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67 results for “wavelets”
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 5. Results from CNN and ANNSVM that used 1Dimg and 2Dimg
<p>We compared the results of CNN_1Dimg, CNN_2Dimg, ANNSVM_1Dimg, and ANNSVM_2Dimg to confirm the validity of ANNSVM when applied to images. The 1Dimg represented the dataset of one-dimensional images, while 2Dimg represented the dataset of twodimensional images. Results are shown in Figure 5. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 1. Example displaying two scatter plots with different characteristics and patterns
<p>In addition to this introductory section, the remainder of this paper is organized as follows. In Section 2, we present previous work related to our present study. In Section 3, we describe details regarding the methodology used in this study. In Section 4, we describe our experiments and results, then discuss our findings. Finally, we summarize the key content of our study in Section 5. </p>
One-dimensional hydrodynamic solution data of wavelet-based adaptive finite volume and discontinuous Galerkin shallow water solvers
<p>Raw data for a series of idealised, one-dimensional hydrodynamic test cases:</p> <ul> <li>dambreakwet (SWASHES 4.1.1)</li> <li>dambreakdry (SWASHES 4.1.2)</li> <li>dambreakmanning (SWASHES 4.1.3)</li> <li>dambreakupslope, dambreakdownslope (<a href="http://doi.org/10.1061/(ASCE)HY.1943-7900.0000494">Kesserwani and Liang 2011</a>)</li> <li>dambreakonehump (<a href="https://doi.org/10.1080/19942060.2011.11015393">Ozmen-Cagatay and Kocaman 2011</a>)</li> <li>lakeatrest (<a href="https://doi.org/10.29007/vm3q">Kesserwani et al. 2018</a>)</li> <li>parabolicbowlswashes (SWASHES 4.2.1)</li> <li>parabolicbowlliangmarche (<a href="https://doi.org/10.1016/j.advwatres.2009.02.010">Liang and Marche 2009</a>)</li> <li>steadysubcritical (SWASHES 3.1.3)</li> <li>steadysupercritical (<a href="https://doi.org/10.2166/hydro.2015.039">Haleem et al. 2015</a>)</li> <li>steadytranscriticalshockless (SWASHES 4.1.4)</li> <li>steadytranscriticalshock (SWASHES 4.1.5) </li> </ul> <p>SWASHES refers to <a href="https://doi.org/10.1002/fld.3741">Delestre et al. 2013</a> </p>
Datasets and Codesets for "Wavelet Decomposition and Neural Networks: A Potent Combination for Short Term Wind Speed and Power Forecasting"
<p>This is the datasets and codesets used in the paper:</p> <p>A. E. Kio, J. Xu, N. Gautam, and Y. Ding, 2024, “Wavelet decomposition and neural networks: A potent combination for short term wind speed and power forecasting,” Frontiers in Energy Research, section of Wind Energy, Vol. 12, pp. 1277464. </p> <p>The PDF file, "Reproducibility Report," explains how to reproduce the results in the tables and figures.</p>
Data set for Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?
<p>This data set is the basis for the publication "Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?", submitted to Journal of Sensors and Sensor Systems.<br> It contains the following data:</p> <p>- "Scipioni_JSSS23_Fig5_WaveletChoice.txt" contains results to obtain the best wavelet for this study. For this, a SAWR (Fig. 3a) signal is noised by an additive Gaussian white noise with different SNR values. The signal is then denoised by wavelets for each SNR value. Results are the new SNR values after denoising.</p> <p>- "Scipioni_JSSS23_Fig9_to_15_F_Ref.txt" contains all the frequencies around F=10700 MHz chosen to test the three methods: Fourier, wavelets, EMD.</p> <p>- 10 files "Scipioni_JSSS23_Fig10_to_14_SAW_EMDvsWavelet_FRef_XX_Occ_100.txt" contain results of the frequency and uncertainty measurement for each noisy SAWR signal versus frequencies and SNR values.</p> <p>- 3 files "Scipioni_JSSS23_Fig17_Tab2_3_Experimental SAWR signal_NoX" contain the values of 3 different experimental SAWR signals.</p>
Estimates of the Wavenumber Wavelet Power Spectrum of Magnetic Fluctuations during Magnetic Reconnection Figure Data
<p>This is data for the publication, "Estimates of the Wavenumber Wavelet Power Spectrum of Magnetic Fluctuations during Magnetic Reconnection".</p>
Supplementary Data to: "Quantifying sub-seasonal growth rate changes in fossil giant clams using wavelet transformation of daily Mg/Ca cycles" in Geochemistry, Geophysics, Geosystems
<p>El/Ca data and high resolution images of 3 laser-ablation tracks on a fossil giant calm. The following Isotopes were monitored <sup>11</sup>B, <sup>23</sup>Na, <sup>24</sup>Mg, <sup>27</sup>Al, <sup>43</sup>Ca, <sup>88</sup>Sr, <sup>89</sup>Y and <sup>138</sup>Ba. The data was measured with laser-ablation inductively coupled plasma mass spectrometry (LA-ICPMS) using a 3 x 33 µm laser slit. El/Ca ratios were calibrated using NIST SRM 612 as bracketing external standard (Jochum et al., 2011) with updated Mg values from Evans & Müller (2018) and <sup>43</sup>Ca as the internal standard; data quantification follows Longerich et al. (1996) and was performed using the software iolite 4 (Paton et al., 2011). For details see main text.</p> <p>References:</p> <p>Evans, D., & Müller, W. (2018). Automated Extraction of a Five-Year LA-ICP-MS Trace Element Data Set of Ten Common Glass and Carbonate Reference Materials: Long-Term Data Quality, Optimisation and Laser Cell Homogeneity. <em>Geostandards and Geoanalytical Research</em>, <em>42</em>(2), 159–188. https://doi.org/10.1111/ggr.12204</p> <p>Jochum, K. P., Weis, U., Stoll, B., Kuzmin, D., Yang, Q., Raczek, I., Jacob, D. E., Stracke, A., Birbaum, K., Frick, D. A., Günther, D., & Enzweiler, J. (2011). Determination of Reference Values for NIST SRM 610–617 Glasses Following ISO Guidelines. <em>Geostandards and Geoanalytical Research</em>, <em>35</em>(4), 397–429. https://doi.org/10.1111/j.1751-908X.2011.00120.x</p> <p>Longerich, H. P., Jackson, S. E., & Günther, D. (1996). Inter-laboratory note. Laser ablation inductively coupled plasma mass spectrometric transient signal data acquisition and analyte concentration calculation. <em>Journal of Analytical Atomic Spectrometry</em>, <em>11</em>(9), 899–904. https://doi.org/10.1039/JA9961100899</p> <p>Paton, C., Hellstrom, J., Paul, B., Woodhead, J., & Hergt, J. (2011). Iolite: Freeware for the visualisation and processing of mass spectrometric data. <em>Journal of Analytical Atomic Spectrometry</em>, <em>26</em>(12), 2508–2518. https://doi.org/10.1039/C1JA10172B</p>
Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].
Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.
Fig. 1 in Classificação Digital de Cicadidae com Wavelets e Support Vector Machines
Fig. 1. Ninfa móvel de Quesada gigas
Fig. 2 in Classificação Digital de Cicadidae com Wavelets e Support Vector Machines
Fig. 2. Cigarras da espécie Quesada gigas, envolvidas nos experimentos
Wavelets of Yellow (laser) and base lines
<p>Collection of wavelets from laser measurements, yellow and base lines. November 15 2019 to January 13 2020</p>
Examining the oil price and renewable energy price nexus: Comparative wavelet analysis for the aftermath of 2008 financial crisis, shale oil crisis and COVID-19 pandemic.
<p><span>In this study, we conducted a wavelet analysis on the dependence between renewable energy indices and Brent oil index (Brent) for the period of 21st November, 2003 till 24th May, 2024. The objective of the paper includes comparing the co-movement of renewable energy stock prices and oil prices during three different crises including the global financial crisis, shale oil crisis and the covid-19 pandemic. We found that the dependence is similar for both Europe and on a global scale during the pre-crises time, where renewable energy prices lead Brent oil prices in the short and medium term. Furthermore, results confirm that there is substitutability between oil prices and renewable energy prices before all crises which shows a positive correlation. The results further show that the short and medium term dependence disappears after the oil crisis and financial crisis which is supported by the sudden loss in demand for oil. These findings show that co-movement changes between the three crises where there is no dependence between the indices after the financial and oil crisis while there is a negative correlation after the covid-19 pandemic. These findings could have significant ramifications for investors seeking to mitigate risks and for policymakers making decisions about supporting the advancement of renewable energy while understanding the change of behaviour between the two crises.</span></p>
Wavelet filters for automated recognition of birdsong in long-time field recordings
<p>1. Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded -- recognising calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone -- are not well developed yet. 2. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimise false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again. 3. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel-Frequency Cepstral Coefficients (MFCC)) on real-world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hrs of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise. 4. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large-scale monitoring of animal species. It can be trained to recognise new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.</p>
Data and Code for "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach"
<p>These files are the supplementary information, including dataset, codes and plots for the research work entitled "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach".</p>
Wavelet variance coefficients of children and adolescents with and without ADHD
<p>Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood. One hallmark in the characterization of pathological processing in ADHD is that attention skills are not impaired per se but more inconsistent and with higher variability compared to typically developing children (TDC). Increased variability in ADHD patients has been found in reaction times, as well as resting-state fMRI (rs-fMRI) brain signals. High variability has been assumed to reflect occasional lapses in attention, linked to intrusions of distracting activity during task performance and/or reduced anti-correlation between regions in the DMN and attention networks. Therefore, Dajani et al. (2019) concluded that it is more likely the dynamics between and within neural networks [i.e., the variability of network processing across time- and frequency-scales], that are affected in ADHD, than functional connectivity [in terms of one coefficient describing the (averaged) correlation between two regions over time]. </p> <p>We determined wavelet variance to quantify these dynamics. We determined wVar at rest and under task in fMRI timeseries of regions of the DMN and the FPN in three different frequency bands: 0.02 to 0.04Hz, 0.04 to 0.08Hz, and 0.08-0.16Hz.</p> <p>We found that wVar differed group specifically between rest and task (significant group X condition interaction: whereas wVar was higher at rest compared to task in TDC, wVar was comparable or even decreased at rest in ADHD. For an external validation of group comparisons in wVar at rest, we determined wVar in rs-fMRI timeseries of a subsample of the Child Mind Institute data set (Functional Connectomes Project International Neuroimaging Data-Sharing Initiative http://dx.doi.org/10.15387/CMI_HBN (2017)). Results replicated our findings in terms of no significant group differences in wVar at rest in combination with similar or lower absolute values in ADHD patients compared to control subjects. </p> <p>In normal processing, high wVar at rest was interpreted as reflecting free fluctuating brain signaling, in comparison to small wVar under task indicating focussed processing. Thus, we conclude that wVar is a sensitive measure of cognitive processing and is even capable of detecting deviant processing in pathological brain function.</p>
Figure 8 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 8. Cross-oalidation algorithm.
Figure 6 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 6. The experimental setup for the proposed application two.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.