Abstract

Abstract
Measuring water motion is essential for oceanography, coastal engineering, and marine environmental monitoring. A wide range of sensing technologies is used to quantify water velocity, wave motion, and flow dynamics, each suited to specific spatial and temporal scales. This paper presents a comprehensive review of modern sensor technologies for marine flow measurement, covering mechanical, electromagnetic, pressure-based, acoustic, optical, MEMS-based, inertial, Lagrangian, and remote-sensing approaches. The operating principles, strengths, and limitations of each technology are examined alongside their suitability for different environments and deployment platforms, including moorings, buoys, vessels, autonomous underwater vehicles, and drifters. Special attention is given to rapidly advancing fields such as MEMS flow sensors, multi-sensor fusion, and hybrid systems that combine inertial, acoustic, and optical data. Applications range from high-resolution turbulence measurements to large-scale current mapping and wave characterization. Remaining challenges include biofouling, performance degradation in energetic shallow waters, uncertainties in indirect velocity estimation, and long-term calibration stability. By synthesizing the state of the art across sensing modalities, this review provides a unified perspective on current technological capabilities and identifies key trends shaping the future of marine flow measurement.

Abstract
Conventional localization systems typically rely on fixed transmission parameters and signal types, limiting their effectiveness in variable and dynamic underwater environments. The present work investigates the potential of adaptable transmission strategies to enhance signal detection estimation for localization purposes. Two widely used signal types, Linear Frequency Modulated (LFM) chirps and BPSK-modulated Msequences, are selected due to their strong autocorrelation properties and robustness to noise. A matched-filter detection approach based on peak correlation is implemented and evaluated. The analysis examines the impact of varying transmission parameters, namely transmission power and signal duration, on detection performance, which inherently influences time-based localization. Results demonstrate that reconfiguring signal parameters significantly reduces estimation dispersion. Moreover, the optimal signal type is shown to depend on the acoustic scenario, with no single waveform consistently outperforming the other. These findings highlight the value of reconfigurable acoustic systems capable of adapting acoustic systems characteristics based on environmental or system feedback, thereby improving localization performance in navigation tasks and dynamic underwater conditions. © 2025 Marine Technology Society.

Abstract
As digital design methodologies and tools are evolving to higher abstraction levels, teaching the low-level concepts of digital electronic system design is becoming increasingly challenging. The raise of the design abstraction level and, more recently, the ability of AI-assisted automated design is pushing the interest of students away from the lower-level details of the digital world. Nevertheless, digital electronic systems are (still) made of transistors, gates and flip-flops, and people do need to keep this basic knowledge to be able to build efficient circuits, understand them and develop the essential electronic design automation tools. For learning these subjects, hands-on experimentation, and learning by doing, is proven to be an effective tool, and when students finally see and feel the results of their designs, motivation raises rapidly. This paper presents the technical aspects of a platform created in the DECEL project to support an FPGA-based remote laboratory based on a commercial single-board computer that can be located somewhere in the Internet. This computer runs a Linux operating system and is based on an AMD/XILINX device that integrates in the same chip an ARM Cortex A9 CPU and a region of FPGA programmable logic. The user develops a digital circuit using standard hardware-description languages (Verilog or VHDL) and runs the implementation tools for the target FPGA using a very simple web interface running in a remote server.

Abstract
This demo shows an infrastructure that allows for easy implementation of real remote labs. In this infrastructure, several nodes are remotely interconnected by publishing/subscribing MQTT messages. There are physical nodes capable of connecting to real circuits and/or sensors/actuators, and virtual nodes that implement simulated versions of circuits that interact remotely with signals from other nodes. The latencies that occur are low enough for groups of students located in different physical locations to benefit from a near real-time experience in interacting with the circuits thus implemented.

Abstract
Pose estimation by computer vision is essential in underwater robot navigation. Several works already use computer vision and ArUco markers for this purpose. The method is widely spread and developed. In terms of software, libraries have already been developed, for instance, the ArUco module in the OpenCV library. However, there is still a need to characterize the relationship between the performance of the system and the computer vision hardware itself, as well as the spatial arrangement of the markers. Another aspect to take into account is the environmental condition. This work seeks to relate these factors to the error resulting from the estimation of relative positions between cameras and markers.