The hemibrain connectome […] covers a large portion of the central fly brain, including the mushroom body and central complex circuits critical for associative learning and fly navigation.
This project is about an autonomous vehicle, based on a modified toy RC car, that can drive along a “road” without any manual interaction required.
To this end, the car’s remote control is modified so it can be attached to a microcontroller, that receives commands from a Python program running on a laptop. The camera, mounted on the top of the car, streams its view wirelessly to a neural net on the laptop, that decides what steering commands are the most appropriate at every time step/frame.
In this post, I will present how to modify the remote control (soldering and mechanical changes), how to extend the car, and how to stream live video, with low latency, from the Raspberry Pi to a laptop using GStreamer and OpenCV. An upcoming post will show a reliable neural net model for automated steering.Continue reading
While adding support for editing and viewing text encoded in UTF-8 to HxD’s hex editor control itself, it turns out I have to query Unicode property tables, that go beyond the basic ones included with Delphi (and most other languages / default libraries).
Parsing the structured text files, provided by the Unicode consortium, at each startup is too inefficient, and merely storing the parsed text into a simple integer array wastes too much memory.
A more efficient storage uses a dictionary-like approach, to compress the needed data using a few layers of indirections, while still giving array-like performance with constant (and negligible) overhead.
In the following, I’ll briefly present the solution I found.Continue reading
There has been interesting research in helping to make machine learning models more understandable, such as Unmasking Clever Hans predictors and assessing what machines really learn. Also see practical implementations of this approach:
- Heatmaps showing which features majorly influenced hand writing, image, or text classification
- Analyzing these heatmaps can point out undesired correlations in the training data, between samples and labels. For example, an image classifiers for train track might rely on objects that are present in each picture (such as , while not being present in pictures of counter examples for horses. This artifact in the collected data set may be subtle, and not noticeable to a human, but would be visible on the heatmap that highlights the critical features in each image that drove the classification.