Experiments on Decision Fusion for Driver Recognition

2007 ◽  
pp. 1-9 ◽  
Author(s):  
Hakan Erdogan ◽  
Aytül Ereil ◽  
Hüseyin Abut
Keyword(s):  
Decision Fusion

scholarly journals Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion

2020 ◽  
Vol E103.D (2) ◽  
pp. 450-453
Author(s):  
Guizhong ZHANG ◽  
Baoxian WANG ◽  
Zhaobo YAN ◽  
Yiqiang LI ◽  
Huaizhi YANG
Keyword(s):  
Sparse Representation ◽  
Steel Structure ◽  
Decision Fusion ◽  
One Class Classification

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

Melanoma Detection Using Decision Fusion of Various Classifiers

2020 ◽  
Author(s):  
Dan Popescu ◽  
Loretta Ichim ◽  
Alexandra Dumitru ◽  
Hassan El-Khatib
Keyword(s):  
Decision Fusion ◽  
Melanoma Detection

Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding

2014 ◽  
Vol 5 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Arpita Sharma ◽  
Samiksha Goel
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Cuckoo Search ◽  
Decision Fusion ◽  
Majority Voting ◽  
Natural Terrain ◽  
Terrain Features ◽  
Level Fusion ◽  
Better Than

This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with similar high accuracies as other well identified land cover types, which was not possible by single analyzers.

Gas Turbine Fault Diagnosis Using Fuzzy-Based Decision Fusion

2009 ◽  
Vol 25 (2) ◽  
pp. 335-343 ◽  
Author(s):  
A. Kyriazis ◽  
K. Mathioudakis
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Decision Fusion

scholarly journals A muscle synergies-based movements detection approach for recognition of the wrist movements

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Aida Masoumdoost ◽  
Reza Saadatyar ◽  
Hamid Reza Kobravi
Keyword(s):  
Decision Fusion ◽  
Classification Performance ◽  
Muscle Synergy ◽  
Fusion Algorithm ◽  
Main Research ◽  
Main Challenge ◽  
Detection Approach ◽  
Average Accuracy ◽  
Bayesian Fuzzy Clustering ◽  
Research Challenge

Abstract Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively.

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