Systems and Control · Cross-listed from q-bio.QM
Machine Learning of Temperature-dependent Chemical Kinetics Using Parallel Droplet Microreactors
Mamoru Saita and Yutaka Hori
Temperature is a fundamental regulator of chemical and biochemical kinetics, yet capturing nonlinear thermal effects directly from experimental data remains a major challenge due to limited throughput and model flexibility. Recent advances in machine learning have enabled flexible modeling beyond conventional physical laws, but most existing strategies remain confined to surrogate models of end-point yields rather than full kinetic dynamics. Consequently, an end-to-end framework that unifies systematic kinetic data acquisition with machine learning based modeling has been lacking. In this paper, we present a unified framework that integrates droplet microfluidics with machine learning for the systematic analysis of temperature-dependent reaction kinetics. The platform is specifically designed to enable stable immobilization and long-term time-lapse imaging of thousands of droplets under dynamic thermal gradients. This configuration yields massively parallel time-resolved
datasets across diverse temperature conditions that capture transient kinetics and provides particularly suitable inputs for training machine-learning models of reaction dynamics. Leveraging these
datasets, we train Neural ODE models, which embed neural networks within differential equations to flexibly represent nonlinear temperature dependencies beyond conventional formulations. We demonstrate accurate prediction of enzymatic kinetics across diverse thermal environments, highlighting the robustness and versatility of the approach. Our framework bridges high-throughput experimental data acquisition with data-driven modeling, establishing a versatile foundation for enhanced predictive ability and rational analysis and design of temperature-sensitive biochemical processes.
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Systems and Control · Cross-listed from math.NA
Mixed formulation and structure-preserving discretization of Cosserat rod dynamics in a port-Hamiltonian framework
Philipp L. Kinon, Simon R. Eugster and Peter Betsch
An energy-based modeling framework for the nonlinear dynamics of spatial Cosserat rods undergoing large displacements and rotations is proposed. The mixed formulation features independent displacement, velocity and stress variables and is further objective and locking-free. Finite rotations are represented using a director formulation that avoids singularities and yields a constant mass
matrix. This results in an infinite-dimensional nonlinear port-
Hamiltonian (PH) system governed by partial differential-algebraic equations with a quadratic energy functional. Using a time-differentiated compliance form of the stress-strain relations allows for the imposition of kinematic constraints, such as inextensibility or shear-
rigidity. A structure-preserving finite element discretization leads to a finite-dimensional system with PH structure, thus facilitating the design of an energy-momentum consistent integration scheme. Dissipative material behavior (via the generalized-Maxwell model) and non-standard actuation approaches (via pneumatic chambers or tendons) integrate naturally into the framework. As illustrated by selected numerical examples, the present framework establishes a new approach to energy-momentum consistent formulations in computational mechanics involving finite rotations.
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Systems and Control
Power feedback strategy based on efficiency trajectory analysis for HCPV sun tracking
Manuel G. Satué, Fernando Castaño, Manuel G. Ortega et al. (+1 more)
This paper presents a control strategy for sun trackers which adapts continuously to different sources of error, avoiding the necessity of any kind of calibration by analyzing the produced electric power to sense the position of the Sun. The proposed strategy is able to meet the strict specifications for HCPV sun trackers despite of mechanical uncertainties (misalignments in the structure itself, misalignment of the
solar modules with respect to the wing, etc.) and installation uncertainties (misalignments of the platform with respect to geographical north). Experimental results with an industrial-grade
solar tracker showing the validity of the proposed control strategy under sunny and moderate cloudy conditions, as well as with different installation precisions by un-calibrating the system on purpose are exposed.
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All authors Manuel G. Satué, Fernando Castaño, Manuel G. Ortega, Francisco R. Rubio
Image and Video Processing
ForeSpeed: A real-world video dataset of CCTV cameras with different settings for vehicle speed estimation
Massimo Iuliani, Blake Sawyer, Marco Fontani et al. (+2 more)
The need to estimate the speed of road vehicles has become increasingly important in the field of
video forensics, particularly with the widespread deployment of CCTV cameras worldwide. Despite the development of various approaches, the accuracy of forensic speed estimation from real-world footage remains highly dependent on several factors, including camera specifications, acquisition methods, spatial and temporal resolution, compression methods, and scene perspective, which can significantly influence performance. In this paper, we introduce ForeSpeed, a comprehensive
dataset designed to support the evaluation of speed estimation techniques in real-world scenarios using CCTV footage. The
dataset includes recordings of a vehicle traveling at known speeds, captured by three digital and three analog cameras from two distinct perspectives. Real-world road metrics are provided to enable the restoration of the scene geometry.
Videos were stored with multiple compression factors and settings, to simulate real world scenarios in which export procedures are not always performed according to forensic standards. Overall, ForeSpeed, includes a collection of 322
videos. As a case study, we employed the ForeSpeed
dataset to
benchmark a speed estimation
algorithm available in a commercial product (Amped FIVE). Results demonstrate that while the method reliably estimates average speed across various conditions, its uncertainty range significantly increases when the scene involves strong perspective distortion. The ForeSpeed
dataset is publicly available to the forensic community, with the aim of facilitating the evaluation of current methodologies and inspiring the development of new, robust solutions tailored to collision investigation and forensic incident analysis.
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All authors Massimo Iuliani, Blake Sawyer, Marco Fontani, David Spreadborough, Martino Jerian
Image and Video Processing · Cross-listed from cs.CV
Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations
Marica Muffoletto, Uxio Hermida, Charlène Mauger et al. (+8 more)
Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates, that provides a common anatomical reference frame for the human heart. Our method predicts NIHCs directly from a limited number of 2D
segmentations (sparse acquisition) and subsequently decodes them into dense 3D
segmentations and high-resolution meshes at arbitrary output resolution. Trained on a large
dataset of 5,000 cardiac meshes, the model achieves high reconstruction accuracy on clinical contours, with mean Euclidean surface errors of 2.51$\pm$0.33 mm in a diseased cohort (n=4549) and 2.3$\pm$0.36 mm in a healthy cohort (n=5576). The NIHC representation enables anatomically coherent reconstruction even under severe slice sparsity and
segmentation noise, faithfully recovering complex structures such as the valve planes. Compared with traditional pipelines, inference time is reduced from over 60 s to 5-15 s. These results demonstrate that NIHCs constitute a robust and efficient anatomical representation for patient-specific 3D cardiac reconstruction from minimal input data.
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All authors Marica Muffoletto, Uxio Hermida, Charlène Mauger, Avan Suinesiaputra, Yiyang Xu, Richard Burns, Lisa Pankewitz, Andrew D McCulloch, Steffen E Petersen, Daniel Rueckert, Alistair A Young
Signal Processing · Cross-listed from cs.LG
Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring
Keivan Faghih Niresi, Jun Qing, Mengjie Zhao et al. (+1 more)
Structural Health Monitoring (SHM) plays a crucial role in maintaining the safety and resilience of infrastructure. As sensor networks grow in scale and complexity, identifying the most informative sensors becomes essential to reduce deployment costs without compromising monitoring quality. While Graph Signal Processing (GSP) has shown promise by leveraging spatial correlations among sensor nodes, conventional approaches often overlook the temporal dynamics of structural behavior. To overcome this limitation, we propose Time-Vertex Machine Learning (TVML), a novel framework that integrates GSP, time-domain analysis, and machine learning to enable interpretable and efficient sensor placement by identifying representative nodes that minimize redundancy while preserving critical information. We evaluate the proposed approach on two bridge
datasets for damage detection and time-varying graph signal reconstruction tasks. The results demonstrate the effectiveness of our approach in enhancing SHM systems by providing a robust, adaptive, and efficient solution for sensor placement.
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All authors Keivan Faghih Niresi, Jun Qing, Mengjie Zhao, Olga Fink
Systems and Control
Stability Analysis of a B-Spline Deep Neural Operator for Nonlinear Systems
Raffaele Romagnoli and Soummya Kar
This paper investigates the stability properties of neural operators through the structured representation offered by the Hybrid B-spline Deep Neural Operator (HBDNO). While existing stability-aware architectures typically enforce restrictive constraints that limit universality, HBDNO preserves full expressive power by representing outputs via B-spline control points. We show that these control points form a natural observable for post-training stability analysis. By applying Dynamic Mode Decomposition and connecting the resulting discrete dynamics to the Koopman operator framework, we provide a principled approach to spectral characterization of learned operators. Numerical results demonstrate the ability to assess stability and reveal future directions for safety-critical applications.
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Signal Processing
Anti-Malicious ISAC: How to Jointly Monitor and Disrupt Your Foes?
Zonghan Wang, Zahra Mobini, Hien Quoc Ngo et al. (+2 more)
Integrated sensing and communication (
ISAC) systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious
ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect legitimate targets from potential detection or exploitation by malicious foes. In this paper, we propose a new wireless proactive monitoring paradigm, where a legitimate monitor intercepts a suspicious communication link while performing cognitive jamming to enhance the monitoring success probability (MSP) and simultaneously safeguard the target. To this end, we derive closed-form expressions of the signal-to-interference-plus-noise-ratio (SINR) at the user (UE), sensing access points (S-APs), and an approximating expression of the SINR at the proactive monitor. Moreover, we propose an optimization technique under which the legitimate monitor minimizes the success detection probability (SDP) of the legitimate target, by optimizing the jamming power allocation over both communication and sensing channels subject to total power constraints and monitoring performance requirement. To enhance the monitor's longevity and reduce the risk of detection by malicious
ISAC systems, we further propose an adaptive power allocation scheme aimed at minimizing the total transmit power at the monitor while meeting a pre-selected sensing SINR threshold and ensuring successful monitoring. Our numerical results show that the proposed
algorithm significantly compromises the sensing and communication performance of malicious
ISAC.
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All authors Zonghan Wang, Zahra Mobini, Hien Quoc Ngo, Hyundong Shin, Michail Matthaiou
Systems and Control
Topology-based Conditions for Multiconsensus under the Signed Friedkin-Johnsen Model
Aashi Shrinate, Tanmay Siddharth and Twinkle Tripathy
In this paper, we address the multiconsensus problem in networked systems, where
agents are partitioned into disjoint subgroups and the states of
agents within a subgroup are driven to consensus. Our objective is to present a distributed control law that leads to multiconsensus in signed digraphs. To this end, we examine the convergence of opinions under the opposing rule-based signed Friedkin-Johnsen (SFJ) model and present conditions that lead to multiconsensus under this model. Interestingly, the proposed conditions depend only on graph
topology and signed interactions and not on the edge weights of the network. Consequently, the proposed SFJ-based control law relaxes the in-degree balance and homogeneity of trust-distrust, frequently assumed in the literature. Finally, we add simulation results to demonstrate the proposed conditions for multiconsensus.
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Systems and Control
Vision-Aided Relative State Estimation for Approach and Landing on a Moving Platform with Inertial Measurements
Tarek Bouazza, Alessandro Melis, Soulaimane Berkane et al. (+2 more)
This paper tackles the problem of estimating the relative position, orientation, and velocity between a
UAV and a planar platform undergoing arbitrary 3D motion during approach and landing. The estimation relies on measurements from Inertial Measurement Units (IMUs) mounted on both systems, assuming there is a suitable communication channel to exchange data, together with visual information provided by an onboard
monocular camera, from which the bearing (line-of-sight direction) to the platform's center and the normal vector of its planar surface are extracted. We propose a cascade observer with a complementary filter on $\mathbf{SO}(3)$ to reconstruct the relative attitude, followed by a linear Riccati observer for relative position and velocity estimation. Convergence of both observers is established under persistently exciting conditions, and the cascade is shown to be almost globally asymptotically and locally exponentially stable. We further extend the design to the case where the platform's rotation is restricted to its normal axis and show that its measured linear acceleration can be exploited to recover the remaining unobservable rotation angle. A sufficient condition for local exponential convergence in this setting is provided. The proposed observers are validated through extensive simulations.
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All authors Tarek Bouazza, Alessandro Melis, Soulaimane Berkane, Robert Mahony, Tarek Hamel
Image and Video Processing
Selective Phase-Aware Training of nnU-Net for Robust Breast Cancer Segmentation in Multi-Center DCE-MRI
Beyza Zayim, Aissiou Ikram and Boukhiar Naima
Breast cancer remains the most common cancer among women and is a leading cause of female mortality. Dynamic contrast-enhanced MRI (DCE-MRI) is a powerful imaging tool for evaluating breast tumors, yet the field lacks a standardized
benchmark for analyzing treatment responses and guiding personalized care. We participated in the MAMA-MIA Challenge's Primary Tumor
Segmentation task and this work presents a proposed selective, phase-aware training framework for the nnU-Net architecture, emphasizing quality-focused data selection to strengthen model robustness and generalization. We employed the No New Net (nnU-Net) framework with a selective training strategy that systematically analyzed the impact of image quality and center-specific variability on
segmentation performance. Controlled experiments on the DUKE, NACT, ISPY1, and ISPY2
datasets revealed that including ISPY scans with motion artifacts and reduced contrast impaired
segmentation performance, even with advanced preprocessing, such as contrast-limited adaptive histogram equalization (CLAHE). In contrast, training on DUKE and NACT data, which exhibited clearer contrast and fewer motion artifacts despite varying resolutions, with early phase images (0000-0002) provided more stable training conditions. Our results demonstrate the importance of phase-sensitive and quality-aware training strategies in achieving reliable
segmentation performance in heterogeneous clinical
datasets, highlighting the limitations of the expansion of naive
datasets and motivating the need for future automation of quality-based data selection strategies.
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Signal Processing
How is remifentanil dosed without dedicated indicator?
Bob Aubouin-Pairault, Mazen Alamir, Benjamin Meyer et al. (+2 more)
This study investigates the paradigm of intraoperative analgesic dosage using a data-driven approach based on retrospective clinical data. Remifentanil, an analgesic widely used during anesthesia, presents a dosing challenge due to the absence of an universally accepted indicator of analgesia. To examine how changes in patient state correlate with adjustments in remifentanil target concentration triggered by the practitioner, we analyzed data from two sources: VitalDB (Seoul, Korea) and PREDIMED (Grenoble, France). Results show that only features derived from arterial pressure are consistently associated with changes in remifentanil targets. This finding is robust across both
datasets despite variations in specific thresholds. In particular, increases in remifentanil targets are associated with high or rising arterial pressure over short periods (1–2 minutes), whereas decreases are linked to low, stable, or declining arterial pressure over longer periods (5–7 minutes). By capturing anesthesiologists' dosing strategies we provide a foundation for the future development of closed-loop control
algorithms. Beyond the specific example of remifentanil's change prediction, the proposed feature generation and associated sparse fitting approach can be applied to other domain where human decision can be viewed as sensors interpretation.
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All authors Bob Aubouin-Pairault, Mazen Alamir, Benjamin Meyer, Rémi Wolf, Kaouther Moussa
Systems and Control
Modular Landfill Remediation for AI Grid Resilience
Qi He and Chunyu Qu
Rising AI electricity demand and persistent landfill methane emissions constitute coupled constraints on U.S. digital infrastructure and decarbonization. While China has achieved a rapid 'de-landfilling' transition through centralized coordination, the U.S. remains structurally 'locked in' to landfilling due to fragmented governance and carbon accounting incentives. This paper proposes a modular legacy landfill remediation framework to address these dual challenges within U.S. institutional constraints. By treating legacy sites as stock resources, the proposed system integrates excavation, screening, and behind-the-meter combined heat and power (CHP) to transform environmental liabilities into resilience assets. A system analysis of a representative AI corridor demonstrates that such modules can mitigate site-level methane by 60-70% and recover urban land, while supplying approximately 20 MW of firm, islandable power. Although contributing only approximately 5% of a hyperscale data center's bulk load, it provides critical microgrid resilience and black-start capability. We conclude that remediation-oriented waste-to-energy should be valued not as a substitute for bulk renewables, but as a strategic control volume for buffering critical loads against grid
volatility while resolving long-term environmental liabilities.
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Systems and Control
Semantic Communication for Rate-Limited Closed-Loop Distributed Communication-Sensing-Control Systems
Guangjin Pan, Ayça Özçelikkale, Christian Häger et al. (+2 more)
The growing integration of distributed integrated sensing and communication (
ISAC) with closed-loop control in intelligent networks demands efficient information transmission under stringent bandwidth constraints. To address this challenge, this paper proposes a unified framework for goal-oriented semantic communication in distributed SCC systems. Building upon Weaver's three-level model, we establish a hierarchical semantic formulation with three error levels (L1: observation reconstruction, L2: state estimation, and L3: control) to jointly optimize their corresponding objectives. Based on this formulation, we propose a unified goal-oriented semantic compression and rate adaptation framework that is applicable to different semantic error levels and optimization goals across the SCC loop. A rate-limited multi-sensor LQR system is used as a case study to validate the proposed framework. We employ a GRU-based AE for semantic compression and a PPO-based rate adaptation
algorithm that dynamically allocates transmission rates across sensors. Results show that the proposed framework effectively captures task-relevant semantics and adapts its resource allocation strategies across different semantic levels, thereby achieving level-specific performance gains under bandwidth constraints.
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All authors Guangjin Pan, Ayça Özçelikkale, Christian Häger, Musa Furkan Keskin, Henk Wymeersch
Systems and Control · Cross-listed from math.OC
Finite-sample guarantees for data-driven forward-backward operator methods
Filippo Fabiani and Barbara Franci
We establish finite sample certificates on the quality of solutions produced by data-based forward-backward (FB) operator splitting schemes. As frequently happens in
stochastic regimes, we consider the problem of finding a zero of the sum of two operators, where one is either unavailable in closed form or computationally expensive to evaluate, and shall therefore be approximated using a finite number of noisy oracle samples. Under the lens of algorithmic stability, we then derive probabilistic bounds on the distance between a true zero and the FB output without making specific assumptions about the underlying data distribution. We show that under weaker conditions ensuring the convergence of FB schemes, stability bounds grow proportionally to the number of iterations. Conversely, stronger assumptions yield stability guarantees that are independent of the iteration count. We then specialize our results to a popular FB
stochastic Nash
equilibrium seeking
algorithm and validate our theoretical bounds on a control problem for smart grids, where the energy price uncertainty is approximated by means of historical data.
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Systems and Control · Cross-listed from physics.optics
Optical design and characterization of a multi-depth vision simulator
Parviz Zolfaghari, Ehsan Varasteh, Koray Kavakli et al. (+3 more)
We present a
vision simulator device (Katsim), a compact near-eye optical display designed for assessing postoperative corrected
vision, preoperative intraocular lens (IOL) assessment, and objective IOL characterization. The system forms a virtual image using an amplitude-modulated LCoS spatial light modulator (AM-SLM), RGB LED illumination, and a high-speed varifocal lens. In the proposed architecture, the LED illumination and varifocal lens diopter changes are triggered in synchrony with the SLM RGB subframes, rendering three depth planes perceptually simultaneously via high-frequency time-multiplexing. Operating at 60 frames per second (fps), the system achieves an effective 180 Hz depth-coded cycle, enabling sharp, multi-depth rendering within a dynamically adjustable depth range from 0.2 m to optical infinity. The system's eyebox is configurable from 1 to 5 mm, while maintaining a fixed spatial location and preserving angular magnification regardless of changes in focus or eyebox size. The designed system features a 9.15-degree field of view. An integrated infrared pupil-tracking module detects non-cataractous regions of the cataractous crystalline lens, and the projected imagery is mechanically steered through those clear zones in real time. The proposed
vision simulator supports both subjective simulation of post-
surgical vision for patient-specific counseling and objective optical evaluation of IOLs, including resolution and contrast fidelity (e.g., modulation transfer function, contrast transfer function, and defocus curves). By decoupling depth modulation from eyebox position and size, the system offers a modular, portable platform that supports enhanced preoperative planning, personalized IOL selection, objective IOL characterization, and use as a novel research
vision tool.
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All authors Parviz Zolfaghari, Ehsan Varasteh, Koray Kavakli, Arda Gulersoy, Afsun Sahin, Hakan Urey
Signal Processing
Energy Optimization for Time-of-Arrival Based Tracking
Luca Reggiani and Arnaldo Spalvieri
The paper analyzes energy allocation in a scenario where the position of a moving target is tracked by exploiting the Time-of-Arrivals of bandwidth-constrained signals received by or transmitted from a fixed number of anchors located at known positions. The signal of each anchor is generated by transmitting a sequence of known symbols, allowing for amplitude and duration (number of symbols) to be different from anchor to anchor. The problem is the minimization of the sum of the energies of the transmitted signals imposing a constraint on the performance of the tracking procedure. Specifically, the constraint is the Posterior Cramer-Rao Bound, below the mean square error achieved by any unbiased
estimator. The main improvement over the previous literature is the derivation of a formula that, at each step of the tracking, allows to calculate in closed form the first-order variation of the Posterior Cramer-Rao Bound as a function of the variation of the total energy. To concretely show the application of our approach, we present also two numerical
algorithms that implement the constrained optimization in the case of signals of fixed amplitude and variable duration transmitted from the anchors in a time division multiplexing scheme.
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Systems and Control
Rapid stabilization of the heat equation with localized disturbance
Patricio Guzmán, Hugo Parada and Christian Calle-Cárdenas
This paper studies the rapid stabilization of a multidimensional heat equation in the presence of an unknown spatially localized disturbance. A novel multivalued feedback control strategy is proposed, which synthesizes the frequency Lyapunov method (introduced by Xiang [41]) with the sign multivalued operator. This methodology connects Lyapunov-based stability analysis with spectral inequalities, while the inclusion of the sign operator ensures robustness against the disturbance. The closed-loop system is governed by a differential inclusion, for which well-posedness is proved via the theory of maximal monotone operators. This approach not only guarantees exponential stabilization but also circumvents the need for explicit disturbance modeling or estimation.
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Signal Processing
Specific Multi-emitter Identification: Theoretical Limits and Low-complexity Design
Yuhao Chen, Boxiang He, Junshan Luo et al. (+3 more)
Specific emitter identification (SEI) distinguishes emitters by utilizing hardware-induced signal imperfections. However, conventional SEI techniques are primarily designed for single-emitter scenarios. This poses a fundamental limitation in distributed wireless networks, where simultaneous transmissions from multiple emitters result in overlapping signals that conventional single-emitter identification methods cannot effectively handle. To overcome this limitation, we present a specific multi-emitter identification (SMEI) framework via multi-label learning, treating identification as a problem of directly decoding emitter states from overlapping signals. Theoretically, we establish performance bounds using Fano's inequality. Methodologically, the multi-label formulation reduces output dimensionality from exponential to linear scale, thereby substantially decreasing computational complexity. Additionally, we propose an improved SMEI (I-SMEI), which incorporates multi-head
attention to effectively capture features in correlated signal combinations. Experimental results demonstrate that SMEI achieves high identification accuracy with a linear computational complexity. Furthermore, the proposed I-SMEI scheme significantly improves identification accuracy across various overlapping scenarios compared to the proposed SMEI and other advanced methods.
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All authors Yuhao Chen, Boxiang He, Junshan Luo, Shilian Wang, Lei Yao, Jing Lei
Signal Processing
Intelligent Sky Mirrors: SAC-Driven MF-RIS Optimization for Secure NOMA in Low-Altitude Economy
Sai Zhao, Fanjin Kong, Dong Tang et al. (+5 more)
Low-altitude economy (LAE) has become a key driving force for smart cities and economic growth. To address spectral efficiency and communication security challenges in LAE, this paper investigates secure energy efficiency (SEE) maximization using intelligent sky mirrors,
UAV-mounted multifunctional reconfigurable intelligent surfaces (MF-RIS) assisting nonorthogonal multiple access (NOMA) systems. These aerial mirrors intelligently amplify legitimate signals while simultaneously generating jamming against eavesdroppers. We formulate a joint optimization problem encompassing
UAV trajectory, base station power allocation, RIS phase shifts, amplification factors, and scheduling
matrices. Given the fractional SEE objective and dynamic
UAV scenarios, we propose a two-layer optimization scheme: SAC-driven first layer for trajectory and power management, and channel
alignment-based second layer for phase optimization. Simulations demonstrate that our proposed scheme significantly outperforms
benchmark approaches.
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All authors Sai Zhao, Fanjin Kong, Dong Tang, Tuo Wu, Shunxing Yang, Kai-Kit Wong, Kin-Fai Tong, Kwai-Man Luk
Audio and Speech Processing · Cross-listed from cs.SD
JoyVoice: Long-Context Conditioning for Anthropomorphic Multi-Speaker Conversational Synthesis
Fan Yu, Tao Wang, You Wu et al. (+22 more)
Large
speech generation models are evolving from single-speaker, short sentence synthesis to multi-speaker, long conversation geneartion. Current long-form
speech generation models are predominately constrained to dyadic, turn-based interactions. To address this, we introduce JoyVoice, a novel anthropomorphic foundation model designed for flexible, boundary-free synthesis of up to eight speakers. Unlike conventional cascaded systems, JoyVoice employs a unified E2E-
Transformer-DiT architecture that utilizes autoregressive hidden representations directly for
diffusion inputs, enabling holistic end-to-end optimization. We further propose a MM-Tokenizer operating at a low bitrate of 12.5 Hz, which integrates multitask semantic and MMSE losses to effectively model both semantic and acoustic information. Additionally, the model incorporates robust text front-end processing via large-scale data perturbation. Experiments show that JoyVoice achieves state-of-the-art results in
multilingual generation (Chinese, English, Japanese, Korean) and zero-shot voice cloning. JoyVoice achieves top-tier results on both the Seed-TTS-Eval
Benchmark and multi-speaker long-form conversational voice cloning tasks, demonstrating superior audio quality and generalization. It achieves significant improvements in prosodic continuity for long-form
speech, rhythm richness in multi-speaker conversations, paralinguistic naturalness, besides superior intelligibility. We encourage readers to listen to the demo at https://jea-
speech.github.io/JoyVoice
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All authors Fan Yu, Tao Wang, You Wu, Lin Zhu, Wei Deng, Weisheng Han, Wenchao Wang, Lin Hu, Xiangyu Liang, Xiaodong He, Yankun Huang, Yu Gu, Yuan Liu, Yuxuan Wang, Zhangyu Xiao, Ziteng Wang, Boya Dong, Feng Dang, Jinming Chen, Jingdong Li, Jun Wang, Yechen Jin, Yuan Zhang, Zhengyan Sheng, Xin Wang
Signal Processing
Wireless sEMG-IMU Wearable for Real-Time Squat Kinematics and Muscle Activation
Marie Jose Perez Peralta, Daniela Flores Casillas, Benjamin Wilson et al. (+3 more)
This work presents the design and implementation of a wireless, wearable system that combines surface electromyography (sEMG) and inertial measurement units (IMUs) to analyze a single lower-limb functional task: the free bodyweight squat in a healthy adult. The system records bipolar EMG from one agonist and one antagonist muscle of the dominant leg (vastus lateralis and semitendinosus) while simultaneously estimating knee joint angle, angular velocity, and angular acceleration using two MPU6050 IMUs. A custom dual-channel EMG front end with differential instrumentation preamplification, analog filtering (5-500 Hz band-pass and 60 Hz notch), high final gain, and rectified-integrated output was implemented on a compact 10 cm x 12 cm PCB. Data are digitized by an ESP32 microcontroller and transmitted wirelessly via ESP-NOW to a second ESP32 connected to a PC. A Python-based graphical user interface (GUI) displays EMG and kinematic signals in real time, manages subject metadata, and exports a summary of each session to Excel. The complete system is battery-powered to reduce electrical risk during human use. The resulting prototype demonstrates the feasibility of low-cost, portable EMG-IMU instrumentation for integrated analysis of muscle activation and squat kinematics and provides a platform for future biomechanical applications in sports performance and rehabilitation.
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All authors Marie Jose Perez Peralta, Daniela Flores Casillas, Benjamin Wilson, Cristian Aviles Medina, Yira Itzae Rendon Hernandez, Vladimir Orrante Bracho
Signal Processing
AI-Driven Subcarrier-Level CQI Feedback
Chengyong Jiang, Jiajia Guo, Yuqing Hua et al. (+2 more)
The Channel Quality Indicator (CQI) is a fundamental component of channel state information (CSI) that enables adaptive modulation and coding by selecting the optimal modulation and coding scheme to meet a target block error rate. While AI-enabled CSI feedback has achieved significant advances, especially in precoding
matrix index feedback, AI-based CQI feedback remains underexplored. Conventional subband-based CQI approaches, due to coarse granularity, often fail to capture fine frequency-selective variations and thus lead to suboptimal resource allocation. In this paper, we propose an AI-driven subcarrier-level CQI feedback framework tailored for 6G and NextG systems. First, we introduce CQInet, an autoencoder-based scheme that compresses per-subcarrier CQI at the user equipment and reconstructs it at the base station, significantly reducing feedback overhead without compromising CQI accuracy. Simulation results show that CQInet increases the effective data rate by 7.6% relative to traditional subband CQI under equivalent feedback overhead. Building on this, we develop SR-CQInet, which leverages super-resolution to infer fine-grained subcarrier CQI from sparsely reported CSI reference signals (CSI-RS). SR-CQInet reduces CSI-RS overhead to 3.5% of CQInet's requirements while maintaining comparable throughput. These results demonstrate that AI-driven subcarrier-level CQI feedback can substantially enhance spectral efficiency and reliability in future wireless networks.
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All authors Chengyong Jiang, Jiajia Guo, Yuqing Hua, Chao-Kai Wen, Shi Jin
Signal Processing
The MIMO-ME-MS Channel: Analysis and Algorithm for Secure MIMO Integrated Sensing and Communications
Seongkyu Jung, Namyoon Lee and Jeonghun Park
This paper addresses precoder design for secure multiple-input multiple-output (
MIMO) integrated sensing and communications (
ISAC) systems. We introduce a
MIMO channel with a multiple-
antenna eavesdropper and a multiple-
antenna sensing receiver (
MIMO-ME-MS) and analyze the fundamental performance limits of this tripartite tradeoff. Using sensing mutual information, we formulate the precoder design as a nonconvex weighted sum rate maximization problem. A high signal-to-noise ratio analysis based on a subspace decomposition characterizes the maximum weighted degrees of freedom. This analysis reveals the structure of a quasi-optimal precoder that must span the “useful subspace” and demonstrates the inadequacy of extending known schemes from simpler wiretap or
ISAC channels. To solve this nonconvex problem, we develop a practical two-stage iterative
algorithm that alternates between a sequential basis construction stage and a power allocation stage that solves the resulting difference-of-convex program. We demonstrate that the proposed method captures the desirable precoder structure identified in our analysis and achieves substantial performance gains in the
MIMO-ME-MS channel.
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Signal Processing
PalpAid: Multimodal Pneumatic Tactile Sensor for Tissue Palpation
Devi Yuliarti, Ravi Prakash, Hiu Ching Cheung et al. (+3 more)
The tactile properties of tissue, such as elasticity and stiffness, often play an important role in
surgical oncology when identifying tumors and pathological tissue boundaries. Though extremely valuable,
robot-assisted surgery comes at the cost of reduced sensory information to the surgeon, with
vision being the primary. Sensors proposed to overcome this sensory desert are often bulky, complex, and incompatible with the
surgical workflow. We present PalpAid, a
multimodal pneumatic tactile sensor to restore touch in
robot-assisted surgery. PalpAid is equipped with a microphone and pressure sensor, converting contact force into an internal pressure differential. The pressure sensor acts as an event detector, while the acoustic signature assists in tissue identification. We show the design, fabrication, and assembly of sensory units with characterization tests for robustness to use, repetition cycles, and integration with a robotic system. Finally, we demonstrate the sensor's ability to classify 3D-printed hard objects with varying infills and soft ex vivo tissues. We envision PalpAid to be easily retrofitted with existing
surgical/general robotic systems, allowing soft tissue palpation.
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All authors Devi Yuliarti, Ravi Prakash, Hiu Ching Cheung, Amy Strong, Patrick J. Codd, Shan Lin
Signal Processing
Reimagining Wireless Connectivity: The FAS-RIS Synergy for 6G Smart Cities
Tuo Wu, Kai-Kit Wong, Jie Tang et al. (+6 more)
Fluid
antenna system (FAS) represents the concept of treating
antenna as a reconfigurable physical-layer resource to broaden system design and network optimization and inspire next-generation reconfigurable
antennas. FAS can unleash new degree of freedom (DoF) via
antenna reconfigurations for novel spatial diversity. Reconfigurable intelligent surfaces (RISs) on the other hand can reshape wireless propagation environments but often face limitations from double path-loss and minimal signal processing capability when operating independently. This article envisions a transformative FAS-RIS integrated architecture for future smart city networks, uniting the adaptability of FAS with the environmental control of RIS. The proposed framework has five key applications: FAS-enabled base stations (BSs) for large-scale beamforming, FAS-equipped user devices with finest spatial diversity, and three novel RIS paradigms – fluid RIS (FRIS) with reconfigurable elements, FAS-embedded RIS as active relays, and enormous FAS (E-FAS) exploiting surface waves on facades to re-establish line-of-sight (LoS) communication. A two-timescale control mechanism coordinates network-level beamforming with rapid, device-level adaptation. Applications spanning from simultaneous wireless information and power transfer (SWIPT) to integrated sensing and communications (
ISAC), with challenges in co-design, channel modeling, and optimization, are discussed. This article concludes with simulation results demonstrating the robustness and effectiveness of the FAS-RIS system.
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All authors Tuo Wu, Kai-Kit Wong, Jie Tang, Junteng Yao, Baiyang Liu, Kin-Fai Tong, Chan-Byoung Chae, Matthew C. Valenti, Kwai-Man Luk
Signal Processing
An Fluid Antenna Array-Enabled DOA Estimation Method: End-Fire Effect Suppression
Jiaji Ren, Ye Tian, Baiyang Liu et al. (+5 more)
Direction of Arrival (DOA) estimation serves as a critical sensing technology poised to play a vital role in future intelligent and ubiquitous communication systems. Despite the development of numerous mature super-resolution
algorithms, the inherent end-fire effect problem in fixed
antenna arrays remains inadequately addressed. This work proposed a novel array architecture composed of fluid
antennas. By exploiting the spatial reconfigurability of their positions to equivalently modulate the array steering vector and integrating it with the classical MUSIC
algorithm, this approach achieved high-precision DOA estimation. Simulation results demonstrated that the proposed method delivers outstanding estimation performance even in highly challenging end-fire regions.
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All authors Jiaji Ren, Ye Tian, Baiyang Liu, Tuo Wu, Wei Liu, Kai-Kit Wong, Kin-Fai Tong, Kwai-Man Luk
Systems and Control · Cross-listed from cs.LG
OPBO: Order-Preserving Bayesian Optimization
Wei Peng, Jianchen Hu, Kang Liu et al. (+1 more)
Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for approximating the black-box objective function, it is well-known that it can fail in high-dimensional space (e.g., dimension over 500). We argue that the reliance of GP on precise numerical fitting is fundamentally ill-suited in high-dimensional space, where it leads to prohibitive computational complexity. In order to address this, we propose a simple order-preserving
Bayesian optimization (OPBO) method, where the surrogate model preserves the order, instead of the value, of the black-box objective function. Then we can use a simple but effective OP neural network (NN) to replace GP as the surrogate model. Moreover, instead of searching for the best solution from the acquisition model, we select good-enough solutions in the ordinal set to reduce computational cost. The experimental results show that for high-dimensional (over 500) black-box optimization problems, the proposed OPBO significantly outperforms traditional BO methods based on regression NN and GP. The source code is available at https://github.com/pengwei222/OPBO.
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All authors Wei Peng, Jianchen Hu, Kang Liu, Qiaozhu Zhai
Signal Processing
FAS-RIS for V2X: Unlocking Realistic Performance Analysis with Finite Elements
Tuo Wu, Xiazhi Lai, Maged Elkashlan et al. (+3 more)
The synergy of fluid
antenna systems (FAS) and reconfigurable intelligent surfaces (RIS) is poised to unlock robust Vehicle-to-Everything (V2X) communications. However, a critical gap persists between theoretical predictions and real-world performance. Existing analyses predominantly rely on the Central Limit
Theorem (CLT), an assumption valid only for a large number of RIS elements, which fails to represent practical, finite-sized deployments constrained by cost and urban infrastructure. This paper bridges this gap by presenting a novel framework that unlocks a realistic performance analysis for FAS-RIS systems with finite elements. Leveraging a Gamma distribution approximation, we derive a new, tractable closed-form expression for the outage probability. Numerical results validate our approach, demonstrating that it offers a significantly more accurate performance characterization than conventional CLT-based methods, particularly in the practical regime of small-scale RIS. This work provides a crucial foundation for the design and deployment of reliable FAS-RIS-aided vehicular networks.
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All authors Tuo Wu, Xiazhi Lai, Maged Elkashlan, Naofal Al-Dhahir, Matthew C. Valenti, Fumiyuki Adachi
Audio and Speech Processing
Enhancing Fully Formatted End-to-End Speech Recognition with Knowledge Distillation via Multi-Codebook Vector Quantization
Jian You, Xiangfeng Li and Erwan Zerhouni
Conventional automatic
speech recognition (ASR) models typically produce outputs as normalized texts lacking punctuation and capitalization, necessitating post-processing models to enhance readability. This approach, however, introduces additional complexity and latency due to the cascaded system design. In response to this challenge, there is a growing trend to develop end-to-end (E2E) ASR models capable of directly predicting punctuation and capitalization, though this area remains underexplored. In this paper, we propose an enhanced fully formatted E2E ASR model that leverages knowledge
distillation (KD) through multi-codebook vector quantization (MVQ). Experimental results demonstrate that our model significantly outperforms previous works in word error rate (WER) both with and without punctuation and capitalization, and in punctuation error rate (PER). Evaluations on the LibriSpeech-PC test-clean and test-other subsets show that our model achieves state-of-the-art results.
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