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Journal articles on the topic 'Adaptive Cancelers'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Samiei, Aria, and Hossein Hashemi. "A Bidirectional Neural Interface SoC With Adaptive IIR Stimulation Artifact Cancelers." IEEE Journal of Solid-State Circuits 56, no. 7 (July 2021): 2142–57. http://dx.doi.org/10.1109/jssc.2021.3056040.

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2

Rahman, Muhammad Zia Ur, Rafi Ahamed Shaik, and D. V. Rama Koti Reddy. "Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring." IEEE Sensors Journal 12, no. 3 (March 2012): 566–73. http://dx.doi.org/10.1109/jsen.2011.2111453.

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3

Miranda, Ricardo Kehrle, João Paulo C. L. da Costa, and Felix Antreich. "High accuracy and low complexity adaptive Generalized Sidelobe Cancelers for colored noise scenarios." Digital Signal Processing 34 (November 2014): 48–55. http://dx.doi.org/10.1016/j.dsp.2014.07.015.

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4

Rahman, Muhammad Zia Ur, G. V. S. Karthik, S. Y. Fathima, and A. Lay-Ekuakille. "An efficient cardiac signal enhancement using time–frequency realization of leaky adaptive noise cancelers for remote health monitoring systems." Measurement 46, no. 10 (December 2013): 3815–35. http://dx.doi.org/10.1016/j.measurement.2013.07.009.

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5

Delmas, J. P. "Adaptive harmonic jammer canceler." IEEE Transactions on Signal Processing 43, no. 10 (1995): 2323–31. http://dx.doi.org/10.1109/78.469857.

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6

Kim. "A Robust Frequency-Domain Multi-Reference Narrowband Adaptive Noise Canceller." Journal of the Acoustical Society of Korea 34, no. 2 (2015): 163. http://dx.doi.org/10.7776/ask.2015.34.2.163.

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7

Moulds, Clinton W. "Adaptive vibration canceller." Journal of the Acoustical Society of America 90, no. 1 (July 1991): 622. http://dx.doi.org/10.1121/1.401222.

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8

Moulds, Clinton W. "Multivariable adaptive vibration canceller." Journal of the Acoustical Society of America 92, no. 1 (July 1992): 627. http://dx.doi.org/10.1121/1.404091.

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9

Gerlach, K. "Adaptive canceler and pulse compressor interactions." IEEE Transactions on Aerospace and Electronic Systems 27, no. 2 (March 1991): 331–42. http://dx.doi.org/10.1109/7.78307.

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10

I., SALEM, HANAFY A., HUSSEIN G., and MOUFID D. "ADAPTIVE SPACE—TIME SIDELOBE CANCELLER." International Conference on Electrical Engineering 2, no. 2 (November 1, 1999): 146–57. http://dx.doi.org/10.21608/iceeng.1999.62302.

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11

Gi-Hong Im and N. R. Shanbhag. "A pipelined adaptive NEXT canceller." IEEE Transactions on Signal Processing 46, no. 8 (1998): 2252–58. http://dx.doi.org/10.1109/78.705453.

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12

Xl, JIANGTAO, and J. F. CHlCHARO. "The cancellation mechanism in adaptive sidelobe cancellers." International Journal of Electronics 79, no. 1 (July 1995): 17–23. http://dx.doi.org/10.1080/00207219508926246.

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13

Jinhui Chao, S. Kawabe, and S. Tsujii. "A new IIR adaptive echo canceler: GIVE." IEEE Journal on Selected Areas in Communications 12, no. 9 (1994): 1530–39. http://dx.doi.org/10.1109/49.339921.

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14

GOMEZ, GABRIEL, and RAYMOND SIFERD. "AN ADAPTIVE NOISE CANCELER IMPLEMENTED WITH CMOS ANALOG TECHNOLOGY." Journal of Circuits, Systems and Computers 06, no. 02 (April 1996): 139–54. http://dx.doi.org/10.1142/s0218126696000121.

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A fully analog implementation of an adaptive noise canceler is presented, including design, simulation, and test results of the fabricated chip. The prototype chip was fabricated using 2-µ CMOS P-Well technology on a 4.0 mm2 die and uses ±5 V power supplies. The static power dissipation is 276 milliwatts. Analog signal processing techniques are used to realize an adaptive system based upon a finite impulse response (FIR) filter and least mean squares (LMS) adaptive algorithm. The circuit is tested as an adaptive noise canceler, where a signal corrupted by noise is the input. The circuit adaptively converges to cancel the noise to produce an output that is the best LMS estimate of the signal. The circuit could be used for other real-time adaptive filter applications or for realizing an on-chip learning algorithm. The implementation illustrates the advantages of an analog system with no requirements for A/D and D/A converters, reduced size of circuit subsystems (e.g. multipliers), and the relatively fast convergence.

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15

Hazim saber, Aws, and Rafid Ahmed Khalil. "FPGA Implementation of Adaptive Noise Canceller." AL-Rafdain Engineering Journal (AREJ) 17, no. 4 (August 28, 2009): 63–72. http://dx.doi.org/10.33899/rengj.2009.43287.

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16

Evci, C. C. "Adaptive echo canceller employing DM encoding." Electronics Letters 22, no. 12 (1986): 664. http://dx.doi.org/10.1049/el:19860454.

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17

Gerlach, K. "Cascaded adaptive canceller using loaded SMI." IEEE Transactions on Aerospace and Electronic Systems 37, no. 2 (April 2001): 710–19. http://dx.doi.org/10.1109/7.937483.

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18

Vanden Berghe, Jeff, and Jan Wouters. "Hearing aid with adaptive noise canceller." Journal of the Acoustical Society of America 118, no. 4 (2005): 2109. http://dx.doi.org/10.1121/1.2125225.

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19

Bertran, Eduard. "A Fully Analog Adaptive-Disturbance Canceller." IEEE Transactions on Instrumentation and Measurement 56, no. 5 (October 2007): 1605–9. http://dx.doi.org/10.1109/tim.2007.904563.

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20

Parsa, V., and P. Parker. "Constrained crosstalk resistant adaptive noise canceller." Electronics Letters 30, no. 16 (August 4, 1994): 1276–77. http://dx.doi.org/10.1049/el:19940871.

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21

Justh, E. W., and F. J. Kub. "Feedback phase-shift compensation for adaptive interference cancellers." Electronics Letters 36, no. 17 (2000): 1503. http://dx.doi.org/10.1049/el:20000994.

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22

Wallace, R. B., and R. A. Goubran. "Improved tracking adaptive noise canceler for nonstationary environments." IEEE Transactions on Signal Processing 40, no. 3 (March 1992): 700–703. http://dx.doi.org/10.1109/78.120817.

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23

Zhang, Zhi Ping, and Xi Hong Wu. "Adaptive Echo Cancellation Combined with Delay Estimation." Applied Mechanics and Materials 303-306 (February 2013): 2072–75. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2072.

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This paper proposed an echo canceller to reduce the computational complexity of the long delay echo cancellation. Two adaptive filters were used for constructing this echo canceller. One was designed as a delay estimator, which used down-sampled sub-band signals to estimate the echo delay time. The other was designed as a short-tap filter to subtract the echo from the recorded signal with delay compensation. Experimental results showed that the output signal-to-noise ratio from the proposed canceller with low complexity is similar to that from the conventional canceller based on a long-tap filter.

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24

Liu, Wenhong. "A Robust and Adaptive Line Echo Canceller." Journal of Electrical and Electronic Engineering 4, no. 3 (2016): 78. http://dx.doi.org/10.11648/j.jeee.20160403.17.

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25

Lee Won, Cheol, Hwa Jeong Kyu, and Hee Youn Dae. "Robust stereophonic subband adaptive acoustic echo canceller." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 455. http://dx.doi.org/10.1016/s0920-5489(99)90967-4.

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26

Mizuno, Kimiyasu, and Hajime Kubota. "Double-talk detection for adaptive noise canceller." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 77, no. 4 (1994): 69–80. http://dx.doi.org/10.1002/ecjc.4430770407.

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27

Godbole, Swati S., and Sanjay B. Pokle. "Implementation of Adaptive Noise Canceller System for Audio-Related Applications." International Journal of Measurement Technologies and Instrumentation Engineering 3, no. 4 (October 2013): 51–67. http://dx.doi.org/10.4018/ijmtie.2013100105.

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This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

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28

De Coster, Luc, Rudy Lauwereins, and J. A. Peperstraete. "Rapid prototyping of an adaptive noise canceler using GRAPE." Signal Processing 64, no. 1 (January 1998): 61–70. http://dx.doi.org/10.1016/s0165-1684(97)00176-x.

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29

Picciolo, Michael, and Karl Gerlach. "Reiterative median cascaded canceler for robust adaptive array processing." IEEE Transactions on Aerospace and Electronic Systems 43, no. 2 (April 2007): 428–42. http://dx.doi.org/10.1109/taes.2007.4285344.

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30

KIJIMOTO, Shinya, and Hiroshi SHIMOJIMA. "Effect of Adaptive Howling Canceler in Active Noise Control." Transactions of the Japan Society of Mechanical Engineers Series C 62, no. 598 (1996): 2272–77. http://dx.doi.org/10.1299/kikaic.62.2272.

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31

Porayath, Rajiv, John F. Doherty, and Steve F. Russell. "An Adaptive Acoustic Echo Canceler for Hands-Free Teleconferencing." Digital Signal Processing 6, no. 1 (January 1996): 29–36. http://dx.doi.org/10.1006/dspr.1996.0004.

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32

Furukawa, Hiroshi, Yukiyoshi Kamio, and Hideichi Sasaoka. "Cochannel interference canceler using a CMA adaptive array antenna." Electronics and Communications in Japan (Part I: Communications) 83, no. 10 (October 2000): 92–102. http://dx.doi.org/10.1002/(sici)1520-6424(200010)83:10<92::aid-ecja11>3.0.co;2-j.

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33

Dimolitsas, Spiros, and James E. Gunn. "A length adaptive, transversal data echo cancelor." Signal Processing 12, no. 3 (April 1987): 321–24. http://dx.doi.org/10.1016/0165-1684(87)90101-0.

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34

Pauline, S. Hannah, Samiappan Dhanalakshmi, R. Kumar, R. Narayanamoorthi, and Khin Wee Lai. "A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal." Journal of Healthcare Engineering 2022 (April 30, 2022): 1–24. http://dx.doi.org/10.1155/2022/3039624.

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Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

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35

Miao, Yingjie, Feifeng Liu, Hongjie Liu, and Hao Li. "Clutter Jamming Suppression for Airborne Distributed Coherent Aperture Radar Based on Prior Clutter Subspace Projection." Remote Sensing 14, no. 23 (November 22, 2022): 5912. http://dx.doi.org/10.3390/rs14235912.

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Airborne distributed coherent aperture radar is of great significance for expanding the detection capability of the system. However, the extra observation dimension introduced by its sparse configuration also deteriorates the performance of traditional adaptive processing in a non-uniform environment. This paper focuses on moving target detection when the system works in a clutter–jamming-coexisting environment. In order to make full use of the specific low-rank structure to reduce the requirement for training data, this paper proposes a two-stage adaptive scheme that cancels jamming and clutter separately. The proposed suppression scheme first excludes the mainlobe jamming component from the training data based on the prior clutter subspace projection and performs intra-node clutter suppression. Then, the remaining jamming is jointly canceled based on the covariance obtained with its inter-pulse mixture model. Numerical examples show that this scheme can effectively reduce the blocking effect of main lobe jamming on high-speed targets but, due to the inaccuracy of the prior subspace, there is a certain additional loss of signal-to-noise ratio for near stationary targets. The simulation also shows that the proposed scheme is equally applicable to systems with a time-varying distributed geometry.

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36

Qian, Bingfeng, and Yize Sun. "A Robust Eigenanalysis Interference Canceler Using Sensitivity-Based Number of Sources Estimation." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1958007. http://dx.doi.org/10.1142/s0218001419580072.

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When the estimation of the number of sources is inaccurate and the expected signal is mixed in the training data, the conventional eigenanalysis interference canceler (EC) will result in the expected signal cancelation, which leads the performance of adaptive beamforming to be degraded significantly. In this paper, a robust eigenanalysis interference canceler (REC) algorithm is proposed to resolve this problem by using the minimum sensitivity-based number of sources estimation. In the proposed method, the number of sources is first pre-estimated by effectively using the minimum sensitivity function. Then, based on the eigenvalue decomposition of the array covariance matrix, the expected signal in the training data is determined and eliminated, so that the corresponding impact of the adaptive weight calculation can be avoided. Finally, the adaptive weight vector is obtained by orthogonally projecting the quiescent weight vector into the interference subspace. Simulation results show that the performance of the output signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm has improved significantly when compared with the conventional adaptive beamforming methods.

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37

Gi Hong Im, Chong Kwan Un, and Jae Chon Lee. "Performance of a class of adaptive data-driven echo cancellers." IEEE Transactions on Communications 37, no. 12 (1989): 1254–63. http://dx.doi.org/10.1109/26.44197.

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38

Putri, Cindhi Kusuma, Rachmad Saptono, Hadiwiyatno Hadiwiyatno, and Septriandi Wira Yoga. "Analysis Of The Effect Filter Order Number On Noise Canceller System Using STM32F4." Jurnal Jartel Jurnal Jaringan Telekomunikasi 12, no. 4 (December 30, 2022): 212–17. http://dx.doi.org/10.33795/jartel.v12i4.366.

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Communication plays an important role in society. This can be seen in the amount of information that is spread, such as information in the form of sound, images, and videos. But communicating is not always easy. Due to the large number of sounds emitted from different sources, other sounds will be disturbed. The interference caused by noise can distort the information signal, leading a sine wave to integrate a minor noise signal. As a result, the receiver cannot differentiate the actual information signal and the added noise. This study proposed a noise-reduction system by analyzing the effect of the number of filter orders on the noise canceler system. The information signal will be processed using the STM32F4 noise canceller system, which will then be filtered using an adaptive filter with a Finite Impulse Response (FIR) structure and the Least Mean Square (LMS) algorithm. The test results show that the best SNR value is obtained at Order 40 of 5.3671 dB at a sound duration of 14 seconds, while the best PSNR value is obtained at Order 40 of 21.3557 at a sound duration of 9 seconds, and the higher the filter order value, the smaller the MSE value.

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39

TOPA, M. D., I. MURESAN, B. S. KIREI, and I. HOMANA. "Digital Adaptive Echo-Canceller for Room Acoustics Improvement." Advances in Electrical and Computer Engineering 10, no. 1 (2010): 50–53. http://dx.doi.org/10.4316/aece.2010.01008.

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40

Tao, Zhen, Mingwei Shen, Chao Liang, Di Wu, and Dai-Yin Zhu. "ROBUST ADAPTIVE SIDELOBE CANCELLER USING SV MISMATCH ESTIMATION." Progress In Electromagnetics Research Letters 74 (2018): 31–38. http://dx.doi.org/10.2528/pierl18010804.

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41

Guozhu Long, D. Shwed, and D. Falconer. "Study of a pole - zero adaptive echo canceller." IEEE Transactions on Circuits and Systems 34, no. 7 (July 1987): 765–69. http://dx.doi.org/10.1109/tcs.1987.1086203.

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42

Buckley, K., and L. Griffiths. "An adaptive generalized sidelobe canceller with derivative constraints." IEEE Transactions on Antennas and Propagation 34, no. 3 (March 1986): 311–19. http://dx.doi.org/10.1109/tap.1986.1143832.

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43

Gao, Shawn X. "Band-limited adaptive feedback canceller for hearing aids." Journal of the Acoustical Society of America 118, no. 3 (2005): 1257. http://dx.doi.org/10.1121/1.2097091.

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44

Jin, Feng, and Siyang Cao. "Automotive Radar Interference Mitigation Using Adaptive Noise Canceller." IEEE Transactions on Vehicular Technology 68, no. 4 (April 2019): 3747–54. http://dx.doi.org/10.1109/tvt.2019.2901493.

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45

Gao, Shawn X. "Band-limited adaptive feedback canceller for hearing aids." Journal of the Acoustical Society of America 123, no. 4 (2008): 1827. http://dx.doi.org/10.1121/1.2909057.

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46

Yu, Shiann-Jeng, and Fang-Biau Ueng. "Blind adaptive beamforming based on generalized sidelobe canceller." Signal Processing 80, no. 12 (December 2000): 2497–506. http://dx.doi.org/10.1016/s0165-1684(00)00141-9.

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47

Horne, E., and EA Soleit. "TMS32020 implementation of an adaptive recursive echo canceller." Microprocessors and Microsystems 12, no. 9 (November 1988): 485–89. http://dx.doi.org/10.1016/0141-9331(88)90113-5.

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48

Picciolo, M. L., and K. Gerlach. "Median cascaded canceller for robust adaptive array processing." IEEE Transactions on Aerospace and Electronic Systems 39, no. 3 (July 2003): 883–900. http://dx.doi.org/10.1109/taes.2003.1238743.

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49

Kuo, Sen M., and Wei M. Peng. "Principle and applications of asymmetric crosstalk-resistant adaptive noise canceler." Journal of the Franklin Institute 337, no. 1 (January 2000): 57–71. http://dx.doi.org/10.1016/s0016-0032(00)00007-7.

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50

HONDA, YUYA, ARATA KAWAMURA, and YOUJI IIGUNI. "Car Noise Suppression Using Adaptive Noise Canceler with Speech Suppressors." Electronics and Communications in Japan 100, no. 12 (November 6, 2017): 14–28. http://dx.doi.org/10.1002/ecj.11997.

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