We initially developed this USB atmospheric pressure monitor to study some operating characteristics of Bosch BMP180 sensor. BMP180 is low cost sensor to measuring barometric pressure and temperature. According to the data sheet this sensor can use to measure pressure ranging between 300hPa to 1100hPa. This sensor is introduced couple of years back but still it is popular due to lower cost and simplicity of it’s interface.
First of all, what is calibration? In a general sense, calibrating a sensor makes the sensor provide the most accurate readings allowed by the sensor’s own precision. As an example, let’s assume for a moment that the earth’s magnetic field and any other stray magnetic fields are shielded and you have a uniform magnetic field generated artificially for the sole purpose of calibration. Let’s say that the field strength is 400 mG (milliGauss), equivalent to 40,000 nT (nanoTesla). Now if you align one axis of your magnetic sensor parallel to the direction of the field, it should read 400mG. If you then carefully rotate your sensor so that the axis is anti-parallel with your field, it will read -400mG. If you didn’t do a good job in either alignments, you will read less values, say 390mG, if you’re off by about 13 degrees, because only a portion of the field, which is a vector, is projected along your magnetic sensor’s axis.
Particle sensors could be cheap and easy to use. Disadvantage of lowest cost PM sensors is lack of “calibration”. The best method to measure particle content dispensed in the air is to collect the air sample and analyse it off-line in the laboratory with proper equipment (not cheap at all). Optical particle counting sensors use the light scattering method to detect and count particles in the operating concentration range in a given environment. A laser light source illuminates a particle as it is pulled through the detection chamber. As particles pass through the laser beam, the light source becomes obscured and is recorded on the photo or light detector. The light is then analyzed and converted to an electrical signal providing particulate size and quantity to predict concentrations in real time.
A how-to on making a Dual-sensor ultrasonic echo locator by lingib, project instructables here:
This instructable explains how to pinpoint the location of an object using an Arduino, two ultrasonic sensors, and Heron’s formula for triangles. There are no moving parts.
Heron’s formula allows you to calculate the area of any triangle for which all sides are known. Once you know the area of a triangle, you are then able to calculate the position of a single object (relative to a known baseline) using trigonometry and Pythagoras.
The accuracy is excellent. Large detection areas are possible using commonly available HC-SR04, or HY-SRF05, ultrasonic sensors.
Construction is simple … all you require is a sharp knife, two drills, a soldering iron, and a wood saw.
The described device is nearly matchbox-sized board (50 x 24 mm) packed with sensors. Auxilary board is 10x50mm with additional sensors. The module is developed around the Murata ABZ LoRa module, which integrates STM32L072 and samtech SX1276 in tiny 12.5 x 11.6 x 1.76 mm package.
A while ago I added the hall effect encoder IC I’ve been using directly to the motor controller PCB. The controller sits directly on the back of the motor (with a magnet added to the motor shaft), and the phase wires solder straight in. I also have a pair of board-mounted XT30 connectors on the DC bus for easy daisy-chaining. Otherwise, the board is basically identical to the previous version of this controller. I’ve now built over a dozen of these, and have had no problems.
Like a million other people on the Internet, I’ve been experimenting with home weather logging. I roll my eyes at the phrase “Internet of Things”, but it’s hard to deny the potential of cheap networked sensors and switches, and a weather logging system is like this field’s Hello World application. Back in June I posted about my initial experiments in ESP8266 weather logging. Since then I’ve finalized the hardware setup, installed multiple nodes around the house, organized a nice web page to analyze all the data, and integrated everything with Amazon Alexa. Time for an update.
It detects humidity through two sensors which are to be used alternatively to let us know when there is water on the ground because it’s raining, or when the water level in a flowerpot is too low and it needs watering.
We have taken a look at the MQ sersor in this post here.
As I said those sensor are electro-chemical. Accuracy of those sensor is not the best. Also they will react to many gases. It means that if you are trying to measure the ppm of a certain gas with this sensor, you will have false measurement values if any of the other gas that the sensor react to, changes.
Here I will “overengeneer” on this type of sensor, trying to correlate the MQ sensor readings to temperature and humidity too, even if this correlation to me is not prominent. The correlation formula I’ve found may be wrong, so let me know if there is something to fix here.
By projecting encoded visible patterns onto an object’s surface (e.g. paper, display, or table), and localizing the user’s fingers with light sensors, Lift offers users a richer interactive space than the device’s existing interfaces. Additionally, everyday objects can be augmented by attaching sensor units onto their surface to accept multi-touch gesture input. We also present two applications as a proof of concept. Finally, results from our experiments indicate that Lift can localize ten fingers simultaneously with accuracy of 0.9 mm and 1.8 mm on two axes respectively and an average refresh rate of 84 Hz with 16.7ms delay on WiFi and 12ms delay on serial, making gesture recognition on noninstrumented objects possible.