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ShareScore release 0.9.0
Dataset results
12 results for “digital signal processing”
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.
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).
A Comparative Clinical Investigation of Two Hearing Instrument Digital Signal Processing
ClinicalTrials.gov study NCT04374851. IPD Sharing: NO. Countries: 1. Publications: 0.
Fig. 1 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 1. Each graph is a frequency plot of a one half-second exemplar sound clip. One exemplar call from each species and an exemplar distress call from an individual Tropisternus blatchleyi (that is indicative of all four beetles' distress calls) is shown transformed into the frequency domain using the fast fourier transformation with normalized amplitudes and plotted from 0–10,000 Hz.
Fig. 5 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 5. Results in T1 (left) and T2 (right).
Fig. 3 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 3. Diagram of the proposed algorithm..
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.