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SD LMS L-Filters for Filtration of Gray Level Images in Timespatial Domain Based on GLCM Features  [cached]
Robert Hudec,Miroslav Benco,Janko Krajcovic
Advances in Electrical and Electronic Engineering , 2008,
Abstract: In this paper, the new kind of adaptive signal-dependent LMS L-filter for suppression of a mixed noise in greyscale images is developed. It is based on the texture parameter measurement as modification of spatial impulse detector structure. Moreover, the one of GLCM (Gray Level Co-occurrence Matrix) features, namely, the contrast or inertia adjusted by threshold as switch between partial filters is utilised. Finally, at the positions of partial filters the adaptive LMS versions of L-filters are chosen.
Mixed Noise Suppression in Color Images by Signal-Dependent LMS L-Filters
R. Hudec
Radioengineering , 2003,
Abstract: The paper is devoted to the signal-dependent (SD) design of adaptiveLMS L-filters with marginal data ordering for color images. The samestem of SD processing of noised grayscale images was applied on noisycolor images. Component-wise and multichannel modifications of SD LMSL-filter in R'G'B' (gamma corrected RGB signals) color space weredeveloped. Both modifications for filtering two-dimensional staticcolor images degraded by mixed noise consisting of additive Gaussianwhite noise and impulsive noise were used. Moreover, single-channelspatial impulse detectors as detectors of impulses and details wereused, too. Considering experimental results, SD modifications ofL-filters for noisy color images can be concluded to yield the bestresults.
FPGA Implementation of Audio Enhancement Using Adaptive LMS Filters
V. Elamaran,A. Aswini,V. Niraimathi,D. Kokilavani
Journal of Artificial Intelligence , 2012,
Abstract: Digital audio has become very popular in the last two decades with the growth of multimedia systems and the World Wide Web. So, audio processing techniques such as filtering, equalization, noise suppression, compression, addition of sound effects and synthesis become necessary in the field of sound engineering. This study has presented some of the audio enhancement techniques using adaptive Least Mean Square (LMS) filters with the Field Programmable Gate Array (FPGA) Architectures which are developed using Xilinx System Generator (XSG). Verilog descriptions from XSG are synthesized to the target FPGA device-a Virtex4 xc4vsx55-12ff1148 and the resource utilization summary for the various alternate LMS architectures is obtained along with the Signal-to-Noise Ratio (SNR) calculations. Results show that Delayed LMS architectures provide a better SNR improvement at the cost of more resource utilizations.
Adaptive Reduced-Rank Processing Using a Projection Operator Based on Joint Iterative Optimization of Adaptive Filters For CDMA Interference Suppression  [PDF]
Rodrigo C. de Lamare,Raimundo Sampaio-Neto
Computer Science , 2013,
Abstract: This paper proposes a novel adaptive reduced-rank filtering scheme based on the joint iterative optimization of adaptive filters. The proposed scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that constitutes the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and simple least-mean squares (LMS) adaptive algorithms for its computationally efficient implementation. Simulation results for a CDMA interference suppression application reveals that the proposed scheme significantly outperforms the state-of-the-art reduced-rank schemes, while requiring a significantly lower computational complexity.
Extension of Impulse Detectors to Spatial Dimension and their Utilization as Switch in the LMS L-SD Filter
R. Hudec,S. Marchevsky
Radioengineering , 2001,
Abstract: In this paper, one kind of adaptive LMS filters based on orderstatistics is used for two-dimensional filtration of noisy greyscaleimages degraded by mixed noise. The signal-dependent adaptive LMSL-filter (L-SD) consists of two normalized constrained adaptive LMSL-filters, because they have better convergence properties than simpleLMS algorithm. Moreover, first filter suppresses the noise inhomogeneous regions and second filter preserves the high components offiltered image. Some versions of spatial order statistic detectors weredeveloped from the impulse detectors and were employed as switchbetween output these filters.
Periodic Noise Suppression from ECG Signal using Novel Adaptive Filtering Techniques
Yogesh Sharma,Anurag Shrivastava
International Journal of Electronics and Computer Science Engineering , 2012,
Abstract: Electrocardiogram signal most commonly known recognized and used biomedical signal for medical examination of heart. The ECG signal is very sensitive in nature, and even if small noise mixed with original signal, the various characteristics of the signal changes, Data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real time heart monitoring systems. Various filters used for removing the noise from ECG signals, most commonly used filters are Notch Filters, FIR filters, IIR filters, Wiener filter, Adaptive filters etc. Performance analysis shows that the best result is obtained by using Adaptive filter to remove various noises from ECG signal and get significant SNR andMSE results. In this paper a novel adaptive approach by using LMS algorithm and delay has shown whichcan be used for pre-processing of ECG signal and give appreciable result.
Combining Several PBS-LMS Filters as a General Form of Convex Combination of Two Filters  [PDF]
A. Fathiyan,M. Eshghi
Journal of Applied Sciences , 2009,
Abstract: Combination approaches can improve the performance of adaptive filters. Recently a convex combination of adaptive filters was proposed to improve the performance of LMS algorithm. Our proposal in this study is to use the PBS-LMS algorithm instead of LMS algorithm in the structure of convex combination. Our simulations showed that this structure not only has the optimality of first one, but also, it has the features of PBS-LMS algorithm such as regularity. By using PBS-LMS algorithm in this structure we saved in total number of samples needed by filter to converge about 22.2%, for example the fast filter converged to the steady state in 352 samples, the slow one in 397 samples and the overall filter in 309 samples. Also, this scheme was generalized, combining multiple PBS-LMS filters with different adaptation step sizes.
Adaptive Order-Statistic LMS Filters
R. Hudec,S. Marchevsky
Radioengineering , 2001,
Abstract: The LMS-based adaptive order-statistic filters are presented in thispaper. The adaptive Ll-filters as extension of the adaptive L-filterfor two-dimensional filtering of noisy greyscale images is studied too.Their adaptation properties are studied by three types of noise, theadditive white Gaussian noise, the impulsive noise or both,respectively. Moreover, the impulsive noise has the fixed noise value(Salt & Pepper noise). The problem of pixel value multiplicity anddetermination its position in the ordered input vector for adaptiveLl-filter is shown in this article. The two types of images withdifferent of image complexity are used to demonstration of the power oftime-spatial ordering.
Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters  [PDF]
Wilmar Hernandez,Jesús De Vicente,Oleg Sergiyenko,Eduardo Fernández
Sensors , 2010, DOI: 10.3390/s100100313
Abstract: In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
Nonlinear Adaptive Filters based on Particle Swarm Optimization  [cached]
Leonardo Journal of Sciences , 2009,
Abstract: This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
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