An Efficient Recognition Algorithm Between Analog and Digital Signals at Low SNR

2010 ◽  
Author(s):  
Chisheng Li ◽  
Shuliang Xu ◽  
Guofeng Zha
Keyword(s):  
Recognition Algorithm ◽  
Digital Signals ◽  
Low Snr ◽  
Efficient Recognition

scholarly journals A Robust Real-Time Automatic Recognition Prototype for Maritime Optical Morse-Based Communication Employing Modified Clustering Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1227 ◽  
Author(s):  
Xiaozheng Wang ◽  
Minglun Zhang ◽  
Hongyu Zhou ◽  
Xinglong Lin ◽  
Xiaomin Ren
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Light Emitting Diode ◽  
Experimental Tests ◽  
Recognition Algorithm ◽  
Automatic Recognition ◽  
Decision Threshold ◽  
Low Snr ◽  
Led Array

In maritime communications, the ubiquitous Morse lamp on ships plays a significant role as one of the most common backups to radio or satellites just in case. Despite the advantages of its simplicity and efficiency, the requirement of trained operators proficient in Morse code and maintaining stable sending speed pose a key challenge to this traditional manual signaling manner. To overcome these problems, an automatic system is needed to provide a partial substitute for human effort. However, few works have focused on studying an automatic recognition scheme of maritime manually sent-like optical Morse signals. To this end, this paper makes the first attempt to design and implement a robust real-time automatic recognition prototype for onboard Morse lamps. A modified k-means clustering algorithm of machine learning is proposed to optimize the decision threshold and identify elements in Morse light signals. A systematic framework and detailed recognition algorithm procedure are presented. The feasibility of the proposed system is verified via experimental tests using a light-emitting diode (LED) array, self-designed receiver module, and microcontroller unit (MCU). Experimental results indicate that over 99% of real-time recognition accuracy is realized with a signal-to-noise ratio (SNR) greater than 5 dB, and the system can achieve good robustness under conditions with low SNR.

scholarly journals Sobre Finura Própria de Grafos

2018 ◽  
Author(s):  
Moysés S. Sampaio Jr. ◽  
Fabiano S. Oliveira ◽  
Jayme L. Szwarcfiter
Keyword(s):  
Recognition Algorithm ◽  
Unit Interval ◽  
Interval Graphs ◽  
Graph Classes ◽  
Efficient Recognition

Both graph classes of k-thin and proper k-thin graphs have recently been introduced generalizing interval and unit interval graphs, respectively. The complexity of the recognition of k-thin and proper k-thin are open, even for fixed k 2. In this work, we introduce a subclass of the proper 2-thin graphs, called proper 2-thin of precedence. For this class, we present a characterization and an efficient recognition algorithm.

scholarly journals A Few-Shot Modulation Recognition Method Based on Pseudo-Label Semi-Supervised Learning

2020 ◽  
Vol 38 (5) ◽  
pp. 1074-1083
Author(s):  
Yunhao Shi ◽  
Hua Xu ◽  
Yinghui Liu
Keyword(s):  
Supervised Learning ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Digital Signals ◽  
Label Algorithm ◽  
Label System ◽  
Model Recognition ◽  
Labeling Method

In order to solve the problem of insufficient labeled samples in modulation recognition, this paper proposes a few-shot modulation recognition algorithm based on pseudo-label semi-supervised learning (pseudo-label algorithm). First of all, high quality artificial feature, excellent classifier and data-labeling method are used to build efficient pseudo label system, and then the pseudo label system is combined with signal classification method based on the deep learning to realize the modulation classification under the condition of a small number of labeled samples and a large number of unlabeled samples. The simulation results show that the pseudo-label algorithm can improve the model recognition performance by 5%-10% when the six kinds of digital signals are classified and identified and its SNR is greater than 5 dB. At the same time, the algorithm has a simple network design and is of great application value.

scholarly journals A Pulse Signal Characteristic Recognition Algorithm Based on Multifractal Dimension

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yi-bing Li ◽  
Wei Nie ◽  
Fang Ye ◽  
Jing-chao Li
Keyword(s):  
Radiation Source ◽  
Impulse Noise ◽  
Fractal Theory ◽  
Pulse Signal ◽  
Recognition Algorithm ◽  
Transient Characteristics ◽  
Signal Characteristic ◽  
Low Snr

In low SNR condition, it is difficult to identify the radio transient characteristics of the signals. To solve this problem, a new recognition algorithm based on multifractal dimension characteristics is proposed. In fractal theory, multifractal dimension is the most sophisticated characterize that can describe the similar characteristics of the signals. Therefore, multifractal dimension is used in this paper to extract the subtle features of different impulse noise signals, in order to achieve the purpose of the classification and identification of the radiation source.

scholarly journals Reconhecimento de Grafos Fino de Precedência

2019 ◽  
Author(s):  
Flavia Bonomo ◽  
Fabiano Oliveira ◽  
Moysés Sampaio Jr ◽  
Jayme Szwarcfiter
Keyword(s):  
Recognition Algorithm ◽  
Interval Graphs ◽  
Efficient Recognition

The class of k-thin graphs have recently been introduced generalizing interval graphs. The complexity of the recognition of k-thin is open, even for fixed k 1. We introduce a subclass of the k-thin graphs, called precedence k-thin graphs, presenting an efficient recognition algorithm based on PQ trees.

Digital Signal Modulation with Recognition Algorithm to Identify the Ability to Optimize the Wavelet

2014 ◽  
Vol 1014 ◽  
pp. 413-416 ◽  
Author(s):  
Shu Mei Yan ◽  
Hui Fang
Keyword(s):  
Wavelet Transform ◽  
Digital Signal ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Discrete Variable ◽  
Modulation Recognition ◽  
Algorithm Optimization ◽  
Digital Signals ◽  
Signal Modulation ◽  
Original Algorithm

Large errors in the digital signal modulation recognition algorithm for the general wavelet transform, we propose a discrete optimization algorithm based on wavelet transform, which is generally improved algorithm based on wavelet transform algorithm, the original algorithm in order to avoid redundancy Yu phenomenon, discrete variable domain transform operation, the wavelet transform and then use the algorithm to improve the amplitude modulation of digital signals, quadrature amplitude modulated signal, a digital frequency-modulated signal and the digital phase modulated signal is identified. Simulation results show that the discrete wavelet transform algorithm optimization wavelet transform algorithm than the digital signal modulation recognition applications to identify errors in the proposed smaller, more accuracy.

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.

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