An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition, and optimization

2000 ◽  
Vol 8 (1) ◽  
pp. 183-191 ◽  
Author(s):  
D. Xue ◽  
Z. Dong
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Real Time ◽  
Control Method ◽  
Traffic Scheduling ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern ◽  
Time Optimal ◽  
Artificial Neural

An ASIC-Based Artificial Neural Network Applied Real-time Speech Recognition SOPC

2016 ◽  
Vol 2 (1) ◽  
pp. 38
Author(s):  
Lam D. Pham ◽  
Hieu M. Nguyen ◽  
Du N. N. T. Nguyen ◽  
Trang Hoang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Real Time ◽  
Silicon Area ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
Real Time Applications ◽  
Artificial Neural

Artificial Neural Network (ANN) is promoted to one of major schemes applied in pattern recognition area. Indeed, many approaches to software-based platforms have proven great performance of ANN. However, developing pattern recognition systems integrating ANN hardware-based architecture has been limited not only by the silicon requirements such as frequency, area, power, or resource but also by high accuracy and real-time applications strictly. Although a considerable number of ANN hardware-based architectures have been proposed currently, they have experienced a deprivation of functions due to both small configurations and ability of reconfiguration. Consequently, achieving an effective ANN hardware-based architecture so as to adapt to not only strict accuracy, enormous configures, or silicon area but also real-time criterion in pattern recognition systems has been really challenged. To tackle these issues, this work has proposed a dynamic structure of three-layer ANN architecture being able to reconfigure for adapting to various real-time applications. What is more, a complete SOPC system integrating proposed ANN hardware has also implemented to apply Vietnamese speech recognition automatically to confirm high recognition probability around 95.2 % towards 20 Vietnamese discrete words. Moreover, experiment results on such ASIC-based architecture have witnessed maximum frequency at 250 MHz on 130nm technology as well as great ability of reconfiguration.

scholarly journals Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3170 ◽  
Author(s):  
Zhang ◽  
Yang ◽  
Qian ◽  
Zhang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Recognition System ◽  
Test Method ◽  
Training Dataset ◽  
Artificial Neural ◽  
Semg Signals

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Pattern Recognition of Vertical Strabismus Using an Artificial Neural Network (StrabNet©)

Strabismus ◽  
2009 ◽  
Vol 17 (4) ◽  
pp. 131-138 ◽  
Author(s):  
Arvind Chandna ◽  
Anthony C. Fisher ◽  
Ian Cunningham ◽  
Deborah Stone ◽  
Maureen Mitchell
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural
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