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• Low power firmware

  Jan Říha • 08/28/2023 at 08:12 • 0 comments

  In this latest update, I'm excited to introduce a new firmware that prioritizes energy efficiency. It leverages the fact that the TinyML board has a neural network accelerator (NDP101) and host processor (ATSAMD21G18) as two separate devices. The NDP101 can process the input audio with extremely low power consumption. While NDP101 is processing the audio, the host MCU can be in sleep mode until the moment the NDP101 recognizes the desired sound pattern. If this happens, the NDP101 wakes up the host MCU via a dedicated interrupt pin. As the host MCU takes the largest portion of energy, this approach will dramatically reduce total power consumption.

  Before the low-power mode was implemented the total current draw was about 15mA. It's now been impressively reduced to under 2mA ! This current is low enough to power the whole device with a small solar panel (keep the battery charged). FW is available on GitHub – Please follow the readme file for further instructions. You can also watch the demonstration video on YT. 

• Road tests

  Jan Říha • 08/23/2023 at 20:35 • 0 comments

  After updating the ML model, I conducted more comprehensive road tests. I captured some compelling footage that demonstrates how the sensor performs under real-world conditions. Particularly, the first scenario is a major concern for deaf drivers.

• ML model update

  Jan Říha • 08/23/2023 at 17:37 • 0 comments

  Today, I want to provide an update regarding the ML model which I have updated. First of all, I will describe an issue with the old model. During the testing I noticed, that detection sensitivity is very poor when I drive a car at moderate/high speed. After analyzing this problem I have found, that this is caused by additional low-frequency noise generated by the car (engine, tire rolling, etc.), that masks the sound of sirens. 

  First I wanted to implement any kind of high-frequency/band-pass filter to analyze and evaluate just the frequency band where a siren typically emits a sound. But I found, that there is no easy way to make it with TinyML board or Edge Impulse studio.

  After this, I decided to create a new ML model that will "learn", that low-frequency noise in a car cabin together with siren sound should result in positive class detection. So I turned my current prototype into a sound recorder and recorded several hours of driving sound. After this, I mixed this with already existing siren sounds.

  I also used a new dataset for siren sounds: sireNNet. This Dataset seems to have better quality as the recordings are cleaner and do not contain any other (often very strange) type of sirens, that were present in the previous dataset. Even though this dataset is much smaller, the observed result in the real driving conditions was much better.

  For a better understanding of how I created the current dataset, please see the block diagram “Dataset_8_2023_diagram.pdf” which I share together with other files. I believe that this might be useful also for other people when trying to build any ML model that should run in a real environment.

  In the block diagram, you'll see that I use varying amplification levels for certain classes. I've discovered that this greatly helps the model in achieving a higher level of abstraction when recognizing sounds. Without this adjustment, the model frequently identified different classes (sounds) based on their average sound level rather than their frequency content.

  My current dataset is again shared on the Edge Impulse Platform here:

  https://studio.edgeimpulse.com/studio/267982

• Li-pol battery safety

  Jan Říha • 08/21/2023 at 19:57 • 0 comments

  Despite the longer inactivity on my project page I have been still actively working on the project. One of the issues that I've been trying to figure out is the Li-pol battery safety concern.

  It's well-known that temperatures inside a car can soar, especially during the summer. Naturally, this raises concerns about whether the battery inside a sensor could overheat, leading to potential fires or explosions.
  

  After delving into the subject, here's what I've found: Standard lithium-polymer (Li-Po) batteries typically have a temperature range of up to 60°C for discharging and up to 45°C for charging. It's not hard to imagine a car's interior exceeding these temperatures. However, even if the temperature goes above these limits, a Li-Po cell remains stable. It's only when you hit the 130-150°C range that the cell becomes thermally unstable, which could result in a thermal runaway where flaming gases are released. But, realistically, reaching such extreme temperatures inside a car is quite improbable. The manufacturer's temperature guidelines seem to be more about minimizing cell degradation than they are about safety.

  Nevertheless, in the interest of maximizing safety, I looked into high-temperature batteries. Although there aren't many options on the market, I did discover that Zoncell produces high-temperature cells. These have a temperature range that extends up to 80°C for discharging (and 45°C for charging). Some might point out that 45°C as a charging limit might not be sufficient, especially since I plan to incorporate solar charging. It's worth noting that the lower charging temperature limit is primarily because of self-heating caused by the charging current. This is especially true for charging currents close to 1C or higher, where the heating effect is significant. However, based on my recent tests, solar panels can only deliver a maximum of 30mA, which is less than 0.1C. Such a low charging current won't result in substantial self-heating, so I'm confident that the true temperature limit is much closer to the 80°C discharge limit.

  So I ordered a few samples of Zoncell LP602530T (datasheet enclosed in shared files) and will consider their further use in the PionEar sensor.
  

• Solar charging test

  Jan Říha • 05/25/2023 at 18:47 • 0 comments

  In the next iteration, I would like to integrate a solar panel into the PionEar sensor to avoid the need for charging. I did some research for suitable low-power solar charger modules with MPTT functionality. I discovered AMELION from @Jasper Sikken. In combination with IXYS SLMD960H09L solar panel, it seemed to be a good starting point. AMELION module is a great solution for low-power applications. It can still provide some current even in the indoor environment. I could measure the following charging currents:

  Indoor: 20 to 50 uA

  Outdoor (cloudy/shadow): 2 to 5 mA

  Outdoor sunlight: 25 to 30 mA

  These numbers seem to be promising. I think that behind the car's front window, there should be plenty of light to keep the battery charged - unless you park in the garage.

• Test in the car

  Jan Říha • 05/23/2023 at 21:10 • 0 comments

  Usually, as a first test for audio event detection, I use YouTube as a source for various audible events. But the real-life test is irreplaceable. In the case of PionEar sensor, it was a bit tricky to catch the ambulance with sirens on and have a camera ready. Last time I spent 2 hours driving around the nearest hospital and wasn't able to meet any car with sirens :-) Today, on my way to work I was lucky and made a shot.

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