An adaptive wave digital filter (WDF) based on the least-mean-square algorithm is presented. The stability can easily be monitored and achieved by observing and controlling the multiplier coefficients of the adaptive ...
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An adaptive wave digital filter (WDF) based on the least-mean-square algorithm is presented. The stability can easily be monitored and achieved by observing and controlling the multiplier coefficients of the adaptive WDF. The author discusses the elementary building blocks of the WDF and derives the algorithm for updating the coefficients of the adaptive WDF. Simulation results are included to demonstrate its potential application in system identification.< >
In this paper, we propose an identification and control method for a control stage with eddy current. The proposed method is that we first obtain the system parameters using an adaptive algorithm and then we design a ...
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In this paper, we propose an identification and control method for a control stage with eddy current. The proposed method is that we first obtain the system parameters using an adaptive algorithm and then we design a model-based controller to achieve good performance. The detailed theoretical analysis is given. The experimental results show that the proposed method can guarantee the parameter convergence and improve the control performance.
Very high level of acoustic noise in fMRI scanner rooms disrupts speech communication between the subject and the physician/researcher. Enhancing speech in such an environment is challenging due to the broadband and d...
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Very high level of acoustic noise in fMRI scanner rooms disrupts speech communication between the subject and the physician/researcher. Enhancing speech in such an environment is challenging due to the broadband and dynamic nature of the noise. Sub-band adaptive methods prove to be very effective in cancelling such noise. In this paper we present the results of using sub-band adaptive methods for enhancing speech corrupted by noise from a 3-Tesla fMRI scanner. We also observe that the performance depends on the synthesis filter bank structure.
We take into account the non-Gaussian properties of textures and we propose a new approach for their characterization based on bidimensional adaptive modeling using higher order statistics. The 2D-OLRIV (bidimensional...
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We take into account the non-Gaussian properties of textures and we propose a new approach for their characterization based on bidimensional adaptive modeling using higher order statistics. The 2D-OLRIV (bidimensional overdetermined lattice recursive instrumental variable) algorithm allows accurate texture model estimation. Sets of 2D-AR coefficients obtained from the 2D reflection coefficients of the lattice model are used to characterize the texture model. This algorithm has the advantage of yielding non-biased estimates of the 2D-AR model even when the texture image is disturbed by Gaussian noise. A multilayer neural network deals with these coefficients in order to classify different textures. In order to evaluate the performance of this approach, the classification sensitivity is evaluated on a set of eight different textures. This characterization approach gives very promising results.
This paper presents a unified framework for adaptive filters based on a line search method. Expressions for this unified framework are derived. Based on this framework new algorithms are derived, namely, diagonal Q-co...
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This paper presents a unified framework for adaptive filters based on a line search method. Expressions for this unified framework are derived. Based on this framework new algorithms are derived, namely, diagonal Q-correlation matrix least square algorithm (DQLS), block diagonal Q-correlation matrix least square algorithm (BDQLS) and their reduced complexity variants. It is shown that both DQLS and BDQLS have less computational complexity compared to EDS and RLS, and better performance than LMS.
Summary form only given, as follows. There are several types of time-varying filters designed for processing of nonstationary data. The old method of dividing the nonstationary sequence into piecewise stationary secti...
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Summary form only given, as follows. There are several types of time-varying filters designed for processing of nonstationary data. The old method of dividing the nonstationary sequence into piecewise stationary sections has been modified to design an adaptive filter for multichannel time-varying seismic data. The design principle is illustrated through some synthetic seismic traces. It is found that the adaptive multichannel filter suppresses the coherent noise present in the seismogram.
This paper is dealing with the reconstruction of the control of preheating mold machine. Old system with relay devices for operation control is replaced by new electronic PLC one. There are designed and created a new ...
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This paper is dealing with the reconstruction of the control of preheating mold machine. Old system with relay devices for operation control is replaced by new electronic PLC one. There are designed and created a new electronic signal grid with fieldbus and new control system using programmable logic controllers(PLCs) as well as human-machine interface(HMI). They are used for control and setting parameters of preheating technology. New algorithms and programs for PLC and HMI have been created, and reparation of mechanical parts of the preheating machine has been performed. Device is after all make changes used for saving time in manufacture. Several kinds of regulators are described adaptive regulator including.
A low-complexity adaptive linear MMSE multiuser detection architecture for estimation of all users in a CDMA system is presented. The order of linear filter is reduced substantially by taking advantage of the knowledg...
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A low-complexity adaptive linear MMSE multiuser detection architecture for estimation of all users in a CDMA system is presented. The order of linear filter is reduced substantially by taking advantage of the knowledge of the signal space for demodulation of all users' signals on the up-link of a multi-access network. This results in fast convergence of the adaptive algorithms and also the feasibility of employing least-squares type of algorithms which are impractical otherwise for systems with large spreading gains. Further reduction in complexity without compromising on performance is shown to be possible by using a set-membership adaptive filtering algorithm that features optimal and sparse updating of the filter coefficients.
Summary form only given. Binary variable order adaptive algorithms like the UMC of Rissanen (1986) and JBIG can be used to losslessly compress non-binary data by splitting the data into planes, each of 1 bit resolutio...
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Summary form only given. Binary variable order adaptive algorithms like the UMC of Rissanen (1986) and JBIG can be used to losslessly compress non-binary data by splitting the data into planes, each of 1 bit resolution, and passing each plane to a separate instance of the algorithm. The UMC algorithm operated in this way is the most powerful lossless signal data compressor the authors are aware of. We attempt to develop an understanding of why this approach is so effective. We investigate the common technique of Gray coding the data before splitting it into single-bit planes and passing to the modeler and coder, and compare it to a simple weighted binary coding. We then propose a non-binary pseudo-Gray code as a method of generating planes of resolution greater than or equal to 1 bit, and compare it with the other conventional methods. The algorithm to generate the pseudo-Gray code is much the same as that for the construction of a binary Gray code, except that instead of minimizing the Hamming distance between neighboring bit planes, we instead minimize the Euclidean distance between adjacent groups of bit planes.
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