Distance measurement using an ultrasonic wave is suitable for environment recognition in autonomous mobile robots. Ultrasonic distance measurement with the pulse-echo method is based on determining the time-of-flight (TOF) of the reflected echoes. The pulse-echo method with pulse compression, the cross-correlation method, can improve the distance resolution and the signal-to-noise ratio (SNR) of the reflected echo. However, cross correlation of the cross-correlation method requires the high-cost digital signal processing. A sensor signal processing method of cross correlation using a delta-sigma modulated single-bit digital signal has been proposed. Cross correlation by single-bit signal processing reduces the calculation cost of cross correlation. Furthermore, cross correlation by single-bit signal processing improves the time resolution of digital signal processing. In this paper, the distance resolution of cross correlation by single-bit signal processing is evaluated by computer simulations and experiments of ultrasonic distance measurement. Distance measurement from the high-time-resolution cross-correlation function obtained by single-bit signal processing realized high distance resolution from experimental results, despite the low calculation costs.

The development of a quantitative diagnostic method for liver fibrosis using an ultrasound B-mode image is highly required. In our previous study, a multi-Rayleigh model was proposed to express a probability distribution of echo envelope amplitude from a fibrotic liver. Using the multi-Rayleigh model, a structure of fibrotic tissue can be quantitatively estimated. In this study, a method of estimating the number of tissue components was proposed to improve the accuracy of estimating a fibrotic tissue structure. Using threshold processing for a squared Mahalanobis distance of moments, which is a statistical property of echo envelope amplitude, the number of tissue components could be quantitatively estimated. The results of evaluation of clinical ultrasound B-mode images using the multi-Rayleigh model with estimation of the number of tissue components well reflected the tissue structural changes caused by liver fibrosis. It was concluded that our proposed method of estimating the number of tissue components improves the accuracy of liver fibrosis evaluation based on the multi-Rayleigh model.

To realize a quantitative diagnosis method of liver fibrosis, we have been developing a modeling method for the probability density function of the echo amplitude. In our previous model, the approximation accuracy is insufficient in regions with hypoechoic tissue such as a nodule or a blood vessel. In this study, we examined a multi-Rayleigh model with three Rayleigh distributions, corresponding to the distribution of the echo amplitude from hypoechoic, normal, and fibrous tissue. We showed quantitatively that the proposed model can model the amplitude distribution of liver fibrosis echo data with hypoechoic tissue adequately using Kullback–Leibler (KL) divergence, which is an index of the difference between two probability distributions. We also found that fibrous indices can be estimated stably using the proposed model even if hypoechoic tissue is included in the region of interest. We conclude that the multi-Rayleigh model with three components can be used to evaluate the progress of liver fibrosis quantitatively.