本文提出了一种在各种电路元件设计中实现人工表面等离子体模式的可靠、可重复的方法。还提出了等离子体结构的第一个等效电路模型，为基于SSP的电路设计提供了一个有见地的指导。如今，电子电路系统正在迅速发展，成为我们日常生活中不可缺少的一部分；然而，集成电路的紧凑性问题仍然是一个艰巨挑战。近年来，人工表面等离子体（SSP）模式被提出作为一种新型的高紧凑型电子电路平台。尽管在这方面已经做了大量的研究工作，但仍然迫切需要一种等离子体电路的系统设计方法。本文对不同的基于SSP的传输线、天线馈送网络和天线进行了设计和实验评估。由于其高场特性的限制，SSP不受传统电路紧凑性限制，能够为未来的电子电路和电磁系统提供替代平台。 This thesis proposes a reliable and repeatable method for implementing Spoof Surface Plasmon (SSP) modes in the design of various circuit components.It also presents the first equivalent circuit model for plasmonic structures, which serves as an insightful guide to designing SSP-based circuits.Today, electronic circuits and systems are developing rapidly and becoming an indispensable part of our daily life; however the issue of compactness in integrated circuits remains a formidable challenge.Recently, the Spoof Surface Plasmon (SSP) modes have been proposed as a novel platform for highly compact electronic circuits.Despite extensive research efforts in this area, there is still an urgent need for a systematic design method for plasmonic circuits.In this thesis, different SSP-based transmission lines, antenna feeding networks and antennas are designed and experimentally evaluated.With their high field confinement, the SSPs do not suffer from the compactness limitations of traditional circuits and are capable of providing an alternative platform for the future generation of electronic circuits and electromagnetic systems.

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Accurate evaluation of bradykinesia plays a crucial role in the diagnosis and therapy effect of Parkinson's disease. However, the subjective assessment shows low consistency among different evaluators, and the objective sensor-based methods cannot accurately distinguish patients with different grades of the 5-point clinical bradykinesia ratings. In this paper, an objective scoring method based on axis-angle representation and multi-class support vector machine (SVM) classi_er was employed to est

这是论文《Gaussian Pyramid Based Multiscale Feature Fusion for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(9), 3312-3324》的代码，更多详情可在纸上找到。 如果你使用这个演示，请引用这篇论文。 要运行此演示，您应该先下载 libsvm-3.20。 libsvm-3.20可从https://www.csie.ntu.edu.tw/~cjlin/libsvm/获得

Measuring the electromagnetic properties of materials has important applications in many fields. In this paper, two electrically small sensors based on the split ring resonators (SRRs) with extended long legs, i.e., two-layer and three-layer magnetic coupled SRRs, are proposed to measure the permittivity of small samples of unknown materials. By virtue of several merits, such as extremely compact size for two-layer and three-layer magnetic coupled SRRs, respectively), high quality factor (Q), and stable resonance. Especially, the proposed three-layer magnetic coupled sensor with opposite splits on the SRRs is able to further improve the quality factor and have better stability compared with the two-layer coupled sensor. By different shifting resonant frequencies instead of the single ones and the polynomial fitting method, the proposed sensors can accuratelycalculatetheunknownpermittivity.Simulatedandexperimentalresultshavevalidatedtheefficacy of the proposed approach and designs. With the features of compact size and lower far-field radiation, the proposed resonators can be combined with various permittivity measurement algorithms to improve the measurement accuracy in a wide range of environments beyond the specific experimental setup.

这是论文“Density Peak Clustering-based Noisy Label Detection for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 2018, (Accepted)”的代码，更多细节可以在论文中找到。 如果你使用这个演示，请引用这篇论文。 要运行此演示，您应该先下载 libsvm-3.22。 libsvm-3.22 可在https://www.csie.ntu.edu.tw/~cjlin/libsvm/ 获得

matlab代码影响基于自动编码器的单图像超分辨率 介绍 单图像超分辨率（SISR）是计算机视觉中的不适定问题，并且在视频编码的背景下显示出其潜力。 自从SRCNN [1]模型首次提出以来，训练基于深度学习的模型来执行超分辨率已成为该领域的当前研究重点。 基于深度学习的超分辨率的当前流程如图1所示。首先使用双三次/ SHVC方法将原始图像降采样为低分辨率图像，然后通过插值方法将低分辨率图像放大。 插值图像用于深度学习模型的训练和测试。 图1：当前基于深度学习的SISR模型的一般结构。 在该项目中，发现不同的下采样方法对基于深度学习的SISR模型的训练和性能有深远的影响。 使用几乎没有别名的下采样和内插方法进行训练对网络恢复高一半频率的信息没有帮助。 基于这些结论，设计了一种可以同时学习下采样和上采样操作的自动编码器模型，希望该自动编码器模型可以学习适当的下采样方法，以便在上半频率范围内获得更多信息。可以恢复的。 测试结果表明，与VDSR [2]模型相比，该自动编码器模型可以实现更高的PSNR值。 自动编码器模型的结构如图2所示。图2：基于自动编码器的SISR模型的结构。 表1给出了测试