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The interference fringes produced by the target movement contain information such as the motion parameters of the moving target and the waveguide invariant of the sound field. However, most of the time required by the estimation algorithm of target motion parameters and waveguide invariant using interference fringes must include the nearest passing point. In order to solve this shortcoming, this paper proposes a new fitting method. The main process is to extract the interference fringes of the target motion from the time-frequency diagram, and use the difference of the interference fringes at different frequencies to predict and fit the interference fringes of the entire motion process of the target, so as to satisfy most of the target motion parameters using interference fringes. And the interference fringe information required by the estimation algorithm of waveguide invariant. The simulation results show that when the interference fringe has no nearest passing point information, the method proposed in this paper can fit the entire interference fringe more accurately, and can obtain the target motion parameters and waveguide invariant. [ABSTRACT FROM AUTHOR]

The aim of an autoencoder neural network is to transform the input data into a lower-dimensional code and then to reconstruct the output from this representation. Applications of autoencoders to classifying sound events in the road traffic have not been found in the literature. The presented research aims to determine whether such an unsupervised learning method may be used for deploying classification algorithms applied to the automatic annotation of road traffic-related events based on noise analysis. Two-dimensional representation of traffic sounds based on 1D convolution was fed the core of autoencoder neural network, and after that classified with seven feed-forward classification subnetworks. Obtained results show that sound recordings can help determine the number of vehicles passing on the road. However, instead of being treated as independent, this method output should be combined with another source of data, e.g., video processing results or microwave radar data readings. Results of vehicle types classification and occupied lane obtained with the use of autoencoder are shown in the paper. [ABSTRACT FROM AUTHOR]

Precise information about the spatial distribution of sound speed in tissue has diagnostic value in itself, and also enables effective aberration correction in standard ultrasonic imaging. An algorithm called Computed Ultrasound Tomography in Echo mode (CUTE) makes it possible to reconstruct quantitative sound speed images. However, the computational cost is high, which is an obstacle to CUTE implementation in real-time imaging systems. This paper presents an improved version of the CUTE algorithm called Quick-CUTE (QCUTE). The CUTE algorithm uses the inverse transformation matrix to reconstruct the sound speed spatial distribution. The Q-CUTE algorithm is based on simplified model with unified integration paths which enables solving the inverse problem without use of a large transformation matrix. The Q-CUTE algorithm was verified through numerical simulations. The obtained results differ from those of the CUTE algorithm but maintain the quantitative character of sound speed imaging. The computational complexity of the QCUTE algorithm is proportional to N while in case of the CUTE it is proportional to N squared (where N is a number of pixels in the sound speed image). This means that the Q-CUTE algorithm allows the quantitative sound speed imaging to operate in real time. [ABSTRACT FROM AUTHOR]

A fast algorithm for the automated detection of buried and proud objects in three-dimensional (3-d) volumetric synthetic aperture sonar (SAS) imagery is proposed. The method establishes the positions of underwater targets by finding localized volumes of strong acoustic returns on or within the sediment. The algorithm relies on an important data-normalization step that is grounded in principled physics-based arguments, and it greatly reduces the amount of data that must be passed to a follow-on classification stage. The promise of the approach is demonstrated for man-made objects present in real, measured SAS data cubes collected at multiple aquatic sites by an experimental volumetric sonar system. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) represents a fast-growing field of great interest specially for aerospace, civil and mechanical engineering. This work proposes an in-flight SHM method for impact detection by ultrasounds, that is based on the dynamic behavior of the inspected structure and is useful to establish critical and dangerous operational conditions. The proposed method can be used to detect impact events both in metallic or composite structures, it is specifically designed to be used on typical fuselage and wing panels and is based on the propagation of Lamb waves in the structure on which PZT sensors are bonded for receiving signals. Algorithms are implemented in order to evaluate the impact location by post-processing the acquired signals. Several test cases are numerically studied before being tested in laboratory and reproduced in-flight. A good agreement between the numerical, laboratory and in-flight results is achieved. [ABSTRACT FROM AUTHOR]