To address the challenges of small inter-class differences in pest details，severe field background interference，and imbalanced sample distribution，a complementary feature fusion dual-stream network for pest recognition is proposed.This network combines the local perception capability of convolutional neural networks with the global modeling ability of the Mamba model，capturing and integrating the global and local information of pest images.A hierarchical multiscale perception module is designed to extract multi-scale image features through grouped hierarchical convolution and enhance pest detail information with a detail enhancement perception strategy.An adaptive focusing Mamba module is designed to locate key pest regions using dynamic convolution operators and reduce complex background interference.Additionally，an attention-weighted fusion module is designed to achieve adaptive interaction and optimization of global and local features through a cross-attention mechanism，further improving the accuracy of semantic expression.A balanced loss function is constructed to mitigate the effects of class imbalance in the dataset.The experimental results show that the network achieves an accuracy of 71.19%on the large-scale pest dataset IP102，and an accuracy of 99.36%on the D0 dataset，demonstrating its ability to effectively identify pest species.

Aiming at the problems of tight spectrum resources，insufficient computing resources，and easy interception and tampering of information transmission in the Internet of Things（IoT）mobile edge computing（MEC）network，a resource allocation scheme for the IRS-assisted user backscatter communication（BC）technology secure offloading perception MEC network is proposed.By integrating cognitive radio（CR），IRS and BC technologies，an optimization problem targeting the maximization of secure MEC network throughput is constructed，and thejoint optimization solution is carried out by using methods based on block coordinate descent（BCD），Lagrangian duality，and quadratic transformation.Simulation results show that compared with the random IRS phase scheme and the random beamforming scheme，the proposed scheme can increase the task computation amount of secondary users by about 260%and 178%，verifying its effectiveness and feasibility under complex constraint conditions.

Aiming at the problems that single-tone excitation sources in antenna pattern testing have difficulty in accurately evaluating the broadband performance of circularly polarized antennas in non-terrestrial networks（NTN），as well as its low efficiency，a broadband excitation source pattern measurement method based on the 5G new radio（NR）demodulation reference signals（demodulation reference signal，DMRS）is proposed.By leveraging the orthogonality of DMRS signals across different ports in the 5G NR service channel，optimal orthogonal DMRS reference signals and excitation signals are designed，enabling high-precision measurement of amplitude and phase across various antenna pointing angles.The antenna radiation pattern is then synthesized with the orthogonal linearly polarized components.Experimental results demonstrate that the proposed method is well-suited for evaluating the broadband performance of circularly polarized antennas in NTN systems，while significantly improves the testing efficiency.

For the low detection accuracy problem of small targets and low-contrast defects on the surface of latex gloves，an improved YOLOv（you only look once version）8n algorithm for defects detection on the surface of latex gloves is proposed.The receptive field attention convolution module is introduced in the feature extraction network to dynamically adjust the spatial feature weights within the receptive field，and to enhance the network's focus on defect features. The C2f module is redesigned based on the proposed multi-scale convolution，which captures the contextual information from shallow features through multi-scale convolutional kernels，and improves the network's ability to extract shallow features.The context and the spatial feature calibration network are added to the feature fusion network，where feature calibration refines and aligns contextual information and spatial features，and further enhances the representation of defect features.Experimental results show that on the homemade dataset，the mean average precision（mAP）of the improved algorithm reaches 93.2%，which is 3.1%higher than that of YOLOv8n.It effectively improves the surface defect detection accuracy of latex gloves.In addition，on the VisDrone2019Det and steel defect detection datasets，the mAP reaches 36.1%and 79.8%，respectively，which are 1.1%and 2.7%higher than that of YOLOv8n，and further verifies the effectiveness of the improved algorithm.