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1,855 results for “Autonomous”
FIGURE 6 in A new species of Amphinemura (Plecoptera: Nemouridae) from Guangxi Zhuang Autonomous Region of southern China
FIGURE 6. Amphinemura baisensis sp. nov. (female). a. Genital sclerites, ventral view. b. Vaginal complex, cleared, dorsal view. c. Vaginal complex, posterior view. d. Vaginal complex, lateral view. e. Vaginal complex, anterior top view. P, pouch; R, rod; S, subtriangular structure under paragenital plates; T, tube.
FIGURE 5 in A new species of Amphinemura (Plecoptera: Nemouridae) from Guangxi Zhuang Autonomous Region of southern China
FIGURE 5. Amphinemura baisensis sp. nov. (female). a. Terminalia, ventral view. b. Vaginal complex, cleared, dorsal view. P, pouch; R, rod; S, subtriangular structure under paragenital plates; T, tube.
FIGURE 4 in A new species of Amphinemura (Plecoptera: Nemouridae) from Guangxi Zhuang Autonomous Region of southern China
FIGURE 4. Amphinemura baisensis sp. nov. (male). a. Terminalia, dorsal view. b. Terminalia, ventral view. c. Epiproct, dorsal view. d. Epiproct, lateral view. e. Left paraproct, ventral view.
FIGURE 3 in A new species of Amphinemura (Plecoptera: Nemouridae) from Guangxi Zhuang Autonomous Region of southern China
FIGURE 3. Amphinemura baisensis sp. nov. (male). a. Terminalia, dorsal view. b. Terminalia, ventral view. c. Terminalia, lateral view. d. Epiproct, posterior view. e. Epiproct apex, dorsal view. f. Tip of epiproct, dorsal view. g. Tip of epiproct, dorsolateral view. h. Spines of left portion of apex of epiproct, dorsal view.
FIGURE 2 in A new species of Amphinemura (Plecoptera: Nemouridae) from Guangxi Zhuang Autonomous Region of southern China
FIGURE 2. Amphinemura baisensis sp. nov. (male). a. Left paraproct, ventral view. b. Median and outer lobes of left paraproct, lateral view. c. Median lobe of left paraproct, caudal view. d. Left paraproct, caudal view. e. Apex of the median left paraproct lobe, caudal view. f. Apex of the outer left paraproct lobe, caudal view.
FIGURE 1 in A new species of Amphinemura (Plecoptera: Nemouridae) from Guangxi Zhuang Autonomous Region of southern China
FIGURE 1. Amphinemura baisensis sp. nov. (male). a. Adult habitus, dorsal view. b–c. Epiproct, dorsal view. d. Epiproct apex, ventral view. e. Epiproct, lateral view. f. Epiproct apex, anterolateral view.
Path Planning for Autonomous Bus Driving in Urban Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Urban Environments".</pre>
Path Planning for Autonomous Bus Driving in Highly Constrained Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Highly Constrained Environments".</pre>
FIGURE 3 in One new species and one new Chinese record of Neoperla (Plecoptera: Perlidae) from Mount Shiwandashan of the Guangxi Zhuang Autonomous Region of southern China
FIGURE 3. Neoperla shiwandashana sp. nov. (male). a. Aedeagus, lateral view. b. Aedeagus, lateral view.
FIGURE 4 in One new species and one new Chinese record of Neoperla (Plecoptera: Perlidae) from Mount Shiwandashan of the Guangxi Zhuang Autonomous Region of southern China
FIGURE 4. Neoperla shiwandashana sp. nov. (male). a. Aedeagus, oblique ventral view. b. Aedeagal sac, lateral view. c. Apical part of aedeagal sac, dorsal view.
FIGURE 5 in One new species and one new Chinese record of Neoperla (Plecoptera: Perlidae) from Mount Shiwandashan of the Guangxi Zhuang Autonomous Region of southern China
FIGURE 5. Neoperla yentu Cao & Bae, 2007 (male). a. Head and pronotum, dorsal view. b. Terminalia, dorsal view. c. Aedeagus, lateral view. d. Aedeagal sac and lobes of aedeagus, lateral view. e. Ventral lobes of aedeagus, oblique ventral view.
FIGURE 2 in One new species and one new Chinese record of Neoperla (Plecoptera: Perlidae) from Mount Shiwandashan of the Guangxi Zhuang Autonomous Region of southern China
FIGURE 2. Neoperla shiwandashana sp. nov. (male). a. Head and pronotum, dorsal view. b. Terminalia, dorsal view. c. Terminalia, dorsal view. d. Terminalia, lateral view.
FIGURE 1 in Phylogenetic and taxonomic appraisal of Neomanoharachariella xizangensis sp. nov. and the first asexual report of Acanthostigmina (Tubeufiaceae, Tubeufiales) from Xizang Autonomous Region, China
FIGURE 1. RAxML tree based on combined ITS, LSU, tef 1-α, and rpb2 dataset analyses. Bootstrap support values for maximum likelihood greater than or equal to 75% and Bayesian posterior probabilities greater than or equal to 0.90 are given above each branch. Ex-type strains are in bold; the newly generated sequences are in red.
FIGURE 3 in Phylogenetic and taxonomic appraisal of Neomanoharachariella xizangensis sp. nov. and the first asexual report of Acanthostigmina (Tubeufiaceae, Tubeufiales) from Xizang Autonomous Region, China
FIGURE 3. Acanthostigmina multiseptatum (HKAS 131155). a Host substrate. b Appearance of colonies on the substrate. c–e, g Conidiophores with conidiogenous cells and conidia. f Conidiophores. h–j Conidia. k Germinated conidium. l, m Front and reverse colonies on PDA. Scale bars: c–k = 20 μm.
FIGURE 2 in Phylogenetic and taxonomic appraisal of Neomanoharachariella xizangensis sp. nov. and the first asexual report of Acanthostigmina (Tubeufiaceae, Tubeufiales) from Xizang Autonomous Region, China
FIGURE 2. Neomanoharachariella xizangensis (HKAS 131153, holotype) a Host substrate. b Appearance of colonies on the substrate. c–f Conidiophores and conidia. g–h, l Conidiogenous cells and conidia. i–k Conidia. m Germinated conidium. n, o Front and reverse colonies on PDA. Scale bars: c–f, m = 50 μm, g–l = 20 μm.
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation
<p><strong>Abstract: </strong></p> <p>Swarms of drones offer an increased sensing aperture, and having them mimic behaviors of natural swarms enhances sampling by adapting the aperture to local conditions. We demonstrate that such an approach makes detecting and tracking heavily occluded targets practically feasible. While object classification applied to conventional aerial images generalizes poorly the randomness of occlusion and is therefore inefficient even under lightly occluded conditions, anomaly detection applied to synthetic aperture integral images is robust for dense vegetation, such as forests, and is independent of pre-trained classes. Our autonomous swarm searches the environment for occurrences of the unknown or unexpected, tracking them while continuously adapting its sampling pattern to optimize for local viewing conditions. We achieved an average positional accuracy of 0.39 m with an average precision of 93.2% and an average recall of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We show that sensor noise can effectively be included in the synthetic aperture image integration process, removing the need for a computationally costly optimization of high-dimensional parameter spaces. Finally, we present a complete hard- and software framework that supports low-latency transmission and fast processing of extensive video and telemetry data.</p>
FIGURE 1. A–J in Lagerstroemia densa (Lythraceae), a new species from Daqing Mountain, Guangxi Zhuang Autonomic Region, China
FIGURE 1. A–J. Lagerstroemia densa (from the holotype). A. Flowering branch and inflorescence. B. Branchlet. C. Single inflorescence. D. Fruiting branch. E. A single flower. F. A single flower. G. Flower with petals removed. H. Cross section of ovary. I. Longitudinal section of ovary with style. J. Fruit (Illustrations X. D. Zhu).
FIGURE 2. A in Lagerstroemia densa (Lythraceae), a new species from Daqing Mountain, Guangxi Zhuang Autonomic Region, China
FIGURE 2. A. Bark of L. densa. B–C. Inflorescences of L. densa. D. Flowers of L. densa. (Photos C.H. Gu).
BadODD: Bangladeshi Autonomous Vehicle Object Detection Dataset
<p>The dataset covers the following 9 districts in Bangladesh: Sylhet, Dhaka, Rajshahi, Mymensingh, Maowa, Chittagong, Sirajganj, Sherpur, and Khulna. Participants will encounter a wide range of road types, including towns, expressways, highways, and village roads. This diversity in locations aims to challenge algorithms to perform well across various driving contexts commonly encountered on Bangladesh roads.</p> <p> </p>
Empowering Autonomous Shuttles with Next-Generation Infrastructure
<p>This dataset accompanies the paper: <strong>Empowering Autonomous Shuttles with Next-Generation Infrastructure </strong>presented at the European Conference on Computer Vision (ECCV) 2024 in Milan. </p> <p>We provide a dataset in the form of CSV-file recordings. The dataset was recorded during the three-day congress IT-TRANS 2024 in Karlsruhe. It contains 207 individual recordings, each recording representing one trip between the two intelligent bus stops, with a total driving duration of 7 hours and 28 minutes. We publish V2X messages, information regarding the autonomous driving of the shuttle, and recorded information from the bus stops and the intelligent traffic light.</p> <p><br>More information about the data and the setup can be read in the paper.<br><br>This work was partially funded by KAMO: Karlsruhe Mobility High Performance Center (www.kamo.one).</p>
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.