Journal articles: 'NON-LINEAR SIGNAL PROCESSING'

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Pagès-Zamora, Alba, and Miguel A. Lagunas. "Fourier models for non-linear signal processing." Signal Processing 76, no. 1 (July 1999): 1–16. http://dx.doi.org/10.1016/s0165-1684(98)00243-6.

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Qu, Yanhuai, Shuai Zhang, and Qingkai Han. "Comparison of Non-linear Signals Analysis Methods." MATEC Web of Conferences 232 (2018): 01014. http://dx.doi.org/10.1051/matecconf/201823201014.

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In modern engineering, linear is relative, while non-linear and non-stationary is absolute. There are many methods in non-linear signals processing. How to select a most suitable analysis method quickly for the nonlinear signal is particularly important, which can improve the signal processing efficiently. In this paper, three common analysis methods for nonlinear signals, Wavelet spectrum, Hilbert spectral analysis (HSA) and Poincaré mapping are researched and analyzed by some typical nonlinear signals from the complex electromechanical model test system. The effectiveness and application scopes of these approaches are obtained, which can provide a theoretical and practical basis for engineering application.

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Lunner, Thomas, Johan Hellgren, Stig Arlinger, and Claus Elberling. "Non-Linear Signal Processing in Digital Hearing Aids." Scandinavian Audiology 27, no. 4 (January 1998): 40–49. http://dx.doi.org/10.1080/010503998420649.

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Bhateja, Vikrant, Rishendra Verma, Rini Mehrotra, and Shabana Urooj. "A Non-Linear Approach to ECG Signal Processing using Morphological Filters." International Journal of Measurement Technologies and Instrumentation Engineering 3, no. 3 (July 2013): 46–59. http://dx.doi.org/10.4018/ijmtie.2013070104.

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Analysis of the Electrocardiogram (ECG) signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.

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Adithya valli, N., and Dr D. Elizabath Rani. "Modified PWNLFM Signal for Side-Lobe Reduction." International Journal of Engineering & Technology 7, no. 4.20 (November 28, 2018): 4. http://dx.doi.org/10.14419/ijet.v7i4.20.22110.

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Many applications in radar systems require low range side-lobe performance which is achieved by pulse compression processing. Most used chirp signal for this processing is linear frequency modulation (LFM) signal but with a presence of first high side-lobe level. Suppression of this side-lobe requires weighting function causing the reduction in signal to noise ratio at the receiver owing to mismatch loss. Non-linear chirp signals are introduced as a solution and became most practiced signals aimed at reducing side-lobes. In this paper, an overall piece wise non-linear frequency modulation chirp signal is designed by merging two stages, one with linear function and the other with a tangent based non-linear function. Simulation results show significant reduction in the sidelobe level of autocorrelation function when NLFM is generated in this method.

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BILLINGS, S. A., and Q. H. TAO. "Model validity tests for non-linear signal processing applications." International Journal of Control 54, no. 1 (July 1991): 157–94. http://dx.doi.org/10.1080/00207179108934155.

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Liu, H., and T. Vinh. "Multi-dimensional signal processing for non-linear structural dynamics." Mechanical Systems and Signal Processing 5, no. 1 (January 1991): 61–80. http://dx.doi.org/10.1016/0888-3270(91)90015-w.

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Smolarik, Lukas, Dusan Mudroncik, and Lubos Ondriga. "ECG Signal Processing." Advanced Materials Research 749 (August 2013): 394–400. http://dx.doi.org/10.4028/www.scientific.net/amr.749.394.

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Electrocardiography (ECG) is a diagnostic method that allows sensing and record the electric activity of heart [. The measurement of electrical activity is used as a standard twelve-point system. At each of these leads to measure the useful signal and interference was measured. The intensity of interference depends on the artefacts (electrical lines, brum, motion artefacts, muscle, interference from the environment, etc.). For correct evaluation of measured signal there is a need to processing the measured signal to suitable form. At present, the use of electrocardiograms with sensors with contact scanning are difficult to set a time so we decided to use the principle of non-contact sensing. Such a device to measure the ECG was constructed under the project. The disadvantage of such devices is a problem with a high level of noise, which degrades a useful signal. The aim of this article is to pre-process the signals obtained from non-contact sensing. The contactless devices are powered from the network and battery. The electrodes were connected by way of Eithoven bipolar leads. Signals were pre-treated with suitable filters so that they are also appropriate for their subsequent analysis. In the filtration ECG signals was used as a method of linear (low pass filter, high pass, IIR (Infinite Impulse Response) peak, notch filter. The results of many signals clearly demonstrate removing noise in the ECG signals to the point that is also suitable for their analysis.

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Bilgehan, Bülent. "Efficient approximation for linear and non‐linear signal representation." IET Signal Processing 9, no. 3 (May 2015): 260–66. http://dx.doi.org/10.1049/iet-spr.2014.0070.

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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

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Tsang, K. M., W. L. Chan, and L. L. Lai. "Rapid On-Line Frequency Determination Using Non-Linear Signal Processing." Electric Power Components and Systems 33, no. 10 (October 2005): 1137–43. http://dx.doi.org/10.1080/15325000590933609.

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Sackey, Samson Hansen, Michael Kwame Ansong, Samuel Nartey Kofie, and Abdul Karim Armahy. "Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems." International Journal of Computer Networks and Communications Security 8, no. 8 (August 30, 2020): 59–66. http://dx.doi.org/10.47277/ijcncs/8(8)1.

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The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used

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Hwang, Wen-Liang, and Andreas Heinecke. "Un-Rectifying Non-Linear Networks for Signal Representation." IEEE Transactions on Signal Processing 68 (2020): 196–210. http://dx.doi.org/10.1109/tsp.2019.2957607.

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Fernandes, Dylan Royce, and Suchetha M. "FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF EMPIRICAL MODE DECOMPOSITION ALGORITHM FOR ELECTROCARDIOGRAM PROCESSING." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 77. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19569.

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The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. Therefore, good quality ECG signal is required. Hilbert-Huang transform (HHT) is a method to analyze non-stationary and non-linear signals. Empirical mode decomposition (EMD) is the core of HHT. EMD breaks down signals into smaller number of components. These components form a complete and nearly orthogonal basis for the original signal. This algorithm is implemented on field-programmable gate array using the process of extrema generation, envelope generation, and stopping criterion.

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DEGTYAREV, ANDREY N., ALEXANDER S. KOZHEMYAKIN, IGOR L. AFONIN, GENNADY V. SLEZKIN, and ALEXANDER L. POLYAKOV. "TWO-STAGE ALGORITHM FOR CONSISTENT SIGNALS FILTERING." H&ES Research 14, no. 3 (2022): 32–38. http://dx.doi.org/10.36724/2409-5419-2022-14-3-32-38.

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Introduction. Introduction. To combat inter-symbol interference resulting from multipath signal propagation, spreading of the signal frequency spectrum, channel equalizing, OFDM-multiplexing with orthogonal frequency division of channels is used. To reduce the influence of non-linear signal distortions, methods of pre-distortion, diversity reception, as well as special algorithms for digital processing of the demodulator output signal are used. The effect of additive noise is reduced by using a filter matched to the signal. Objective. It is advisable to develop a matched filtering algorithm that allows one to simultaneously reduce the influence of additive noise, non-linear signal distortions and its multipath propagation on the correct message reception. The idea of selecting the orthogonality weight can also be used to achieve the goal. Result. To reduce the influence of non-linear distortions on the correct reception of a message under conditions of multipath signal propagation and additive interference, it is proposed to use two stages of signal processing. The first step is to minimize the noise dispersion caused by intersymbol interference and non-linear signal distortions. The specified dispersion is minimized by determining the orthogonality weight of the basis-functions that make up the signal. The second step is to use a classic matched filter. The proposed two-stage matched filtering algorithm makes it possible to simultaneously reduce the influence of non-linear distortions, intersymbol interference resulting from multipath propagation, and additive interference on the correct signal reception. Signals distorted and delayed in time relative to the main signal are proposed to be considered as additive interference.

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Lagunas, Miguel A., Ana Perez-Neira, and José Rubio. "NDM: 1-Bit Delta-Sigma Converter with Non-Linear Loop." MATEC Web of Conferences 292 (2019): 04005. http://dx.doi.org/10.1051/matecconf/201929204005.

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In this paper we propose to introduce a new processing scheme in the basic loop of a Delta Sigma (ΔΣ) analog-to-digital converter. This processing confers extra gains of the converter over both the quantization error and the channel noise. This is an advance with respect to all cases found in the literature, where the desired signal is not protected against channel noise. Also, the proposed processing is simple and contrasts with the existing architectures, which produce better quality at the expense of sensitivity to implementation imperfections due to the presence of multiples loops in the corresponding architecture. Furthermore, the in-phase/quadrature components structure of a band pass signal has not been used to improve the performance of ΔΣ converters.

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Li, Bin, Weisi Guo, Xiang Wang, Yansha Deng, Yueheng Lan, Chenglin Zhao, and Arumugam Nallanathan. "CSI-Independent Non-Linear Signal Detection in Molecular Communications." IEEE Transactions on Signal Processing 68 (2020): 97–112. http://dx.doi.org/10.1109/tsp.2019.2957636.

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Diaz‐Ramirez, Victor H., and Vitaly Kober. "Robust speech processing using local adaptive non‐linear filtering." IET Signal Processing 7, no. 5 (July 2013): 345–59. http://dx.doi.org/10.1049/iet-spr.2011.0206.

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Ansari, M. Samar, and Syed Atiqur Rahman. "DVCC-Based Non-linear Feedback Neural Circuit for Solving System of Linear Equations." Circuits, Systems, and Signal Processing 30, no. 5 (January 14, 2011): 1029–45. http://dx.doi.org/10.1007/s00034-010-9261-x.

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Klepka, Andrzej, Wieslaw Jerzy Staszewski, Kajetan Dziedziech, and Francesco Aymerich. "Non-Linear Vibro-Acoustic Wave Modulations - Analysis of Different Types of Low-Frequency Excitation." Key Engineering Materials 569-570 (July 2013): 924–31. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.924.

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Signal processing method based on wavelet transform used in non-linear acoustic test is presented in the paper. The method is applied for sidebands identification in response signal acquired during vibro-acoustic modulation test of impacted carbon fiber reinforced plate (CFRP). The plate was impacted with known energy using drop-weight testing machine. The modulation effect in investigated specimen results from the interaction of low and high frequency excitation with damage. The paper investigates different than mono-harmonic low-frequency excitation usually used in non-linear acoustics tests. Application of aperiodic low-frequency excitation signal allows to omit the modal test, where natural frequency of the structure are estimated. However, this requires the use of dedicated signal processing methods.

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Yang, Lin. "Application of Local Wave Method in the Structural Health Monitoring Signal Decomposition." Applied Mechanics and Materials 457-458 (October 2013): 969–73. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.969.

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Health monitoring of the bridge structure has gradually become one of the hot topics. The signal decomposition technology is the key technique of the bridge structural health monitoring. The traditional data analysis and processing methods, which can only be applied to stationary or linear signal processing, have significant limitations. However, the structural response signals tested are mostly non-stationary and nonlinear. So methods that can effectively analyze non-stationary and nonlinear signal are urgently needed. Based on the summarization and analysis of the shortage of wavelet analysis method, the application of local wave method for data processing and analysis in structural health monitoring is put forward. The feasibility and superiority of local wave method is discussed. Experimental simulation results show that the application of local wave method in bridge health monitoring signal decomposition is feasible.

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Fan, Zhi Ping, Tian Sheng Hong, Zhi Zhuan Liu, and Zheng Zhe Jing. "Improve the Envelope of EMD with Piecewise Linear Fractal Interpolation." Key Engineering Materials 439-440 (June 2010): 390–95. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.390.

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Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing non-stationary signals as sums of zero-mean amplitude modulation frequency modulation components. The traditional EMD algorithm adopts the cubic spline interpolation as an effective tool processing non-stationary signal, but it cannot effectively extract the characteristic frequencies from a highly non-stationary signal, and the overshoots and the undershoots may become a common phenomenon during the decomposition process. In order to solve the problem, we presents the piecewise linear fractal interpolation as the spline interpolating. Finally, we will use the simulation signal to verify the effectiveness of the improved EMD.

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Wen, Zhi, Chen Lu, and Hong Mei Liu. "Feature Extraction Technology for Rolling Bearings Based on Local Tangent Space Alignment." Applied Mechanics and Materials 764-765 (May 2015): 274–79. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.274.

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Health assessment and fault diagnosis for rolling bearings mostly adopt traditional methods, such as time-frequency, spectral, and wavelet packet analyses, to extract the feature vector. These methods are suitable for processing data with a linear structure. However, for the non-linear and non-stationary signal, the result of these methods is not ideal. Thus, this study proposes a suitable method to extract the feature vector in nonlinear signals. Local tangent space alignment of a manifold algorithm is employed to extract the feature vector from the rolling bearings. Results verify the advantage of the manifold algorithm for non-linear and non-stationary signals.

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Sicuranza, Giovanni L., and Alberto Carini. "Unconstrained linear combination of even mirror Fourier non‐linear filters." IET Signal Processing 8, no. 6 (August 2014): 612–21. http://dx.doi.org/10.1049/iet-spr.2013.0256.

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Joshi, Sunil, and Gaurav Sharma. "Non-linear Distortion & its Reduction Techniques for Coherent Optical OFDM System: A Review." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 8 (August 31, 2015): 13–16. http://dx.doi.org/10.53555/nneee.v2i8.179.

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In this paper the literature review is done for different compensation technique to reduce the nonlinear distortion for optical coherent system. Comparative analysis done so for dispersion compensation fiber, method of Fiber Bragg Grating, digital signal processing (DSP), pre and post symmetric-DCF techniques. The digital signal processing is very practical for all type of nonlinear distortion is minimized while dispersion technique is use to compensate the dispersion loss only and other methods are useful to reduce nonlinear distortion.

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Lavanya, S., S. Prabakaran, and N. Ashok Kumar. "A Deep Learning Technique for Detecting High Impedance Faults in Medium Voltage Distribution Networks." Engineering, Technology & Applied Science Research 12, no. 6 (December 1, 2022): 9477–82. http://dx.doi.org/10.48084/etasr.5288.

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Utility companies always struggle with the High Impedance Fault (HIF) in the electrical distribution systems. In this article, the current signal is seen in situations involving 10,400 different samples, with and without HIF, like linear, non-linear load, and capacitance switching. A better method that processes signals very fast and with low sample rates, requiring less memory and computational labor, is demonstrated by Mathematical Morphology (MM). For HIF identification, Deep Convolution Neural Networks (DCNNs) are being developed. This paper presents a novel method for signal processing with low sample rates, high signal processing speed, and low computational and memory requirements. The suggested six-layer DCNN is compared with other models, such as the four-layer and eight-layer DCNN models and the results are discussed.

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Gaudette, Jason E., and James A. Simmons. "Linear time-invariant (LTI) modeling for aerial and underwater acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A95. http://dx.doi.org/10.1121/10.0018285.

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Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.

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Yu, Yan Xin, Chun Yang Wang, Yu Chen, and Hong Yan Sun. "A Fast Algorithm of Linear Canonical Transformation for Radar Signal Processing System." Advanced Materials Research 1049-1050 (October 2014): 1245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1245.

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Linear canonical transformation is a new signal processing tools developing in recent years. As a unified multi-parameter linear integral transform, linear canonical transformation has its unique advantages when dealing with non-stationary signal. However, from the existing literatures, the basic theoretical system is not perfect, some of the theories associated with signal processing needs to be further established or strengthened, the research of linear canonical transformation has important theoretical significance and practical significance, but linear canonical transformation needs a lot of calculation, it is not like Fourier transform, fractional Fourier transform, Fresnel transform and scale operator, they have already been widely used in various fields of expertise, in order to reduce the amount of calculation, this paper puts forward a fast algorithm which uses duality theorem of linear canonical transformation to reduce the amount of calculation, it can quickly complete the operation when we use linear canonical transformation to process the signal during radar signal processing, the time for normal algorithm is 5s, the fast algorithm needs only 0.2s.

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Ali Naz, S., M. J. Grimble, and P. Majecki. "Multi-channel restricted structure estimators for linear and non-linear systems." IET Signal Processing 5, no. 4 (2011): 407. http://dx.doi.org/10.1049/iet-spr.2008.0158.

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Li, Meng. "Rolling Bearing Fault Diagnosis Based on Fractal Dimension." Advanced Materials Research 430-432 (January 2012): 2050–53. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.2050.

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Fractal, as a new technique of signal processing, is suitable for analyzing the non-linear fault signals of rotating machine. By researching the characteristic of non-linear vibration signals of rolling bearings, a study of box dimension in analyzing the vibration signals and diagnosing the fault pattern of rolling bearings is proposed. Box dimension algorithm is presented in details and quantificational calculating of non-linear vibration signals generated by bearing system is also discussed. Experimental results show that kinematics mechanisms of rolling bearing result in the different working state, so the box dimensions are different evidently. The application of box dimension in monitoring working state is a new approach to promote the accuracy of rolling bearings.

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CIFUENTES FIALLOS, PABLO, JORDI ROMEU GARBI, and TERESA PAMIES GOMEZ. "LOW-COST DEVICE FOR FAULT DIAGNOSIS IN BEARINGS BASED ON THE HILBERT-HUANG TRANSFORM." DYNA 98, no. 5 (September 1, 2023): 484–90. http://dx.doi.org/10.6036/10875.

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In order to monitor the condition of machinery complex industrial environments, high-cost equipment is required for signal acquisition and processing. However, low-cost sensor nodes with high processing capability are a potential solution to improve diagnostic systems. This paper presents a low-cost device for fault diagnosis based on the vibration response in rotating machines with the implementation of the Hilbert-Huang transform (HHT) analysis to extract the main characteristics of the signal. HHT, used to analyze non-linear and non-stationary signals, incorporates an Empirical Mode Decomposition (EMD) process. Processing is carried out in an embedded system to acquire vibration response data and extract signal characteristics that allow condition monitoring. As a result of local processing in the vibratory measurement device in an embedded system, it is achieved to decompose the signal in order to the characteristic failure in the bearing and transmit the alarm to a hub. This eliminates the need for a central diagnostic system and reduces the total cost of the system. Keywords: vibration detection; fault diagnosis; empirical mode decomposition (EMD); local processing, embedded systems.

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Li, Jin, Jing-tian Tang, and Xiao Xiao. "De-Noising Algorithm for Magnetotelluric Signal Based on Mathematical Morphology Filtering." Noise & Vibration Worldwide 42, no. 11 (December 2011): 65–72. http://dx.doi.org/10.1260/0957-4565.42.11.65.

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In this paper, an effective de-noising algorithm based on mathematical morphology filtering for magnetotelluric sounding data is presented. Magnetotelluric signals are nonlinear, non-stationary, non-minimum phase, they do not meet the basic requirements of the Fourier transform based on the traditional power spectrum estimation. Mathematical morphology filtering is a new signal analysis method developed in recent years for dealing with non-linear, non-stationary signal. This paper briefly introduce the mathematical morphology filtering basic principles and algorithms. According to the properties of structuring elements, the mathematical morphology filtering is designed. Analysis structuring elements type selection program by filtering performance. Based on the measured signal processing, we discussed its application in magnetotelluric sounding data processing and strong interference separation. Experimental results indicate that the proposed method is feasible and can effectively eliminate larger scale disturbance and baseline drift of magnetotelluric sounding data. In addition, the method is efficient to keep the main characteristics of the original signals, and is helpful to improve signal quality and information interpretability for magnetotelluric sounding data.

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Deng, Wei Bo, Hong Xin Sun, Ying Ning Dong, and Qiang Yang. "Optimal Design of Non-Uniform Linear Array Using Genetic Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 3686–91. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3686.

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An arrangement method for non-uniform linear array using genetic algorithm (GA) is proposed. It is a general purpose method and needs only the angle range of the signal directions and the desired aperture of the array. It has few parameters, simple processing steps and a strong stabilization. It can be applied to optimize arbitrary array configuration. Modified multiple signal classification (MMUSIC) algorithm is discussed for estimating coherent sources using the non-uniform linear array. Simulation results show that the performance of the direction of arrival (DOA) estimation has been improved effectively on contrast with other array structures, the validity of the proposed method is proved.

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McEwan, A. L., and S. Collins. "Efficient, ROM-less DDFS using non-linear interpolation and non-linear DAC." Analog Integrated Circuits and Signal Processing 48, no. 3 (May 15, 2006): 231–37. http://dx.doi.org/10.1007/s10470-006-7741-5.

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Manoharan, Samuel, and Narain Ponraj. "Analysis of Complex Non-Linear Environment Exploration in Speech Recognition by Hybrid Learning Technique." December 2020 2, no. 4 (February 19, 2021): 202–9. http://dx.doi.org/10.36548//jiip.2020.4.005.

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Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the combination of statistical-based design and Support Vector Machine (SVM) based learning techniques. Dynamic Bayes network is a dominant technique related to speech processing characterizing stack co-occurrences. This method is derived from mathematical and statistical formalism. It is also used to predict the word sequences along with the posterior probability method with the help of phonetic word unit recognition. This research involves the complexities of signal processing that it is possible to combine sentences with various types of noises at different signal-to-noise ratios (SNR) along with the measure of comparison between the two techniques.

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Manoharan, Samuel, and Narain Ponraj. "Analysis of Complex Non-Linear Environment Exploration in Speech Recognition by Hybrid Learning Technique." December 2020 2, no. 4 (February 19, 2021): 202–9. http://dx.doi.org/10.36548/jiip.2020.4.005.

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Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the combination of statistical-based design and Support Vector Machine (SVM) based learning techniques. Dynamic Bayes network is a dominant technique related to speech processing characterizing stack co-occurrences. This method is derived from mathematical and statistical formalism. It is also used to predict the word sequences along with the posterior probability method with the help of phonetic word unit recognition. This research involves the complexities of signal processing that it is possible to combine sentences with various types of noises at different signal-to-noise ratios (SNR) along with the measure of comparison between the two techniques.

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Yu, W.-W., U. R. Acharya, T.-C. Lim, and H. W. Low. "Non-linear analysis of body responses to functional electrical stimulation on hemiplegic subjects." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 223, no. 6 (May 22, 2009): 653–62. http://dx.doi.org/10.1243/09544119jeim535.

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Functional electrical stimulation (FES) is a method of applying low-level electrical currents to restore or improve body functions lost through nervous system impairment. FES is applied to peripheral nerves that control specific muscles or muscle groups. Application of advanced signal computing techniques to the medical field has helped to achieve practical solutions to the health care problems accurately. The physiological signals are essentially non-stationary and may contain indicators of current disease, or even warnings about impending diseases. These indicators may be present at all times or may occur at random on the timescale. However, to study and pinpoint these subtle changes in the voluminous data collected over several hours is tedious. These signals, e.g. walking-related accelerometer signals, are not simply linear and involve non-linear contributions. Hence, non-linear signal-processing methods may be useful to extract the hidden complexities of the signal and to aid physicians in their diagnosis. In this work, a young female subject with major neuromuscular dysfunction of the left lower limb, which resulted in an asymmetric hemiplegic gait, participated in a series of FES-assisted walking experiments. Two three-axis accelerometers were attached to her left and right ankles and their corresponding signals were recorded during FES-assisted walking. The accelerometer signals were studied in three directions using the Hurst exponent H, the fractal dimension (FD), the phase space plot, and recurrence plots (RPs). The results showed that the H and FD values increase with increasing FES, indicating more synchronized variability due to FES for the left leg (paralysed leg). However, the variation in the normal right leg is more chaotic on FES.

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Ji-jun, Mahmoudi, Baleanu, and Maleki. "On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors." Symmetry 11, no. 6 (June 20, 2019): 820. http://dx.doi.org/10.3390/sym11060820.

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In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.

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Ciampa, M. "Continuous LTI Input–Output Stable Systems on $${L^{p}(\mathbb {R})}$$ and $${\mathscr {D'}_{L^{p}}(\mathbb {R})}$$ Associated with Differential Equations: Existence, Invertibility Conditions and Inversion." Circuits, Systems, and Signal Processing 40, no. 9 (March 18, 2021): 4301–45. http://dx.doi.org/10.1007/s00034-021-01689-7.

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AbstractA usual problem in analog signal processing is to ascertain the existence of a continuous single-input single-output linear time-invariant input–output stable system associated with a linear differential equation, i.e., of a continuous system such that, for every input signal in a given space of signals, yields an output, in the same space, which verifies the equation with known term the input, and to ascertain the existence of its inverse system. In this paper, we consider, as space of signals, the usual Banach space of $${L^{p}}$$ L p functions, or the space of distributions spanned by $${L^{p}}$$ L p functions and by their distributional derivatives, of any order (input spaces which include signals with not necessarily left-bounded support), we give a systematic theoretical analysis of the existence, uniqueness and invertibility of continuous linear time-invariant input–output stable systems (both causal and non-causal ones) associated with the differential equation and, in case of invertibility, we characterize the continuous inverse system. We also give necessary and sufficient conditions for causality. As an application, we consider the problem of finding a suitable almost inverse of a causal continuous linear time-invariant input–output stable non-invertible system, defined on the space of finite-energy functions, associated with a simple differential equation.

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Marsi, Stefano, Jhilik Bhattacharya, Romina Molina, and Giovanni Ramponi. "A Non-Linear Convolution Network for Image Processing." Electronics 10, no. 2 (January 17, 2021): 201. http://dx.doi.org/10.3390/electronics10020201.

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This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the input data. This type of layers performs, as we demonstrate, a non-linear transfer function. The features generated by these layers, compared to the ones generated by canonical CNN layers, are more complex and more suitable to fit to the local characteristics of the images. Networks composed by these non-linear layers offer performance comparable with or superior to the ones which use canonical Convolutional Networks, using fewer layers and a significantly lower number of features. Several applications of these newly conceived networks to classical image-processing problems are analyzed. In particular, we consider: Single-Image Super-Resolution (SISR), Edge-Preserving Smoothing (EPS), Noise Removal (NR), and JPEG artifacts removal (JAR).

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Chen, Jian Hui. "An Improved EMD Method and its Application in Nonstationary Signals Analysis." Advanced Materials Research 429 (January 2012): 313–17. http://dx.doi.org/10.4028/www.scientific.net/amr.429.313.

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Empirical mode decomposition (EMD) method based on HHT has exhibited unique advantages such as adaptability and highly efficiency in many nonlinear, nonstationary signals processing applications. It breaks the uncertainty principle limit, but the traditional EMD still has its deficiencies. In this article, we construct a new wavelet which has excellent decomposing-frequency performance and energy concentration, and then an improved EMD method based on this wavelet is presented. Results of numerical simulation show the validity and efficiency of the method proposed in paper are better than traditional one. Furthermore, some foreseeable trends of time-frequency distribution technologies are described. The systems in reality, strictly speaking, tend to non-linear, so most practical signals are non-stationary random signals. Nonlinear, nonstationary signals analysis is a very significant and difficult problem in almost all technical fields such as automation, communication, aerospace- engineering, biomedicine, structural fault diagnosis and so on. Owed to the rapid development of large scale integrated circuit technology and artificial intelligence, the exploration of signal processing theories have got a sharply impetus. A series of new modern signal processing theories and methods have appeared to meet the need of time-frequency joint analysis of nonlinear, non-Gaussian and non-stationary signals, including discrete short-time Fourier transform, wavelet transform, Hilbert-Huang transform and so on. Time-frequency joint analysis can observe the evolution of the signal in the time domain and the frequency domain simultaneously, provide local time-frequency characteristics of the signal.

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Kumar, Mohit, and P. Keith Kelly. "Non-Linear Signal Processing Methods for UAV Detections from a Multi-Function X-Band Radar." Drones 7, no. 4 (April 6, 2023): 251. http://dx.doi.org/10.3390/drones7040251.

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This article develops the applicability of non-linear processing techniques such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA), and Multiple-input-multiple-output (MIMO) for the purpose of enhanced UAV detections using portable radar systems. The combined scheme has many advantages and the potential for better detection and classification accuracy. Some of the benefits are discussed here with a phased array platform in mind, the novel portable phased array Radar (PWR) by Agile RF Systems (ARS), which offers quadrant outputs. CS and IAA both show promising results when applied to micro-Doppler processing of radar returns owing to the sparse nature of the target Doppler frequencies. This shows promise in reducing the dwell time and increases the rate at which a volume can be interrogated. Real-time processing of target information with iterative and non-linear solutions is possible now with the advent of GPU-based graphics processing hardware. Simulations show promising results.

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Palmieri, Francesco A. N. "Learning Non-Linear Functions With Factor Graphs." IEEE Transactions on Signal Processing 61, no. 17 (September 2013): 4360–71. http://dx.doi.org/10.1109/tsp.2013.2270463.

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Fan, Jicong, and Tommy W. S. Chow. "Non-linear matrix completion." Pattern Recognition 77 (May 2018): 378–94. http://dx.doi.org/10.1016/j.patcog.2017.10.014.

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Butyrskiy, E. "Couscous-constant approximation in signal filtration task." National Security and Strategic Planning 2021, no. 1 (May 5, 2021): 34–43. http://dx.doi.org/10.37468/2307-1400-2021-1-34-43.

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The paper considers the task of assessing the state of a nonlinear dynamic system, based on the couscous-linear approximation of non-linear functions included in the state and observa-tion equation. Examples of the method presented in filtration tasks are given and it is shown that the use of couscous-linear approximation allows at least half the margin of sampling error by the Kalman-Busey filter compared to the first-order approximation. Dynamic systems and processing algorithms in the form of vector-matrix equations have been obtained for multidi-mensional systems.

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García-Raffi, Luis, E. Jiménez Fernández, and Enrique Sánchez Pérez. "A Non-linear Approach to Signal Processing by Means of Vector Measure Orthogonal Functions." Publications of the Research Institute for Mathematical Sciences 49, no. 2 (2013): 241–69. http://dx.doi.org/10.4171/prims/105.

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Gupta, Payal, and Monika Agrawal. "Higher-Order Statistics-Based Non-uniform Linear Array for Underdetermined DoA Estimation of Non-circular Signals." Circuits, Systems, and Signal Processing 41, no. 5 (January 27, 2022): 2719–49. http://dx.doi.org/10.1007/s00034-021-01903-6.

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Roshanmanesh, Sanaz, Farzad Hayati, and Mayorkinos Papaelias. "Utilisation of Ensemble Empirical Mode Decomposition in Conjunction with Cyclostationary Technique for Wind Turbine Gearbox Fault Detection." Applied Sciences 10, no. 9 (May 11, 2020): 3334. http://dx.doi.org/10.3390/app10093334.

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In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.

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Rodenacker, Karsten, Klaus Hahn, Gerhard Winkler, and Dorothea P. Auer. "SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA." Image Analysis & Stereology 19, no. 3 (May 3, 2011): 189. http://dx.doi.org/10.5566/ias.v19.p189-194.

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Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.

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Qian, Rong, Defu Jiang, and Wei Fu. "FPGA implementation of closed‐loop compensation for LFMCW signal non‐linear distortions." IET Signal Processing 13, no. 2 (April 2019): 192–98. http://dx.doi.org/10.1049/iet-spr.2018.5298.

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Journal articles on the topic 'NON-LINEAR SIGNAL PROCESSING'

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