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Journal articles on the topic 'Adaptive Filters'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Pichardo, Eduardo, Ángel Vázquez, Esteban R. Anides, Juan C. Sánchez, Hector Perez, Juan G. Avalos, and Giovanny Sánchez. "A Dual Adaptive Filter Spike-Based Hardware Architecture for Implementation of a New Active Noise Control Structure." Electronics 10, no. 16 (August 12, 2021): 1945. http://dx.doi.org/10.3390/electronics10161945.

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Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.

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2

Brewster, R. L. "Adaptive Filters." Electronics and Power 32, no. 7 (1986): 545. http://dx.doi.org/10.1049/ep.1986.0315.

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3

Cowan, Colin F. N., and Peter M. Grant. "Adaptive filters." Signal Processing 9, no. 2 (September 1985): 138. http://dx.doi.org/10.1016/0165-1684(85)90050-7.

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4

Fettweis, A., and A. Zalnieriunas. "Adaptive filters." Signal Processing 13, no. 3 (October 1987): 335–36. http://dx.doi.org/10.1016/0165-1684(87)90136-8.

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5

Cowan, C. F. N. "Adaptive filters." IEE Proceedings F Communications, Radar and Signal Processing 134, no. 3 (1987): 209. http://dx.doi.org/10.1049/ip-f-1.1987.0043.

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6

Harteneck, M., J. M. Pàez-Borrallo, and R. W. Stewart. "An oversampled subband adaptive filter without cross adaptive filters." Signal Processing 64, no. 1 (January 1998): 93–101. http://dx.doi.org/10.1016/s0165-1684(97)00179-5.

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7

Jain, Deepanjali. "A Novel LMS Algorithm Applied to Adaptive Noise Cancellation with Varying Parameters." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 47–51. http://dx.doi.org/10.22214/ijraset.2022.40179.

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Abstract: Adaptive filters have become active area of research in the field of communication system. This paper explores the novel concept of adaptive noise cancellation (ANC) using least-mean-square (LMS) adaptive filters. The model of the LMS-ANC is designed and simulated in MATLAB environment. The proposed algorithm utilizes adaptive filters to evaluate gradients accurately which results in good adaptation, stability and performance. The objective of this investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters. The results are obtained with the help of adaptive algorithm with variable step size and filter order in order to deliver high convergence speed and stability of the error signal. Keywords: Adaptive Noise cancellation, LMS algorithm, MATLAB, Filter order, Step size.

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M. Africa, Aaron Don, John Arvin Mercado, and Joshua Kenichi Sim. "Development of an adaptive finite impulse response filter optimization algorithm using rough set theory." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (July 1, 2023): 238. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp238-247.

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Signal processing is crucial that as one sends information, there is a corresponding process to encode, decode, and clean the signal of unwanted noise and disruptions via use of filters. Due to the environment and how unpredictable it can be and how noise can come from almost anywhere, the typical filter to be used are adaptive filters. Adaptive filters are non-linear filters and have been used regularly regarding adaptive signal processing, this means that the filter changes accordingly and adapts to the environmental noise surrounding it. The world today has numerous applications for adaptive filters such as channel equalization and acoustic noise cancellation. This incentivizes the further development of this specific technology and the constant research that is ongoing. The main component when it comes to adaptive filtering is the algorithms used for the filter. This research compares the least mean square (LMS) and recursive least square (RLS) algorithms concerning their effectiveness in filtering out unwanted acoustic noises. The paper will cover the design and implementation of an optimized rough set based adaptive finite impulse response (FIR) filter for acoustic noise cancellation. The microstrip and bowtie antenna were used to relay the data. The software MATLAB was used for the simulation.

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Li, Song, Roman Schlieper, and Jürgen Peissig. "Semi-adaptive active noise cancellation headphones." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4717–23. http://dx.doi.org/10.3397/in-2021-2808.

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Active noise cancellation (ANC) headphones are becoming increasingly important as they can effectively attenuate perceived ambient noise. Fixed filters are commonly applied in commercially available ANC headphones due to their robustness. However, they are not capable of adapting to changes that occur in dynamic environments, resulting in degraded ANC performance. In contrast, adaptive filters are able to update the ANC filters to compensate for noise in dynamic environments, but large estimation errors can occur due to a sudden change in direction/type of noise or secondary path. Some studies have suggested an ANC system by combining fixed and adaptive filters. Based on this mechanism, we propose a semi-adaptive ANC system in which the fixed and adaptive filters are weighted in real-time. Initially, the weighting for the fixed filter dominates the whole system to ensure the robustness of the ANC system. Then, the residual error provided by the adaptive filter is simulated and compared to the real measured one to determine the relative weighting between the fixed and adaptive filters. In this study, this approach is applied to a feedback ANC system. Simulation results show that our proposed approach achieves high noise attenuation performance while maintaining robustness with time-varying secondary paths.

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10

Ristau, J. P., and Wooil M. Moon. "Adaptive filtering of random noise in 2-D geophysical data." GEOPHYSICS 66, no. 1 (January 2001): 342–49. http://dx.doi.org/10.1190/1.1444913.

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Random noise is often a problem in geophysical data visualization because it obscures fine details and complicates identification of image features. Adaptive filters have recently been used to suppress speckle (random) noise in synthetic aperture radar (SAR) images. SAR data are similar to seismic reflection data, both in their data acquisition approach and in their final data processed format. The nature of the random noise associated is also very similar, and adaptive filters can be applied to reduce random noise in both types of data sets. In this paper several popular adaptive filters—the Lee filter, the Frost filter, and the Kuan filter, which have been used frequently for speckle reduction in SAR data—are tested on Lithoprobe (AGT) deep seismic reflection data and on one set of oil industry shallow seismic reflection data. In addition, a standard band‐pass filter, which is common in many seismic data processing packages, is tested with the oil industry test data. Performance of the adaptive filters is also tested on Radarsat SAR data. The random (speckle) noise in both the Lithoprobe and the Radarsat (SAR) data sets is statistically very similar, and the adaptive filters tested successfully suppressed random noise while minimizing blurring. Among the tested filters the enhanced Lee filter performed best, closely followed by the enhanced Frost filter and the Kuan filter. The background noise in the oil industry seismic data is statistically quite different from the above two data sets; the results obtained were less than satifactory, although they were still encouraging. With the oil industry data, the enhanced Frost and Kuan filters performed better than the enhanced Lee filter. The commonly used band‐pass filter successfully removed the background (random) noise, but it also suppressed the reflection events, making the final result less desirable.

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11

RYU, GEUN-TAEK, DAE-SUNG KIM, DAE-YOUNG LEE, SUNG-HWAN HAN, and HYEON-DEOK BAE. "CONVERGENCE IMPROVEMENT OF ADAPTIVE LATTICE ALGORITHM WITH FUZZY BASED ADAPTIVE GAIN." Journal of Circuits, Systems and Computers 09, no. 01n02 (February 1999): 125–32. http://dx.doi.org/10.1142/s0218126699000116.

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The choice of the adaptive gain is important to the performance of LMS-based adaptive filters. Depending on application areas, the realization structure of the filters is also important. This letter presents an adaptive lattice algorithm which adjusts the adaptive gain of LMS using fuzzy if-then rules determined by matching input and output variables during adaptation procedure. In each lattice filter stage, this filter adjusts the adaptive gain as the output of the fuzzy logic which has two input variables, normalized squared forward prediction error and one step previous adaptive gain. The proposed algorithm is applied to echo canceling problem of long distance communication channel. The simulation results are compared with NLMS on TDL and lattice structures.

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12

Siswanto, Antonius, Cheng-Yuan Chang, and Sen M. Kuo. "Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises." Sensors 20, no. 22 (November 23, 2020): 6693. http://dx.doi.org/10.3390/s20226693.

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Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.

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13

Raheem, Syed, and Dr Subhashish Bose. "Subband Adaptive Filter in Signal Processing Application." Revista Gestão Inovação e Tecnologias 11, no. 4 (August 4, 2021): 4096–109. http://dx.doi.org/10.47059/revistageintec.v11i4.2434.

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Owing to the powerful digital signal processors and the improvement of advanced edge adaptive algorithms there are an extraordinary number of various applications in which adaptive filters are utilized. Subband adaptive filtering algorithms can build the assembly pace of framework ID undertakings when the info signal is hued. The adaptive filter can filter the dubious noise signal, track the difference in the signal, and consistently change the boundaries to accomplish the ideal filtering impact. Another standardized subband adaptive filtering algorithm has been proposed, whose primary benefit is the lower computational intricacy when contrasted with best in class subband approaches, while keeping up comparable union execution. A connection between the adaptive subband coefficients and the ideal full band move work is determined, and the algorithm is demonstrated to create an asymptotically unprejudiced arrangement. The proficiency of the adaptive filters basically relies upon the plan procedure utilized and the algorithm of variation. The adaptive filters can be analogical plans, digital or blended which show their benefits and inconveniences, for instance, the analogical filters are low power consuming and fast response, however they address balance issues, which influence the activity of the variation algorithm.

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14

Mathews, V. J. "Adaptive polynomial filters." IEEE Signal Processing Magazine 8, no. 3 (July 1991): 10–26. http://dx.doi.org/10.1109/79.127998.

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15

Mitzenmacher, Michael, Salvatore Pontarelli, and Pedro Reviriego. "Adaptive Cuckoo Filters." ACM Journal of Experimental Algorithmics 25 (November 8, 2020): 1–20. http://dx.doi.org/10.1145/3339504.

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16

Petkovic, Dragan. "Adaptive digital filters." Vojnotehnicki glasnik 50, no. 1 (2002): 61–72. http://dx.doi.org/10.5937/vojtehg0201061p.

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17

Driscoll, T., J. Quinn, S. Klein, H. T. Kim, B. J. Kim, Yu V. Pershin, M. Di Ventra, and D. N. Basov. "Memristive adaptive filters." Applied Physics Letters 97, no. 9 (August 30, 2010): 093502. http://dx.doi.org/10.1063/1.3485060.

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18

den Brinker, A. C. "Adaptive Orthonormal Filters." IFAC Proceedings Volumes 26, no. 2 (July 1993): 23–28. http://dx.doi.org/10.1016/s1474-6670(17)48215-2.

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19

Kari, Dariush, Ali H. Mirza, Farhan Khan, Huseyin Ozkan, and Suleyman S. Kozat. "Boosted adaptive filters." Digital Signal Processing 81 (October 2018): 61–78. http://dx.doi.org/10.1016/j.dsp.2018.07.012.

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20

Karraz, George. "Effect of adaptive line enhancement filters on noise cancellation in ECG signals." Serbian Journal of Electrical Engineering 18, no. 3 (2021): 291–302. http://dx.doi.org/10.2298/sjee2103291k.

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Power line interference is the main noise source that contaminates Electrocardiogram (ECG) signals and measurements. In recent years, adaptive filters with different approaches have been investigated to eliminate power line interference in ECG waveforms. Adaptive line enhancement filter is a special type of adaptive filter that, unlike other adaptive filters, does not require a reference signal and has potential application in ECG signal filtering. In this paper, a selflearning filter based on an adaptive line enhancement (ALE) filter is proposed to remove power line interference in ECG signals. We simulate the adaptive filter in MATLwith a noisy ECG signal and analyze the performance of algorithms in terms of signal-to-noise ratio (SNR) improvement. The proposed algorithm is validated with Physikalisch-Technische Bundesanstalt (PTB) ECG signals database. Additive white gaussian noise is added to the raw ECG signal. Influential parameters on the ALE filter performance such as filter delay, the convergence factor, and the filter length are analyzed and discussed.

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21

Nalli, Praveen Kumar, Kalyan Sagar Kadali, Ramu Bhukya, Y. T. R. Palleswari, Asapu Siva, and S. Pragaspathy. "Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2089/1/012020.

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Abstract The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.

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Vered, Yoav, and Stephen J. Elliott. "Hybrid analog-sampled adaptive active noise control." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A124. http://dx.doi.org/10.1121/10.0018380.

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Feedforward adaptive controllers are widely used for active noise control. For good broadband performance, the secondary path delay must be shorter than that of the primary path. In this work, a hybrid analog-sampled adaptive feedforward controller is developed to eliminate the additional delay associated with the sampling. An adaptive finite-impulse-response system is used for the sampled controller, and a state filtered adaptive linear combiner is used for the analog controller. Two approximations are used for the linear combiner. The first minimizes the added electrical noise by having individual state filters, all driven by the reference signal, while the second gives a superior bandwidth by driving each component filter with the output of the previous one. A sampled version of the normalized projection algorithm is developed for the analog controller. The different error paths used to filter the reference signal for the controllers’ adaptation are emphasized. Finally, the performance of the proposed controller is assessed. The results highlight the capabilities of the suggested controller and the different state filters, especially their ability to achieve significant attenuation levels close to the causality limit and when long delays of the reference signal are required.

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23

Verhoeven, J. T. M., and J. M. Thijssen. "Improvement of Lesion Detectability by Speckle Reduction Filtering: A Quantitative Study." Ultrasonic Imaging 15, no. 3 (July 1993): 181–204. http://dx.doi.org/10.1177/016173469301500301.

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An objective measure (Lesion Signal-to-Noise Ratio) quantifying the detectability of lesions in echographic images was employed. This measure was used to determine the performance of digital speckle reduction filters, which were applied to computer simulated ultrasound B-mode images. One linear (mean filter) and two nonlinear filters (median and L2-mean filters) have been investigated. A comparison was made between fixed and adaptive versions of these filters. The influence of the size of the filter window on the Lesion Signal-to-Noise Ratio was systematically investigated. Also, the effect of the shape of the filter window is illustrated. The difference in performance of the linear and nonlinear filters was found to be small. Adaptive filters did not perform significantly better than fixed filters. The maximum improvement of lesion detectability was in the order of 40 percent. The choice of a correct window size was critical. For all types of filters, an optimum window size appeared to be present in the curves relating the Lesion Signal-to-Noise Ratio to this size.

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Saxena, Ms Chhavi, Dr P. D. Murarka, and Dr Hemant Gupta. "ECG Signals Processing using Adaptive Linear Filters." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 496–502. http://dx.doi.org/10.31142/ijtsrd2342.

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Mo, Jing, Wei He, Dan Su, and Jing Wei Wu. "Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System." Advanced Materials Research 886 (January 2014): 390–93. http://dx.doi.org/10.4028/www.scientific.net/amr.886.390.

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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.

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Naghizadeh, Mostafa, and Mauricio D. Sacchi. "f-x adaptive seismic-trace interpolation." GEOPHYSICS 74, no. 1 (January 2009): V9—V16. http://dx.doi.org/10.1190/1.3008547.

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We use exponentially weighted recursive least squares to estimate adaptive prediction filters for frequency-space [Formula: see text] seismic interpolation. Adaptive prediction filters can model signals where the dominant wavenumbers vary in space. This concept leads to an [Formula: see text] interpolation method that does not require windowing strategies for optimal results. In other words, adaptive prediction filters can be used to interpolate waveforms that have spatially variant dips. The interpolation method’s performance depends on two parameters: filter length and forgetting factor. We pay particular attention to selection of the forgetting factor because it controls the algorithm’s adaptability to changes in local dip. Finally, we use synthetic- and real-data examples to illustrate the performance of the proposed adaptive [Formula: see text] interpolation method.

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Abhigna, Kandala. "Integrating OpenCV and Pandas for Enhanced Image Filtering and Color Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (June 30, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem36213.

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Image filtering and color detection in OpenCV is performed using functions like cv2.filter2D() for filtering, and cv2.cvtColor() for color space conversion and detection. These functions can be combined with other image processing techniques such as thresholding, erosion and dilation, and edge detection to perform more advanced tasks. The process typically starts with loading an image into Python using OpenCV's imread() function. From there, the image may be converted to a different color space using cvtColor() to make it easier to detect certain colors or features. Various image filters can then be applied to enhance the image and reduce noise or other unwanted features. This can include techniques such as blurring, sharpening, or edge detection, depending on the desired outcome. Once the image has been filtered, color detection can be performed using thresholding, which involves setting a threshold value and creating a binary image based on whether each pixel falls above or below the threshold. This can help to identify specific colors or features in the picture. Pandas are particularly useful for working with image data as it provides a flexible and efficient data structure called a DataFrame. This makes it easy to perform data manipulation tasks such as selecting, filtering, and grouping data, which can be useful for image processing tasks. Meanwhile, OpenCV provides a comprehensive set of tools for image filtering and color detection. These tools include a variety of filters, such as Gaussian, Median, and Bilateral filters, as well as thresholding techniques, such as binary and adaptive thresholding, which can be used to detect specific colors or features in an image. Keywords:- Image Processing, OpenCV, Color Detection, Image Filtering, cv2.filter2D, cv2.cvtColor, Thresholding, Erosion and Dilation, Edge Detection, Pandas DataFrame, Gaussian Filter, Median Filter, Bilateral Filter, Adaptive Thresholding, Feature Detection..

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Lazaro-Gredilla, Miguel, Luis A. Azpicueta-Ruiz, Aníbal R. Figueiras-Vidal, and Jerónimo Arenas-Garcia. "Adaptively Biasing the Weights of Adaptive Filters." IEEE Transactions on Signal Processing 58, no. 7 (July 2010): 3890–95. http://dx.doi.org/10.1109/tsp.2010.2047501.

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Stordal, Andreas S., and Hans A. Karlsen. "Large Sample Properties of the Adaptive Gaussian Mixture Filter." Monthly Weather Review 145, no. 7 (July 2017): 2533–53. http://dx.doi.org/10.1175/mwr-d-15-0372.1.

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In high-dimensional dynamic systems, standard Monte Carlo techniques that asymptotically reproduce the posterior distribution are computationally too expensive. Alternative sampling strategies are usually applied and among these the ensemble Kalman filter (EnKF) is perhaps the most popular. However, the EnKF suffers from severe bias if the model under consideration is far from linear. Another class of sequential Monte Carlo methods is kernel-based Gaussian mixture filters, which reduce the bias but maintain the robustness of the EnKF. Although many hybrid methods have been introduced in recent years, not many have been analyzed theoretically. Here it is shown that the recently proposed adaptive Gaussian mixture filter can be formulated in a rigorous Bayesian framework and that the algorithm can be generalized to a broader class of interpolated kernel filters. Two parameters—the bandwidth of the kernel and a weight interpolation factor—determine the filter performance. The new formulation of the filter includes particle filters, EnKF, and kernel-based Gaussian mixture filters as special cases. Techniques from particle filter literature are used to calculate the asymptotic bias of the filter as a function of the parameters and to derive a central limit theorem. The asymptotic theory is then used to determine the parameters as a function of the sample size in a robust way such that the error norm vanishes asymptotically, whereas the normalized error is sample independent and bounded. The parameter choice is tested on the Lorenz 63 model, where it is shown that the error is smaller or equal to the EnKF and the optimal particle filter for a varying sample size.

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Wu, Jie, Zuren Feng, and Zhigang Ren. "Improved structure-adaptive anisotropic filter based on a nonlinear structure tensor." Cybernetics and Information Technologies 14, no. 1 (March 1, 2014): 112–27. http://dx.doi.org/10.2478/cait-2014-0009.

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Abstract A variety of structure-adaptive filters are proposed to overcome the blurred effects of image structures caused by the classical Gaussian weighted mean filter. However, two major issues are needed to be dealt with carefully for structure-adaptive anisotropic filters. One is to properly construct the filter kernel and the other is to accurately estimate the orientation of the image structures. In this paper we propose to improve the structure-adaptive anisotropic filtering approach based on the nonlinear structure tensor (NLST) analysis technique. According to the anisotropism measurements of image structures, a new kernel construction method is designed to make the filter shape fine adapted to image features. Through the accurately estimated orientation of the image structures, the filter kernels are then properly aligned to perform the filtering process. Experimental results show that the proposed filter denoises the noisy images carefully and image features, such as corners and junctions are well preserved. Compared with some other known filters, the proposed filter obtains great improvements both in Mean Square Error (MSE) and visual quality.

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Petrovic, Predrag. "Possible solution of parallel FIR filter structure." Serbian Journal of Electrical Engineering 2, no. 1 (2005): 21–28. http://dx.doi.org/10.2298/sjee0501021p.

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In this paper, a parallel form FIR adaptive filter structure with RLS (Recursive Least Squares) type adaptive algorithm is proposed. The proposed parallel form FIR structure consists of a recursive orthogonal transform stage and sparse FIR sub filters operating in parallel. The adaptive algorithm used to update coefficient vector of the sparse filters is implemented by using modified Hopfield networks. This structure implements the RLS-type adaptive algorithm, without an explicit matrix inversion avoiding numerical instability problems. Simulation results which show the desirable features of proposed structure are given.

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Mvuma, Aloys. "Adaptive IIR Filters for Single Interference Suppression in a BPSK DS CDMA System In Rayleigh Fading Channel." Tanzania Journal of Engineering and Technology 30, no. 1 (June 30, 2007): 65–73. http://dx.doi.org/10.52339/tjet.v30i1.398.

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In this paper, effect of a single narrow-band interference (NBI) on bit error rate (BER) performance for a binary phase shift keying (BPSK) synchronous direct-sequence code-division multiple access (DS CDMA) communication system operating in a frequency nonselective Rayleigh fading channel is analyzed. Second-order adaptive infinite impulseresponse (IIR) notch filters with plain gradient algorithm (GA) for suppression of NBI in the DS CDMA system are proposed. A general closed-form BER expression for the DS CDMA system with NBI suppression second order adaptive IIR notch filters is derived based on the standard Gaussian approximation (SGA) method. BER expressions are then derived for the allpass filter-based adaptive IIR notch filter and adaptive IIR notch filter with constrained poles and zeros, the two structures that are commonly found in literature. It is observed that both adaptive IIR notch filter structures exhibit comparable BER performance. Extensive computer simulation results are presented to verify the accuracy and limitations of the analysis.

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Sivakumar, R., M. K. Gayathri, and D. Nedumaran. "Speckle Filtering of Ultrasound B-Scan Images- A Comparative Study of Single Scale Spatial Adaptive Filters, Multiscale Filter and Diffusion Filters." International Journal of Engineering and Technology 2, no. 6 (2010): 514–23. http://dx.doi.org/10.7763/ijet.2010.v2.174.

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Wu, Yunfeng, and Rangaraj M. Rangayyan. "An Unbiased Linear Adaptive Filter with Normalized Coefficients for the Removal of Noise in Electrocardiographic Signals." International Journal of Cognitive Informatics and Natural Intelligence 3, no. 4 (October 2009): 73–90. http://dx.doi.org/10.4018/jcini.2009062305.

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The authors propose an unbiased linear adaptive filter (ULAF) to eliminate high-frequency random noise in electrocardiographic (ECG) signals. The ULAF does not contain a bias in its summation unit, and the filter coefficients are normalized. During the adaptation process, the normalized coefficients are updated with the steepest-descent algorithm in order to achieve efficient filtering of noisy ECG signals. The authors tested the ULAF with ECG signals recorded from 16 subjects, and compared the performance of the ULAF with that of the least-mean-square (LMS) and recursive-least-squares (RLS) adaptive filters. The filtering performance was quantified in terms of the root-mean-squared error (RMSE), normalized correlation coefficient (NCC), and filtered noise entropy (FNE). A template derived from each ECG signal was used as the reference to compute the measures of filtering performance. The results indicated that the ULAF was able to provided noise-free ECG signals with an average RMSE of 0.0287, which was lower than the second best RMSE (0.0365) obtained with the LMS filter. With respect to waveform fidelity, the proposed ULAF provided the highest average NCC (0.9964) among the three filters studied. In addition, the ULAF effectively removed more noise measured by FNE versus the LMS and RLS filters in most of the ECG signals tested.

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Bonny, Sarungbam, and Yambem Jina Chanu. "Speckle Reduction in SAR Images by Using Homogeneity NeighShrink." European Journal of Engineering and Technology Research 1, no. 4 (July 27, 2018): 9–14. http://dx.doi.org/10.24018/ejeng.2016.1.4.176.

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Synthetic Aperture Radar (SAR) image suffers from severe artifacts caused by speckle noise which is multiplicative in nature. Some of the adaptive filters such as the Lee filter, the Frost filter and the Kuan filter are the well known speckle filters. These filters adapt the filter coefficients based on the pixels within a fixed moving window. Though it removes speckle noise well in the homogeneous regions, it leaves noise in the heterogeneous areas to preserve the edges and fine details or smoothes the edges to remove the noise in this area. In order to reduce the speckle noise with the preservation of edges, a new method based on wavelet analysis and adaptive mean filtering is proposed in this paper. A comparative study with other methods show that proposed method is better in preserving edges and fine details while reducing the speckle noise.

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Liu, Zongxiang, Chunmei Zhou, and Junwen Luo. "Adaptive Marginal Multi-Target Bayes Filter without Need for Clutter Density for Object Detection and Tracking." Applied Sciences 13, no. 19 (October 7, 2023): 11053. http://dx.doi.org/10.3390/app131911053.

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The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA(2) errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA(2) errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters.

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37

Befigiannis, G. N., E. N. Demiris, and S. D. Likothanassis. "Evolutionary Nonlinear Multimodel Partitioning Filters." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 1 (January 20, 2001): 8–14. http://dx.doi.org/10.20965/jaciii.2001.p0008.

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The problem of designing adaptive filters for nonlinear systems is faced in this work. The proposed evolution program combines the effectiveness of multimodel adaptive filters and the robustness of genetic algorithms (GAs). Specifically, a bank of different extended Kalman filters is implemented. Then, the a posteriori probability that a specific model of the bank of conditional models is the true one can be used as a GA fitness function. The superiority of the algorithm is that it evolves concurrently the models’ population with initial conditions. Thus, this procedure alleviates extended Kalman filter sensitivity in initial conditions, by estimating the best values. In addition to this, adaptive implementation is proposed that relieves the disadvantage of time-consuming GA implementation. Finally, a variety of defined crossover and mutation operators is investigated in order to accelerate the algorithm’s convergence.

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38

Forssen, U. "Adaptive bilinear digital filters." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 40, no. 11 (1993): 729–35. http://dx.doi.org/10.1109/82.251842.

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Baik, H. K., and V. J. Mathews. "Adaptive lattice bilinear filters." IEEE Transactions on Signal Processing 41, no. 6 (June 1993): 2033–46. http://dx.doi.org/10.1109/78.218134.

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40

den Brinker, A. C. "Laguerre-domain adaptive filters." IEEE Transactions on Signal Processing 42, no. 4 (April 1994): 953–56. http://dx.doi.org/10.1109/78.285660.

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41

Strobach, P. "Low-rank adaptive filters." IEEE Transactions on Signal Processing 44, no. 12 (1996): 2932–47. http://dx.doi.org/10.1109/78.553469.

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Fejzo, Z., and H. Lev-Ari. "Adaptive Laguerre-lattice filters." IEEE Transactions on Signal Processing 45, no. 12 (1997): 3006–16. http://dx.doi.org/10.1109/78.650260.

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Orfanidis, S., and L. Vail. "Zero-tracking adaptive filters." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 6 (December 1986): 1566–72. http://dx.doi.org/10.1109/tassp.1986.1164995.

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El-Sharkawy, M. A., and B. Peikari. "Multistage adaptive stochastic filters." IEEE Transactions on Circuits and Systems 35, no. 8 (1988): 929–35. http://dx.doi.org/10.1109/31.1839.

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Szu, H. H., and R. A. Messner. "Adaptive invariant novelty filters." Proceedings of the IEEE 74, no. 3 (1986): 518–19. http://dx.doi.org/10.1109/proc.1986.13494.

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Lopes, Wilder Bezerra, and Cassio Guimaraes Lopes. "Geometric-Algebra Adaptive Filters." IEEE Transactions on Signal Processing 67, no. 14 (July 15, 2019): 3649–62. http://dx.doi.org/10.1109/tsp.2019.2916028.

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Den Brinker, A. C. "Adaptive modified Laguerre filters." Signal Processing 31, no. 1 (March 1993): 69–79. http://dx.doi.org/10.1016/0165-1684(93)90102-g.

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Ansari, N., and Z. Z. Zhang. "Generalised adaptive neural filters." Electronics Letters 29, no. 4 (1993): 342. http://dx.doi.org/10.1049/el:19930231.

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Kasparis, T. "Adaptive mixed-rank filters." Electronics Letters 29, no. 22 (1993): 1933. http://dx.doi.org/10.1049/el:19931287.

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Won, Chee Sun. "Grid adaptive interpolation filters." Electronics Letters 49, no. 3 (January 2013): 181–82. http://dx.doi.org/10.1049/el.2012.2481.

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