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Journal articles on the topic 'Adaptive signal processing – Mathematics'

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Published: 4 June 2021

Last updated: 28 January 2023

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1

Perić, Zoran, Vlado Delić, Zoran Stamenković, and David Pokrajac. "Advanced Signal Processing and Adaptive Learning Methods." Computational Intelligence and Neuroscience 2019 (November 3, 2019): 1–2. http://dx.doi.org/10.1155/2019/5428615.

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2

Zou, Dong Lan. "Research on the Sensor Coarse Signal Processing Model Based on Adaptive Genetic Algorithm." Applied Mechanics and Materials 443 (October 2013): 342–45. http://dx.doi.org/10.4028/www.scientific.net/amm.443.342.

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With the rapid development of electronic information science and network transmission technology, the signal processing technology has been widely applied to various fields, which is the most important component of signal detection and transmission, and the key signal processing technology for processing sensor crude signals. Based on this, the experimental system of sensor coarse signal processing model is established, and in the experimental system, the transformer can carry out signal recognition for voltage and current, the use of PC microcontroller and embedded AD converter carries out analog / digital conversion for sensor crude signal. For the amplification process of sensor coarse signal, the use of adaptive genetic algorithm carries out mathematical modeling, the realization of the signal identification, acquisition and processing functions through software programming control. Finally, the intelligent processing of sensor coarse signal is successfully completed by the experiment system, and the signal processing effect is given as well.

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3

Uskovas, Gediminas, Algimantas Valinevicius, Mindaugas Zilys, Dangirutis Navikas, Michal Frivaldsky, Michal Prauzek, Jaromir Konecny, and Darius Andriukaitis. "A Novel Seismocardiogram Mathematical Model for Simplified Adjustment of Adaptive Filter." Electronics 11, no. 15 (August 5, 2022): 2444. http://dx.doi.org/10.3390/electronics11152444.

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Nonclinical measurements of a seismocardiogram (SCG) can diagnose cardiovascular disease (CVD) at an early stage, when a critical condition has not been reached, and prevents unplanned hospitalization. However, researchers are restricted when it comes to investigating the benefits of SCG signals for moving patients, because the public database does not contain such SCG signals. The analysis of a mathematical model of the seismocardiogram allows the simulation of the heart with cardiovascular disease. Additionally, the developed mathematical model of SCG does not totally replace the real cardio mechanical vibration of the heart. As a result, a seismocardiogram signal of 60 beats per min (bpm) was generated based on the main values of the main artefacts, their duration and acceleration. The resulting signal was processed by finite impulse response (FIR), infinitive impulse response (IRR), and four adaptive filters to obtain optimal signal processing settings. Meanwhile, the optimal filter settings were used to manage the real SCG signals of slowly moving or resting. Therefore, it is possible to validate measured SCG signals and perform advanced scientific research of seismocardiogram. Furthermore, the proposed mathematical model could enable electronic systems to measure the seismocardiogram with more accurate and reliable signal processing, allowing the extraction of more useful artefacts from the SCG signal during any activity.

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4

Lee, Kwan-Hyeong. "A Study on Target Detection using Covariance Correlation Matrix of Spatial Adaptive Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 236–42. http://dx.doi.org/10.17762/turcomat.v12i5.884.

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In this paper, we study for direction of arrival estimation of the desired target in spatial adaptive processing system. The interference signal removed by using the optimal weight of the covariance correlation matrix in order to estimate desired target signal. The spatial adaptive processing system updates the weight of the direction of arrival algorithm to estimate the desired signal. The weight update use an adaptive algorithm such as MUSIC. The optimal weight is obtained by Lagrange multiplier and the covariance correlation matrix. The covariance correlation matrix applies signal phase matching and uses the output power spectrum of the direct of arrival algorithm to estimate the desired target direction. We compare the performance of the proposed method with the existing method by computer simulation. The existing method has poor resolution due to phase errors of 5o and -3o in the estimation of three targets [10o, 20o, 30o]. While, the method proposed in this study accurately estimated the desired three targets. This study proved that the proposed method is superior to the existing method as a result simulation result.

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5

Ghalyan, Najah F., Asok Ray, and William Kenneth Jenkins. "A Concise Tutorial on Functional Analysis for Applications to Signal Processing." Sci 4, no. 4 (October 21, 2022): 40. http://dx.doi.org/10.3390/sci4040040.

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Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated and developed through collaborative efforts of engineers and mathematicians who used results from Hilbert spaces, Hardy spaces, weak topology, and other topics of functional analysis to establish essential analytical structures for many subfields in signal processing. This paper presents a concise tutorial for understanding the theoretical concepts of the essential elements in functional analysis, which form a mathematical framework and backbone for central topics in signal processing, specifically statistical and adaptive signal processing. The applications of these concepts for formulating and analyzing signal processing problems may often be difficult for researchers in applied sciences and engineering, who are not adequately familiar with the terminology and concepts of functional analysis. Moreover, these concepts are not often explained in sufficient details in the signal processing literature; on the other hand, they are well-studied in textbooks on functional analysis, yet without emphasizing the perspectives of signal processing applications. Therefore, the process of assimilating the ensemble of pertinent information on functional analysis and explaining their relevance to signal processing applications should have significant importance and utility to the professional communities of applied sciences and engineering. The information, presented in this paper, is intended to provide an adequate mathematical background with a unifying concept for apparently diverse topics in signal processing. The main objectives of this paper from the above perspectives are summarized below: (1) Assimilation of the essential information from different sources of functional analysis literature, which are relevant to developing the theory and applications of signal processing. (2) Description of the underlying concepts in a way that is accessible to non-specialists in functional analysis (e.g., those with bachelor-level or first-year graduate-level training in signal processing and mathematics). (3) Signal-processing-based interpretation of functional-analytic concepts and their concise presentation in a tutorial format.

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6

KIM, KEONWOOK, and ALAN D. GEORGE. "PARALLEL SUBSPACE PROJECTION BEAMFORMING FOR AUTONOMOUS, PASSIVE SONAR SIGNAL PROCESSING." Journal of Computational Acoustics 11, no. 01 (March 2003): 55–74. http://dx.doi.org/10.1142/s0218396x0300181x.

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Adaptive techniques can be applied to improve performance of a beamformer in a cluttered environment. The sequential implementation of an adaptive beamformer, for many sensors and over a wide band of frequencies, presents a serious computational challenge. By coupling each transducer node with a microprocessor, in-situ parallel processing applied to an adaptive beamformer on a distributed system can glean advantages in execution speed, fault tolerance, scalability, and cost. In this paper, parallel algorithms for Subspace Projection Beamforming (SPB), using QR decomposition on distributed systems, are introduced for in-situ signal processing. Performance results from parallel and sequential algorithms are presented using a distributed system testbed comprised of a cluster of computers connected by a network. The execution times, parallel efficiencies, and memory requirements of each parallel algorithm are presented and analyzed. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of future advanced beamforming algorithms in a scalable fashion.

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7

Ni, Chenqiang, He Xue, Shuai Wang, Xiurong Fang, and Hongliang Yang. "Crack Growth Signal Processing Approach Combining Wavelet Threshold Denoising and Variable Amplitude DCPD Technique." Mathematical Problems in Engineering 2021 (October 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5510361.

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The direct current potential drop (DCPD) method is widely used in laboratory environments to monitor the crack initiation and propagation of specimens. In this study, an anti-interference signal processing approach, combining wavelet threshold denoising and a variable current amplitude DCPD signal synthesis technique, was proposed. Adaptive wavelet threshold denoising using Stein’s unbiased risk estimate was applied to the main potential drop signal and the reference potential signal under two different current amplitudes to reduce the interference caused by noise. Thereafter, noise-reduced signals were synthesized to eliminate the time-varying thermal electromotive force. The multiplicative interference signal was eliminated by normalizing the main potential drop signal and the reference potential drop signal. This signal processing approach was applied to the crack growth monitoring data of 316 L stainless steel compact tension specimens in a laboratory environment, and the signal processing results of static cracks and propagation cracks under different load conditions were analyzed. The results showed that the proposed approach can significantly improve the signal-to-noise ratio as well as the accuracy and resolution of the crack growth measurement.

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8

Tkachuk, O. V. "OPTIMAL IMAGE SIGNALS PROCESSING ON THE NOISE BACKGROUND IN THE INFORMATION SYSTEM WITH ADAPTIVE ANTENNA ARRAY." Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì -, no. 2(17) (2020): 29–36. http://dx.doi.org/10.32684/2412-5288-2020-2-17-29-36.

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The method to restore image signals against the arbitrary intensity noise background in the information radio engineering system with adaptive antenna array has been developed. In order to use methods developed for processing one-dimensional signals for image recovery in the information system with adaptive antenna array, the transition from the two-dimensional array to vector representation is carried out. Mathematical model of narrowband signal formed at input of antenna array elements in space-time sense is obtained. Correlation matrices of image carrier signals, interference and noise are considered and features of adaptive processing of image signals coming from several sources are observed. An expression was found for the likelihood function if the incoming vector process is a multivariate stationary Gaussian process with a non-zero mean. According to the maximum likelihood criterion, the expression for the system of optimal independent parametric weight vectors necessary for image signals restoring against the arbitrary intensity noise background coming from several independent sources in the information system with adaptive antenna array is obtained. In accordance to this system, a signal processing algorithm is built in the adaptive processor of N-dimensional adaptive antenna array. A simulation model of image signal restores coming from one source in the information system with adaptive antenna array against the arbitrary intensity noise background coming from several independent sources is built. It is shown that the use of weight coefficients calculated on the basis of the correlation matrix of observations, due to its properties, does not allow dividing the set of correlated image signals. The direction of further development of the obtained results in the class of invariant methods is determined.

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9

Gong, Tianzhuo, and Sibing Sun. "Feature Extraction of Music Signal Based on Adaptive Wave Equation Inversion." Advances in Mathematical Physics 2021 (October 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/8678853.

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The digitization, analysis, and processing technology of music signals are the core of digital music technology. There is generally a preprocessing process before the music signal processing. The preprocessing process usually includes antialiasing filtering, digitization, preemphasis, windowing, and framing. Songs in the popular wav format and MP3 format on the Internet are all songs that have been processed by digital technology and do not need to be digitalized. Preprocessing can affect the effectiveness and reliability of the feature parameter extraction of music signals. Since the music signal is a kind of voice signal, the processing of the voice is also applicable to the music signal. In the study of adaptive wave equation inversion, the traditional full-wave equation inversion uses the minimum mean square error between real data and simulated data as the objective function. The gradient direction is determined by the cross-correlation of the back propagation residual wave field and the forward simulation wave field with respect to the second derivative of time. When there is a big gap between the initial model and the formal model, the phenomenon of cycle jumping will inevitably appear. In this paper, adaptive wave equation inversion is used. This method adopts the idea of penalty function and introduces the Wiener filter to establish a dual objective function for the phase difference that appears in the inversion. This article discusses the calculation formulas of the accompanying source, gradient, and iteration step length and uses the conjugate gradient method to iteratively reduce the phase difference. In the test function group and the recorded music signal library, a large number of simulation experiments and comparative analysis of the music signal recognition experiment were performed on the extracted features, which verified the time-frequency analysis performance of the wave equation inversion and the improvement of the decomposition algorithm. The features extracted by the wave equation inversion have a higher recognition rate than the features extracted based on the standard decomposition algorithm, which verifies that the wave equation inversion has a better decomposition ability.

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10

Zhen, Jingran. "Rotating Machinery Fault Diagnosis Based on Adaptive Vibration Signal Processing under Safety Environment Conditions." Mathematical Problems in Engineering 2022 (May 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/1543625.

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At present, the degree of industrialization in China is deepening, and various types of production equipment appear. However, during the startup and operation of mechanical equipment, fracture and wear will occur due to various factors. Therefore, once the mechanical equipment fails, it must be diagnosed as soon as possible to avoid serious economic losses and casualties. Rotating machinery is an important power device, so it is necessary to regularly detect and monitor equipment signals to avoid the consequences of wrong control methods. In this study, the fault diagnosis of rotating machine based on adaptive vibration signal processing is studied under the safe environmental conditions. The fault diagnosis process of rotating machinery is to first collect vibration signals, then process signal noise reduction, and then extract fault characteristic signals to further identify and classify fault status and diagnose fault degree. This study briefly introduces several rotating machinery vibration signal processing methods and identifies the fault state of the rotating machine based on the high-order cumulant. By building a DDS fault diagnosis test bench, the chaotic particle swarm parameter optimization algorithm is used to calculate the accurate stochastic resonance parameters. After noise processing, the high-frequency part is significantly reduced. The results show that, after stochastic resonance wavelet decomposition and denoising processing, the number of intrinsic functions can be significantly reduced, the fault frequency can be increased, the high-frequency noise can be reduced, and the fault analysis accuracy can be improved. We identify the fault state of rotating machinery based on the high-order cumulant, train the four states of the bearing, and compare the four types of faults, no fault, inner ring fault, rolling element fault, and outer ring fault through the comparison of the actual test set and the predicted test set. It is concluded that the rotating machinery fault belongs to the rolling element fault and the identification accuracy rate is 95%. Finally, based on the LMD morphological filtering, the rotating machinery fault diagnosis is carried out, and the feature extraction is carried out based on the LMD algorithm to decompose the bearing fault signal. Finally, the result after the morphological filtering and LMD decomposition and extraction can avoid noise interference.

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11

HINAMOTO, T. "Special Section on Adaptive Signal Processing and Its Applications." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 3 (March 1, 2005): 619. http://dx.doi.org/10.1093/ietfec/e88-a.3.619.

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12

Totsky, Alexander V., Dmitriy V. Fevralev, Vladimir V. Lukin, Vladimir Ya Katkovnik, Dmitriy V. Paliy, Karen O. Egiazarian, Oleksiy B. Pogrebnyak, and Jaakko T. Astola. "Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction." Circuits, Systems & Signal Processing 25, no. 3 (June 2006): 315–42. http://dx.doi.org/10.1007/s00034-004-1028-9.

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13

Gaydecki, Patrick. "The Foundations of Digital Signal Processing Using Signal Wizard Systems®." International Journal of Electrical Engineering & Education 49, no. 3 (July 2012): 310–20. http://dx.doi.org/10.7227/ijeee.49.3.10.

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Signal Wizard Systems® is a digital signal processing (DSP) research venture within the School of EEE at the University of Manchester, UK. It specialises in the development and supply of real-time DSP products for audio signal analysis and processing. The unique and underpinning philosophy of these products is their ease of use. The systems require minimal knowledge of DSP theory on the part of the user and none of the mathematics associated with digital filter design. Filters and other algorithms can be designed in seconds, downloaded and executed in real time with just a few mouse clicks. Since 2004 Signal Wizard products have been sold all over the world for applications ranging from noise suppression, adaptive filtering and system modelling to musical instrument research. In particular, their ease of use ensures that they are ideally suited for teaching simple and more advanced concepts in DSP both at undergraduate and postgraduate level. For this purpose, a DSP laboratory teaching package has been developed using the Signal Wizard range of devices, and has proven an invaluable tool for training our student cohort in the practical aspects of DSP engineering design and programming.

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14

Kumar, Krishna, Rajlaxmi Pandey, Sankha Subhra Bhattacharjee, and Nithin V. George. "Exponential Hyperbolic Cosine Robust Adaptive Filters for Audio Signal Processing." IEEE Signal Processing Letters 28 (2021): 1410–14. http://dx.doi.org/10.1109/lsp.2021.3093862.

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15

Haykin, S. "Lessons on adaptive systems for signal processing, communications, and control." IEEE Signal Processing Magazine 16, no. 5 (September 1999): 39–48. http://dx.doi.org/10.1109/msp.1999.790980.

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16

Liu, W., V. K. Prasanna, and M. Lee. "Parallel Implementation of a Class of Adaptive Signal Processing Applications." Algorithmica 30, no. 4 (October 1, 2001): 645–84. http://dx.doi.org/10.1007/s00453-001-0031-9.

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17

Bruni, Vittoria, Daniela De Canditiis, and Domenico Vitulano. "Local Sorting for Adaptive Signal Regularization." IEEE Signal Processing Letters 17, no. 7 (July 2010): 691–94. http://dx.doi.org/10.1109/lsp.2010.2051616.

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18

Raza, Hasan, Ishtiaq Ahmad, Noor M. Khan, Waseem Abbasi, Muhammad Shahid Anwar, Sadique Ahmad, and Mohammed A. El-Affendi. "Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms." Mathematics 10, no. 23 (December 5, 2022): 4600. http://dx.doi.org/10.3390/math10234600.

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The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on 2×2, 3×3, and 4×4 MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on 3×3 and 4×4 MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time.

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19

da Silva, Felipe B., and Wallace A. Martins. "Data-Selective Volterra Adaptive Filters." Circuits, Systems, and Signal Processing 37, no. 10 (January 29, 2018): 4651–64. http://dx.doi.org/10.1007/s00034-018-0765-0.

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20

Chen, Qian, Kai Gu, and Li Li Zhai. "Analysis of Adaptive Beamforming Based on Convex Optimization." Applied Mechanics and Materials 651-653 (September 2014): 2262–68. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2262.

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Most of the signal in the communication system have the cyclostationary property. Many algorithms based on the cyclostationary of the signal in the array signal processing have been exploited. They can well work without knowing the steering vector of interested signal, thus they all belong to the blind algorithms. When there is cycle frequency error, a mathematical analysis of gradient decent-based algorithm is provided in this paper. It pointed out that due to the zero point effect of the sinc function, the above approach have periodic deterioration as the number of snapshot increasing. Hence, in this paper a novel robust algorithm based on conjugate gradient, which can be used to extract signals with cyclostationarity with the cycle frequency error, is proposed. Because of its fast convergence, periodic nulls can be circumvented, and the steering vector of interested signal is estimated. Then we use traditional beamformer to avoid the influence of cycle frequency error. Simulation experiments show that our new algorithm performs well under cycle frequency mismatches.

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21

Wang, Pin, Peng Wang, and En Fan. "Neural Network Optimization Method and Its Application in Information Processing." Mathematical Problems in Engineering 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/6665703.

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Neural network theory is the basis of massive information parallel processing and large-scale parallel computing. Neural network is not only a highly nonlinear dynamic system but also an adaptive organization system, which can be used to describe the intelligent behavior of cognition, decision-making, and control. The purpose of this paper is to explore the optimization method of neural network and its application in information processing. This paper uses the characteristic of SOM feature map neural network to preserve the topological order to estimate the direction of arrival of the array signal. For the estimation of the direction of arrival of single-source signals in array signal processing, this paper establishes a uniform linear array and arbitrary array models based on the distance difference vector to detect DOA. The relationship between the DDOA vector and the direction of arrival angle is regarded as a mapping from the DDOA space to the AOA space. For this mapping, through derivation and analysis, it is found that there is a similar topological distribution between the two variables of the sampled signal. In this paper, the network is trained by uniformly distributed simulated source signals, and then the trained network is used to perform AOA estimation effect tests on simulated noiseless signals, simulated Gaussian noise signals, and measured signals of sound sources in the lake. Neural network and multisignal classification algorithms are compared. This paper proposes a DOA estimation method using two-layer SOM neural network and theoretically verifies the reliability of the method. Experimental research shows that when the signal-to-noise ratio drops from 20 dB to 1 dB in the experiment with Gaussian noise, the absolute error of the AOA prediction is small and the fluctuation is not large, indicating that the prediction effect of the SOM network optimization method established in this paper does not vary. The signal-to-noise ratio drops and decreases, and it has a strong ability to adapt to noise.

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Jeyaraj, Pandia Rajan, and Edward Rajan Samuel Nadar. "Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal." Multidimensional Systems and Signal Processing 31, no. 4 (February 17, 2020): 1299–316. http://dx.doi.org/10.1007/s11045-020-00710-7.

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23

Konopel'kin, M. Yu, S. V. Petrov, and D. A. Smirnyagina. "Implementation of stochastic signal processing algorithms in radar CAD." Russian Technological Journal 10, no. 5 (October 21, 2022): 49–59. http://dx.doi.org/10.32362/2500-316x-2022-10-5-49-59.

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Objectives. In 2020, development work on the creation of a Russian computer-assisted design system for radars (radar CAD) was completed. Radar CAD provides extensive opportunities for creating simulation models for developing the hardware-software complex of radar algorithms, which take into account the specific conditions of aerospace environment observation. The purpose of the present work is to review and demonstrate the capabilities of radar CAD in terms of implementing and testing algorithms for processing stochastic signals.Methods. The work is based on the mathematical apparatus of linear algebra. Analysis of algorithms characteristics was carried out using the simulation method.Results. A simulation model of a sector surveillance radar with a digital antenna array was created in the radar CAD visual functional editor. The passive channel included the following algorithms: algorithm for detecting stochastic signals; algorithm for estimating the number of stochastic signals; direction finding algorithm for stochastic signal sources; adaptive spatial filtering algorithm. In the process of simulation, the algorithms for detecting and estimating the number of stochastic signals produced a correct detection sign and an estimate of the number of signals. The direction-finding algorithm estimated the angular position of the sources with an accuracy of fractions of degrees. The adaptive spatial filtering algorithm suppressed interfering signals to a level below the antenna's intrinsic noise power.Conclusions. The processing of various types of signals can be simulated in detail on the basis of the Russian radar CAD system for the development of functional radar models. According to the results of the simulation, coordinates of observing objects were obtained and an assessment of the effectiveness of the algorithms was given. The obtained results are fully consistent with the theoretical prediction. The capabilities of radar CAD systems demonstrated in this work can be used by specialists in the field of radar and signal processing.

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Gao, Yuxin, and Fuzhong Wang. "Multiple Signal Processing in Adaptive Cascaded-Bistable Stochastic Resonance System." Journal of Computational and Theoretical Nanoscience 10, no. 4 (April 1, 2013): 996–98. http://dx.doi.org/10.1166/jctn.2013.2798.

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Chaoang, Xiao, Tang Hesheng, and Ren Yan. "Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model." Measurement and Control 53, no. 3-4 (January 25, 2020): 649–61. http://dx.doi.org/10.1177/0020294019898725.

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Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.

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Nayak, N. R., P. K. Dash, B. N. Sahu, and Ranjeeta Bisoi. "A New Adaptive Multiscale Morphological Filter and Robust RVFLN Classifier for Distributed Generation Systems during Islanding and Non-Islanding Events." International Journal on Electrical Engineering and Informatics 12, no. 3 (September 30, 2020): 494–518. http://dx.doi.org/10.15676/ijeei.2020.12.3.6.

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A new method for islanding and non-islanding disturbances detection and classification is proposed for a multiple PV based distributed generation (DG) system utilizing adaptive multi-scale morphological filter (AMF) and random vector functional link network (RVFLN) classifier. In comparison to different signal analysis techniques, the mathematical MF, that has wide application in power signals, EEG signal analysis, image processing, pattern recognition, etc. posses the benefit of easy execution, fast processing, and minimal computations. Further it is well known that a single scale morphological filter has limited noise filtering capacity and may also filter useful signal disturbance components resulting in erroneous detection of disturbance signals in microgrid. Therefore, an adaptive multiscale combined morphological filter is presented in this paper built on the concept of multiscale overall filtering which has better denoising effect and can retain useful signals better than the traditional filter. The proposed technique is built upon the measurement of voltage signal samples and the processing of these signals through AMF has been done for feature extraction. The extracted features are then employed as inputs to an efficient, fast, and easily implementable randomized network based classifier (RVFLN) which is made robust to reject the presence noise and outliers in the signal data. The outputs exhibited from the suggested technique concludes that it is s very fast and accurate technique for the detection and classification of islanding and non-islanding events in comparison to the widely used approaches.

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Lou, Yuang, Chrysostomos L. Nikias, and Anastasios N. Venetsanopoulos. "Efficient VLSI array processing structures for adaptive quadratic digital filters." Circuits, Systems, and Signal Processing 7, no. 2 (June 1988): 253–73. http://dx.doi.org/10.1007/bf01602100.

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Zhang, Ming, and M. H. Er. "Robust adaptive beamforming for broadband arrays." Circuits Systems and Signal Processing 16, no. 2 (March 1997): 207–16. http://dx.doi.org/10.1007/bf01183275.

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29

Zerguine, Azzedine. "Variable Weight Mixed-Norm Adaptive Algorithm." Circuits, Systems & Signal Processing 21, no. 6 (December 2002): 547–66. http://dx.doi.org/10.1007/s00034-002-1010-3.

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Maleki, Nafiseh, and Masoumeh Azghani. "Sparse Mixed Norm Adaptive Filtering Technique." Circuits, Systems, and Signal Processing 39, no. 11 (June 23, 2020): 5758–75. http://dx.doi.org/10.1007/s00034-020-01432-8.

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31

Madan, Bharat B., and Sydney R. Parker. "On adaptive beamforming in correlated noise." Circuits, Systems, and Signal Processing 7, no. 3 (September 1988): 327–43. http://dx.doi.org/10.1007/bf01599974.

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QIAN, TAO, I. T. HO, I. T. LEONG, and YANBO WANG. "ADAPTIVE DECOMPOSITION OF FUNCTIONS INTO PIECES OF NON-NEGATIVE INSTANTANEOUS FREQUENCIES." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 05 (September 2010): 813–33. http://dx.doi.org/10.1142/s0219691310003791.

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We introduce the concept of adaptive decomposition of signals into basic building blocks, of which each of the non-negative analytic instantaneous frequency are called mono-components. We propose certain methods based on p-starlike functions and Fourier expansions for such decomposition. We justify the terminology mono-component used in signal analysis.

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ONCHIŞ, DARIAN M., and ESPERANZA M. SÚAREZ SÁNCHEZ. "THE FLEXIBLE GABOR-WAVELET TRANSFORM FOR CAR CRASH SIGNAL ANALYSIS." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 04 (July 2009): 481–90. http://dx.doi.org/10.1142/s0219691309003045.

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This paper is concerned with the spectral decomposition and the adaptive analysis of data coming from car crash simulations. The mathematical ingredient of the proposed signal processing technique is the flexible Gabor-wavelet transform or the α-transform that reliably detects both high and low frequency components of such complicated short-time signals. We go from the functional treatment of this wavelet-type transform to its numerical implementation and we show how it can be used as an improved tool for spectral investigations compared to the short-time Fourier transform or the classical wavelet transform.

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Pauline, S. Hannah, Samiappan Dhanalakshmi, and R. Kumar. "Variable-Stage Cascaded Adaptive Filter Technique for Signal De-Noising Application." Circuits, Systems, and Signal Processing 41, no. 4 (October 26, 2021): 1972–2006. http://dx.doi.org/10.1007/s00034-021-01868-6.

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Yang, Ying, Yusen Wei, and Ming Yang. "Signal Denoising Based on the Adaptive Shrinkage Function and Neighborhood characteristics." Circuits, Systems, and Signal Processing 33, no. 12 (June 18, 2014): 3921–30. http://dx.doi.org/10.1007/s00034-014-9834-1.

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36

Lu, Zilin, Nuan Xia, Liang Sun, Wenxing Xu, Guangcheng Zhang, Haiyue Dou, and Qifeng Jiang. "An Alternative Adaptive Method for Seismic Data Denoising and Interpolation." Mathematical Problems in Engineering 2020 (August 30, 2020): 1–16. http://dx.doi.org/10.1155/2020/6295902.

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Seismic data denoising and interpolation are generally essential steps for reflection processing and imaging workflow especially for the complex surface geologic conditions and the irregular acquisition field area. The rank-reduction method is a valid way for the attenuation of random noise and data interpolation by selecting the suitable threshold, i.e., the rank of the useful signals. However, it is difficult for the traditional rank-reduction method to select an appropriate threshold. In this paper, we propose an adaptive rank-reduction method based on the energy entropy to automatically estimate the rank as the threshold for seismic data processing and interpolation. This method considers the energy entropy into the traditional rank-reduction method. The energy entropy of signals can be used to indicate the energy intensity of a signal component in the total energy. The difference of the energy entropy between the useful signals and random noise is perceived as a measurement for selecting the appropriate threshold. Synthetic and field examples indicate that the proposed method can well achieve the attenuation of random noise and interpolation automatically without the estimation of the ranks and demonstrate the feasibility of the new adaptive method in seismic data denoising and interpolation.

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QIAN, TAO, LIMING ZHANG, and HONG LI. "MONO-COMPONENTS VS IMFs IN SIGNAL DECOMPOSITION." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 03 (May 2008): 353–74. http://dx.doi.org/10.1142/s0219691308002392.

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The concepts of intrinsic mode functions and mono-components are investigated in relation to the empirical mode decomposition. Mono-components are defined to be the functions for which non-negative analytic instantaneous frequency is well defined. We show that a great variety of functions are mono-components based on which adaptive decomposition of signals are theoretically possible. We justify the role of empirical mode decomposition in signal decomposition in relation to mono-components.

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Figueroa, J. L., J. E. Cousseau, and R. J. P. de Figueiredo. "A Simplicial Canonical Piecewise Linear Adaptive Filter." Circuits, Systems and Signal Processing 23, no. 5 (October 2004): 365–86. http://dx.doi.org/10.1007/s00034-004-0808-6.

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Du, K. L., and M. N. S. Swamy. "A Class of Adaptive Cyclostationary Beamforming Algorithms." Circuits, Systems & Signal Processing 27, no. 1 (January 4, 2008): 35–63. http://dx.doi.org/10.1007/s00034-007-9009-4.

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Ashiba, H. I., K. H. Awadalla, S. M. El-Halfawy, and F. E. Abd El-Samie. "Adaptive Least Squares Interpolation of Infrared Images." Circuits, Systems, and Signal Processing 30, no. 3 (December 21, 2010): 543–51. http://dx.doi.org/10.1007/s00034-010-9243-z.

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Jeong, Jae Jin, and SeungHun Kim. "Robust Adaptive Filter Algorithms Against Impulsive Noise." Circuits, Systems, and Signal Processing 38, no. 12 (May 14, 2019): 5651–64. http://dx.doi.org/10.1007/s00034-019-01135-9.

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Sahay, Peeyush, B. S. Teza, Pranav Kulkarni, P. Radhakrishna, and Vikram M. Gadre. "Adaptive Generalised Fractional Spectrogram and Its Applications." Circuits, Systems, and Signal Processing 39, no. 12 (May 20, 2020): 5982–6033. http://dx.doi.org/10.1007/s00034-020-01442-6.

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Lee, Kwan-Hyeong. "A Study on Radar Target Detection using Space Time Adaptive Processing Algorithm and LCMV Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 243–48. http://dx.doi.org/10.17762/turcomat.v12i5.886.

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In this paper, we study the directionof arrival estimation of the desired target in adaptive array MV algorithm to update the weight, and the optimized weight removes the interference signal. The target signal is estimated using the optimized weight vector and the high resolution the direction of arrival estimation MUSIC algorithm. We calculate the inverse of the correlation matrix using the QR method to reduce the processing power consumption of the optimized weight. The optimal weight vector is applied to the proposed algorithm to estimate the desired target direction from the output power spectrum. The performance of the proposed method is compared with the existing method by simulation. The experimental method estimates three targets from the antenna received signal. The existing method did not estimate the three desired targets at [-30o,-20, -10o]. The proposed method accurately estimates the desired three targets at [-30o,-20, -10o]. In the [-10o, 0, 10o] target estimation, the existing method reduces the estimated resolution of the target, but the proposed method accurately estimates the target. We proved that the proposed method in the simulation was superior to the existing method.

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Zhao, Kang, Zhiwen Liu, Shuli Shi, Yulin Huang, and Yougen Xu. "Augmented Joint Domain Localized Method for Polarimetric Space–Time Adaptive Processing." Circuits, Systems, and Signal Processing 40, no. 7 (February 8, 2021): 3592–608. http://dx.doi.org/10.1007/s00034-020-01634-0.

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JAILLET, FLORENT, and BRUNO TORRÉSANI. "TIME-FREQUENCY JIGSAW PUZZLE: ADAPTIVE MULTIWINDOW AND MULTILAYERED GABOR EXPANSIONS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 02 (March 2007): 293–315. http://dx.doi.org/10.1142/s0219691307001768.

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We describe a new adaptive multiwindow Gabor expansion, which dynamically adapts the windows to the signal's features in time-frequency space. The adaptation is based on local time-frequency sparsity criteria, and also yields as by-product an expansion of the signal into layers corresponding to different windows. As an illustration, we show that simply using two different windows with different sizes leads to decompositions of audio signals into transient and tonal layers. We also discuss potential applications to transient detection and denoising.

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Wang, Xiaolan, Tongzhou Wang, Lili Su, Yansong Wang, Dongpo Yang, Chao Yang, and Ningning Liu. "Adaptive Active Vehicle Interior Noise Control Algorithm Based on Nonlinear Signal Reconstruction." Circuits, Systems, and Signal Processing 39, no. 10 (April 16, 2020): 5226–46. http://dx.doi.org/10.1007/s00034-020-01410-0.

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YAN, ZHONGHONG, JIAN PING LI, YONG QIN YANG, and YUAN YAN TANG. "STUDY ON RECURSIVE CONSTRUCTION METHOD OF BIORTHOGONAL WAVELETS FOR SIGNAL PROCESSING." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 02 (June 2004): 197–206. http://dx.doi.org/10.1142/s0219691304000470.

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Wavelet has been applying in signal analyzing, image processing model recognizing, computer sense etc. But among them biorthogonal wavelets with symmetry characteristics (or antisymmetry) in the image compressing, signal examination has more special functions, this paper research a recursive construction method, at the same time, It is valuable to notice that our recursive methods are not the same as the W. Seldens's lifting scheme, the new technique has important mean to adaptive signal processing and more application: such as for QMF•CQF etc. filters. It is very easy to choose the wavelet bases match to questions by dynamically.

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Kelleci, Burak, Timothy Wayne Fischer, Aydın İlker Karşılayan, Kai Shi, and Erchin Serpedin. "Adaptive Narrowband Interference Suppression in Multiband OFDM Receivers." Circuits, Systems & Signal Processing 27, no. 4 (June 3, 2008): 475–89. http://dx.doi.org/10.1007/s00034-008-9041-z.

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Ahmad, Mohammad Shukri, Osman Kukrer, and Aykut Hocanin. "Robust Recursive Inverse Adaptive Algorithm in Impulsive Noise." Circuits, Systems, and Signal Processing 31, no. 2 (August 24, 2011): 703–10. http://dx.doi.org/10.1007/s00034-011-9341-6.

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Wang, Shi-Yuan, Chao Yin, Shu-Kai Duan, and Li-Dan Wang. "A Modified Variational Bayesian Noise Adaptive Kalman Filter." Circuits, Systems, and Signal Processing 36, no. 10 (February 1, 2017): 4260–77. http://dx.doi.org/10.1007/s00034-017-0497-6.

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