The Experts below are selected from a list of 210 Experts worldwide ranked by ideXlab platform
Hu, Mendeley Q Data) - One of the best experts on this subject based on the ideXlab platform.
-
UAV-derived waterfowl thermal imagery dataset
2020Co-Authors: Hu, Mendeley Q Data)Abstract:This dataset is the research data for: Accurately Wildlife Censusing by Using Deep Learning for UAV-Based Thermal Imageries The dataset (00_UAV-derived Waterfowl Thermal Imagery Dataset) captured in a playa wetland area of Nebraska, the United States, during the spring season which contain 355 drone-derived thermal images at 200 feet AGL flight height with more than one wildlife present in each image (512x640 px, 7.5cm/pixel GSD) and 187 images without wildlife presented. The corresponding label was given in a CSV file with five columns where the first column indicates the image name, and the last four columns indicate the position of each waterfowl in the corresponding image (every row indicates a single wildlife). Every wildlife was represent by a bounding box defined by the last four columns (the position of the Upper Left Corner the bounding box was expressed as (x,y) which is equal to the (column, row) of the image, the width and height indicate the pixel additions based on the Upper Left Corner (x, y) in the column and row direction respectively). Corresponding RGB images were also given in the '01 RGB Images' file. The dataset can be used as training data for automated detection with machine learning or deep learning algorithm. The orthomosaic of Smith WPA, which contains 2915 wildlife, was given in '02 Test Orthomosaic'. The orthomosiac can be used as test data. The corresponding Label was also given in the same format as above
-
UAV-derived waterfowl thermal imagery dataset
2020Co-Authors: Hu, Mendeley Q Data)Abstract:This dataset is the research data for: Accurately Wildlife Censusing by Using Deep Learning for UAV-Based Thermal Imageries The dataset (00_UAV-derived Waterfowl Thermal Imagery Dataset) was captured in a playa wetland area (Straightwater WMA) of Nebraska, the United States, during the spring season at 200 feet AGL flight height. The dataset contains 355 thermal images with more than one wildlife presented (512x640 px, 7.5cm/pixel GSD) and 187 images without wildlife presented. The corresponding label was given in a CSV file with five columns where the first column indicates the image name, and the last four columns indicate the position of each wildlife in the corresponding image (every row indicates a single wildlife). Every wildlife was represented by a bounding box defined by the last four columns (the position of the Upper Left Corner the bounding box was expressed as (x,y) which is equal to the (column, row) of the image, the width and height indicate the pixel additions based on the Upper Left Corner (x, y) in the column and row direction respectively). The dataset can be used as training data for automated detection with machine learning or deep learning algorithm. The ccorresponding RGB images were also given in the '01 RGB Images' file. The dataset can be used as visual references to label the thermal training dataset. The orthomosaic of Smith WPA, which contains 2915 wildlife, was given in '02 Test Orthomosaic'. The orthomosiac can be used as test data. The corresponding labels were also given in the same format as above.
Wen Chen - One of the best experts on this subject based on the ideXlab platform.
-
Direct Single-Step Measurement of Hadamard Spectrum Using Single-Pixel Optical Detection
IEEE Photonics Technology Letters, 2019Co-Authors: Yin Xiao, Lina Zhou, Wen ChenAbstract:Spectrum can be acquired by Hadamard transform, and the spectrum energy concentrates on the Upper-Left Corner according to the chosen Walsh-ordering Hadamard bases. Hence, measurement of the main spectrum coefficients can facilitate the reconstruction of objects. In this letter, we propose a method for directly calculating each Hadamard spectrum coefficient using only single-step measurement in single-pixel imaging (SPI) for high-contrast object reconstruction. The proposed method can significantly reduce the number of measurements in the SPI. In addition, an effective noise suppression strategy is further developed to recover high-contrast objects. The proposed method in the SPI is also tested in scattering environment. Experimental work is conducted to verify the feasibility and effectiveness of the proposed method.
Myles Wortham - One of the best experts on this subject based on the ideXlab platform.
-
Corrigendum to: “ On sequences of Toeplitz matrices over finite fields” [Linear Algebra Appl. 561 (2019) 63–80]
Linear Algebra and its Applications, 2020Co-Authors: Geoffrey L. Price, Myles WorthamAbstract:Abstract For each non-negative integer n let A n be an n + 1 by n + 1 Toeplitz matrix over a finite field, F, and suppose for each n that A n is embedded in the Upper Left Corner of A n + 1 . We study the structure of the sequence ν = { ν n : n ∈ Z + } , where ν n = null ( A n ) is the nullity of A n . For each n ∈ Z + and each nullity pattern ν 0 , ν 1 , … , ν n , we count the number of strings of Toeplitz matrices A 0 , A 1 , … , A n with this pattern. As an application we present an elementary proof of a result of D. E. Daykin on the number of n × n Toeplitz matrices over G F ( 2 ) of any specified rank.
-
On sequences of Toeplitz matrices over finite fields
Linear Algebra and its Applications, 2019Co-Authors: Geoffrey L. Price, Myles WorthamAbstract:Abstract For each non-negative integer n let A n be an n + 1 by n + 1 Toeplitz matrix over a finite field, F, and suppose for each n that A n is embedded in the Upper Left Corner of A n + 1 . We study the structure of the sequence ν = { ν n : n ∈ Z + } , where ν n = null ( A n ) is the nullity of A n . For each n ∈ Z + and each nullity pattern ν 0 , ν 1 , … , ν n , we count the number of strings of Toeplitz matrices A 0 , A 1 , … , A n with this pattern. As an application we present an elementary proof of a result of D. E. Daykin on the number of n × n Toeplitz matrices over G F ( 2 ) of any specified rank.
-
On Toeplitz matrices over GF(2)
2018Co-Authors: Geoffrey L. Price, Myles WorthamAbstract:For each positive integer $n$ let $\mathcal{A}_n$ be a Toeplitz matrix over $GF(2)$ and suppose for each $n$ that $\mathcal{A}_n$ is the Upper Left Corner of $\mathcal{A}_{n+1}$. We study the structure of the sequence $\nu = \{\nu_n :n \in \mathbb{N}\}$, where $\nu_n = \text{null}(\mathcal{A}_n)$ is the nullity of $\mathcal{A}_n$. As an application we recover a result of D. E. Daykin on the number of $n\times n$ Toeplitz matrices over $GF(2)$ of any specified rank.\\ Keywords: Toeplitz matrix, nullity sequence, rank, finite fields
Jamil H. Kazmi - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of socio-environmental impacts of the loss of urban trees and vegetation in Lahore, Pakistan: a review of public perception
Ecological Processes, 2016Co-Authors: Safdar Ali Shirazi, Jamil H. KazmiAbstract:In the original publication of this article (Shirazi et al., 2016) the author figured out that in the Fig. 1, the Upper Left Corner Map of Pakistan was inappropriate as per the national policy. In map of Pakistan, Jammu & Kashmir (occupied by India) must be shown annexed with Pakistan while mentioning Azad Jammu & Kashmir as disputed territory.
Claudia Bonfiglioli - One of the best experts on this subject based on the ideXlab platform.
-
Up-and-Left as a spatial cue of leadership.
The British journal of social psychology, 2017Co-Authors: Maria Paola Paladino, Mara Mazzurega, Claudia BonfiglioliAbstract:Cues of leadership are features that signal who is (or who is expected to be) the leader in a specific context. Although their use is widespread, empirical research is scarce, especially for spatial positioning as a sign of leadership. Based on work on spatial biases, we suggest here that the Upper-Left Corner of a page is a spatial position associated with leadership. In the present studies (N = 455), we investigated this hypothesis and showed that a layout with a photograph positioned in the Upper-Left Corner (compared to the Upper-right, lower-Left, or lower-right Corner) led people to infer that the person portrayed in the photograph had a leading (vs. subordinate) role in the organization. Participants also thought that the Upper-Left Corner was the ideal spatial position to convey a leading (vs. subordinate) role in an organization. Implications of these results for symbols of leadership and spatial biases are discussed.
