results: achieved an average accuracy rate of 93.36%, outperformed contemporary deep learning models, better computational performance ($\sim$20$\times$ fewer operations)Here’s the full text in Simplified Chinese:for: 这个研究旨在为 радио галактик类型分类做出一种有效的方法。methods: 我们使用了COSFIRE滤波器，它具有适应形态和方向的能力，同时具有解释性、学习自由、旋转快速等优点。results: 我们在一个标准的 радио галактик数据集上进行了实验，包括1180个训练样本和404个测试样本。结果显示，我们的方法实现了93.36%的平均准确率，超越了当前的深度学习模型，并在这个数据集上达到了最佳的成绩。此外，COSFIRE滤波器在计算性能方面也表现出了优势，相比同精度的 denseNet 模型，它的计算量只需20倍以下。这些发现证明了 COSFIRE 滤波器在 радио галактик类型分类中的效iveness。Abstract
Radio galaxies exhibit a rich diversity of characteristics and emit radio emissions through a variety of radiation mechanisms, making their classification into distinct types based on morphology a complex challenge. To address this challenge effectively, we introduce an innovative approach for radio galaxy classification using COSFIRE filters. These filters possess the ability to adapt to both the shape and orientation of prototype patterns within images. The COSFIRE approach is explainable, learning-free, rotation-tolerant, efficient, and does not require a huge training set. To assess the efficacy of our method, we conducted experiments on a benchmark radio galaxy data set comprising of 1180 training samples and 404 test samples. Notably, our approach achieved an average accuracy rate of 93.36\%. This achievement outperforms contemporary deep learning models, and it is the best result ever achieved on this data set. Additionally, COSFIRE filters offer better computational performance, $\sim$20$\times$ fewer operations than the DenseNet-based competing method (when comparing at the same accuracy). Our findings underscore the effectiveness of the COSFIRE filter-based approach in addressing the complexities associated with radio galaxy classification. This research contributes to advancing the field by offering a robust solution that transcends the orientation challenges intrinsic to radio galaxy observations. Our method is versatile in that it is applicable to various image classification approaches.
摘要
Radio галактики表现出丰富多样性，通过多种辐射机制发射电波，这使得它们的分类成为一项复杂的挑战。为了解决这个挑战，我们提出了一种创新的电波 галакти子分类方法使用COSFIRE筛选器。这些筛选器具有适应形状和orientation的图像模式的能力。COSFIRE方法是可解释的、学习无需、旋转快速、高效的，并不需要庞大的训练集。为评估我们的方法的有效性，我们在一个标准电波 галакти子数据集上进行了实验，该数据集包括1180个训练样本和404个测试样本。结果显示，我们的方法实现了93.36%的准确率，这比当今的深度学习模型高出了一些，同时也是这个数据集上的最佳成绩。此外，COSFIRE筛选器在计算性能方面比 denseNet 基于的竞争方法更好，在同等准确率下，它们的操作数量只需 $\sim$20 $\times$ fewer。我们的发现证明了 COSFIRE 筛选器基于的方法在电波 галакти子分类中的效iveness。这种方法不仅可以应用于电波 галакти子分类，还可以扩展到其他图像分类领域。
Wireless Regional Imaging through Reconfigurable Intelligent Surfaces: Passive Mode
results: 论文提出了一种基于多个反射器的线性成像通道模型，并提出了一种计算成像理论来恢复SOI中信号强度分布。furthermore, the paper proposes an amplitude-only imaging algorithm to mitigate the problem of phase unpredictability. Finally, the performance of the imaging algorithm is verified by proof-of-concept experiments under reasonable parameter settings.Abstract
In this paper, we propose a multi-RIS-aided wireless imaging framework in 3D facing the distributed placement of multi-sensor networks. The system creates a randomized reflection pattern by adjusting the RIS phase shift, enabling the receiver to capture signals within the designated space of interest (SoI). Firstly, a multi-RIS-aided linear imaging channel modeling is proposed. We introduce a theoretical framework of computational imaging to recover the signal strength distribution of the SOI. For the RIS-aided imaging system, the impact of multiple parameters on the performance of the imaging system is analyzed. The simulation results verify the correctness of the proposal. Furthermore, we propose an amplitude-only imaging algorithm for the RIS-aided imaging system to mitigate the problem of phase unpredictability. Finally, the performance verification of the imaging algorithm is carried out by proof of concept experiments under reasonable parameter settings.
摘要
在这篇论文中，我们提出了一种基于多个反射层设备（RIS）的无线影像框架，面临分布式感知网络的分布式部署。系统通过调整RIS阶段偏移，使接收器能够在指定空间 интере（SoI）内捕捉信号。首先，我们提出了一种基于多个RIS的线性影像通道模型。我们介绍了一种计算影像理论框架，以回收SOI中信号强度分布。对于RIS协助影像系统，我们分析了多个参数对系统性能的影响。实验结果证明了我们的提议的正确性。此外，我们提出了一种幂only影像算法，以 Mitigate phase unpredictability问题。最后，我们验证了影像算法的性能通过理想参数设置的证明。