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The low resolution of ultrasonic images is mainly caused by the convolution between the reflect function of the measured tissues and the system impulse response. In this paper, the system impulse response is first estimated on the envelope ultrasonic image data using higher-order spectra. Subsequently, wavelet-based deconvolution is implemented. The algorithm proposed in this paper removes the coupling problem between the tissue signals and the impulse response, which has been a puzzle in homomorphic deconvolution. It also preserves spatial components of ultrasonic images much better compared to Fourier domain deconvolution. The resulting images behave clearer in velamen boundary and tissue layers, and the texture information also appears more detailed. The deconvolved images achieve good resolution gains in both axial and lateral directions.

Early fault detection and diagnosis of rolling element bearing is of paramount importance in wind turbines as it contributes to around 70% of gearbox and 21%–70% of generator failure. When a rolling element bearing strikes a local fault in the inner or outer race, a shock (high frequency) is introduced, and repetitive impact occurs due to continuous rotation. Extracting the fault-sensitive repetitive impact frequency component from the measured signal containing multiple frequencies (discrete gear and shaft frequency, bearing fault frequency and high-frequency noise) is challenging. This paper presents two vibration techniques based on enhanced envelope analysis and blind deconvolution technique for bearing fault identification. The improved envelope analysis diagnosis bearing faults using the three-step process of removing gear and shaft frequency components by auto-regression model, followed by spectral kurtosis to extract fault-sensitive features and envelope analysis to identify bearing faults.

On the contrary, the enhanced blind deconvolution extracts the fault-sensitive component by finding the best inverse finite impulse response filter from the measured vibration signal by adaptively demodulating resonance bands due to repetitive impact and reducing the periodic noise component in a single step. The application of the two bearing fault diagnosis techniques and their comparative study has been demonstrated through numerical simulations and two industrial testrig bearing benchmark datasets. Investigations concluded that both methods extract the transient impulse features due to bearing fault; however, the enhanced blind deconvolution technique outperforms the envelope analysis in the case of measured vibration signal with outliers.

In this paper, we consider the use of blind deconvolution for optoacoustic (photoacoustic) imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the system. The method is demonstrated with optoacoustic measurement obtained from six-day-old mice, imaged in the near-infrared using a broadband hydrophone in a circular scanning configuration. We find that estimates of the unknown point spread function, achieved by blind deconvolution, improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

This paper presents a theoretical foundation for the newly developed methodology that enables the prediction of blood pressures based on the heart sounds measured directly on the chest of a patient. The key to this methodology is the separation of heart sounds into first heart sound and second heart sound, from which components attributable to four heart valves, i.e.: mitral; tricuspid; aortic; and pulmonary valve-closure sounds are separated. Since human physiology and anatomy can vary among people and are unknown a priori, such separation is called blind source separation. Moreover, the sources locations, their surroundings and boundary conditions are unspecified. Consequently, it is not possible to obtain an exact separation of signals. To circumvent this difficulty, we extend the point source separation method in this paper to an inhomogeneous fluid medium, and further combine it with iteration schemes to search for approximate source locations and signal propagation speed. Once these are accomplished, the signals emitted from individual sources are separated by deconvoluting mixed signals with respect to the identified sources. Both numerical simulation example and experiment have demonstrated that this approach can provide satisfactory source separation results.