ライフサイエンスコーパス: wavelet

1 on down-sampling operation and footprint in wavelet.

2 and of short tones that consist of a single wavelet.

3 e fit of the remainder of the spectrum using wavelets.

4 ates are modeled non-parametrically by using wavelets.

5 wed conduction and pivot points of reentrant wavelets.

6 tain brief bursts of high-frequency (600 Hz) wavelets.

7 bursts and single spikes phase-locked to EEG wavelets.

8 maintained by dynamically changing multiple wavelets.

9 ate capable of accommodating many meandering wavelets.

10 eled using a series of short-duration (<1 s) wavelets.

11 to multiple sources, up to diffuse bi-atrial wavelets.

12 d frequently breaking up to produce multiple wavelets.

13 the most common patterns of AF were multiple wavelets (92), with pulmonary vein (69) and non-pulmonar

14 dard was the binding potential obtained with wavelet-aided parametric imaging (WAPI BPND).

15 The wavelet-aided parametric imaging method was used to obta

16 omic intervals and novel peak calling with a wavelet algorithm.

17 alysed the instantaneous fluctuations in the wavelet amplitude of the field potential oscillation rec

18 ation (ENSO) cycles was examined using cross-wavelet analyses and convergent cross mapping (CCM).

19 Further cross-wavelet analyses between our calcite delta(18)O and atmo

20 Fourth, the wavelet analyses demonstrate that all these patterns are

21 Cross-correlations and wavelet analyses highlighted 2 major changes in influenz

22 We used wavelet analyses of the single trial data to estimate to

23 The frequency domain methods of Fourier and wavelet analyses provide compact summaries of temporal a

24 Spectral and wavelet analyses reveal that the cycles have a periodici

25 m 38 countries eligible for inclusion in the wavelet analyses.

26 A wavelet analysis identified a significant ( p < 0.01) 2.

27 Our findings confirm the robustness of NVC wavelet analysis in Neonatal Encephalopathy related to H

28 Wavelet analysis indicates that most of the differences

29 time samples, scales and electrodes, through wavelet analysis of multi-channel ictal EEG.

30 Wavelet analysis of outbreak time series suggested clima

31 quantify neurovascular coupling (NVC) using wavelet analysis of the dynamic coherence between amplit

32 Wavelet analysis of the raw response curves showed that

33 For six selected sections of coast, wavelet analysis of the shoreline change signal indicate

34 Wavelet analysis revealed a consistent pattern of DBT an

35 Wavelet analysis reveals full-width half-maximum oscilla

36 ctors of protein structure and properties by wavelet analysis technique; (ii) extracting novel wavele

37 We perform a wavelet analysis to analyze localized variations of powe

38 Here I show that application of wavelet analysis to experimentally manipulated plankton

39 s are affected before others, we used Morlet wavelet analysis to obtain high temporal resolution in t

40 Here we apply for the first time wavelet analysis to the PSII RC 2DES data to obtain time

41 Wavelet analysis was used to extract a denoised measure

42 To address this, we coupled wavelet analysis with hidden Markov models for unbiased

43 ons of IVUS, such as integrated backscatter, wavelet analysis, and virtual histology, have focused on

44 Using a combination of methods - wavelet analysis, economic analysis, statistical and dis

45 Using country-level wavelet analysis, we identified whether a 12 month perio

46 still quite unknown in the AFM context: the wavelet analysis.

47 Both wavelet and correlation methods also identified ABPopt w

48 Both wavelet and correlation methods distinguished functional

49 etween the spatial derivatives of the mapped wavelet and the finite difference operator over all prop

50 a wavelet transform, using the Morlet mother wavelet and wavelet phase coherence, to determine the fr

51 well with previous patterns identified using wavelets and confirm the highly non-stationary behaviour

52 rkov Models, maximum likelihood, regression, wavelets and genetic algorithms.

53 enobiotics (DoGEX) was developed, which uses wavelets and morphological analysis to process liquid ch

54 mmon techniques (e.g., principal components, wavelets) and GMM fitting parameters (e.g., Gaussian dis

55 trum, Enright, Empirical Mode Decomposition, Wavelet, and Detrended Fluctuation analyses.

56 summarize the potential of state of the art wavelets, and in particular wavelet statistical methodol

57 However, the new continuous CS wavelet approach allows simultaneous analysis of surface

58 In addition, the wavelet approach reduced the regional variation from 75%

59 lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to es

60 Wavelets are more efficient and faster than Fourier meth

61 We propose that the likely source of these wavelets are pneumatic pulses caused by opening and clos

62 Multiple excitation wavelets are present during ventricular fibrillation (VF

63 EEG wavelets at 600 Hz may therefore permit non-invasive ass

64 ptively with different velocities and source wavelet bandwidths, the method is capable to maximise th

65 In addition, wavelet based CS approximations, founded on a new contin

66 ations of a more general class of continuous wavelet -based waveforms.

67 Wavelet-based (wavelet-Fourier analysis [WFA]), Fourier-

68 laser light on sperm motility using a novel wavelet-based algorithm that allows for direct measureme

69 le adaptive histogram equalization (MAHE), a wavelet-based algorithm, was investigated as a method of

70 dinal autoantibody profiles by using a novel wavelet-based algorithm.

71 Here, we employ a recently developed wavelet-based analytical approach to examine how differe

72 pogenic drivers of urban water demand (using wavelet-based approaches) and (2) the relative contribut

73 neuromagnetic activation were assessed with wavelet-based beamformer analyses.

74 c sources were volumetrically estimated with wavelet-based beamformer at 2.5 mm resolution.

75 and potentially more germane alternative to wavelet-based classification techniques that rely on mor

76 The tested wavelet-based compression method proved to be an accurat

77 t functional networks were constructed using wavelet-based correlations over 90 brain regions.

78 Here, we implement a wavelet-based de-noising algorithm (PURE-LET) to enhance

79 ified linear discriminant analysis (LDA) and wavelet-based differentiation, were employed in a study

80 outperformed classical linear-nonlinear and wavelet-based feature representations that build on exis

81 We obtain and integrate wavelet-based features and statistics-based features of

82 In this article, we apply the wavelet-based functional mixed model methodology to anal

83 The proposed 2D wavelet-based image analysis effectively detected phosph

84 The proposed wavelet-based image analysis provides, for the first tim

85 nd 40:1 by using a two-dimensional JPEG 2000 wavelet-based image compression method.

86 ed to 30:1, 20:1, and 10:1 levels by using a wavelet-based JPEG2000 method, resulting in 400 test cas

87 The wavelet-based method achieves an overall high performanc

88 nalysis our results showed that the proposed wavelet-based methodology can effectively quantify the f

89 propriate mathematic models before and after wavelet-based noise reduction to get flow estimates.

90 dy was to evaluate a newly developed, novel, wavelet-based noise-reduction approach that can objectiv

91 the application of a novel multidimensional wavelet-based noise-reduction protocol.

92 This wavelet-based parametric functional mapping has been sta

93 By means of wavelet-based phonovibrographic analysis, a set of three

94 Scale specificity is achieved by an optional wavelet-based smoothing operation.

95 ted state ab-initio molecular dynamics and a wavelet-based time-dependent frequency analysis of nonst

96 Wavelet-based transformation and shape-based morphology

97 Widespread use and systematic application of wavelet-based XAS can potentially reveal in greater deta

98 ace scattering coefficient maps with a novel wavelet-based-curve-fitting method that provides signifi

99 Through wavelet-basis regularization, our method sharpens signal

100 Differences between wavelet coefficient matrices revealed several heterogene

101 domains were identified from differences in wavelet coefficient matrices, and there was good agreeme

102 ficiently without maintaining the compressed wavelet coefficient matrix of the original data set.

103 Once the pure dissimilarity plots ad optimal wavelet coefficients are selected, different ANN models

104 Specifically, the wavelet coefficients are treated as the pixels of a bloc

105 ined when using Fisher's criterion to choose wavelet coefficients for compression.

106 d by applying ALS to partially reconstructed wavelet coefficients generated from two-way NLWC.

107 We choose the former, where the wavelet coefficients of the multiresolution decompositio

108 Finally, the local wavelet coefficients of the PET image are substituted by

109 ncy CS method that utilizes transformed post-wavelet coefficients to calculate the frequency saliency

110 scale-specific quantization of biorthogonal wavelet coefficients was developed for the compression o

111 their contributions via soft thresholding of wavelet coefficients.

112 ssimilarity pure spectra of OPA and selected wavelet coefficients.

113 Wavelet coherence analysis of neurovascular coupling in

114 STM retention was examined by computing EEG wavelet coherence during the retention period of a modif

115 cient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12-15 h

116 Wavelet coherence values (close to 1 and phase shifts of

117 lysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra.

118 P = .05 and favored interpretation with the wavelet-compressed reconstructed images.

119 Linear wavelet compression (LWC) applied to IMS data may cause

120 Nonlinear wavelet compression (NLWC) precisely preserves the peak

121 Nonlinear wavelet compression (NLWC) preserves peak shape and can

122 eater compression ratios compared to regular wavelet compression and interpretable models.

123 constructed ion mobility spectra from linear wavelet compression is problematic in that artifact peak

124 A two-way nonlinear wavelet compression method that incorporates alternating

125 se a signal-to-noise gain can be achieved by wavelet compression.

126 y k-means clustering, and differentiation by wavelet convolution showed distinct response patterns wi

127 elationship between the onset of significant wavelet cross spectrum (WCS) and tremor onset was determ

128 A population-averaged wavelet cross-spectrum displayed a strong tendency for o

129 s in neural activity were analyzed using the wavelet cross-spectrum, and its statistical significance

130 The wavelet crosscorrelation transforms time-offset data int

131 se issues using data-driven offset stacking, wavelet-crosscorrelation filtering, and radiation-patter

132 A wavelet decomposition algorithm is described, and thresh

133 thylation and phosphorylation using SERS and wavelet decomposition data analysis techniques.

134 itatively, we first obtain a multiresolution wavelet decomposition of each.

135 core of two given RNAs was designed based on wavelet decomposition of their numerical representation.

136 s developed using total signal variation and wavelet decomposition to identify spike, seizure, and ot

137 frequencies of oscillation using continuous wavelet decomposition to non-parametrically model change

138 ty were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range

139 rocess of analogue-to-digital conversion and wavelet decomposition, we develop the notion of quantal

140 mass accuracy of the precursor ions, and (3) wavelet denoising of the mass spectra prior to fragment

141 E with iterative deconvolution incorporating wavelet denoising.

142 signal-to-noise variations was addressed by wavelet denoising.

143 The higher wavelet density in the EBZ was caused by increased (P:<0

144 resonance energy (smFRET) trajectories using wavelet detail thresholding and Bayesian inference is pr

145 iteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated vari

146 cess of a quantitative trait on the basis of wavelet dimension reduction.

147 velet embryo features by multi-resolution 2D wavelet discrete transform, followed by min-redundancy m

148 ur scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual

149 by enforcing joint multicoil sparsity in the wavelet domain (SPARSE-SPACE).

150 We present a limit test that compares the wavelet domain signal-to-noise ratios at the energies of

151 hase reconstruction technique implemented in wavelet domain to achieve at least 2-fold reduction in t

152 bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising

153 entifies potential regulatory regions in the wavelet domain.

154 ation are often inaccurate in either time or wavelet domain.

155 y saliency information of images in the post-wavelet domain.

156 ed to the compressed data in the Fourier and wavelet domains.

157 We propose to identify the wavelet embryo features by multi-resolution 2D wavelet d

158 roperties of sensor time series by computing wavelet entropy of and correlation between time series,

159 We also propose a 3D anisotropic wavelet feature extractor for extracting textural featur

160 et analysis technique; (ii) extracting novel wavelet features that can capture hidden components from

161 We make use of wavelet features, original expression values, difference

162 The standard deviations of the wavelet-filtered BP signals during tilt and TWR overlaid

163 rincipal component analysis (PCA), different wavelet filters may provide different mathematical persp

164 The daublet wavelet filters were selected, because they worked well

165 Wavelet-based (wavelet-Fourier analysis [WFA]), Fourier-based (fast Fou

166 e and inaccurate in the sense that peaks and wavelet functions do not directly correspond to the unde

167 esolution MS, in which features are peaks or wavelet functions, are parameter-sensitive and inaccurat

168 Analysis of wavelet functions, derived from a mathematical model of

169 Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws en

170 We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect tru

171 series into the time-frequency domain using wavelets, histories of biodiversity are shown to be simi

172 Using a novel wavelet image filter, we first demonstrated that rectang

173 onstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

174 Multiple wavelets in MI dogs were associated with significantly h

175 I conclude by discussing the use of wavelets in modeling biological structures.

176 owadays there is a growing interest in using wavelets in the analysis of biological sequences and mol

177 ial duration and rotors, as well as wave and wavelets in the atria, and thereby mimics mechanistic th

178 odel degenerated into persistent and erratic wavelets, leading to fibrillation.

179 also degenerated into persistent and erratic wavelets, leading to fibrillation.

180 The wavelet local multiple correlation (WLMC) is introduced

181 he most reproducible feature family, and the wavelet low-pass filter applied horizontally and vertica

182 te difference by introducing a time-to-space wavelet mapping.

183 very tracer tested under all conditions, the wavelet method improved the shape of blood and tissue ti

184 false positive rate, the sensitivity of our WAVELET method is higher than other methods.

185 Note that although the wavelet method is well known, its application into the E

186 We tested whether a wavelet method that uses near-infrared spectroscopy (NIR

187 Using a novel wavelet method, we quantified the amplitude and phase of

188 Wavelet methodology had less index variability with smal

189 Thus, wavelet methodology using NIRS may offer an accurate vas

190 findings with a structural analysis based on wavelet methods of the major branches of chemokine recep

191 We used wavelet methods to disaggregate synchronous fluctuations

192 ndices were calculated using correlation and wavelet methods, including the pressure reactivity index

193 NVC coupling is assessed by a wavelet metric estimation of percent time of coherence b

194 It employs wavelet multi-resolution analysis to extract time-freque

195 and introduce a method based on statistical wavelet multiresolution texture analysis to quantitative

196 A wavelet-neural network signal processing method has demo

197 If the wavelet noise-reduction technique was not used, the corr

198 s in tissue that tend to fractionate to form wavelets of excitation.

199 l waves, fibrillatory conduction of multiple wavelets or rapid focal activity.

200 The underlying wavelet organization of VF is unclear.

201 ethylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the l

202 both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet tran

203 s and non-solenoid proteins using stationary wavelet packet transform (SWPT).

204 d the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and

205 We use wavelet phase analysis, which allows for dynamical non-s

206 Wavelet phase coherence and phase difference around the

207 A wavelet phase coherence method was used to estimate the

208 We used wavelet transform and wavelet phase coherence methods to analyse integrated sk

209 ansform, using the Morlet mother wavelet and wavelet phase coherence, to determine the frequencies an

210 High cross wavelet power indicates that biodiversity is most simila

211 , we introduce a more robust transform-based wavelet pressure reactivity index (wPRx) and compare its

212 potentials via frequency, time-frequency, or wavelet representations, and adaptive models that estima

213 ail coefficients), we used the discrete Haar wavelet shrinkage technique to transform an inherently h

214 nlike conventional nonparametric modeling of wavelet shrinkage, we incorporate mathematical aspects o

215 By transforming the spectrum into wavelet space, the pattern-matching problem is simplifie

216 iguous activation state-underlie reentry and wavelet splitting and represent the sources of VF.

217 state of the art wavelets, and in particular wavelet statistical methodology, in different areas of m

218 al model consists of first- and higher-order wavelet statistics.

219 We developed a wavelet stimulus that evoked rich population responses a

220 ince its divergence from chimpanzee and used wavelet techniques to study, simultaneously for multiple

221 y but in which, through the concatenation of wavelets, the phase changes randomly every few cycles.

222 are based on the mutual information between wavelet time series, and estimated for each trial window

223 ent time-frequency decomposition with Morlet wavelets to determine power of 4-80 Hz oscillations.

224 ent time-frequency decomposition with Morlet wavelets to determine the power of 4-30 Hz oscillations.

225 ession in simulated LCR size (from sparks to wavelets to global waves), LCR rhythmicity, and decrease

226 demonstrate the influence of noise modelled wavelets to sort overlapping spikes.

227 oherence (WTC) analysis, specifically by the wavelet total pixel number of significant coherences wit

228 utilizes the one-dimensional (1D) continuous wavelet transform (CWT) of linearized fluorescence reson

229 Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm h

230 on chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compou

231 reduce the features generated by continuous wavelet transform (CWT).

232 med using techniques based on the continuous wavelet transform (CWT).

233 based on the multiresolution property of the wavelet transform (WT).

234 In this work novel wavelet transform analysis techniques are used to detect

235 pport of DFT calculations and advanced EXAFS wavelet transform analysis.

236 t implements spectral analysis by continuous wavelet transform and machine learning methods for chara

237 Using continuous 2-dimensional wavelet transform and time series analyses, we found tha

238 We used wavelet transform and wavelet phase coherence methods to

239 esentation of images in V1 is described by a wavelet transform and, therefore, that the properties of

240 ntage of the multiresolution property of the wavelet transform applied to both functional and structu

241 In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH dat

242 tween aEEG and NIRS-SctO2 was assessed using wavelet transform coherence (WTC) analysis, specifically

243 d electromyography impulses were derived for wavelet transform coherence and causality analyses of th

244 estimated using a methodology that features Wavelet Transform Coherency (WTC).

245 The wavelet transform decodes the information contained in t

246 hat low frequency power spectral density and wavelet transform features (10 30 Hz) were the best perf

247 uorescence (XRF) spectra based on continuous wavelet transform filters, and the method is applied to

248 A wavelet transform multiscale analysis shows these tumble

249 The linear combination fitting and wavelet transform of EXAFS data revealed noticeable diff

250 and relate it to the DNA sequence by using a wavelet transform of read information from the sequencer

251 x was calculated by taking the cosine of the wavelet transform phase-shift between ABP and ICP.

252 combination of fluorescence spectroscopy and wavelet transform processing technique.

253 ivided into eight regions of interest, and a wavelet transform protocol was applied to images and tim

254 Results indicate that the wavelet transform techniques developed herein are a prom

255 Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequen

256 The method uses the continuous wavelet transform to filter the signal and noise compone

257 multiresolution properties of the continuous wavelet transform to fluorescence resonance energy trans

258 At a recent meeting, the wavelet transform was depicted as a small child kicking

259 correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a k

260 on's correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium tran

261 a particular type of computation, known as a wavelet transform, determining the firing rate of V1 neu

262 Our approach utilizes the wavelet transform, is free of distributional assumptions

263 Wavelet transform, mask construction, and sparse-partial

264 t time fourier transform, multitaper method, wavelet transform, or Hilbert transform.

265 We analysed data with a wavelet transform, using the Morlet mother wavelet and w

266 ct of growth time was directly observed with wavelet transform, which could not be observed using the

267 -magnification lens-based microscope using a wavelet transform-based colorization method.

268 to calculate the traditional PRx and a novel wavelet transform-based wPRx.

269 ard neural network (OPA-FFNN) and continuous wavelet transform-feed forward neural network (CWT-FFNN)

270 brain activity was obtained by means of the wavelet transform.

271 and a computerized method, the t-continuous wavelet transform.

272 g method in the macaque monkey combined with wavelet transform.

273 z were quantitatively determined with Morlet wavelet transform.

274 the source level was extracted by means of a wavelet transform.

275 The transformation is realized by a fast wavelet transform.

276 Wavelet-transform analyses of the Fe K-edge EXAFS spectr

277 This study used wavelet transformation and Fourier analysis to assess th

278 gions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the b

279 Morlet wavelet transformation of the leading eigenvector-derive

280 hen optimized for animal vocalizations and a wavelet transformation when optimized for non-biological

281 candidate peaks after reducing noise through wavelet transformation.

282 AFS) analysis, the systematic application of wavelet transformed (WT) XAS is shown to disclose the ph

283 The linear and nonlinear discrete wavelet transforms (DWTs) were used to compress matrix-a

284 rometry data that uses translation-invariant wavelet transforms and performs peak detection using the

285 Wavelet transforms are a useful approach for isolating i

286 agonal matrices F are the simplest examples; wavelet transforms are more subtle.

287 aximum likelihood, stochastic resonance, and wavelet transforms have been used previously to preproce

288 using fast Fourier transform and continuous wavelet transforms show quantitatively that the periodic

289 of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concept

290 at filters have, particularly in the case of wavelet transforms.

291 ssion along the chromatographic dimension by wavelet transforms.

292 ssion scheme based on a novel application of wavelet tries as well as a highly accurate lossy compres

293 ces conduction velocity, converting multiple-wavelet VF into VF with a focal source anchored to the P

294 renergic receptor blockade converts multiple-wavelet VF to focal-source VF and that this focal source

295 Propranolol converted multiple-wavelet VF to slow VF with reentry localized to the PM.

296 uous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of th

297 Wavelets were characterized, the frequency content of op

298 The above-noted nonstationarity implies that wavelets, which do not assume stationarity, show promise

299 By integrating Haar wavelets with Hidden Markov Models, we achieve drastical

300 ionship between PRx and wPRx (r = 0.73), and wavelet wPRx was more reliable in time (ratio of between