This paper introduces new algorithms designed specifically for smart tomato harvesting, which combines predictions from two independently trained YOLOv8 (You Only Look Once version 8) models, each specialized on different datasets to detect tomatoes and classify their ripeness stages—ripe, unripe, and semi-ripe—while also computing their center points. To enhance detection accuracy under varying field conditions, multiple fusion strategies were developed, including a hybrid algorithm that integrates union and confidence-weighted summation methods. The Hybrid Algorithm achieved the best performance, surpassing the original models and other fusion techniques. It attained F1 scores of 0.697 and 0.694 at confidence thresholds of 0.5 and 0.9, respectively, compared to the original models' F1 scores of 0.670 and 0.648 at confidence 0.5, and 0.481 and 0.497 at confidence 0.9. This corresponds to an improvement of 4.0 % and 7.6 % at confidence 0.5, and 44.3 % and 39.6 % at confidence 0.9, demonstrating the hybrid algorithm's stability and superiority, particularly at higher thresholds. Furthermore, the system utilizes a high-resolution RGB (Red, Green, Blue) camera for real-time image capture, enhancing model performance in complex agricultural environments. This study validates the effectiveness of confidence-based fusion in developing robust and accurate vision systems for precision agriculture.

This paper proposes the experimental proof of concept of a new topology of the Common-Rail injection system for diesel engines. The main difference with respect to the traditional Common-Rail system consists in the introduction of a Continuously Variable Transmission (CVT) between the engine and the high-pressure pump. This approach helps reducing the high-pressure pump speed and, consequently, the cuts energy consumption of the system. The proof of concept is designed by adopting a CVT available on the market. The experimental results are encouraging and promising for future developments of the proposed approach.

This paper proposes a new topology of the Common-Rail injection system. This injection system has been the milestone for the success of the diesel engines in the automotive market for over three decades. The innovative aspect of the new topology consists in the introduction of a Continuously Variable Transmission (CVT) between the engine and the high-pressure pump. This technical solution aims at reducing the speed of the high-pressure pump and, consequently, the energy consumption of the system. Moreover, a control strategy is designed to regulate the common-rail pressure by adjusting the CVT ratio and the Fuel Metering Unit outflow. Simulation results are encouraging and promising for future developments of the proposed approach.

In this paper, we present a diesel engine common-rail pressure controller using a Model Reference Adaptive Control (MRAC) approach. The proposed control strategy is designed using the only common rail pressure differential equation and considering all the measurable system parameter variation (e.g the high pressure pump speed). The resulting control strategy looks simple and allows a fast and precise regulation of the common-rail pressure. The tracking performances are compared with those obtained using a standard PID controller that represents the state of art in commercial applications.

A frame of modular design problems and requirements for underactuated mechanisms is discussed as related to robotic fingers. The proposed modular mechanism is connected sequentially by series units of underactuated mechanisms, which have the feature of passive self-adaptive in grasp operation and uniformizable in design procedure. The design considerations for modular underactuated mechanism are outlined. Optimality criteria are analyzed with the aim to formulate a general design algorithm. An example of a four-phalanx modular robotic finger is studied as an improvement of new version LARM Hand with the aim to show the practical feasibility for the proposed modular concepts and design methods.

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In this paper a humanoid robot named as WABIAN-RIV (WAseda BIpedal humANoid Refined IV) is analyzed in terms of stiffness characteristics. This paper proposes basic models and a formulation in order to deduce the stiffness matrix as a function of the most important stiffness parameters of the WABIAN architecture. The proposed formulation is useful for numerical estimation of stiffness performances.