Emulation of Circuits under Test Using Low-Cost Embedded Platforms

Electrical engineering education requires the development of the specific ability and skills to address the design and assembly of practical electronic circuits, as well as the use of advanced electronic instrumentation. However, for electronic instrumentation courses or any other related specialty that pursues to gain expertise testing a physical system, the circuit assembly process itself can represent a bottleneck in a practical session. The time dedicated to the circuit assembly is subtracted both to the measurements and the final decision-making time. Therefore, the student's practical experience is limited. This article presents a reconfigurable physical system based on the Arduino (TM) shield pin-out, which (after specific programming) can virtually behave as a device under test to carry out measurement procedures on it, emulating any system or process. Although it has been mainly oriented to the Arduino boards, it is possible to add different control devices with a connector compatible. The user does not need to assemble any circuit. Our approach does not only pursue the correct instrument handling as a goal, but it also immerses the student in the context of the functional theory of the proposed circuit under test. Consequently, the same emulation platform can be utilized for other techno-scientific specialties, such as electrical engineering, automatic control systems or physics courses. Besides that, it is a compact product that can be adapted to the needs of any teaching institution.This work was performed as an innovation and teaching improvement project and supported by grant SOL-201700083174-TRA from Vicerrectorado de Recursos Docentes y de la Comunicacion, University of Cadiz

Design of an FPGA Based High-Speed Data Acquisition System for Frequency Scanning Interferometry Long Range Measurement

Frequency Scanning Interferometry (FSI) has become a popular method for long-range, target based, distance measurements. However, the cost of developing such systems, particularly the electronic components required for high-speed data acquisition, remains a significant concern. In this paper, we present a cost-effective, FPGA-based real-time data acquisition system specifically designed for FSI, with a focus on long absolute distance measurements. Our design minimizes the use of third-party intellectual property (IP) and is fully compatible with the Xilinx FPGA 7 series families. The hardware employs a 160 MS/s, 16-bit dual-channel ADC interfaced to the FPGA via a Low Voltage Differential Signal (LVDS). The proposed system incorporates an external sampling clock, referred to as the K- clock, which linearizes the laser's tuning rate, enabling optical measurements to be sampled at equal optical frequency intervals rather than equal time intervals. Additionally, we present the design of a high-speed, 160 MS/s ADC module for the front-end analogue signal interface and the LVDS connection to the chosen FPGA. We demonstrate that the digitized data samples can be efficiently transmitted to a PC application via a USB interface for further processing

FPGA-Based Urinalysis Using Principal Component Analysis

Urinalysis is considered to be a common test performed in laboratory in order to diagnose Urinary Tract Infection (UTI). It undergoes three stages, which include macroscopic, dipstick, and microscopic analysis. This paper describes a way of performing urinalysis for UTI detection using the Principal Component Analysis (PCA) implemented using a Field Programmable Gate Array (FPGA). Input to the system is from five ion-selective sensors that measure five different components specifically sodium, nitrite, nitrate, potassium, and pH level of a urine sample. Tests show that the system obtained an accuracy of 94.13% for standard urinalysis showing the accuracy of sensors and measurements used. To be able to detect the presence of UTI in urines, an outlier detection method Principal Component Analysis (PCA), was used. PCA is a tool used in reducing multidimensional data to lesser dimensions while keeping all the information. An accuracy of 83.33% in detecting UTI infection was achieved. The accuracy of FPGA implementation of PCA was compared with MATLAB calculation results and an accuracy of 99.917% was achieved

Design and Development of Intelligent Navigation Control Systems for Autonomous Robots that Uses Neural Networks and Fuzzy Logic Techniques and Fpga For Its Implementation

This research compares the behavior of three robot navigation controllers namely: PID, Artificial Neural Networks (ANN), and Fuzzy Logic (FL), that are used to control the same autonomous mobile robot platform navigating a real unknown indoor environment that contains simple geometric-shaped static objects to reach a goal in an unspecified location. In particular, the study presents and compares the design, simulation, hardware implementation, and testing of these controllers. The first controller is a traditional linear PID controller, and the other two are intelligent non-linear controllers, one using Artificial Neural Networks and the other using Fuzzy Logic Techniques. Each controller is simulated first in MATLAB® using the Simulink Toolbox. Later the controllers are implemented using Quartus ll® software and finally the hardware design of each controller is implemented and downloaded to a Field-Programmable Gate Array (FPGA) card which is mounted onto the mobile robot platform. The response of each controller was tested in the same physical testing environment using a maze that the robot should navigate avoiding obstacles and reaching the desired goal. To evaluate the controllers\u27 behavior each trial run is graded with a standardized rubric based on the controllers\u27 ability to react to situations presented within the trial run. The results of both the MATLAB® simulation and FPGA implementation show the two intelligent controllers, ANN and FL, outperformed the PID controller. The ANN controller was marginally superior to the FL controller in overall navigation and intelligence