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10 results for “bandwidth data”
Bandwidth and Service Placement data from the CityLab testbed
<p>A dataset that contains bandwidth data from the nodes of Fed4Fire+ CityLab testbed in Antwerp, Belgium. </p>
Examining the Effects of Resolution Bandwidth when Measuring Compound Radar Waveforms Data
<p>Data used by NTIA/ITS TR-23-566 Examining the Effects of Resolution Bandwidth when Measuring Compound Radar Waveforms.</p>
COLOURATION IN 2.5D LOCAL WAVE FIELD SYNTHESIS USING SPATIAL BANDWIDTH-LIMITATION -- Data
<p>This data set contains stimuli and results from an experiment for human perception of colouration in Stereophonics, Wave Field Synthesis, Near-Field-Compensated Higher-Order Ambisonics and Local Wave Field Synthesis simulated via binaural synthesis using head related impulse responses (HRIRs). The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the directory `brs`. The employed stimuli is contained in `stimuli`. The Multiple Stimulus with Hidden Reference and Anchor (MUSHRA) ratings are stored in `results` together with a text file to describe the content in more detail. The `analysis` directory includes scripts for the statistical evaluation in the publication. The source code is licensed under GPL v3.</p> <p>Abstract of the Publication:</p> <p>Sound Field Synthesis techniques, such as Wave Field Synthesis aim at a physically accurate reproduction of a desired sound field inside an extended listening area. This area is surrounded by loudspeakers individually driven by their respective driving signals. Due to practical limitations, artefacts impair the synthesis accuracy resulting in a perceivable change in timbre compared to the desired sound field. Recently, an approach for so-called Local Wave Field Synthesis was published which enhances the reproduction accuracy in a limited region by applying a spatial bandwidth limitation in the circular/spherical harmonics domain to the desired sound field. This paper reports on a listening experiment comparing conventional Sound Field Synthesis techniques with the mentioned approach. Also the influence of the different parametrisations for Local Wave Field Synthesis is investigated. The results show that the enhanced reproduction accuracy in Local Wave Field Synthesis leads to an improvement with regard to the perceived colouration.</p>
Data and data analysis codes for Palacios et al. "Single-domain Bose condensate magnetometer achieves energy resolution per bandwidth below ℏ"
<p>Data and data analysis codes for the article "Single-domain Bose condensate magnetometer achieves energy resolution per bandwidth below ℏ" by S. Palacios, et al. https://arxiv.org/abs/2108.11716</p>
Data and code from "Full bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene micro-transistor depth neural probes"
<p>Data and python code for reproducing the main results of the paper "Full bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene micro-transistor depth neural probes."</p>
GATEMAN project -- Wide-bandwidth, high-precision GNSS and jammer raw data
<p>This dataset contains high quality GNSS and jammer raw data generated during the in-lab validation activities of jamming detection and localization performed in the frame of the GATEMAN project. These files are grouped for each type of validation scenario defined. The scenarios are combinations of three different GNSS and three different jamming signals.</p> <p><strong>Content:</strong></p> <p>The archive contains a folder for each validation scenario. Each folder contains two files. One contains the GNSS data and the second file contains jamming data. A function to read the data with GNU Octave/Matlab is also included.</p> <p>├── GALE1+AMtone<br> │ ├── AMtone@20MSps.bin<br> │ └── GALE1@20MSps.bin<br> ├── GALE1+Chirp10MHz<br> │ ├── Chirp10MHz@20MSps.bin<br> │ └── GALE1@20MSps.bin<br> ├── GALE1+Chirp20MHz<br> │ ├── Chirp20MHz@20MSps.bin<br> │ └── GALE1@20MSps.bin<br> ├── GPSL1+AMtone<br> │ ├── AMtone@20MSps.bin<br> │ └── GPSL1@20MSps.bin<br> ├── GPSL1+Chirp10MHz<br> │ ├── Chirp10MHz@20MSps.bin<br> │ └── GPSL1@20MSps.bin<br> ├── GPSL1+Chirp20MHz<br> │ ├── Chirp20MHz@20MSps.bin<br> │ └── GPSL1@20MSps.bin<br> ├── GPSL5+AMtone<br> │ ├── AMtone@20MSps.bin<br> │ └── GPSL5@20MSps.bin<br> ├── GPSL5+Chirp10MHz<br> │ ├── Chirp10MHz@20MSps.bin<br> │ └── GPSL5@20MSps.bin<br> ├── GPSL5+Chirp20MHz<br> │ ├── Chirp20MHz@20MSps.bin<br> │ └── GPSL5@20MSps.bin<br> └── readBinData.m</p> <p> </p> <p><strong>Data format:</strong></p> <p>Each file stores the received baseband samples as an array of complex, 16-bit signed integer data (range -32,768 to 32,767) of the corresponding signal. The data is stored in Big-endian, network order format, i.e. the most-significant byte occupies the lowest memory address. The real and imaginary components of the data correspond to the in-phase (I) and quadrature-phase (Q) data, respectively. I and Q-samples are interleaved [I, Q, I, Q, ...] in the array.</p> <p> </p> <p><strong>Data:</strong></p> <p>The recorded data of each validation scenario consists of two files, one containing the GNSS signal (L1, L5 or E1 band), the other one containing the jamming signal (tone, chirp 10 & 20 MHz bandwidth). The RF GNSS signal was generated with a GNSS signal generator, it contains only a single spreading sequence. The RF jamming signal was generated with a Vector Signal Transceiver. Both signals of a scenario were recorded synchronously with an USRP at an I\Q rate of 20 MS/s.</p> <p>GNSS signals:</p> <ul> <li>Startime: 30.05.2018 at 12:00 (approximately)</li> <li>Latitude: N 50 deg, 50.4864 min</li> <li>Longitude: E 4 deg, 21.3499 min</li> <li>Altitude: 50m</li> <li>Trajectory: Static</li> <li>Ephemeris file: brdc1500.18.n</li> </ul> <p><br> GNSS band PRN Elevation (deg) Azimuth (deg) Power (dBm)<br> GPS L1 4 85 187 -70<br> GPS L5 4 85 193 -70<br> Galileo E1 E14 66 274 -68.5</p> <p>Jamming signals:</p> <ul> <li>The tone is simple a sinosoidal waveform in the centre of the GNSS bands.</li> <li>The chirps are linear frequency modulated waveforms (sawtooth) in the centre of the GNSS bands with sweep range of 10 MHz and 20 MHz and sweep period of 8.64 µs.</li> </ul> <p> </p> <p> </p> <p>The data uploaded here are a truncated samples. Longer samples of data are available upon request.</p>
Data Processing Pipeline and Products from the NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths
<p>This Zenodo dataset contains the data processing pipeline, as well as the data products, corresponding to the scientific journal paper "NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths". The processing pipeline is structured as follows:</p> <ol> <li>1_fit_dispersion_3terms.py reads the .tim files (containing the times-of-arrival), creates the broadband and narrowband datasets, and fits a dispersion model to both datasets using three parameters.</li> <li>2_plot_fits_differences.py creates the residual plots showing the differences in the fitted values of the parameters.</li> <li>3_autocovariance.py calculates the autocovariance function of the differences in the fitted values, and creates the corresponding plots.</li> <li>All the files starting with "plot" are convenience scripts for creating the plots presented in the paper.</li> <li>All the files starting with "sophia" are utility functions created by the authors that are used in the main pipeline.</li> <li>The folder "NANOGrav_12yv4" contains the dataset analyzed in this work.</li> <li>The folder "NG_timing_analysis" contains utility functions created by the NANOGrav collaboration that are used in the main pipeline.</li> </ol> <p>Please do not hesitate to send all your questions, concerns, or commentaries to sophia.sosa@nanograv.org</p>
Data from: High bandwidth synaptic communication and frequency tracking in human neocortex
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Data from: The allometry of sound frequency bandwidth in songbirds
<p>Theory predicts that allometric constraints on sound production should be stronger for the lower frequencies of vocalizations than for their higher frequencies, which could originate an allometry for sound frequency bandwidth, but this was never tested. Using song recordings of ca. 1000 passerine species (from >75% passerine genera), we show a significantly steeper allometry for the lower than the higher song frequencies, resulting in a positive allometry of frequency bandwidth: larger species can use wider bandwidths than smaller species. The bandwidth allometry exists in songbirds (oscines), but not non-oscine passerines, indicating that it emerges from a combination of physical constraint and the evolved behavior of oscines: unlike the narrow bandwidths of most non-oscine songs, the learned songs of oscines often use wide bandwidths that can be limited by both the lower and upper limits of sound frequency production. This bandwidth allometry has implications for several research topics in acoustic communication.</p>
Data from: The allometry of sound frequency bandwidth in songbirds
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Allen Brain Atlas
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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.