scholarly journals Modification of notes recognition algorithm with a sharp change of the pitch frequency

2020 ◽  
Vol 1680 ◽  
pp. 012055
Author(s):  
A Yu Yakimuk ◽  
E S Kataeva ◽  
M N Golovchiner
Keyword(s):  
Recognition Algorithm ◽  
Sharp Change ◽  
Pitch Frequency

scholarly journals Track Damage Recognition Algorithm Based on Improved Convolution

2020 ◽  
Author(s):  
Qiang Jia ◽  
Feng Han ◽  
Yuan Li ◽  
Miaomiao Qi ◽  
Zhibo Liu
Keyword(s):  
Edge Detection ◽  
Structural Damage ◽  
Rapid Development ◽  
Recognition Algorithm ◽  
Sharp Change ◽  
Damage Recognition ◽  
Structure Recognition ◽  
Convolution Model ◽  
Inspection And Maintenance ◽  
Image Edge

Railway inspection and maintenance in uninhabited high-cold areas are facing great challenges according to the rapid development of Sino-Russian railway. In this paper, an algorithm is proposed, which is image edge detection track structure recognition and damage detection based on improved convolution model. This paper use image acquisition method and improved two-dimensional convolution to image filtering, thus the original image matrix is processed by determinant transformation to enhance image boundary elements. On the basis of the linear characteristics obtained from edge detection, the fastener cartridge and steel rails are identified. The damage and position of steel rails are judged by the sharp change of alignment. The image recognition and verification of the existing railway demonstrates that the method has the following advantages: It improves the rate of recognition structure and has certain adaptability. At the same time, the corresponding position of the structure can be determined, which is beneficial to the identification of structural damage. Besides, it plays an important role in the daily operation and maintenance of track.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Measurements method of the audio recordings acoustic quality indicator prepared for registration and processing in the Unified Biometric System

2019 ◽  
pp. 40-46 ◽  
Author(s):  
V.V. Savchenko ◽  
A.V. Savchenko
Keyword(s):  
Quality Control ◽  
Small Sample Size ◽  
Personal Data ◽  
Digital Processing ◽  
Small Sample ◽  
Practical Implementation ◽  
Acoustic Measurements ◽  
Pitch Frequency ◽  
Wide Range ◽  
Automated Quality Control

We consider the task of automated quality control of sound recordings containing voice samples of individuals. It is shown that in this task the most acute is the small sample size. In order to overcome this problem, we propose the novel method of acoustic measurements based on relative stability of the pitch frequency within a voice sample of short duration. An example of its practical implementation using aninter-periodic accumulation of a speech signal is considered. An experimental study with specially developed software provides statistical estimates of the effectiveness of the proposed method in noisy environments. It is shown that this method rejects the audio recording as unsuitable for a voice biometric identification with a probability of 0,95 or more for a signal to noise ratio below 15 dB. The obtained results are intended for use in the development of new and modifying existing systems of collecting and automated quality control of biometric personal data. The article is intended for a wide range of specialists in the field of acoustic measurements and digital processing of speech signals, as well as for practitioners who organize the work of authorized organizations in preparing for registration samples of biometric personal data.

Hybrid Face Recognition Algorithm for Wireless Sensor Network

2017 ◽  
Vol 13 (3) ◽  
pp. 267-281
Author(s):  
Matheel E. Abdulmunem E. Abdulmunem ◽  
◽  
Fatima B. Ibrahim
Keyword(s):  
Face Recognition ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Recognition Algorithm ◽  
Wireless Sensor

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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