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.

Audio Processing with The QuadMic 4-Microphone Array on the Raspberry Pi

The QuadMic Array is a 4-microphone array based around the AC108 quad-channel analog-to-digital converter (ADC) with Inter-IC Sound (I2S) audio output capable of interfacing with the Raspberry Pi. The QuadMic can be used for applications in voice detection and recognition, acoustic localization, noise control, and other applications in audio and acoustic analysis. The QuadMic will be connected to the header of a Raspberry Pi 4 and used to record simultaneous audio data from all four microphones. Some signal processing routines will be developed as part of an acoustic analysis with the four microphones. Algorithms will be introduced that approximate acoustic source directivity, which can help with understanding and characterizing noise sources, room and spatial geometries, and other aspects of acoustic systems. Python is also used for the analysis. Additionally, visualizations will aid in the understanding of the measurements and subsequent analyses conducts in this tutorial.

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

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

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.

Geographic Visualizations in Python with Cartopy

Cartopy is a cartographic Python library that was developed for applications in geographic data manipulation and visualization. It is the successor to the the Basemap Toolkit, which was the previous Python library used for geographic visualizations. Cartopy can be used to plot satellite data atop realistic maps, visualize city and country boundaries, track and predict movement based on geographic targeting, and a range of other applications relating to geographic-encoded data systems. In this tutorial, Anaconda 3 will be used to install Cartopy and related geographic libraries. As an introduction to the library and geographic visualizations, some simple tests will be conducted to ensure that the Cartopy library was successfully installed and is working properly. In subsequent tutorials: shapefiles will be used as boundaries, realistic city streets will be mapped, and satellite data will be analyzed.

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.

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’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.

Arduino + VL53L1X Time of Flight Distance Measurement

Time of flight (ToF) is an approximation of the time it takes a traveling wave to come in contact with a surface and reflect back to the source. Time of flight has applications in automotive obstacle detection, resolving geographic surface composition, and computer vision and human gesture recognition. In the application here, the VL53L1X ToF sensor will be used to track the displacement of a ping pong ball falling down a tube. We can predict the acceleration and behavior of a falling ping pong ball by balancing the forces acting on the ball, and ultimately compare the theory to the actual displacement tracked by the time of flight sensor.

How to Create a Rotating Globe Using Python and the Basemap Toolkit