Biblio

The main limitation of acoustic particle separation for microfluidic application is its low sorting efficiency. This is due to the weak coupling of surface acoustic waves (SAWs) into the microchannel. In this work, we demonstrate bulk acoustic wave (BAW) particle sorting using capacitive micromachined ultrasonic transducers (CMUTs) for the first time. A collapsed mode CMUT was driven in air to generate acoustic pressure within the silicon substrate in the in-plane direction of the silicon die. This acoustic pressure was coupled into a water droplet, positioned at the side of the CMUT die, and measured with an optical hydrophone. By using a beam steering approach, the ultrasound generated from 32 CMUT elements were added in-phase to generate a maximum peak-to-peak pressure of 0.9 MPa. Using this pressure, 10 µm latex beads were sorted almost instantaneously.

It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D power amplifier, a typical nonlinear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. First, the nonlinearity of the sonar transmitter is studied in-depth, and the nonlinearity of the power amplifier is modeled and its nonlinearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and, then, the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then, the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the nonlinear characteristics of the signal. By this way, this method can enhance the communication safety of the UASNs.

In this paper, we propose a theoretical framework to investigate the eavesdropping behavior in underwater acoustic sensor networks. In particular, we quantify the eavesdropping activities by the eavesdropping probability. Our derived results show that the eavesdropping probability heavily depends on acoustic signal frequency, underwater acoustic channel characteristics (such as spreading factor and wind speed) and different hydrophones (such as isotropic hydrophones and array hydrophones). Simulation results have further validate the effectiveness and the accuracy of our proposed model.