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• The data model with the bistatic FDA-MIMO radar based on the subarray scheme is presented. • The real-valued subspace method for parameter estimation is provided. • The problem of false range estimation caused by phase ambiguity is analyzed and solved. • An original method is proposed to match the 3D parameter for multi-objective case. The frequency diverse array (FDA) is mainly applied to achieving target localization under the complicated electromagnetic jamming condition. The bistatic multiple-input-multiple-output (MIMO) radar with FDA has gained comprehensive attention in recent years. An innovative method covering the transmitting subarray scheme and the unitary estimation of signal parameters via rotational invariance technology (ESPRIT) is proposed for joint direction of departure (DOD), direction of arrival (DOA), and range estimation. First, a non-overlapping subarray scheme is designed for the transmitting array to decouple DOD and range. For the purpose of enhancing the estimation accuracy and reducing the computational complexity, the real-valued rotational invariance matrix is obtained via applying the unitary transformation to the extended data. The removal method is proposed to avoid the periodic phase ambiguity. The pairing method is put forward to achieving the match of three-dimensional parameter (DOD, DOA and range) for multiple targets. The computational complexity of the proposed algorithm and conventional ESPRIT algorithm are also provided. Finally, extensive experiment results indicate that the proposed algorithm shows the superior performance than the ESPRIT algorithm. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI) have inspired researchers to explore machine learning (ML)-based optimization and reverse design techniques for photonic crystal fibers (PCFs). These studies often seek to improve model generalization, particularly for data that the model has not previously encountered. Traditional centralized training methods are challenging for devices with limited resources, as they rely on aggregating expansive datasets, which is hindered by constraints in storage capacity and communication efficiency. This paper introduces an innovative distributed framework for optimizing PCF parameters, utilizing decentralized training to amalgamate knowledge across various institutions while maintaining data privacy. Each institution develops a lightweight neural network using a small subset of local data, contributing to the construction of a collective and robust global model. This approach is advantageous for both internal and external applications in PCF engineering. Rigorous empirical experiments conducted with real-world PCF optimization data substantiate the efficacy and benefits of the proposed framework. This framework shows promise in achieving an equilibrium between data protection and resource efficiency, offering a novel platform for the reverse design of microstructured optical fibers. [ABSTRACT FROM AUTHOR]

Accurate and massive medical image annotation data is crucial for diagnosis, surgical planning, and deep learning in the development of medical images. However, creating large annotated datasets is challenging because labeling medical images is complicated, laborious, and time-consuming and requires expensive and professional medical skills. To significantly reduce the cost of labeling, an interactive image annotation framework based on composite geodesic distance is proposed, and medical images are labeled through segmentation. This framework uses Attention U-net to obtain initial segmentation based on adding user interaction to indicate incorrect segmentation. Another Attention U-net takes the user's interaction with the initial segmentation as input. It uses a composite geodesic distance transform to convert the user's interaction into constraints, giving accurate segmentation results. To further improve the labeling efficiency for large datasets, this paper validates the proposed framework against the segmentation background of a self-built prostate MRI image datasets. Experimental results show that the proposed method achieves higher accuracy in less interactive annotation and less time than traditional interactive annotation methods with better Dice and Jaccard results. This has important implications for improving medical diagnosis, surgical planning, and the development of deep-learning models in medical imaging. [ABSTRACT FROM AUTHOR]

As a result of rapid industrialization and urbanization, China is now facing a host of environmental problems that have serious health implications. Studies of air pollution's impact on human health are vital in many fields, including epidemiology, environmental science, and the social sciences. To ensure the effective growth of socioeconomic sectors, it is critical to investigate the effect of socioeconomic factors on primary air pollutant particulate matter (PM 2.5) and the driving mechanism. We conducted group-wise (i.,e. divide data in 5 different periods, D1 (2002–2006), D2 (2007–2011), D3 (2012–2016), D4 (2017–2021) and D5(2002–2021) spatial autocorrelation and spatial panel regression analyses of PM 2.5 emissions using panel data from 34 provincial-level administrative units in China from 2002 to 2021 to understand the factors influencing air pollutant emissions. This study adds to the literature by considering comprehensive features and spatial effects in the panel-data econometric framework of the different areas. The spatial features analysis reveals that pollutant emissions in these regions decreased during the study period, although socioeconomic and natural factors are essential sources of PM 2.5. PM 2.5 emissions also showed significant positive spatial autocorrelations. Several statistical tests were run to examine the spatial autocorrelation among the regions. The results of a random effect regression model and geometric weighted regression (GWR) revealed that both socioeconomic and natural factors were statistically significant for PM 2.5 , though to varying degrees depending on region type. Positive and statistically significant results were obtained for China when considering the impacts of urban population, urban green space, economic growth, and economic spending. China has a positive and significant link with the exploitation of energy and natural resources. In light of these findings, we have developed several ideas for addressing air pollution and improving environmental sustainability, such as increasing regional collaboration and reforming the economy. [ABSTRACT FROM AUTHOR]

Background: The study aimed to observe the internal structure of coconuts from two locations (coastal and non-coastal) using computed tomography (CT)., Methods: Seventy-six mature coconuts were collected from Wenchang and Ding'an cities in Hainan Province. These coconuts were scanned four times using CT, with a two-week interval between each scan. CT data were post-processed to reconstruct two-dimensional slices and three-dimensional models. The density and morphological parameters of coconut structures were measured, and the differences in these characteristics between the two groups and the changes over time were analyzed., Results: Time and location had interactive effects on CT values of embryos, solid endosperms and mesocarps, morphological information such as major axis of coconut, thickness of mesocarp, volume of coconut water and height of bud ( p  < 0.05)., Conclusions: Planting location and observation time can affect the density and morphology of some coconut structures., Competing Interests: Li’an Luo is an employee of Siemens Healthineers, but she was not involved in the analysis of the data and had no control of data or information submitted for publication. There is no conflict of interest or industry support in this study. The other authors declare that they have no competing interests., (©2024 Lin et al.)

Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer from poor image quality and loss of crucial details due to inadequate handling of semantic information and limited feature extraction capabilities. This paper introduces a novel medical image fusion technique leveraging unsupervised image segmentation to enhance the semantic understanding of the fusion process. The proposed method, named DUSMIF, employs a multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms to refine the feature extraction and fusion processes. An innovative approach that utilizes unsupervised image segmentation to extract semantic information is introduced, which is then integrated into the fusion process. This not only enhances the semantic relevance of the fused images but also improves the overall fusion quality. The paper proposes a sophisticated network structure that extracts and fuses features at multiple scales and across multiple branches. This structure is designed to capture a comprehensive range of image details and contextual information, significantly improving the fusion outcomes. Multiple attention mechanisms are incorporated to selectively emphasize important features and integrate them effectively across different modalities and scales. This approach ensures that the fused images maintain high quality and detail fidelity. A joint loss function combining content loss, structural similarity loss, and semantic loss is formulated. This function not only guides the network in preserving image brightness and texture but also ensures that the fused image closely resembles the source images in both content and structure. The proposed method demonstrates superior performance over existing fusion techniques in objective assessments and subjective evaluations, confirming its effectiveness in enhancing the diagnostic utility of fused medical images., (© 2024. The Author(s).)

Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep one extracted from each dense block to form a residual block. We add attention mechanism to the network to extract the key features and suppress the irrelevant ones. We fuse the output feature maps from all dense blocks and input them to the reconstruction output layer. We compare PFFADN with the state-of-the-art denoising algorithms on retinal OCT images. Experiments show that our method has better improvement in denoising performance.

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interactions stand out as an intuitive and straightforward approach compared to alternatives such as graffiti, bounding boxes, and extreme points. To improve the model's ability to interpret click-based interactions, we propose a comprehensive interactive segmentation framework that leverages an iterative weighted loss function based on user clicks. To enhance the segmentation capabilities of the Plain-ViT backbone, we introduce a Residual Multi-Headed Self-Attention encoder with hierarchical inputs and residual connections, offering multiple perspectives on the data. This innovative architecture leads to a remarkable improvement in segmentation model performance. In this research paper, we assess the robustness of our proposed framework using a self-compiled T2-MRI image dataset of the prostate and three publicly available datasets containing images of other organs. Our experimental results convincingly demonstrate that our segmentation model surpasses existing state-of-the-art methods. Furthermore, the incorporation of an iterative loss function training strategy significantly accelerates the model's convergence rate during interactions. In the prostate dataset, we achieved an impressive Intersection over Union (IoU) score of 88.11% and Number of Clicks(NoC) at 80% are 7.03 clicks.

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.

Objective: This study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT)., Methods: Currently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU)., Results: Cropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them., Conclusion: This U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation severely hinders the optimization of coconut breeding. To address this issue, we propose a new model based on the improved architecture of Deeplab V3+. We replace the original ASPP(Atrous Spatial Pyramid Pooling) structure with a dense atrous spatial pyramid pooling module and introduce CBAM(Convolutional Block Attention Module). This approach resolves the issue of information loss due to sparse sampling and effectively captures global features. Additionally, we embed a RRM(residual refinement module) after the output level of the decoder to optimize boundary information between organs. Multiple model comparisons and ablation experiments are conducted, demonstrating that the improved segmentation algorithm achieves higher accuracy when dealing with diverse coconut organ CT(Computed Tomography) images. Our work provides a new solution for accurately segmenting internal coconut organs, which facilitates scientific decision-making for coconut researchers at different stages of growth., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Zhang, Chen, Sun, Huang, Che, Li and Lin.)

Background and Objective: Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging., Methods: This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism., Results: The results of our proposed network's prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively., Conclusions: Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance., Competing Interests: Declaration of competing interest All authors declare no commercial conflicts of interests other than per capita or grant funding to their institutions for patient accrual. The funding bodies had no role in the analysis or reporting of results and did not review or edit the text of the manuscript., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In the downlink communication, it is currently challenging for ground users to cope with the uncertain interference from aerial intelligent jammers. The cooperation and competition between ground users and unmanned aerial vehicle (UAV) jammers leads to a Markov game problem of anti-UAV jamming. Therefore, a model-free method is adopted based on multi-agent reinforcement learning (MARL) to handle the Markov game. However, the benchmark MARL strategies suffer from dimension explosion and local optimal convergence. To solve these issues, a novel event-triggered multi-agent proximal policy optimization algorithm with Beta strategy (ETMAPPO) is proposed in this paper, which aims to reduce the dimension of information transmission and improve the efficiency of policy convergence. In this event-triggering mechanism, agents can learn to obtain appropriate observation in different moment, thereby reducing the transmission of valueless information. Beta operator is used to optimize the action search. It expands the search scope of policy space. Ablation simulations show that the proposed strategy achieves better global benefits with fewer dimension of information than benchmark algorithms. In addition, the convergence performance verifies that the well-trained ETMAPPO has the capability to achieve stable jamming strategies and stable anti-jamming strategies. This approximately constitutes the Nash equilibrium of the anti-jamming Markov game., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength. In addition, a low-cost data augmentation strategy is introduced to break the data size bottleneck common in data-driven methods so that the NN can still achieve superior performance with small-scale data. In summary, the demodulation system provides an efficient and reliable solution for multi-point monitoring of large structures based on FBG array sensors.

Purpose: Automatic segmentation of prostate magnetic resonance (MR) images is crucial for the diagnosis, evaluation, and prognosis of prostate diseases (including prostate cancer). In recent years, the mainstream segmentation method for the prostate has been converted to convolutional neural networks. However, owing to the complexity of the tissue structure in MR images and the limitations of existing methods in spatial context modeling, the segmentation performance should be improved further., Methods: In this study, we proposed a novel 3D pyramid pool Unet that benefits from the pyramid pooling structure embedded in the skip connection (SC) and the deep supervision (DS) in the up-sampling of the 3D Unet. The parallel SC of the conventional 3D Unet network causes low-resolution information to be sent to the feature map repeatedly, resulting in blurred image features. To overcome the shortcomings of the conventional 3D Unet, we merge each decoder layer with the feature map of the same scale as the encoder and the smaller scale feature map of the pyramid pooling encoder. This SC combines the low-level details and high-level semantics at two different levels of feature maps. In addition, pyramid pooling performs multifaceted feature extraction on each image behind the convolutional layer, and DS learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task., Results: Experiments on 3D prostate MR images of 78 patients demonstrated that our results were highly correlated with expert manual segmentation. The average relative volume difference and Dice similarity coefficient of the prostate volume area were 2.32% and 91.03%, respectively., Conclusion: Quantitative experiments demonstrate that, compared with other methods, the results of our method are highly consistent with the expert manual segmentation., (© 2022 American Association of Physicists in Medicine.)

Introduction: The fusion of infrared and visible images can improve image quality and eliminate the impact of changes in the agricultural working environment on the information perception of intelligent agricultural systems., Methods: In this paper, a distributed fusion architecture for infrared and visible image fusion is proposed, termed RADFNet, based on residual CNN (RDCNN), edge attention, and multiscale channel attention. The RDCNN-based network realizes image fusion through three channels. It employs a distributed fusion framework to make the most of the fusion output of the previous step. Two channels utilize residual modules with multiscale channel attention to extract the features from infrared and visible images, which are used for fusion in the other channel. Afterward, the extracted features and the fusion results from the previous step are fed to the fusion channel, which can reduce the loss in the target information from the infrared image and the texture information from the visible image. To improve the feature learning effect of the module and information quality in the fused image, we design two loss functions, namely, pixel strength with texture loss and structure similarity with texture loss., Results and Discussion: Extensive experimental results on public datasets demonstrate that our model has superior performance in improving the fusion quality and has achieved comparable results over the state-of-the-art image fusion algorithms in terms of visual effect and quantitative metrics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Feng, Wu, Lin and Huang.)