In this paper, an ultra-wideband (UWB) filter with a notch band using T-shaped step impedance resonator loaded with cross-shaped open stubs has been presented. The notch band is formed by utilizing intrinsic zero. The characteristics of the new quad-mode resonator have been analyzed using odd-even mode analysis method. It can be shown that it is the intrinsic zero that generates the notch band. In addition, wide tunable notch band form 5–9.3 GHz can be achieved. A UWB filter with a notch band centered at 6.25 GHz using the proposed quad-mode resonator has been designed, fabricated, and tested. Experiment results show that the attenuation in notch frequency is >22 dB while the return loss of the simulation and measurement results are 22 dB/20 dB and 14.5 dB/11.2 dB in the lower and upper passband, respectively, which illustrate that the simulation and measurement results are in agreement.

This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.

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At present, the study of upper-limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper-limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine- (FSM-) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

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