Nonlinear signal processing
This letter presents an effective digital passive intermodulation (PIM) cancellation technique for multi-band multiple-input multiple-output frequency-division duplexing radios for 5G and beyond. We begin by introducing a universal PIM signal model for multi-band radios with an arbitrary number of transmit--receive antennas, which enables the calculation of basis functions at all intermodulation frequencies. After establishing this signal model, a comparative analysis of state-of-the-art behavioral models is conducted in the context of PIM modeling.
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This letter presents an effective digital passive intermodulation (PIM) cancellation technique for multi-band multiple-input multiple-output frequency-division duplexing radios for 5G and beyond. We begin by introducing a universal PIM signal model for multi-band radios with an arbitrary number of transmit--receive antennas, which enables the calculation of basis functions at all intermodulation frequencies. After establishing this signal model, a comparative analysis of state-of-the-art behavioral models is conducted in the context of PIM modeling.
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A PCB 130D20 microphone is kept in a stand and facing towards the tool tip to capture the emitted sound. The microphone is connected to the computer through a specially designed signal conditioner. GoldWave software is used to record the captured sound with sampling frequency set to 44100Hz. A series of machining experiments were conducted on a Turning machine(XLTURN from MTAB) with Carbide Insert and aluminium work piece of 38mm diameter. Throughout the experiment a constant feed rate set at 0.5 mm/rotation was uniformly maintained.
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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".
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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".
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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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This dataset contains parameter values of the model presented by the manuscript titled "Model-Based Extraction of T2D Diagnostic Information from Continuous Glucose Monitoring". The parameters are obtained by first extracting hyperglycemic excursions, i.e. peaks, from continuous glucose data. A simple mathematical model describing glucose homeostasis is fitted to each peak, resulting in a collection of model parameters for each peak.
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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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