A Practical Underwater Information Sensing System Based on Intermittent Chaos Under the Background of Lévy Noise

The Gaussian noise model has been chosen for underwater information sensing tasks under substantial interference for most of the research at present. However, it often contains a strong impact and does not conform to the Gaussian distribution. In this paper, a practical underwater information sensing system is proposed based on intermittent chaos under the background of Lévy noise. In this system, a novel Lévy noise model is presented to describe the underwater natural environment interference and estimate its parameters, which can better describe the impact characteristics of the underwater environment. Then an underwater environment sensing method of dual-coupled intermittent chaotic Duffing oscillator is improved by using the variable step-size method and scale transformation. The simulation results show that the method can sense weak signals and estimate their frequencies under the background of strong Lévy noise, and the estimation error is as low as 0.03%. Compared with the intermittent chaos of the single Duffing oscillator and the intermittent chaotic Duffing of double coupling, the minimum SNR ratio threshold has been reduced by 11.5dB and 6.9dB, respectively, and the computational cost significantly reduced, and the sensing efficiency is significantly improved.

Wearable devices in diving: A systematic review (Preprint)

BACKGROUND Wearable devices have grown enormously in importance in recent years. While wearables have generally been well studied, they have not yet been discussed in the underwater environment. OBJECTIVE The reason for this systematic review was to systematically search for the wearables for underwater operation used in the scientific literature, to make a comprehensive map of their capabilities and features, and to discuss the general direction of development. METHODS In September 2021, we conducted an extensively search of existing literature in the largest databases using keywords. For this purpose, only articles were used that contained a wearable or device that can be used in diving. Only articles in English were considered, as well as peer-reviewed articles. RESULTS In the 36 relevant studies that were found, four device categories could be identified: safety devices, underwater communication devices, head-up displays and underwater human-computer interaction devices. CONCLUSIONS The possibilities and challenges of the respective technologies were considered and evaluated separately. Underwater communication has the most significant influence on future developments. Another topic that has not received enough attention is human-computer interaction.

Analysis of Security Attacks and Taxonomy in Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSN) have gained more attention from researchers in recent years due to their advancement in marine monitoring, deployment of various applications, and ocean surveillance. The UWSN is an attractive field for both researchers and the industrial side. Due to the harsh underwater environment, own capabilities, and open acoustic channel, it is also vulnerable to malicious attacks and threats. Attackers can easily take advantage of these characteristics to steal the data between the source and destination. Many review articles are addressed some of the security attacks and taxonomy of the Underwater Wireless Sensor Networks. In this study, we have briefly addressed the taxonomy of the UWSNs from the most recent research articles related to the well-known research databases. This paper also discussed the security threats on each layer of the Underwater Wireless sensor networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues.

Using Environmental Physiology to Teach Physiological Regulation

Environmental Physiology at Boise State University, Idaho, is a multidisciplinary course that expands students’ understanding of human regulatory physiology through acute and chronic responses to environmental extremes. Students explore the physics (pressure, fluid dynamics, gas laws, sound, and light) of the underwater environment, marine flora and fauna adaptations to this environment, and the human experience within this environment. Included is completion of the Professional Association of Dive Instructors (PADI) Open Water Scuba Certification. The course culminates in an international dive trip where course concepts are further demonstrated and explored, and conservation activities are undertaken.

A Velocity-Independent DOA Estimator of Underwater Acoustic Signals Via An Arbitrary Cross-Linear Nested Array

In this paper, an estimator for underwater DOA estimation is proposed by using a cross-linear nested array with arbitrary cross angle. The estimator excludes the variation acoustic velocity by deriving the geometric relation of the cross-linear array on the proposed algorithm. Therefore, compared with traditional DOA estimation algorithms via linear array, this estimator eliminates systematic errors caused by the uncertainty factor of the acoustic velocity in the underwater environment. Compared with the traditional acoustic velocity independent algorithm, this estimator uses the nested array and improves the performance of DOA estimation. In addition, the estimator is based on arbitrary angle of the cross-linear array, so it is more flexible in practical applications. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance in underwater environments where the actual acoustic velocity is not accurate.

Construction Method of Swimming Pool Intelligent Assisted Drowning Detection Model Based on Computer Feature Pyramid Networks

Swimming pool intelligent assisted drowning detection is an important research content in the field of drowning rescue. A large number of scholars track drowning targets in real time through underwater intelligent monitoring system, and use it to build a reliable swimming pool intelligent assisted drowning detection model to reduce the risk of drowning. For the complex underwater environment of the swimming pool, the previous detection model has been difficult to adapt to the practical demand. In this regard, based on the summary of the previous swimming pool intelligent assisted drowning detection models and the computer feature pyramid networks, the feature stratification of the swimming pool intelligent assisted drowning detection image is completed, and then the final swimming pool intelligent assisted drowning detection results are obtained through the YOLO principle. After analysis, it is confirmed that the accuracy rate of swimming pool intelligent assisted drowning detection of this method is significantly improved, which can provide effective data theoretical guidance for swimming pool intelligent assisted drowning rescue and has significant practical advantages.

A Novel Feature-Based Detector for Underwater Acoustic Beacon Signals Using Superimposed Envelope Spectrum of Multi-Pulses

For tackling the challenge of in-time searching a sea-crashed plane, it is critical to develop a convenient and reliable detector for the underwater beacon signal. In the application of signal detection, a conventional detector such as linear correlation (LC) is used based on the assumption of Gaussian white noise, but it has turned out to be a poor choice in a sophisticated underwater environment. To address this issue, a novel feature-based detector using superimposed envelope spectrum (SES) of multi-pulses is proposed in this paper. The proposed detector firstly extracts the envelopes of the received multi-pulse signals and superimposes the envelopes according to the known period. Then, the harmonic features of the SES are derived and utilized in the feature judgment to make the final decision. The proposed method is evaluated together with several existing state-of-the-art detectors, including the matched filter (MF), the generalized likelihood ratio test (GRLT) detector, and the periodogram of the directly dislocation superposition (PDDS) detectors with constant false alarm probability. Compared with the conventional detectors, it is found that the proposed SES detector is more robust against the colored noise, the random phase, and the channel distortions caused by the sophisticated underwater environment. Simulation results show that, given a detection probability value of 90% and a false alarm probability value of 1%, the proposed detector shows a gain of 3–12 dB compared with the best one of the MF, GRLT, and the PDDS detectors under distorted channels in terms of signal-to-noise ratio (SNR) requirements, respectively. Experimental results based on lake trial data have also verified the validity and feasibility of the proposed feature-based detector.