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.

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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.

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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.

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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.

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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.

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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.

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Satellite Imagery Analysis in Python Part I: GOES-16 Data, netCDF Files, and The Basemap Toolkit

In this tutorial series, Python’s Basemap toolkit and several other libraries are utilized to explore the publicly-available Geostationary Operational Environmental Satellite-16 (GOES-16). In this first entry, the following will be introduced: acquisition of satellite data, understanding of satellite data files, mapping of geographic information in Python, and plotting satellite land surface temperature (LST) on a map.

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Arduino Heart Rate Monitor Using MAX30102 and Pulse Oximetry

Pulse oximetry monitors the oxygen saturation in blood by measuring the magnitude of reflected red and infrared light [read more about pulse oximetry here and here]. Pulse oximeteters can also approximate heart rate by analyzing the time series response of the reflected red and infrared light . The MAX30102 pulse oximeter is an Arduino-compatible and inexpensive sensor that permits calculation of heart rate using the method described above. In this tutorial, the MAX30102 sensor will be introduced along with several in-depth analyses of the red and infrared reflection data that will be used to calculate parameters such as heart rate and oxygen saturation in blood.

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DIY Laser Pointer Cat Toy

This is a quick and fun tutorial on how to create an inexpensive laser pointer cat toy using DIY parts either found around the maker space or bought for other projects. It is an inexpensive and fun project that almost any maker or engineer could put together in a matter of minutes. It uses a cheap laser that can be found in many electronics kits (I’m using a red, 650 nm), a small button, and a 3.7V LiPo battery.

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SAMD21 M0 Mini Arduino Board

In this tutorial, the SAMD21 M0 Mini is introduced, which is a variation of the Arduino Zero (SAMD21 48MHz). The SAMD21 board will be tested specifically in its speed and compatibility with several Arduino libraries. Particularly, the SAMD21 is the most powerful when harnessing its speed, but also in other areas such as analog to digital conversion. The SAMD21 core is a 32-bit microcontroller that will likely replace the traditional ATmega328 (8-bit microcontroller) over time. The SAMD21 core boasts 48MHz clock speeds in contrast to the 20MHz ATmega boards, while also being fully-compatible with many of the capabilities of the Arduino platform.

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WeMos D1 Mini ESP8266 Arduino WiFi Board

The WeMos D1 Mini is an inexpensive ESP8266-based WiFi board that is low-profile but just as powerful as any NodeMCU or ESP8266-based microcontroller. The D1 Mini is incredibly versatile because it is inexpensive, WiFi-enabled, and fully compatible with the Arduino platform. In this tutorial, the ESP8266 library and board manager will be introduced in order to get the D1 Mini acting as an Arduino board. Then, a simple web page will be introduced with the intention of harnessing the WiFi capabilities of the module. The D1 Mini will act as a web server, allowing any WiFi-connected device to interact with the board and control its pins wirelessly.

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Arduino Optical Fingerprint Sensor (AS608)

Optical fingerprint sensors take low-resolution snapshots of the tip of a finger and create arrays of identifiers that are then used to uniquely identify a given fingerprint. The AS608 is capable of storing up to 128 individual fingerprints. This tutorial will introduce the AS608 Arduino-compatible fingerprint sensor and how to validate and reject fingerprints based on the enrolled fingerprint information that will be given to the sensor. The fingerprint algorithm is handled by the AS608 and Arduino, so this tutorial will focus on implementation and putting the pieces together to make a working fingerprint sensor with Arduino.

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Arduino Interrupts with PIR Motion Sensor

The basics of Arduino’s hardware interrupt is explored through the use of a passive infrared (PIR) sensor. The passive infrared sensors used here operate at voltages from 2.7V - 5V and use very little energy when operating in the non-tripped state. The PIR sensor is ultimately tripped by an infrared source, typically human body heat (or another animal with similar radiative emission). When the PIR sensor is tripped it sends a HIGH signal to its OUT pin, which will be read by the Arduino’s interrupt pin (pin 2 or 3 on the Uno board). This process seems trivial, but when done correctly can save massive amounts of energy when dealing with battery-powered systems, as in home automation.

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Smartphone Arduino Weighing Scale with Load Cell and HX711

In this tutorial, I introduce an Arduino-based weighing scale that uses a load cell, analog-to-digital converter, and calibrated mass. I introduce calibration with known masses to create a powerful and accurate weighing system that can be used for highly accurate measurement purpose such as: chemistry, horticulture, cooking, and much more!

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Arduino Weighing Scale with Load Cell and HX711

In this tutorial, I introduce an Arduino-based weighing scale that uses a load cell, analog-to-digital converter, and calibrated mass. I introduce calibration with known masses to create a powerful and accurate weighing system that can be used for highly accurate measurement purpose such as: chemistry, horticulture, cooking, and much more!

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Multiple Object Detection with Python and Raspberry Pi

The picamera and edge detection routines will be used to identify individual objects, predict each object’s color, and approximate each object’s orientation (rotation). By the end of the tutorial, the user will be capable of dividing an image into multiple objects, determining the rotation of the object, and drawing a box around the subsequent object.

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Image Processing Object Detection with Raspberry Pi and Python

In this entry, image processing-specific Python toolboxes are explored and applied to object detection to create algorithms that identify multiple objects and approximate their location in the frame using the picamera and Raspberry Pi. The methods used in this tutorial cover edge detection algorithms as well as some simple machine learning algorithms that allow us to identify individual objects in a frame.

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Image Processing with Raspberry Pi and Python

The Raspberry Pi has a dedicated camera input port that allows users to record HD video and high-resolution photos. Using Python and specific libraries written for the Pi, users can create tools that take photos and video, and analyze them in real-time or save them for later processing. In this tutorial, I will use the 5MP picamera v1.3 to take photos and analyze them with Python and an Pi Zero W. This creates a self-contained system that could work as an item identification tool, security system, or other image processing application. The goal is to establish the basics of recording video and images onto the Pi, and using Python and statistics to analyze those images.

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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.

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Arduino SD Card Module Data Logger

This tutorial will explore the range of capabilities available to the Arduino SD library by using a real-world example of data logging. The SD library allows users to read/write, list files, create/remove files, and make/delete directories. Additionally, we will develop an algorithm that creates a new file every time the Arduino board is restarted, which will prevent overwriting of existing data records. The resulting data file will be in comma separated format and contain multiple data points, including a time stamp in milliseconds since the program started. Therefore, it is important to record the program start time. For very accurate time monitoring tasks, a real-time clock is recommended, however, for the experiments conducted here, relative time suffices.

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