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1,855 results for “Autonomous”
Autonomous And Sustained Perching On Smooth Surfaces
<p><strong>In this video the results are shown of the extended work on aerial perching, which was started in the work “Mechanism For Perching On Smooth Surfaces Using Aerial Impacts”. </strong></p> <p> </p> <p><strong>The experiment in this video shows the capabilities of an automated and self-regulating perching drone. After the user gives the ‘go’ signal, the drone autonomously impacts the surface and slows down the rotors after successfully attaching. The drone measures the pressure in the vacuum-cup using a pressure-sensor. If the vacuum gets below a threshold level, meaning that the vacuum is about to become too low to remain perched, the drone reactivates and actively presses the vacuum-cup to regain the vacuum. A significant force is required to repress the vacuum-cup, which was done using the knowledge gained from the work “Application Of Substantial And Sustained Force To Vertical Surfaces Using A Quadrotor”.</strong></p> <p> </p> <p><strong>Using the approach described above and the integrated sensing capabilities, the drone is capable of safely remaining airborne (mostly perched on a smooth surface) for 45 minutes(!) on a single battery and having the drone turned on only for about 3 minutes.</strong></p>
Bilateral Human-Robot Control for Semi-Autonomous UAV Navigation
<p><strong>This video demonstrates the work towards a novel control architecture for UAV navigation. In general, UAVs are not easy to operate and skilled pilots are required for a good performance in manual flight. However, currently it is impossible to capture every possible situation an UAV could encounter in the autonomous control. To avoid overly complicated control, a semi-autonomous control approach can be used, so the drone is partly autonomously and partly manually piloted. The novelty of the approach presented here is in the way this semi-autonomy is defined. </strong></p> <p><strong>As the UAV regularly operates autonomously, it is not desirable to switch to manual control in dangerous procedures. Instead, a more supervisory method of control can be applied in which the UAV is always controlled by the onboard computer, but the boundaries of control are controlled by the operator. Whenever a situation requires bigger risks, the operator is informed requested by the drone for help, which he\she can offer by softening certain boundaries of the UAV.</strong></p> <p><strong>This video demonstrates the concept.</strong></p>
Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The image sequences of sideward walking and turning are shown in Figure 6 respectively.</p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
Рис. 2. Живые Meghimatium bilineatum иЗ бассейна Среднего Амура. Фото В.П. Макаренко. in First record of the rare slug species Meghimatium bilineatum (Benson, 1842) (Gastropoda: Eupulmonata: Philomycidae) in the Jewish Autonomous Region (Middle Amur basin)
Рис. 2. Живые Meghimatium bilineatum иЗ бассейна Среднего Амура. Фото В.П. Макаренко.
Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05
<i>This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 30.54 GB of MP4 files, which were trimmed into 11362 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.65% of these extracted images are useful and 0.35% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 93.52 %, Q2: 5.31 %, Q5: 1.17 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 2.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.137 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 28.1 GB of MP4 files, which were trimmed into 9884 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 52.75% of these extracted images are useful and 47.25% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 94.76 %, Q2: 5.02 %, Q5: 0.23 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 3.0 m and 30.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.787 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28
<i>This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 32.76 GB of MP4 files, which were trimmed into 12042 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 2.63% of these extracted images are useful and 97.37% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 93.32 %, Q2: 4.33 %, Q5: 2.35 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.699 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Fig. 2 in First record of the rare slug species Meghimatium bilineatum (Benson, 1842) (Gastropoda: Eupulmonata: Philomycidae) in the Jewish Autonomous Region (Middle Amur basin)
Fig. 2. Alive Meghimatium bilineatum from the Middle Amur basin. Photo by V.P. Makarenko.
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 24.11 GB of MP4 files, which were trimmed into 8401 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.99% of these extracted images are useful and 0.01% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 18.95 %, Q2: 63.13 %, Q5: 17.92 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 30.32 GB of MP4 files, which were trimmed into 10244 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 86.22% of these extracted images are useful and 13.78% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 24.24 %, Q2: 71.19 %, Q5: 4.57 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Linear vs. non-linear metrics of Autonomic Nervous System: study on healthy volunteers during controlled breathing
<h1>Please cite this article as reference article:</h1> <p>Uryga A, Najda M, Berent I, Mataczyński C, Urbański P, Kasprowicz M, Buchner T. The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability. Physiol Meas. 2024 Sep 16;45(9). doi: 10.1088/1361-6579/ad7778. </p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (dataset 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (dataset 1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes 49 healthy volunteers (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes 21 healthy volunteers (14 females, 7 males, median age: 22 years, range: 18-31 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in all subjects in dataset 1; CNAP, CNSystems Medizintechnik GmbH, Graz, Austria and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li>Protocol: after a resting epoch lasted at least 5 minutes, a controlled breathing session was initiated with 5-minute recordings at each of the respiratory rate: 6, 10 or 15 breaths/min (0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome.</li> </ul> <h1>Data description</h1> <ul> <li>Type of database (database 1/database 2)</li> <li>Type of device used to ABP measurement</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Autonomic Nervous System parameters including:</li> </ul> <p>- <strong>Phase-Rectified Signal Averaging</strong> - a non-linear approach used to quantify the acceleration (AC) and deceleration (DC) capacity of the heart; more details could be found here: <em>Campana L M, Owens R L, Clifford G D, Pittman S D and Malhotra A 2010 Phase-rectified signal averaging as a sensitive index of autonomic changes with aging J Appl Physiol 108 1668–73</em></p> <p>- <strong>Entropy</strong>: multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn) functions calculated for R-R intervals, which were implemented in NeuroKit2</p> <ul> <li> <strong>Heart rate variability (HRV) metrics</strong>: In the frequency domain, the Lomb–Scargle periodogram was used to determine the power spectral density of the interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz). Additionally, the total power of the HRV signal (TP, 0.04–0.40 Hz) and the ratio between low and high-frequency components (LF/HF) were calculated. In the time domain, the following metrics were determined: the standard deviation of the R-R intervals (SDNN) and the square root of the mean of the squared successive differences between adjacent R-R intervals (RMSSD), mean of the R-R intervals (meanNN), and the proportion of R-R intervals greater than 20 ms or 50 ms, out of the total number of R-R intervals (pNN20 and pNN50, respectively); appropriate functions were implemented in NeuroKit2</li> </ul> <p> </p> <p>Update ------version 2</p> <p>After the revision process, SDNNref was added, defined according to formula presented in paper of Monfredi et al. (Monfredi O, Lyashkov AE, Johnsen AB, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014 Dec;64(6):1334-43)</p>
Nornir: A Customizable Framework for Autonomic and Power-Aware Applications
<p><strong><em>Abstract: </em></strong><em>A desirable characteristic of modern parallel applications is the ability to dynamically select the amount of resources to be used to meet requirements on performance or power consumption. In many cases, providing explicit guarantees on performance is of paramount importance. In streaming applications, this is related with the concept of elasticity, i.e. being able to allocate the proper amount of resources to match the current demand as closely as possible. Similarly, in other scenarios, it may be useful to limit the maximum power consumption of an application to do not exceed the power budget. In this paper we propose Nornir, a customizable C++ framework for autonomic and power-aware parallel applications on shared memory multicore machines. Nornir can be used by autonomic strategy designers to implement new algorithms and by application users to enforce requirements on applications.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p>
Experimental Evaluation LTV-MPC Stability for Autonomous Driving
<p>This video presents the experimental evaluation of LTV-MPC stability at Scania’s test track facilities located in Södertälje, Sweden, south of Stockholm.</p>
Surface Type Classification for Autonomous Robot Indoor Navigation - Dataset
<p>Surface Type Recognition with Inertial Measurement Unit (IMU).</p> <p>The dataset contains time series samples with 10 features each, related to orientation, velocity and acceleration. Each time series (of lenght 128) includes its corresponding surface type annotation.</p> <p>The data has been also divided in groups for easier cross-validation (80 groups present)</p> <p>A total of 9 different surface types are present in the dataset.</p> <p>"X_data.npy" contains the time series samples of dimension 7626x10x128<br> "label.npy" contains the label information for each sample (dimension 7626x1)<br> "groups.npy" contains the group information for each sample (dimension 7626x1)<br> "details.csv" contains for each sample the group information and the corresponding label</p> <p> </p>
Evolution of Formal Model-based Assurance Cases for Autonomous Robots: Supplemental Material
<p>This report contains supplemental material for the paper Evolution of Formal Model-based Assurance Cases for Autonomous Robots accepted at Software Engineering and Formal Methods 2019 in Oslo. This material provides more details about the two discussed assurance case patterns, their implementation in Isabelle/SACM the instantiation of these patterns for the presented example, as well as Isabelle skripts for the theoretical part and the example.</p>
Sample files for auto_SAR_Ocean_Contrast (autonomous Ocean Contrast Estimation for SAR Images)
<p>Sample input and output files for the open source code auto_SAR_Ocean_Contrast (autonomous Ocean Contrast Estimation for SAR Images).</p> <p>auto_SAR_Ocean_Contrast is a python module for estimating the contrast in a SAR image of the ocean surface, relative to clean water pixels. The code is primarily intended to identify oil slicks, but can be used to identify any radar-dark feature in a scene that is not entirely radar-dark. The contrast ratio is often referred to as the damping ratio in the scientific literature concerning mineral oil slicks.</p> <p>github repository:</p>
Leveraging Multi-Agent Systems and Decentralised Autonomous Organisations for Tax Credit Tracking: A Case Study of the Superbonus 110% in Italy
<p>The present dataset was obtained as output of the simulation of Secure Fiscal Credit Model (SCFM) demonstrator. The software, inspectable at the link in the repository url, allows to simulate the workflow and payments involved in the execution of Italian Superbonus 110% fiscal credits model. Two different Scenarios were tested, parameters are reported in the following table.</p> <table> <tbody> <tr> <td> </td> <td><strong>GC payment</strong></td> <td><strong>Tech Asseveration</strong></td> <td><strong>Open</strong></td> <td><strong>Anticipation</strong></td> <td><strong>SAL1</strong></td> <td><strong>SAL2</strong></td> <td><strong>EOW</strong></td> <td><strong>Tech fee</strong></td> </tr> <tr> <td><strong>Scenario 1</strong></td> <td>50%</td> <td>50%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> <tr> <td><strong>Scenario 2</strong></td> <td>25%</td> <td>25%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> </tbody> </table> <p>File named with S1 prefix refer to Scenario 1, while those with S2 prefix refer to Scenario 2.</p>
EMS3D-KITTI-Synthetic: A Synthetic 3D Dataset in KITTI Format with Balanced EMS Vehicle Distribution for Autonomous Driving AI Model Training
<p>A 3D synthetic dataset in KITTI format, focused on emergency vehicles such as ambulances and police cars. The dataset was generated across 8 towns within the CARLA simulator and converted into the KITTI format, ensuring compatibility for direct use in AI model training for autonomous driving applications.</p>
Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications: Example Scenario with Comments
<p>This example scenario with comments has been created in conjunction with the report “A Framework for Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications” to be published by the IEEE Standards Association (IEEE SA) Research Group on Issues of AI and Autonomy in Defense Systems (the Research Group). It provides insights into the Research Group's working methods and process.</p>
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.