Systems and Control
Convex Model Predictive Control for Safe Output Consensus of Nonlinear Multi-Agent Systems
Chao Wang, Shuyuan Zhang and Lei Wang
Nonlinear dynamics and safety constraints typically result in a nonlinear programming problem when applying model predictive control to achieve safe output consensus. To avoid the heavy computational burden of solving a nonlinear programming problem directly, this paper proposes a novel Convex Model Predictive Control (CMPC) approach based on a Sequential Quadratic Programming (SQP) scheme. The core of our method lies in transforming the nonlinear constraints into linear forms: we linearize the system dynamics and convexify the discrete-time high-order control barrier functions using a proposed tangent-line projection method. Consequently, the original problem is reduced to a quadratic program that can be iteratively solved within the SQP scheme at each time step of CMPC. Furthermore, we provide the formal guarantee of the convergence of the SQP scheme, and subsequently guarantee the recursive feasibility and stability of CMPC. Simulations on multi-
agent systems with unicycle dynamics demonstrate a 35-52 times reduction in computation time compared with baseline methods, confirming the suitability of the proposed approach for real-time safe output consensus control.
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Audio and Speech Processing
VoCodec: An Efficient Lightweight Low-Bitrate Speech Codec
Leyan Yang, Ronghui Hu, Yang Xu et al. (+1 more)
Recent advancements in end-to-end neural
speech codecs enable compressing audio at extremely low bitrates while maintaining high-fidelity reconstruction. Meanwhile, low computational complexity and low latency are crucial for real-time communication. In this paper, we propose VoCodec, a
speech codec model featuring a computational complexity of only 349.29M multiply-accumulate operations per second (MACs/s) and a latency of 30 ms. With the competitive vocoder Vocos as its backbone, the proposed model ranked fourth on Track 1 in the 2025 LRAC Challenge and achieved the highest subjective evaluation score (MUSHRA) on the clean
speech test set. Additionally, we cascade a lightweight neural network at the front end to extend its capability of
speech enhancement. Experimental results demonstrate that the two systems achieve competitive performance across multiple evaluation metrics.
Speech samples can be found at https://acceleration123.github.io/.
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All authors Leyan Yang, Ronghui Hu, Yang Xu, Jing Lu
Systems and Control
Complex Aircraft Maneuvering using Reinforcement-Learning-Augmented Sliding Mode-Based Control
Imran Sayyed and Nandan Kumar Sinha
Learning-based controllers leverage nonlinear couplings and enhance transients but seldom offer guarantees under tight input constraints. Robust feedback like sliding-mode control (SMC) provides these guarantees but is conservative in isolation. This paper creates a learning-augmented framework where a deep reinforcement learning policy produces learned nominal maneuvering commands and an SMC law imposes actuator limits, bounds learned authority and guarantees robustness. The policy is modeled as a matched, bounded input, and Lyapunov-based conditions link SMC gains to the nominal compensation mismatch, guaranteeing stability under saturation. This formulation is applicable to nonlinear, underactuated plants with hard constraints. To illustrate the methodology, the method is applied to a six-degree-of-freedom aircraft model and compared with Reinforcement Learning and isolated SMC. Simulation results show that the hybrid controller improves transient behavior and reduces control oscillations compared to standalone RL and SMC controllers. A Monte Carlo evaluation over 1000 randomized initial flight conditions further confirms that the controller consistently converges to the target attitude with small terminal tracking error, demonstrating robustness to variation in the initial-condition envelope. Even using it with partially trained policies, SMC component of the control stabilizes transients, whereas fully trained policies provide faster convergence, reduced constraint violations, and robustness. These results illustrate that learning-augmented control offers superior performance with robustness guarantees under tight input constraints.
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Signal Processing
When Is Distributed Nonlinear Aggregation Private? Optimality and Information-Theoretical Bounds
Wenrui Yu, Jaron Skovsted Gundersen, Richard Heusdens et al. (+1 more)
Nonlinear aggregation is central to modern distributed systems, yet its
privacy behavior is far less understood than that of linear aggregation. Unlike linear aggregation where mature mechanisms can often suppress information leakage, nonlinear operators impose inherent structural limits on what
privacy guarantees are theoretically achievable when the aggregate must be computed exactly. This paper develops a unified information-theoretic framework to characterize
privacy leakage in distributed nonlinear aggregation under a joint adversary that combines passive (honest-but-curious) corruption and eavesdropping over communication channels. We cover two broad classes of nonlinear aggregates: order-based operators (maximum/minimum and top-$K$) and robust aggregation (median/
quantiles and trimmed mean). We first derive fundamental lower bounds on leakage that hold without sacrificing accuracy, thereby identifying the minimum unavoidable information revealed by the computation and the transcript. We then propose simple yet effective
privacy-preserving distributed
algorithms, and show that with appropriate randomized initialization and parameter choices, our proposed approaches can attach the derived optimal bounds for the considered operators. Extensive experiments validate the tightness of the bounds and demonstrate that network
topology and key algorithmic parameters (including the stepsize) govern the observed leakage in line with the theoretical analysis, yielding actionable guidelines for
privacy-preserving nonlinear aggregation.
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All authors Wenrui Yu, Jaron Skovsted Gundersen, Richard Heusdens, Qiongxiu Li
Systems and Control
Guiding vector field-based guidance under wind disturbances applied to a tailsitter UAV
Evangelos Ntouros and Ewoud J. J. Smeur
This paper develops a guidance control law based on a parametric Guiding Vector Field (GVF) and integrates it with a state-of-the-art acceleration and attitude control architecture for tailsitters. The resulting framework enables a direct comparison between traditional trajectory-tracking guidance and GVF-based path-following guidance using a realistic tailsitter model operating under windy conditions. Through extensive simulations, it is shown that for agile flight scenarios with wind and small initial position error, both guidance strategies achieve comparable tracking performance, indicating that the additional complexity introduced by the GVF formulation is not always justified. However, the GVF-based approach exhibits an advantage when initial deviation from the path is present, yielding smooth and well-behaved convergence toward the desired path. Two additional contributions support this evaluation. First, a modification of the parametric GVF is proposed that guarantees exponential stability of the tracking error dynamics for a single integrator system. Second, the differential flatness transform of a tailsitter vehicle is extended to account for explicit knowledge of the wind velocity vector.
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Signal Processing
Physics-Aware RIS Codebook Compilation for Near-Field Beam Focusing under Mutual Coupling and Specular Reflections
Alexandros I. Papadopoulos, Maria Anna Pistela, Dimitrios Tyrovolas et al. (+5 more)
Next-generation wireless networks are envisioned to achieve reliable, low-latency connectivity within environments characterized by strong multipath and severe channel variability. Programmable wireless environments (PWEs) address this challenge by enabling deterministic control of electromagnetic (EM) propagation through software-defined reconfigurable intelligent surfaces (RISs). However, effectively configuring RISs in real time remains a major bottleneck, particularly under near-field conditions where mutual coupling and specular reflections alter the intended response. To overcome this limitation, this paper introduces MATCH, a physics-based codebook compilation
algorithm that explicitly accounts for the EM coupling among RIS unit
cells and the reflective interactions with surrounding structures, ensuring that the resulting codebooks remain consistent with the physical characteristics of the environment. Finally, MATCH is evaluated under a full-wave simulation framework incorporating mutual coupling and secondary reflections, demonstrating its ability to concentrate scattered energy within the focal region, confirming that physics-consistent, codebook-based optimization constitutes an effective approach for practical and efficient RIS configuration.
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All authors Alexandros I. Papadopoulos, Maria Anna Pistela, Dimitrios Tyrovolas, Antonios Lalas, Konstantinos Votis, Sotiris Ioannidis, George K. Karagiannidis, Christos Liaskos
Signal Processing
6G OFDM Communications with High Mobility Transceivers and Scatterers via Angle-Domain Processing and Deep Learning
Mauro Marchese, Musa Furkan Keskin, Henk Wymeersch et al. (+1 more)
High-mobility communications, which are crucial for next-generation wireless systems, cause the orthogonal frequency division multiplexing (OFDM) waveform to suffer from strong intercarrier interference (ICI) due to the Doppler effect. In this work, we propose a novel receiver architecture for OFDM that leverages the angular domain to separate multipaths. A block-type pilot is sent to estimate direction-of-arrivals (DoAs), propagation delays, and channel gains of the multipaths. Subsequently, a decision-directed (DD) approach is employed to estimate and iteratively refine the Dopplers. Two different approaches are investigated to provide initial Doppler estimates: an error vector magnitude (EVM)-based method and a deep learning (DL)-based method. Simulation results reveal that the DL-based approach allows for constant bit error rate (BER) performance up to the maximum 6G speed of 1000 km/h.
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All authors Mauro Marchese, Musa Furkan Keskin, Henk Wymeersch, Pietro Savazzi
Signal Processing
Monostatic ISAC Without Full Buffers: Revisiting Spatial Trade-Offs Under Bursty Traffic
Mauro Marchese, Musa Furkan Keskin, Pietro Savazzi et al. (+1 more)
This work investigates the spatial trade-offs arising from the design of the transmit beamformer in a monostatic integrated sensing and communication (
ISAC) base station (BS) under bursty traffic, a crucial aspect necessitated by the integration of communication and sensing functionalities in next-generation wireless systems. In this setting, the BS does not always have data available for transmission. This study compares different
ISAC policies and reveals the presence of multiple effects influencing
ISAC performance: signal-to-noise ratio (SNR) boosting of data-aided strategies compared to pilot-based ones, saturation of the probability of detection in data-aided strategies due to the non-full-buffer assumption, and, finally, directional masking of sensing targets due to the relative position between target and user. Simulation results demonstrate varying impact of these effects on
ISAC trade-offs under different operating conditions, thus guiding the design of efficient
ISAC transmission strategies.
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All authors Mauro Marchese, Musa Furkan Keskin, Pietro Savazzi, Henk Wymeersch
Systems and Control · Cross-listed from cs.RO
Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration
Shibo Shao, Dong Zhou, Guanghui Sun et al. (+2 more)
Existing spacecraft rendezvous and docking control methods largely rely on predefined dynamic models and often exhibit limited robustness in realistic on-orbit environments. To address this issue, this paper proposes an Imitation Learning-based spacecraft rendezvous and docking control framework (IL-SRD) that directly learns control policies from expert demonstrations, thereby reducing dependence on accurate modeling. We propose an anchored decoder target mechanism, which conditions the decoder queries on state-related anchors to explicitly constrain the control generation process. This mechanism enforces physically consistent control
evolution and effectively suppresses implausible action deviations in sequential prediction, enabling reliable six-degree-of-freedom (6-DOF) rendezvous and docking control. To further enhance stability, a temporal aggregation mechanism is incorporated to mitigate error accumulation caused by the sequential prediction nature of
Transformer-based models, where small inaccuracies at each time step can propagate and amplify over long horizons. Extensive simulation results demonstrate that the proposed IL-SRD framework achieves accurate and energy-efficient model-free rendezvous and docking control. Robustness evaluations further confirm its capability to maintain competitive performance under significant unknown disturbances. The source code is available at https://github.com/Dongzhou-1996/IL-SRD.
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All authors Shibo Shao, Dong Zhou, Guanghui Sun, Liwen Zhang, Mingxuan Jiang
Audio and Speech Processing
ImmersiveFlow: Stereo-to-7.1.4 spatial audio generation with flow matching
Zining Liang, Runbang Wang, Yujia Xiao et al. (+2 more)
Immersive spatial audio has become increasingly critical for applications ranging from AR/VR to home entertainment and automotive sound systems. However, existing generative methods remain constrained to low-dimensional formats such as binaural audio and First-Order Ambisonics (FOA). Binaural rendering is inherently limited to headphone playback, while FOA suffers from spatial aliasing and insufficient resolution for high-frequency. To overcome these limitations, we introduce ImmersiveFlow, the first end-to-end generative framework that directly synthesizes discrete 7.1.4 format spatial audio from stereo input. ImmersiveFlow leverages Flow Matching to learn trajectories from stereo inputs to multichannel spatial features within a pretrained VAE latent space. At inference, the Flow Matching model predicted latent features are decoded by the VAE and converted into the final 7.1.4 waveform. Comprehensive objective and subjective evaluations demonstrate that our method produces perceptually rich sound fields and enhanced spatial impression, significantly outperforming traditional upmixing techniques.
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All authors Zining Liang, Runbang Wang, Yujia Xiao, Xuzhou Ye, Qiuqiang Kong
Image and Video Processing
PYVALE: A Fast, Scalable, Open-Source 2D Digital Image Correlation (DIC) Engine Capable of Handling Gigapixel Images
Joel Hirst, Lorna Sibson, Adel Tayeb et al. (+10 more)
Background: Digital Image Correlation (DIC) is a widely used full-field measurement technique, but both open-source and commercial packages often have limitations such as operating-system restrictions, lack of support for deployment on computing clusters, and poor scalability to gigapixel-scale images common in Scanning Electron Microscopy DIC (SEM-DIC). Objective: Pyvale is an open-source software package designed for sensor simulation, uncertainty quantification, placement optimization, and calibration/validation. A key component of this is the development of a dedicated 2D DIC module intended for standalone use and integration within broader workflows. Methods: Pyvale provides a user-friendly Python interface with performant compiled routines underneath. At its core is a multithreaded, reliability-guided DIC
algorithm. Its open-source MIT license enables wide deployment, including on computing clusters and in automated pipelines. Results: Benchmarking with the publicly available 2D DIC challenge 2.0
dataset shows that Pyvale achieves metrological performance comparable to existing commercial and open-source DIC codes. It can correlate gigapixel-scale image pairs in under 5 minutes on high-specification desktop workstations, with
memory peaking at approximately 50 GB. Conclusions: Pyvale's strong metrological foundation, coupled with its scalability for SEM-DIC, positions it as a platform for sustained, community-driven development. Its design and licensing provide a foundation for future improvements in open-source DIC and integration into experimental design and validation workflows.
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All authors Joel Hirst, Lorna Sibson, Adel Tayeb, Ben Poole, Megan Sampson, Wiera Bielajewa, Michael Atkinson, Alex Marsh, Rory Spencer, Rob Hamill, Cory Hamelin, Allan Harte, Lloyd Fletcher
Signal Processing · Cross-listed from cs.RO
Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design
Kaleem Arshid, Ali Krayani, Lucio Marcenaro et al. (+2 more)
This paper proposes an Active Inference-based framework for autonomous trajectory design in
UAV swarms. The method integrates probabilistic
reasoning and self-learning to enable distributed mission allocation, route ordering, and motion planning. Expert trajectories generated using a Genetic
Algorithm with Repulsion Forces (GA-RF) are employed to train a hierarchical World Model capturing swarm behavior across mission, route, and motion levels. During online operation,
UAVs infer actions by minimizing divergence between current beliefs and model-predicted states, enabling adaptive responses to dynamic environments. Simulation results show faster convergence, higher stability, and safer
navigation than Q-Learning, demonstrating the scalability and cognitive grounding of the proposed framework for intelligent
UAV swarm control.
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All authors Kaleem Arshid, Ali Krayani, Lucio Marcenaro, David Martin Gomez, Carlo Regazzoni
Signal Processing
Fluid Antenna Relay (FAR)-assisted Communication with Hybrid Relaying Scheme Selection
Ruopeng Xu, Songling Zhang, Zhaohui Yang et al. (+3 more)
In this paper, we investigate a fluid
antenna relay (FAR)-assisted communication system with hybrid relaying scheme selection. By leveraging statistical channel state information (CSI) and distribution characteristics of fluid
antenna system (FAS), we approximate the outage probability (OP) with different relaying schemes utilizing a Gaussian copula-based method. Each relay node follows the OP-minimized principle to choose the forwarding schemes. To reduce self-interference and avoid multi-user interference, half-duplex relays and frequency division multiple access schemes are considered, respectively. On this basis, we formulate a sum-rate maximization problem to mitigate the rate loss introduced by the half-duplex mode. To solve this problem, we first transform the original nonconvex problem into a power control optimization problem by obtaining the closed form of bandwidth allocation and substituting it into the original problem. Then, we solve the power control optimization problem with a low complexity method. Simulation results verify the effectiveness of our proposed
algorithm to improve the sum rate of the system.
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All authors Ruopeng Xu, Songling Zhang, Zhaohui Yang, Mingzhe Chen, Zhaoyang Zhang, Kai-Kit Wong
Systems and Control
From Vertices to Convex Hulls: Certifying Set-Wise Compatibility for CBF Constraints
Shima Sadat Mousavi, Xiao Tan and Aaron D. Ames
This paper develops certificates that propagate compatibility of multiple control barrier function (CBF) constraints from sampled vertices to their convex hull. Under mild concavity and affinity assumptions, we present three sufficient feasibility conditions under which feasible inputs over the convex hull can be obtained per coordinate, with a common input, or via convex blending. We also describe the associated computational methods, based on interval intersections or an offline linear program (LP). Beyond certifying compatibility, we give conditions under which the quadratic-program (QP) safety filter is affine in the state. This enables explicit implementations via convex combinations of vertex-feasible inputs. Case studies illustrate the results.
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Signal Processing
Angular Sensing by Highly Reconfigurable Pixel Antennas with Joint Radiating Aperture and Feeding Ports Reconfiguration
Zixiang Han, Hanning Wang, Shiwen Tang et al. (+1 more)
Angular sensing capability is realized using highly reconfigurable pixel
antenna (HRPA) with joint radiating aperture and feeding ports reconfiguration. Pixel
antennas represent a general class of reconfigurable
antenna designs in which the radiating surface, regardless of its shape or size, is divided into sub-wavelength elements called pixels. Each pixel is connected to its neighboring elements through
radio frequency switches. By controlling pixel connections, the pixel
antenna topology can be flexibly adjusted so that the resulting radiation pattern can be reconfigured. However, conventional pixel
antennas have only a single, fixed-position feeding port, which is not efficient for angular sensing. Therefore, in this work, we further extend the reconfigurability of pixel
antennas by introducing the HRPA, which enables both geometry control of the pixel
antenna and switching of its feeding ports. The model of the proposed HRPA, including both
circuit and radiation parameters, is derived. A codebook is then defined, consisting of pixel connection states and feeding port positions for each sensing area. Based on this codebook, an efficient optimization approach is developed to minimize the Cram\acute{\mathrm{\mathbf{e}}}r-Rao lower bound (CRLB) and obtain the optimal HRPA geometries for angular sensing within a given area. Numerical results show that the HRPA reduces the angle estimation error by more than 50% across the full three-dimensional sphere when compared with a conventional uniform planar array of the same size. This demonstrates the effectiveness of the proposed approach and highlights the potential of HRPA for integrated sensing and communication systems.
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All authors Zixiang Han, Hanning Wang, Shiwen Tang, Yujie Zhang
Systems and Control
Report on Earth Observation Missions and Ground Station Management using On-Demand Satellite Operation System
Yuji Sakamoto
Since the launch of its first satellite in 2009, Tohoku University has continuously developed and operated Earth observation satellites and engineering demonstration satellites in the 50cm-class and CubeSat-class (up to 3U). The 50cm-class satellite launched into operation in 2021 enabled efficient operations through cloud-based management functions for both the satellite and ground stations, including automatic command generation. By 2022, up to eight operational satellites were simultaneously managed on a daily basis using three ground stations (Sendai, Hakodate, and Sweden). This paper presents the operational achievements to date and introduces the system that supports efficient satellite operations
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Systems and Control
System Analysis and Pre-Flight Evaluation of Deployable Solar Panels for 3U CubeSat HOKUSHIN-1
Yuji Sakamoto, Masaki Aoi, Sho Suzuki et al. (+8 more)
This paper describes the system design methodology derived from the development and evaluation tests of deployable
solar panels to be mounted on a 3U CubeSat. The study mainly includes structural analysis, thermal analysis, and a review of vibration test results. Hokkaido University is developing the 3U CubeSat HOKUSHIN-1 in collaboration with Tohoku University and Muroran Institute of Technology. Deployable
solar panels are a key technology for future planned lunar exploration missions, as they enable power-intensive communication and propulsion required for orbit control. The satellite also demonstrates a newly developed compact and efficient propulsion system. The satellite has dimensions of approximately 10x10x34 cm, a mass of 3.99 kg, and will be deployed into a circular orbit at an altitude of about 400 km with an orbital inclination of 51.6 degrees from the International Space Station.
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All authors Yuji Sakamoto, Masaki Aoi, Sho Suzuki, Takumi Haga, Shumpei Hosokawa, Yuma Abe, Yuya Tasaki, Tsuyoshi Totani, Sou Nakamura, Masaharu Uchiumi, Shinya Fujita
Systems and Control
Lessons Learned from Structural Design and Vibration Testing of 50-kg Microsatellites Deployed from the International Space Station
Yuji Sakamoto, Junichi Kurihara, Shinya Fujita et al. (+2 more)
Hokkaido University and Tohoku University have been developing and operating a constellation of 50-cm-class microsatellites for Earth observation. DIWATA-1, launched in 2016, was deployed into a circular orbit at an altitude of approximately 400 km from the International Space Station (ISS). For the subsequent satellite developed in 2021, the structural design and vibration test campaign were optimized to meet a strict one-year development schedule. This paper summarizes how the structural design of the previous satellite was reviewed and updated, and how the vibration test was successfully completed in a single trial to minimize schedule and technical risks. These lessons learned provide valuable insights, as there are only a limited number of reported cases of 50-kg-class microsatellites deployed from the ISS.
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All authors Yuji Sakamoto, Junichi Kurihara, Shinya Fujita, Yuji Sato, Toshinori Kuwahara
Signal Processing
Integrated Sensing and Semantic Communication with Adaptive Source-Channel Coding
Haotian Wang, Dan Wang, Xiaodong Xu et al. (+3 more)
Semantic communication has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes an adaptive source-channel coding and beamforming design framework for integrated sensing and SemCom systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is also derived for target position under imperfect time synchronization. To facilitate the joint optimization, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget. Subsequently, an alternative optimization
algorithm composed of successive convex approximation and fractional programming is proposed to address this problem by decoupling it into two subproblems for coding rate and beamforming designs, respectively. Simulation results demonstrate that our proposed scheme outperforms the conventional deep joint source-channel coding -water filling-zero forcing
benchmark.
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All authors Haotian Wang, Dan Wang, Xiaodong Xu, Chuan Huang, Hao Chen, Nan Ma
Audio and Speech Processing · Cross-listed from cs.SD
UNMIXX: Untangling Highly Correlated Singing Voices Mixtures
Jihoo Jung, Ji-Hoon Kim, Doyeop Kwak et al. (+3 more)
We introduce UNMIXX, a novel framework for multiple singing voices separation (MSVS). While related to
speech separation, MSVS faces unique challenges: data scarcity and the highly correlated nature of singing voices mixture. To address these issues, we propose UNMIXX with three key components: (1) musically informed mixing strategy to construct highly correlated, music-like mixtures, (2) cross-source
attention that drives representations of two singers apart via reverse
attention, and (3) magnitude penalty loss penalizing erroneously assigned interfering energy. UNMIXX not only addresses data scarcity by simulating realistic training data, but also excels at separating highly correlated mixtures through cross-source interactions at both the architectural and loss levels. Our extensive experiments demonstrate that UNMIXX greatly enhances performance, with SDRi gains exceeding 2.2 dB over prior work.
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All authors Jihoo Jung, Ji-Hoon Kim, Doyeop Kwak, Junwon Lee, Juhan Nam, Joon Son Chung
Signal Processing
Movable Antenna Enhanced MIMO Communications with Spatial Modulation
Kaihe Wang, Ran Yang, Lipeng Zhu et al. (+3 more)
Movable
antenna (MA) has demonstrated great potential in enhancing wireless communication performance. In this paper, we investigate an MA-enabled multiple-input multiple-output (
MIMO) communication system with spatial modulation (SM), which improves communication performance by utilizing flexible MA placement while reducing the cost of RF chains. To this end, we propose a joint transceiver design framework aimed at minimizing the bit error rate (BER) based on the maximum minimum distance (MMD) criterion. To address the intractable problem, we develop an efficient iterative
algorithm based on alternating optimization (AO) and successive convex approximation (SCA) techniques. Simulation results demonstrate that the proposed
algorithm achieves rapid convergence performance and significantly outperforms the existing
benchmark schemes.
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All authors Kaihe Wang, Ran Yang, Lipeng Zhu, Rongyan Xi, Yue Xiu, Zhongpei Zhang
Systems and Control
Sensing-Limited Control of Noiseless Linear Systems Under Nonlinear Observations
Ming Li, Fan Liu, Yifeng Xiong et al. (+2 more)
This paper investigates the fundamental information-theoretic limits for the control and sensing of noiseless linear dynamical systems subject to a broad class of nonlinear observations. We analyze the interactions between the control and sensing components by characterizing the minimum information flow required for stability. Specifically, we derive necessary conditions for mean-square observability and stabilizability, demonstrating that the average directed information rate from the state to the observations must exceed the intrinsic expansion rate of the unstable dynamics. Furthermore, to address the challenges posed by non-Gaussian distributions inherent to nonlinear observation channels, we establish sufficient conditions by imposing regularity assumptions, specifically log-concavity, on the system's probabilistic components. We show that under these conditions, the divergence of differential
entropy implies the convergence of the estimation error, thereby closing the gap between information-theoretic bounds and estimation performance. By establishing these results, we unveil the fundamental performance limits imposed by the sensing layer, extending classical data-rate constraints to the more challenging regime of nonlinear observation models.
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All authors Ming Li, Fan Liu, Yifeng Xiong, Jie Xu, Tao Liu
Audio and Speech Processing
Adaptive Speaker Embedding Self-Augmentation for Personal Voice Activity Detection with Short Enrollment Speech
Fuyuan Feng, Wenbin Zhang, Yu Gao et al. (+3 more)
Personal Voice Activity Detection (PVAD) is crucial for identifying target speaker segments in the mixture, yet its performance heavily depends on the quality of speaker
embeddings. A key practical limitation is the short enrollment
speech–such as a wake-up word–which provides limited cues. This paper proposes a novel adaptive speaker
embedding self-augmentation strategy that enhances PVAD performance by augmenting the original enrollment
embeddings through additive fusion of keyframe
embeddings extracted from mixed
speech. Furthermore, we introduce a long-term adaptation strategy to iteratively refine
embeddings during detection, mitigating speaker temporal variability. Experiments show significant gains in recall, precision, and F1-score under short enrollment conditions, matching full-length enrollment performance after five iterative updates. The source code is available at https://anonymous.4open.science/r/ASE-PVAD-E5D6 .
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All authors Fuyuan Feng, Wenbin Zhang, Yu Gao, Longting Xu, Xiaofeng Mou, Yi Xu
Systems and Control · Cross-listed from cs.CV
Spatial-VLN: Zero-Shot Vision-and-Language Navigation With Explicit Spatial Perception and Exploration
Lu Yue, Yue Fan, Shiwei Lian et al. (+4 more)
Zero-shot
Vision-and-
Language Navigation (VLN)
agents leveraging Large
Language Models (LLMs) excel in generalization but suffer from insufficient spatial perception. Focusing on complex continuous environments, we categorize key perceptual bottlenecks into three spatial challenges: door interaction,multi-room
navigation, and ambiguous instruction execution, where existing methods consistently suffer high failure rates. We present Spatial-VLN, a perception-guided exploration framework designed to overcome these challenges. The framework consists of two main modules. The Spatial Perception Enhancement (SPE) module integrates panoramic filtering with specialized door and region experts to produce spatially coherent, cross-view consistent perceptual representations. Building on this foundation, our Explored Multi-expert
Reasoning (EMR) module uses parallel LLM experts to address waypoint-level semantics and region-level spatial transitions. When discrepancies arise between expert predictions, a query-and-explore mechanism is activated, prompting the
agent to actively probe critical areas and resolve perceptual ambiguities. Experiments on VLN-CE demonstrate that Spatial VLN achieves state-of-the-art performance using only low-cost LLMs. Furthermore, to validate real-world applicability, we introduce a value-based waypoint sampling strategy that effectively bridges the Sim2Real gap. Extensive real-world evaluations confirm that our framework delivers superior generalization and robustness in complex environments. Our codes and
videos are available at https://yueluhhxx.github.io/Spatial-VLN-web/.
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All authors Lu Yue, Yue Fan, Shiwei Lian, Yu Zhao, Jiaxin Yu, Liang Xie, Feitian Zhang
Audio and Speech Processing
CodeSep: Low-Bitrate Codec-Driven Speech Separation with Base-Token Disentanglement and Auxiliary-Token Serial Prediction
Hui-Peng Du, Yang Ai, Xiao-Hang Jiang et al. (+2 more)
This paper targets a new scenario that integrates
speech separation with
speech compression, aiming to disentangle multiple speakers while producing discrete representations for efficient transmission or storage, with applications in online meetings and dialogue archiving. To address this scenario, we propose CodeSep, a codec-driven model that jointly performs
speech separation and low-bitrate compression. CodeSep comprises a residual vector quantizer (RVQ)-based plain neural
speech codec, a base-
token disentanglement (BTD) module, and parallel auxiliary-
token serial prediction (ATSP) modules. The BTD module disentangles mixed-
speech mel-spectrograms into base
tokens for each speaker, which are then refined by ATSP modules to serially predict auxiliary
tokens, and finally, all
tokens are decoded to reconstruct separated waveforms through the codec decoder. During training, the codec's RVQ provides supervision with permutation-invariant and teacher-forcing-based cross-
entropy losses. As only base
tokens are transmitted or stored, CodeSep achieves low-bitrate compression. Experimental results show that CodeSep attains satisfactory separation performance at only 1 kbps compared with baseline methods.
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All authors Hui-Peng Du, Yang Ai, Xiao-Hang Jiang, Rui-Chen Zheng, Zhen-Hua Ling
Signal Processing
Robust Beamforming and Time Allocation for Time-Division Cell-Free Near-Field ISAC
Chaedam Son and Si-Hyeon Lee
In this paper, we propose a time-division near-field integrated sensing and communication (
ISAC) framework for
cell-free multiple-input multiple-output (
MIMO), where sensing and downlink communication are separated in time. During the sensing phase, user locations are estimated and used to construct location-aware channels, which are then exploited in the subsequent communication phase. By explicitly modeling the coupling between sensing-induced localization errors and channel-estimation errors, we capture the tradeoff between sensing accuracy and communication throughput. Based on this model, we jointly optimize the time-allocation ratio, sensing covariance
matrix, and robust downlink beamforming under imperfect channel state information (CSI). The resulting non-convex problem is addressed via a semidefinite programming (SDP)-based reformulation within an alternating-optimization framework. To further reduce computational complexity, we also propose two low-complexity suboptimal designs: an error-ignorant scheme and a maximum ratio transmission (MRT)-based scheme. Simulation results show that the proposed scheme significantly improves localization accuracy over far-field and monostatic setups, thereby reducing channel estimation errors and ultimately enhancing the achievable rate. Moreover, the error-ignorant scheme performs well under stringent sensing requirements, whereas the MRT-based scheme remains robust over a wide range of sensing requirements by adapting the time-allocation ratio, albeit with some beamforming loss.
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Signal Processing
Energy-Based Cell Association in Nonuniform Renewable Energy-Powered Cellular Networks: Analysis and Optimization of Carbon Efficiency
Yuxi Zhao, Vicente Casares-Giner, Vicent Pla et al. (+4 more)
The increasing global push for carbon reduction highlights the importance of integrating renewable energy into the supply chain of cellular networks. However, due to the
stochastic nature of renewable energy generation and the uneven load distribution across base stations, the utilization rate of renewable energy remains low. To address these challenges, this paper investigates the trade-off between carbon emissions and downlink throughput in cellular networks, offering insights into optimizing both network performance and sustainability. The renewable energy state of base station batteries and the number of occupied channels are modeled as a quasi-birth-death process. We construct models for the probability of channel blocking, average successful transmission probability for users, downlink throughput, carbon emissions, and carbon efficiency based on
stochastic geometry. Based on these analyses, an energy-based
cell association scheme is proposed to optimize the carbon efficiency of cellular networks. The results show that, compared to the closest
cell association scheme, the energy-based
cell association scheme is capable of reducing the carbon emissions of the network by 13.0% and improving the carbon efficiency by 11.3%.
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All authors Yuxi Zhao, Vicente Casares-Giner, Vicent Pla, Luis Guijarro, Iztok Humar, Yi Zhong, Xiaohu Ge
Audio and Speech Processing
Improving Audio Question Answering with Variational Inference
Haolin Chen
Variational inference (VI) provides a principled framework for estimating posterior distributions over model parameters, enabling explicit modeling of weight uncertainty during optimization. By capturing this uncertainty, VI improves the reliability of predictions, yielding better calibrated outputs. In this work, we investigate the benefits of VI for challenging
multimodal understanding and
reasoning by applying the Improved Variational Online Newton (IVON), a recent VI optimizer, to fine-tuning a
multimodal large
language model on audio question answering tasks. Our results show that VI not only enhances predictive accuracy but also significantly improves calibration, reducing the model's overconfidence. These advances further support risk-sensitive applications such as selective prediction, where reliable confidence estimates are crucial.
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Systems and Control · Cross-listed from cs.LG
Arkaprava Gupta, Nicholas Carter, William Zellers et al. (+5 more)
Deep
Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life and cause side effects while failing to adapt to changing neural dynamics. Recent reinforcement learning approaches improve adaptability, yet most rely on deep neural networks that require offline training and are computationally too expensive for implantable hardware. This paper presents a resource-conscious adaptive DBS framework based on a Time- and Threshold-Triggered Pruned Multi-Armed Bandit (T3P MAB)
algorithm. The proposed method jointly tunes stimulation frequency and amplitude, avoids prior training, and remains transparent enough to support clinician-guided adjustment. Using a computational basal ganglia-thalamic model, we show that T3P converges faster than competing MAB methods and outperforms deep-RL baselines in suppressing pathological beta-band activity while reducing stimulation power. We implemented it on different microcontrollers and report detailed energy measurements, showing convergence in under two minutes and suitability for resource-constrained implantable systems. These results support lightweight bandit-based control as a practical path toward personalized, energy-efficient DBS.
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All authors Arkaprava Gupta, Nicholas Carter, William Zellers, Prateek Ganguli, Benedikt Dietrich, Vibhor Krishna, Parasara Sridhar Duggirala, Samarjit Chakraborty
Systems and Control
From Noise to Knowledge: System Identification with Systematic Polytope Construction via Cyclic Reformulation
Hiroshi Okajima, Shun Shirahama, Tatsunori Hayashi et al. (+1 more)
Model-based robust control requires not only accurate nominal models but also systematic uncertainty representations to guarantee stability and performance. However, constructing polytopic uncertainty models typically demands multiple experiments or a priori structural assumptions.This paper proposes an identification framework based on intentional periodicity induction, in which cyclic reformulation with period $N$ is applied to a linear time-invariant system to interpret noise-induced parameter fluctuations as a structured manifestation of estimation uncertainty. The $N$ parameter sets obtained from a single identification experiment – which would coincide in the noise-free case – are used as polytope vertices, providing systematic control over the granularity of the uncertainty description through the choice of $N$. The practical utility of the constructed polytope is demonstrated through robust $H_\infty$ state-feedback synthesis via LMI optimization at the polytope vertices; the synthesis uses only noisy identification data and is shown across Monte Carlo trials to stabilize the true plant with only marginal conservatism. Complementarily, a diagnostic assessment based on the best in-polytope point confirms that the polytope captures meaningful uncertainty information. For a third-order system under Gaussian and uniform noise, a comparison with bootstrap-inspired resampling baselines indicates that cyclic reformulation provides a competitive or favorable trade-off by utilizing the full data record; the construction is further validated on a fourth-order
MIMO system.
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All authors Hiroshi Okajima, Shun Shirahama, Tatsunori Hayashi, Nobutomo Matsunaga