Статті в журналах: "Single value decomposition"

1

KOH, MIN-SUNG. "A QUINTET SINGULAR VALUE DECOMPOSITION THROUGH EMPIRICAL MODE DECOMPOSITIONS." Advances in Adaptive Data Analysis 06, no. 02n03 (April 2014): 1450010. http://dx.doi.org/10.1142/s1793536914500101.

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A particular quintet singular valued decomposition (Quintet-SVD) is introduced in this paper via empirical mode decompositions (EMDs). The Quintet-SVD results in four specific orthogonal matrices with a diagonal matrix of singular values. Furthermore, this paper shows relationships between the Quintet-SVD and traditional SVD, generalized low rank approximations of matrices (GLRAM) of one single matrix, and EMDs. One application of the Quintet-SVD for speech enhancement is shown and compared with an application of traditional SVD.

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2

Muafak Rashied, Manar. "Iraqi Plate Number Recognition Using Single Value Decomposition (SVD)." Diyala Journal For Pure Science 14, no. 2 (April 1, 2018): 140–52. http://dx.doi.org/10.24237/djps.1402.391a.

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3

Zhao, Xuezhi, and Bangyan Ye. "Separation of Single Frequency Component Using Singular Value Decomposition." Circuits, Systems, and Signal Processing 38, no. 1 (May 25, 2018): 191–217. http://dx.doi.org/10.1007/s00034-018-0852-2.

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4

Rahul, Mayur, Vinod Kumar, Vikash Yadav, and Rishabh. "Movie Recommender System using Single Value Decomposition and K-means Clustering." IOP Conference Series: Materials Science and Engineering 1022 (January 19, 2021): 012100. http://dx.doi.org/10.1088/1757-899x/1022/1/012100.

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5

Lantsov, V. N., and I. S. Melnik. "Development of an equalizer using singular value decomposition." Journal of Physics: Conference Series 2373, no. 2 (December 1, 2022): 022030. http://dx.doi.org/10.1088/1742-6596/2373/2/022030.

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Abstract The problems of wireless communication systems are investigated. A model and hardware implementation of a system for estimating and correcting amplitude distortions of a radio signal using an algorithm based on Singular Value Decomposition (SVD) has been developed. The analysis of the efficiency of the system when processing the LTE signal is performed. The model was obtained in the Matlab system with modeling the propagation environment, as well as time and frequency tuning blocks using the LTE Toolbox library. FPGA resources with hardware implementation are estimated. Testing the signal-to-noise ratio of the two-antenna equalizer revealed its advantage over the single-antenna configuration by 2.8 dB.

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6

Kanjilal, P. P., S. Palit, and G. Saha. "Fetal ECG extraction from single-channel maternal ECG using singular value decomposition." IEEE Transactions on Biomedical Engineering 44, no. 1 (1997): 51–59. http://dx.doi.org/10.1109/10.553712.

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7

Jackson, G. M., I. M. Mason, and S. A. Greenhalgh. "Principal component transforms of triaxial recordings by singular value decomposition." GEOPHYSICS 56, no. 4 (April 1991): 528–33. http://dx.doi.org/10.1190/1.1443068.

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Polarization analysis can be achieved efficiently by treating a time window of a single‐station triaxial recording as a matrix and doing a singular value decomposition (SVD) of this seismic data matrix. SVD of the triaxial data matrix produces an eigenanalysis of the data covariance (cross‐energy) matrix and a rotation of the data onto the directions given by the eigenanalysis (Karhunen‐Loève transform), all in one step. SVD provides a complete principal components analysis of the data in the analysis time window. Selection of this time window is crucial to the success of the analysis and is governed by three considerations: the window should contain only one arrival; the window should be such that the signal‐to‐noise ratio is maximized; and the window should be long enough to be able to discriminate random noise from signal. The SVD analysis provides estimates of signal, signal polarization directions, and noise. An F‐test is proposed which gives the confidence level for the hypothesis of rectilinear polarization. This paper illustrates the analysis and interpretation of synthetic rectilinearly and elliptically polarized arrivals at a single triaxial station by SVD.

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8

Feng, Feng, Hamido Fujita, Young Bae Jun, and Madad Khan. "Decomposition of Fuzzy Soft Sets with Finite Value Spaces." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/902687.

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The notion of fuzzy soft sets is a hybrid soft computing model that integrates both gradualness and parameterization methods in harmony to deal with uncertainty. The decomposition of fuzzy soft sets is of great importance in both theory and practical applications with regard to decision making under uncertainty. This study aims to explore decomposition of fuzzy soft sets with finite value spaces. Scalar uni-product and int-product operations of fuzzy soft sets are introduced and some related properties are investigated. Usingt-level soft sets, we define level equivalent relations and show that the quotient structure of the unit interval induced by level equivalent relations is isomorphic to the lattice consisting of allt-level soft sets of a given fuzzy soft set. We also introduce the concepts of crucial threshold values and complete threshold sets. Finally, some decomposition theorems for fuzzy soft sets with finite value spaces are established, illustrated by an example concerning the classification and rating of multimedia cell phones. The obtained results extend some classical decomposition theorems of fuzzy sets, since every fuzzy set can be viewed as a fuzzy soft set with a single parameter.

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9

Barnova, Katerina, Radana Kahankova, Rene Jaros, Martina Litschmannova, and Radek Martinek. "A comparative study of single-channel signal processing methods in fetal phonocardiography." PLOS ONE 17, no. 8 (August 19, 2022): e0269884. http://dx.doi.org/10.1371/journal.pone.0269884.

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Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

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10

Acosta, Martha N., Edgar Gomez, Francisco Gonzalez-Longatt, Manuel A. Andrade, Ernesto Vazquez, and Emilio Barocio. "Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements." IEEE Access 9 (2021): 125999–6010. http://dx.doi.org/10.1109/access.2021.3111006.

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11

So, H. C., Frankie K. W. Chan, and Weize Sun. "Efficient frequency estimation of a single real tone based on principal singular value decomposition." Digital Signal Processing 22, no. 6 (December 2012): 1005–9. http://dx.doi.org/10.1016/j.dsp.2012.05.010.

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12

Fang, Liang, and Hongchun Sun. "Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery." Applied Sciences 8, no. 9 (August 23, 2018): 1441. http://dx.doi.org/10.3390/app8091441.

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A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.

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13

Li, Linao, and Xinlao Wei. "Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition." Energies 14, no. 24 (December 20, 2021): 8579. http://dx.doi.org/10.3390/en14248579.

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Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

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14

Tang, Zhou, and Wang. "Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System." Applied Sciences 9, no. 15 (July 29, 2019): 3067. http://dx.doi.org/10.3390/app9153067.

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The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is the combination of the OFDM and MIMO technologies, which could improve the system capacity and make efficient utilization of the frequency spectrum. This paper utilizes space-time block coding (STBC) to achieve diversity gains and combat the channel fading. However, channel estimation is an essential block for space-time block decoding (STBD). Many channel estimation methods are utilized for the single antenna OFDM system, but they cannot be directly applied to the multiple antennas system due to the interference from other antennas. In this paper, orthogonal pilot sequences are designed to suppress the interference of pilot symbols from other transmit antennas. This paper also derives a minimum mean square error (MMSE) channel estimation method in MIMO-OFDM systems. The MMSE method involves the inverse operation of the channel autocorrelation matrix, which has a large calculation complexity. To further reduce the complexity of the MMSE method, the singular value decomposition (SVD) is used to decompose the channel autocorrelation matrix, which avoids the inverse operation. Simulation results verify that the SVD channel estimation method with comb-type pilots and STBC can be effectively adapted to multipath propagation conditions.

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15

Kaur, Sumit, R. K. Bansal, Mamta Mittal, Lalit Mohan Goyal, Iqbaldeep Kaur, Amit Verma, and Le Hoang Son. "Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images." Journal of the Indian Society of Remote Sensing 47, no. 3 (January 16, 2019): 427–37. http://dx.doi.org/10.1007/s12524-019-00946-2.

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16

Eissens-van der Laan, Monique, Manda Broekhuis, Marjolein van Offenbeek, and Kees Ahaus. "Service decomposition: a conceptual analysis of modularizing services." International Journal of Operations & Production Management 36, no. 3 (March 7, 2016): 308–31. http://dx.doi.org/10.1108/ijopm-06-2015-0370.

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Purpose – Applying “modularity” principles in services is gaining in popularity. The purpose of this paper is to enrich existing service modularity theory and practice by exploring how services are being decomposed and how the modularization aim and the routineness of the service(s) involved may link to different decomposition logics. The authors argue that these are fundamental questions that have barely been addressed. Design/methodology/approach – The authors first built a theoretical framework of decomposition steps and the design choices involved that distinguished six decomposition logics. The authors conducted a systematic literature search that generated 18 empirical articles describing 16 service modularity cases. The authors analysed these cases in terms of decomposition logic and two main contingencies: modularization aim and service routineness. Findings – Only three of the 18 articles explicitly addressed the service decomposition by reflecting on the underlying design choices. By unravelling the decomposition in each case, the authors were able to identify the decomposition logic and found four of the six theoretically derived logics: single-level process oriented; single-level outcome oriented; multilevel outcome oriented; and multilevel combined orientation. Although the authors did not find a direct relationship between the modularization aim and the decomposition logic, the authors did find that single-level decomposition logics seem to be mainly applied in non-routine service offerings whereas the multilevel ones are mainly applied in routine service offerings. Originality/value – By contributing to a common understanding of modular service decomposition and proposing a framework that explicates the design choices involved, the authors enable an enhanced application of the modularity concept in services.

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17

Li, Haijun, Yongpeng Zhao, Changxi Ma, Ke Wang, Xiaoting Huang, and Wentao Zhang. "Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM." Journal of Advanced Transportation 2022 (September 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/2589681.

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Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed to perform short-term predictions on each component separately. The predicted value of each component is accumulated to obtain the original passenger flow’ predicted result. The example shows that, compared with the single LSTM and other hybrid models, the proposed method offers a greater overall prediction accuracy in the experimental days, and the method has specific applicability.

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18

Xu, Sheng, Min Zhang, Siyu Li, Moyu Yi, Shigen Shen, Hongyan Zeng, Jinze Du, and Yong Pan. "Non-Isothermal Decomposition Kinetic of Polypropylene/Hydrotalcite Composite." Journal of Nanoscience and Nanotechnology 19, no. 11 (November 1, 2019): 7493–501. http://dx.doi.org/10.1166/jnn.2019.16675.

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P3O5-10 pillared Mg/Al hydrotalcite (HTs) as a functional fire-retarding filler was successfully prepared by impregnation-reconstruction, where the HTs was used to prepare polypropylene (PP) and HTs composite (PP/HTs). Thermal decomposition was crucial for correctly identifying the thermal behavior for the PP/HTs, and studied using thermogravimetry (TG) at different heating rates. Based on single TG curves and Málek method, as well as 41 mechanism functions, the thermal decompositions of the PP/HTs composite and PP in nitrogen atmosphere were studied under non-isothermal conditions. The mechanism functions of the thermal decomposition reactions for the PP/HTs composite and PP were separately “chemical reaction F3” and “phase boundary reaction R2,” which were also in good agreement with corresponding experimental data. It was found that the addition of the HTs increased the apparent activation energy Ea of the PP/HTs comparing to the PP, which improved the thermal stability of the polypropylene. A difference in the set of kinetic and thermodynamic parameters was also observed between the PP/HTs and PP, particularly with respect to lower ΔS≠ value assigned to higher thermal stability of the PP/HTs composite.

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19

Hu, Wanqi, Huiting Liu, Dicheng Chen, Tianyu Qiu, Hongwei Sun, Chunyan Xiong, Jianzhong Lin, Di Guo, Hao Chen, and Xiaobo Qu. "Coil Combination of Multichannel Single Voxel Magnetic Resonance Spectroscopy with Repeatedly Sampled In Vivo Data." Molecules 26, no. 13 (June 25, 2021): 3896. http://dx.doi.org/10.3390/molecules26133896.

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Magnetic resonance spectroscopy (MRS), as a noninvasive method for molecular structure determination and metabolite detection, has grown into a significant tool in clinical applications. However, the relatively low signal-to-noise ratio (SNR) limits its further development. Although the multichannel coil and repeated sampling are commonly used to alleviate this problem, there is still potential room for promotion. One possible improvement way is combining these two acquisition methods so that the complementary of them can be well utilized. In this paper, a novel coil-combination method, average smoothing singular value decomposition, is proposed to further improve the SNR by introducing repeatedly sampled signals into multichannel coil combination. Specifically, the sensitivity matrix of each sampling was pretreated by whitened singular value decomposition (WSVD), then the smoothing was performed along the repeated samplings’ dimension. By comparing with three existing popular methods, Brown, WSVD, and generalized least squares, the proposed method showed better performance in one phantom and 20 in vivo spectra.

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20

Meng, Tao, Huanchang Wei, Feng Gao, and Huichao Shi. "Measurement of Flow Fluctuation in the Flow Standard Facility Based on Singular Value Decomposition." Sensors 21, no. 20 (October 15, 2021): 6850. http://dx.doi.org/10.3390/s21206850.

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In order to accurately evaluate the flow stability of the flow standard facility, the flow fluctuation in the standard facility needs to be accurately measured. However, the flow fluctuation signal is always superimposed with the fluctuation signal of the measuring flowmeter or measurement system (mainly noise), which leads to inaccurate measurement of the flow fluctuation and even an unreliable evaluation result of the flow stability. In addition, when there are multiple fluctuation sources, flow fluctuations with different frequencies are superimposed together, which is extremely unfavorable for evaluating the impact of flow fluctuation with different single frequencies. In this paper, a new measuring method was proposed to obtain the fluctuation signal and the flow fluctuation based on singular value decomposition (SVD). Simulation experiments on the fluctuation signal (single frequency and multiple frequencies) under different levels of noise were conducted, and simulation results showed that the proposed method could accurately obtain the fluctuation signal and the flow fluctuation, even under high noise. Finally, an experimental platform was set-up based on a water flow standard facility and a flow fluctuation generator, and experiments on the output signal of a venturi flowmeter were carried out. The experiment results showed that the proposed method could effectively obtain the fluctuation signal and accurately measure the flow fluctuation.

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21

Qu, Gang, Xiangfeng Meng, Xiulun Yang, Huazheng Wu, Pengwei Wang, Wenqi He, and Hongyi Chen. "Optical color watermarking based on single-pixel imaging and singular value decomposition in invariant wavelet domain." Optics and Lasers in Engineering 137 (February 2021): 106376. http://dx.doi.org/10.1016/j.optlaseng.2020.106376.

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22

Ren, Hongrui, and Xiaoman Feng. "Calculating vertical deformation using a single InSAR pair based on singular value decomposition in mining areas." International Journal of Applied Earth Observation and Geoinformation 92 (October 2020): 102115. http://dx.doi.org/10.1016/j.jag.2020.102115.

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23

Edelman, Alan, and Sungwoo Jeong. "On the Cartan decomposition for classical random matrix ensembles." Journal of Mathematical Physics 63, no. 6 (June 1, 2022): 061705. http://dx.doi.org/10.1063/5.0087010.

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We complete Dyson’s dream by cementing the links between symmetric spaces and classical random matrix ensembles. Previous work has focused on a one-to-one correspondence between symmetric spaces and many but not all of the classical random matrix ensembles. This work shows that we can completely capture all of the classical random matrix ensembles from Cartan’s symmetric spaces through the use of alternative coordinate systems. In the end, we have to let go of the notion of a one-to-one correspondence. We emphasize that the KAK decomposition traditionally favored by mathematicians is merely one coordinate system on the symmetric space, albeit a beautiful one. However, other matrix factorizations, especially the generalized singular value decomposition from numerical linear algebra, reveal themselves to be perfectly valid coordinate systems that one symmetric space can lead to many classical random matrix theories. We establish the connection between this numerical linear algebra viewpoint and the theory of generalized Cartan decompositions. This, in turn, allows us to produce yet more random matrix theories from a single symmetric space. Yet, again, these random matrix theories arise from matrix factorizations, though ones that we are not aware have appeared in the literature.

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24

Muraleedharan, Karuvanthodi, and Labeeb Pasha. "Thermal decomposition of potassium titanium oxalate." Journal of the Serbian Chemical Society 76, no. 7 (2011): 1015–26. http://dx.doi.org/10.2298/jsc100615083m.

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The thermal decomposition of potassium titanium oxalate (PTO) was studied using non-isothermal thermogravimetry at different heating rates under a nitrogen atmosphere. The thermal decomposition of PTO proceeds mainly through five stages forming potassium titanate. The theoretical and experimental mass loss data are in good agreement for all stages of the thermal decomposition of PTO. The third thermal decomposition stage of PTO, the combined elimination of carbon monoxide and carbon dioxide, were subjected to kinetic analyses both by the method of model fitting and by the model free approach, which is based on the isoconversional principle. The model free analyses showed that the combined elimination of carbon monoxide and carbon dioxide and formation of final titanate in the thermal decomposition of PTO proceeds through a single step with an activation energy value of about 315 kJ mol-1.

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25

Loukhaoukha, Khaled. "Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain." Journal of Optimization 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/921270.

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We present a new optimal watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) using multiobjective ant colony optimization (MOACO). A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs) instead of a single scaling factor (SSF). Determining the optimal values of the multiple scaling factors (MSFs) is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.

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26

Bergamo, Genevile Carife, Carlos Tadeu dos Santos Dias, and Wojtek Janusz Krzanowski. "Distribution-free multiple imputation in an interaction matrix through singular value decomposition." Scientia Agricola 65, no. 4 (2008): 422–27. http://dx.doi.org/10.1590/s0103-90162008000400015.

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Some techniques of multivariate statistical analysis can only be conducted on a complete data matrix, but the process of data collection often misses some elements. Imputation is a technique by which the missing elements are replaced by plausible values, so that a valid analysis can be performed on the completed data set. A multiple imputation method is proposed based on a modification to the singular value decomposition (SVD) method for single imputation, developed by Krzanowski. The method was evaluated on a genotype × environment (G × E) interaction matrix obtained from a randomized blocks experiment on Eucalyptus grandis grown in multienvironments. Values of E. grandis heights in the G × E complete interaction matrix were deleted randomly at three different rates (5%, 10%, 30%) and were then imputed by the proposed methodology. The results were assessed by means of a general measure of performance (Tacc), and showed a small bias when compared to the original data. However, bias values were greater than the variability of imputations relative to their mean, indicating a smaller accuracy of the proposed method in relation to its precision. The proposed methodology uses the maximum amount of available information, does not have any restrictions regarding the pattern or mechanism of the missing values, and is free of assumptions on the data distribution or structure.

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27

Liu, Dong, Xu Lai, Zhihuai Xiao, Dong Liu, Xiao Hu, and Pei Zhang. "Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition." Shock and Vibration 2020 (July 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/6542913.

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Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods.

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28

Guler, Demet Cilden, Ece S. Conguroglu, and Chingiz Hajiyev. "Single-Frame Attitude Determination Methods for Nanosatellites." Metrology and Measurement Systems 24, no. 2 (June 27, 2017): 313–24. http://dx.doi.org/10.1515/mms-2017-0023.

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AbstractSingle-frame methods of determining the attitude of a nanosatellite are compared in this study. The methods selected for comparison are: Single Value Decomposition (SVD), q method, Quaternion ESTimator (QUEST), Fast Optimal Attitude Matrix (FOAM) − all solving optimally the Wahba’s problem, and the algebraic method using only two vector measurements. For proper comparison, two sensors are chosen for the vector observations on-board: magnetometer and Sun sensors. Covariance results obtained as a result of using those methods have a critical importance for a non-traditional attitude estimation approach; therefore, the variance calculations are also presented. The examined methods are compared with respect to their root mean square (RMS) error and variance results. Also, some recommendations are given.

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29

Zou, Wan Jie, Chuan Gao Li, and Yun Xia Zhang. "The Random Response of a Single Degree of Freedom Generalized Maxwell Damping Structure." Applied Mechanics and Materials 744-746 (March 2015): 1648–53. http://dx.doi.org/10.4028/www.scientific.net/amm.744-746.1648.

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Using the model of stationary white noise excitation, defining the exact analytic method of random response and nearer value of earthquake action about single degree of freedom generalized Maxwell damping, firstly transform the motion equation into standard form, with the Laplace transform and Inverse Laplace transform method, obtained the exact analytical formula of structural response, calculate the response variance by the complex modal method and frequency domain decomposition and make comparisons, The response variance decomposition for the first standard vibrator and second order of the standard vibrator, According to the corresponding relationship between maximum response of the second order vibrator and The design response spectrum, calculated the maximum response by it, base on the maximum response proportion of the first standard vibrator and second, obtained the design of the structural response values and its corresponding earthquake force.

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30

Sirat, J. Ariel. "A FAST NEURAL ALGORITHM FOR PRINCIPAL COMPONENT ANALYSIS AND SINGULAR VALUE DECOMPOSITION." International Journal of Neural Systems 02, no. 01n02 (January 1991): 147–55. http://dx.doi.org/10.1142/s0129065791000145.

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We propose a fast neural algorithm to perform Principal Component Analysis (PCA) of a set of examples. It is obtained by simplification of current neural learning rules for PCA. First, we use a single binary neuron to extract a given component by a Hebb-type learning rule (Self-Organized Perceptron). This rule rapidly yields the first principal component. Moreover, as the neuron is binary, convergence is easily interpreted in terms of geometry and trajectory. Then successive components are obtained after projection of examples on the subspace which is supplementary to already learnt components. This avoids mixing the components as in, for example, the “Subspace Method” recently proposed by Oja.11 We have tested this approach on a gaussian distribution of examples: the quality of results is identical to that obtained with methods which diagonalize the correlation matrix computed from the set of examples. A variant of the algorithm is also proposed to perform the Singular Value Decomposition (SVD) which “diagonalizes” an asymmetrical matrix. Performances are as satisfactory as for PCA. Complexity and performances of a VLSI implementation are then estimated from the specifications of the neural VLSI developed in our laboratory. Comparison with hardware implementations of non-neural approaches (SVD) seems to favor neatly the neural approach: the expected speed increase is at least two orders of magnitude for a 100-dimensional SVD calculation.

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31

Chen, Baojia, Baoming Shen, Fajun Zhang, Wenrong Xiao, Fafa Chen, Hongliang Tian, and Shu Chen. "Operation reliability evaluation of cutting tools based on singular value decomposition transform and support vector space." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 2 (April 12, 2018): 175–85. http://dx.doi.org/10.1177/1748006x18766125.

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The traditional reliability evaluation method based on large sample statistics is inefficient for a single or a small batch computer numerical control turning cutting tool due to the inadequate description of time, dynamic process, inaccurate model and individualization. To solve the problem, a new operation reliability evaluation method based on singular value decomposition transform and support vector space is proposed. In this new method, the singular value decomposition is used for the dimensionality reduction of high-dimensional feature data so as to reduce the computational complexity and the redundant components. The hypersphere space of the similar data is established based on the dimension reduction data. The relative distance between the sample points and the hypersphere is then calculated and used to describe the performance of the tool. The semi-normal function is introduced to define the mapping relationship of the relative distance and the operation reliability of the tool. Finally, two cutting tools in the experiment are taken as the research example to verify the effectiveness of the method. The result shows that this method can evaluate the operation reliability of the tool effectively and the singular value decomposition dimensionality reduction improves the accuracy of the evaluation. It provides a new theoretical and practical support for the reliability evaluation of small sample data.

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32

Dong, Shaojiang, Baoping Tang, and Yan Zhang. "A repeated single-channel mechanical signal blind separation method based on morphological filtering and singular value decomposition." Measurement 45, no. 8 (October 2012): 2052–63. http://dx.doi.org/10.1016/j.measurement.2012.05.003.

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33

Pardhu, Thottempudi, and Vijay Kumar. "Novel Implementations of Clutter and Target Discrimination Using Threshold Skewness Method." Traitement du Signal 38, no. 4 (August 31, 2021): 1079–85. http://dx.doi.org/10.18280/ts.380418.

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Now a day’s defence applications associated to novel, army and military war fields are required wall imaging discrimination. As of now many wall-imaging techniques are designed but cannot discriminate the target and clutter with accurate working. Therefore, a novel advance wall image tracking method is required for differentiate the clutter and human target. In this research work single value decomposition technique is used to estimate the range bin behind the wall target. In order to track the target and clutter single-value-decomposition (SVD) is not sufficient, so that along this SVD, threshold skewness (TS) method has been presented. Combination of SVD-TS giving the accurate long range-bin sensing and directed the human’s targets. SVD-TS method is a statistical scheme, which can realise the amplitude ranges through large number of range-bin scans. This technique improves the accuracy by 98.6%, skewness by 8%, and normalised power by 98.9%. These SVD-TS method is more efficient and compete with existed techniques.

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34

WANG, HAIYING, SNEHASIS MUKHOPADHYAY, and SHIAOFEN FANG. "FEATURE DECOMPOSITION ARCHITECTURES FOR NEURAL NETWORKS: ALGORITHMS, ERROR BOUNDS, AND APPLICATIONS." International Journal of Neural Systems 12, no. 01 (February 2002): 69–81. http://dx.doi.org/10.1142/s0129065702001011.

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In recent years, systems consisting of multiple modular neural networks have attracted substantial interest in the neural networks community because of various advantages they offer over a single large monolithic network. In this paper, we propose two basic feature decomposition models (namely, parallel model and tandem model) in which each of the neural network modules processes a disjoint subset of the input features. A novel feature decomposition algorithm is introduced to partition the input space into disjoint subsets solely based on the available training data. Under certain assumptions, the approximation error due to decomposition can be proved to be bounded by any desired small value over a compact set. Finally, the performance of feature decomposition networks is compared with that of a monolithic network in real-world bench-mark pattern recognition and modeling problems.

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35

Liu, Rui. "Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning." Shock and Vibration 2021 (October 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/9544997.

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The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.

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Kwon, Soonil, Mark O. Goodarzi, Kent D. Taylor, Jinrui Cui, Y. D. Ida Chen, Jerome I. Rotter, Willa Hsueh, and Xiuqing Guo. "A Multinomial Ordinal Probit Model with Singular Value Decomposition Method for a Multinomial Trait." Journal of Probability and Statistics 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/419832.

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We developed a multinomial ordinal probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously for association with multidisease status when sample size is much smaller than the number of SNPs. The validity and performance of the method was evaluated via simulation. We applied the method to our real study sample recruited through the Mexican-American Coronary Artery Disease study. We found 3 genes (SORCS1, AMPD1, and PPARα) to be associated with the development of both IGT and IFG, while 5 genes (AMPD2, PRKAA2, C5, TCF7L2, and ITR) with the IGT mechanism only and 6 genes (CAPN10, IL4, NOS3, CD14, GCG, and SORT1) with the IFG mechanism only. These data suggest that IGT and IFG may indicate different physiological mechanism to prediabetes, via different genetic determinants.

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M. Ahmed, Hanaa, and Maisa'a A. A. Khohder. "Steganography Arabic Text Based on Natural Language Process Documents." Journal of Education College Wasit University 1, no. 25 (January 14, 2018): 457–80. http://dx.doi.org/10.31185/eduj.vol1.iss25.132.

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: Obscurity is a main reason whereas computers can not know natural language. It have made great transaction steps trend developing instrument to morphological and syntactic analyzers for Arabic . One of the manners used in security areas is steganography. The rapid development of steganography scripts, it is a large security and confidentiality problem, it becomes necessary to find appropriate protection because of the significance, accuracy and sensitivity of the data during transmitted. In this research is offer in a new method and to use one level to hide, this level is hiding by embedding and addition. The one level is embed a secret message twice, one bit in the LSB in the FFT and the addition of one kashida and add Single-Double Quotation in the same secret message. Using Random Singular Value Decomposition (RSVD) is NRG to find positions that are hiding within the text. Linguistic steganography is covering all the techniques that deal with using written natural language to hide secret message. in this research presents a linguistic steganography for scripts written in Arabic language, using kashida, Single-Double Quotation and Fast Fourier Transform on the bases of using new technique entitled Random Singular Value Decomposition (RSVD) as allocation to hide secret message. The proposed approach is an attempt to present a transform linguistic steganography using one level for hiding to improve implementation of kashida and Single-Double Quotation , and improve the security of the secret message by using Random Singular Value Decomposition (RSVD). Are testing this method in terms of security and capacity, transparency, and robustness and this is way better than previous methods. The proposed algorithm ideal steganography properties.

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38

Vasco, D. W. "Deriving source-time functions using principal component analysis." Bulletin of the Seismological Society of America 79, no. 3 (June 1, 1989): 711–30. http://dx.doi.org/10.1785/bssa0790030711.

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Abstract Factors such as source complexity, microseismic noise, and lateral heterogeneity all introduce nonuniqueness into the source-time function. The technique of principal component analysis is used to factor the moment tensor into a set of orthogonal source-time functions. This is accomplished through the singular value decomposition of the time-varying moment tensor. The adequacy of assuming a single source-time function may then be examined through the singular values of the decomposition. The F test can also be used to assess the significance of the various principal component basis functions. The set of significant basis functions can be used to test models of the source-time functions, including multiple sources. Application of this technique to the Harzer nuclear explosion indicated that a single source-time function was found to adequately explain the moment tensor. It consists of a single pulse appearing on the diagonal elements of the moment-rate tensor. The decomposition of the moment tensor for a deep teleseism in the Bonin Islands revealed three basis functions associated with relatively large singular values. The F test indicated that only two of the principal components were significant. The principal component associated with the largest singular value consists of a large pulse followed 16-sec later by a diminished pulse. The second principal component, a long-period oscillation, appears to be a manifestation of the poor resolution of the moment-rate tensor at low frequencies.

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39

Dong, Xiaoni, Dongqin Fan, Guangrui Wen, Xiaodong Zhang, and Zhifen Zhang. "A new gearbox compound fault recognition approach based on improved double wavelet packet transform." Insight - Non-Destructive Testing and Condition Monitoring 62, no. 4 (April 1, 2020): 232–37. http://dx.doi.org/10.1784/insi.2020.62.4.232.

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A new approach for the diagnosis of compound faults in gearboxes is proposed in this paper. To extract characteristics in the frequency band of non-stationary raw vibration signals, a double-tree complex wavelet packet transform (DTCWPT) is used to decompose the signals. A singular value spectrum (SVP) is generated by performing singular value decomposition (SVD) on the matrix formed by all of the components. The new analysis method, DTCWPT-SVP, is used to diagnose different operating conditions of a gearbox, including single faults and compound faults, with a k-nearest neighbour (kNN) classifier. The results show that the minimum recognition accuracy using DTCWPT-SVP is 91.9% with different values of k in the kNN and that DTCWPT has better performance in signal decomposition than discrete wavelet packet transform (DWPT). Furthermore, the decomposition level used in DTCWPT is analysed in this paper. For the gearbox vibration problem, a level 2 or 3 DTCWPT can achieve good performance.

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40

Lu, Xiao, Xin Dong, Haixia Wang, and Baoye Song. "On Fixed-Point Smoothing for Descriptor Systems with Multiplicative Noise and Single Delayed Observations." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/658153.

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Optimal fixed-point smoothing problem for the descriptor systems with multiplicative noises is considered, where instantaneous and delayed observations are available. Standard singular value decomposition is used to give the restricted equivalent delayed system, where the observations also include two different types of measurements. Reorganized innovation lemma and projection theorem are used to give the fixed-point smoother for the restricted equivalent delayed system. The fixed-point smoother is given in terms of recursive Riccati equations.

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41

NEGI, S., and S. CHATURVEDI. "NORMAL MODE ANALYSIS OF A SINGLE-WALLED CARBON NANOTUBE BASED ON MOLECULAR DYNAMIC: A SINGULAR VALUE DECOMPOSITION STUDY." International Journal of Nanoscience 09, no. 05 (October 2010): 471–86. http://dx.doi.org/10.1142/s0219581x10007125.

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The complete set of significant normal modes of a single-walled carbon nanotube has been extracted using singular value decomposition analysis of this molecular dynamics data. The first part of this study focuses on an isolated single-walled carbon nanotube performed with NVE Molecular Dynamic simulations. Singular value decomposition analysis is then done on this data. Normal modes are excited with an initial radial stretching given to all the atomic coordinates. For the case with 5% initial radial stretching given to the carbon nanotube, the two strongest modes involve radial breathing motion combined with a very slow rotational motion of individual rings of the nanotube. There is good agreement between the calculated frequency of radial breathing modes and published experimental measurements, as also the inverse scaling of this frequency with tube diameter. The coupling between these two motions weakens for a smaller initial perturbation. The next eight most significant modes are divided into two classes. The first class is characterized by mz = 0, i.e., axial uniformity and produces azimuthal variation in the radial positions of atoms, with a finite azimuthal mode number. The second class of modes has mθ = 0, with mz = 1 and 2, are with radial uniformity and leads to shifts in the X- and Y-centroid locations of different rings. Mode frequency and the associated spatial distortion are thus obtained for all the above-mentioned modes. Under NPT conditions, similar to laboratory conditions, i.e., at a constant temperature and pressure, mode frequencies change only slightly, but the hierarchy of modes is slightly different. External excitation produced at one of the normal mode frequencies, corresponding to centroid motion with (mθ = 0, mz = 1), shows a significant and steady increase in the amplitude of centroid displacement. Excitation at the second harmonic frequency leads to an initial increase in displacement amplitude, but eventual saturation. These conclusions are important for the application of carbon nanotubes in nanodevices, e.g., as nanomotors.

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42

Diblík, Josef, Denis Khusainov, Oleksandra Kukharenko, and Zdeněk Svoboda. "Solution of the First Boundary-Value Problem for a System of Autonomous Second-Order Linear Partial Differential Equations of Parabolic Type with a Single Delay." Abstract and Applied Analysis 2012 (2012): 1–27. http://dx.doi.org/10.1155/2012/219040.

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The first boundary-value problem for an autonomous second-order system of linear partial differential equations of parabolic type with a single delay is considered. Assuming that a decomposition of the given system into a system of independent scalar second-order linear partial differential equations of parabolic type with a single delay is possible, an analytical solution to the problem is given in the form of formal series and the character of their convergence is discussed. A delayed exponential function is used in order to analytically solve auxiliary initial problems (arising when Fourier method is applied) for ordinary linear differential equations of the first order with a single delay.

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43

Huang, Qinyuan, Qiang Li, Maoxia Ran, Xin Liu, and Ying Zhou. "Threshold-Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic-Based Internal Defect Detection of Arc Magnets." Shock and Vibration 2021 (March 13, 2021): 1–21. http://dx.doi.org/10.1155/2021/6636873.

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The acoustic-based internal defect detection is essential to ensure the quality of arc magnets efficiently. Swarm decomposition (SWD) is conducive to processing acoustic signals, but it is still confronted with threshold optimization problems. Especially, the existing optimization methods for the SWD thresholds are merely available for a single signal with exclusive characteristics, instead of the various signals with similar characteristics. Therefore, a threshold-optimized SWD using grey wolf optimizer (GWO) is proposed to solve these issues and applied to detect the internal defects of arc magnets. In this method, a fitness function is designed to indicate the relationship between the SWD thresholds and the overall decomposition effect of similar signals. The minimum value of it corresponds to the threshold setting yielding the optimal decomposition. GWO is used for searching such a minimum value, and the obtained optimal threshold setting allows SWD to decompose any signal into a series of oscillatory components. The frequency information in the two oscillatory components with the highest energy ratio is extracted as the internal defect features. Random forest is carried out to identify these features. Experimentally, the detection accuracy reaches above 97%, and the detection speed per single arc magnet does not exceed 3.4 seconds. The proposed method cannot only determine the unified threshold setting of SWD for similar signals but also achieve an accurate, rapid detection for the internal defects.

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44

Goding, Eric, and Crista Arangala. "A mathematical analysis of world cup advertisements." Discrete Mathematics, Algorithms and Applications 08, no. 04 (November 8, 2016): 1650062. http://dx.doi.org/10.1142/s1793830916500622.

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Qualitative content analysis is the most common method to compare advertisements cross-culturally or cross-generationally. However, quantitative methods, such as chi-square or Fisher tests, can also be used. In this paper, we introduce results for Fisher tests, seriation and single-value decomposition that prove useful in determining similarities and differences in cultural appeals in advertising.

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45

Abdelaziz, Abdulrahman B., Mohammad A. Rahimi, Muhammad R. Alrabeiah, Ahmed B. Ibrahim, Ahmed S. Almaiman, Amr M. Ragheb, and Saleh A. Alshebeili. "Photoplethysmography Data Reduction Using Truncated Singular Value Decomposition and Internet of Things Computing." Electronics 12, no. 1 (January 2, 2023): 220. http://dx.doi.org/10.3390/electronics12010220.

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Biometric-based identity authentication is integral to modern-day technologies. From smart phones, personal computers, and tablets to security checkpoints, they all utilize a form of identity check based on methods such as face recognition and fingerprint-verification. Photoplethysmography (PPG) is another form of biometric-based authentication that has recently been gaining momentum, because it is effective and easy to implement. This paper considers a cloud-based system model for PPG-authentication, where the PPG signals of various individuals are collected with distributed sensors and communicated to the cloud for authentication. Such a model incursarge signal traffic, especially in crowded places such as airport security checkpoints. This motivates the need for a compression–decompression scheme (or a Codec for short). The Codec is required to reduce the data traffic by compressing each PPG signal before it is communicated, i.e., encoding the signal right after it comes off the sensor and before it is sent to the cloud to be reconstructed (i.e., decoded). Therefore, the Codec has two system requirements to meet: (i) produce high-fidelity signal reconstruction; and (ii) have a computationallyightweight encoder. Both requirements are met by the Codec proposed in this paper, which is designed using truncated singular value decomposition (T-SVD). The proposed Codec is developed and tested using a publicly available dataset of PPG signals collected from multiple individuals, namely the CapnoBase dataset. It is shown to achieve a 95% compression ratio and a 99% coefficient of determination. This means that the Codec is capable of delivering on the first requirement, high-fidelity reconstruction, while producing highly compressed signals. Those compressed signals do not require heavy computations to be produced as well. An implementation on a single-board computer is attempted for the encoder, showing that the encoder can average 300 milliseconds per signal on a Raspberry Pi 3. This is enough time to encode a PPG signal prior to transmission to the cloud.

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46

Zhang, Zhenming, Chenlei Liu, Rui Wang, Jian Li, Di Xiahou, Qinzhe Liu, Shi Cao, and Shengrui Zhou. "Mechanical Fault Diagnosis of a Disconnector Operating Mechanism Based on Vibration and the Motor Current." Energies 15, no. 14 (July 18, 2022): 5194. http://dx.doi.org/10.3390/en15145194.

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The mechanical fault diagnosis of a disconnector operating mechanism using a single signal is not sufficiently accurate and reliable. To address this problem, this paper proposes a new fault diagnosis method based on the vibration signal and the motor current signal. First, based on the analysis of the motor stator current signal envelope, segmented envelope RMS values are extracted. Then, the vibration signal of the operating mechanism is processed with VMD (Variational Mode Decomposition). In this paper, the number of modal decompositions K is selected according to the envelope entropy. Second, the effective value of the current segment envelope is fused with the energy entropy value of each IMF component to construct the feature parameters for fault identification. Finally, a fusion weighting algorithm using AdaBoost is proposed to train an SVM as a strong classifier to improve the correct fault diagnosis rate. In this paper, the proposed new diagnosis method is applied to a 220 kV disconnector operating mechanism. The algorithm can effectively identify three operating states of a disconnector operating mechanism.

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47

Wong, Pak Kin, Jian-Hua Zhong, Zhi-Xin Yang, and Chi Man Vong. "A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 6 (November 14, 2016): 1146–61. http://dx.doi.org/10.1177/0954406216632022.

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This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.

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48

Wang, Min, Wei Yan, and Shudao Zhou. "Image Denoising Using Singular Value Difference in the Wavelet Domain." Mathematical Problems in Engineering 2018 (2018): 1–19. http://dx.doi.org/10.1155/2018/1542509.

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Singular value (SV) difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD) is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.

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49

Sha'abani, Mohd Nurul Al Hafiz, Norfaiza Fuad, and Norezmi Jamal. "Eye Blink Artefact Removal of Single Frontal EEG Channel Algorithm using Ensemble Empirical Mode Decomposition and Outlier Detection." Malaysian Journal of Fundamental and Applied Sciences 17, no. 6 (December 31, 2021): 731–41. http://dx.doi.org/10.11113/mjfas.v17n6.2287.

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Recently, the emergence of various applications to use EEG has evolved the EEG device to become wearable with fewer electrodes. Unfortunately, the process of removing artefact becomes challenging since the conventional method requires an additional artefact reference channel or multichannel recording to be working. By focusing on frontal EEG channel recording, this paper proposed an alternative single-channel eye blink artefact removal method based on the ensemble empirical mode decomposition and outlier detection technique. The method removes the segment of the potential eyeblinks artefact on the residual of a pre-determined level of decomposition. An outlier detection technique is introduced to identify the peak of the eyeblink based on the extreme value of the residual signal. The results showed that the corrected EEG signal achieved high correlation, low RMSE and have small differences in PSD when compared to the reference clean EEG. Comparing with an adaptive Wiener filter technique, the corrected EEG signal by the proposed method had better signal-to-artefact ratio.

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50

Borkar, Samarth, and Sanjiv V. Bonde. "A Fusion Based Visibility Enhancement of Single Underwater Hazy Image." International Journal of Advances in Applied Sciences 7, no. 1 (March 1, 2018): 38. http://dx.doi.org/10.11591/ijaas.v7.i1.pp38-45.

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<span lang="EN-IN">Underwater images are prone to contrast loss, limited visibility, and undesirable color cast. For underwater computer vision and pattern recognition algorithms, these images need to be pre-processed. We have addressed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respectively. To appraise the work conducted, the visual and quantitative analysis is performed. The restored images demonstrate improved contrast and effective enhancement in overall image quality and visibility. The proposed algorithm performs on par with the recent underwater dehazing techniques.</span>

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