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67 results for “wavelets”
Figure 5 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 5. Examples of images used: high, low, and zero density, respectioely.
Figure 3 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 3. The experimental setup for the proposed application one.
Wavelet variance coefficients of children and adolescents with and without ADHD
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Wavelet filters for automated recognition of birdsong in long-time field recordings
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Continuous Wavelet Transform and Short-Time Fourier Transform used to generate S1_Table2
<p>Continuous Wavelet Transform and Short-Time Fourier Transform true vs. false recognition accuracies: the t-test was used to compare CWT and STFT recognition accuracies with mean <em>f</em>NIRS classification accuracy. The result was used to generate S1_Table2.</p>
Wavelets and base lines for Yellow experiments
<p>Collection of the wavelets I made, together with the collections of baselines</p>
Facial Features of Air Gun Array Wavelets in the Time-Frequency Domain under Practical Conditions
<p>The data in folder (a) include mass of air in the bubble, variation in the temperature of the gas inside the bubble, bubble radius, bubble wall velocity, notional signature and far-field signature simulated by Van der Waals air-gun model. The actual data in folder (b) are the far-field wavelets at different locations measured by China Ocean University on the northern slope of the South China Sea in April 2017. The simulated data are the far-field wavelets corresponding to the actual locations simulated by Van der Waals air-gun model. The data in the folder (c) includes simulated three dimensional acoustic field and facial evaluation data for long array, square array, simultaneously fired vertical array, and time-delayed vertical array.</p>
Wavelet Domain Compensation of Frequency Dispersion of UWB Electromagnetic Waves for Time-Reversal Imaging (dataset)
<p>These files are the simulation data used to create the figures illustrated in the relevant journal paper. Each filename indicates which figure number it relates to. These files are text files. The first row in each file describes the content of each of its columns.</p> <p> </p>
COVID-19 and Wavelet Coherence
<p>Data is related to COVID-19, Crude oil prices, and atmospheric CO<sub>2</sub>.</p>
Data from: Choosing wavelet methods, filters, and lengths for functional brain network construction
Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)—each essential parameters in wavelet-based methods—on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.
Data from: Dual tree complex wavelet transform based signal denoising method exploiting neighbourhood dependencies and goodness of fit test
A novel signal denoising method is proposed whereby goodness of fit (GOF) test in combination with a majority classifications based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DTCWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.
Figure 7 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 7. The SVM structure used in application two. Similar to AP1, 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.
Data from: Wavelet domain radiofrequency pulse design applied to magnetic resonance imaging
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Data from: Dual tree complex wavelet transform based signal denoising method exploiting neighbourhood dependencies and goodness of fit test
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Data from: Choosing wavelet methods, filters, and lengths for functional brain network construction
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Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data
GEO Series GSE37524. Homo sapiens. 2 samples. Type: Other.
Period wavelet movies of spreadouts, ex vivo models of somitogenesis, subjected to 170-min periodic pulses of 2 uM DAPT
<p>Here are period wavelet movies (as .tif), generated using a wavelet analysis workflow developed by Gregor Mönke, of timelapse imaging of a dynamic Notch signaling reporter (i.e. LuVeLu) in spreadouts, ex vivo models of somitogenesis. Spreadouts were subjected to 170-min periodic pulses of 2 uM DAPT. Also here are binary masks generated in Fiji based on threshold of LuVeLu intensity. These files are used to run an accompanying Python script, available at: <a href="https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/PeriodGradientAnalysis">https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/PeriodGradientAnalysis</a></p>
Fig. 3 in Classificação Digital de Cicadidae com Wavelets e Support Vector Machines
Fig. 3. Modelo da SVM utilizada nos experimentos
Wavelet Analysis of Electromyography (EMG) in Cerebral Palsy
ClinicalTrials.gov study NCT00504049. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Smart Sensing Using Wavelets Project
<p>Further refinements to the FOSS technologies are focusing on &ldquo;smart&rdquo; sensing techniques that adjust sensing parameters as needed in real time so that only the necessary amount of data is acquired &ndash; no more, no less. Traditional FOSS signal processing is based on Fourier transforms that break up the length of the fiber into analysis sections that are equal in length along the whole fiber. If high resolution is required along one portion of the fiber, the whole fiber must be processed at that resolution. Wavelet transforms make it possible to efficiently break up the length of the fiber into analysis sections that vary in length. If high resolution is required along one portion of the fiber, only that portion is processed at high resolution, and the rest of the fiber can be processed at the lower resolution.</p><p><strong>Work to date</strong>: The team has developed a C language prototype of a wavelet-based signal processing algorithm. This static form currently operates at half the speed of the Fourier-based algorithm but it will be able to operate 3-4 times faster when optimized.&nbsp;The team has also developed a LabVIEW prototype of the adaptive form of the algorithm. This form provides real-time adaptive spatial resolution.&nbsp;For example: when strain on a wing increases during flight, the software will automatically increase the resolution on the part of the fiber that is under strain.</p><p><strong>Looking ahead</strong>: Next steps involve optimizing the code and implementing it on a digital signal processing (DSP) chip.&nbsp;A patent application for the wavelet algorithm has been filed with the USPTO.</p><p><strong>Benefits</strong></p><ul><li><strong>Improved efficiency</strong>: Offers precision measurement only where it is needed rather than on the entire fiber</li><li><strong>Faster signal processing</strong>: Data can be processed at different resolutions at different fiber segments</li><li><strong>Adaptive</strong>: Automatically selects optimum resolution based on data received</li></ul><p><strong>Applications</strong></p><ul><li>Strain sensing</li><li>Temperature measurements</li></ul>
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.