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Radio frequency connectors are widely used in microwave equipment and radio frequency circuits. Connector degradation in harsh environment may lead to the deterioration of signal integrity and communication quality. In this paper, the effects of connector dielectric degradation on signal transmission in humid environment were presented by the way of experimental tests and theoretical analysis. The equivalent dielectric constant of the degraded dielectric was calculated and a circuit model was developed to explain these effects. The simulation results of the circuit model were in good agreements with the measurement results. In addition, the impacts of dielectric degradation on digital signal waveform and eye diagram were predicted using the model. The results of this research provided a better understanding of the influence of connector dielectric degradation on signal transmission in humid environment.

In the communication systems, passive intermodulation (PIM) induced by the nonlinear effect of coaxial connectors may impact the transmission quality of analog modulated signals, when the analog modulated signals are transmitted through coaxial connectors. In this paper, based on the traditional polynomial model of PIM products, an empirical expression of PIM products in coaxial connectors was deduced from a series of PIM two-tone tests, curve fitting and coefficient extraction. For different signal modulated modes, the simulation results of PIM characteristics of analog modulated signals in coaxial connectors were respectively obtained using the empirical expression applied to ADS simulator. In addition, the Adjacent Channel Power Ratio (ACPR) metric was introduced to quantify the effect of PIM interference in the modulated signal frequency band, providing a theoretical basis for evaluating the performance of analog modulated signals in coaxial connectors against PIM interference.

RF connectors generally have slight nonlinear characteristics. The nonlinearity of connectors can cause passive intermodulation (PIM) interference. The RF connectors will degrade due to the vibration environment, which will aggravate the PIM interference and affect the transmission quality of communication systems. In this paper, a vibration device is designed for a series of accelerated degradation tests of N-type RF connectors. The contact stress is selected as the index to quantify the degradation degree of the connectors. The main findings of the test are that passive intermodulation is greatly affected by vibration contact pressure. The parameters of the theoretical model are modified through the test results of a series of vibration comparative experiments, and a developed high-order power series model is proposed to obtain a higher accuracy prediction of harmonic power.

Bar charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step. Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage component detection framework for bar charts that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.

Chart images are commonly used for data visualization. Automatically reading the chart values is a key step for chart content understanding. Charts have a lot of variations in style (e.g. bar chart, line chart, pie chart and etc.), which makes pure rule-based data extraction methods difficult to handle. However, it is also improper to directly apply end- to-end deep learning solutions since these methods usually deal with specific types of charts. In this paper, we propose an unified method ChartOCR to extract data from various types of charts. We show that by combing deep framework and rule-based methods, we can achieve a satisfying generalization ability and obtain accurate and semantic-rich intermediate results. Our method extracts the key points that define the chart components. By adjusting the prior rules, the framework can be applied to different chart types. Experiments show that our method achieves state-of-the- art performance with fast processing speed on two public datasets. Besides, we also introduce and evaluate on a large dataset ExcelChart400K for training deep models on chart images. The code and the dataset are publicly available at https://github.com/soap117/DeepRule.