arXiv subjects

Zhizhan Wang

Publications and source records attributed to Zhizhan Wang.

2 recordsLinked to original sources

arXiv2026

Non-Invasive Inspection of Water Canals Using Dronar

Open concrete canals play a vital role in water transportation, serving as primary water infrastructure for millions of people across the Phoenix, Arizona, metro area. Over time, the concrete canals can experience a range of issues, including canal lining deformation, cracked concrete, and sediment buildup on the canal floor. Identifying such critical issues is a resource-intensive process, which currently happens only during four-year dry-up cycles. This prevents the maintenance crew from prioritizing operations on the most affected canal segments. To address this issue, the research team has developed and verified an easily deployable and non-invasive method to inspect canal beds without draining the water. This inspection system integrates affordable, off-the-shelf drone and sonar technology (termed dronar). This dronar system includes a consumer-grade sonar system integrated into an unmanned surface vehicle (USV) that carries the sonar transducer just under the surface of the canal water. This paper presents a proof-of-concept demonstration of the dronar system across three field tests on the Arizona Canal in Phoenix. DownScan depth profiles from the sedimented canal segment were consistently shallower than profiles from the same segment after cleaning, with offsets of up to 15 cm observed along the track. Repeated runs over the clean segment produced closely overlapping DownScan depth profiles, confirming that the dronar yields repeatable measurements across the natural variation of the canal bed. These results establish the dronar as a viable proof-of-concept tool for non-invasive canal bed inspection.

cs.RO↗

arXiv2021

Channel Estimation for Underwater Acoustic OFDM Communications: An Image Super-Resolution Approach

In this paper, by exploiting the powerful ability of deep learning, we devote to designing a well-performing and pilot-saving neural network for the channel estimation in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. By considering the channel estimation problem as a matrix completion problem, we interestingly find it mathematically equivalent to the image super-resolution problem arising in the field of image processing. Hence, we attempt to make use of the very deep super-resolution neural network (VDSR), one of the most typical neural networks to solve the image super-resolution problem, to handle our problem. However, there still exist significant differences between these two problems, we thus elegantly modify the basic framework of the VDSR to design our channel estimation neural network, referred to as the channel super-resolution neural network (CSRNet). Moreover, instead of training an individual network for each considered signal-to-noise ratio (SNR), we obtain an unified network that works well for all SNRs with the help of transfer learning, thus substantially increasing the practicality of the CSRNet. Simulation results validate the superiority of the CSRNet against the existing least square (LS) and deep neural network (DNN) based algorithms in terms of the mean square error (MSE) and the bit error rate (BER). Specifically, compared with the LS algorithm, the CSRNet can reduce the BER by 44.74% even using 50% fewer pilots.

cs.IT↗