[1] Fang Lanting, Wu Lenan, Zhang Yudong,. Signal classification systemusing global-local feature extraction algorithm [J]. Journal of Southeast University (English Edition), 2017, 33 (4): 432-436. [doi:10.3969/j.issn.1003-7985.2017.04.007]
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Signal classification systemusing global-local feature extraction algorithm(
)
基于全局-局部特征提取算法的信号分类系统
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 33
- Issue:
- 2017 4
- Page:
- 432-436
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2017-12-30
Info
- Title:
- Signal classification systemusing global-local feature extraction algorithm
- 基于全局-局部特征提取算法的信号分类系统
- Author(s):
- Fang Lanting, Wu Lenan, Zhang Yudong
- School of Information Science and Engineering, Southeast University, Nanjing 210096, China
- Keywords:
- continuous wavelet transform(CWT); support vector machine(SVM); global-local features; signal classification
- PACS:
- TP391
- DOI:
- 10.3969/j.issn.1003-7985.2017.04.007
- Abstract:
- A continuous wavelet transform(CWT)and global-local feature(GLF)extraction-based signal classification algorithm is proposed to improve the signal classification accuracy. First, the CWT is utilized to generate the time-frequency scalogram. Then, the GLF extraction method is proposed to extract features from the time-frequency scalogram. Finally, a classification method based on the support vector machine(SVM)is proposed to classify the extracted features. Experimental results show that the extended binary phase shift keying(EBPSK)bit error rate(BER)of the proposed classification algorithm is 1.3×10-5 under the environment of additional white Gaussian noise with the signal-to-noise ratio of -3 dB, which is 24 times lower than that of the SVM-based signal classification method. Meanwhile, the BER using the GLF extraction method is 13 times lower than the one using the global feature extraction method and 24 times lower than the one using the local feature extraction method.
- 为了提高信号分类的准确度, 提出了一种基于连续小波变换和全局-局部特征提取的信号分类算法.首先, 对信号进行小波变换, 生成时域-频域系数矩阵.然后, 提出了一种全局-局部特征提取算法, 该算法可以有效地提取时域-频域系数矩阵的特征信息.最后, 使用支持向量机分析方法提取到的特征信息, 输出分类结果.仿真结果表明, 在信噪比为-3 dB的高斯白噪声环境下, 所提出的信号分类算法对EBPSK信号分类的误码率为1.3×10-5, 该误码率比基于支持向量机的信号分类算法低24倍, 同时, 使用全局-局部特征提取算法的误码率比仅使用全局特征提取算法低13倍, 比仅使用局部特征提取算法低24倍.
References:
[1] Ju M, Kim I M. Error performance analysis of BPSK modulation in physical-layer network-coded bidirectional relay networks [J]. IEEE Transactions on Communications, 2010, 58(10): 2770-2775. DOI:10.1109/tcomm.2010.082010.090256.
[2] Schlegel C, Truhachev D. Multiple access demodulation in the lifted signal graph with spatial coupling [J]. IEEE Transactions on Information Theory, 2013, 59(4): 2459-2470. DOI:10.1109/tit.2012.2232965.
[3] Takahashi H, Kosugi T, Hirata A, et al. 10-Gbit/s BPSK modulator and demodulator for a 120-GHz-band wireless link [J]. IEEE Transactions on Microwave Theory and Techniques, 2011, 59(5): 1361-1368. DOI:10.1109/tmtt.2010.2097603.
[4] Linn Y. A self-normalizing symbol synchronization lock detector for QPSK and BPSK [J]. IEEE Transactions on Wireless Communications, 2006, 5(2): 347-353. DOI:10.1109/twc.2006.1611058.
[5] Murray V, Rodríguez P, Pattichis M S. Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy [J]. IEEE Transactions on Image Processing, 2010, 19(5): 1138-1152. DOI:10.1109/TIP.2010.2040446.
[6] Chen X Q, Wu L N. Nonlinear demodulation and channel coding in EBPSK scheme [J]. The Scientific World Journal, 2012, 2012: 180469-1-180469-7. DOI:10.1100/2012/180469.
[7] Bialasiewicz J T, González D, Balcells J, et al. Wavelet-based approach to evaluation of signal integrity [J]. IEEE Transactions on Industrial Electronics, 2013, 60(10): 4590-4598. DOI:10.1109/tie.2012.2217713.
[8] Banerjee S, Mitra M. Application of cross wavelet transform for ECG pattern analysis and classification [J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(2): 326-333. DOI:10.1109/TIM.2013.2279001.
[9] Karamzadeh N, Doloei G J, Reza A M. Automatic earthquake signal onset picking based on the continuous wavelet transform [J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 2666-2674. DOI:10.1109/tgrs.2012.2213824.
[10] Costa F B. Fault-induced transient detection based on real-time analysis of the wavelet coefficient energy [J]. IEEE Transactions on Power Delivery, 2014, 29(1): 140-153. DOI:10.1109/tpwrd.2013.2278272.
[11] Fang L T, Wu L N, Zhang Y D. A novel demodulation system based on continuous wavelet transform [J]. Mathematical Problems in Engineering, 2015, 2015: 513849-1-513849-9. DOI:10.1155/2015/513849.
[12] Zhang T D, Dong Z C, Phillips P, et al. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning [J]. Frontiers in Computational Neuroscience, 2015, 9: 66. DOI:10.3389/fncom.2015.00066.
[13] Le T P, Paultre P. Modal identification based on continuous wavelet transform and ambient excitation tests [J]. Journal of Sound and Vibration, 2012, 331(9): 2023-2037. DOI:10.1016/j.jsv.2012.01.018.
[14] Jiang X, Ma Z J, Ren W X, Crack detection from the slope of the mode shape using complex continuous wavelet transform [J]. Computer Aided Civil and Infrastructure Engineering, 2012, 27(3): 187-201. DOI:10.1111/j.1467-8667.2011.00734.x.
[15] Zhang Y D, Dong Z C, Wang S H, et al. Preclinical diagnosis of magnetic resonance(MR)brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine(GEPSVM)[J]. Entropy, 2015, 17(4): 1795-1813. DOI:10.3390/e17041795.
Memo
- Memo:
-
Biographies: Fang Lanting(1990—), female, graduate; Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.
Foundation items: The National Key Technology R&D Program(No.2012BAH15B00), the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX150076).
Citation: Fang Lanting, Wu Lenan, Zhang Yudong. Signal classification system using global-local feature extraction algorithm[J].Journal of Southeast University(English Edition), 2017, 33(4):432-436.DOI:10.3969/j.issn.1003-7985.2017.04.007.