Continuous Wavelet Transform Applications In Steganography

Varuikhin, Vladmir; Levina, Alla

doi:10.1016/j.procs.2021.04.179

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…This signal is called ‘microscope’ [8]. Varushin et al.’s wave transforms are an important tool in mathematical analysis and are considered to have a wide range of applications: many branches of mathematics: signal analysis and image processing; quantum mechanics, theoretical physics; military countermeasures and weapons intelligence; computer classification and recognition; artificial synthesis of music and language; Medical Imaging and Diagnosis: Processing of Seismic Survey Data; Diagnosis of Major Mechanical Damage [9]. In their work, Kuo et al revealed that it is used for numerical analysis, surface construction, and solving differential equations; signal analysis including filters, noise suppression, compression, and so on; image processing including image compression, classification, identification, diagnosis, and disinfection; and in medicine, signal attenuation, character recognition, and character size reduction can reduce the duration and improve accuracy of B‐ultrasound, CT and MRI in clinical imaging [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Heart rate monitoring of physical fitness training load based on wavelet transform

Zhang¹,

Sharma

Samori

2022

The Journal of Engineering

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The detection of fluctuations is a crucial step in the process of isolating heart rate oscillation signals. This is necessary to circumvent the issue wherein a variety of auditory signals will invariably interfere with the collection of centre velocity signals during physical activity, which has a significant impact on the detection efficiency of automated testing. To obtain heart rate fluctuation signals that are more precise, the paper proposes a method of R‐wave identification that is based on heartbeat conversion while the athlete is exercising. The proposed model relies heavily on the technique of wave analysis to filter out the background noise of the heartbeat when the athlete exercises and isolates the R‐wave signal of the heartbeat from the background noise. The proposed algorithm is evaluated in terms of noise interference that removes approximately 90% of noise, the heart rate data which is obtained after the noise is normally distributed, the average detection rate of the R‐wave algorithm is 98.65%. It has been demonstrated that the detection accuracy of the proposed algorithm is sufficient to meet the requirements of generating heart rate oscillatory signals.

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Section: Literature Reviewmentioning

confidence: 99%