A novel method for camera calibration in underwater environments using convolutional networks is presented. Two modified neural network architectures, ZCalibAquaNet and BCalibAquaNet, were developed for calibration matrix estimation from chessboard images in underwater environments. The process began with the training of the InceptionResNetV2 neural network from scratch, followed by the adjustment of the dense layers for regression and calibration matrix estimation. To ensure a suitable underwater environment, the dataset used in this work was created by the authors, using a ZED 2 stereo camera with a resolution of 1280 x 720 pixels and a baseline of 12 cm. A total of 2700 images were captured in an underwater environment. Three distinct scenes clean, green and blue waters were used to study the network performance under different lighting and color conditions. For network training, the Mean Squared Error (MSE) was used as the loss function, and the L 2 norm was applied to the dense layers for 256 epochs. Additionally, 3D reconstruction of objects with known geometry in underwater environments was performed. The results showed that both networks, ZCalibAquaNet and BCalibAquaNet, provided better camera calibration and improved the measurement quality in underwater environments compared to Zhang and Bouquet models.

Peristaltic pumps represent a fundamental component of hemodialysis machines. They facilitate the transfer of fluids, particularly in the collection and treatment of blood. This study aims to improve pump precision and reliability by reducing steady-state error and optimizing flow consistency, measured in milliliters per minute. A detailed characterization established the relationship between revolutions per minute (RPM) and flow rate (mL/min), with redundant mass and volume measurements supporting accuracy. To model the system’s behavior, two non-linear functions and one linear function were compared, with the polynomial model proving the most accurate and revealing the pump’s inherently non-linear flow behavior. A proportional–integral (PI) controller was then applied, and optimized through step input and non-linear least squares fitting. A key aspect of this study is a comparative validation against a commercial hemodialysis machine, configured identically with the same blood circuit diameter, tubing brand, and filter, in order to ensure equivalency in conditions. Results showed a maximum flow rate error of 0.5296%, highlighting the integration of control and characterization methods that enhance system precision, dependability, and reproducibility—critical factors for ensuring the safety and effectiveness of hemodialysis treatments.

This study presents the design and evaluation of AquaSignalNet, a deep learning-based system for recognizing underwater visual commands to enable the autonomous navigation of a Remotely Operated Vehicle (ROV). The system is built on a ResNet50 V2 architecture and trained with a custom dataset, UVSRD, comprising 33,800 labeled images across 12 gesture classes, including directional commands, speed values, and vertical motion instructions. The model was deployed on a Raspberry Pi 4 integrated with a TIVA C microcontroller for real-time motor control, a PID-based depth control loop, and an MPU9250 sensor for orientation tracking. Experiments were conducted in a controlled pool environment using printed signal cards to define two autonomous trajectories. In the first trajectory, the system achieved 90% success, correctly interpreting a mixed sequence of turns, ascents, and speed changes. In the second, more complex trajectory, involving a rectangular inspection loop and multi-layer navigation, the system achieved 85% success, with failures mainly due to misclassification resulting from lighting variability near the water surface. Unlike conventional approaches that rely on QR codes or artificial markers, AquaSignalNet employs markerless visual cues, offering a flexible alternative for underwater inspection, exploration, and logistical operations. The results demonstrate the system’s viability for real-time gesture-based control.

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision.