scholarly journals Impacts of stochastic models on real-time 3D UAV mapping

2014 ◽  
Vol XL-1 ◽  
pp. 35-44
Author(s):  
M. Alqurashi ◽  
J. Wang
Keyword(s):  
Stochastic Model ◽  
Real Time ◽  
Stochastic Models ◽  
Feature Matching ◽  
Critical Role ◽  
Image Feature ◽  
Mapping Process ◽  
Camera Orientation ◽  
Camera Position

In UAV mapping using direct geo-referencing, the formation of stochastic model generally takes into the account the different types of measurements required to estimate the 3D coordinates of the feature points. Such measurements include image tie point coordinate measurements, camera position measurements and camera orientation measurements. In the commonly used stochastic model, it is commonly assumed that all tie point measurements have the same variance. In fact, these assumptions are not always realistic and thus, can lead to biased 3D feature coordinates. Tie point measurements for different image feature objects may not have the same accuracy due to the facts that the geometric distribution of features, particularly their feature matching conditions are different. More importantly, the accuracies of the geo-referencing measurements should also be considered into the mapping process. In this paper, impacts of typical stochastic models on the UAV mapping are investigated. It has been demonstrated that the quality of the geo-referencing measurements plays a critical role in real-time UAV mapping scenarios.

scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
yateng bai ◽  
xiaoping ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals Local Deep Descriptor for Remote Sensing Image Feature Matching

2019 ◽  
Vol 11 (4) ◽  
pp. 430 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Deep Learning ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Training Dataset ◽  
Feature Descriptor ◽  
Remote Sensing Images

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

Predicting the Real-Time Multivariate Madden–Julian Oscillation Index through a Low-Order Nonlinear Stochastic Model

2015 ◽  
Vol 143 (6) ◽  
pp. 2148-2169 ◽  
Author(s):  
Nan Chen ◽  
Andrew J. Majda
Keyword(s):  
Stochastic Model ◽  
Real Time ◽  
Stochastic Models ◽  
Multiplicative Noise ◽  
Hidden Variables ◽  
Ensemble Prediction ◽  
Madden Julian Oscillation ◽  
The Real ◽  
Nonlinear Stochastic Model ◽  
Low Order

Abstract A new low-order nonlinear stochastic model is developed to improve the predictability of the Real-time Multivariate Madden–Julian oscillation (MJO) index (RMM index), which is a combined measure of convection and circulation. A recent data-driven, physics-constrained, low-order stochastic modeling procedure is applied to the RMM index. The result is a four-dimensional nonlinear stochastic model for the two observed RMM variables and two hidden variables involving correlated multiplicative noise defined through energy-conserving nonlinear interaction. The special structure of the low-order model allows efficient data assimilation for the initialization of the hidden variables that facilitates the ensemble prediction algorithm. An information-theoretic framework is applied to the calibration of model parameters over a short training phase of 3 yr. This framework involves generalizations of the anomaly pattern correlation, the RMS error, and the information deficiency in the model forecast. The nonlinear stochastic models show skillful prediction for 30 days on average in these metrics. More importantly, the predictions succeed in capturing the amplitudes of the RMM index and the useful skill of forecasting strong MJO events is around 40 days. Furthermore, information barriers to prediction for linear models imply the necessity of the nonlinear interactions between the observed and hidden variables as well as the multiplicative noise in these low-order stochastic models.

scholarly journals Developing and Evaluating A Real-time and Cost-Efficient Architecture for COVID-19 Using Internet of Health Things

2021 ◽  
Author(s):  
Ahmad Malekian Borujeni ◽  
Mahmood Fathy ◽  
Nasser Mozayani
Keyword(s):  
Real Time ◽  
Care Service ◽  
Health Care Service ◽  
Critical Role ◽  
Hierarchical Architecture ◽  
Task Management ◽  
Network Usage ◽  
Cost Efficient ◽  
Common Architecture

Abstract Coronavirus is spreading an outbreak in the world and the medical staffs have been coming under further strain with increasing in patients referring to the hospital. Real-time health monitoring systems play a critical role in prevention, control the pandemic disease, and enhance the entire health care service delivery in real-time, and influence on visiting the hospital visiting the hospital. In this paper, a hierarchical architecture for the Internet of Things is improved using resource and task management techniques for reducing emergency response time and enhanced the patients’ quality of experiences considering energy consumption, network delay, cost of execution in the cloud and network usage. In this proposed scenario, the task is divided into subtasks and processes depending on their functions with different execution times. This method also has led to improving the fog architecture. This proposed method is modeled are evaluated in terms of the specified parameters, using the iFogsim toolkit. The results showed an improvement of 80 percent in their parameters such as cost of execution, total network usage, and average delays, compared to the cloud and other common architecture.

scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
Yateng Bai ◽  
Xiaoping Ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals Application Layer ARQ Algorithm for Real-Time Multi-Source Data Streaming in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5763
Author(s):  
Mohammed Amin Lamri ◽  
Albert Abilov ◽  
Danil Vasiliev ◽  
Irina Kaisina ◽  
Anatoli Nistyuk
Keyword(s):  
Real Time ◽  
Packet Loss ◽  
Data Flow ◽  
Critical Role ◽  
Buffer Management ◽  
Base Station ◽  
Video Data ◽  
Application Layer ◽  
Delay Jitter

Because of the specific characteristics of Unmanned Aerial Vehicle (UAV) networks and real-time applications, the trade-off between delay and reliability imposes problems for streaming video. Buffer management and drop packets policies play a critical role in the final quality of the video received by the end station. In this paper, we present a reactive buffer management algorithm, called Multi-Source Application Layer Automatic Repeat Request (MS-AL-ARQ), for a real-time non-interactive video streaming system installed on a standalone UAV network. This algorithm implements a selective-repeat ARQ model for a multi-source download scenario using a shared buffer for packet reordering, packet recovery, and measurement of Quality of Service (QoS) metrics (packet loss rate, delay and, delay jitter). The proposed algorithm MS-AL-ARQ will be injected on the application layer to alleviate packet loss due to wireless interference and collision while the destination node (base station) receives video data in real-time from different transmitters at the same time. Moreover, it will identify and detect packet loss events for each data flow and send Negative-Acknowledgments (NACKs) if packets were lost. Additionally, the one-way packet delay, jitter, and packet loss ratio will be calculated for each data flow to investigate the performances of the algorithm for different numbers of nodes under different network conditions. We show that the presented algorithm improves the QoS of the video data received under the worst network connection conditions. Furthermore, some congestion issues during deep analyses of the algorithm’s performances have been identified and explained.

Development and Application of Quality Control System for Highway Subgrade Construction

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Tong Wang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Detection Method ◽  
System Development ◽  
Effective Control ◽  
Construction Process ◽  
Development Mode ◽  
The Road ◽  
Whole Process

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

CONTROL IN REAL TIME THE DENSITY OF PRODUCT AT VARIOUS STAGES OF THE TECHNOLOGICAL PROCESS OF SUGAR PRODUCTION

2020 ◽  
Author(s):  
S.B. Kudryashev ◽  
◽  
N.S. Assev ◽  
R.D. Belashov ◽  
V.A. Naumenko ◽  
...  
Keyword(s):  
Real Time ◽  
Technological Process ◽  
Sugar Industry ◽  
Sugar Production ◽  
Production Processes ◽  
Quality Of Products

The article is devoted to solving one of the most important problems of the development of the sugar industry in Russia – the modernization of sugar production processes. Today, sugar production is actively being modernized, shifting most of its processes to the path of avomatization and optimization to improve the quality of products. This article describes one of the main ways to obtain information about the concentration of sucrose in syrup in the production of sugar.

Motivating the staff of Jazan University

2013 ◽  
Vol 10 (1) ◽  
pp. 1261-1267
Author(s):  
Ali Medabesh
Keyword(s):  
Human Capital ◽  
Quality Of Service ◽  
Economic System ◽  
Endogenous Growth ◽  
Employee Satisfaction ◽  
Development Process ◽  
Empirical Investigation ◽  
Critical Role ◽  
Financial Investment

The quality of public services and the yield of organizations are not limited to the financial investment and innovation solely. Human capital plays a critical role in the growth and excellence in institutions, but its contribution remains dependent on several factors. Its role is not limited on quantitative and qualitative accumulating, because it should be coherent and integrated in the development process. The theories of endogenous growth contributed to account for the disparity in levels of development between countries, by assuming that the extent of human capital response or inversely lack of responsiveness the economic system. This inaction is usually the prime cause of the deterioration of the quality of service and lack of satisfaction of the citizens, in addition of the lack of employee satisfaction about the circumstances of his work. Hence, arose the significance of several research about the mechanisms of reducing non-enthusiasm for the job or complacency professional and indifference. Staff of Jazan University has been chosen as a context of the empirical investigation of this study. The data has been collected using a well designed questionnaire and analyzed by SPSS program.

Sign in / Sign up

Export Citation Format

Share Document