scholarly journals Real time model of a lock gate system upon fingerprint with microcontroller

2021 ◽  
Vol 21 (1) ◽  
pp. 37
Author(s):  
Maath Jasem Mahammad ◽  
Yousif Al Mashhadany ◽  
Abdulkader Mohammad Eyada
Keyword(s):  
Real Time ◽  
Optical Sensor ◽  
Input Data ◽  
Main Part ◽  
Ease Of Use ◽  
Digital Data ◽  
Time Model ◽  
Design And Implementation ◽  
Gate System ◽  
Lock Gate

<span>Safety is one of the most important things that we are keen to provide all aspects of our lives. Keyless lock gate with fingerprint access has many advantages over traditional systems such as passwords. Therefore, fingerprint identification represents the main part, which require that system is cheap, practical, easy to use and allows fast and securely. This paper discusses the design and implementation with real time model of a practical lock gate system for the achievement of high security by using fingerprint purification to control the mechanism of opening doors in banks, warehouses, laboratories and places of interest. In this paper, the microcontroller technology in the Ardonio board was used with an optical sensor to take the fingerprint, convert it into digital data, and insert it into a definition window to unify all input data and store it for use when compared to the system experience. After obtaining the result of the comparison, the appropriate commands are prepared for operating the servo of the lock mechanism for the doors used. In the testing of the system, the results are very accurate. The system also has other advantages such as small size and low power required for operation and ease of use. </span>

Design and Implementation of Low Bit-rate Real-time Video Encoder Based on DSP

2011 ◽  
Vol 30 (4) ◽  
pp. 945-948
Author(s):  
Shao-hua Liu ◽  
Zhi-hui Xiong ◽  
Wei-dong Bao ◽  
Mao-jun Zhang
Keyword(s):  
Real Time ◽  
Bit Rate ◽  
Video Encoder ◽  
Low Bit Rate ◽  
Design And Implementation

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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