arXiv subjects

Yuanqing Xia

Publications and source records attributed to Yuanqing Xia.

At least 19 recordsLinked to original sources

arXiv2026

On Diagonalizable Systems with Random Structure

Diagonalizability plays an important role in the analysis and design of multivariable systems. A structured matrix is called structurally diagonalizable if almost all of its numerical realizations, obtained by assigning real values to its free entries, are diagonalizable. Structural diagonalizability is useful for the verification and optimization of various structural system properties. In this paper, we study the asymptotic probability distribution of structural diagonalizability for structured systems whose system matrices are represented by directed Erdős-Rényi random graphs. Leveraging a recently established graph-theoretic characterization of structural diagonalizability, we analyze the distribution of structurally diagonalizable graphs under different edge-density regimes. For dense graphs, we prove that the system is almost always structurally diagonalizable. For graphs of medium density, we derive tight upper and lower bounds on the asymptotic probability of structural diagonalizability. For extremely sparse graphs, we show that this probability approaches 0. The theoretical results are validated through extensive numerical simulations with varying numbers of vertices and connection probabilities.

math.OC↗

arXiv2026

Physics-informed sparse identification-based tube model predictive control for aerial vehicles

Autonomous aerial vehicles necessitate control strategies that balance computational efficiency with robust performance in dynamic operational environments. This paper proposes a model predictive control (MPC) framework for aerial platforms that leverages physics-informed machine learning (PIML) to achieve an optimal balance between computational tractability and robust performance. At the core of the proposed approach lies a sparse, control-affine model identified via the PIML method, which provides a parsimonious yet interpretable representation of the system dynamics by embedding first-principles knowledge and learning residual uncertainties from operational data. This model is incorporated within a robust MPC scheme that adopts a high-order Runge-Kutta discretization to ensure prediction accuracy and an adaptive tube-based mechanism to guarantee constraint satisfaction under uncertainty. The online adaptation of the tube, directly informed by the residual error of the PIML model, ensures robust stability without introducing excessive conservatism. Rigorous theoretical proofs are provided to establish recursive feasibility and stability. Numerical simulations and experiments on a quadrotor demonstrate that our method significantly reduces computational load compared to nonlinear MPC and robust MPC using a first-principles high-fidelity model, while outperforming PID, nonlinear MPC, neural-network-based MPC, and fixed-tube robust MPC in tracking performance and robustness, showcasing the practical efficiency of the proposed PIML-based control synthesis for resource-constrained aerial systems.

eess.SY↗

arXiv2026

Optimization-based Safe Trajectory Planning for Autonomous Ground Vehicle in Multi-Floor Scenarios

The development of trajectory planning strategies for autonomous ground vehicles (AGVs) represents a prevailing research interest within the domain of intelligent transportation systems. This paper introduces a trajectory planning framework tailored for multi-floor scenarios. The framework consists of two main modules: the task planning module and the trajectory planning module. The task planning module involves a strategic selection phase, where a task planning strategy based on generalized voronoi diagrams (GVD) and multi-objective algorithms is proposed to select the floor exits for each floor. The trajectory planning module utilizes optimization-based methods to generate high-quality trajectories, and a warm-started hierarchical planning framework is designed to ensure rapid convergence. Additionally, for handling complex obstacle constraints, a correlation constraint calculation method is designed for reducing obstacle constraints in trajectory planning. Finally, the feasibility and effectiveness of the proposed framework are verified through simulations.

cs.RO↗

arXiv2026

Towards Efficient and Secure Cloud-Assisted Autonomous Systems: A Review of Architectures, Algorithms, Security, and Deployment Challenges

Networked Control Systems (NCSs) have been instrumental in realizing fully connected and responsive intelligent environments within the context of real-time virtual control and management. However, traditional NCSs face considerable challenges in handling the vast amounts of data generated by large-scale control applications, particularly in terms of data acquisition, storage, and computational processing. To address these challenges, the emergence of cloud computing and advancements in control theory have empowered the new paradigm known as Cloud Control Systems (CCSs). Recently, CCSs have received substantial attention from industries for their potential properties, such as large-scale data management, complex computations, and data-centric optimized decisions. This study presents an extensive review of recent progress in CCSs spanning over multiple studies published between 2012 and 2025. Specifically, the focus is on providing a taxonomy of the current findings in CCS research, encompassing various perspectives, such as its efficient implementations in industrial automation, security and privacy considerations, and cloud-based control techniques. Each category is examined in depth through selected state-of-the-art analyses of different approaches and contrasting methodologies. Furthermore, we discuss future directions aimed at designing more efficient and practical CCSs. The insights gained from this study can help researchers, practitioners, and decision-makers in their domain for effective CCS design and deployment.

eess.SY↗

arXiv2026

Artificial Intelligence Driven Channel Coding and Resource Optimization for Wireless Networks: A Systematic Survey

The ongoing evolution of 5G and its enhanced version, 5G+, has significantly transformed the telecommunications landscape, driving an unprecedented demand for ultra-high-speed data transmission, ultra-low latency, and resilient connectivity. These capabilities are essential for enabling mission-critical applications such as the Internet of Things, autonomous vehicles, and smart city infrastructures. This survey investigates the important role of Artificial Intelligence (AI) in addressing the key challenges faced by 5G/5G+ networks, including interference mitigation, dynamic resource allocation, and maintaining seamless network operation. The study particularly focuses on AI-driven innovations in coding theory, which offer advanced solutions to the limitations of conventional error correction and modulation techniques. By employing deep learning, reinforcement learning, and neural network-based approaches, including convolutional neural networks, recurrent neural networks, and Transformer-based models, this research demonstrates significant advancements in error correction performance, decoding efficiency, and adaptive transmission strategies. Additionally, the integration of AI with emerging technologies, such as massive multiple-input and multiple-output, intelligent reflecting surfaces, and privacy-enhancing mechanisms, is discussed, highlighting their potential to propel the next generation of wireless networks. This survey provides an insightful overview of the transformative impact of AI on modern wireless communication, establishing a foundation for scalable, adaptive, and more efficient network architectures.

eess.SP↗

arXiv2026

Cost-Aware Distributed Online Learning with Strict Rejection Behavior against Adversarial Agents

Distributed online learning in Internet of Things(IoT)-enabled multi-agent systems(MASs) is highly vulnerable to persistent adversarial interactions, particularly when malicious agents cannot be fully isolated during the transient learning stage. Existing resilient learning methods mainly focus on convergence preservation or malicious suppression, while the resulting evolution inefficiency caused by repeated corrective adaptation remains largely unexplored. To address this issue, this paper develops a cost-aware distributed online learning framework with a strict rejection behavior against adversarial agents. The proposed mechanism suppresses harmful assimilation of suspicious neighboring information and reveals a previously overlooked side effect, that is, the strict rejection may induce heterogeneous transient evolution among neighboring normal agents, leading to evolution desynchronization across the network. To mitigate this effect, a two-time-scale adaptive evolution regulation architecture is further developed, in which the outer layer dynamically adjusts the long-term evolution-rate schedule while the inner layer preserves robust online learning. Theoretical analysis establishes the dynamic tracking property of the outer-layer update and proves that the proposed regulation mechanism attenuates the propagation of strict-rejection-induced evolution desynchronization. Numerical simulations and a satellite-assisted IoT monitoring scenario demonstrate that the proposed method achieves robust and low-cost distributed online learning under persistent malicious interference.

cs.MA↗

arXiv2025

Data-driven functional state estimation of complex networks

The internal state of a dynamical system, a set of variables that defines its evolving configuration, is often hidden and cannot be fully measured, posing a central challenge for real-time monitoring and control. While observers are designed to estimate these latent states from sensor outputs, their classical designs rely on precise system models, which are often unattainable for complex network systems. Here, we introduce a data-driven framework for estimating a targeted set of state variables, known as functional observers, without identifying the model parameters. We establish a fundamental functional observability criterion based on historical trajectories that guarantees the existence of such observers. We then develop methods to construct observers using either input-output data or partial state data. These observers match or exceed the performance of model-based counterparts while remaining applicable even to unobservable systems. The framework incorporates noise mitigation and can be easily extended to nonlinear networks via Koopman embeddings. We demonstrate its broad utility through applications including sensor fault detection in water networks, load-frequency control in power grids, and target estimation in nonlinear neuronal systems. Our work provides a practical route for real-time target state inference in complex systems where models are unavailable.

eess.SY↗

arXiv2025

Robust Set Partitioning Strategy for Malicious Information Detection in Large-Scale Internet of Things

With the rapid development of the Internet of Things (IoT), the risks of data tampering and malicious information injection have intensified, making efficient threat detection in large-scale distributed sensor networks a pressing challenge. To address the decline in malicious information detection efficiency as network scale expands, this paper investigates a robust set partitioning strategy and, on this basis, develops a distributed attack detection framework with theoretical guarantees. Specifically, we introduce a gain mutual influence metric to characterize the inter-subset interference arising during gain updates, thereby revealing the fundamental reason for the performance gap between distributed and centralized algorithms. Building on this insight, the set partitioning strategy based on Grassmann distance is proposed, which significantly reduces the computational cost of gain updates while maintaining detection performance, and ensures that the distributed setting under subset partitioning preserves the same theoretical performance bound as the baseline algorithm. Unlike conventional clustering methods, the proposed set partitioning strategy leverages the intrinsic observational features of sensors for robust partitioning, thereby enhancing resilience to noise and interference. Simulation results demonstrate that the proposed method limits the performance gap between distributed and centralized detection to no more than 1.648$\%$, while the computational cost decreases at an order of $O(1/m)$ with the number of subsets $m$. Therefore, the proposed algorithm effectively reduces computational overhead while preserving detection accuracy, offering a practical low-cost and highly reliable security detection solution for edge nodes in large-scale IoT systems.

cs.DC↗

arXiv2025

Stochastic Tube-based Model Predictive Control for Cyber-Physical Systems under False Data Injection Attacks with Bounded Probability

This paper addresses the challenge of amplitude-unbounded false data injection (FDI) attacks targeting the sensor-to-controller (S-C) channel in cyber-physical systems (CPSs). We introduce a resilient tube-based model predictive control (MPC) scheme. This scheme incorporates a threshold-based attack detector and a control sequence buffer to enhance system security. We mathematically model the common FDI attacks and derive the maximum duration of such attacks based on the hypothesis testing principle. Following this, the minimum feasible sequence length of the control sequence buffer is obtained. The system is proven to remain input-to-state stable (ISS) under bounded external disturbances and amplitude-unbounded FDI attacks. Moreover, the feasible region under this scenario is provided in this paper. Finally, the proposed algorithm is validated by numerical simulations and shows superior control performance compared to the existing methods.

eess.SY↗

arXiv2025

Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments

The rapid detection of abnormal body temperatures in urban populations is essential for managing public health risks, especially during outbreaks of infectious diseases. Multi-drone thermal screening systems offer promising solutions for fast, large-scale, and non-intrusive human temperature monitoring. However, trajectory planning for multiple drones in complex urban environments poses significant challenges, including collision avoidance, coverage efficiency, and constrained flight environments. In this study, we propose an enhanced trust region sequential convex optimization (TR-SCO) algorithm for optimal trajectory planning of multiple drones performing thermal screening tasks. Our improved algorithm integrates a refined convex optimization formulation within a trust region framework, effectively balancing trajectory smoothness, obstacle avoidance, altitude constraints, and maximum screening coverage. Simulation results demonstrate that our approach significantly improves trajectory optimality and computational efficiency compared to conventional convex optimization methods. This research provides critical insights and practical contributions toward deploying efficient multi-drone systems for real-time thermal screening in urban areas. For reader who are interested in our research, we release our source code at https://github.com/Cherry0302/Enhanced-TR-SCO.

cs.RO↗

arXiv2025

UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments

In post-disaster scenarios, rapid and efficient delivery of medical resources is critical and challenging due to severe damage to infrastructure. To provide an optimized solution, we propose a cooperative trajectory optimization and task allocation framework leveraging unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study integrates a Genetic Algorithm (GA) for efficient task allocation among multiple UAVs and UGVs, and employs an informed-RRT* (Rapidly-exploring Random Tree Star) algorithm for collision-free trajectory generation. Further optimization of task sequencing and path efficiency is conducted using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Simulation experiments conducted in a realistic post-disaster environment demonstrate that our proposed approach significantly improves the overall efficiency of medical rescue operations compared to traditional strategies. Specifically, our method reduces the total mission completion time to 26.7 minutes for a 15-task scenario, outperforming K-Means clustering and random allocation by over 73%. Furthermore, the framework achieves a substantial 15.1% reduction in total traveled distance after CMA-ES optimization. The cooperative utilization of UAVs and UGVs effectively balances their complementary advantages, highlighting the system's scalability and practicality for real-world deployment.

cs.RO↗

arXiv2025

Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance

This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem-combining UAV dynamics, STL satisfaction, and collision avoidance-is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics.

eess.SY↗

arXiv2025

RCGNet: RGB-based Category-Level 6D Object Pose Estimation with Geometric Guidance

While most current RGB-D-based category-level object pose estimation methods achieve strong performance, they face significant challenges in scenes lacking depth information. In this paper, we propose a novel category-level object pose estimation approach that relies solely on RGB images. This method enables accurate pose estimation in real-world scenarios without the need for depth data. Specifically, we design a transformer-based neural network for category-level object pose estimation, where the transformer is employed to predict and fuse the geometric features of the target object. To ensure that these predicted geometric features faithfully capture the object's geometry, we introduce a geometric feature-guided algorithm, which enhances the network's ability to effectively represent the object's geometric information. Finally, we utilize the RANSAC-PnP algorithm to compute the object's pose, addressing the challenges associated with variable object scales in pose estimation. Experimental results on benchmark datasets demonstrate that our approach is not only highly efficient but also achieves superior accuracy compared to previous RGB-based methods. These promising results offer a new perspective for advancing category-level object pose estimation using RGB images.

cs.CV↗

arXiv2025

A Note on Structural Controllability and Observability Indices

In this note, we investigate the structural controllability and observability indices of structured systems. We provide counter-examples showing that an existing graph-theoretic characterization for the structural controllability index (SCOI) may not hold, even for systems with self-loop at every state node. We further demonstrate that this characterization actually provides upper bounds, and extend them to new graph-theoretic characterizations applicable to systems that are not necessarily structurally controllable. Additionally, we reveal that an existing method may fail to obtain the exact SCOI. Consequently, complete graph-theoretic characterizations and polynomial-time computation of SCOI remain open. Given this, we present an efficiently computable tight lower bound, whose tightness is validated by numerical simulations. All these results apply to the structural observability index by the duality between controllability and observability.

eess.SY↗

arXiv2024

Radiant: Large-scale 3D Gaussian Rendering based on Hierarchical Framework

With the advancement of computer vision, the recently emerged 3D Gaussian Splatting (3DGS) has increasingly become a popular scene reconstruction algorithm due to its outstanding performance. Distributed 3DGS can efficiently utilize edge devices to directly train on the collected images, thereby offloading computational demands and enhancing efficiency. However, traditional distributed frameworks often overlook computational and communication challenges in real-world environments, hindering large-scale deployment and potentially posing privacy risks. In this paper, we propose Radiant, a hierarchical 3DGS algorithm designed for large-scale scene reconstruction that considers system heterogeneity, enhancing the model performance and training efficiency. Via extensive empirical study, we find that it is crucial to partition the regions for each edge appropriately and allocate varying camera positions to each device for image collection and training. The core of Radiant is partitioning regions based on heterogeneous environment information and allocating workloads to each device accordingly. Furthermore, we provide a 3DGS model aggregation algorithm that enhances the quality and ensures the continuity of models' boundaries. Finally, we develop a testbed, and experiments demonstrate that Radiant improved reconstruction quality by up to 25.7\% and reduced up to 79.6\% end-to-end latency.

cs.CV↗

arXiv2024

Rank Resilience of Pattern Matrices against Structured Perturbations with Applications

In structured system theory, a pattern matrix is a matrix with entries either fixed to zero or free to take arbitrary numbers. The (generic) rank of a pattern matrix is the rank of almost all its realizations. The resilience of various system properties is closely tied to the rank resilience of the corresponding pattern matrices. Yet, existing literature predominantly explores the latter aspect by focusing on perturbations that change the zero-nonzero structure of pattern matrices, corresponding to link additions/deletions. In this paper, we consider the rank resilience of pattern matrices against structured perturbations that can arbitrarily alter the values of a prescribed set of entries, corresponding to link weight variations. We say a pattern matrix is structurally rank $r$ resilient against a perturbation pattern if almost all realizations of this pattern matrix have a rank not less than $r$ under arbitrary complex-valued realizations of the perturbation pattern. We establish a generic property in this concept. We then present combinatorial necessary and sufficient conditions for a rectangular pattern matrix to lose full rank under given perturbation patterns. We also generalize them to obtain a sufficient condition and a necessary one for losing an arbitrarily prescribed rank. We finally show our results can be applied to the resilience analysis of various properties of structured (descriptor) systems, including controllability and input-state observability, as well as characterizing zero structurally fixed modes.

cs.IT↗

arXiv2024

Two-Dimensional Quaternion Linear Canonical Transform A Novel Framework for Probability Modeling

The linear canonical transform (LCT) serves as a powerful generalization of the Fourier transform (FT), encapsulating various integral transforms within a unified framework. This versatility has made it a cornerstone in fields such as signal processing, optics, and quantum mechanics. Extending this concept to quaternion algebra, the Quaternion Fourier Transform (QFT) emerged, enriching the analysis of multidimensional and complex-valued signals. The Quaternion Linear Canonical Transform (QLCT), a further generalization, has now positioned itself as a central tool across various disciplines, including applied mathematics, engineering, computer science, and statistics. In this paper, we introduce the Two Dimensional Quaternion Linear Canonical Transform (2DQLCT) as a novel framework for probability modeling. By leveraging the 2DQLCT, we aim to provide a more comprehensive understanding of probability distributions, particularly in the context of multi-dimensional and complex-valued signals. This framework not only broadens the theoretical underpinnings of probability theory but also opens new avenues for researchers

math.FA↗

arXiv2024

Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach

How to achieve precise distributed optimization despite unknown attacks, especially the Byzantine attacks, is one of the critical challenges for multiagent systems. This paper addresses a distributed resilient optimization for linear heterogeneous multi-agent systems faced with adversarial threats. We establish a framework aimed at realizing resilient optimization for continuous-time systems by incorporating a novel self-triggered hybrid detection approach. The proposed hybrid detection approach is able to identify attacks on neighbors using both error thresholds and triggering intervals, thereby optimizing the balance between effective attack detection and the reduction of excessive communication triggers. Through using an edge-based adaptive self-triggered approach, each agent can receive its neighbors' information and determine whether these information is valid. If any neighbor prove invalid, each normal agent will isolate that neighbor by disconnecting communication along that specific edge. Importantly, our adaptive algorithm guarantees the accuracy of the optimization solution even when an agent is isolated by its neighbors.

eess.SY↗