An image zooming method based on the coupling threshold in the wavelet packet transform domain

Liu, Chenhua; Wang, Anhong

doi:10.1088/1361-6501/ad031a

Cited by 2 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where is the wavelet approximation component, is the detail component [12] at all levels, and is the number of wavelet decomposition stages [13]. For unification, denote by , then can be defined as ( 4).…”

Section: A Feature Extraction Technology-wavelet Entropymentioning

confidence: 99%

“…In both hidden and output layers, all neurons have to employ a nonlinear activation function to calculate their output values. For the -th neuron on the -th hidden layer, its activation function can be expressed as (11), (12).…”

Section: B Feedforward Neural Networkmentioning

confidence: 99%

“…As shown in (11) (12), the input value of any neuron in the hidden layer is the difference between and the threshold . This difference will be used as a variable in the activation function to calculate the output value [30].…”

Section: B Feedforward Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

Multiple Sclerosis Recognition by Wavelet Entropy and Self-adaptive PSO

Hou

2023

Preprint

View full text Add to dashboard Cite

Multiple sclerosis is a chronic, autoimmune disease that mainly affects the central nervous system, including the brain, spinal cord, and optic nerve. This disease can cause clinical symptoms such as cognitive decline, muscle weakness, spasms, and fatigue in patients, and the onset tends to be younger. Current medication can only prevent or alleviate symptoms, so early diagnosis of this disease can increase patients' chances of treatment. Although the use of nuclear magnetic resonance detection can improve the efficiency of early auxiliary diagnosis, it still requires experienced doctors to spend too much time and energy on comprehensive judgment. To reduce the time cost and improve the efficiency of multiple sclerosis diagnosis, this article proposes a detection and recognition algorithm for multiple sclerosis based on wavelet entropy and self-adaptive particle swarm optimization. Firstly, a triple discrete wavelet transform is performed on the brain image of multiple sclerosis, and then 10 wavelet entropies are extracted from the decomposed wavelet subbands, which can reduce the feature dimensions of the image; Then, the algorithm of self-adaptive particle swarm optimization is used to optimize the feedforward neural network, in order to obtain the optimal connection weights and thresholds during the training process. The result of the model is with an average sensitivity of 92.29±1.89, specificity of 92.54±0.67, precision of 92.48±0.59, accuracy of 92.42±0.88, and F1 score of 84.85±1.74, Matthews correlation coefficient of 92.37±0.96, and Fowlkes223 Mallows Index of 92.38±0.96. The experimental results indicate that this algorithm has a very important data support role in detecting multiple sclerosis.

show abstract

Section: A Feature Extraction Technology-wavelet Entropymentioning

confidence: 99%

Section: B Feedforward Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Sclerosis Recognition by Wavelet Entropy and Self-adaptive PSO

Hou

2023

Preprint

View full text Add to dashboard Cite

show abstract

Performance degradation assessment of rolling bearing under vibration signal monitoring based on optimized variational mode decomposition and improved fuzzy support vector data description

Hu,

Chen,

et al. 2024

Journal of Applied Physics

View full text Add to dashboard Cite

Performance degradation assessment methods for rolling bearings under vibration signal monitoring typically involve extracting signal degradation features and inputting them directly into unoptimized assessment models. However, this method often fails to characterize degradation trends and detect early faults in bearings effectively. Moreover, it is susceptible to interference from outliers and false signal fluctuations, posing challenges for accurate performance degradation assessment. To solve the above problems, a novel performance degradation assessment method of rolling bearings based on optimized variational mode decomposition to construct the maximum information degradation feature set and improved hippopotamus optimization algorithm optimized fuzzy support vector data description is proposed. This method effectively suppresses the signal degradation feature pollution caused by the redundancy of irrelevant frequency components, realizes the screening of effective degradation feature sets, and further improves the accuracy of bearing performance degradation assessment. Through experimental verification, this research method uses early healthy rolling bearing samples to establish an assessment model, which can adaptively determine the starting point and degradation trend of bearing degradation. In comparison with other methods for performance degradation assessment under vibration monitoring, it offers distinct advantages.

show abstract

An image zooming method based on the coupling threshold in the wavelet packet transform domain

Cited by 2 publications

References 48 publications

Multiple Sclerosis Recognition by Wavelet Entropy and Self-adaptive PSO

Multiple Sclerosis Recognition by Wavelet Entropy and Self-adaptive PSO

Performance degradation assessment of rolling bearing under vibration signal monitoring based on optimized variational mode decomposition and improved fuzzy support vector data description

Contact Info

Product

Resources

About