Abstract
The growing needs in exploring ocean resources have been pushing the length and complexity of autonomous underwater vehicle (AUV) missions, leading to more stringent energy requirements. A promising approach to reduce the energy consumption of AUVs is to use variable buoyancy systems (VBSs) as a replacement or complement to thruster action, since VBSs only require energy consumption during limited periods of time to control the vehicle's floatation. This paper presents the development of an electrohydraulic VBS to be included in an existing AUV for shallow depths of up to 100 m. The device's preliminary mechanical design is presented, and a mathematical model of the device's power consumption is developed, based on data provided by the manufacturer. Taking a standard mission profile as an example, a comparison between the energy consumed using thrusters and the designed VBS is presented and compared.

Abstract
Corners usually appear very distinct from the rest of the scene in a mechanical scanning imaging sonar (MSIS) image, generally characterized by sharp intensities. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their location provides a way to compute the vehicle position. The combination of some basic operations typically used for image segmentation have great potential to detect and localize corners in sonar images automatically. This article proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. The developed algorithms are evaluated with ground truth, and their performance is analyzed following a few relevant metrics for autonomous navigation.

Abstract
In this paper we evaluate the long-term deployment feasibility of a large-scale network of abandoned underwater sensors, where power is provided by autonomous underwater vehicles (AUVs) in periodic visits.

Abstract
The thermocline is a relatively narrow vertical region that separates the mixed layer at the surface from the deep-water layer. In this region, the gradient of temperature with respect to depth is higher than in the rest of the water column. The characteristics of the thermocline have strong impact in marine biology, since it may trap high-nutrient organisms, and it also affects sound propagation, with direct impact on underwater acoustic communications and military operations. Under adaptive sampling, Autonomous Underwater Vehicles are practical tools for efficient ocean observation. In this work, we describe an implementation of an Extremum Seeking Controller that performs identification and tracking of thermoclines at its point of highest temperature gradient in a completely autonomous way. The vehicle chosen to perform this tracking was an autonomous vertical profiler, and the algorithms were validated using both real and simulated data.

Abstract
The energy requirements of thruster driven autonomous underwater vehicle (AUV) missions have been growing in recent years. Their complexity and length are continuously increasing due to the growth of undersea exploration. The use of variable buoyancy systems (VBS) can potentially lead to energy savings since consumption is only required for buoyancy changes. As such, energy is only spent during limited periods of time, as opposed to thruster driven systems, where consumption is typically continuous. In this work, an energetic comparison between thruster and VBS driven devices is performed for a specific mission profile and a defined set of parameters. The influence of the mission parameters is studied in order to determine which system leads to the lowest energy consumption. For the case study presented, it is shown that the use of VBS over thrusters can lead to considerable energetic savings. © 2019 IEEE.

Abstract
Measuring and forecasting changes in coastal and deep-water ecosystems and climates requires sustained long-term measurements from marine observation systems. One of the key considerations in analyzing data from marine observatories is quality assurance (QA). The data acquired by these infrastructures accumulates into Giga and Terabytes per year, necessitating an accurate automatic identification of false samples. A particular challenge in the QA of oceanographic datasets is the avoidance of disqualification of data samples that, while appearing as outliers, actually represent real short-term phenomena, that are of importance. In this paper, we present a novel cross-sensor QA approach that validates the disqualification decision of a data sample from an examined dataset by comparing it to samples from related datasets. This group of related datasets is chosen so as to reflect upon the same oceanographic phenomena that enable some prediction of the examined dataset. In our approach, a disqualification is validated if the detected anomaly is present only in the examined dataset, but not in its related datasets. Results for a surface water temperature dataset recorded by our Texas A&M-Haifa Eastern Mediterranean Marine Observatory (THEMO)-over a period of 7 months, show an improved trade-off between accurate and false disqualification rates when compared to two standard benchmark schemes.