KEYWORDS: Matrices, Multiple input multiple output, Antennas, Singular value decomposition, Signal to noise ratio, Analog electronics, Design, Mathematical optimization, Telecommunications, Systems modeling
Hybrid precoding combines the advantages of analogue and digital techniques and has become the preferred precoding method for achieving a balance between high performance and cost-effectiveness in large-scale MIMO systems by simplifying hardware requirements and reducing energy consumption.In this study, a novel hybrid precoding method based on Joint Space Division Multiplexing (JSDM) and Successive Interference Cancellation (SIC) techniques is proposed for multi-user 3D MIMO systems.User grouping and statistical information based analogue pre-coding using JSDM is firstly used to form a suppression of major interferences, whereas SIC further optimises the digital pre-coded joint combining matrix to finely tune the data stream for each user by sequentially detecting and removing interference from the decoded signals. The experimental results show that the hybrid precoding scheme based on JSDM and SIC can significantly improve the spectral efficiency and BER performance in multi-user scenarios.
Salient object detection is a fundamental problem in the research of image and vision. However, traditional models have low confidence and low recall. Although deep learning methods can better locate objects, the boundaries are often not detailed enough. To address these issues, we propose a salient object detection model (RF2Net) that combines traditional level set methods with deep learning. RF2Net incorporates the idea of level set structured loss and reverse attention mechanism on the basis of F3Net. First, RF2Net uses a new loss function that combines BCE(Binary Cross Entropy) loss, weight level set loss and weight MAE(Mean Absolute Error) loss with multi-indicator joint supervision. Through the role of the level set loss operator, it is possible to better focus on the whole of the image instead of pixel-by-pixel supervision like the BCE loss. The introduction of the reverse attention mechanism can effectively reduce the noise during feature fusion between layers and achieve the purpose of improving accuracy. The experiments are compared with 12 state-of-the-art methods on 4 datasets, and MAE, maxF and avgF are all higher than other algorithms in HKU-IS dataset. At the same time, we also conduct ablation experiments on the DUTS dataset and the ECSSD dataset to verify the effectiveness of the algorithm. The ablation experimental results show that the proposed algorithm can effectively improve the effect of salient object detection.
In aquaculture, the normal growth of fish is closely related to the density of aquaculture. Therefore, it is of great significance to use remote sensing images to accurately segment the cages in a specific sea area at a macro level. This research proposes an accurate segmentation scheme for remote sensing cages based on U-Net and voting mechanism. Firstly, a remote sensing cage segmentation (RSCS) data set is produced, which includes fifty-three high-resolution cage images with inconsistent resolution. Secondly, by using random cropping and data enhancement operations on the training samples, three training sets with image block sizes of 256×256 pixels, 512×512 pixels, and 1024×1024 pixels are created. And through the introduction of U-Net network, three training sets of different sample sizes are trained separately and three trained models are generated. Then, after reasonably filling the test image, a window sliding overlap cropping method is adopted. The high-resolution remote sensing cage test images are sequentially cut into the image blocks for segmentation, and the segmented image blocks are spliced and combined into the binary segmentation image by the mean method. Finally, for each image, the three binary segmentation images generated by different trained models are used to vote for each pixel. The experimental results show that by testing three remote sensing images of Li'an Port, Xincun Port and Potou Port, the Mean Intersection over Union (mIoU) is 0.865. Our data and code can be available online.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.