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24 results for “Detection distance”
Ant Resource Detection Distance at the Caxiuana National Forest in Brazil 2017-2018
Environmental change scenarios of low precipitation forecast species loss in tropical regions. These losses can affect generalist species that provide important ecosystem services, such as controlling the rate at which nutrients become available for uptake by other organisms in tropical forests. Here, we use a long-term rainwater exclusion experiment in primary Amazonian rainforest to test whether induced water stress affects the detection distance of food resources (baits) in a generalist ant guild (number of colonies, richness, and composition) that remove resources on the ground. We found that (i) overall the distance of resource removal by generalist ants did not change with drought; (ii) however, comparing ant species that occurred in drought-induced and control environments, workers walked shorter distances in the drought habitat; (iii) the number of resources detected by ant colonies in the drought-induced habitat decreased by 50%. Although generalist ants are considered resilient to habitat disturbance, the effect of reduced rainfall can negatively affect the services mediated by them. The rate of removal and consumption of resources in tropical forests may be related to abundance of generalist ants; losses in both nest density and walking distances may cause cascading effects on ecosystem processes and the services they mediate.
Dataset of Detection Distances to Small Bodies using Spacecraft Cameras
<p>The dataset contains the detection distances to small bodies (in kilometres) considering three different spacecraft camera setups for the full list of known objects by the Minor Planet Center catalogue (https://www.minorplanetcenter.net). A separate ASCII file has been created per each considered phase angle.</p> <p>The generation of the dataset as well as the simulation settings are detailed in the following paper:</p> <p>Franzese, Hein, Modelling Detection Distances to Small Bodies Using Spacecraft Cameras, <em>Modelling</em> <strong>2023</strong>, <em>4</em>(4), 600-610; <a href="https://doi.org/10.3390/modelling4040034">https://doi.org/10.3390/modelling4040034</a></p> <p>The columns of the dataset are as follows:</p> <ol> <li>Object: The object's numerical identifier.</li> <li>MPC Designation: The object designation of the Minor Planet Center.</li> <li>Name: The object name, if available.</li> <li>HP Cam & rp: Object detection distance in km considering the high-performance camera and the object at perihelion</li> <li>HP Cam & ra: Object detection distance in km considering the high-performance camera and the object at aphelion</li> <li>MP Cam & rp: Object detection distance in km considering the medium performance camera and the object at perihelion</li> <li>MP Cam & ra: Object detection distance in km considering the medium performance camera and the object at aphelion</li> <li>LP Cam & rp: Object detection distance in km considering the low-performance camera and the object at perihelion</li> <li>LP Cam & ra: Object detection distance in km considering the low-performance camera and the object at aphelion.</li> </ol> <p>Note that the detection distances refer to the following phase angles: 0 deg, 15 deg, 30 deg, 60 deg, and 90 deg.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 17. The results of calculating the Hausdorff distances
<p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods<br> (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the<br> Corpus Calosum segmentation. </p> <p> </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 16. Results of calculating the Hausdorff distances
<p>Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a subject Normal following segmentation of the Corpus Calosum.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 15. The results of calculating the Hausdorff distances
<p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the Corpus Calosum segmentation.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 12. Results of calculating the Hausdorff distances
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 11. Results of calculating the Hausdorff distance
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p> Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) .and the ground truth about a MCI subject following segmentation of the hippocampus.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
Dataset for the Julia code titled "Image Similarity Detection using ORB & Julia : Leveraging Hamming Distance & Keypoint Optimization"
<p>The above Dataset contains</p> <p>1) Real PCB Pics</p> <p>2) Real Car Fusebox pics</p> <p>3) Real & Filtered Human face pics</p> <p>4) Readme file</p> <p>The above Dataset is designed to be used for the Julia Code titled "Image Similarity Detection using ORB & Julia : Leveraging Hamming Distance & Keypoint Optimization". The Code should be run on either similar PCB pics or Car Fusebox pics or Human Face Pics(Vikrant or Mat or Paris). Proper Choice of Hamming Distance & the number of Keypoints is necessary to achieve the desired number of similar images. </p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
data from "Temperate bats may alter calls to partially compensate for weather-induced changes in detection distance"
<p>Echolocation call parameters of three species of European bats collected between 2017-2019, recorded with a microphone array. This dataset is associated to the manuscript "Temperate bats may alter calls to partially compensate for weather-induced changes in detection distance". Included is the R script used for statistical analyses, as well as the R script used to model detection distances of different targets by bats across weather conditions.</p>
DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method
<p>DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method</p>
How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed
<p>These datasets provide precipitation data over Central Plateau watershed of Iran. Four different variants of the Inverse Distance Weighting (IDW) method are utilized to create these datasets. Two out of four IDW variants are proposed and developed by the authors to enhance the performance of the available standard models.</p> <p> </p> <p>Data Format: xlsx</p> <p>Spatial Resolution: ~0.08˚ & 0.25˚</p> <p>Spatial Coverage: Central Plateau watershed, Iran (48˚07'E to 61˚25'E - 26˚33'N to 37˚27'N)</p> <p>Temporal Resolution: Monthly</p> <p>Temporal Coverage: 2005 - 2015 (Inclusively)</p> <p> </p> <p><strong>How to cite:</strong></p> <p><strong>Arash Ghomlaghi, Mohsen Nasseri & Bardia Bayat (2022): How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed, Hydrological Sciences Journal, DOI: 10.1080/02626667.2022.2124874</strong></p>
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
Aerial survey is an important, widely employed approach for estimating free‐ranging wildlife over large or inaccessible study areas. We studied how a distance covariate influenced probability of double‐observer detections for birds counted during a helicopter survey in Canada's central Arctic. Two observers, one behind the other but visually obscured from each other, counted birds in an incompletely shared field of view to a distance of 200 m. Each observer assigned detections to one of five 40‐m distance bins, guided by semi‐transparent marks on aircraft windows. Detections were recorded with distance bin, taxonomic group, wing‐flapping behavior, and group size. We compared two general model‐based estimation approaches pertinent to sampling wildlife under such situations. One was based on double‐observer methods without distance information, that provide sampling analogous to that required for mark–recapture (MR) estimation of detection probability, urn:x-wiley:20457758:media:ece34824:ece34824-math-0001, and group abundance, urn:x-wiley:20457758:media:ece34824:ece34824-math-0002, along a fixed‐width strip transect. The other method incorporated double‐observer MR with a categorical distance covariate (MRD). A priori, we were concerned that estimators from MR models were compromised by heterogeneity in urn:x-wiley:20457758:media:ece34824:ece34824-math-0003 due to un‐modeled distance information; that is, more distant birds are less likely to be detected by both observers, with the predicted effect that urn:x-wiley:20457758:media:ece34824:ece34824-math-0004 would be biased high, and urn:x-wiley:20457758:media:ece34824:ece34824-math-0005 biased low. We found that, despite increased complexity, MRD models (ΔAICc range: 0–16) fit data far better than MR models (ΔAICc range: 204–258). However, contrary to expectation, the more naïve MR estimators of urn:x-wiley:20457758:media:ece34824:ece34824-math-0006 were biased low in all cases, but only by 2%–5% in most cases. We suspect that this apparently anomalous finding was the result of specific limitations to, and trade‐offs in, visibility by observers on the survey platform used. While MR models provided acceptable point estimates of group abundance, their far higher stranded errors (0%–40%) compared to MRD estimates would compromise ability to detect temporal or spatial differences in abundance. Given improved precision of MRD models relative to MR models, and the possibility of bias when using MR methods from other survey platforms, we recommend avian ecologists use MRD protocols and estimation procedures when surveying Arctic bird populations.
Data for: An experimental assessment of detection dog ability to locate great crested newts (Triturus cristatus) at a channeled distance and through soil
<p>Detection dogs are increasingly used to locate cryptic wildlife species, but their use for amphibians is still rather underexplored. In the present paper we focus on the great crested newts (<em>Triturus cristatus</em>), a European species which is experiencing high conservation concerns across its range, and assess the ability of a trained detection dog to locate individuals during their terrestrial phase. More specifically, we used a series of randomised, double-blinded experiments to document whether a range of distances between target newts and the detection dog affects the ability of localisation, and to assess the ability and efficiency of target newt detection in simulated subterranean refugia through 20 cm of two common soil types (clay and sandy soil, both with and without air vents to mimic mammal burrows, a common refuge used by <em>T. cristatus</em>). The detection dog accurately located all individual <em>T. cristatus</em> across the entire range of tested distances (25 cm - 2 m). The substrate trials revealed that the detection dog could locate individuals also through soil. As expected, the detection time during the soil discrimination trials was significantly reduced for treatments with vents. Contrary to existing studies with detection dogs in human forensic contexts, however, detection was generally faster for <em>T. cristatus</em> under clay soil compared to sandy soil. Our study provides a general baseline for the use of detection dogs in locating <em>T. cristatus</em> and similar amphibian species during their terrestrial phase. </p>
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
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Data for: An experimental assessment of detection dog ability to locate great crested newts (Triturus cristatus) at a channeled distance and through soil
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Data from: Multi-level patterns in population genetics: variogram series detects a hidden isolation-by- distance- dominated structure of Scandinavian brown bears Ursus arctos
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Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
<p><strong>Datasets for PILOT</strong></p> <p>Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods for the analysis of single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be compared with other samples. We propose here patient Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories. We evaluate PILOT and competing approaches in disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, and genes from large and complex single-cell and pathomics data.</p>
Data from: Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias?
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