Status Report 8 – Team A2: Project LAKE – Logging of Acoustic Keyboard Emanations

Kevin

This week I finished the placement and routing of the new PCB. It is 2”x1.5” which should allow for portability as well as easy integration into any package size. While routing, I used many pours this time to connect power between the two power management pins and from external power. This should help to produce cleaner power, leading to better signal integrity.
The top layer is a ground pour, similar to the last revision, to make routing much simpler. This time, however, the bottom layer is a 3.3V pour, which allowed me to much more easily route power the devices that needed it.
I also continued researching the Power Level Difference method of sound reduction. I found several descriptive papers that I can possible recreate the method from if we deem it necessary.

Next week I will be working on integrating our signal processing code together to make it much easier to use, and make it a complete system. Ronit, James and I will be spending most of our time working on tuning our design and code to get the results we need.
I will also be placing the order for our last PCB and any parts we need.

This week, I rewrote much of the Matlab code in order to allow the different components (filtering, keystroke detection, clustering, etc.) to fit and interface together. This will allow us to test the full system and see how it performs at the current stage in development
I optimized the keystroke separation and feature extraction algorithms to run much faster, allowing us to process 10-minute recordings within 30 seconds.
Lastly, I modified the current data receiving server and TDoA algorithm to accept timestamps taken from an NTP server, allowing for much more accurate determination of the start time of each audio clip.

This upcoming week, we will begin to fully integrate the different components of the system. We will need to work toward fine tuning the signal processing and machine learning portions of the project.

We had a bug whereby the esp32 would collect data via DMA even when the device was not connected to the laptop, as a result we were not starting the recordings of the two mics at the same time and thus we were unable to collect accurate TDOA.
The fix was making the mic sleep before collecting data and clear the buffers via a soft reset before reconnecting to the laptop.
To further improve the tdoa data, we now have the the nodes synchronise time with network time protocol . The laptop then tells them to start collecting data some time in the future, this ensures that the tdoa data is being recorded from the same period in time.
Machine learning is still still a challenge, i explored the naive bayes approach more as per professor Mai’s advice.
We are also looking at existing substitution cipher solver and seeing if we can leverage them.

The next week will be more machine learning. We need to find an efficient method of cracking substitution ciphers.

This week, we have completed most of the work for the final revision of our PCB. We have also began finalizing the software running on our ESP32, allowing for NTP synchronization and removing bugs like failing to clear buffers between clips.
We are in the process of organizing the code in order to allow for integration of the individual components of the system.

In the following weeks, we will need to divert much more attention to the machine learning aspects of the project, as well as to fine tune much of the signal processing algorithms in order to be more robust and effective.