This paper presents the evaluation of the performance, in terms of uncertainty, of a tool designed to estimate the main parameters of a model of a photovoltaic panel (PVP) under real and/or simulated working conditions. The presented tool permits the characterization of the panel, and it is useful to predict its behavior in whatever working condition; in this way, it is possible to compare the actual and expected performance to prevent any decrease in the output power, so permitting the replacement of the monitored module before it goes out of order or its efficiency falls under a given threshold. The well-known two-diode model is used to estimate the parameters of the electrical equivalent circuit of the PVP and to simulate the I -V and P-V characteristic curves in any given environmental condition of irradiance and/or temperature. The model and the estimation algorithm are implemented with MATLAB functions, whereas data acquisition and result presentation are managed by a LabVIEW graphics user interface. The presented tool has been validated against an experimentally characterized PVP. The environmental parameters of the model such as irradiance and temperature have been set (with their respective uncertainties) during simulations or directly measured during the outdoor tests, whereas the others parameters have been evaluated using a best-fit algorithm on the measured data. The estimation is based on the minimization of a new objective function and on a modified expression of the model resistances, which differ from those mentioned in the available literature. After a review of the state of the art, this paper provides the description of the estimation technique and its validation by means of simulations and experiments. Some results are also provided to illustrate the performance of the proposed test method.

In this editorial, an overview of the content of the Special Issue on “Wireless Sensor Networks for Environmental Monitoring” is provided

This paper describes a theoretical approach to evaluate the uncertainty on the series and shunt resistances estimated by the seven-parameter (double diode) model of a photovoltaic (PV) cell using data commonly provided by panel manufacturers, measured environmental parameters, and semiempirical equations. After a brief survey on the state of the art and the treatment of the double-diode model, the procedure proposed by the authors, to estimate the unknown parameters, is illustrated. The theoretical expression of the uncertainty, which affects the estimation of the series and shunt resistances (namely, and ) of a PV cell, is then derived. A statistical analysis performed by means of a Monte Carlo simulation is in agreement with the theoretical expression of the uncertainty.

The purpose of this paper is to assess the effect of different noise reduction filters on computed tomography (CT) images. In particular, denoising filters based on the combination of Gaussian and Prewitt operators and on anisotropic diffusion are proposed. Simulation results show that the proposed techniques increase the image quality and allow the use of a low-dose CT protocol.

This paper explores the use of deep learning-based computer vision for real-time monitoring of the flow in intravenous (IV) infusions. IV infusions are among the most common therapies in hospitalized patients and, given that both over-infusion and under-infusion can cause severe damages, monitoring the flow rate of the fluid being administered to patients is very important for their safety. The proposed system uses a camera to film the IV drip infusion kit and a deep learning-based algorithm to classify acquired frames into two different states: frames with a drop that has just begun to take shape and frames with a well-formed drop. The alternation of these two states is used to count drops and derive a measurement of the flow rate of the drip. The usage of a camera as sensing element makes the proposed system safe in medical environments and easier to be integrated into current health facilities. Experimental results are reported in the paper that confirm the accuracy of the system and its capability to produce real-time estimates. The proposed method can be therefore effectively adopted to implement IV infusion monitoring and control systems.