Yaqing Tu, Huiyue Yang, Haitao Zhang and Xiangyu Liu
In this paper, we focus on CMF signal processing and aim to resolve the problems of precision sharp-decline occurrence when using adaptive notch filters (ANFs) for tracking the signal frequency for a long time and phase difference calculation depending on frequency by the sliding Goertzel algorithm (SGA) or the recursive DTFT algorithm with negative frequency contribution. A novel method is proposed based on feedback corrected ANF and Hilbert transformation. We design an index to evaluate whether the ANF loses the signal frequency or not, according to the correlation between the output and input signals. If the signal frequency is lost, the ANF parameters will be adjusted duly. At the same time, singular value decomposition (SVD) algorithm is introduced to reduce noise. And then, phase difference between the two signals is detected through trigonometry and Hilbert transformation. With the frequency and phase difference obtained, time interval of the two signals is calculated. Accordingly, the mass flow rate is derived. Simulation and experimental results show that the proposed method always preserves a constant high precision of frequency tracking and a better performance of phase difference measurement compared with the SGA or the recursive DTFT algorithm with negative frequency contribution
The following work is an analysis of flatness deviations of a workpiece made of X2CrNiMo17-12-2 austenitic stainless steel. The workpiece surface was shaped using efficient machining techniques (milling, grinding, and smoothing). After the machining was completed, all surfaces underwent stylus measurements in order to obtain surface flatness and roughness parameters. For this purpose the stylus profilometer Hommel-Tester T8000 by Hommelwerke with HommelMap software was used. The research results are presented in the form of 2D surface maps, 3D surface topographies with extracted single profiles, Abbott-Firestone curves, and graphical studies of the Sk parameters. The results of these experimental tests proved the possibility of a correlation between flatness and roughness parameters, as well as enabled an analysis of changes in these parameters from shaping and rough grinding to finished machining. The main novelty of this paper is comprehensive analysis of measurement results obtained during a three-step machining process of austenitic stainless steel. Simultaneous analysis of individual machining steps (milling, grinding, and smoothing) enabled a complementary assessment of the process of shaping the workpiece surface macro- and micro-geometry, giving special consideration to minimize the flatness deviations
J. Švehlíková, M. Kania, M. Turzová, E. Hebláková, M. Tyšler and R. Maniewski
Identification of Ischemic Lesions Based on Difference Integral Maps, Comparison of Several ECG Intervals
Ischemic changes in small areas of myocardium can be detected from difference integral maps computed from body surface potentials measured on the same subject in situations with and without manifestation of ischemia. The proposed method for their detection is the inverse solution with 2 dipoles. Surface potentials were recorded at rest and during stress on 10 patients and 3 healthy subjects. Difference integral maps were computed for 4 intervals of integration of electrocardiographic signal (QRST, QRSU, STT and STU) and their properties and applicability as input data for inverse identification of ischemic lesions were compared. The results showed that better (more reliable) inverse solutions can be obtained from difference integral maps computed either from QRST or from STT interval of integration. The average correlation between these maps was 97%. The use of the end of U wave instead of the end of T wave for interval of integration did not improve the results.
To examine the correlation of driver visual behaviors and subjective levels of fatigue, a total of 36 commercial drivers were invited to participate in 2-h, 3-h, and 4-h naturalistic driving tests during which their eye fixation, saccade, blinking variables, and self-awareness of their fatigue levels were recorded. Then, one-way ANOVA was applied to analyze the variations of each variable among different age groups over varying time periods. The statistical analysis revealed that driving duration had a significant effect on the variation of visual behaviors and feelings of fatigue. After 2h of driving, only the average closure duration value and subjective level of fatigue had an increase of one-fifth or more. After 4h of driving, however, all these variables had a significant change except for the number of saccades and pupil diameter measurements. Particularly, driver saccadic eye movement was more sensitive to driving fatigue, and the elderly were more likely to be affected by the duration of the drive. Finally, a predictor of driver fatigue was determined to detect the real-time level of fatigue and alert at the critical moment.
Toshifumi Minemoto, Teijiro Isokawa, Haruhiko Nishimura and Nobuyuki Matsui
Hebbian learning rule is well known as a memory storing scheme for associative memory models. This scheme is simple and fast, however, its performance gets decreased when memory patterns are not orthogonal each other. Pseudo-orthogonalization is a decorrelating method for memory patterns which uses XNOR masking between the memory patterns and randomly generated patterns. By a combination of this method and Hebbian learning rule, storage capacity of associative memory concerning non-orthogonal patterns is improved without high computational cost. The memory patterns can also be retrieved based on a simulated annealing method by using an external stimulus pattern. By utilizing complex numbers and quaternions, we can extend the pseudo-orthogonalization for complex-valued and quaternionic Hopfield neural networks. In this paper, the extended pseudo-orthogonalization methods for associative memories based on complex numbers and quaternions are examined from the viewpoint of correlations in memory patterns. We show that the method has stable recall performance on highly correlated memory patterns compared to the conventional real-valued method.
V. Juráš, P. Szomolányi, S. Gäbler, I. Frollo and S. Trattnig
The Relationship between MR Parameters and Biomechanical Quantities of Loaded Human Articular Cartilage in Osteoarthritis: An In-Vitro Study
The aim of this study was to assess the changes in MRI parameters during applied load directly in MR scanner and correlate these changes with biomechanical parameters of human articular cartilage. Cartilage explants from patients who underwent total knee replacement were examined in the micro-imaging system in 3T scanner. Respective MRI parameters (T1 without- and T1 with contrast agent as a marker of proteoglycan content, T2 as a marker of collagen network anisotropy and ADC as a measure of diffusivity) were calculated in pre- and during compression state. Subsequently, these parameters were compared to the biomechanical properties of articular cartilage, instantaneous modulus (I), equilibrium modulus (Eq) and time of tissue relaxation (τ). Significant load-induced changes of T2 and ADC were recorded. High correlation between T1Gd and I (r = 0.6324), and between ADC and Eq (r = -0.4884) was found. Multi-parametric MRI may have great potential in analyzing static and dynamic biomechanical behavior of articular cartilage in early stages of osteoarthritis (OA).
S. Solis-Najera, F. Vazquez, R. Hernandez, O. Marrufo and A.O. Rodriguez
A surface radio frequency coil was developed for small animal image acquisition in a pre-clinical magnetic resonance imaging system at 7 T. A flexible coil composed of two circular loops was developed to closely cover the object to be imaged. Electromagnetic numerical simulations were performed to evaluate its performance before the coil construction. An analytical expression of the mutual inductance for the two circular loops as a function of the separation between them was derived and used to validate the simulations. The RF coil is composed of two circular loops with a 5 cm external diameter and was tuned to 300 MHz and 50 Ohms matched. The angle between the loops was varied and the Q factor was obtained from the S11 simulations for each angle. B1 homogeneity was also evaluated using the electromagnetic simulations. The coil prototype was designed and built considering the numerical simulation results. To show the feasibility of the coil and its performance, saline-solution phantom images were acquired. A correlation of the simulations and imaging experimental results was conducted showing a concordance of 0.88 for the B1 field. The best coil performance was obtained at the 90° aperture angle. A more realistic phantom was also built using a formaldehyde-fixed rat phantom for ex vivo imaging experiments. All images showed a good image quality revealing clearly defined anatomical details of an ex vivo rat.
Amira Mohamed, Shady S. Refaat and Haitham Abu-Rub
Smart grid (SG) is the solution to solve existing problems of energy security from generation to utilization. Examples of such problems are disruptions in the electric grid and disturbances in the transmission. SG is a premium source of Big Data. The data should be processed to reveal hidden patterns and secret correlations to extrapolate the needed values. Such useful information obtained by the so-called data analytics is an essential element for energy management and control decision towards improving energy security, efficiency, and decreasing costs of energy use. For that reason, different techniques have been developed to process Big Data. This paper presents an overview of these techniques and discusses their advantages and challenges. The contribution of this paper is building a recommender system using different techniques to overcome the most obstacles encountering the Big Data processes in SG. The proposed system achieves the goals of the future SG by (i) analyzing data and executing values as accurately as possible, (ii) helping in decision-making to improve the efficiency of the grid, (iii) reducing cost and time, (iv) managing operating parameters, (v) allowing predicting and preventing equipment failures, and (vi) increasing customer satisfaction. Big Data process enables benefits that were never achieved for the SG application.
Radial Ball Bearing Inner Race Defect Width Measurement using Analytical Wavelet Transform of Acoustic and Vibration Signal
In the present work, an experiment is carried out with a customized test setup where the seeded defects are introduced in the form of an axial groove on the inner race of a radial ball bearing. The nature of the acoustic and vibration signal bursts, and their correlation with the inner race defects, are established and estimated. Experimental investigation reveals that the analytical wavelet transform (AWT) is an effective tool for analyzing the acoustic and vibration signals, transmitted from the bearing, in order to characterize and measure the defect size. In the recent work, AWT followed by the time marginal integration (TMI) have been implemented on acoustic and vibration signals of a defective radial bearing. Size of the defect in the inner race of bearing is corroborated well with AWT scalogram. The segregation of the defect is carried out on TMI graph across the highest amplitude spike, which is due to signal burst (due to a contact of ball with bearing inner race defect). This manual demarcation on TMI graph in time axis provides the time duration (contact between a ball and the inner race defect). Using this time duration of the ball passed over bearing inner race defect, RPM of shaft mounted across bearing, and the fundamental train frequency, the defect width is estimated. The deviation of the measured width from the actual, using the proposed method, is sought below 5%. Summarizing, the proposed method can be reckoned a suitable and reliable measurement of radial bearing inner race defect width from acoustic and vibration signal.
Ondřej Macíček, Radovan Jiřík, Jan Mikulka, Michal Bartoš, Andrea Šprláková-Puková, Miloš Keřkovský, Zenon Starčuk, Karel Bartušek and Torfinn Taxt
Dynamic contrast enhanced MRI (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) are perfusion imaging techniques used mainly for clinical and preclinical measurement of vessel permeability and capillary blood flow, respectively. It is advantageous to apply both methods to exploit their complementary information about the perfusion status of the tissue. We propose a novel acquisition method that combines advantages of the current simultaneous and sequential acquisition. The proposed method consists of a DCE-MRI acquisition interrupted by DSC-MRI acquisition. A new method for processing of the DCE-MRI data is proposed which takes the interleaved acquisition into account. Analysis of both the DCE- and DSC-MRI data is reformulated so that they are approximated by the same pharmacokinetic model (constrained distributed capillary adiabatic tissue homogeneity model). This provides a straightforward evaluation of the methodology as some of the estimated DCE- and DSC-MRI perfusion parameters should be identical. Evaluation on synthetic data showed an acceptable precision and no apparent bias introduced by the interleaved character of the DCE-MRI acquisition. Intravascular perfusion parameters obtained from clinical glioma data showed a fairly high correlation of blood flow estimates from DCE- and DSC-MRI, however, an unknown scaling factor was still present mainly because of the tissue-specific relaxivity. The results show validity of the proposed acquisition method. They also indicate that simultaneous processing of both DCE- and DSC-MRI data with joint estimation of some perfusion parameters (included in both DCE- and DSC-MRI) might be possible to increase the reliability of the DCE- and DSC-MRI methods alone.