Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification

Ice environments pose challenges for conventional underwater acoustic localization techniques due to theirmultipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), for passive localization and tracking of single moving, on-ice acoustic sources using two underwater acoustic vector sensors. We incorporate ordinal classification as a localization approach and compare the results with other standard methods. We conduct experiments passively recording the acoustic signature of an anthropogenic source on the ice and analyze these data. The results demonstrate that Vision Transformers are a strong contender for tracking moving acoustic sources on ice. Additionally, we show that classification as a localization technique can outperform regression for networks more suited for classification, such as the CNN and ViTs

Detecting fish aggregations from reef habitats mapped with high resolution side scan sonar imagery

As part of a multibeam and side scan sonar (SSS) benthic survey of the Marine Conservation District (MCD) south of St. Thomas, USVI and the seasonal closed areas in St. Croix—Lang Bank (LB) for red hind (Epinephelus guttatus) and the Mutton Snapper (MS) (Lutjanus analis) area—we extracted signals from water column targets that represent individual and aggregated fish over various benthic habitats encountered in the SSS imagery. The survey covered a total of 18 km2 throughout the federal jurisdiction fishery management areas. The complementary set of 28 habitat classification digital maps covered a total of 5,462.3 ha; MCDW (West) accounted for 45% of that area, and MCDE (East) 26%, LB 17%, and MS the remaining 13%. With the exception of MS, corals and gorgonians on consolidated habitats were significantly more abundant than submerged aquatic vegetation (SAV) on unconsolidated sediments or unconsolidated sediments. Continuous coral habitat was the most abundant consolidated habitat for both MCDW and MCDE (41% and 43% respectively). Consolidated habitats in LB and MS predominantly consisted of gorgonian plain habitat with 95% and 83% respectively. Coral limestone habitat was more abundant than coral patch habitat; it was found near the shelf break in MS, MCDW, and MCDE. Coral limestone and coral patch habitats only covered LB minimally. The high spatial resolution (0.15 m) of the acquired imagery allowed the detection of differing fish aggregation (FA) types. The largest FA densities were located at MCDW and MCDE over coral communities that occupy up to 70% of the bottom cover. Counts of unidentified swimming objects (USOs), likely representing individual fish, were similar among locations and occurred primarily over sand and shelf edge areas. Fish aggregation school sizes were significantly smaller at MS than the other three locations (MCDW, MCDE, and LB). This study shows the advantages of utilizing SSS in determining fish distributions and density

Acoustic oceanographic buoy (version 1)

SiPLAB Report 06/05, FCT, University of Algarve,2005.One of the known impairements in the application of ocean acoustic tomography in operational scenarios has been the size, weight and difficulty of operation of actual ocean going equipment, such as hydrophone arrays and acoustic sources. The Acoustic Oceanographic Buoy - AOB is meant to be, at its final stage, an easy to deploy and easy to maintain autonomous vertical array that puts together in a single system acoustic and non-acoustic sensors, self-storing of geotime and GPS referred data, on the buoy preprocessing capabilities and network seamless integrability and data online transfer via a wireless lan high speed link. These capabilities make the AOB a versatile system for a variety of applications such as ocean tomography, underwater communications, rapid environmental assessment (REA), passive and multistatic sonar and underwater target tracking. The AOB version 1 described in this report was developed between 2003 and 2004 and tested at sea during the MREA’03 and MREA’04 sea trials

Vision applications in agriculture

From early beginnings in work on the visual guidance of tractors, the National Centre for Engineering in Agriculture has built up a portfolio of projects in which machine vision plays a prominent part. This presentation traces the history of this research, including some highly unusual topics

Automated soil hardness testing machine

This paper describes the design and performance of a mechatronic system for controlling a standard drop-hammer mechanism that is commonly used in performing outdoor soil or ground hardness tests. A low-cost microcontroller is used to control a hydraulic actuator to repeatedly lift and drop a standard free-falling weight that strikes a pipe (sampler) which is pushed deeper into the ground with each impact. The depth of the sampler pipe and position of the hydraulic cylinder are constantly monitored and the number of drops, soil penetration data and other variables are recorded in a database for future analysis. This device, known as the “EVH Trip Hammer”, allows the full automation and faster completion of what is typically a very labour-intensive and slow testing process that can involve human error and the risk of human injuries