Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance.

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency.

Bioinspired Control Architecture for Adaptive and Resilient Navigation of Unmanned Underwater Vehicle in Monitoring Missions of Submerged Aquatic Vegetation Meadows.

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure.

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys.

Simultaneous Sensing and Actuating Capabilities of a Triple-Layer Biomimetic Muscle for Soft Robotics.

This work presents the fabrication and characterization of a triple-layered biomimetic muscle constituted by polypyrrole (PPy)-dodecylbenzenesulfonate (DBS)/adhesive tape/PPy-DBS demonstrating simultaneous sensing and actuation capabilities. The muscle was controlled by a neurobiologically inspired cortical neural network sending agonist and antagonist signals to the conducting polymeric layers. Experiments consisted of controlled voluntary movements of the free end of the muscle at angles of ±20°, ±30°, and ±40° while monitoring the muscle's potential response.

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things.

Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination.

Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas.

The world's oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species.

Underwater Acoustic Impulsive Noise Monitoring in Port Facilities: Case Study of the Port of Cartagena.

Recording underwater impulsive noise data is an important aspect of mitigating its environmental impact and improving maritime environmental management systems. This paper describes the method used and results of the spatial monitoring of both the baseline noise level and the impulsive noise sources in the Port of Cartagena. An autonomous vessel was equipped with a smart digital hydrophone with a working frequency range between 10 and 200 kHz and a received voltage response (RVR) of, approximately, -170 dB re 1V/µPa.

Smart Industrial IoT Monitoring and Control System Based on UAV and Cloud Computing Applied to a Concrete Plant.

Unmanned aerial vehicles (UAVs) are now considered one of the best remote sensing techniques for gathering data over large areas. They are now being used in the industry sector as sensing tools for proactively solving or preventing many issues, besides quantifying production and helping to make decisions. UAVs are a highly consistent technological platform for efficient and cost-effective data collection and event monitoring.