Articles de revues : « Multivariate filter »

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Vitek, Francis. "A Closed Form Multivariate Linear Filter." IMF Working Papers 18, no. 275 (2018): 1. http://dx.doi.org/10.5089/9781484388785.001.

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Chen, Qiuhui, Charles A. Micchelli, Silong Peng, and Yuesheng Xu. "Multivariate Filter Banks Having Matrix Factorizations." SIAM Journal on Matrix Analysis and Applications 25, no. 2 (2003): 517–31. http://dx.doi.org/10.1137/s0895479802412735.

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WHITCHER, BRANDON, and PETER F. CRAIGMILE. "MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 04 (2004): 567–87. http://dx.doi.org/10.1142/s0219691304000652.

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We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

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García Infante, Juan Carlos, José de J. Medel Juárez, and Juan Carlos Sánchez García. "Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (MIMO)." Ingeniería e Investigación 31, no. 1 (2011): 184–92. http://dx.doi.org/10.15446/ing.investig.v31n1.20569.

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Multivariate identifier filters (multiple inputs and multiple outputs - MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948). This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back-propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error.

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International Monetary Fund. "Estimating Potential Output with a Multivariate Filter." IMF Working Papers 10, no. 285 (2010): 1. http://dx.doi.org/10.5089/9781455210923.001.

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Kang, Jieun, Heung-Kyu Ko, Ji Hoon Shin, et al. "Practice patterns of retrievable inferior vena cava filters and predictors of filter retrieval in patients with pulmonary embolism." Vascular Medicine 22, no. 6 (2017): 512–17. http://dx.doi.org/10.1177/1358863x17726596.

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Retrievable inferior vena cava (IVC) filters are increasingly used in patients with venous thromboembolism (VTE) who have contraindications to anticoagulant therapy. However, previous studies have shown that many retrievable filters are left permanently in patients. This study aimed to identify the common indications for IVC filter insertion, the filter retrieval rate, and the predictive factors for filter retrieval attempts. To this end, a retrospective cohort study was performed at a tertiary care center in South Korea between January 2010 and May 2016. Electronic medical charts were reviewed for patients with pulmonary embolism (PE) who underwent IVC filter insertion. A total of 439 cases were reviewed. The most common indication for filter insertion was a preoperative/procedural aim, followed by extensive iliofemoral deep vein thrombosis (DVT). Retrieval of the IVC filter was attempted in 44.9% of patients. The retrieval success rate was 93.9%. History of cerebral hemorrhage, malignancy, and admission to a nonsurgical department were the significant predictive factors of a lower retrieval attempt rate in multivariate analysis. With the increased use of IVC filters, more issues should be addressed before placing a filter and physicians should attempt to improve the filter retrieval rate.

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Jonsson, Lena, Elzbieta Plaza, and Bengt Hultman. "Experiences of nitrogen and phosphorus removal in deep-bed filters in the Stockholm area." Water Science and Technology 36, no. 1 (1997): 183–90. http://dx.doi.org/10.2166/wst.1997.0042.

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Experimental studies of nutrient removal in a discontinuous downflow two-media filter on a pilot plant scale are described and compared with previously reported results from a continuous upflow filter (DynaSand) studied at full scale. Under controlled conditions both filters show low effluent values of total nitrogen and phosphorus. Removal mechanisms were more complex in the discontinuous downflow filter owing to the use of two media, deposition of sludge on the filter surface, and changes of the biological activity over time after backwashing. The discontinuous downflow filter was much influenced by the formation of nitrogen gas bubbles clogging the filter, while the influence of nitrogen gas formation in the DynaSand filter was minor. Multivariate methods were used to evaluate factors influencing nitrate and phosphate removal and the time passing before the filter became clogged.

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Blagrave, Patrick, Roberto Garcia-Saltos, Douglas Laxton, and Fan Zhang. "A Simple Multivariate Filter for Estimating Potential Output." IMF Working Papers 15, no. 79 (2015): 1. http://dx.doi.org/10.5089/9781475565133.001.

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ur Rehman, Naveed, and Danilo P. Mandic. "Filter Bank Property of Multivariate Empirical Mode Decomposition." IEEE Transactions on Signal Processing 59, no. 5 (2011): 2421–26. http://dx.doi.org/10.1109/tsp.2011.2106779.

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Chen, Qiuhui, Charles A. Micchelli, and Yuesheng Xu. "Biorthogonal multivariate filter banks from centrally symmetric matrices." Linear Algebra and its Applications 402 (June 2005): 111–25. http://dx.doi.org/10.1016/j.laa.2004.12.028.

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Lee, J., and D. W. Kim. "Efficient multivariate feature filter using conditional mutual information." Electronics Letters 48, no. 3 (2012): 161. http://dx.doi.org/10.1049/el.2011.3063.

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Chen, Qiuhui, Guangbin Ren, Paula Cerejeiras, and Uwe Kaehler. "Non-separable multivariate filter banks from univariate wavelets." Mathematische Nachrichten 286, no. 10 (2013): 951–69. http://dx.doi.org/10.1002/mana.201000100.

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Sum, Stephen T., and Steven D. Brown. "Standardization of Fiber-Optic Probes for Near-Infrared Multivariate Calibrations." Applied Spectroscopy 52, no. 6 (1998): 869–77. http://dx.doi.org/10.1366/0003702981944418.

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The standardization of Fourier transform near-infrared (FT-NIR) spectrometers equipped with fiber-optic probes was investigated. FT-NIR spectra of caustic brines for an industrial process were measured on two different instruments. Calibration transfer across the instruments and probes was studied by employing calibration models built on one instrument to predict properties from spectra measured on the other. The transfer was examined by using spectra without and with preprocessing. The preprocessing methods included a Savitzky–Golay (SG) derivative polynomial filter, a procedure based on a finite impulse response (FIR) filter, and a combination of both. In addition to being a preprocessing technique, the FIR filter is also a standardization method that transforms the instrument response function of one instrument to match that of another. The transformation is performed over a moving processing window without the use of transfer standards. In this study, application of the FIR filter to first-derivative spectra provided the best multivariate calibration models and led to the successful transfer of calibration across different probes and spectrometers.

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Azevedo, João Valle e. "A multivariate band-pass filter for economic time series." Journal of the Royal Statistical Society: Series C (Applied Statistics) 60, no. 1 (2010): 1–30. http://dx.doi.org/10.1111/j.1467-9876.2010.00734.x.

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Poloni, Federico, and Giacomo Sbrana. "MULTIVARIATE TREND–CYCLE EXTRACTION WITH THE HODRICK–PRESCOTT FILTER." Macroeconomic Dynamics 21, no. 6 (2016): 1336–60. http://dx.doi.org/10.1017/s1365100515000887.

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The Hodrick–Prescott filter represents one of the most popular methods for trend–cycle extraction in macroeconomic time series. In this paper we provide a multivariate generalization of the Hodrick–Prescott filter, based on the seemingly unrelated time series approach. We first derive closed-form expressions linking the signal–noise matrix ratio to the parameters of the VARMA representation of the model. We then show that the parameters can be estimated using a recently introduced method, called “Moment Estimation Through Aggregation (META).” This method replaces traditional multivariate likelihood estimation with a procedure that requires estimating univariate processes only. This makes the estimation simpler, faster, and better behaved numerically. We prove that our estimation method is consistent and asymptotically normal distributed for the proposed framework. Finally, we present an empirical application focusing on the industrial production of several European countries.

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Luong, Van Su, Minhhuy Le, Khoa Dang Nguyen, Dang-Khanh Le, and Jinyi Lee. "Electromagnetic Testing of Moisture Separation Reheater Tube based on Multivariate Singular Spectral Analysis." Applied Sciences 10, no. 11 (2020): 3954. http://dx.doi.org/10.3390/app10113954.

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Moisture separator reheater (MSR) tubing systems are an important part of a pressurized-water power plant to increase the efficiency of the heat transfer rate. The MSR tubes are finned tubes which are made of ferritic stainless steel (SS439) with a high strength and corrosion resistance characteristics. However, corrosion can appear along with the fins after a long period of operation of the MSR tubes that requires nondestructive testing (NDT) of the MSR tubes’ periodically. Electromagnetic testing (ET) is an efficient NDT method for the inspection of far-side corrosion in the MSR tubes. However, the ET sensor signal is affected by signal noise from the fins. Material degradation that make it challenging to inspect and evaluate the corrosion. In this study, we proposed three ET methods, including magnetic flux leakage testing, eddy current testing and partial saturation eddy current testing, and incorporated with a multivariate singular spectral analysis (MSSA) filter to improve the detectability of the corrosion in the MSR tubes. The proposed MSSA filter was compared with the multivariate wavelet transform filter and Gabor transform filter, and the results showed more efficient and stable results of the MSSA filter in the extraction of the corrosion signal.

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Meierhofer, Regula, Pascal Rubli, Jared Oremo, and Alloyce Odhiambo. "Does Activated Silver Reduce Recontamination Risks in the Reservoirs of Ceramic Water Filters?" Water 11, no. 5 (2019): 1108. http://dx.doi.org/10.3390/w11051108.

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Efforts to provide safe water are challenged by recontamination and regrowth of pathogens in treated water during storage. This study evaluated the potential of metallic silver with a chemically etched surface to reduce recontamination risks during water storage in ceramic water filters. Batch experiments were conducted in the laboratory with water storage buckets containing three configurations of varying amounts of silver. Field trials in a rural area in Kenya assessed the effect of the same configurations in the storage buckets of locally produced ceramic pot filters without colloidal silver coating. The tests revealed that the etched silver slightly reduced microbiological recontamination risks during water storage despite the low diffusion of silver ions (<5 µg Ag/L). The effect was strongly influenced by water chemistry parameters. A statistically significant difference in the removal of E. coli (Δ Log Removal Value (LRV) = 0.6) and total coliforms (Δ LRV = 1.7) was found between households using a filter with silver in the water reservoir and those using a filter without silver. Multivariate regression of water handling factors and hygiene practices on filter performance revealed that the presence of silver in the reservoir and cleaning the filter element with a brush were associated with a better filter performance.

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AMMICHE, M., and A. KOUADRI. "False Alarms Rate Reduction Using Filtered Monitoring Indices." Algerian Journal of Signals and Systems 2, no. 1 (2017): 40–50. http://dx.doi.org/10.51485/ajss.v2i1.31.

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False alarms are the major problem in fault detection when using multivariate statistical process monitoring such as principal component analysis (PCA), they affect the detection accuracy and lead to make wrong decisions about the process operation status. In this work, filtering the monitoring indices is proposed to enhance the detection by reducing the number of false alarms. The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. Signal to Noise Ratio (SNR), False Alarms Rate (FAR) and the detection time of the fault were used as criteria to compare their performance and their filtering action influence on monitoring. The algorithms were applied to cement rotary kiln data; real data, to remove spikes and outliers on the monitoring indices of PCA, and then, the filtered signals were used to supervise the system. The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussed.

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Barginear, M. F., M. L. Lesser, M. Strakhan, et al. "Anticoagulation vs. vena caval filters in cancer patients with venous thromboembolism: Improved survival with anticoagulation." Journal of Clinical Oncology 24, no. 18_suppl (2006): 8553. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.8553.

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8553 Background: Cancer patients (pts) have an increased incidence of venous thromboembolism (VTE). Inferior vena caval (IVC) filters are used extensively in the US; over 40,000 are inserted annually. The impact on survival of cancer pts receiving IVC filters has not been studied. Methods: A retrospective study examined 154 consecutive cancer pts with VTE in order to compare the effects of IVC filter placement with anticoagulation (AC) therapy on overall survival (OS), as measured from the time of VTE. Pts were classified into three treatment groups: AC (n=58), IVC filter (73), or combination IVC filter+AC (23). OS was analyzed using survival analysis methods (e.g., Kaplan-Meier Product-Limit Method, logrank test). Multivariate Cox regression was used to account for the possible confounders: ECOG performance status (PS) and type of thrombus. Results: Treatment groups did not differ with respect to age, gender, or albumin levels. Groups did differ for PS (P<0.033 chi-square test, better PS was associated with AC only) and for type of thrombus (P<0.007 chi-square, DVT was associated with AC, PE with IVC, and DVT/PE with AC+IVC). Median OS was significantly greater in pts treated with AC (11 mos) compared to IVC filters (3 mos) or IVC+AC (3.5 mos) (p<0.0046). IVC pts were 1.73 times more at risk of death than AC only (hazard ratio; 95%CI 1.18–2.52). Multivariate analysis revealed that PS and type of thrombus were not confounders and had no effect on OS. Conclusion: AC should be considered primary therapy for cancer pts with VTE. IVC filters should be considered a therapeutic option only when AC is strictly contraindicated. Prospective randomized trials are needed to address this issue. No significant financial relationships to disclose.

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HAN, Min, and Ya-Nan WANG. "Multivariate Time Series Online Predictor with Kalman Filter Trained Reservoir." Acta Automatica Sinica 36, no. 1 (2010): 169–73. http://dx.doi.org/10.3724/sp.j.1004.2010.00169.

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Alichi, Ali, Olivier Bizimana, Douglas Laxton, et al. "Multivariate Filter Estimation of Potential Output for the United States." IMF Working Papers 17, no. 106 (2017): 1. http://dx.doi.org/10.5089/9781475598384.001.

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Rutan, Sarah C., Eric Bouveresse, Kevin N. Andrew, Paul J. Worsfold, and D. L. Massart. "Correction for drift in multivariate systems using the Kalman filter." Chemometrics and Intelligent Laboratory Systems 35, no. 2 (1996): 199–211. http://dx.doi.org/10.1016/s0169-7439(96)00052-4.

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Chen, Qiuhui, Charles A. Micchelli, and Yuesheng Xu. "On the matrix completion problem for multivariate filter bank construction." Advances in Computational Mathematics 26, no. 1-3 (2007): 173–204. http://dx.doi.org/10.1007/s10444-005-7535-9.

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Qiaofang, Lian, Xiao Hongying, and Chen Qiuhui. "Some properties on multivariate filter banks with a matrix factorization." Progress in Natural Science 15, no. 2 (2005): 115–25. http://dx.doi.org/10.1080/10020070512331341860.

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Haibach, Frederick G., Ashley E. Greer, Maria V. Schiza, Ryan J. Priore, Olusola O. Soyemi, and Michael L. Myrick. "On-line reoptimization of filter designs for multivariate optical elements." Applied Optics 42, no. 10 (2003): 1833. http://dx.doi.org/10.1364/ao.42.001833.

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Buss, Ginters. "Real-Time Signal Extraction with Regularized Multivariate Direct Filter Approach." Journal of Forecasting 35, no. 3 (2015): 206–16. http://dx.doi.org/10.1002/for.2352.

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Marhaba, Bassel, and Mourad Zribi. "A Novel Combination Method for Image Restoration Based on Bootstrap Filter and DWT." MATEC Web of Conferences 261 (2019): 06002. http://dx.doi.org/10.1051/matecconf/201926106002.

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In our paper, we propose a novel combination of two methods based on the bootstrap filter. We will use the fusion technique to combine the restored images from the bootstrap multivariate kernel density filter and the bootstrap kernel-diffeomorphism filter. Experimental results have shown that our method has proved the image restoration results.

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Singh, Omkar. "Physiological Time Series Processing via Empirical Wavelet Transform." Advanced Science, Engineering and Medicine 12, no. 5 (2020): 582–87. http://dx.doi.org/10.1166/asem.2020.2557.

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This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.

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Cheng, Xiaoyu, Maoxing Wen, Cong Gao, and Yueming Wang. "Hyperspectral Anomaly Detection Based on Wasserstein Distance and Spatial Filtering." Remote Sensing 14, no. 12 (2022): 2730. http://dx.doi.org/10.3390/rs14122730.

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Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral AD method based on Wasserstein distance (WD) and spatial filtering (called AD-WDSF). Based on the assumption that both background and anomaly targets obey the multivariate Gaussian distribution, background and anomaly target distributions are estimated in the local regions of HSIs. Subsequently, the anomaly intensity of test pixels centered in the local regions are determined via measuring the WD between background and anomaly target distributions. Lastly, spatial filters, i.e., guided filter (GF), total variation curvature filter (TVCF), and Maxtree filter, are exploited to further refine detection results. Experimental results conducted on two real hyperspectral data sets demonstrate that the proposed method achieves competitive detection performance compared with the state-of-the-art AD methods.

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Qin, Ke, Raofen Wang, and Yu Zhang. "Filter Bank-Driven Multivariate Synchronization Index for Training-Free SSVEP BCI." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 934–43. http://dx.doi.org/10.1109/tnsre.2021.3073165.

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Dong, Wenhui, and Xingbao Gao. "Error estimate for the approximate solution to multivariate feedback particle filter." Pure and Applied Mathematics Quarterly 18, no. 3 (2022): 1113–46. http://dx.doi.org/10.4310/pamq.2022.v18.n3.a9.

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Dong, Qi, and Xiangbo Liu. "Multivariate filter estimation and ARDL model analysis of China’s potential output." Applied Economics Letters 25, no. 18 (2017): 1327–32. http://dx.doi.org/10.1080/13504851.2017.1420873.

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Wu, Wen, Sarah C. Rutan, Antonietta Baldovin, and Désiré-Luc Massart. "Feature selection using the Kalman filter for classification of multivariate data." Analytica Chimica Acta 335, no. 1-2 (1996): 11–22. http://dx.doi.org/10.1016/s0003-2670(96)00347-9.

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KOH, MIN-SUNG, DANILO P. MANDIC, and ANTHONY G. CONSTANTINIDES. "THEORY OF DIGITAL FILTER BANKS REALIZED VIA MULTIVARIATE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 06, no. 01 (2014): 1450001. http://dx.doi.org/10.1142/s1793536914500010.

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Undecimated and decimated multivariate empirical mode decomposition filter banks (MEMDFBs) are introduced in order to incorporate MEMD equipped with downsampling into any arbitrary tree structure and provide flexibility in the choice of frequency bands. Undecimated MEMDFBs show the same results as those of original MEMD for an octave tree structure. Since the exact cut-off frequencies of MEMD are not known (i.e. due to data-driven decomposition), employing just simple downsampling in MEMD might cause aliasing. However, decimated MEMDFBs in this paper achieve perfect reconstruction with aliasing cancelled for any arbitrary tree. Applications of decimated/undecimated MEMDFBs for speech/audio and image signals are also included. Since decimated MEMDFBs can be applied into any arbitrary tree structure, this extends into MEMD packets. Arbitrary tree structures in decimated MEMDFBs also lead to more diverse choices in frequency bands for various multivariate applications requiring decimations.

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Borowski, Matthias, Karen Schettlinger, and Ursula Gather. "Multivariate Real-Time Signal Extraction by a Robust Adaptive Regression Filter." Communications in Statistics - Simulation and Computation 38, no. 2 (2009): 426–40. http://dx.doi.org/10.1080/03610910802514972.

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Prats-Montalbán, José Manuel, Marina Cocchi, and Alberto Ferrer. "N-way modeling for wavelet filter determination in multivariate image analysis." Journal of Chemometrics 29, no. 6 (2015): 379–88. http://dx.doi.org/10.1002/cem.2717.

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Rice, P. M., K. B. Alexander, and I. M. Anderson. "Multivariate Statistical Analysis of Spectrum Lines From Si3N4 Grain Boundaries." Microscopy and Microanalysis 3, S2 (1997): 945–46. http://dx.doi.org/10.1017/s1431927600011612.

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It is well known that the high-temperature properties of polycrystalline Si3N4 ceramics are strongly influenced by the nanometer-scale glassy phase at the grain boundaries. We have recently analyzed the variation of the near-edge fine structure (ELNES) of the Si-L2,3 edges using a combination of TEM spectrum-line acquisition with an imaging filter and multivariate statistical analysis.The glassy phase at the Si3N4 grain boundaries is easily damaged by the the fine probes usually used in scanning transmission electron microscopy to acquire ELNES data. Thus an alternative method using a Gatan imaging filter (GIF), called TEM spectrum-line analysis, was used. In this mode, energy-loss spectra are dispersed along one axis of the CCD detector, while the orthogonal axis displays the spatial variation across the interface. In this mode, a slotted washer is used as the GIF entrance aperture, with its minor axis parallel to the energy-dispersion direction of the spectrometer.

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Jin, Qi Bing, and Rong Li. "A V-Norm Decoupling IMC Method with Filters Based on Inverted Decoupling." Applied Mechanics and Materials 197 (September 2012): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amm.197.311.

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A V-norm Decoupling internal model control (IMC) method with filters based on inverted decoupling for multivariate stable object is proposed in this paper. The actual industrial process is very difficult to obtain an accurate model, which makes the control effect not satisfactory. To solve this problem, the V-norm decoupling controller is designed on the basis of the inverted decoupling, and a filter is added in front of the controller to reduce coupling and increase robustness. Compared with traditional multivariable controller designed method, the method of designing the internal model controller in this paper is simpler and less calculation. Finally, the Wood/Berry model is taken as the simulated object to verify the controller design method is reasonable. The results show that V-norm decoupling internal model controller method is effective and feasible, even the system model is mismatched.

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Shcherban, O. G., D. M. Lazurenko, I. V. Shcherban, and N. E. Kirilenko. "The Synthesis of an Adaptive Filter for Automatic Detection and Time Localization of Premotor EEG Patterns of the Human Brain." INFORMACIONNYE TEHNOLOGII 27, no. 9 (2021): 484–93. http://dx.doi.org/10.17587/it.27.484-493.

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An adaptive low-pass filter has been synthesized for automatic patterns detection of arbitrary mental movements in records of multidimensional electroencephalograms (EEG). The filter is based on the multivariate singular spectrum analysis. The effective bandwidth of the filter corresponds to the spectrum of the sought patterns of mental activity on the observed EEG time interval. The use of the synthesized filter provided a reliable automatic search for patterns and the correct determination of their time boundaries. The correctness of the results has been confirmed in experiments with 24 volunteers.

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Li, Mingai, Ruotu Wang, Jinfu Yang, and Lijuan Duan. "An Improved Refined Composite Multivariate Multiscale Fuzzy Entropy Method for MI-EEG Feature Extraction." Computational Intelligence and Neuroscience 2019 (March 28, 2019): 1–12. http://dx.doi.org/10.1155/2019/7529572.

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Feature extraction of motor imagery electroencephalogram (MI-EEG) has shown good application prospects in the field of medical health. Also, multivariate entropy-based feature extraction methods have been gradually applied to analyze complex multichannel biomedical signals, such as EEG and electromyography. Compared with traditional multivariate entropies, refined composite multivariate multiscale fuzzy entropy (RCmvMFE) overcomes the defect of unstable entropy values caused by the scale factor increase and is beneficial towards obtaining richer feature information. However, the coarse-grained process of RCmvMFE is mean filtered, which weakens Gaussian noise and is powerless against random impulse noise interference. This yields poor quality feature information and low accuracy classification. In this paper, RCmvMFE is improved (IRCmvMFE) by using composite filters in the coarse-grained procedure to enhance filter performance. Median filters are employed to remove the impulse noise interference from multichannel MI-EEG signals, and these filtered MI-EEGs are further smoothed by the mean filters. The multiscale IRCmvMFEs are calculated for all channels of composite filtered MI-EEGs, forming a feature vector, and a support vector machine is used for pattern classification. Based on two public datasets with different motor imagery tasks, the recognition results of 10 × 10-fold cross-validation achieved 99.43% and 99.86%, respectively, and the statistical analysis of experimental results was completed, showing the effectiveness of IRCmvMFE, as well. The proposed IRCmvMFE-based feature extraction method is superior compared to entropy-based and traditional methods.

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Lim, Jong Gwan, Mi-hye Kim, and Sahngwoon Lee. "Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/280140.

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Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers,k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposed multivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures.

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HUANG, YONGDONG, and ZHENGXING CHENG. "PARAMETRIZATION OF COMPACTLY SUPPORTED TRIVARIATE ORTHOGONAL WAVELET FILTER." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (2007): 627–39. http://dx.doi.org/10.1142/s0219691307001938.

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Multivariate wavelets analysis is a powerful tool for multi-dimensional signal processing, but tensor product wavelets have a number of drawbacks. In this paper, we give an algorithm of parametric representation compactly supported trivariate orthogonal wavelet filter, which simplifies the study of trivariate orthogonal wavelet. Four examples are also given to demonstrate the method.

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Song, Peter Xue-Kun. "Monte Carlo Kalman filter and smoothing for multivariate discrete state space models." Canadian Journal of Statistics 28, no. 3 (2000): 641–52. http://dx.doi.org/10.2307/3315971.

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Ma, Junxia, Weili Xiong, Jing Chen, and Ding Feng. "Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter." IET Control Theory & Applications 11, no. 6 (2017): 857–69. http://dx.doi.org/10.1049/iet-cta.2016.1033.

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Sales-Mayor, F., and R. E. Wyatt. "A two-stage filter for smoothing multivariate noisy data on unstructured grids." Computers & Mathematics with Applications 47, no. 6-7 (2004): 877–91. http://dx.doi.org/10.1016/s0898-1221(04)90072-7.

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Labani, Mahdieh, Parham Moradi, Fardin Ahmadizar, and Mahdi Jalili. "A novel multivariate filter method for feature selection in text classification problems." Engineering Applications of Artificial Intelligence 70 (April 2018): 25–37. http://dx.doi.org/10.1016/j.engappai.2017.12.014.

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Burtseva, Tatyana, Nataliya Mironova, Elena Volk, and Alexey Chaykovskiy. "Marketing assessment of innovation potential of water filter manufacturers." E3S Web of Conferences 258 (2021): 06067. http://dx.doi.org/10.1051/e3sconf/202125806067.

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The study focuses on analyzing the products of water filter manufacturers using marketing tools. In the course of the study, factorial, cluster, regression analysis, as well as multivariate scaling were used. As a result, it was revealed that 65% of buyers rely on 4 main factors when buying filter products: price-value ratio, assortment depth, assortment saturation, and advertising support.

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Popov, Andrey A., and Adrian Sandu. "A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions." Nonlinear Processes in Geophysics 26, no. 2 (2019): 109–22. http://dx.doi.org/10.5194/npg-26-109-2019.

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Abstract. Ever since its inception, the ensemble Kalman filter (EnKF) has elicited many heuristic approaches that sought to improve it. One such method is covariance localization, which alleviates spurious correlations due to finite ensemble sizes by using relevant spatial correlation information. Adaptive localization techniques account for how correlations change in time and space, in order to obtain improved covariance estimates. This work develops a Bayesian approach to adaptive Schur-product localization for the deterministic ensemble Kalman filter (DEnKF) and extends it to support multiple radii of influence. We test the proposed adaptive localization using the toy Lorenz'96 problem and a more realistic 1.5-layer quasi-geostrophic model. Results with the toy problem show that the multivariate approach informs us that strongly observed variables can tolerate larger localization radii. The univariate approach leads to markedly improved filter performance for the realistic geophysical model, with a reduction in error by as much as 33 %.

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Tropp, Joshua, Michael H. Ihde, Abagail K. Williams, et al. "A sensor array for the discrimination of polycyclic aromatic hydrocarbons using conjugated polymers and the inner filter effect." Chemical Science 10, no. 44 (2019): 10247–55. http://dx.doi.org/10.1039/c9sc03405f.

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Vezzulli, Fosca, Gabriele Rocchetti, Milena Lambri, and Luigi Lucini. "Metabolomics Combined with Sensory Analysis Reveals the Impact of Different Extraction Methods on Coffee Beverages from Coffea arabica and Coffea canephora var. Robusta." Foods 11, no. 6 (2022): 807. http://dx.doi.org/10.3390/foods11060807.

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An untargeted metabolomics approach combined with sensory analysis was used to depict the impact of different traditional Italian extraction methods (i.e., Espresso, Neapolitan, Moka) along with Filter, on Coffea arabica and Coffea canephora var. robusta beverages. To this aim, polyphenols, Maillard reaction products, and coffee metabolites were screened by high resolution mass spectrometry and elaborated through both unsupervised and supervised multivariate statistical approaches. Multivariate statistics showed a distinctive chemical profile for Espresso preparation, while Moka and Neapolitan were very similar. The orthogonal projection to latent structures and discriminant analysis allowed the identification of 86 compounds showing a high VIP discrimination score (i.e., > 0.8). The 2,5-dimethyl-3-(methyldithio)-furan was a marker for the Filter preparation, while 1,2-disinapoylgentiobiose characterized both Filter and Neapolitan extractions. Caffeine (known to be a bitter compound) accumulated highly in Filter vs. Espresso, although at the sensory profile, bitterness was more perceived in Espresso. Vegetal aroma carried by pyrazines, pyridines, and phenolic acids were markers of Espresso, with Robusta showing higher values than Arabica. Notwithstanding, our findings showed that the extraction process played a hierarchically higher role in driving the chemical composition of the beverages when compared to coffee species.

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