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In this paper, we propose an algorithm for estimating the beam aperture of the acoustic transducers by using the geometric backscattering model for side-scan sonar systems. The geometric backscattering model is developed to describe the propagation paths of the signal transmitted from the transducers towards the seabed and backscatters to the hydrophones. To evaluate our proposed algorithm, we have developed a side-scan sonar system. The side-scan sonar system uses two transducers, operating on two different frequencies and focusing on two different wave beams, to scan the images of the seabed. The proposed algorithm provides the estimated beam apertures of each transducer. Our obtained results agree quite well with the parameters provided by the manufacturers. Moreover, these results are used to calibrate the scanned images. We provide the scanned sonar 3D images of the Dong Do lakebed, Vietnam, to justify our proposal.

This paper considers hiding messages in overt transmissions with a full-duplex receiver, which emits artificial noise to secure its transmission connection while a transmitter opportunistically sends a covert message to a covert user. The warden’s uncertainties in decoding the overt message and artificial-noise-received power are exploited to hide messages. Then, the covert throughput accompanied with the warden’s average detection error probability are determined. The results show that increasing the transmit power of artificial noise or improving secure connection at the overt user will improve the covert performance. The results also show that the covert performance is improved when the self-interference cancellation is improved at the full-duplex receiver or when the warden is located close to the full-duplex receiver, indicating the positive impact of the overt performance on the covert performance.

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.

Tin sulfide (SnS) is known for its effective gas-detecting ability at low temperatures. However, the development of a portable and flexible SnS sensor is hindered by its high resistance, low response, and long recovery time. Like other chalcogenides, the electronic and gas-sensing properties of SnS strongly depend on its surface defects. Therefore, understanding the effects of its surface defects on its electronic and gas-sensing properties is a key factor in developing low-temperature SnS gas sensors. Herein, using thin SnS films annealed at different temperatures, we demonstrate that SnS exhibits n-type semiconducting behavior upon the appearance of S vacancies. Furthermore, the presence of S vacancies imparts the n-type SnS sensor with better sensing performance under UV illumination at room temperature (25 °C) than that of a p-type SnS sensor. These results are thoroughly investigated using various experimental analysis techniques and theoretical calculations using density functional theory. In addition, n-type SnS deposited on a polyimide substrate can be used to fabricate high-stability flexible sensors, which can be further developed for real applications.