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.
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The analysis of time series considers one of the mathematical and statistical methods in explanation of the nature phenomena and its manner in a specific time period.
Because the studying of time series can get by building, analysis the models and then forecasting gives the priority for the practicing in different fields, therefore the identification and selection of the model is of great importance in spite of its difficulties.
The selection of a standard methods has the ability for estimation the errors in the estimated the parameters for the model, and there will be a balance between the suitability and the simplicity of the model.
In the analysis of d
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Recently Tobit Quantile Regression(TQR) has emerged as an important tool in statistical analysis . in order to improve the parameter estimation in (TQR) we proposed Bayesian hierarchical model with double adaptive elastic net technique and Bayesian hierarchical model with adaptive ridge regression technique .
in double adaptive elastic net technique we assume different penalization parameters for penalization different regression coefficients in both parameters λ1and λ2 , also in adaptive ridge regression technique we assume different penalization parameters for penalization different regression coefficients i
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Among more than 200 different human papilloma viral genotypes, the association of low oncogenic risk-HPV genotypes have been recognized with a variety of oral, oropharyngeal, nasopharyngeal benign tumors as well as non-neoplastic polyposis and papillomas and adenoid hypertrophy. This prospective case- control study aims to determine the rate of DNA detection of HPV genotype 6/11 in nasopharyngeal adeno- tonsillar tissues from a group of patients subjected to adenoctomy for adenoid hypertrophy . A total number of nasopharyngeal adeno-tonsillar tissue specimens from pediatric patients with adenoid hypertrophy were enrolled; 40 nasopharyngeal adeno-tonsillar tissues from patients with adenoid hypertrophy, and 20 normal nasal tissue specimen
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The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such ha
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Objective: Aimed to asses the role of PT estimation in early diagnosis and predicting the extent and the outcome of head injury with ICerH and/ or Contusion
Method :PT was measured by Digiclot 818
Group –1: One hundred consecutive head injured patients admitted at Neurosurgical and Al Ramadi teaching hospitals were initially estimated for prothrombin time and subsequently scanned
Group-2 : Two hundred twenty five consecutive non scanned head injured patients admitted to Neurosurgical and Al Ramadi teaching hospitals were estimated with prothrombin time at the time of insult and subsequently for the next two weeks Al – Kindy Col Med J 2012; Vol. 8 No. 1 P: 54
Clinical and neurological evaluation (GCS) score in addition to
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In this work, two different structures are proposed which is fuzzy real normed space (FRNS) and fuzzy real Pre-Hilbert space (FRPHS). The basic concept of fuzzy norm on a real linear space is first presented to construct space, which is a FRNS with some modification of the definition introduced by G. Rano and T. Bag. The structure of fuzzy real Pre-Hilbert space (FRPHS) is then presented which is based on the structure of FRNS. Then, some of the properties and related concepts for the suggested space FRN such as -neighborhood, closure of the set named , the necessary condition for separable, fuzzy linear manifold (FLM) are discussed. The definition for a fuzzy seminorm on is also introduced with the prove that a fuzzy seminorm on
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A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.
A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh
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