Methodology for Detecting the Mental Activity of the Brain by Wavelet Analysis of the Electroencephalogram

Yumatov, E. A.

doi:10.1007/s10527-023-10223-9

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In signal processing, wavelet analysis has become a mainstream mathematical analysis method, which is widely used in various signal-related elds such as medical diagnosis and natural disaster signal analysis [15]. Wavelet packet decomposition method is developed on the basis of wavelet transform, and at the same time makes up for some defects in the wavelet, and develops into a commonly used timefrequency analysis method.…”

Section: Wavelet Transformsmentioning

confidence: 99%

Research on the quality evaluation method of copper and aluminum diffusion bonding

Li,

Hou,

Yun

et al. 2024

Preprint

View full text Add to dashboard Cite

The application of copper-aluminum welded pole pieces is extensive in the new energy manufacturing sectors, such as automotive batteries. Diffusion bonding is one of the main ways of copper-aluminum connection, which is very important for the safe use of the components in terms of the quality of its connection. First, a water-immersion ultrasonic inspection platform was built to carry out inspection research on internal defects in copper-aluminum welding. In order to address the problem of overlapping signal features at the interface, the signal is decomposed and reconstructed by applying the wavelet decomposition method using the water-immersion focusing transmission method. The reconstructed signal is then decomposed in three layers, and the energy features of the last layer are extracted to form energy feature vectors for the qualitative analysis of different defect types. Secondly, different welding area samples were prepared and the peak power spectral density was extracted as an interface characterization parameter. Tensile tests were conducted on samples with varying weld areas, and the measured maximum pull-out force was correlated with the characteristic parameters of ultrasonic testing. The results demonstrate the effective capability of wavelet packet analysis in identifying un-welded, weak bond, and micro-porous defects at the diffusion welding interface. A higher peak value of power spectral density indicates superior welding quality. Establishing a reasonable threshold for peak power spectral density can serve as a reliable criterion for assessing the condition of copper and aluminum diffusion bonding.

show abstract

Section: Wavelet Transformsmentioning

confidence: 99%