Systems and Control
Real-Time Forward Kinematics and Jacobians for Control of an MRI-Guided Magnetically Actuated Robotic Catheter
Ran Hao, Yuttana Itsarachaiyot, Yen-Chun Chen et al. (+1 more)
This paper presents a forward kinematics and analytical Jacobian computation approach for real-time control of a novel
magnetic resonance imaging (MRI)-actuated robotic catheter. The MRI-actuated robotic catheter is modeled as a series of rigid and flexible segments and actuated by
magnetic torques generated on a set of current-carrying microcoils embedded on the catheter body by the
magnetic field of the MRI scanner. First, a real-time forward kinematic modeling approach of the robotic catheter employing the static Cosserat-rod theory is presented. Second, the analytical calculation approach of the forward kinematic Jacobians of the proposed forward kinematic model is presented. The accuracy, reproducibility, and computational efficiency of the proposed methods are evaluated using a robotic catheter prototype with a single coil set, where catheter tip trajectories collected by a catadioptric stereo camera tracking system are validated using the desired tip trajectories. Experimental results demonstrate that the proposed method can successfully control the catheter in an open loop to perform complex trajectories with real-time computational efficiency, paving the way for accurate closed-loop control with real-time MR-imaging feedback.
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All authors Ran Hao, Yuttana Itsarachaiyot, Yen-Chun Chen, M. Cenk Çavuşoğlu
Systems and Control
Global Frequency Reference Tracking as an Oscillation Suppression Mechanism in VSM Primary Control: A Coupled-Oscillator Study
Taha Saeed Khan
Synchronization in power systems is traditionally achieved through physical network coupling, whereby inverter-based resources (IBRs) and synchronous machines converge to a common frequency via oscillatory swing dynamics. In conventional operation, secondary control acts on a slow time scale and is typically engaged only after the primary dynamics have largely settled. As a result, in the absence of an explicit global reference, disturbances can induce prolonged transients and large phase excursions. This work considers a setting in which the total active power balance is known and maintained at all times, and proposes a control architecture for virtual synchronous machine (VSM) based inverters in which all units track a broadcast global frequency reference. Under this assumption, synchronization is transformed from a mutual oscillator locking problem into a reference tracking problem. Using a second order swing network model, we show that
embedding a simple proportional integral (PI) frequency controller can significantly improves transient behavior. A washout mechanism ensures that the additional control action vanishes in steady state, thereby preserving network determined power sharing. Simulations on a three oscillator network demonstrate reduced frequency overshoot, elimination of underdamped oscillations, and lower angular stress compared to conventional open loop synchronization, highlighting the effectiveness of a global frequency reference as a coordination mechanism for grid-forming inverter networks.
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Signal Processing · Cross-listed from cs.IT
User-Centric Cell-Free Massive MIMO Enhanced by Fluid-Antenna Access Points: Uplink Analysis
Maryam Olyaee, Giovanni Interdonato and Stefano Buzzi
In this paper, we investigate
cell-free massive
MIMO (CF-mMIMO) systems in which access points (APs) are equipped with fluid
antennas (FAs) and develop a comprehensive framework for channel estimation,
antenna port selection, and uplink spectral efficiency (SE) optimization. We propose a generalized LMMSE-based uplink channel estimation scheme that dynamically activates FA ports during pilot transmission, efficiently exploiting
antenna reconfigurability under practical training constraints. Building on this, we design a distributed port selection strategy that minimizes per-AP channel estimation error by exploiting spatial correlation among FA ports. We systematically analyze the impact of
antenna geometry and spatial correlation using the Jakes' channel model for different AP array configurations, including uniform linear and planar arrays. We then derive SINR expressions for centralized and distributed uplink processing and obtain a closed-form uplink SE expression for centralized maximum-ratio combining using the use-and-then-forget bound. Finally, we propose an alternating-optimization framework to select FA port configurations that maximize the uplink sum SE. Numerical results show that the proposed FA-aware channel estimation and port optimization strategies greatly reduce channel estimation error and significantly improve sum-SE over fixed-
antenna and non-optimized FA baselines, confirming FAs as a key enabler for scalable, adaptive CF-mMIMO networks.
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Signal Processing
Flexible Intelligent Metasurface for Downlink Communications under Statistical CSI
Vaibhav Kumar, Anastasios Papazafeiropoulos, Pandelis Kourtessis et al. (+4 more)
Flexible intelligent metasurface (FIM) is a recently developed, groundbreaking hardware technology with promising potential for 6G wireless systems. Unlike conventional rigid
antenna array (RAA)-based transmitters, FIM-assisted transmitters can dynamically alter their physical surface through morphing, offering new degrees of freedom to enhance system performance. In this letter, we depart from prior works that rely on instantaneous channel state information (CSI) and instead address the problem of average sum spectral efficiency maximization under statistical CSI in a FIM-assisted downlink multiuser multiple-input single-output setting. To this end, we first derive the spatial correlation
matrix for the FIM-aided transmitter and then propose an iterative FIM optimization
algorithm based on the gradient projection method. Simulation results show that with statistical CSI, the FIM-aided system provides a significant performance gain over its RAA-based counterpart in scenarios with strong spatial channel correlation, whereas the gain diminishes when the channels are weakly correlated.
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All authors Vaibhav Kumar, Anastasios Papazafeiropoulos, Pandelis Kourtessis, John Senior, Marwa Chafii, Dimitra I. Kaklamani, Iakovos S. Venieris
Signal Processing
Masked Sequence Autoencoding for Enhanced Defect Visualization in Active Infrared Thermography
Mohammed Salah, Eman Ouda, Stefano Sfarra et al. (+2 more)
Active infrared thermography (AIRT) became a crucial tool in aerospace non-destructive testing (NDT), enabling the detection of hidden defects and anomalies in materials by capturing thermal responses over time. In AIRT, autoencoders are widely used to enhance defect detection by reducing the dimensionality of thermal data and improving the signal-to-noise ratio. However, traditional AIRT autoencoders often struggle to disentangle subtle defect features from dominant background responses, leading to suboptimal defect analysis under varying material and inspection conditions. To overcome this challenge, this work proposes a Masked CNN-
Attention Autoencoder (AIRT-Masked-CAAE) that integrates convolutional feature extraction with
attention mechanisms to capture both local thermal patterns and global contextual dependencies. The AIRT-Masked-CAAE introduces a masked sequence autoencoding strategy, where the network learns to infer missing thermal responses from surrounding contextual cues, while suppressing background redundancy. In addition, the proposed masked sequence autoencoding approach enables training on only a subset of the thermal sequence, while providing generalizable latent representations and reducing training time by a factor of 30. The AIRT-Masked-CAAE framework was evaluated using specimens made of PVC, CFRP, and PLA. The results demonstrate that the AIRT-Masked-CAAE surpasses state-of-the-art AIRT autoencoders in terms of contrast, signal-to-noise ratio (SNR), and metrics based on neural networks.
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All authors Mohammed Salah, Eman Ouda, Stefano Sfarra, Davor Svetinovic, Yusra Abdulrahman
Signal Processing · Cross-listed from cs.CV
Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection
Runwei Guan, Jianan Liu, Shaofeng Liang et al. (+7 more)
4D millimeter-wave (mmWave) radar has been widely adopted in autonomous driving and
robot perception due to its low cost and all-weather robustness. However, point-cloud-based radar representations suffer from information loss due to multi-stage signal processing, while directly utilizing raw 4D radar tensors incurs prohibitive computational costs. To address these challenges, we propose WRCFormer, a novel 3D object detection framework that efficiently fuses raw 4D radar cubes with camera images via decoupled multi-view radar representations. Our approach introduces two key components: (1) A Wavelet
Attention Module embedded in a wavelet-based Feature Pyramid Network (FPN), which enhances the representation of sparse radar signals and image data by capturing joint spatial-frequency features, thereby mitigating information loss while maintaining computational efficiency. (2) A Geometry-guided Progressive Fusion mechanism, a two-stage query-based fusion strategy that progressively aligns multi-view radar and visual features through
geometric priors, enabling modality-agnostic and efficient integration without overwhelming computational overhead. Extensive experiments on the K-Radar
benchmark show that WRCFormer achieves state-of-the-art performance, surpassing the best existing model by approximately 2.4% in all scenarios and 1.6% in sleet conditions, demonstrating strong robustness in adverse weather.
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All authors Runwei Guan, Jianan Liu, Shaofeng Liang, Fangqiang Ding, Shanliang Yao, Xiaokai Bai, Daizong Liu, Tao Huang, Guoqiang Mao, Hui Xiong
Systems and Control
A Bezier Curve Based Approach to the Convexification of the AC Optimal Power Flow Problem
Carlos Arturo Saldarriaga-Cortes, Carlos Adrian Correa-Florez, Maximiliano Bueno-Lopez et al. (+1 more)
The Alternating Current Optimal Power Flow (ACOPF) problem remains one of the most fundamental yet computationally challenging tasks in power systems operation and planning due to its nonconvex, nonlinear, and
multimodal nature. This paper proposes a convex reformulation of the AC power flow problem by introducing auxiliary variables to isolate nonlinear terms, applying logarithmic transformations to exploit product-sum properties, and approximating with Bezier curves using a novel convexifying butterfly shaped function. This model is intended for assessing and operating weak power systems that face challenges with reactive power supply and overall network robustness. Its formulation closely mirrors the AC formulation, particularly regarding active and reactive power dispatch and network voltage levels. The proposed model achieves convergence on large test systems (e.g., IEEE 118 bus) in seconds and is validated against exact AC solutions. This convex formulation stands out not only for its mathematical transparency and intuitive structure but also for its ease of validation and implementation, making it an accessible and reliable tool for researchers and system operators for energy planning. The numerical analysis conducted on the IEEE 118 bus system yielded average percentage errors in the state variables specifically, the magnitudes and angles of nodal voltages of just 0.0008 percentage and 0.014 degree, respectively, when compared with the precise AC formulation. These results underscore the high accuracy and reliability of the proposed methodology.
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All authors Carlos Arturo Saldarriaga-Cortes, Carlos Adrian Correa-Florez, Maximiliano Bueno-Lopez, Maria Victoria Gasca-Segura
Systems and Control · Cross-listed from cs.RO
PreGME: Prescribed Performance Control of Aerial Manipulators based on Variable-Gain ESO
Mengyu Ji, Shiliang Guo, Zhengzhen Li et al. (+3 more)
An aerial manipulator, comprising a multirotor base and a robotic arm, is subject to significant dynamic coupling between these two components. Therefore, achieving precise and robust motion control is a challenging yet important objective. Here, we propose a novel prescribed performance motion control framework based on variable-gain extended state observers (ESOs), referred to as PreGME. The method includes variable-gain ESOs for real-time estimation of dynamic coupling and a prescribed performance flight control that incorporates error trajectory constraints. Compared with existing methods, the proposed approach exhibits the following two characteristics. First, the adopted variable-gain ESOs can accurately estimate rapidly varying dynamic coupling. This enables the proposed method to handle manipulation tasks that require aggressive motion of the robotic arm. Second, by prescribing the performance, a preset error trajectory is generated to guide the system
evolution along this trajectory. This strategy allows the proposed method to ensure the tracking error remains within the prescribed performance envelope, thereby achieving high-precision control. Experiments on a real platform, including aerial staff twirling, aerial mixology, and aerial cart-pulling experiments, are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that even under the dynamic coupling caused by rapid robotic arm motion (end-effector velocity: 1.02 m/s, acceleration: 5.10 m/s$^2$), the proposed method achieves high tracking performance.
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All authors Mengyu Ji, Shiliang Guo, Zhengzhen Li, Jiahao Shen, Huazi Cao, Shiyu Zhao
Signal Processing
Confidence analysis-based hybrid heartbeat detection for ballistocardiogram using template matching and deep learning
Dongli Cai, Xihe Chen, Yaosheng Chen et al. (+3 more)
Heartbeat interval can be detected from ballistocardiogram (BCG) signals in a non-contact manner. Conventional methods achieved heartbeat detection from different perspectives, where template matching (TM) and deep learning (DL) were based on the similarity of neighboring heartbeat episodes and robust spatio-temporal characteristics, respectively, and thus, performed varied from case to case. Inspired by the above facts, we propose confidence analysis-based hybrid heartbeat detection using both TM and DL, and further explore the advantages of both methods in various scenarios. To be specific, the confidence of the heartbeat detection results was evaluated by the consistency of signal morphology and the variability of the detected heartbeat intervals, which could be formulated by the averaged correlation between each heartbeat episode and the detected template and the normalized standard deviation among detected heartbeat intervals, respectively, where the results with higher confidence were remained. In order to validate the effectiveness of the proposed hybrid method, we conducted experiments using practical clinical BCG
dataset with 34 subjects including 924,235 heartbeats. Numerical results showed that the proposed hybrid method achieved an average absolute interval error of 20.73 ms, yielding a reduction of 29.28 ms and 10.13 ms compared to solo TM and DL methods, respectively. Besides, case study showed the robustness of heartbeat detection of TM and DL to individual differences and signal quality, respectively, and in turn, validated that the hybrid method could benefit from the complementary advantages of both methods, which demonstrated the superiority of the proposed hybrid method in practical BCG monitoring scenarios.
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All authors Dongli Cai, Xihe Chen, Yaosheng Chen, Hong Xian, Baoxian Yu, Han Zhang
Systems and Control
Weak state synchronization of homogeneous multi-agent systems with adaptive protocols
Anton A. Stoorvogel, Ali Saberi, Zhenwei Liu et al. (+1 more)
In this paper, we study scale-free weak synchronization for multi-
agent systems (MAS). In other words, we design a protocol for the
agents without using any knowledge about the network. We do not even require knowledge about the connectivity of the network. Each protocol contains an adaptive parameter to tune the protocol automatically to the demands of the network.
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All authors Anton A. Stoorvogel, Ali Saberi, Zhenwei Liu, Tayaba Yeasmin
Audio and Speech Processing
Spatial Interpolation of Room Impulse Responses based on Deeper Physics-Informed Neural Networks with Residual Connections
Ken Kurata, Gen Sato, Izumi Tsunokuni et al. (+1 more)
The room impulse response (RIR) characterizes sound propagation in a room from a loudspeaker to a microphone under the linear time-invariant assumption. Estimating RIRs from a limited number of measurement points is crucial for sound propagation analysis and visualization. Physics-informed neural networks (PINNs) have recently been introduced for accurate RIR estimation by
embedding governing physical laws into deep learning models; however, the role of network depth has not been systematically investigated. In this study, we developed a deeper PINN architecture with residual connections and analyzed how network depth affects estimation performance. We further compared activation functions, including tanh and sinusoidal activations. Our results indicate that the residual PINN with sinusoidal activations achieves the highest accuracy for both interpolation and extrapolation of RIRs. Moreover, the proposed architecture enables stable training as the depth increases and yields notable improvements in estimating reflection components. These results provide practical guidelines for designing deep and stable PINNs for acoustic-inverse problems.
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All authors Ken Kurata, Gen Sato, Izumi Tsunokuni, Yusuke Ikeda
Systems and Control
Distributed Fusion Estimation with Protecting Exogenous Inputs
Liping Guo, Jimin Wang, Yanlong Zhao et al. (+1 more)
In the context of distributed fusion estimation, directly transmitting local estimates to the fusion center may cause a
privacy leakage concerning exogenous inputs. Thus, it is crucial to protect exogenous inputs against full eavesdropping while achieving distributed fusion estimation. To address this issue, a noise injection strategy is provided by injecting mutually independent noises into the local estimates transmitted to the fusion center. To determine the covariance
matrices of the injected noises, a constrained minimization problem is constructed by minimizing the sum of mean square errors of the local estimates while ensuring ({\epsilon}, {\delta})-differential
privacy. Suffering from the non-convexity of the minimization problem, an approach of relaxation is proposed, which efficiently solves the minimization problem without sacrificing differential
privacy level. Then, a differentially private distributed fusion estimation
algorithm based on the covariance intersection approach is developed. Further, by introducing a feedback mechanism, the fusion estimation accuracy is enhanced on the premise of the same ({\epsilon}, {\delta})-differential
privacy. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed
algorithms, and the trade-off between differential
privacy level and fusion estimation accuracy.
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All authors Liping Guo, Jimin Wang, Yanlong Zhao, Ji-Feng Zhang
Signal Processing · Cross-listed from cs.IT
Covering in Hamming and Grassmann Spaces: New Bounds and Reed–Solomon-Based Constructions
Samin Riasat and Hessam Mahdavifar
We study covering problems in Hamming and Grassmann spaces through a unified coding-theoretic and information-theoretic framework. Viewing covering as a form of quantization in general metric spaces, we introduce the notion of the average covering radius as a natural measure of average distortion, complementing the classical worst-case covering radius. By leveraging tools from one-shot rate-distortion theory, we derive explicit non-asymptotic random-coding bounds on the average covering radius in both spaces, which serve as fundamental performance
benchmarks. On the construction side, we develop efficient puncturing-based covering
algorithms for generalized Reed–Solomon (GRS) codes in the Hamming space and extend them to a new family of subspace codes, termed character-Reed–Solomon (CRS) codes, for Grassmannian quantization under the chordal distance. Our results reveal that, despite poor worst-case covering guarantees, these structured codes exhibit strong average covering performance. In particular, numerical results in the Hamming space demonstrate that RS-based constructions often outperform random codebooks in terms of average covering radius. In the one-dimensional Grassmann space, we numerically show that CRS codes over
prime fields asymptotically achieve average covering radii within a constant factor of the random-coding bound in the high-rate regime. Together, these results provide new insights into the role of algebraic structure in covering problems and high-dimensional quantization.
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Systems and Control
A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments
Si-Yu Xiao, Xin-Di Zhao, Xiang-Zhan Wang et al. (+8 more)
Casing collar locator (CCL) measurements are widely used as reliable depth markers for positioning downhole instruments in cased-hole operations, enabling accurate depth control for operations such as perforation. However, autonomous collar recognition in downhole environments remains challenging because CCL signals are often corrupted by toolstring- or casing-induced
magnetic interference, while stringent size and power budgets limit the use of computationally intensive
algorithms and specific operations require real-time, in-situ processing. To address these constraints, we propose Collar Recognition Nets (CRNs), a family of domain-specific lightweight 1-D convolutional neural networks for collar signature recognition from streaming CCL waveforms. With depthwise separable convolutions and input pooling, CRNs optimize efficiency without sacrificing accuracy. Our most compact model achieves an F1-score of 0.972 on field data with only 1,985~parameters and 8,208~MACs, and deployed on an ARM Cortex-M7 based embedded system using TensorFlow Lite for Microcontrollers (TFLM) library, the model demonstrates a throughput of 1,000 inference per second and 343.2 {\mu}s latency, confirming the feasibility of robust, autonomous, and real-time collar recognition under stringent downhole constraints.
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All authors Si-Yu Xiao, Xin-Di Zhao, Xiang-Zhan Wang, Tian-Hao Mao, Ying-Kai Liao, Xing-Yu Liao, Yu-Qiao Chen, Jun-Jie Wang, Shuang Liu, Tu-Pei Chen, Yang Liu
Image and Video Processing · Cross-listed from cs.CV
Hash Grid Feature Pruning
Yangzhi Ma, Bojun Liu, Jie Li et al. (+2 more)
Hash grids are widely used to learn an implicit neural field for Gaussian splatting, serving either as part of the
entropy model or for inter-frame prediction. However, due to the irregular and non-uniform distribution of Gaussian splats in 3D space, numerous sparse regions exist, rendering many features in the hash grid invalid. This leads to redundant storage and transmission overhead. In this work, we propose a hash grid feature
pruning method that identifies and prunes invalid features based on the coordinates of the input Gaussian splats, so that only the valid features are encoded. This approach reduces the storage size of the hash grid without compromising model performance, leading to improved rate-distortion performance. Following the Common Test Conditions (CTC) defined by the standardization committee, our method achieves an average bitrate reduction of 8% compared to the baseline approach.
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All authors Yangzhi Ma, Bojun Liu, Jie Li, Li Li, Dong Liu
Systems and Control
Assessment of a Hybrid Energy System for Reliable and Sustainable Power Supply to Boru Meda Hospital in Ethiopia
Tegenu Argaw Woldegiyorgis, Hong Xian Li, Fekadu Chekol Admassu et al. (+5 more)
This study aims to evaluate the techno-economic feasibility of hybrid energy systems (HES) including Grid for providing reliable and sustainable power to Boru Meda Hospital, Ethiopia. HOMER pro 3.11.2 was used to design and evaluate a novel, integrated optimization and comparative assessment of diverse HRES, specif ically adjusted to the energy consumptions and available resources of the Hospital. The scenario evaluation showed that interconnecting photovoltaic (PV), biomass generator (BG), wind power (WP), diesel generator (DG), battery, and converter can effectively provide the Hospital's daily energy consumption of 11,214.66 kWh while conforming reliability and reducing emissions. The PV/BG/batt/conv configuration emerged as the most cost-effective and sustainable alternative, attaining the lowest LCOE of \$0.339/kWh, an NPC of \$25.7 million, and a 100% renewable energy fraction with simple pay back of 7.26 yr. As a result, the operational cost associated with the consumption of 500.00 L of diesel per month can be entirely avoided. The DG-integrated hybrids exhibit advanced techno-economic capability with significant worth, strong ROI (20%) and IRR (18%), endorsed by fast capital recovery (7.21-8.71 years). Overall, the hybrid system offers an optimal balance of cost, reliability, and sustainability, making it a promising and scalable solution for electrification of energy scare institution and areas in Ethiopia, thereby contributing to national sustainable energy development goals.
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All authors Tegenu Argaw Woldegiyorgis, Hong Xian Li, Fekadu Chekol Admassu, Merkebu Gezahegne, Abdurohman Kebede, Tadese Abera, Haris Ishaq, Eninges Asmare
Signal Processing
Generalizable Learning for Massive MIMO CSI Feedback in Unseen Environments
Haoyu Wang, Zhi Sun, Shuangfeng Han et al. (+2 more)
Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (
MIMO) systems. Nevertheless, the generalizability of current deep learning-based CSI feedback
algorithms cannot be guaranteed in unseen environments, which induces a high deployment cost. In this paper, the generalizability of deep learning-based CSI feedback is promoted with physics interpretation. Firstly, the distribution shift of the cluster-based channel is modeled, which comprises the multi-cluster structure and single-cluster response. Secondly, the physics-based distribution
alignment is proposed to effectively address the distribution shift of the cluster-based channel, which comprises multi-cluster decoupling and fine-grained
alignment. Thirdly, the efficiency and robustness of physics-based distribution
alignment are enhanced. Explicitly, an efficient multi-cluster decoupling
algorithm is proposed based on the Eckart-Young-Mirsky (EYM)
theorem to support real-time CSI feedback. Meanwhile, a hybrid criterion to estimate the number of decoupled clusters is designed, which enhances the robustness against channel estimation error. Fourthly, environment-generalizable neural network for CSI feedback (EG-CsiNet) is proposed as a novel learning framework with physics-based distribution
alignment. Based on extensive simulations and sim-to-real experiments in various conditions, the proposed EG-CsiNet can robustly reduce the generalization error by more than 3 dB compared to the state-of-the-arts.
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All authors Haoyu Wang, Zhi Sun, Shuangfeng Han, Xiaoyun Wang, Zhaocheng Wang
Signal Processing
On the Impact of Phase Errors in Phase-Dependent Amplitudes of Near-Field RISs
Ke Wang, Chan-Tong Lam, Benjamin K. Ng et al. (+1 more)
This paper investigates mutual coupling between phase-dependent amplitudes (PDAs) and designed phase shifts within pixels of near-field (NF) reconfigurable intelligent surfaces (RISs) in the presence of phase errors (PEs). In contrast to existing research that treats phase shifts with errors (PSEs) and the PDAs separately, we introduce a remaining power (RP) metric to quantify the proportion of power preserved in the signals reflected by the RIS, and we prove its asymptotic convergence to theoretical values by leveraging extended Glivenko-Cantelli
theorem. Then, the RP of signals passing through RIS pixels is jointly examined under combined phase and amplitude uncertainties. In addition, we propose four pixel reflection models to capture practical conditions, and we derive approximate
polynomial upper bounds for the RP with error terms by applying Taylor expansion. Furthermore, based on Friis transmission formula and projected aperture, we propose a general NF channel model that incorporates the coupling between the PSEs and the PDAs. By using Cauchy-Bunyakovsky-Schwarz inequality and Riemann sums, we derive a closed-form upper bound on spectral efficiency, and the bound becomes tighter as the pixel area decreases. We reveal that as the RIS phase shifts approach the ends of their range, the RP under independent and identically distributed PEs is smaller than that under fully correlated PEs, whereas this relationship reverses when the phase shifts are near the middle of their range. Neglecting the PEs in the PDAs leads to an overestimation of the RIS performance gain, explaining the discrepancies between theoretical and measured results.
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All authors Ke Wang, Chan-Tong Lam, Benjamin K. Ng, Yue Liu
Systems and Control
Reach-Avoid Differential game with Reachability Analysis for UAVs: A decomposition approach
Minh Bui, Simon Monckton and Mo Chen
Reach-avoid (RA) games have significant applications in security and defense, particularly for unmanned aerial vehicles (
UAVs). These problems are inherently challenging due to the need to consider obstacles, consider the
adversarial nature of opponents, ensure optimality, and account for nonlinear dynamics. Hamilton-Jacobi (HJ) reachability analysis has emerged as a powerful tool for tackling these challenges; however, while it has been applied to games involving two spatial dimensions, directly extending this approach to three spatial dimensions is impossible due to high dimensionality. On the other hand, alternative approaches for solving RA games lack the generality to consider games with three spatial dimensions involving
agents with non-trivial system dynamics. In this work, we propose a novel framework for dimensionality reduction by decomposing the problem into a horizontal RA sub-game and a vertical RA sub-game. We then solve each sub-game using HJ reachability analysis and consider second-order dynamics that account for the defender's acceleration. To reconstruct the solution to the original RA game from the sub-games, we introduce a HJ-based tracking control
algorithm in each sub-game that not only guarantees capture of the attacker but also tracking of the attacker thereafter. We prove the conditions under which the capture guarantees are maintained. The effectiveness of our approach is demonstrated via numerical simulations, showing that the decomposition maintains optimality and guarantees in the original problem. Our methods are also validated in a Gazebo physics simulator, achieving successful capture of quadrotors in three spatial dimensions space for the first time to the best of our knowledge.
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Signal Processing · Cross-listed from cs.LG
A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm
Shuai Chen, Yang Song, Chen Wang et al. (+1 more)
Electronic nose (E-nose) systems face two interconnected challenges in open-set gas recognition: feature distribution shift caused by signal drift and decision boundary failure induced by unknown gas interference. Existing methods predominantly rely on Euclidean distance or conventional classifiers, failing to account for anisotropic feature distributions and dynamic signal intensity variations. To address these issues, this study proposes the Spherical Normalization coupled Mahalanobis (SNM) module, a universal post-processing module for open-set gas recognition. First, it achieves
geometric decoupling through cascaded batch and L2 normalization, projecting features onto a unit hypersphere to eliminate signal intensity fluctuations. Second, it utilizes Mahalanobis distance to construct adaptive ellipsoidal decision boundaries that conform to the anisotropic feature geometry. The architecture-agnostic SNM-Module seamlessly integrates with mainstream backbones including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and
Transformer. Experiments on the public Vergara
dataset demonstrate that the
Transformer+SNM configuration achieves near-theoretical-limit performance in discriminating among multiple target gases, with an AUROC of 0.9977 and an unknown gas detection rate of 99.57% at 5% false positive rate, significantly outperforming state-of-the-art methods with a 3.0% AUROC improvement and 91.0% standard deviation reduction compared to Class Anchor Clustering (CAC). The module maintains exceptional robustness across five sensor positions, with standard deviations below 0.0028. This work effectively addresses the critical challenge of simultaneously achieving high accuracy and high stability in open-set gas recognition, providing solid support for industrial E-nose deployment.
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All authors Shuai Chen, Yang Song, Chen Wang, Ziran Wang
Systems and Control
A Time-Barrier Lyapunov Condition for Predefined-Time Stability
Özhan Bingöl
Predefined-time stability enables convergence within a user-specified time independent of initial conditions. Existing results are predominantly based on autonomous Lyapunov inequalities, where the predefined-time is realized through integral bounds on state-dependent decay and therefore acts as an upper bound rather than a structurally enforced deadline. This paper introduces a time-barrier predefined-time stability concept in which convergence is enforced through a nonautonomous Lyapunov mechanism that intrinsically restricts the remaining available time. A sufficient Lyapunov-based condition is established, guaranteeing convergence before the predefined deadline via divergence of a time-dependent barrier. It is further shown that this mechanism cannot be reproduced by classical autonomous predefined-time stability formulations, thereby constituting a distinct stability notion. The proposed approach provides a concise and transparent means of enforcing hard convergence deadlines in nonlinear systems.
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Image and Video Processing · Cross-listed from cs.CV
Plug In, Grade Right: Psychology-Inspired AGIQA
Zhicheng Liao, Baoliang Chen, Hanwei Zhu et al. (+3 more)
Existing AGIQA models typically estimate image quality by measuring and aggregating the similarities between image
embeddings and text
embeddings derived from multi-grade quality descriptions. Although effective, we observe that such similarity distributions across grades usually exhibit
multimodal patterns. For instance, an image
embedding may show high similarity to both "excellent" and "poor" grade descriptions while deviating from the "good" one. We refer to this phenomenon as "semantic drift", where semantic inconsistencies between text
embeddings and their intended descriptions undermine the reliability of text-image shared-space learning. To mitigate this issue, we draw inspiration from psychometrics and propose an improved Graded Response Model (GRM) for AGIQA. The GRM is a classical assessment model that categorizes a subject's ability across grades using test items with various difficulty levels. This paradigm aligns remarkably well with human quality rating, where image quality can be interpreted as an image's ability to meet various quality grades. Building on this philosophy, we design a two-branch quality grading module: one branch estimates image ability while the other constructs multiple difficulty levels. To ensure monotonicity in difficulty levels, we further model difficulty generation in an arithmetic manner, which inherently enforces a unimodal and interpretable quality distribution. Our Arithmetic GRM based Quality Grading (AGQG) module enjoys a plug-and-play advantage, consistently improving performance when integrated into various state-of-the-art AGIQA frameworks. Moreover, it also generalizes effectively to both natural and screen content image quality assessment, revealing its potential as a key component in future IQA models.
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All authors Zhicheng Liao, Baoliang Chen, Hanwei Zhu, Lingyu Zhu, Shiqi Wang, Weisi Lin
Image and Video Processing
SwinCCIR: An end-to-end deep network for Compton camera imaging reconstruction
Minghao Dong, Xinyang Luo, Xujian Ouyang et al. (+1 more)
Compton cameras (CCs) are a kind of gamma cameras which are designed to determine the directions of incident gammas based on the Compton scatter. However, the reconstruction of CCs face problems of severe artifacts and deformation due to the fundamental reconstruction principle of back-projection of Compton cones. Besides, a part of systematic errors originated from the performance of devices are hard to remove through calibration, leading to deterioration of imaging quality. Iterative
algorithms and deep-learning based methods have been widely used to improve reconstruction. But most of them are optimization based on the results of back-projection. Therefore, we proposed an end-to-end deep learning framework, SwinCCIR, for CC imaging. Through adopting swin-
transformer blocks and a transposed convolution-based image generation module, we established the relationship between the list-mode events and the radioactive source distribution. SwinCCIR was trained and validated on both simulated and practical
dataset. The experimental results indicate that SwinCCIR effectively overcomes problems of conventional CC imaging, which are expected to be implemented in practical applications.
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All authors Minghao Dong, Xinyang Luo, Xujian Ouyang, Yongshun Xiao
Systems and Control · Cross-listed from cs.RO
Active Constraint Learning in High Dimensions from Demonstrations
Zheng Qiu, Chih-Yuan Chiu and Glen Chou
We present an iterative active constraint learning (ACL)
algorithm, within the learning from demonstrations (LfD) paradigm, which intelligently solicits informative demonstration trajectories for inferring an unknown constraint in the demonstrator's environment. Our approach iteratively trains a Gaussian process (GP) on the available demonstration
dataset to represent the unknown constraints, uses the resulting GP posterior to query start/goal states, and generates informative demonstrations which are added to the
dataset. Across simulation and hardware experiments using high-dimensional nonlinear dynamics and unknown nonlinear constraints, our method outperforms a baseline, random-sampling based method at accurately performing constraint inference from an iteratively generated set of sparse but informative demonstrations.
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Image and Video Processing · Cross-listed from cs.CV
Improved cystic hygroma detection from prenatal imaging using ultrasound-specific self-supervised representation learning
Youssef Megahed, Robin Ducharme, Inok Lee et al. (+4 more)
Cystic hygroma is a high-risk prenatal ultrasound finding that portends high rates of chromosomal abnormalities, structural malformations, and adverse pregnancy outcomes. Automated detection can increase reproducibility and support scalable early screening programs, but supervised deep learning methods are limited by small labelled
datasets. This study assesses whether ultrasound-specific self-supervised pretraining can facilitate accurate, robust deep learning detection of cystic hygroma in first-trimester ultrasound images. We fine-tuned the Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), pretrained on over 370,000 unlabelled ultrasound images, for binary classification of normal controls and cystic hygroma cases used in this study. Performance was evaluated on the same curated ultrasound
dataset, preprocessing pipeline, and 4-fold cross-validation protocol as for the DenseNet-169 baseline, using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC-AUC). Model interpretability was analyzed qualitatively using Score-CAM visualizations. USF-MAE outperformed the DenseNet-169 baseline on all evaluation metrics. The proposed model yielded a mean accuracy of 0.96, sensitivity of 0.94, specificity of 0.98, and ROC-AUC of 0.98 compared to 0.93, 0.92, 0.94, and 0.94 for the DenseNet-169 baseline, respectively. Qualitative Score-CAM visualizations of model predictions demonstrated clinical relevance by highlighting expected regions in the fetal neck for both positive and negative cases. Paired statistical analysis using a Wilcoxon signed-rank test confirmed that performance improvements achieved by USF-MAE were statistically significant (p = 0.0057).
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All authors Youssef Megahed, Robin Ducharme, Inok Lee, Inbal Willner, Adrian D. C. Chan, Mark Walker, Steven Hawken