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 wed conduction and pivot points of reentrant wavelets.

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

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

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

7 maintained by dynamically changing multiple wavelets.

8 ate capable of accommodating many meandering wavelets.

9 ularity points (PSs), which flank individual wavelets.

10 At baseline, VF was maintained by 5.3+/-1 wavelets.

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

12 d frequently breaking up to produce multiple wavelets.

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

14 ates are modeled non-parametrically by using wavelets.

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

16 on from rapid automatic activity to multiple wavelet AF.

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

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

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

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

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

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

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

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

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

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

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

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

29 Wavelet analysis indicates that most of the differences

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

31 Wavelet analysis of outbreak time series suggested clima

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

33 Wavelet analysis of the raw response curves showed that

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

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

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

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

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

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

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

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

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

54 (0.45-2.75 mL/g/min), flow achieved with the wavelet approach correlated extremely closely with value

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

56 Flow estimates without and with the wavelet approach were compared with those obtained using

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

58 Wavelets are more efficient and faster than Fourier meth

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

60 Multiple excitation wavelets are present during ventricular fibrillation (VF

61 unctional reentrant wave fronts and multiple wavelets are present during ventricular fibrillation (VF

62 As wavelets are typically effective at representing point s

63 Firstly, wavelets are used to smooth G+C profiles to locate chara

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

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

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

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

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

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

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

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

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

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

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

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

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

86 The wavelet-based method achieves an overall high performanc

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

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

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

90 The wavelet-based noise-reduction method effectively and obj

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

92 was selected to evaluate the effects of the wavelet-based noise-reduction protocol.

93 This wavelet-based parametric functional mapping has been sta

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

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

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

97 Wavelet-based transformation and shape-based morphology

98 h Joint Photographic Experts Group (JPEG) or wavelet-based trellis-coded quantization (WTCQ) algorith

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

100 We investigate which wavelet bases and threshold functions are overall most a

101 Through wavelet-basis regularization, our method sharpens signal

102 We show that wavelet change-point performs well for smoothing hydropa

103 Differences between wavelet coefficient matrices revealed several heterogene

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

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

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

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

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

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

110 sed on performing a chi(2) statistics on the wavelet coefficients of a profile; thus we do not need t

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

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

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

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

115 their contributions via soft thresholding of wavelet coefficients.

116 ssimilarity pure spectra of OPA and selected wavelet coefficients.

117 Wavelet coherence analysis of neurovascular coupling in

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

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

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

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

122 Nonlinear wavelet compression (NLWC) precisely preserves the peak

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

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

125 x 512 matrix) was transmitted after JPEG or wavelet compression by using point-to-point and Web-base

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

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

128 With wavelet compression, there was loss of 1% modulation wit

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

130 sed to 1:1, 10:1, and 20:1 ratios with lossy wavelet compression.

131 imal signal) diminished only with 20:1 lossy wavelet compression.

132 diminished with any tested level of JPEG or wavelet compression.

133 were mild and were equivalent with JPEG and wavelet compression.

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

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

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

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

138 A non-parametric method, based on a wavelet data-dependent threshold technique for change-po

139 A wavelet decomposition algorithm is described, and thresh

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

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

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

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

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

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

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

147 on, which is sustained by multiple reentrant wavelets defined by anatomic and/or functional barriers,

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

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

150 E with iterative deconvolution incorporating wavelet denoising.

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

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

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

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

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

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

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

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

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

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

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

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

163 entifies potential regulatory regions in the wavelet domain.

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

165 Procainamide decreases the number of wavelets during VF by preventing spontaneous wave breaks

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

181 Multiple wavelets in MI dogs were associated with significantly h

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

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

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

185 more complex, sustaining multiple reentrant wavelets in the free wall and lateral appendage.

186 on, we analyzed the lifespan and dynamics of wavelets in VF, using a new method of phase mapping that

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

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

189 plane X-ray transform of f by an appropriate wavelet-like system for chin,k.

190 This wavelet-like tight frame is the pushout to chin,k, via t

191 We first construct a wavelet-like tight frame on the X-ray bundle chin,k-the

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

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

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

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

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

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

198 s a growing literature on wavelet theory and wavelet methods showing improvements on more classical t

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

200 st parameter-rich versions, both Fourier and wavelet models become equivalent to the unrestricted-rat

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

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

203 A wavelet-neural network signal processing method has demo

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

205 n,k, of an orthonormal basis of tensor Meyer wavelets on Euclidean space Rk(n-k) x Rn-k.

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

207 The underlying wavelet organization of VF is unclear.

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

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

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

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

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

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

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

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

216 15)O, which was not affected markedly by the wavelet process.

217 The use of a wavelet protocol allows near-optimal noise reduction, ma

218 nd differentiating capacity derived from the wavelet protocol were compared with those obtained from

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

220 In this model, multiple wavelets resulting from wavebreaks do not appear to be r

221 Secondly, a wavelet scalogram is used as a measure for sequence prof

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

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

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

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

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

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

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

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

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

231 There is a growing literature on wavelet theory and wavelet methods showing improvements

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

233 ) at 10 microgram/mL decreased the number of wavelets to 3.5+/-1 (P<0.05).

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

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

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

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

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

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

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

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

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

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

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

245 We used wavelet transform and wavelet phase coherence methods to

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

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

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

249 The wavelet transform decodes the information contained in t

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

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

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

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

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

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

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

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

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

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

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

261 odel the same pure variables for the partial wavelet transform, although for the Fourier and complete

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

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

264 Wavelet transform, mask construction, and sparse-partial

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

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

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

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

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

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

271 z were quantitatively determined with Morlet wavelet transform.

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

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

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

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

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

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

278 Morlet wavelet transformation of the leading eigenvector-derive

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

280 candidate peaks after reducing noise through wavelet transformation.

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

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

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

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

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

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

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

288 sform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and mode

289 ssion along the chromatographic dimension by wavelet transforms.

290 Fourier series, and the other from discrete wavelet transforms.

291 ther an artificial obstacle affects multiple-wavelet VF in real tissue is unclear.

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

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

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

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

296 aphic recordings of ventricular fibrillation wavelets were analyzed and transformed into an amplitude

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