The integration of Artificial Intelligence with the Internet of Things significantly enhances the functionality of vehicle–road cooperation (VRC) systems by enabling smarter, real-time decision-making and resource optimization across interconnected vehicular networks. To tackle the challenges associated with resource constraints, this study introduces a method where vehicle users can offload tasks to nearby roadside units (RSUs) or service-oriented vehicles to ensure timely application execution. However, this task offloading introduces additional transmission delays and energy expenditures. Consequently, this article first conceptualizes the computation offloading problem, aiming to minimize the total task processing time and energy consumption under the constraints of resources provided by RSUs and service-oriented vehicles. We model the computation offloading issue within the VRC framework as a Markov decision process (MDP) and propose a multiagent reinforcement learning-based resource scheduling method. Each vehicle, acting as an intelligent agent, interacts with and influences decisions within this environment. The method integrates the twin delayed deep deterministic policy gradient algorithm to train deep neural networks for deciding on task offloading and computational resource allocation. Simulation results demonstrate that compared to existing algorithms, the proposed method more effectively utilizes the computational resources available through RSUs and service-oriented vehicles within the VRC system. It achieves joint optimization of latency and energy consumption, thus validating the efficacy of the proposed approach in enhancing the operational efficiency and sustainability of urban transportation systems.

This article studies synchronization issues for a class of discrete-time fractional-order quaternion-valued uncertain neural networks (DFQUNNs) using nonseparation method. First, based on the theory of discrete-time fractional calculus and quaternion properties, two equalities on the nabla Laplace transform and nabla sum are strictly proved, whereafter three Caputo difference inequalities are rigorously demonstrated. Next, based on our established inequalities and equalities, some simple and verifiable quasi-synchronization criteria are derived under the quaternion-valued nonlinear controller, and complete synchronization is achieved using quaternion-valued adaptive controller. Finally, numerical simulations are presented to substantiate the validity of derived results.

This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

Exploring the stability of delta fractional order systems is essential for them to be used properly in various applications. Since the existing researches usually focused on the system with$\alpha \in (0,1)$, it is natural to ponder a parallel case with extra order. Motivated by this need, this study addresses the stability of delta delay fractional order systems with$\alpha \in (1,2)$systematically. The main difficulties lie in the approximation of stable region and the derivation of the relevant linear matrix inequalities (LMI) conditions caused by the complicated stable region of the suggested system. Firstly, a novel clustering region is constructed and it is proved that such a region is the subset of the considered stable region. Besides, the scheme on how to construct alternative approximation regions is discussed tentatively. Secondly, the stability conditions are formulated in terms of LMIs, which are sufficient and necessary to evaluate all the eigenvalues of system matrix locating at the approximation region. Thirdly, the complex decision matrices are replaced by the real ones and the formulation is therefore more tractable. Finally, the validity and applicability of the proposed approaches are demonstrated by simulation study.

This paper introduces the True-Neighbor Template (TNT), a novel, minutiae-only, fingerprint representation and matching approach for authentication. The TNT representation approach overcomes consistency-limitations affecting many existing approaches that stem from relative distortion and spurious minutiae. The TNT matching approach maximizes exploitation of captured fingerprint complexity. A standard benchmark experiment, using the FVC protocol and FVC2006 and FVC2002 databases, is used for evaluation. TNT demonstrates generally-superior neighbor-selection-consistency regarding several established approaches, including: fixed radius, k-nearest neighbors, fixed sectors, and Voronoi diagram. TNT demonstrates generally-superior authentication performance regarding several well-known approaches, including: Bozorth, K-plet, and Minutia Cylinder-Code (MCC).