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97 results for “signal processing”
Eco-evolutionary processes underlying early warning signals of population declines
<p>Datasets for the paper appearing in Journal of Animal ecology : "Eco-evolutionary processes underlying early warning signals of population declines". Also GitHub repository link :<a href="https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA">https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA</a></p>
Monitoring valve activity in M.edulis and M.galloprovincialis: Valve signal processing
<p>This repository provides access to the metadata and scripts used to monitor valve gaping activity of bivalves using valvometry (Valve-Trek ; Technosmart Europe srl, www.technosmart.eu). The dataset contains valve activity records in csv format per individual sampled from February to May 2023 at Agon-Coutainville (Normandy, France), as well as r scripts for processing, formatting and analyzing valve gaping data.</p>
The Place of Signal processing
<p>“The Place of Signal processing” 2012. In: Workshop at Conference on Biomedical<br /> Science, by Scientific Society of Medical Engineering of Islamic Azad university of<br /> Tabriz. Presenter: Dr.Mirhadi Seyed Arabi, Dr.Ali Gouya and Dr.Hamid Mirzaei,<br /> 2012.</p>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>PSO is used to identify the best solution from collection of solution. It is a computational<br> method that optimizes a problem by iteratively trying to improve a candidate solution with regard to<br> a given measure of quality. PSO optimizes a problem by having a population of candidate solutions,<br> here dubbed particles, and moving these particles around in the search-space according to simple<br> mathematical formulae over the particle's position and velocity. Each particle's movement is<br> influenced by its local best known position but, is also guided toward the best known positions in<br> the search-space, which are updated as better positions are found by other particles. This is expected<br> to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no<br> assumptions about the problem being optimized and can search very large spaces of candidate<br> solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found.<br> The following Figure 5 explains the basic flow of PSO process.</p>
Supplementary materials (processed data) for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."
<p>Processed data for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."</p>
Time-Lapse Self-Potential Signals from Microbial Processes: a Laboratory Perspective
<p>#Datasets for 3D self-potential monitoring experiments for microbial processes under laboratory-controlled conditions</p> <p>The three datasets presented here are the measured time-lapse self-potential (SP) data described in the paper 'Time-Lapse Self-Potential Signals from Microbial Processes: a Laboratory Perspective'.</p> <p>The data contains three SP datasets and resistivity data. Each SP dataset contains 193 columns, the data in the first column is the time and the unit is seconds, and columns 2 to 193 are SP data in mV corresponding to the 192 electrode measurements.</p> <p>1) Stability test in water: The measured data is named water_background_data.txt, which record the time-lapse SP signals measured by the SP monitoring system in the stability test experiment.</p> <p>2) Microbial Processes monitoring experiment 2: The measured data is named Experiment2_data.txt, which record the time-lapse SP signals measured by microbial processes in organic matter.</p> <p>3) Microbial Processes monitoring experiment 3: The measured data is named Experiment3_data.txt, which record the time-lapse SP signals measured by microbial processes in organic matter.</p> <p>4) Resistivity data: The measured data is named Resistivity_measurement_data.txt, which record three experimental materials' resistivity measured by the DER25718 instrument.</p>
Sex-specific speed-accuracy tradeoffs shape neural processing of acoustic signals in a grasshopper
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Data from: Combinatorial signal processing in an insect
Human language is combinatorial: phonemes are grouped into syllables, syllables into words, and so on. The capacity for combinatorial processing is present to different degrees in some mammals and birds. We tested for basic combinatorial processing in an insect against two competing hypotheses: beginning rule (where the early signal portions play a stronger role in acceptability); and no rule (where the order of signal elements plays no role in signal acceptability). We worked with Enchenopa treehoppers, whose vibrational signals consist of a whine (W) followed by pulses (P). The combinatorial rule hypothesis predicts females will prefer any stimuli containing the natural-combination (WP or PWP) over reverse-order stimuli (PW). The beginning rule hypothesis predicts that females will prefer stimuli with natural beginnings (WP or W) over stimuli with modified beginnings (PW or PWP). The no rules hypothesis predicts no preferences in stimuli acceptability. In playback experiments using laser vibrometry, females preferred natural-combination signals regardless of the beginning element (WP or PWP) and discriminated against reverse-order signals (PW) or individual elements (W or P). Finding support for the combinatorial rule hypothesis in insects suggests that this capability represents a common solution to the problems presented by complex communication.
Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation
<p>Phenotypic integration is an important metric that describes the degree of covariation among traits in a population, and is hypothesized to arise due to selection for shared functional processes. Our ability to identify the genetic and/or developmental underpinnings of integration is marred by temporally overlapping cell-, tissue-, and structure-level processes that serve to continually 'overwrite' the structure of covariation among traits through ontogeny. Here we examine whether traits that are integrated at the phenotypic level, also exhibit a shared genetic basis (e.g., pleiotropy). We micro-CT scanned two hard tissue traits, and two soft tissue traits (mandible, pectoral girdle, atrium, and ventricle respectively) from an F<sub>5</sub> hybrid population of Lake Malawi cichlids, and used geometric morphometrics to extract 3D shape information from each trait. Given the large degree of asymmetric variation that may reflect developmental instability, we separated symmetric- from asymmetric-components of shape variation. We then performed quantitative trait loci (QTL) analysis to determine the degree of genetic overlap between shapes. While we found ubiquitous associations among traits at the phenotypic level, except for a handful of notable exceptions, our QTL analysis revealed few overlapping genetic regions. Taken together, this indicates developmental interactions can play a large role in determining the degree of phenotypic integration among traits, and likely obfuscate the genotype to phenotype map, limiting our ability to gain a comprehensive picture of the genetic contributors responsible for phenotypic divergence.</p>
ND250 as a prediction error signal in orthographic processing: insights from the comparison of handwritten and printed words
<p><span>This dataset contains electroencephalography (EEG) recordings and behavioral data from a study investigating the neural mechanisms of visual word recognition in native Chinese speakers. The study used a color decision task, where participants viewed printed and handwritten Chinese single-character words varying in lexical frequency (high-frequency vs. low-frequency). The primary aim was to examine the N250 ERP component, a 250-ms difference in brain activity observed between certain word types, and determine whether it reflects activation of the orthographic lexicon or a prediction error signal during orthographic processing. The findings suggest that the N250 is related to prediction error, providing support for the Interactive Account of orthographic processing.</span></p>
Geophysical Signals from Magma Propagation: Experimental and Processed Data
<p>This repository contains the experimental data analyzed and interpreted in the manuscript titled "<em>Geophysical signals induced by magma propagation: Insights from analog experiments</em>" by S. Furst, J. Vandemeulebrouck, and V. Pinel. It includes video recordings, timelapse photos, accelerometer data, and deformation data. Additionally, there are three MATLAB scripts for post-processing the timelapse photos following the approach described in the manuscript. The results of the MFP analysis on the accelerometer data, as well as the outcomes from the COMSOL Multiphysics simulations, are also included in the repository.</p>
'Signal integrity' in the Audiovisual Media Services Directive: Process, positions and policies examined and explained
<p>Tables 1 & 2 can be used as complementary material for readers of the forthcoming article (2021) in the Journal of Digital Media & Policy entitled <em>‘Signal integrity’ in the Audiovisual Media Services Directive: Process, positions and policies examined and explained.</em> Table 1 looks at the Mapping of modifications, processes and cases referred to by Article 7b and Recital 26 exemptions, while Table 2 presents the stakeholders' argumentation relating to signal integrity based on economic and cultural positions, and the scope of the measure. For the online version of the article, these tables are referred to in-text in the shape of hyperlinks and full links in the printed version.</p> <p>The article aims to get a broad picture of current practices, positions and policy on signal integrity. Based on the findings, this article proposes a clear framework for the implementation of Article 7b. It identifies the key considerations made in relation to exemptions and consent and reveals the positions of the key stakeholders and the vacuum that is exists at the level of MS regulation. Therefore, the concept of ‘signal integrity’ and the legal protections it sets on broadcasters’ signal will be explored.</p>
ENST-Drums: an extensive audio-visual database for drum signals processing
<p>The <strong>ENST-Drums database</strong> is a large and varied research database for automatic drum transcription and processing:</p> <ul> <li>Three professional drummers specialized in different music genres were recorded.</li> <li>Total duration of audio material recorded per drummer is around 75 minutes.</li> <li>Each drummer played his own drum kit.</li> <li>Each sequence used either sticks, rods, brushes or mallets to increase the diversity of drum sounds.</li> <li>The drum kits themselves are varied, ranging from a small, portable, kit with two toms and 2 cymbals, suitable for jazz and latin music ; to a larger rock drum set with 4 toms and 5 cymbals.</li> </ul> <p>Each sequence is recorded on 8 individual audio channels, is filmed from two angles, and is fully annotated</p> <p>A large part of ENST-Drums is publicly available <strong>under some conditions</strong>. These conditions include:</p> <ul> <li>The use and exploitation of the database should be limited to <strong>research</strong> purposes. No commercial use is possible.</li> <li>The database is distributed under the licence "Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"</li> <li>Any document describing a research work where ENST-Drums was used should include a reference to ENST-Drums and to the paper <em>Olivier Gillet and Gaël Richard. ENST-Drums: an extensive audio-visual database for drum signals processing, In Proc of ISMIR'06, Victoria, Canada, 2006.</em></li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank:</p> <ul> <li>The 3 drummers: Louis Cavé, Bertrand Clouard and Frédéric Rottier.</li> <li>E. Thiévon (author) and Play Music Publishing (publisher) for the background accompaniment sequences.</li> </ul> <p>The authors wish to acknowledge the support of the French ministry of research (<a href="http://recherche.ircam.fr/equipes/analyse-synthese/musicdiscover">ACI-MusicDiscover</a> project) and of the European Commission under the <a href="http://www.k-space.eu/">FP6-027026-K-SPACE</a> contract.</p>
Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking
<p>Example IMU data and scripts for reconstructing the trajectory of a sacral IMU and validation with motion capture data. Also contains example code of gait event detection and synchronization.</p>
Reproducibility Report for the paper "Vibration signal-assisted endpoint detection for long-stretch, ultraprecision polishing processes"
<p>This reproducibility report includes the datasets and computer code used for reproducing the results in the paper, Jin, Bukkapatnam, Hayes, and Ding, 2023, “Vibration signal-assisted endpoint detection for long-stretch, ultraprecision polishing processes,” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol.145, pp. 061007.</p>
EEG Dataset and Processing script Associated with the Manuscript, "Using the Time-varying Drift Rate, the Signal Suppression and the Utility Maximisation to Account for Road Crossing Decisions".
<p>The repository archived the behavioural data (zip files) associated with the EEG experiment and the MATLAB script for EEG processing. BDFs stored the down-sampled EEG. </p>
Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation
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Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking
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Data from: Combinatorial signal processing in an insect
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ScienceDex guides
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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.