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Solar Panel Characterization and Experiments with Arduino

Solar Panel Characterization and Experiments with Arduino

In this tutorial, the aim is to characterize a solar panel by varying the load at (near) peak solar insolation to identify the panel's nominal values such as open-circuit voltage, short-circuit current, max power voltage and current, and max power output. These values help users understand the expectations from a photovoltaic array and how their power needs may be met with a given PV system. An Arduino board will be used to log the current and voltage values outputted from a small solar panel. The current and voltage are measured using a 16-bit analog-to-digital converter power module, the INA226, which will allow us to track the power outputted from the photovoltaic panel. A potentiometer acting as a rheostat will serve as the varying load on the system, which will be used to identify the peak power points of the system. Finally, analyses will be conducted in Python 3, which will allow us to identify the peak power region and also the total power outputted over a duration of 24 hours.

Listening to Your Pipes with a MEMS Microphone and Raspberry Pi

Listening to Your Pipes with a MEMS Microphone and Raspberry Pi

A new type of water meter produced by Water Wise Controls (WaWiCo) introduces a novel method for water metering: non-invasive acoustic analysis. Their USB water metering kit allows users to listen to their pipes without the need for plumbing work. In this tutorial, the acoustic profile of a piping system will be explored using a Raspberry Pi computer, the Python programming language, and a WaWiCo USB water meter kit. The resulting analysis will allow users to identify the acoustic profile of their piping system and determine when water is flowing. This is the first of a series of entries into non-invasive water metering from WaWiCo, where open-source technologies will be used to characterize a piping system based on the acoustic profile of a user's home or apartment.

Distance Detection with the TF-Luna LiDAR and Raspberry Pi

Distance Detection with the TF-Luna LiDAR and Raspberry Pi

The TF-Luna is an 850nm Light Detection And Ranging (LiDAR) module developed by Benewake that uses the time-of-flight (ToF) principle to detect objects within the field of view of the sensor. The TF-Luna is capable of measuring objects 20cm - 8m away, depending on the ambient light conditions and surface reflectivity of the object(s) being measured. A vertical cavity surface emitting laser (VCSEL) is at the center of the TF-Luna, which is categorized as a Class 1 laser, making it very safe for nearly all applications [read about laser classification here]. The TF-Luna has a selectable sample rate from 1Hz - 250Hz, making it ideal for more rapid distance detection scenarios. In this tutorial, the TF-Luna is wired to a Raspberry Pi 4 computer via the mini UART serial port and powered using the 5V pin. Python will be used to configure and test the LiDAR module, with specific examples and use cases.

Calibration of a Magnetometer with Raspberry Pi

Calibration of a Magnetometer with Raspberry Pi

In this tutorial, methods for calibrating a magnetometer aboard the MPU9250 is explored using our Calibration Block. The magnetometer is calibrated by rotating the IMU 360° around each axis and calculating offsets for hard iron effects. Python is again used as the coding language on the Raspberry Pi computer in order to communicate and record data from the IMU via the I2C bus. The second half of this tutorial gives a full calibration routine for the IMU's accelerometer, gyroscope, and magnetometer. The final implementation will allow for moderate (first-order) calibration of the MPU9250 under reasonable conditions, requiring only the calibration block and IMU. Finally, the complete final code will save the coefficients for each sensor for future use in direct applications without the need for constant calibration. The use of the calibration coefficients will allow for improved estimates of orientation, displacement, vibration, and other relevant control and measurement analyses.

Gyroscope and Accelerometer Calibration with Raspberry Pi

Gyroscope and Accelerometer Calibration with Raspberry Pi

This is the second entry into the series entitled "Calibration of an Inertial Measurement Unit (IMU) with Raspberry Pi" where the gyroscope and accelerometer are calibrated using our Calibration Block. Python is used as the coding language on the Raspberry Pi to find the calibration coefficients for the two sensors. Validation methods are also used to integrate the IMU variables to test the calibration of each sensor. The gyroscope shows a fairly accurate response when calibrated and integrated, and found to be within a degree of the actual rotation test. The accelerometer was slightly less accurate, likely due to the double integration required to approximate displacement and the unbalanced table upon which the IMU was calibrated. Filtering methods are also introduced to smooth the accelerometer data for integration. The final sensor, the magnetometer (AK8963), will be calibration in the next iteration of this series.

Recording Stereo Audio on a Raspberry Pi

Recording Stereo Audio on a Raspberry Pi

The INMP441 MEMS microphone is used to record audio using a Raspberry Pi board through the inter-IC sound (I2S or I2S) bus. The I2S standard uses three wires to record data, keep track of timing (clock), and determine whether an input/output is in the left channel or right channel. First, the Raspberry Pi (RPi) needs to be prepped for I2S communication by creating/enabling an audio port in the RPi OS system. This audio port will then be used to communicate with MEMS microphones and consequently record stereo audio (one left channel, one right channel). Python iS then used to record the 2-channel audio via the pyaudio Python audio library. Finally, the audio data will be visualized and analyzed in Python with simple digital signal processing methods that include Fast Fourier Transforms (FFTs), noise subtraction, and frequency spectrum peak detection.

Capacitive Soil Moisture Sensor Calibration with Arduino

Capacitive Soil Moisture Sensor Calibration with Arduino

Soil moisture can be measured using a variety of different techniques: gravimetric, nuclear, electromagnetic, tensiometric, hygrometric, among others. The technique explored here uses a gravimetric technique to calibrate a capacitive-type electromagnetic soil moisture sensor. Capacitive soil moisture sensors exploit the dielectric contrast between water and soil, where dry soils have a relative permittivity between 2-6 and water has a value of roughly 80. Accurate measurement of soil water content is essential for applications in agronomy and botany - where the under- and over-watering of soil can result in ineffective or wasted resources. With water occupying up to 60% of certain soils by volume, depending on the specific porosity of the soil, calibration must be carried out in every environment to ensure accurate prediction of water content. Luckily, the accuracy of measurement devices has been increasing while the cost of the sensors have been decreasing. In this experiment, an Arduino board will be used to read the analog signal from the capacitive sensor, which will output voltage values which can be calibrated to volumetric soil moisture content via gravimetric methods.

Arduino, Data Analysis, Electronics, EngineeringJoshua HriskoJune 15, 2020Most Popular 2021, Most Popular 2020, Capacitive Soil Moisture, Soil Moisture Sensor, Arduino, Engineer, Engineering, Soil, Moisture, Sensor, Agronomy, Capacitive Soil Moisture Sensor Comments

High Resolution Thermal Camera with Raspberry Pi and MLX90640

High Resolution Thermal Camera with Raspberry Pi and MLX90640

Thermal cameras are similar to standard cameras in that they use light to record images. The most significant distinction is that thermal cameras detect and filter light such that only the infrared region of the electromagnetic spectrum is recorded, not the visible region [read more about infrared cameras here]. Shortly after the discovery of the relationship between radiation and the heat given off by black bodies, infrared detectors were patented as a way to predict temperature via non-contact instrumentation. In recent decades, as integrated circuits shrink in size, infrared detectors have become commonplace in applications of non-destructive testing, medical device technology, and motion detection of heated bodies. The sensor used here is the MLX90640 [datasheet], which is a 768 pixel (24x32) thermal camera. It uses an array of infrared detectors (and likely filters) to detect the radiation given off by objects. Along with a Raspberry Pi computer, the MLX90640 will be used to map and record fairly high-resolution temeperature maps. Using Python, we will be able to push the RPI to its limits by interpolating the MLX90640 to create a 3 frame-per-second (fps) thermal camera at 240x320 pixel resolution.

Force Sensitive Resistors (FSRs) with Arduino

Force Sensitive Resistors (FSRs) with Arduino

A force sensitive resistor (FSR) is comprised of a conductive polymer material pressed between two electrode layers, giving it the ability to electrically respond to changes in stress and strain. FSRs are often used in ergonomic or rehabilitation applications where pressure is applied from human interaction and the response is recorded or translated. Force sensitive resistors are incredibly useful for human interactivity because of their slim profile, inexpensive construction, and multiplicative geometries. The sensor used in this tutorial is the RP-S40-ST, which is a 40mm x 40mm thin film FSR. An Arduino board will be used to read the analog signals outputted by the FSR in a voltage divider configuration, where the force applied to the FSR can be approximated using the sensor’s calibration curve.

Visualizing COVID-19 Data in Python

Visualizing COVID-19 Data in Python

This blog post is aimed at creating meaningful visualizations that may or may not be available elsewhere, while instructing users on how to source, analyze, and visualize COVID-19 infection case and rate data using Python. All of the data used herein is publicly available for anyone interested in replicating the figures, with code and links where necessary. The methods used here have been uniquely conceived and developed by Maker Portal, and in no way reflect preferred methods of either the government or any other private entities. Several Python toolboxes will be implemented below, and it is recommended that users install and verify their functionality before attempting to replicate the forthcoming figures.

Radar Emulator with Arduino + Python

Radar Emulator with Arduino + Python

In this tutorial, an ultrasonic sensor (HC-SR04) will be used in place of a radio emitter; and a plan position indicator will be constructed in Python by recording the angular movements of a servo motor. An Arduino board will both record the ranging data from the ultrasonic sensor while also controlling and outputting the angular position of the servo motor. This will permit the creation of a PPI for visualizing the position of various objects surrounding the radar system.

Google Trends x Yahoo Finance Analysis in Python

Google Trends x Yahoo Finance Analysis in Python

Python has a multitude of libraries dedicated to scraping the internet in various ways. For example, Google Trends is a product produced by Google that analyzes search history and publishes the popularity of search terms over time. One user created an algorithm to pull trend data from Google using Python in a package called pytrends. Another such library uses Python to pull stock information from Yahoo Stocks in a package called yfinance. Both of these libraries will be used to plot and compare finance and trend data over time using Python scripts. The methods outlined in this tutorial could be applied to areas in finance, data analytics, and data visualization in general.

Python FTP for Data Mining and Analysis

Python FTP for Data Mining and Analysis

Python’s file transfer protocol (FTP) library is used to parse weather station data from the publicly available automated surface observing system (ASOS) from the U.S.A.’s National Climatic Data Center (NCDC). Several programmatic tools available in Python are used to automate the parsing of weather data, as well as visualizing the resulting data.

Accelerometer, Gyroscope, and Magnetometer Analysis with Raspberry Pi Part I: Basic Readings

Accelerometer, Gyroscope, and Magnetometer Analysis with Raspberry Pi Part I: Basic Readings

A Raspberry Pi will be used to read the MPU9250 3-axis acceleration, 3-axis angular rotation speed, and 3-axis magnetic flux (MPU9250 product page can be found here). The output and limitations of the MPU9250 will be explored, which will help define the limitations of applications for each sensor. This is only the first entry into the MPU9250 IMU series, where in the breadth of the articles we will apply advanced techniques in Python to analyze each of the 9-axes of the IMU and develop real-world applications for the sensor, which may be useful to engineers interested in vibration analysis, navigation, vehicle control, and many other areas.

Arduino GPS Tracker

Arduino GPS Tracker

The NEO-6 is a miniature GPS module designed by u-blox to receive updates from up to 22 satellite on 50 different channels that use trilateration to approximate fixed position of a receiver device every second (or less, for some modules). The particular module used in this tutorial, the NEO-6M, is capable of updating its position every second and communicates with an Arduino board using UART serial communication. The NEO-6M uses the National Marine Electronics Association (NMEA) protocol which provides temporal and geolocation information such as Greenwich Mean Time (GMT), latitude, longitude, altitude, and approximate course speed. The NEO-6M and Arduino board will also be paired with an SD module to create a portable logger that acts as a retrievable GPS tracker.

MPU6050 Arduino High-Frequency Accelerometer and Gyroscope Data Saver

MPU6050 Arduino High-Frequency Accelerometer and Gyroscope Data Saver

The MPU6050 is a 6-DoF (degree of freedom) accelerometer and gyroscope that is designed for inexpensive, small-scale, and efficient approximation of motion. Accelerometers and gyroscopes are used in smart phones for orientation detection, vibration analysis in vehicles and machines, and even camera stabilization and motion tracking. There are countless applications for accelerometers and gyroscopes, and with devices as accessible as the MPU6050, we can really test the limits of the technology.

Infrared Thermometry Theory and Applications with Arduino and Python

Infrared Thermometry Theory and Applications with Arduino and Python

In this tutorial, I will explore black body radiation, infrared detectors, and the relationship between temperature and emissivity - all with the intention of exploring how infrared (IR) detectors measure temperature from a distance. Arduino will be used, along with an MLX90614 IR thermometer, and a thermocouple for true-temperature approximation of each object. Planck’s discovery of energy quanta and their relationship to thermodynamics is the basis for radiation detectors and infrared temperature sensors. We will use Planck’s law to derive a usable equation that can relate the radiation measured by an infrared sensor to the temperature of a radiative object.

Satellite Imagery Analysis in Python Part III: Land Surface Temperature and The National Land Cover Database (NLCD)

Satellite Imagery Analysis in Python Part III: Land Surface Temperature and The National Land Cover Database (NLCD)

The third entry of the satellite imagery analysis in Python uses land surface temperature (LST) as the data variable along with land cover information from the national (U.S.) database. The land cover information will allow us to create a relationship between land cover type and its respective heating (or cooling) contribution to the earth’s surface. Land cover is used in many applications ranging from algorithm development to military applications and crop surveying, not to mention applications in water management and drought awareness.

Satellite Imagery Analysis in Python Part II: GOES-16 Land Surface Temperature (LST) Manipulation

Satellite Imagery Analysis in Python Part II: GOES-16 Land Surface Temperature (LST) Manipulation

For part II, the focus shifts from the introduction of file formats and libraries to the geospatial analysis of satellite images. Python will again be used, along with many of its libraries. Land Surface Temperature will again be used as the data information, along with shapefiles used for geometric boundary setting, as well as information about buildings and land cover produced by local governments - all of which are used in meteorological and weather research and analyses.

MATLAB Datalogger with Arduino

MATLAB Datalogger with Arduino

In this tutorial, MATLAB is introduced as an interface for data acquisition with an Arduino board. The Arduino, in this particular case, will communicate with a Windows computer via the serial port and send data from an Arduino-compatible sensor, which will subsequently be read by MATLAB through its serial communication library. Serial communication from hardware to MATLAB is very simple and requires only a few lines of code. I will also introduce a real-time analysis and plotting routine to visualize the Arduino data as it arrives in real time. This particular method of data analysis and visualization in real time is incredibly useful for engineers interested in experimentation where microcontrollers and sensors may be used, along with complex data acquisition systems.