scholarly journals Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yunfei Chen ◽  
Yang Liu ◽  
Xintao Fan
Keyword(s):  
Clustering Algorithm ◽  
Recognition Rate ◽  
Signal Acquisition ◽  
Signal Source ◽  
Large Signal ◽  
Feature Identification ◽  
Motion Platform ◽  
Communication Signal ◽  
Spectrum Feature ◽  
Envelope Extraction

In order to solve the problem of large signal acquisition error caused by radio wave multipath effect in indoor environment, firstly, the signal source carried on the motion platform is collected for spectrum signal, and the signal processed by wavelet threshold denoising algorithms extracted and stored for spectrum feature extraction. Then, after data training and identification, the signal source is input into the system in random mode for identification. The experimental results show that the improved fuzzy clustering algorithm (FCA) is 12.7% higher than the spectrum envelope extraction method (SEEM) in the recognition rate of spectrum characteristics of different modes of signal source.

Online Biological Literature MRI Image Classification Study

2013 ◽  
Vol 694-697 ◽  
pp. 2336-2340
Author(s):  
Yun Feng Yang ◽  
Feng Xian Tang
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Feature Identification ◽  
Identification Method ◽  
Image Library ◽  
Text Information ◽  
Correct Recognition Rate ◽  
Biological Literature ◽  
Mri Image

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

COARSE-TO-FINE CLASSIFIER ENSEMBLE SELECTION USING CLUSTERING AND GENETIC ALGORITHMS

2009 ◽  
Vol 23 (06) ◽  
pp. 1083-1106 ◽  
Author(s):  
YOUNG-WON KIM ◽  
IL-SEOK OH
Keyword(s):  
Genetic Algorithms ◽  
Clustering Algorithm ◽  
Statistical Significance ◽  
Recognition Rate ◽  
Hybrid Genetic Algorithm ◽  
Classifier Ensemble ◽  
Optimal Ensemble ◽  
Ensemble Selection ◽  
Selection Stage ◽  
Coarse To Fine

A good classifier ensemble should show high complementarity among classifiers to produce a high recognition rate and it should also have a small size to be efficient. This paper proposes a classifier ensemble selection algorithm operating in a coarse-to-fine paradigm. For the algorithm to be successful, the original classifier pool should be sufficiently diverse. So this paper produces a large classifier pool by combining several different classification algorithms and several feature subsets. The coarse selection stage reduces greatly the size of the classifier pool using a clustering algorithm. The fine selection finds the near-optimal ensemble using genetic algorithms. A hybrid genetic algorithm with improved searching capability is also proposed. The experimentation used handwritten numeral datasets and UCI datasets. The experimental results and the test of statistical significance showed that the proposed algorithm is superior to the conventional ones.

scholarly journals Using Machine Learning Algorithms to Recognize Shuttlecock Movements

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Wang
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Research Strategies ◽  
Image Capture ◽  
Motion Recognition ◽  
Motion Characteristics

Shuttlecock is an excellent traditional national sport in China. Because of its simplicity, convenience, and fun, it is loved by the broad masses of people, especially teenagers and children. The development of shuttlecock sports into a confrontational event is not long, and it takes a period of research to master the tactics and strategies of shuttlecock sports. Based on this, this article proposes the use of machine learning algorithms to recognize the movement of shuttlecock movements, aiming to provide more theoretical and technical support for shuttlecock competitions by identifying features through actions with the assistance of technical algorithms. This paper uses literature research methods, model methods, comparative analysis methods, and other methods to deeply study the motion characteristics of shuttlecock motion, the key algorithms of machine learning algorithms, and other theories and construct the shuttlecock motion recognition based on multiview clustering algorithm. The model analyzes the robustness and accuracy of the machine learning algorithm and other algorithms, such as a variety of performance comparisons, and the results of the shuttlecock motion recognition image. For the key movements of shuttlecock movement, disk, stretch, hook, wipe, knock, and abduction, the algorithm proposed in this paper has a good movement recognition rate, which can reach 91.2%. Although several similar actions can be recognized well, the average recognition accuracy rate can exceed 75%, and even through continuous image capture, the number of occurrences of the action can be automatically analyzed, which is beneficial to athletes. And the coach can better analyze tactics and research strategies.

scholarly journals A generation and distribution system of clock signal source for signal acquisition system

2021 ◽  
Author(s):  
Lei Zhang ◽  
Yuanyuan Zhang ◽  
Ziqian Shang ◽  
Yanrui Su ◽  
Fabao Yan ◽  
...  
Keyword(s):  
Distribution System ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Clock Signal ◽  
Signal Source

scholarly journals Research on Action Recognition Method of Dance Video Image Based on Human-Computer Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
FenTian Peng ◽  
Hongkai Zhang
Keyword(s):  
Human Computer Interaction ◽  
Action Recognition ◽  
Clustering Algorithm ◽  
Recognition Rate ◽  
Video Image ◽  
Recognition Method ◽  
Skeleton Model ◽  
Video Images ◽  
Motion Characteristics ◽  
Computer Interaction

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.

scholarly journals A Neural Network Classification Model Based on Covering and Immune Clustering Algorithm

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Fang Zhao
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Clustering Algorithm ◽  
Distributed Storage ◽  
Recognition Rate ◽  
Original Data ◽  
Classification Model ◽  
Computing Technique ◽  
Neural Network Classification ◽  
Hidden Layer

Inspired by the information processing mechanism of the human brain, the artificial neural network (ANN) is a classic data mining method and a powerful soft computing technique. The ANN provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. The constructive neural network (CNN) is a popular emerging neural network model suitable for processing largescale data. In this paper, a novel neural network classification model was established based on the covering algorithm (CA) and the immune clustering algorithm (ICA). The CA is highly comprehensible, and enjoys fast computing speed, and high recognition rate. However, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. To solve the problem, the ICA was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. The CA and the ICA work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. The number of hidden layer neurons equals the number of covering. In addition, the McCulloch-Pitts (M-P) neurons were adopted for the output layer. Based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. The introduction of the ICA fully solves the defect of the CA. Finally, our neural network classification model was verified through experiments on real-world datasets.

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