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Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.

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Electrical treeing degradation is associated with partial discharge activity. Here we relate the growth of electrical trees with the correlation dimension of the reconstructed dynamic object obtained from the nonlinear time series of the partial discharges from the tree propagation, and with the box-counting fractal dimension of the resulting 3D structure of the trees. The growth of trees at 8, 10 and 12 kV has been analyzed; different PD dynamics were found, depending on both the voltage and the stage of growth. A lower fractal dimension is shown to be related to a lower correlation dimension. The results show that nonlinear time series analysis can be an alternative method of analyzing tree growth and associated partial discharges.

A novel three-dimensional interface using immersive augmented reality to perform real-time visual analysis of structural models is presented. The interface integrates and builds on the functionalities of two commercial tools: ‘Leonar3Do’, for visual inspection in a fully three-dimensional immersive environment and ‘SAP 2000’, for structural analysis and simulation. The resulting interface allows the user to visualize the structural design model in three-dimensions, apply forces/loads directly with a three-dimensional physical pointer to indicate their magnitudes and directions and meanwhile observe the behavior of the structure under this action in fully perceived three-dimension. It integrates traditional structural analysis software, three-dimensional viewing and immersive virtual reality environment. The interface facilitates understanding of the different interactions between the structural components, detection of possible structural design weaknesses and improvement of the structural model in order to quickly develop better virtual prototypes.

In collective sports, reactive agility training methodologies allow to evaluate and improve the player performance, being able to consider a mixture of technical, tactical, physical, and psychological abilities, similarly to real game-play situations. In this article, we present a new methodology for reactive agility training (neural training), the technological setup for the methodology, and a new footstep tracking algorithm, as the key element for automating the speed data gathering process, necessary for obtaining the relevant variables of the neural training approach. This new methodology is oriented to accurately measure two of the most relevant variables for reactive agility training: total response time (sprint time) and response correctness, related to a stimuli sequence presented to a player. The stimuli were designed to properly represent realistic competitive conditions for player training, contextualized to soccer. In order to automate the gathering process, a new computer vision based automatic footstep detection algorithm has been integrated to the system. The algorithm combines Kalman Filters, segmentation techniques, and perspective geometry, for obtaining highly precise detections of the moment a relevant footstep occurs in real-time, reaching a precision higher than 97%. Plus, the algorithm does not require any special marker, invasive sensor, or clothing constraint on the player.

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