Hyperbolic wavelet power spectra of nonstationary signals

Lê, Khoa N.

doi:10.1117/1.1608002

Cited by 22 publications

(7 citation statements)

References 35 publications

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“…EEG recordings were divided into sub-band frequencies such as delta (δ), theta (θ), alpha (α) and beta (β) by using DWT. Then a set of power features in time-domain was extracted from the wavelet sub-band frequencies δ (0-4 Hz), θ (4-8 Hz), α (8-16 Hz) and β (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32). Statistical features are indicative factors in determining qualities of signs.…”

Section: Resultsmentioning

confidence: 99%

Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

Yildirim

Koçer

Ekmekçi

2019

International Journal of Applied Mathematics Electronics and Computers

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Artificial Neural Networks (ANNs) that are the ability to learn from their environment in order to improve their performance are widely used in numerous applications. The Backpropagation (BP) Algorithm is one of the most popular and effective model of ANNs. However, since it uses gradient descent algorithm, which attempts to minimize the error of the network by moving gradient of the error curve, easily get trapped at local minima. In order to avoid this problem and to obtain a better classifier, we proposed an ANNs and Swarm Intelligence (SI) method where Artificial Bee Colony and Particle Swarm Optimization algorithms were operated for the Multilayer Perceptron Neural Network. Two Electroencephalogram (EEG) datasets were used to test to test the accuracy and success of the study performed. Compared to conventional-MLPNN, higher success values were obtained on each dataset with the proposed methods. Experimental results demonstrate that combined SI and MLPNN algorithm has been increased the success of BP algorithm by avoiding local minima. For ABC data, respectively, ABC-MLPNN and PSO-MLPNN methods, 79.00% and 75.50% respectively for Boston data and 91.67% and 88.33% respectively for Selcuk data were obtained. On the other hand, with the MLPNN algorithm, the success rate was 68.50% for Boston data and 81.67% for Selcuk data. These results show that the success of MLPNN algorithm significantly increases with the weights obtained by using SI. In addition to this, this study showed that the SI-MLPNN algorithm can be used on non-linear and highly complex EEG data.

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Section: Resultsmentioning

confidence: 99%

Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

Yildirim

Koçer

Ekmekçi

2019

International Journal of Applied Mathematics Electronics and Computers

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“…Engineering degrees require much use of numerical calculation and logical thinking (Le et al 2003). If students do not have a high degree of knowledge, it may affect the continuity of their study.…”

Section: Degree Of Difficultymentioning

confidence: 99%

“…For ease of understanding, the Fourier transform can be viewed as a mapping the energy distribution in the signal in the frequency domain at which harmonic peaks or dominant peaks represent the peak energy concentration in the waveform. For example, the Fourier transform of a constant signal, which is continuous from −∞ to +∞, is an impulse whose energy concentration is theoretically perfect (Le 2003).…”

Section: The Fourier Transformmentioning

confidence: 99%

How to retain postgraduate students in engineering programmes: a practical perspective

Lê

Tam

2008

European Journal of Engineering Education

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Six factors for pursuing an engineering postgraduate programme at Griffith University including (i) programme quality; (ii) employment prospects; (iii) practicality; (iv) personal interest; (v) popularity; and (vi) reputation; and 11 factors for not pursuing this engineering programme including (i) employment prospects; (ii) degree of difficulty; (iii) loss of interest; (iv) interaction with classmates and lecturers;(v) financial difficulty; (vi) programme quality; (vii) persuasion from other institutions; (viii) obligation to family; (ix) career change; (x) self-confidence; and (xi) relocation are studied in this paper. A questionnaire survey on factors for pursuing and against pursuing the programme is conducted. Individual interviews are conducted to gather further information and clarification on the survey. F-tests, relative important index values and spectral behaviour of all factors are estimated. It is found that employment prospects and programme quality are the major factors for pursuing and for not pursuing the programme.

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“…The nth-order hyperbolic kernel was briefly discussed in [1], and it appears that its performance in cross-term suppression, auto-term support and noise robustness improves as the kernel order n increases. Fundamental background on the hyperbolic kernel and Cohen's time-frequency distributions can be found in [1,6] and references therein.…”

Section: Motivationmentioning

confidence: 99%

On mathematical derivations of auto-term functions and signal-to-noise ratios of the Choi–Williams, first- and nth-order hyperbolic kernels

Lê

Dabke

Egan

2006

Digital Signal Processing

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Hyperbolic wavelet power spectra of nonstationary signals

Cited by 22 publications

References 35 publications

Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

How to retain postgraduate students in engineering programmes: a practical perspective

On mathematical derivations of auto-term functions and signal-to-noise ratios of the Choi–Williams, first- and nth-order hyperbolic kernels

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