
This paper introduces an intelligent water-saving irrigation system designed to address critical challenges in precision agriculture, such as inefficient water use and poor terrain adaptability. The system integrates advanced computer
vision, robotic control, and real-time stabilization technologies via a multi-sensor fusion approach. A lightweight YOLO model, deployed on an embedded
vision processor (K210), enables real-time plant container detection with over 96% accuracy under varying lighting conditions. A simplified hand-eye calibration
algorithm-designed for 'handheld camera'
robot arm configurations-ensures that the end effector can be precisely positioned, with a success rate exceeding 90%. The active leveling system, driven by the STM32F103ZET6 main control chip and JY901S inertial measurement data, can stabilize the irrigation platform on slopes up to 10 degrees, with a response time of 1.8 seconds. Experimental results across three simulated agricultural environments (standard greenhouse, hilly terrain, complex lighting) demonstrate a 30-50% reduction in water consumption compared to conventional flood irrigation, with water use efficiency exceeding 92% in all test cases.
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All authors ZhengKai Huang, YiKun Wang, ChenYu Hui, XiaoCheng
Image and Video Processing
USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding
Youssef Megahed, Robin Ducharme, Aylin Erman et al. (+3 more)
Ultrasound imaging is one of the most widely used diagnostic modalities, offering real-time, radiation-free assessment across diverse clinical domains. However, interpretation of ultrasound images remains challenging due to high noise levels, operator dependence, and limited field of view, resulting in substantial inter-observer variability. Current Deep Learning approaches are hindered by the scarcity of large labeled
datasets and the domain gap between general and sonographic images, which limits the transferability of models pretrained on non-medical data. To address these challenges, we introduce the Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), the first large-scale self-supervised MAE framework pretrained exclusively on ultrasound data. The model was pre-trained on 370,000 2D and 3D ultrasound images curated from 46 open-source
datasets, collectively termed OpenUS-46, spanning over twenty anatomical regions. This curated
dataset has been made publicly available to facilitate further research and reproducibility. Using a
Vision Transformer encoder-decoder architecture, USF-MAE reconstructs masked image patches, enabling it to learn rich, modality-specific representations directly from unlabeled data. The pretrained encoder was fine-tuned on three public downstream classification
benchmarks: BUS-BRA (breast cancer), MMOTU-2D (ovarian tumors), and GIST514-DB (gastrointestinal stromal tumors). Across all tasks, USF-MAE consistently outperformed conventional CNN and ViT baselines, achieving F1-scores of 81.6%, 79.6%, and 82.4%, respectively. Despite not using labels during pretraining, USF-MAE approached the performance of the supervised foundation model UltraSam on breast cancer classification and surpassed it on the other tasks, demonstrating strong cross-anatomical generalization.
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All authors Youssef Megahed, Robin Ducharme, Aylin Erman, Mark Walker, Steven Hawken, Adrian D. C. Chan
Audio and Speech Processing
Adapting Speech Foundation Models for Unified Multimodal Speech Recognition with Large Language Models
Jing-Xuan Zhang, Genshun Wan, Jin Li et al. (+3 more)
While
speech foundation models (SFMs) have demonstrated remarkable performance in audio-only tasks, their adaptation to
multimodal scenarios remains underexplored. This work presents UASR-LLM, a novel framework that adapts frozen SFMs to unified visual
speech recognition (VSR), automatic
speech recognition (ASR), and audio-visual
speech recognition (AVSR) by leveraging large
language models (LLMs) as text decoders. Visual representations are injected into multiple SFM layers via visual injection modules, enabling
multimodal fusion and unified representation learning. The augmented SFMs are connected to decoder-only LLMs through a feed-forward adaptor, where concatenated representations and instruction
prompts guide transcription. We propose a two-stage training strategy consisting of visual injection pretraining followed by
speech recognition finetuning. The pretraining stage aligns audio, visual, and audio-visual representations within the frozen SFM backbone, while the finetuning stage integrates LLMs for unified optimization across
speech recognition tasks. Experimental results demonstrate superior performance over state-of-the-art baselines across VSR, ASR, and AVSR under both clean and noisy conditions. Ablation studies further confirm generalization across various SFMs and LLMs, validating the effectiveness of the proposed training strategy.
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All authors Jing-Xuan Zhang, Genshun Wan, Jin Li, Jianqing Gao, Duo Zhao, Zhen-Hua Ling
Audio and Speech Processing
DiffRhythm 2: Efficient and High Fidelity Song Generation via Block Flow Matching
Yuepeng Jiang, Huakang Chen, Ziqian Ning et al. (+7 more)
Generating full-length, high-quality songs is challenging, as it requires maintaining long-term coherence both across text and music modalities and within the music modality itself. Existing non-autoregressive (NAR) frameworks, while capable of producing high-quality songs, often struggle with the
alignment between lyrics and vocal. Concurrently, catering to diverse musical preferences necessitates reinforcement learning from human feedback (RLHF). However, existing methods often rely on merging multiple models during multi-preference optimization, which results in significant performance degradation. To address these challenges, we introduce DiffRhythm 2, an end-to-end framework designed for high-fidelity, controllable song generation. To tackle the lyric
alignment problem, DiffRhythm 2 employs a semi-autoregressive architecture based on block flow matching. This design enables faithful
alignment of lyrics to singing vocals without relying on external labels and constraints, all while preserving the high generation quality and efficiency of NAR models. To make this framework computationally tractable for long sequences, we implement a music variational autoencoder (VAE) that achieves a low frame rate of 5 Hz while still enabling high-fidelity audio reconstruction. In addition, to overcome the limitations of multi-preference optimization in RLHF, we propose cross-pair preference optimization. This method effectively mitigates the performance drop typically associated with model merging, allowing for more robust optimization across diverse human preferences. We further enhance musicality and structural coherence by introducing
stochastic block representation
alignment loss.
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All authors Yuepeng Jiang, Huakang Chen, Ziqian Ning, Jixun Yao, Zerui Han, Di Wu, Meng Meng, Jian Luan, Zhonghua Fu, Lei Xie
Systems and Control · Cross-listed from cs.RO
End-to-End Design and Validation of a Low-Cost Stewart Platform with Nonlinear Estimation and Control
Benedictus C. G. Cinun, Tua A. Tamba, Immanuel R. Santjoko et al. (+3 more)
This paper presents the complete design, control, and experimental validation of a low-cost Stewart platform prototype developed as an affordable yet capable robotic testbed for research and education. The platform combines off the shelf components with 3D printed and custom fabricated parts to deliver full six degrees of freedom motions using six linear actuators connecting a moving platform to a fixed base. The system software integrates dynamic modeling, data acquisition, and real time control within a unified framework. A robust trajectory tracking controller based on feedback linearization, augmented with an LQR scheme, compensates for the platform's nonlinear dynamics to achieve precise motion control. In parallel, an Extended Kalman Filter fuses IMU and actuator encoder feedback to provide accurate and reliable state estimation under sensor noise and external disturbances. Unlike prior efforts that emphasize only isolated aspects such as modeling or control, this work delivers a complete hardware-software platform validated through both simulation and experiments on static and dynamic trajectories. Results demonstrate effective trajectory tracking and real-time state estimation, highlighting the platform's potential as a cost effective and versatile tool for advanced research and educational applications.
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All authors Benedictus C. G. Cinun, Tua A. Tamba, Immanuel R. Santjoko, Xiaofeng Wang, Michael A. Gunarso, Bin Hu
Signal Processing
PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming
Zhaoming Hu, Ruikang Zhong, Xidong Mu et al. (+2 more)
A pinching-
antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching
antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the $\max$ operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO)
algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay
equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional
MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.
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All authors Zhaoming Hu, Ruikang Zhong, Xidong Mu, Dengao Li, Yuanwei Liu
Signal Processing
Intelligent Multimodal Multi-Sensor Fusion-Based UAV Identification, Localization, and Countermeasures for Safeguarding Low-Altitude Economy
Yi Tao, Zhen Gao, Fangquan Ye et al. (+9 more)
The development of the low-altitude economy has led to a growing prominence of uncrewed aerial vehicle (
UAV) safety management issues. Therefore, accurate identification, real-time localization, and effective countermeasures have become core challenges in airspace security assurance. This paper introduces an integrated
UAV management and control system based on deep learning, which integrates
multimodal multi-sensor fusion perception, precise positioning, and collaborative countermeasures. By incorporating deep learning methods, the system combines
radio frequency (RF) spectral feature analysis, radar detection, electro-optical identification, and other methods at the detection level to achieve the identification and classification of
UAVs. At the localization level, the system relies on multi-sensor data fusion and the air-space-ground integrated communication network to conduct real-time tracking and prediction of
UAV flight status, providing support for early warning and decision-making. At the countermeasure level, it adopts comprehensive measures that integrate “soft kill” and “hard kill”, including technologies such as electromagnetic signal jamming,
navigation spoofing, and physical interception, to form a closed-loop management and control process from early warning to final disposal, which significantly enhances the response efficiency and disposal accuracy of low-altitude
UAV management.
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All authors Yi Tao, Zhen Gao, Fangquan Ye, Jingbo Xu, Tao Song, Weidong Li, Yu Su, Lu Peng, Xiaomei Wu, Tong Qin, Zhongxiang Li, Dezhi Zheng
Signal Processing
Clinic-Oriented Feasibility of a Sensor-Fused Wearable for Upper-Limb Function
Thanyanee Srichaisak, Arissa Ieochai and Aueaphum Aueawattthanaphisut
Background: Upper-limb weakness and tremor (4–12 Hz) limit activities of daily living (ADL) and reduce adherence to home rehabilitation. Objective: To assess technical feasibility and clinician-relevant signals of a sensor-fused wearable targeting the triceps brachii and extensor pollicis brevis. Methods: A lightweight node integrates surface EMG (1 kHz), IMU (100–200 Hz), and flex/force sensors with on-device INT8 inference (Tiny 1D-CNN/
Transformer) and a safety-bounded assist policy (angle/torque/jerk limits; stall/time-out). Healthy adults (n = 12) performed three ADL-like tasks. Primary outcomes: Tremor Index (TI), range of motion (ROM), repetitions (Reps min$^{-1}$). Secondary: EMG median-frequency slope (fatigue trend), closed-loop latency, session completion, and device-related adverse events. Analyses used subject-level paired medians with BCa 95% CIs; exact Wilcoxon $p$-values are reported in the Results. Results: Assistance was associated with lower tremor prominence and improved task throughput: TI decreased by $-0.092$ (95% CI [$-0.102$, $-0.079$]), ROM increased by $+12.65\%$ (95% CI [$+8.43$, $+13.89$]), and Reps rose by $+2.99$ min$^{-1}$ (95% CI [$+2.61$, $+3.35$]). Median on-device latency was 8.7 ms at a 100 Hz loop rate; all sessions were completed with no device-related adverse events. Conclusions:
Multimodal sensing with low-latency, safety-bounded assistance produced improved movement quality (TI $\downarrow$) and throughput (ROM, Reps $\uparrow$) in a pilot technical-feasibility setting, supporting progression to IRB-approved patient studies. Trial registration: Not applicable (pilot non-clinical).
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Signal Processing
RMD: Robust Modal Decomposition with Constrained Bandwidth
Wang Hao, Kuang Zhang, Hou Chengyu et al. (+3 more)
Modal decomposition techniques, such as Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Singular Spectrum Analysis (SSA), have advanced time-frequency signal analysis since the early 21st century. These methods are generally classified into two categories: numerical optimization-based methods (EMD, VMD) and spectral decomposition methods (SSA) that consider the physical meaning of signals. The former can produce spurious modes due to the lack of physical constraints, while the latter is more sensitive to noise and struggles with nonlinear signals. Despite continuous improvements in these methods, a modal decomposition approach that effectively combines the strengths of both categories remains elusive. Thus, this paper proposes a Robust Modal Decomposition (RMD) method with constrained bandwidth, which preserves the intrinsic structure of the signal by mapping the time series into its trajectory-GRAM
matrix in phase space. Moreover, the method incorporates bandwidth constraints during the decomposition process, enhancing noise resistance. Extensive experiments were conducted to validate its performance: on the synthetic
dataset front, we focused on low-SNR sine wave separation tasks and nonlinear signal processing experiments to verify the ability of RMD to extract weak signals and handle nonlinear distortions; on real-world data, we tested it on a real-collected millimeter-wave micro-motion energy
dataset to demonstrate its practical applicability in radar-related micro-motion signature analysis. All code and
dataset samples are publicly available on GitHub for reproducibility: https://github.com/Einstein-sworder/RMD.
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All authors Wang Hao, Kuang Zhang, Hou Chengyu, Yang Yifan, Tan Chenxing, Fu Weifeng
Systems and Control · Cross-listed from cs.RO
Never Too Rigid to Reach: Adaptive Virtual Model Control with LLM- and Lyapunov-Based Reinforcement Learning
Jingzehua Xu, Yangyang Li, Yangfei Chen et al. (+2 more)
Robotic arms are increasingly deployed in uncertain environments, yet conventional control pipelines often become rigid and brittle when exposed to perturbations or incomplete information. Virtual Model Control (VMC) enables compliant behaviors by
embedding virtual forces and mapping them into joint torques, but its reliance on fixed parameters and limited coordination among virtual components constrains adaptability and may undermine stability as task objectives evolve. To address these limitations, we propose Adaptive VMC with Large
Language Model (LLM)- and Lyapunov-Based Reinforcement Learning (RL), which preserves the physical interpretability of VMC while supporting stability-guaranteed online adaptation. The LLM provides structured priors and high-level
reasoning that enhance coordination among virtual components, improve sample efficiency, and facilitate flexible adjustment to varying task requirements. Complementarily, Lyapunov-based RL enforces theoretical stability constraints, ensuring safe and reliable adaptation under uncertainty. Extensive simulations on a 7-DoF Panda arm demonstrate that our approach effectively balances competing objectives in dynamic tasks, achieving superior performance while highlighting the synergistic benefits of LLM guidance and Lyapunov-constrained adaptation.
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All authors Jingzehua Xu, Yangyang Li, Yangfei Chen, Guanwen Xie, Shuai Zhang