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5 results for “Traffic Conditions”
Data from: Experimental investigation of ant traffic under crowded conditions.
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Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Acoustic parameters of Phyllodytes luteolus in conditions of low and high noise from vehicle traffic
<p>Acoustic communication in animals can be affected by multiple biotic (intra and interspecific) and abiotic (e.g., wind and rain) natural noises. In addition, human beings produce additional novel sources of noise, which can reduce or inhibit the reception of acoustic signals by conspecifics, leading to behavioral changes. In this study, we investigated whether sound of conspecifics and road noise additively affect the acoustic parameters of the advertisement call of males of a Yellow Heart-tongued Frog (<em>Phyllodytes luteolus</em>). We hypothesized that males that vocalize in larger choruses (many males) and in areas close to highways (anthropic noise) will increase their temporal and spectral acoustic parameters, respectively, to avoid acoustic signal masking. We recorded the vocalizations of 38 males in environments close (N=18) to and distant (N=20) from highways in different social contexts (many or few individuals in the chorus). Contrary to our expectation, the results indicated that individuals exposed to road noise had lower dominant frequency calls than those from natural areas, and that the density of males in the chorus had no influence on the acoustic parameters. Furthermore, we found a positive relationship between body size and intensity, indicating that larger individuals can emit calls that can reach greater distances. The advertisement call of <em>Phyllodytes luteolus</em> has a high dominant frequency, with little overlap with the frequency of anthropic noises (roads), which may explain its presence and reproductive success of this species in bromeliads from urbanized areas.</p>
Acoustic parameters of Phyllodytes luteolus in conditions of low and high noise from vehicle traffic
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MuFoRa – A Multimodal Dataset of Traffic Elements Under Controllable and Measured Adverse Weather Conditions of Fog and Rain
<div> <p>This is a novel multimodal dataset of traffic elements consisting of image and point cloud files in typical adverse weather situations especially suited for autonomous driving sensors and algorithms performance characterization. The specialty of this dataset arises from parametric acquisition under controllable and measured fog (quantified as visibility) and rain intensity conditions in the indoor test facility of CARISMA - THI in collaboration with HM. </p> <p> </p> </div>
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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.