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2,353 results for “channel”
Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources
<p>The Pyramic Dataset contains recordings done using the<br> <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an<br> anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x<br> noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different<br> heights. The audio samples recorded are</p> <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> <p>This dataset allows to evaluate the performance of array processing algorithms<br> on real-life recordings done using MEMS microphones similar to those used in<br> mobile phones with all the non-idealities involved. The dataset is suitable for both 2D<br> and 3D scenarios. By subsampling the 48<br> microphones, a large number of array configurations can be tested. Example of<br> algorithms are:</p> <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> <p>Another application is the generation of realistic room impulse by combining<br> the impulse responses of microphones from sources at multiple angles with a<br> variant of the image source model.</p> <p>In addition to the raw (compressed or not) and segmented<br> recordings, the impulse responses of all the microphones for every source<br> locations were recovered from the exponential sweep measurements and are<br> distributed together with the dataset. The initial manual measurement of loudspeakers<br> and microphones locations was improved upon using a blind calibration method.</p> <p>This record contains</p> <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> <p>The raw measurements in wav format are available as a separate <a href="https://zenodo.org/deposit/1209005">record</a> (10.5281/zenodo.1209005).</p> <p>The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> (a copy is available in pyramic-dataset-doc-d2a456b4.zip) and follow the instructions in the README. The version on github is most up-to-date. If possible, please use that one.</p>
Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources (Raw)
<p>The Pyramic Dataset contains recordings done using the<br> <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an<br> anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x<br> noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different<br> heights. The audio samples recorded are</p> <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> <p>This dataset allows to evaluate the performance of array processing algorithms<br> on real-life recordings done using MEMS microphones similar to those used in<br> mobile phones with all the non-idealities involved. The dataset is suitable for both 2D<br> and 3D scenarios. By subsampling the 48<br> microphones, a large number of array configurations can be tested. Example of<br> algorithms are:</p> <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> <p>Another application is the generation of realistic room impulse by combining<br> the impulse responses of microphones from sources at multiple angles with a<br> variant of the image source model.</p> <p>In addition to the raw (compressed or not) and segmented<br> recordings, the impulse responses of all the microphones for every source<br> locations were recovered from the exponential sweep measurements and are<br> distributed together with the dataset. The initial manual measurement of loudspeakers<br> and microphones locations was improved upon using a blind calibration method.</p> <ul> </ul> <p>This record contains only the raw measurements in wav format and archive of the documentation and code.</p> <p>The post-processed data is available in a separate <a href="https://zenodo.org/record/1209563">record</a> that contains:</p> <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> <p>The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> and download the data as needed into the unzipped archive. Then follow the instructions in README.md.</p>
Detection of a Timing Channel in an UPPAAL Model of a Cyber-Manufacturing System
<p>Model of a cyber-manufacturing system for the UPPAAL model checker, including a mitigation of a timing channel.</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Development dataset
<p>This repository contains the development data of task 5 of the DCASE 2018 challenge. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 development dataset' consists of data collected by 4 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files along with the ground truth. In total 72984 segments are made available, leading to approximately 200 hours of data.</p> <p>More information about the challenge and the specific dataset can be found <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">here</a>. Information solely related to the content of the dataset is available in 'DCASE2018-task5-dev.doc.zip'. <br> <br> <strong>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-dev.doc.zip).</strong></p>
Sample single frequency plots of various channels against Altitude and Azimuth for LOFAR single station
<p>Sample of outputs indicating the variation in stokes and linear polarisation channels for LOFAR single station</p>
MOM6 output from a zonally reentrant Southern Ocean-like channel
<p>This dataset contains output from MOM6 output from a zonally reentrant Southern Ocean-like channel used for investigating the relative role of the baroclinic and barotropic dynamics in establishing eddy-saturated states.</p>
Figure 11 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 11. Variabilities of high meiobenthic taxa by their abundance (103 ind./m2) (colored bars) and by their total number (dotted line): A – in the bottom sediments, B – in the biofouling of the channel walls at the depth of 1.5 meters.
Figure 8 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 8. Quantitative representation of macrozoobenthos in the channel wall biofouling at a depth of 1.5 m: A – by abundance (103 ind./m2), B – by biomass (g/m2).
Figure 6 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 6. Channel wall biofoulings at the point 4: A, B – underwater edge on the northeast wall; C – at the depth of 1.5 m on the northeast wall; D, E – underwater edge on the southeast wall; F – at the depth of 1.5 m on the southeast wall.
Figure 5. A in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 5. A – General view of the biofouling at the point 3, B – Porifera colonies, C – Mussels druze on the halyard.
Figure 2 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 2. Topography of anthropogenic objects in the underground channel on the base of submarines in Balaklava and benthic sampling stations. Topography of anthropogenic objects in the underground channel at the submarine base in Balaklava and the benthos sampling station.
Figure 1 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 1. Museum of Military History of Fortifications "Balaklava" in Mount Tavros: A – entrance to the Museum from the side of the Balaklava Bay embankment (photo by A. Zatsepin); B – northern entrance part of the channel from the side of the embankment of the bay; C – southern entrance part of the channel; D, E – internal view of the channel with artificial illumination in its different parts.
Figure 9 in First researches of the underwater ecosystem communities of an underground channel built in 1950s (Balaklava bay, Sevastopol)
Figure 9. The species number ratio of higher taxa in the channel walls macrobiofouling, in comparison with similar data on rocks other areas of the Crimean coast.
Fig. 4 in Temporal variation of epi- and endofaunal assemblages associated with the red sponge Tedania ignis on a rocky shore (São Sebastião Channel), SE Brazil
Fig. 4. MDS ordination of epifaunal (open symbols) and endofaunal (closed symbols) assemblages associated with Tedania ignis at Praia das Cigarras, São Sebastião, São Paulo, Brazil during one year. Stress = 0.06.
Fig. 3 in Temporal variation of epi- and endofaunal assemblages associated with the red sponge Tedania ignis on a rocky shore (São Sebastião Channel), SE Brazil
Fig. 3. Temporal variation of the main taxonomic groups of epifaunal (open symbols) and endofaunal (closed symbols) assemblages associated with Tedania ignis at Praia das Cigarras, São Sebastião, São Paulo, Brazil. Values are means ± SE.
Fig. 2 in Temporal variation of epi- and endofaunal assemblages associated with the red sponge Tedania ignis on a rocky shore (São Sebastião Channel), SE Brazil
Fig. 2. Variation of density, taxon richness and Shannon diversity index of epifaunal (open symbols) and endofaunal (closed symbols) assemblages associated with Tedania ignis in relation to sponge biomass and organic matter content at Praia das Cigarras, São Sebastião, São Paulo, Brazil.
Fig. 1 in Temporal variation of epi- and endofaunal assemblages associated with the red sponge Tedania ignis on a rocky shore (São Sebastião Channel), SE Brazil
Fig. 1. Temporal variation of sponge biomass, and density, taxon richness and Shannon diversity index of epifaunal (open symbols) and endofaunal (closed symbols) assemblages associated with Tedania ignis at Praia das Cigarras, São Sebastião, São Paulo, Brazil. Values are means ± SE.
3D printer audio and vibration side channels
<p>The dataset focuses on side channel data, i.e., sound and vibration, of 3D printers. The dataset aims to enable further research in cyber-physical system security and explore the vulnerability of fused deposition modeling to side chanel attacks.</p> <p>In particular, the datset consists mainly of sound and vibration data that were collected from two different 3D printers (bambu lab P1P and A1mini), using two different sensor systems. The first method is based on an iPhone, whereas the second one is based on a Teensy microcontroller. Both systems record sound at 44.1kHz sampling frequency. The vibrations are based on acceleration data sampled at 100 Hz and 500 Hz for the iPhone and the Teensy 4.0, respectively. Furthermore, the diversity of the dataset for both Teensy and iPhone methods was achieved using 12 different 3D designs. The dataset also inludes the source 3D CAD and sliced toolpath files of the objects that were printed, videos of the printing process, and recordings of the backround noise that exists in the data recordings. A table of contents is also provided in the files.</p>
FIGURE 5 in The main channel and river confluences as spawning sites for migratory fishes in the middle Uruguay River
FIGURE 5 | Longitudinal profile of the Uruguay River depicting the upper, middle and lower reaches, the position of dams and the number of migratory species recorded in studies that sampled ichthyoplankton.
FIGURE 3 in The main channel and river confluences as spawning sites for migratory fishes in the middle Uruguay River
FIGURE 3 | Proportion Capture (%) of the different embryonic development stages of fishes captured in the middle Uruguay River and tributaries, between October 2016 and January 2017. Degree of embryonic development: Segmentation (S), Head-Tail (HT) and Free-Tail (FT).
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.