I wanted a hard copy of an eBook I had that is out of print. There are many resources out there for binding books. Many recommend using acid free PVA glue. I can’t speak to how it compares to other glues, but “Aleene’s Tacky Glue” is a PVA glue, available acid free, which was available at craft stores in my area.

This post will focus on prepping an eBook for print. As US Letter is the common paper size here, which is too big for a book, I decided to print 4 pages per US letter page, 2 pages per side, each 5.5" wide by 8.5" tall.

I wanted to build my own vision model for a few reasons:

1. I wanted to learn how
2. In my limited experience with OpenALPR, it looked like it was missing some license plates that seemed fairly readable to my eyes - could I possibly do better training my own model?
3. Just the way it is built, I know I wouldn’t be able to get OpenALPR to run faster on my Pi - I wouldn’t be able to get it to run faster by off loading image processing to a VPU like the Myriad X in my Oak D camera.
4. The gen2-license-plate-recognition example provided by Luxonis, built from Intel’s Model Zoo, does not work well with Ontario license plates

The first step was building a library of images to train a model with. I sorted through hundreds of images I’d taken on rides in September, and selected 65 where the photos were clear, and there were license plates in the frame. As this was my first attempt, I wasn’t going to worry about sub-optimal situations (out of focus, low light, over exposed, etc…). I then had to annotate the images - draw boxes around the license plates in the photos, and “tag” them as plates. I looked at a couple tools - I started with Microsoft’s VoTT, but ended up using labelimg. Labelimg was efficient, with great keyboard shortcuts, and used the mouse scroll wheel to control zoom, which was great for labeling small details in larger photos.

One of the features I have in mind for my bicycle dashcam was license plate recognition. In parts 1, 2 and 3, I experimented with the OpenALPR license plate recognition library and a couple different Pi cameras. I encountered a few challenges:

• Image quality challenges: out-of-focus images, warped images due to the “rolling shutter” of the Pi camera
• Field of view: capturing more than just the license plate
• Speed: Only able to process 1 image every 8 seconds on my Pi 3

I acquired the Luxonis Oak-D AI accelerated camera to experiment with different image sensors which could potentially address my image quality challenges, stereo vision/depth sensing provided interesting capabilities, and the AI acceleration to increase the speed. This spring, I mounted it to my bike and started capturing images on my rides.

Ever since the Espressif’s ESP8266 wi-fi capable microcontroller was launched, I’ve been thinking about all the possibilities for low cost network connected devices. And, nothing world changing, but I have used it to build a data logging CO2 monitor and a device to control my old TV with Alexa.

I have been thinking of NEW possibilities as I see development boards with the ESP8266’s successor, the ESP32, with a small screen, for less than $20CAD shipped from Aliexpress. What can I build with a really tiny internet connected dashboard? So I ordered a Lilygo TTGO.

I continue to experiment with how a dashcam can assist urban cyclists. This time, I’ve started a fresh design with a different idea, a new camera, new models, and new code, which I’m submitting as an entry for the Toronto ♥️’s Bikes Make-a-Thon.

I enjoy biking from my home in North York, near Mel Lastman Square, to my office near Union Station during the week. The most harrowing part of this ride is the Yonge-401 interchange, which requires two lane changes with fast moving traffic from the 401 on and off ramp.

By Richard Audette, richard@hotelexistence.ca

Introduction

I first heard about measuring CO2 as a risk proxy for Covid-19 from a Radiolab podcast. Covid-19 can be transmitted through respiratory droplets produced during exhalation. CO2 is also exhaled. We can’t easily measure Covid-19 droplets in the air, but we can inexpensively measure CO2.

A CO2 measurement greater than atmospheric levels (400 ppm) is a sign of air that has recently been exhaled. If a person were infected with Covid-19, aerosols containing Covid-19 could be lingering in the air. Proper ventilation decreases the concentration of aerosols that may be suspended in the air, as well as the concentration of CO2. As such, measuring CO2 is potentially a useful proxy indicator for airborne infection risk. A “HIGH” CO2 reading doesn’t mean there are Covid-19 aerosols, but a “LOW” reading suggests a low probability of infectious aerosols.