Papers by Keyword: Adaptive Filter

Abstract: Piezoelectric actuator (PEA) is widely applied in micro/nanopositioning system. However, its inherent hysteresis limits its application. Modeling of hysteresis plays an important role in solving this problem. Linear play operators (LPO) adaptive hysteresis model is introduced in this paper. LPO operators are used to replace delay operators of adaptive transversal filter to compose a new serial structure of adaptive transversal filter model, and LMS (Least Mean Square) algorithm is used to adjust the weight values. As hysteresis loop of piezoelectric actuator is asymmetric and LPO operator is symmetric, a modified LPO (MLPO) adaptive filter is proposed for asymmetric hysteresis effect. At last, the two LPO filters are applied to model hysteresis characteristic of Piezoelectric actuator, and the modeling effect is verified via a micro-positioning system experiment platform based on Piezoelectric actuator. Experimental results show that the modified LPO filters can achieve better accurate hysteresis modeling.

Abstract: Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

Abstract: In this paper, an adaptive filter method for Chirp signal in additional white Gaussian noise (AWGN) is studied. First, we analyze conception and character of FRFT (Fractional Fourier Transform), and the results shows that Chirp signal has energy concentration character in FRFT domain. Then, an adaptive filtering method is introduced in FRFT domain, and a test is done for separating Chirp signal from AWGN by the filtering method. Last, performance of this filtering method is studied, and the SNR improvement expression is concluded to verifying its advantages, it depends on the performance of adaptive filter basically. At last, the results show that this filtering algorithm is simple in computation and easy in implementation.

Abstract: Displacement is a good descriptor of the structural behavior and safety status. However, measuring displacement of structures under dynamic excitations is still a challenging task. Videogrammetry shows great potential for dynamic displacement measurement, benefiting from its non-contact and long-distance characteristics. Nevertheless, its all-weather performance has to be fully evaluated before gaining wide applications. This study therefore carried out an investigation into the environmental effects of the all-weather videogrammetry for structural dynamic displacement monitoring. First, long-term outdoor dynamic displacement monitoring tests were carried out. Virtual structural displacement was generated by a motion simulation device and monitored by a commercialized industrial digital camera. The adaptive filter was then employed to filter out noises, which had the primary input of the major displacement component and the reference input of the minor displacement component. The results show that the adaptive filter is well capable of filtering out noises and the measurement accuracy of videogrammetry is significantly enhanced.

Abstract: The speech signal usually could not be extracted correctly from the digital speech communication system with strong interference. As for this kind of system, the common fixed coefficient digital filters (FIR, IIR) are unable to achieve the best effect of filtering. Whereas the adaptive filter could extract the available signals properly by adjusting the filter coefficient automatically without knowing the change characteristics of the noise signal. In this paper, we designed an adaptive noise cancellation filter based on LMS algorithm on the DSP chip and verification of the filter was done on the TMS320C5509 platform. The results show that the adaptive noise cancellation designed in this paper could extract the available signals properly and improve the quality of the speech communication.

Abstract: This paper studies a new QR decomposition adaptive filtering algorithm for acoustic echo cancellation (AEC). Based on the p-TA-QR-LS algorithm [1] and an efficient voice activity detection technique, the proposed algorithm can distinguish the significant and insignificant input data periods. The resultant variable mode p-TA-QR-LS algorithm can work between two modes (p=1and N) and is thus suitable for AEC application where reusing significant input data can enhance convergence and the computation cost can be saved when the input is relatively weak.

Abstract: In order to eliminate the speech signal fading induced by noise, this paper proposes a new variable leaky LMS algorithm on the basis of the adaptive filter in combination with the advantages of the leakage LMS algorithm and the variable step size LMS algorithm, and analyzes its principle. After the new algorithm is used to simulate speech with noise, the result shows that the proposed algorithm has a good ability to suppress Gaussian noise and excellent tracking performance, proving the correctness of the algorithm and the feasibility of simulation.

Abstract: This paper studies a new QR decomposition adaptive filtering algorithm for acoustic echo cancellation (AEC). Based on the p-TA-QR-LS algorithm [ and an efficient voice activity detection technique, the proposed algorithm can distinguish the significant and insignificant input data periods. The resultant variable mode p-TA-QR-LS algorithm can work between two modes (p=1and N) and is thus suitable for AEC application where reusing significant input data can enhance convergence and the computation cost can be saved when the input is relatively weak.

Abstract: It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

Abstract: In the speech emotion recognition process, How to obtain effective characteristic parameters from the emotional data including the noise is one of the significant and difficult problem. This paper first removes the gauss white noise with the adaptive filter. Then the Mel Frequency Cepstrum Coefficients (MFCC) based on Empirical Mode Decomposition (EMD) is extracted and with its difference parameter to improve. At last we present an effective method for speech emotion recognition based on Fuzzy Least Squares Support Vector Machines (FLSSVM) so as to realize the speech recognition of four main emotions, i.e, anger, happy, surprise and natural. The experiment results show that this method has the better anti-noise effect when compared with traditional Support Vector Machines (SVM).