Research on the Postural Stability of Underwater Bottom Platforms with Different Burial Depths

The bottom platform is an important underwater sensor that can be used in communications, early warning, monitoring, and other fields. It may be affected by earthquakes, winds, waves, and other loads in the working environment, causing changes in posture and affecting its sensing function. Therefore, it is of practical engineering significance to analyze the force conditions and posture changes in the bottom platform. In order to solve the problem of postural stability of the underwater bottom platform, this paper establishes a fluid and structural simulation model of the underwater bottom platform. First, computational fluid dynamics (CFD) technology is used to solve the velocity distribution and forces in the watershed around the bottom platform under a 3 kn ocean current, where the finite element method (FEM) numerical calculation method is used to solve the initial equilibrium state of the bottom platform after it is buried. On this basis, this paper calculates the forces on the bottom platform and the posture of the bottom platform at different burial depths under the action of ocean currents. Additionally, the effects of different burial depths on the maximum displacement, deflection angle, and postural stability of the bottom platform are studied. The calculation results show that when the burial depth is greater than 0.6 m, and the deflection angle of the bottom platform under the action of the 3 kn sea current is less than 5°, the bottom platform can maintain a stable posture. This paper could be used to characterize the postural stability of underwater bottom platforms at different burial depths for the application of underwater sensors in ocean engineering.

An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted.

Easy-to-Prepare Flexible Multifunctional Sensors Assembled with Anti-Swelling Hydrogels

Recent years have witnessed the development of flexible electronic materials. Flexible electronic devices based on hydrogels are promising but face the limitations of having no resistance to swelling and a lack of functional integration. Herein, we fabricated a hydrogel using a solvent replacement strategy and explored it as a flexible electronic material. This hydrogel was obtained by polymerizing 2-hydroxyethyl methacrylate (HEMA) in ethylene glycol and then immersing it in water. The synergistic effect of hydrogen bonding and hydrophobic interactions endows this hydrogel with anti-swelling properties in water, and it also exhibits enhanced mechanical properties and outstanding self-bonding properties. Moreover, the modulus of the hydrogel is tissue-adaptable. These properties allowed the hydrogel to be simply assembled with a liquid metal (LM) to create a series of structurally complex and functionally integrated flexible sensors. The hydrogel was used to assemble resistive and capacitive sensors to sense one-, two-, and three-dimensional strains and finger touches by employing specific structural designs. In addition, a multifunctional flexible sensor integrating strain sensing, temperature sensing, and conductance sensing was assembled via simple multilayer stacking to enable the simultaneous monitoring of underwater motion, water temperature, and water quality. This work demonstrates a simple strategy for assembling functionally integrated flexible electronics, which should open opportunities in next-generation electronic skins and hydrogel machines for various applications, especially underwater applications.

Underwater Acoustic Signal Acquisition and Sensing Using a Ring Vector Sensor Communication Receiver: Theory and Experiments

Signal acquisition and sensing in underwater systems and applications is typically a challenging issue due to the small signal strength within the background noise. Here, we present a ring vector sensor communication receiver that can significantly improve signal acquisition, by utilizing the underwater acoustic vector field components, compared to the scalar component. The vector sensor receiver is a multichannel receiver that measures particle velocities, which are vector components of the underwater acoustic field, in addition to the scalar field component. According to the combination of our measured experimental data with our signal acquisition performance analysis, the introduced ring vector sensor receiver exhibits higher signal acquisition probabilities for the vector components compared to the scalar component. This can be attributed to certain characteristics of the vector field components. Another advantage of this multichannel receiver is that combining all of its channels can further increase the signal acquisition and packet detection probability in underwater communication systems compared to a single-channel approach.

Integer Arithmetic Algorithm for Fundamental Frequency Identification of Oceanic Currents

Underwater sensor networks play a crucial role in collecting valuable data to monitor offshore aquaculture infrastructures. The number of deployed devices not only impacts the bandwidth for a highly constrained communication environment, but also the cost of the sensor network. On the other hand, industrial and literature current meters work as raw data loggers, and most of the calculations to determine the fundamental frequencies are performed offline on a desktop computer or in the cloud. Belonging to the edge computing research area, this paper presents an algorithm to extract the fundamental frequencies of water currents in an underwater sensor network deployed in offshore aquaculture infrastructures. The target sensor node is based on a commercial ultra-low-power microcontroller. The proposed fundamental frequency identification algorithm only requires the use of an integer arithmetic unit. Our approach exploits the mathematical properties of the finite impulse response (FIR) filtering in the integer domain. The design and implementation of the presented algorithm are discussed in detail in terms of FIR tuning/coefficient selection, memory usage and variable domain for its mathematical formulation aimed at reducing the computational effort required. The approach is validated using a shallow water current model and real-world raw data from an offshore aquaculture infrastructure. The extracted frequencies have a maximum error below a 4%.

Localization Approach for Underwater Sensors in the Magnetic Silencing Facility Based on Magnetic Field Gradients

Localization of the underwater magnetic sensor arrays plays a pivotal role in the magnetic silencing facility. A localization approach is proposed for underwater sensors based on the optimization of magnetic field gradients in the inverse problem of localization. In the localization system, a solenoid coil carrying direct current serves as the magnetic source. By measuring the magnetic field generated by the magnetic source in different positions, an objective function is established. The position vector of the sensor is determined by a novel multi-swarm particle swarm optimization with dynamic learning strategy. Without the optimization of the magnetic source's positions, the sensors' positions, especially in the z-axis direction, struggle to meet the requested localization. A strategy is proposed to optimize the positions of the magnetic source based on magnetic field gradients in the three directions of x, y and z axes. Compared with the former method, the model experiments show that the proposed method could achieve a 10 cm location error for the position type 2 sensor and meet the request of localization.

Water-Borne Fabrication of Stretchable and Durable Microfibers for High-Performance Underwater Strain Sensors

The development of stretchable strain sensors having high linearity and sensitivity, low hysteresis, and fast response to reliably monitor fast human motions is challenging. In this study, hydrogel-based strain sensors in the form of microfibers comprising tough double-network hydrogels or organogels and multi-walled carbon nanotubes (CNTs) are fabricated using aqueous microfluidic devices. Owing to the shear thinning effect on the microchannel, the CNTs can be aligned parallel to the flow direction, which increases the linearity of the sensor up to a strain of 400% and provides high durability over 50,000 strain cycles at 300% elongation. Owing to the negligible hysteresis, high resolution of 0.1%, and low response time of ∼30 ms, the strain sensors enable the quantitative conversion of the measured resistance change to the extent of stimulus and the simple detection of the motion. The developed sensors can be stably used to detect human motions in real time in both air and water. Furthermore, the developed material system demonstrates the potential for use in the fabrication of pressure sensors.

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper.

Performance study of the application of Artificial Neural Networks to the completion and prediction of data retrieved by underwater sensors

This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited.