Backscatter properties of two-layer phantoms using a high-frequency ultrasound annular array

In a previous study, an annular-array transducer was employed to characterize homogeneous scattering phantoms and excised rat livers using backscatter envelope statistics and frequency domain analysis. A sound field correction method was also applied to take into account the average attenuation of the entire scattering medium. Here, we further generalized the evaluation of backscatter coefficient (BSC) using the annular array in order to study skin tissues with a complicated structure. In layered phantoms composed of two types of media with different scattering characteristics, the BSC was evaluated by the usual attenuation correction method which revealed an expected large difference from the predicted BSC. In order to improve the BSC estimate, a correction method that applied the attenuation of each layer as a reference combined with a method that corrects based on the attenuation of the analysis position were applied. It was found that the method using the average attenuation of each layer is the most effective. This correction method is well adapted to the extended depth of field provided by an annular array.

Liquidity connectedness in cryptocurrency market

We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

A novel method for diagnosing rolling bearing surface damage by electric impedance analysis

This paper presents a novel condition monitoring method for rolling bearings, based on measuring the electric bearing impedance. The method can diagnose the presence of damage by frequency-domain analysis, and its extension along the raceway by time-domain analysis. The latter enables the assessment of the severity and the progression of bearing damage. A fatigue test shows that the occurrence of pittings in the bearing raceways causes characteristic peaks in the impedance signal, and that the duration of the peaks increases during damage progression. A second test series with artificial damage shows that the duration of the peaks depends on the bearing load and the length of the damage along the raceway and confirms the explanation hypothesis.

A Fault Diagnosis Method of Rolling Mill Bearing at Low Frequency and Overload Condition Based on Integration of EEMD and GA-DBN

To solve the problems of difficult fault signal recognition and poor diagnosis effect of different damage in the same position in rolling mill bearing at low speed, a fault diagnosis method of rolling mill bearing based on integration of EEMD and DBN was proposed. The vibration signals in horizontal, axial, and vertical directions were decomposed and reconstructed by EEMD, and frequency domain analysis was carried out by using refined spectrum. Then, the signal's time-frequency domain index, rolling force, and torque component feature vector were input into genetic algorithm (GA) to optimize DBN model classification. In order to verify the effectiveness of the method, the experimental study was carried out on the two-high experimental rolling mill. The results show that EEMD combined with thinning spectrum can solve the problem of fault feature extraction well. Compared with time-frequency domain characteristic input, the prediction accuracy of DBN model is obviously improved. And the accuracy of GA-DBN model is higher, and the accuracy is 98.3%, and the time taken to diagnose is significantly reduced. Finally, the fault classification of different parts of bearings and the fault diagnosis of different damage in the same part are realized, which provides a good theoretical basis for the fault diagnosis of low-speed bearings and has important engineering significance.

EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.