|
|
In-vivo Mapping of Human Skin Anisotropy Using Multi-directional Ultrasonic Probe
Tokar, Daniel ; Hradilová, Jana ; Převorovský, Zdeněk
In this paper, investigation of anisotropic behavior of forearm and back skin tissue is presented. Knowledge in this research field is of great interest mainly in dermatology, plastic surgery and regenerative therapies. Anisotropy characteristics of the skin are determined by ultrasonic wave propagation velocity using special multi-directional flexible ultrasonic probe, which enables local investigation of skin anisotropy in vivo. Assessing data, obtained from local measurements of human back, enables visualization of anisotropy map of the back skin tissue. Findings in current state of human skin anisotropy using the multi-directional ultrasonic probe provide an easy method to evaluate the local anisotropy of inter-individual skin tissue.
|
|
|
Multilevel Analysis of Continuous Acoustic Emission Records
Chlada, Milan ; Převorovský, Zdeněk
The latest acoustic emission (AE) systems provide continuous recording of high-frequency signals registered during longtime monitoring of various processes in materials. Recorded data represent extremely large amount of information to analyze, however, it reflects the health of the structure. Therefore, in the last years the attention is paid to the diagnostic method of continuous AE also as a part of Structural Health Monitoring (SHM) systems. For example it can disclose the early damage phases (cracks) or certain imperfections in rotating gearboxes, the leakage of liquids from pressure vessels and many other material defects. This method requires new signal processing and analysis approaches, which are different from the burst AE. The paper deals with the analysis of continuous AE, recorded during the test of renovated gearbox at different flight modes. As an alternative to classical spectrogram, so-called multilevel countogram based on the signal wavelet decomposition is proposed and discussed.
|
|
|
Statistical Expectation of High Energy Physics Data Sets Separation Algorithms
Hakl, František
Article focuses on the application of the basic results of the statistical learning theory known as Probabilistic Approximately Correct learning in the evaluation and post-processing of unique physical data obtained from the detectors of particle accelerators. The aim of this article is not direct separation of the measured data but evaluation of the appropriateness of separation methods used. The main principles and results of the PAC learning theory are briefly summarized, the main characteristics of selected multivariable data separation algorithms are studied from the VC-dimension point of view. Finally, based on actual data sets obtained from Tevatron D$\emptyset$ experiment, some practical hints for separation method selection and numerical computation are derived.
|
host ::
RSS.