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.
Recently, I completed a mini project together with two of my friends. So I am going to take this opportunity to share the project that we have made, we named it the Bridge Monitoring System (BMS) using Wireless Sensor Network (WSN). We are required to design an embedded system that is related with disaster management, either mitigation, preparedness, response or rehabilitation. To give you a high level overview of this project, basically we created three sensor nodes that acquire sensor measurement and transmit to central hub through wireless network. The sensor network works in a many-to-one fashion and data processing is done on the central hub. All the sensor measurement from each node is also displayed on the Host PC for user interface. Therefore, in this article, I am going to walk through some details of the project and how it works.
You may recall the article I wrote a couple of years ago about a nearly identical Doppler sensor, the HB100.
While the HB100 is using a 10.525GHz frequency, this new module uses 24.125GHz! This has the main advantage of being compatible with European regulations (ETSI #300 400) and having good penetration in dry materials. Moreover, as the main frequency is higher the patch antennas are smaller, hence the tiny 25x25x6mm module.
This motion sensor can easily be purchased on eBay under the name CDM324. Oddly enough, looking for “cdm324” on your favorite search engine won’t bring any interesting results.
I therefore spent several hours tracing the origins of this tiny sensor. I finally arrived to the conclusion that it likely is a clone of the InnoSenT IPM 165, which is itself very similar to the AP96 from Agilsense.
After electing to use the PING))) sensor exactly as directed, I needed to build the rest of my circuit. I wanted to build something robust that would mount nicely on the wall of my dad’s garage. Figuring that the sensor would likely need to be placed down low by the car’s bumper, I decided on a two-component design consisting of a small sensor and a large visible display that could be mounted at eye-level.