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Time filter

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Zhang L.G.,Yanshan University | Jin M.,Yanshan University | Jin J.,Hebei University of Technology | Yu G.H.,Audio Visual Machinery Research Institute
Advanced Materials Research | Year: 2014

ASM is a statistical model applied to match contours of non-rigid object. The actual contour may much different from the initial contour and the result is likely to converge to an error contour. Kalman filter is adopted to track the current frame for the prediction and acts as the initial state of the ASM, and then applies the ASM to correct the contour of the object. Experimental results show that the method proposed in this paper allows the model to converge to the target contour quickly and accurately. It has good stability and robustness. © (2014) Trans Tech Publications, Switzerland.


Jin M.,Yanshan University | Zhang K.-N.,Yanshan University | Jin J.,Hebei University of Technology | Yu G.-H.,Audio Visual Machinery Research Institute | Li W.-C.,Yanshan University
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2014

The particle swarm optimization static calibration method of micro-electro-mechanical system accelerometer based on ellipsoidal restriction is studied. According to the output mathematical model of the micro-electro-mechanical system accelerometer, the standard particle swarm optimization algorithm is taken to estimate scale factors, bias, and installation errors in the output mathematical model. The experimental results show that the method can eliminate the random errors in the sampling process effectively. With the introduction of particle swarm optimization algorithm, the outputs of the micro-electro-mechanical system accelerometer can reflect their true values.


Jin M.,Yanshan University | Zhang L.,Yanshan University | Jin J.,Hebei University of Technology | Yu G.,Audio Visual Machinery Research Institute | Liu H.,Yanshan University
Gaojishu Tongxin/Chinese High Technology Letters | Year: 2014

A study on the data reusability of motion capture, the most promising technology in character animation, was conducted, and a motion retargeting method based on physical constraint was proposed to improve the quality of the new motions created by using the captured data. The method adopts the technology of displacement mapping to set the target motion trajectory, and keeps original motion details. To guarantee the physical authenticity of character motions, it uses the UKF algorithm to solve nonlinear constraints. The experimental results demonstrate that the proposed method can create natural motions under the premise of keeping original motion characteristics to enhance the reusability of the captured motion data. ©, 2014, Inst. of Scientific and Technical Information of China. All right reserved.


Jin M.,Yanshan University | Zhao J.,Yanshan University | Jin J.,Hebei University of Technology | Yu G.,Audio Visual Machinery Research Institute | Li W.,Yanshan University
Measurement: Journal of the International Measurement Confederation | Year: 2014

In the inertial motion capture system, the model complexity and the large amount of computation make the completion of the orientation estimation algorithm rely solely on PC. Because the data processing speed is slow, it is difficult to realize high-speed motion tracking in the embedded system. In order to further expand the application of the motion tracking technology, this paper introduces a two-step Kalman filter, which is suitable for the embedded system. The filter is composed of two sub filters, and is adaptively adjusted based on the variance matching of fuzzy logic. IMU orientation is calculated based on the filtered acceleration vector and the estimated yaw. This approach simplifies the mathematical model, reduces the matrix operations and improves the speed of computation. © 2013 Elsevier B.V. All rights reserved.


Zang L.-G.,Yanshan University | Zang Y.-M.,Yanshan University | Jin M.,Yanshan University | Jin J.,Hebei University of Technology | Yu G.-H.,Audio Visual Machinery Research Institute
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2015

P300 is widely used in the field of brain cognition and BCI, however, its intensity is so weak that it is easy to suffer from interferences of environment and artifacts of blink, ECG, and EMG and normally submerged in EEG. In order to separate P300 from interferences speedily and efficiently, the feature of P300 in time, frequency and spatial domain are analyzed. A method is proposed based on BSS to extract P300 feature, combining coherence average, wavelet transformation and BSS. A new technique is described aiming at selecting ICs corresponding to P300 after BSS to multi-lead EEG. Three BSS algorithms, Informax, FastICA and AMUSE are compared in the performance of P300 feature extraction. Experimental results verify that the proposed method has an obvious improvement in P300 feature extraction compared with the other methods using temporal and frequency characteristics of P300 only. © 2015, Chinese Society for Measurement. All right reserved.

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