To address the problems of less semantic information and low measurement accuracy when the SSD (single shot multibox detector) algorithm detects small targets, an MPH-SSD (multiscale pyramid hybrid SSD) algorithm that integrates the attention mechanism and multiscale double pyramid feature enhancement is proposed in this paper. In this algorithm, firstly, the SSD algorithm is used to extract the feature map of small targets, and the shallow feature enhancement module is added to expand the receptive field of the shallow feature layer so as to enrich the semantic information in the feature layer for small targets and improve the expression ability of shallow features. The processed shallow feature layer and deep feature layer are fused at multiple scales, and the semantic information and location information are fused together to obtain a feature map with rich information. Secondly, the cascaded double pyramid structure is used to transfer from the deep layer to the shallow layer so that the context information between different feature layers can be effectively transferred and the feature information can be further strengthened. The hybrid attention mechanism can retain more context information in the network, adaptively adjust the feature map after addition and fusion, and reduce the background interference. The experimental analysis of MPH-SSD algorithm on Pascal VOC and MS COCO datasets shows that the map of this algorithm is 87.7% and 51.1%, respectively. The results show that the MPH-SSD algorithm can make better use of the feature information in the shallow feature layer in the process of small target detection and has better detection performance for small targets.

In the process of the development of image processing technology, image segmentation is a very important image processing technology in the field of machine vision, pedestrian detection, medical imaging, and so on. However, the traditional image segmentation technology cannot solve the problems of reflection and uneven illumination. This paper presents a local threshold segmentation method based on FPGA, which can automatically select the optimal threshold according to different gray levels of images. First, the image is processed by mean filtering to remove noise interference in the image. Then, the idea of the mean value of the local neighborhood block and the Gaussian weighted sum in the local neighborhood is used to deal with the reflective and uneven light on the image. The process is designed and realized on FPGA. Finally, the design algorithm is verified by ModelSim simulation software and QT5 software. The experimental results show that the algorithm can effectively solve the problems of reflection and uneven illumination on the image surface, and the segmentation effect is significantly improved compared with the fixed threshold algorithm and Otsu algorithm. It also has certain reference value in medicine, agriculture, engineering, and other fields.

Currently, the reconfigurable intelligent surface (RIS) has been applied to improve the physical layer security in wireless networks. In this paper, we focus on the secure transmission in RIS-aided multiple-input single-output (MISO) systems. Specifically, by assuming that only imperfect channel state information (CSI) of the eavesdropper can be obtained, we investigated the robust secrecy energy efficiency (SEE) optimization via jointly designing the active beamforming (BF), artificial noise (AN) at Alice, and the passive phase shifter at the RIS. The formulated problem is hard to handle due to the complicated secrecy rate expression as well as the infinite constraints introduced by the CSI uncertainties. By utilizing the Taylor expansion, we transformed the fractional programming into a convex problem, while all the constraints are approximated via the successive convex approximation and constrained concave-convex procedure. Then, by using the extended S-Lemma, we transform the infinite constraints into linear matrix inequality, which is convex. Finally, an alternate optimization (AO) algorithm was proposed to solve the reformulated problem. Simulation results demonstrated the performance of the proposed design.

This paper investigates the transmission design in a multiple-input single-output (MISO) Terahertz network assisted by an intelligent reflecting surface (IRS). Specifically, we consider the sum-rate maximization design by jointly optimizing the active beamforming (BF) and the phase shifters. To solve the formulated nonconvex problem, we utilize the Lagrangian dual transform based method to obtain an equivalent problem. Then, an alternating optimization (AO) algorithm is proposed, where the unit modulus constraint (UMC) of the IRS is handled by a computational efficient adaptive gradient descent (AGD) method. Finally, simulation results demonstrate the promising performance of the proposed design.