scholarly journals The Effect of Visitors on Zoo Reptile Behaviour during the COVID-19 Pandemic

2021 ◽  
Vol 2 (4) ◽  
pp. 664-676
Author(s):  
Kimberley C. Carter ◽  
Isabel A. T. Keane ◽  
Lisa M. Clifforde ◽  
Lewis J. Rowden ◽  
Léa Fieschi-Méric ◽  
...  
Keyword(s):  
High Resolution ◽  
Species Selection ◽  
Low Resolution ◽  
Gekko Gecko ◽  
Video Footage ◽  
Reptile Species ◽  
Zoo Animals ◽  
Species Specific ◽  
The Uk ◽  
Resolution Dataset

Visitors to zoos can have positive, neutral, or negative relationships with zoo animals. This makes human–animal interactions (HAIs) an essential component of welfare and an important consideration in species selection for zoo exhibits and in enclosure designs. We measured the effect of visitors on reptiles by comparing open and closed periods during the lockdowns in response to the COVID-19 pandemic in the UK in a low-resolution dataset for thirteen species of reptiles and a high-resolution dataset focussing on just one of these. Scan sampling on thirteen reptile species (two chelonians and eleven squamates) showed species-specific differences in response to the presence/absence of visitors, with most taxa being only weakly affected. High-resolution scan sampling via video footage of an off-show and on-show enclosure was carried out for tokay geckos (Gekko gecko) over the open and closed periods. In this part of the study, tokay geckos were significantly more visible during zoo closure than when visitors were present on-exhibit, but there was no change in off-show animals, indicating the effect of visitors as opposed to other factors, such as seasonality, which applied equally to both on- and off-show animals. The high-resolution study showed that a significant effect was present for tokay geckos, even though the low-resolution suggested that they were more weakly affected than other taxa. Our results indicate that, for cryptic species such as this, more intensive sampling may be required to properly understand visitor effects. Our data do not allow the interpretation of effects on welfare but show that such assessments require a species-specific approach.

scholarly journals A Generalizable Sample Resolution Augmentation Method for Mechanical Fault Diagnosis Based on ESPCN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhenyun Chu ◽  
Shanshan Ji ◽  
Jinrui Wang ◽  
Xiaoyu Wang ◽  
Zongzhen Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Resolution ◽  
Data Augmentation ◽  
Simple Algorithm ◽  
Fault Classification ◽  
Training Dataset ◽  
Low Resolution ◽  
Stacked Autoencoders ◽  
Set Up ◽  
Resolution Dataset

Data augmentation has become a hot topic in the field of mechanical intelligent fault diagnosis. It can expand the limited training dataset by generating simulated samples, but there is still no effective method augmenting the resolution of low resolution sample. In this paper, a simple algorithm, namely, efficient subpixel convolutional neural network (ESPCN), is proposed to solve this deficiency. The ESPCN model performs the arrange operation on the raw low resolution data through the subpixel layer and outputs the result of four-channel multifeature maps. Then, the sample resolution is increased to four times compared with the raw low resolution sample. Finally, the generated high resolution dataset is employed to train the stacked autoencoders (SAE) for fault classification, and the raw high resolution dataset is used for testing. Two fault diagnosis cases with different sample dimensions and rotating speeds are set up to simulate the low resolution situation, and the experimental results verify the feasibility of the proposed algorithm.

MULTI-ORDER HYDROLOGIC POSITION: A HIGH-RESOLUTION DATASET FOR THE CONTERMINOUS UNITED STATES

2018 ◽  
Author(s):  
Kenneth Belitz ◽  
◽  
Richard B. Moore ◽  
T.L. Arnold ◽  
J.B. Sharpe ◽  
...  
Keyword(s):  
United States ◽  
High Resolution ◽  
Resolution Dataset

scholarly journals A Forensic Detection Method for Hallucinogenic Mushrooms via High-Resolution Melting (HRM) Analysis

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 199
Author(s):  
Xiaochun Zhang ◽  
Huan Yu ◽  
Qi Yang ◽  
Ziwei Wang ◽  
Ruocheng Xia ◽  
...  
Keyword(s):  
High Resolution ◽  
Dna Barcoding ◽  
High Resolution Melting ◽  
Its Region ◽  
Its Sequencing ◽  
Melting Temperatures ◽  
Hrm Analysis ◽  
National Drug ◽  
Species Specific ◽  
Its1 And Its2

In recent years, trafficking and abuse of hallucinogenic mushrooms have become a serious social problem. It is therefore imperative to identify hallucinogenic mushrooms of the genus Psilocybe for national drug control legislation. An internal transcribed spacer (ITS) is a DNA barcoding tool utilized for species identification. Many methods have been used to discriminate the ITS region, but they are often limited by having a low resolution. In this study, we sought to analyze the ITS and its fragments, ITS1 and ITS2, by using high-resolution melting (HRM) analysis, which is a rapid and sensitive method for evaluating sequence variation within PCR amplicons. The ITS HRM assay was tested for specificity, reproducibility, sensitivity, and the capacity to analyze mixture samples. It was shown that the melting temperatures of the ITS, ITS1, and ITS2 of Psilocybe cubensis were 83.72 ± 0.01, 80.98 ± 0.06, and 83.46 ± 0.08 °C, and for other species, we also obtained species-specific results. Finally, we performed ITS sequencing to validate the presumptive taxonomic identity of our samples, and the sequencing output significantly supported our HRM data. Taken together, these results indicate that the HRM method can quickly distinguish the DNA barcoding of Psilocybe cubensis and other fungi, which can be utilized for drug trafficking cases and forensic science.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

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