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1,855 results for “Autonomous”
Fig. 4 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 4. Meotipa pseudomultuma sp. nov., ♀, holotype (HNU802). A. Epigyne, ventral view. B. Vulva, dorsal view. Abbreviations: see Material and methods. Scale bars = 0.1 mm.
Fig. 2 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 2. Meotipa lingulata sp. nov., ♀, holotype (HNU800). A. Epigyne, ventral view. B. Vulva, dorsal view. Abbreviations: see Material and methods. Scale bars = 0.1 mm.
Fig. 1 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 1. Meotipa lingulata sp. nov., ♀, holotype (HNU800). A–C. Habitus. A. Dorsal view. B. Ventral view. C. Lateral view. D–E. Epigyne, ventral view. F. Vulva, dorsal view (E–F, after digestion with pancreatin). Abbreviations: see Material and methods. Scale bars: A–C = 1 mm; D–F = 0.1 mm.
Fig. 3 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 3. Meotipa pseudomultuma sp. nov., ♀, holotype (HNU802). A–C. Habitus. A. Dorsal view. B. Ventral view. C. lateral view. D–E. Epigyne, ventral view. F. Vulva, dorsal view (E–F, after digestion with pancreatin). Abbreviations: see Material and methods. Scale bars: A–C = 0.5 mm; D–F = 0.1 mm.
YY1 mutations disrupt corticogenesis through a cell type-specific rewiring of cell-autonomous and non-cell-autonomous transcriptional programs
<p>This supplementary data includes counts from bulk and pseudobulk omic experiments, h5ad for single-cell experiments, and outputs of differential expression and enrichments performed on different omics assays.</p>
Bridging the Gap between Autonomous and Predetermined Paradigms: The Role of Sampling in Evaluative Learning
<p>While most evaluative learning paradigms remove participants’ autonomy over the information they receive, other research traditions have demonstrated that information sampling has an important role in learning. We investigate the impact of information sampling on a central evaluative learning paradigm: evaluative conditioning. We compare a traditional evaluative conditioning paradigm with a paradigm in which participants have autonomy over the stimulus pairings they receive. Participants in the high-autonomy condition show a strong preference for positively paired CSs. Nevertheless, the strength of evaluative conditioning effects was independent of autonomy. Moreover, high-autonomy participants, but not their low-autonomy counterparts, demonstrate a relationship between sampling frequency and evaluations, in line with the interpretation that sampled stimuli become more positive, whereas ignored stimuli become more negative over the course of the learning phase. The present research provides a cornerstone for integrating several research traditions within and beyond the evaluative learning literature, providing a foundation for new insights and more comprehensive theories of evaluative learning.</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
A Comprehensive Solution for Securing Connected and Autonomous Vehicles (presentation video)
<p>Video recording of the online presentation for the publication M. Kamal et al., "A Comprehensive Solution for Securing Connected and Autonomous Vehicles," 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022, pp. 790-795, doi: 10.23919/DATE54114.2022.9774594.</p>
Annual river runoff in the Xinjiang Uygur Autonomous Region, China.
<p>The annual river runoff in the Xinjiang Uygur Autonomous Region, China during the period 1980-2010.</p>
The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere (dataset)
<p>Dataset accompanying the journal article titled "The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere". Preprint: doi.org/10.5194/egusphere-2022-33</p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</p>
Figure 3 in Phytoseiid mites of Ruyuan Yao Autonomous County, China (Acari: Mesostigmata, Phytoseiidae)
Figure 3 Female of Typhlodromus ruyuanensis sp. nov. a – Dorsal shield; b – Ventral idiosoma; c – Chelicera; d – Spermatheca; e – Leg IV, genu – basitarsus.
Figure 2 in Phytoseiid mites of Ruyuan Yao Autonomous County, China (Acari: Mesostigmata, Phytoseiidae)
Figure 2 Female of Phytoseius subcapitatus sp. nov. a – Dorsal shield; b – Ventral idiosoma; c – Chelicera; d – Spermatheca; e – Leg IV, genu – basitarsus.
Figure 1 in Phytoseiid mites of Ruyuan Yao Autonomous County, China (Acari: Mesostigmata, Phytoseiidae)
Figure 1 Female of Euseius hamiltonii sp. nov. a – Dorsal shield; b – Ventral idiosoma; c – Chelicera; d – Spermatheca; e – Leg IV, genu – basitarsus.
How Ornithopters Can Perch Autonomously On A Branch
<p>Data used in the publication "How Ornithopters Can Perch Autonomously On A Branch", including both design and experimental data.</p>
Figure 5. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The behavior module determines the target position and orientation according to the results<br> of localization and the sensor measurements, and then constructs an action series which consists of<br> the elementary gaits to realize omni directional walking. The implementation of forward walking is<br> applying Virtual Slope Walking in the sagittal plane with the Lateral Swing Movement for lateral<br> stability. The sideward walking and turning is realized by carefully designing the key frames. All of<br> above gait is generated by connecting the key frames with smooth sinusoids. The forward walking<br> speed of PERSIA Humanoid Robot is 25cm/s. The image sequences of forward walking are shown<br> in Figure 5.</p>
Figure 2. Mechanical construction of the PERSIA humanoid robots-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 2 shows one of the constructions used for our robots. Knee joints are considered to<br> bend in both directions which help faster response of the robot in backward walking. Efforts have<br> been made to hold the proportions as much as possible human like. The PERSIA robot is 38cm tall<br> and weighs about 1.6 kg. It has 18 degrees of freedom: 5 in each leg, 3 in each hand and 2 in head.<br> To facilitate exchange of the players, all robots use mechanically the same structure.</p>
Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 4 shows the block diagram of the software which runs in the robot’s main processor.<br> The program consists of 4 main blocks:<br> • Hardware Interface: Contains all low level routines to access hardware of the robot including<br> sensors and actuators.<br> • Vision: Contains image processing algorithms such as recognition of landmarks and other<br> object. Self localization is done using particle filtering. Particles are scored by comparing a<br> simulated image from each particle with the current frame captured by camera. Using<br> “Sampling-Importance Resampling” method, a new distribution of the particles is created after<br> each step.<br> Particles are also updated using a motion model. Final distribution of the particles converges to<br> the real pose of the robot.<br> • Planning: Planning system of the robot is based on a multi layer, and multi thread structure.<br> The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario<br> which runs in parallel with the scenarios in the other layers. A scenario in a higher level can<br> terminate and change the scenario running in the lower level; however it is usually done in<br> synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as<br> stopping the walking motion while one of the feet is still in the air).<br> • Network: Mainly responsible for the wireless communication of the robot with the other robots<br> or the referee box. This is done via WLAN.<br> • Motion Control: manages all the actuators of the robot, and controls locomotion or any other<br> action of the robot according to the requests from Cognition.<br> • Sensor Control: manages other sensors, and interacts with the Sub-Controller.</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.