Selection of feature analysis electronic nose signals based on the correlation between gas sensor and herbal phytochemical.

U.K. Mohamad Yusof; A. Che Soh; N.F. M. Radzi; A.J. Ishak; M.K. Hassan S.A. Ahmad; S. Khamis

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Selection of feature analysis electronic nose signals based on the correlation between gas sensor and herbal phytochemical.

机译：基于气体传感器和草药植物化学成分之间的相关性，选择特征分析电子鼻信号。

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《Australian Journal of Basic and Applied Sciences 》 |2015年第2015期| 共7页
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U.K. Mohamad Yusof; A. Che Soh; N.F. M. Radzi; A.J. Ishak; M.K. Hassan S.A. Ahmad; S. Khamis;
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中图分类经济计划与管理 ;
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2. Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals [J] . Huichun Yu, Jun Wang, Yongwei Wang Sensors . 2009 ,第10期

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3. Feature extraction of electronic nose for classification of indoor pollution gases based on kernel entropy component analysis [J] . Jia Yan, Shukai Duan, Lidan Wang, International Journal of Intelligent Systems Technologies and Applications . 2017 ,第2期

机译：基于核熵分析分析的室内污染气体分类的电子鼻子特征提取
4. Rapid Discrimination of Indonesian Herbal Medicines by using Electronic Nose Based on Array of Commercial Gas Sensors [C] . Fajar Hardoyono, Kuwat Triyana, Bambang Heru Iswanto International Seminar on Instrumentation, Measurement and Metrology . 2015

机译：基于商用气体传感器阵列的电子鼻除电子鼻迅速辨别印度尼西亚草药
5. Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas-sensing electronic nose systems. [D] . Polikar, Robi. 2000

机译：应用于气敏电子鼻系统的算法，用于增强模式可分离性，特征选择和增量学习。
6. Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals [O] . Huichun Yu, Yongwei Wang, Jun Wang 2009

机译：基于电子鼻传感器信号主成分分析特征值的线性判别分析和反向传播神经网络技术识别茶叶贮藏时间
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）
8. Selection Criteria of Test Signals for Correlation-Based Wire Fault Analysis (Preprint) [R] . Telasula, V. , Furse, C. , Lo, C. 2006

机译：基于相关的线路故障分析测试信号的选择标准（预印本）

Selection of feature analysis electronic nose signals based on the correlation between gas sensor and herbal phytochemical.

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