# Nonlinear signal processing

The HQA1K dataset was developed for assessing the quality of Computer Generated Holography (CGH) image renderings based on direct human input.

HQA1K is comprised of 1,000 pairs of natural images matched to simulated CGH renderings of various quality levels. The result is a diverse set of data for evaluating image quality algorithms and models.

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A physical memristor is introduced into heterogeneous coupled neurons. Using the physical memristor, various complex firing patterns are observed. By analyzing the effects of electromagnetic induction on the firing patterns of the coupled neurons, multiple firing patterns are found, such as two coexisting chaotic firing patterns, transient chaos, and some other complex firing patterns. The memristive coupled neurons can better simulate the firing activity of the biological nervous system. In addition, phase synchronization of the coupled neurons is analyzed.

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针对固有参数不确定和部分未知参数的桥式起重机系统在起重作业过程中的负载摆动和小车跟踪定位问题，提出了一种基于RBF神经网络的动态滑模鲁棒控制算法。闭环误差的李雅普诺夫稳定性通过控制算法在理论上得到了验证。控制算法以滑模控制思想为结构框架。滑模面设计考虑了控制输出变化的影响，通过设置模糊规则使滑模切换项自适应，并进一步推导了RBF神经网络的自适应规律，以适应桥式起重机系统中复杂和未知的动态部件， 渲染桥式起重机系统的运行，无需任何系统参数作为信号输入。为了证明该策略影响下更高的控制精度，与文献中提出的分层滑模控制方法进行了比较[27]，结果表明，基于上述算法设计的控制器输出功率更加稳定，且不存在长期高频抖动， 虽然其对负载的抗摆动控制效果也比任何其他控制器具有更好的鲁棒性能。指出当系统参数发生一定程度的变化时，也非常重要。同时基于该控制系统的小车也可以在零过冲下实现稳定的跟踪和定位。

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These data is state estimation accuracy of the proposed algorithm.

These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.

The data are explained below:

rmse_ckf_1 and rmse_ckf_2 are the position accuracy and speed accuracy of CKF，respectively.

rmse_ukf_1 and rmse_ukf_2 are the position accuracy and speed accuracy of UKF，respectively.

rmse_ssmckf1_2 and rmse_ssmckf1_2 are the position accuracy and speed accuracy of SSM-RCKF when the similarity function is selected as exponentiac function, respectively.

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Ground Penetrating Radar (GPR) has a wide range of applications such as detection of buried mines, pipes and wires. GPR has been used as a near-surface remote sensing technique, and its working principle is based on electromagnetic (EM) wave theory. Here proposed data set is meant for data driven surrogate modelling based Buried Object Characterization. The considered problem of estimating geophysical parameters of a buried object is 2D. The training and testing scenarios include B-scan images (2D data), which contain 16 pairs of A-scan (concatenated forms of A-scans).

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<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.

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**A Cu-doped TiO2 -x nanoscale memristor is fabricated, whose faithful mathematical model is established based on the memristive behaviors and its switching mechanism. Using this model, a chaotic system is constructed and its complex dynamics are investigated by numerical simulations. Furthermore, hardware experiments are also designed to verify the model in chaotic circuit.**

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This dataset is proposed for human activity recognition tasks. The static activities including sitting, standing, and laying, as well as walking, running, cycling, and walking upstairs/downstairs. Each activity lasts for 2 minutes, 50 subjects were involved in the experiments.

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A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

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