Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation.
This article presents the development of a low-cost control hardware in the loop platform for the validation and analysis of controllers used for the management of power sharing between the main grid and a DC microgrid. The platform is made up of two parts: a main grid interconnection system emulator (MGISE) and a controller under test (CUT). The MGISE operates on a 260 V DC bus and includes a 1000 W photovoltaic array, a DC variable load and a single H full bridge converter (HFBC). The CUT includes a phase locked loop and a main cascade control structure composed of two PI controllers. Both the MGISE and the CUT were embedded on an NI myRIO-1900 development board and programmed using LabVIEW virtual instrumentation software. These devices communicate with each other using analog signals representing the AC side current, the DC side voltage, and the HFBC control signal. The MGISE operates with an integration time of 6 µs and its performance is validated by comparing it with a simulation in PSIM. The integration time of the MGISE, the development boards used, as well as its programming environment, and the results obtained from the comparison with PSIM simulation, show that the proposed platform is useful for the validation of controllers for power sharing, with a simple implementation process compared to other hardware description methods and with a low-cost platform.
This paper presents the development of a virtual didactic tool for students of mechatronic engineering taking an intelligent system course. The objective of the tool is for students to learn the structures for fuzzy control systems. This tool makes it easier for students to understand the behavior of the membership functions of input and output variables, the evaluation of the set of fuzzy rules, and the method of defuzzification, giving the students the possibility of applying a fuzzy controller in industrial processes using a data acquisition board. The proposed tool was developed with the virtual instrumentation software LabVIEW. It has the advantage that students can manipulate the internal structure of the fuzzy logic control system in a unique window where students can analyze the behavior of internal signals by looking at the response graphs. The fuzzy controller can be easily translated to a real application by using LabVIEW compatible hardware. To have feedback from students on the use of the tool and to understand if this tool allows an improvement in their academic performance, a 2-hour workshop on the proposed application was given to a group of 93 students. At the end of the workshop, a knowledge assessment and a perception survey were applied to the participants. The academic performance achieved by students who were given the workshop using the proposed teaching tool was compared with the academic performance of students who witnessed the workshop using Matlab tools. The statistical analysis of the results obtained for the knowledge assessment shows that the students that had taken the workshop using the proposed teaching tool had better compression of the topic compared to the students that had taken the workshop using the Fuzzy Logic Toolbox provided by Matlab MathWorks. The students that had taken the workshop using the proposed teaching tool obtained a mean grade of 89.63/100, while students that had taken the workshop using Matlab’s tools obtained a mean grade of 69.85/100. Also, the students’ perception of the proposed tool was that it allowed the design of fuzzy control systems in a simple and intuitive way.
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