As the rise of fresh e-supplier, cold chain logistic has become the hot topics in China. But due to its special timeliness, it is necessary to optimize its vehicle routing. Firstly, we...

Reducing X-ray tube current is one of the widely used methods for decreasing the radiation dose. Unfortunately, the signal-to-noise ratio (SNR) of the projection data degrades simultaneously. To improve the quality of reconstructed images, a dictiona

In existing coverage challenges within wireless sensor networks, traditional sensor perception models often fail to accurately represent the true transmission characteristics of wireless signals. In more complex application scenarios such as warehousing, residential areas, etc., this may lead to a large gap between the expected effect of actual coverage and simulated coverage. Additionally, these models frequently neglect critical factors such as sensor failures and malfunctions, which can significantly affect signal detection. To address these limitations and enhance both network performance and longevity, this study introduces a perception model that incorporates path loss and false alarm probability. Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. The study begins by deriving a logarithmic-based path loss model for wireless signals. It then employs the Neyman–Pearson criterion to formulate a maximum detection probability model under conditions where the cost function and prior probability are unknown, constraining the false alarm rate. Simulated experiments are conducted to assess the influence of various model parameters on detection probability, providing comparative analysis against traditional perception models. Ultimately, an optimization model for WSN coverage, based on combined detection probability, is developed and solved using an intelligent optimization algorithm. The experimental results indicate that the proposed model more accurately captures the signal transmission and detection characteristics of sensor nodes in WSNs. In the coverage area of the same size, the coverage of the model constructed in this paper is compared with the traditional 0/1 perception model and exponential decay perception model. The model can achieve full coverage of the area with only 50 nodes, while the exponential decay model requires 54 nodes, and the coverage of the 0/1 model is still less than 70% at 60 nodes. According to the simulation experiments, it can be basically proved that the WSN node optimization coverage strategy based on the proposed model provides an effective solution for improving network performance and extending network lifespan.

Harris Hawks Optimization (HHO) simulates the cooperative hunting behavior of Harris hawks and it has the advantages of fewer control parameters, simple principles, and excellent exploitation ability. However, HHO also has the disadvantages of slow convergence and easy falling into local optimality. Aiming at the above shortcomings, this paper proposes a Multi-strategy Enhanced Harris Hawks Optimization (MEHHO). Firstly, the map-compass operator and Cauchy mutation strategy are used to increase the population diversity and improve the ability of the algorithm to jump out of the local optimal. Secondly, a spiral motion strategy is introduced to improve the exploration phase to enhance search efficiency. Finally, the convergence speed and accuracy of the algorithm are improved by greedy selection to fully retain the dominant individuals. The global search capability of the proposed MEHHO is verified by 28 benchmark test functions, and then the parameters of the deep learning network used for channel estimation are optimized by using the MEHHO to verify the practicability of the MEHHO. Experimental results show that the proposed MEHHO has more advantages in solving global optimization problems and improving the accuracy of the channel estimation method based on deep learning.

In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement.