This paper focuses on the study of topological features in teleportation-based quantum computation as well as aims at presenting a detailed review on teleportaiton-based quantum computation (Gottesman and Chuang, Nature 402, 390, 1999). In the extended Temperley-Lieb diagrammatical approach, we clearly show that such topological features bring about the fault-tolerant construction of both universal quantum gates and four-partite entangled states more intuitive and simpler. Furthermore, we describe the Yang-Baxter gate by its extended Temperley-Lieb configuration, and then study teleportation-based quantum circuit models using the Yang-Baxter gate. Moreover, we discuss the relationship between the extended Temperley-Lieb diagrammatical approach and the Yang-Baxter gate approach. With these research results, we propose a worthwhile subject, the extended Temperley-Lieb diagrammatical approach, for physicists in quantum information and quantum computation.

As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing’s inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.

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The controlling of robotic arms based on brain–computer interface (BCI) can revolutionize the quality of life and living conditions for individuals with physical disabilities. Invasive electroencephalography (EEG)-based BCI has been able to control multiple degrees of freedom (DOFs) robotic arms in three dimensions. However, it is still hard to control a multi-DOF robotic arm to reach and grasp the desired target accurately in complex three-dimensional (3D) space by a noninvasive system mainly due to the limitation of EEG decoding performance. In this study, we propose a noninvasive EEG-based BCI for a robotic arm control system that enables users to complete multitarget reach and grasp tasks and avoid obstacles by hybrid control. The results obtained from seven subjects demonstrated that motor imagery (MI) training could modulate brain rhythms, and six of them completed the online tasks using the hybrid-control-based robotic arm system. The proposed system shows effective performance due to the combination of MI-based EEG, computer vision, gaze detection, and partially autonomous guidance, which drastically improve the accuracy of online tasks and reduce the brain burden caused by long-term mental activities.

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