It is difficult to perform real time object detection in Uncrewed Aerial Vehicle(UAV) based video surveillance because of the dynamic movement of camera, scale, occlusion, variation of illumination and limited power availability on-board of the computer. Purpose: In this paper of researches, the author suggests the proposed UAV Spatial-Temporal Network(UAV-STNet), which is a hybrid model of spatio-temporal deep learning model that is expected to improve the accuracy of the detection, balance of time, and real-time performance. Approaches: techniques: The proposed framework incorporates U-Net representing the system of total encoder involving extraction of multiscale space features mode, Transformer attention module which will involve global contextual modeling and Long Short-Term Memory(LSTM) network which will involve learning the short-term dependency in sequential frames. The model has been lives with and examined against a personalised UAV video dataset using a trade mark assortment of generally utilised measures of detection, including precision, recollection, mAPat 0.5 and inference velocity (FPS). Findings: UAV-STNet gave 94.5% precision, 93.1% recall and 93.2% mAP @0.5 and also 45 FPS. Better accuracy-tolerance efficient state is observed in comparative comparison with SSD, YOLOv3 and faster R-CNN which are evidently small object in motion affected scenes. Conclusions: The spatial, contextual and temporal modelling applied as a single-stop end-to-end architecture shows a powerful and computationally efficient solution of the topic of detecting real-time UAV video items, that brings benefits in the stability and reliability of intelligent surveillance systems using aerial vehicles.
We have presented the distribution of the exponentiated expanded power function (EEPF) with four parameters, where this distribution was created by the exponentiated expanded method created by the scientist Gupta to expand the exponential distribution by adding a new shape parameter to the cumulative function of the distribution, resulting in a new distribution, and this method is characterized by obtaining a distribution that belongs for the exponential family. We also obtained a function of survival rate and failure rate for this distribution, where some mathematical properties were derived, then we used the method of maximum likelihood (ML) and method least squares developed (LSD)
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The article examines the definition of the verb in the form of the imperative mood (imperative) _ general information about the verb in Russian .
A verb in Russian is one of the parts of speech that unify all parts of speech, expressing the meaning of action, movement, process in grammatical forms of time, type, mood, face and voice. The imperative mood is a grammatical feature of the verb expressed through several forms of the verb that urges someone to do things. In other words, imperative mood or (imperative) : - one of the single meaning of the form . The form of the imperative expresses a supplicatio
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This research aims to clarify the role of Information Technology Competency (ITC) with dimensions' (IT Usage, IT Knowledge, and IT Operations) as an independent variable in the activation of Human Resources Management Practices (HRM Practices) as a dependent variable with dimensions' (Training and Development, Recruitment, Job Design, and Performance appraisal). Based on this, the correlation and effect relationships between the independent and dependent variables are determined by formulating two main hypotheses. There are a significant relationship and effect of IT competency with HRM practices within the dimensions. Furthermore, the scope and population of this research are the Informatics and Communications P
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In this paper, we used maximum likelihood method and the Bayesian method to estimate the shape parameter (θ), and reliability function (R(t)) of the Kumaraswamy distribution with two parameters l , θ (under assuming the exponential distribution, Chi-squared distribution and Erlang-2 type distribution as prior distributions), in addition to that we used method of moments for estimating the parameters of the prior distributions. Bayes
Preparation and Identification of some new Pyrazolopyrin derivatives and their Polymerizations study