Whistle detection and classification for whales based on convolutional neural networks

Passive acoustic observation of whales is an increasingly important tool for whale research. Accurately detecting whale sounds and correctly classifying them into corresponding whale species are essential tasks, especially in the case when two species of whales vocalize in the same observed area. Whistles are vital vocalizations of toothed whales, such as killer whales and long-finned pilot whales. In this paper, based on deep convolutional neural networks (CNNs), a novel method is proposed to detect and classify whistles of both killer whales and long-finned pilot whales. Compared with traditional methods, the proposed one can automatically learn the sound characteristics from the training data, without specifying the sound features for classification and detection, and thus shows better adaptability to complex sound signals. First, the denoised sound to be analyzed is sent to the trained detection model to estimate the number and positions of the target whistles. The detected whistles are then sent to the trained classification model, which determines the corresponding whale species. A GUI interface is developed to assist with the detection and classification process. Experimental results show that the proposed method can achieve 97% correct detection rate and 95% correct classification rate on the testing set. In the future, the presented method can be further applied to passive acoustic observation applications for some other whale or dolphin species

Spread-spectrum techniques for environmentally-friendly underwater acoustic communications

PhD ThesisAnthropogenic underwater noise has been shown to have a negative impact on marine life. Acoustic data transmissions have also been shown to cause behavioural responses in marine mammals. A promising approach to address these issues is through reducing the power of acoustic data transmissions. Firstly, limiting the maximum acoustic transmit power to a safe limit that causes no injury, and secondly, reducing the radius of the discomfort zone whilst maximising the receivable range. The discomfort zone is dependent on the signal design as well as the signal power. To achieve these aims requires a signal and receiver design capable of synchronisation and data reception at low-received-SNR, down to around −15 dB, with Doppler effects. These requirements lead to very high-ratio spread-spectrum signaling with efficient modulation to maximise data rate, which necessitates effective Doppler correction in the receiver structure. This thesis examines the state-of-the-art in this area and investigates the design, development and implementation of a suitable signal and receiver structure, with experimental validation in a variety of real-world channels. Data signals are designed around m-ary orthogonal signaling based on bandlimited carrierless PN sequences to create an M-ary Orthogonal Code Keying (M-OCK) modulation scheme. Synchronisation signal structures combining the energy of multiple unique PN symbols are shown to outperform single PN sequences of the same bandwidth and duration in channels with low SNR and significant Doppler effects. Signals and receiver structures are shown to be capable of reliable communications with band of 8 kHz to 16 kHz and transmit power limited to less than 170.8 dB re 1 μPa @ 1m, or 1W of acoustic power, over ranges of 10 km in sea trials, with low-received-SNR below −10 dB, at data rates of up to 140.69 bit/s. Channel recordings with AWGN demonstrated limits of signal and receiver performance of BER 10−3 at −14 dB for 35.63 bit/s, and −8.5 dB for 106.92 bit/s. Piloted study of multipath exploitation showed this performance could be improved to −10.5 dB for 106.92 bit/s by combining the energy of two arrival paths. Doppler compensation techniques are explored with experimental validation showing synchronisation and data demodulation at velocities over ranges of ±2.7m/s. Non-binary low density parity check (LDPC) error correction coding with M-OCK signals is investigated showing improved performance over Reed-Solomon (RS) coding of equivalent code rate in simulations and experiments in real underwater channels. The receiver structures are implemented on an Android mobile device with experiments showing live real-time synchronisation and data demodulation of signals transmitted through an underwater channel.UK Engineering and Physical Sciences Research Council (EPSRC): PhD Doctoral Training Account (DTA)

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Oceanus.

v. 34, no. 1 (1991

EFFICIENT DYNAMIC ADDRESSING BASED ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS

This thesis presents a study about the problem of data gathering in the inhospitable underwater environment. Besides long propagation delays and high error probability, continuous node movement also makes it difficult to manage the routing information during the process of data forwarding. In order to overcome the problem of large propagation delays and unreliable link quality, many algorithms have been proposed and some of them provide good solutions for these issues, yet continuous node movements still need attention. Considering the node mobility as a challenging task, a distributed routing scheme called Hop-by-Hop Dynamic Addressing Based (H2- DAB) routing protocol is proposed where every node in the network will be assigned a routable address quickly and efficiently without any explicit configuration or any dimensional location information. According to our best knowledge, H2-DAB is first addressing based routing approach for underwater wireless sensor networks (UWSNs) and not only has it helped to choose the routing path faster but also efficiently enables a recovery procedure in case of smooth forwarding failure. The proposed scheme provides an option where nodes is able to communicate without any centralized infrastructure, and a mechanism furthermore is available where nodes can come and leave the network without having any serious effect on the rest of the network. Moreover, another serious issue in UWSNs is that acoustic links are subject to high transmission power with high channel impairments that result in higher error rates and temporary path losses, which accordingly restrict the efficiency of these networks. The limited resources have made it difficult to design a protocol which is capable of maximizing the reliability of these networks. For this purpose, a Two-Hop Acknowledgement (2H-ACK) reliability model where two copies of the same data packet are maintained in the network without extra burden on the available resources is proposed. Simulation results show that H2-DAB can easily manage during the quick routing changes where node movements are very frequent yet it requires little or no overhead to efficiently complete its tasks

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties