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97 results for “signal processing”
Experimental signals and processed data used in the research work "On-chip phonon-magnon reservoir for neuromorphic computing"
<p>The data set includes the raw experimental data, processed experimental data, and numerically modeled dependencies in the respective folders:</p><p>The raw experimental data (magnon readout, as measured) are given for all processed signals presented in the respective figures (folders 'Figure2', 'Figure3' and 'Sup Figure1') and used for the ANN training (folder '3x3Sets & AugmentedVisualShapes'). </p><p>The signals presented in the folders '\3x3Sets & AugmentedVisualShapes\3x3SET##' are named following the scheme shown in Fig. 3a. The signals in the folder \3x3Sets & AugmentedVisualShapes\RandomizedShapes' are simulated using the procedure described in the Methods section as "Drawing of randomized visual shapes". The sets of calculated statistical parameters used for the shapes' recognition are in the folder ''\3x3Sets & AugmentedVisualShapes\Parameters'</p><p>The waveforms for the trajectories formed by randomly selected 4, 5, 6, and 7 discrete positions and their statistical parameters are presented in the corresponding folder. </p>
Acoustic Emission Dataset for Multi-Laser LPBF Systems: Supporting DUAL DISCO Signal Processing Technique
<p>This dataset contains acoustic emission (AE) signals collected during experiments with multi-laser Laser Powder Bed Fusion (LPBF) systems. The data supports the research presented in the paper titled "DUAL DISCO: A Novel Approach to Acoustic Emission Monitoring in Multi-Laser LPBF Systems." The dataset is designed to facilitate the development and validation of advanced signal processing techniques, specifically the DUAL DISCO method, which aims to disentangle and analyze AE signals from simultaneous laser operations.</p> <p><strong>Contents:</strong></p> <ul> <li> <p><strong>Raw_data.zip:</strong> This file contains the AE signals used for training. The data was recorded using two condenser microphones positioned around the LPBF build plate, capturing signals from both sequential and simultaneous laser operations across various melting regimes (conduction and keyhole modes).</p> </li> <li> <p><strong>Raw_data_test.zip:</strong> This file includes the AE signals used for testing, recorded under different experimental conditions to evaluate the generalization capabilities of signal processing algorithms.</p> </li> <li> <p><strong>params.xlsx:</strong> This spreadsheet provides the ground truth labels for the training data, indicating the melting regime (conduction or keyhole) for each signal in Raw_data. </p> </li> <li> <p><strong>params_test.xlsx:</strong> This spreadsheet contains the ground truth labels for the test data, similarly indicating the melting regime for each signal in Raw_data_test.</p> </li> </ul> <p><strong>Applications:</strong></p> <p>This dataset is intended for researchers and practitioners in the field of additive manufacturing and signal processing. It can be used to:</p> <ul> <li>Develop and test new algorithms for AE signal processing in multi-laser LPBF systems.</li> <li>Explore the acoustic characteristics of different melting regimes.</li> <li>Enhance the understanding of process monitoring techniques in additive manufacturing.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>The dataset was collected using the AddUp FormUp 350 machine and is part of a research project supported by the Bern Economic Development Agency. Special thanks to Thomas Rytz for technical support.</p>
Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes
<p>Data underlying the figures in the publication “Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes”, published in<em> Nat. Nanotechnol., </em><strong>2020</strong>, 15, 914–921. <a href="https://doi.org/10.1038/s41565-020-0761-y">https://doi.org/10.1038/s41565-020-0761-y</a></p> <p>Table of contents:</p> <p><strong>1. Dataset</strong>; Excel file containing the numerical data for <em>Figure 3</em>: Metathesis kinetics, mutant screenings and crowding.</p> <p><strong>2. Experimental Information</strong>; Word file containing the experimental protocols for synthesis and analysis.</p> <p> </p> <p> </p>
Fig. 4. A in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 4. A half-second vocalization by Tropisternus blatchleyi from 0–5,000 Hz changed into the frequency domain using the Fast Fourier Transformation. Active call frequency regions are at 1,100 Hz and 4,400 Hz. The first feature for T. blatchleyi divides the sum of the points in the active frequency band by the sum of the points in the inactive band, yielding a large number in T. blatchleyi exemplar calls.
Fig. 3 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 3. The classifier algorithm with two features shown in a two-dimensional graph. Beetle call data and noise data are classified based on two beetle call features: difference in active frequency range shape (x-axis and in Matlab™ as a template) and a ratio of amplitudes in an active and non-active frequency range (y-axis). The algorithm is shown as a solid black line. Most beetle calls fall within the correct classification in the lower left-hand corner, however, some fall outside the equation and are classified as noise. Likewise, noises are occasionally classified as beetles. As more features are added, the algorithm becomes multidimensional.
Fig. 2 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 2. Five distress calls by Berosus pantherinus transformed into the frequency domain using the fast fourier transformation from 0–12,000 Hz (x-axis). Wide active frequency bands can be seen from 1,500–6,000 Hz and 7,000–9,500 Hz. The feature for distress calls is the sum of the data points between 1,000–6,000 Hz with an amplitude (y-axis) greater than 2.
Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study
<p><strong>Dataset for the publication: 'Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study'<br> Rigoni et al. 2023, Clinical Neurophysiology, doi: </strong><a href="https://doi.org/10.1016/j.clinph.2023.05.012">https://doi.org/10.1016/j.clinph.2023.05.012</a></p> <p><strong>Dataset description</strong></p> <p><em>func_data.mat</em> : source-reconstructed EEG traces stored in a Fieldtrip format (data for each subj)</p> <p><em>struct_data</em>: consensus structural connectome</p> <p><em>ROIpatch </em>: mesh used to plot Fig4</p> <p><em>SC_surrogates (W0)</em>: 1000 degree-preserving surrogates of the structural connectome computed with the null_model_und_sign function of the Brani Connectivity Toolbox; W0 are used for broadcasting analyses, section 2.6.2</p> <p><em>SC_surrogates_harmonics (U0)</em> : network harmonics of the surrogate structural connectomes (W0); U0 are used for broadcasting analyses, section 2.6.2<br> <em>BD_surr </em>: Broadcasting-direction (BD) of the degree-preserving surrogates of the structural connectome W0; used to produce FigS3 (data for each subj)</p> <p><em>PHI </em>: matrix of +1/-1 to generate the functional surrogates used to define significance of SDI, as described in section 2.6.3</p> <p><em>data_GSP2_surr </em>: SDI of the functional surrogates used to define significance of SDI, as described in section 2.6.3 (data for each subj)</p> <p> </p> <p>Abbreviations:</p> <p>EEG= electroencephalography;</p> <p>SDI= structure-decoupling index</p>
Novel Cardiac Signal Processing System
ClinicalTrials.gov study NCT04112433. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Study of a Signal-processing Algorithm Aiming at Improving Speech-in-noise Intelligibility in Normal-hearing and Hearing-impaired Persons
ClinicalTrials.gov study NCT04775810. IPD Sharing: NO. Countries: 1. Publications: 17.
Shaft Misalignment Data for: Inter-component Phase Processing of Quasipolyharmonic Signals
<p>This dataset contains observations of vibration of test stand in three states: normal state, horizontal and vertical parallel misalignment. Full dataset is avaliable at:</p> <p>https://data.mendeley.com/datasets/pt9mjcvghd/1</p>
Data from: Processing of simple and complex acoustic signals in a tonotopically organized ear
Processing of complex signals in the hearing organ remains poorly understood. This paper aims to contribute to this topic by presenting investigations on the mechanical and neuronal response of the hearing organ of the tropical bushcricket species Mecopoda elongata to simple pure tone signals as well as to the conspecific song as a complex acoustic signal. The high-frequency hearing organ of bushcrickets, the crista acustica (CA), is tonotopically tuned to frequencies between about 4 and 70 kHz. Laser Doppler vibrometer measurements revealed a strong and dominant low-frequency-induced motion of the CA when stimulated with either pure tone or complex stimuli. Consequently, the high-frequency distal area of the CA is more strongly deflected by low-frequency-induced waves than by high-frequency-induced waves. This low-frequency dominance will have strong effects on the processing of complex signals. Therefore, we additionally studied the neuronal response of the CA to native and frequency-manipulated chirps. Again, we found a dominant influence of low-frequency components within the conspecific song, indicating that the mechanical vibration pattern highly determines the neuronal response of the sensory cells. Thus, we conclude that the encoding of communication signals is modulated by ear mechanics.
Spoofing Signal Generation Algorithm using the Processing Information of a Receiver for Authentic GPS Signals
<p>Experimental results analyzation files.</p>
Dataset Used in "Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes"
<p>This is the dataset used in Ding, Zeng, and Zhou, 2006, “Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes.” <em>Journal of Quality Technology</em>, Vol. 38(3), pp. 199-216. This data file has 528 data records, two fewer than the 530 number mentioned in the paper.</p>
Fig. 2 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 2. IoT node prototype.
Fig. 6. T3 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 6. T3 test results.
Fig. 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 4. Organization diagram of tests T1 (left above), T2 (left below) and T3 (right).
Fig. 7. Packets received during testing using 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 7. Packets received during testing using 4 nodes.
Fig. 1 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 1. Mesh network topology (left) and star topology (right).
CP1150 Sound Processor Speech Perception Compared With the Next Generation of Signal Processing Technology
ClinicalTrials.gov study NCT05286385. IPD Sharing: NO. Countries: 1. Publications: 0.
Hearing Aid Signal Processing Comparative Study
ClinicalTrials.gov study NCT04839289. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.