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Infinite delays, especially mixed infinite delays (MIDs), always pose a great challenge for exponentially stability analysis of neural networks (NNs). In this article, we construct a new Lyapunov functional that contains an auxiliary function with the ability to compress infinite time delays to bounded ones, which can remove some of the previous assumptions on NNs systems. Then, several new sufficient conditions to guarantee the exponential stabilization of state-dependent switched inertial NNs with MIDs are derived under the designed adaptive intermittent controller. Finally, numerical simulations are provided to illustrate the validity of the obtained results.

5G is a promising technology for improving the Quality of Service (QoS) in Internet of Vehicles (IoV) applications, including Vehicular Edge Computing (VEC). However, 5G networks have a limited communication range due to their radio-frequency properties, which can be a challenge in dynamic IoV environments. To address this issue, we propose a VEC architecture based on heterogeneous cellular networks, in which vehicles can select the appropriate communication network by classifying tasks according to their maximum tolerable latency. In order to further enhance the overall performance of the VEC system, we developed an efficient scheme that optimizes task offloading decisions and computation resource allocation in the proposed architecture. We analyze and formulate the optimization problem and use the linear relaxation improved branch-and-bound algorithm to solve it. Through extensive simulations, we demonstrate that the proposed scheme is superior to other solutions in computing latency, energy consumption, and failure rate.