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In industrial Internet of Things, the combination of specific emitter identification (SEI) and key authenticated technologies can effectively resist spoofing attacks and improve system security. However, most existing SEI approaches extract features based on real valued operations and only work in static scenario. This motivated us to develop a novel SEI method tasked with: exploiting the high potential model for SEI based on the inphase/quadrature (I/Q) signal that is represented by complex number, and realizing rapid reconstruction of the model in the face of dynamic scenarios. To this end, in this article, we introduce a two-stage cross-domain identification model. First, a complex-valued separate residual network (CVSRN) with novel separate residual modules is proposed as the pretrained model. The CVSRN can automatically extract effective inherent features directly from raw signals in an end-to-end manner, which favors complex-valued signals that are found in two distinct signal paths. Second, three transfer strategies are proposed to achieve rapid construction of the target SEI model. They leverage the knowledge learned from the pretrained CVSRN to facilitate the recognition of a new but similar emitters. We benchmark our proposed approach against four state-of-the-art SEI methods on real-world data and exhibit that it is not only competitive but also able to cope with complex dynamic scenarios.

With the development of vehicular technology, autonomous driving has experienced explosive growth, making it an important and popular research field. In autonomous driving scenarios, autonomous driving tasks are often neural network-based intelligent tasks with different levels of complexity. These intelligent tasks usually have different requirements for hardware resources such as computational performance, memory capacity and power consumption. Additionally, the evaluation becomes even more challenging due to the different system architectures and resource configurations of hardware devices from different manufacturers. It is crucial to accurately estimate the resource requirements of different intelligent tasks on different hardware platforms in advance.To address this issue, this paper proposes a multi-dimensional resource modeling method based on deep learning techniques and constructs an evaluation model to predict the consumption of three resources: computing performance, memory usage and power consumption.Experiments demonstrate that for the performance, memory and power metrics, the evaluation model built using this method for predicting seven mainstream neural networks achieves average error rates of 6.78%, 8.86% and 7.89% respectively on the NVIDIA Jetson AGX Xavier platform, and the average error rates for the same metrics are 8.05%, 9.25% and 3.67% respectively on the Cambricon MLU220 platform.

The problem of task allocation of Unmanned aerial vehicle (UAV) systems is a hot issue in scientific research. Aiming at overcome the shortcomings of existing algorithms in terms of load balancing and execution efficiency, an algorithm Improved Self-Organizing Mapping (ISOM) is proposed in this paper. Firstly, considering the two factors of the UAV flight distance and the mission execution time, the load balance of the UAV is designed. The sum of the track lengths of multiple UAVs and the total time required by multiple UAVs to complete all the tasks in the task area are used to evaluate the quality of cooperative task assignment of multiple UAVs. Secondly, the learning rate and neighborhood function of nonlinear change are designed to ensure the stability and accelerate its convergence. With the increase of the number of iterations, the radius of the neighborhood gradually decreases, and the renewal range of the superior neighborhood is determined by the nonlinear function. Finally, in order to solve the problem of new tasks of UAV and UAV failure, the attention mechanism based on the ISOM algorithm is introduced. Different attention is allocated by the distance between the UAV and the mission points to ensure the missions can be fully executed. Compared with algorithms PSO-GA, Gurobi and ORTools, the time to complete the task is effectively reduced, respectively. At the same time, the algorithm is verified in a large task environment. When the number of tasks is 200 and the number of UAVs is 8, in the TSPLIB task set, the simulation results demonstrate the high efficiency and flexibility of the proposed algorithm.