In Multi-access Edge Computing (MEC) environments, Non-Orthogonal Multiple Access (NOMA) technology significantly improves spectrum efficiency and system access capacity through power-domain multiplexing. However, it also exposes edge servers to more complex resource contention and security risks. In particular, during Distributed Denial-of-Service (DDoS) attacks, conventional mitigation mechanisms often overlook users' quality of experience (QoE), which can lead to prolonged service degradation. To address this challenge, this paper investigates a QoE-guaranteed Edge DDoS Mitigation problem (QEDM) in NOMA-assisted MEC systems by jointly considering request scheduling and power allocation. First, the QEDM problem is modeled as a finite-strategy potential game, and a distributed game-theoretic algorithm, QEDMGame, is proposed. Then, through a multi-player best-response mechanism, the system converges to a Nash equilibrium, thereby achieving joint optimization of service rate and QoE. Next, experimental results on real-world base station deployment data demonstrate that QEDMGame outperforms several representative baseline methods. Additionally, theoretical analysis shows that QEDMGame exhibits strong convergence properties and performance guarantees.

The state estimation problem of linear time-invariant (LTI) systems is investigated in this article. Distributed observers are designed to estimate the complete states of LTI systems under time-varying delays. A class of edge-based switching event-triggered mechanisms (SETMs) are designed for distributed observers to continuously estimate the states of the target system by the discrete relative state information, reducing the consumption of communication/computation resources significantly. The positive lower bounds of intersampling times are guaranteed, which further avoids Zeno behaviors. A unified framework for the stability analysis of the estimation error system under the SETM is presented, and the sufficient conditions for the asymptotic state estimation of the distributed observer are derived. Finally, numerical simulations are given to illustrate the effectiveness of the proposed method.

As an original inverter control method, the virtual synchronous generator (VSG) control technology has aroused much attention in recent years since it can achieve better dynamic voltage regulation when micro grid (MG) operates on island or load changes suddenly. Further, aiming at the superior voltage control of VSG, this paper proposed a double closed loop control strategy based on finite-time control (FTC) to improve the dynamic performance of the system. Compared with the traditional proportional integral (PI) control strategy, the proposed strategy can achieve the quick respond speed and strongly robustness when the event of load change occurs. Finally, the simulation and experimental results demonstrate the feasibility and correctness of the proposed control method.

When the islanded microgrid is subject to a large power disturbance, the fixed-parameter virtual synchronous generator (VSG) control can provide inertia and damping for the system, but it cannot guarantee the frequency adjustment performance and the control flexibility is poor. To solve this problem, this paper proposes an improved adaptive virtual synchronous generator control strategy. In particular, the proposed method introduces the frequency deviation and frequency change rate into the VSG virtual parameters, adaptively adjusting the inertia and damping parameters of VSG system to improve the frequency stability of microgrid when power suddenly changed. Both MATLAB/Simulink simulation and hardware-in-the-loop results based on StarSim demonstrate the superiority of the proposed method compared with fixed-parameter VSG control.

This paper presents a novel fault-tolerant control method for a class of discrete-time and nonGaussian stochastic systems, where two subsystems are connected in series so as to operate in a collaborative way. For such systems, the output probability density function of the second subsystem is taken as the output of the whole system. The proposed method includes the design of a fault diagnosis (FD) algorithm for the first subsystem and the establishment of a fault-tolerant control algorithm for the second subsystem. At first, linear matrix inequality techniques are used to construct the FD algorithm for the first subsystem. Once the fault is diagnosed, a fault-tolerant control algorithm is designed using the well-known optimal norm-based iterative learning control approach. Different from the existing fault tolerant controller methods, the proposed fault-tolerant control is designed not for the faulty subsystem but for the healthy subsystem. As a result, when a fault occurs in the first subsystem, the reconfigured controller for the healthy second subsystem can accommodate the fault and guarantee that the whole system will still exhibit good operational performance. A simulated example is used to demonstrate the collaborative fault-tolerant control effect and desired results have been obtained.

Feature matching is a challenging problem at the heart of numerous computer graphics and computer vision applications. We present the SuperMatching algorithm for finding correspondences between two sets of features. It does so by considering triples or higher order tuples of points, going beyond the pointwise and pairwise approaches typically used. SuperMatching is formulated using a supersymmetric tensor representing an affinity metric that takes into account feature similarity and geometric constraints between features: Feature matching is cast as a higher order graph matching problem. SuperMatching takes advantage of supersymmetry to devise an efficient sampling strategy to estimate the affinity tensor, as well as to store the estimated tensor compactly. Matching is performed by an efficient higher order power iteration approach that takes advantage of this compact representation. Experiments on both synthetic and real data show that SuperMatching provides more accurate feature matching than other state-of-the-art approaches for a wide range of 2D and 3D features, with competitive computational cost.

Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitations (even fail) with small data stream. In fact, there exist many real instances with small data stream. In the paper, we propose a novel incremental extremely random forest algorithm, dealing with online learning classification with small streaming data. In our method, arriving examples are stored at the leaf nodes and used to determine when to split the leaf nodes combined with Gini index, so the trees can be expanded efficiently with a few examples. Our algorithm has been applied to solve both online learning and video object tracking problems, and the results on UCI datasets and challenging video sequences demonstrate its effectiveness and robustness.

Stochastic distribution control systems aims at the controller design so as to realize a shape control of the distributions of certain random variables in the process. Once the probability density functions (PDFs) of these variables are used to describe their distributions, the control task is to obtain control signals so that the output PFDs of the system are made to follow their target PDFs. In this paper a survey of the recent developments on the research of stochastic distribution control systems will be made.