EM interference and xenon flash bulbs

I’ve started looking at bumblebee flight paths (especially during initial orientation flights from the nest). To this end I’ve wanted to increase the acquisition frequency of the system.

I found that firing more frequently than about every 1.6s seconds lead to marked decreases in the actual brightness of the flash. Instead I’ve rebuilt the electronics to allow the flashes to be fired sequentially. I have previously done this but found often the flashes would end up firing together. I assumed my circuit had a short, but I found the same happened again in my new circuit. I experimented for a while adding various capacitors across the supply rail (thinking maybe there was a ‘spike’ coming back through the two trigger wires from the flash), with the same thinking I added a reversed diode across those lines to catch any such spike. None of this helped.

I eventually found that spreading the flashes out across my desk helped a lot… this wasn’t expected, and suggested the problem was likely to be electromagnetic interference. The flash going off involves considerable current*, which even given the small size of the flash probably will equate to quite a large whole-spectrum EM pulse.

After chatting with helpful people on freenode’s ##electronics, I tried out tin-foil, and found (as long as each flash & wire was wrapped in its own foil & not touching) the 4 flashes would fire independently.

Quick foil experiment!

I’ll try out using some household build thermal insulation tape to shield the flashes next as that will be a little more tidy. I also found direct sunlight got the black casings quite hot so they will probably benefit from the silver foil for that reason too.

*back of the envelope calculation: If the flash (at 1/32) is abour 3J over 10us (=300kW) with say, 300V across it, then the current will be on the order of 300kW/300=1kA.

Remove first digit from Swiss Coordinates

Quick tip. I’m using data from air pollution sensors provided here. The Koordinaten are of the form 2’710’500 / 1’259’810. These use the Swiss coordinate system. pyproj is the defacto standard for doing coordinate processing, this page also helped.

But I could make it work, those coordinate values were too big.

Eventually I realised one needs to remove the leading 1 and 2 from the two numbers. This is mentioned in the wikipedia article:

In order to nonetheless achieve a clear distinction between the two systems, an additional digit was added to the coordinates of LV95: any East coordinate (E) now starts with a 2, and any North coordinate (N) with a 1. Consequently, LV95 coordinates are given by pairs of 7-digit numbers, whereas LV03 used pairs of 6-digit numbers – for instance the coordinates (2 600 000m E / 1 200 000m N) in LV95 would be expressed as (600 000m E / 200 000m N) in LV03.


In summary, my code now looks like:

from pyproj import Proj, transform
sites = []
sites.append({'name':'Zurich, Schimmelstrasse (ZH)','E':681942,'N':247245,'height':415})
sites.append({'name':'Zurich, Heubeeribüel (ZH)','E':685126,'N':248460,'height':610})
pWorld = Proj(init='epsg:4326')
pCH = Proj(init='epsg:21781')
for site in sites:
    print(transform(pCH,pWorld, site['E'], site['N']))

Tensorflow and Matrices containing Variables

Recently Pablo, Dennis and I were wondering what the best way to build Tensors with variables inside. I’ve found three ways (that largely mirror the numpy equivalents). Basically just different combinations of stacking, concatting, reshaping and gathering. [related SO question]

import tensorflow as tf
import numpy as np

a = tf.Variable(1.0,dtype=np.float32)
b = tf.Variable(2.0,dtype=np.float32)
with tf.GradientTape() as t:
    #these lines are equivalent:
    M = tf.reshape(tf.gather([a**2,b**2,a**2/2,1],[0,2,3,1]),[2,2])
    M = tf.reshape(tf.stack([a**2,a**2/2,1,b**2]),[2,2])
    M = tf.concat([tf.stack([[a**2,a**2/2]]),tf.stack([[1,b**2]])],0)
    gradients = t.gradient(tf.linalg.det(M),[a,b])
[<tf.Tensor: shape=(), dtype=float32, numpy=7.000001>, <tf.Tensor: shape=(), dtype=float32, numpy=4.0000005>]

I thought I’d just add that, one (possibly unwise) default behaviour of the gradient method is, if one were to ask for the derivative of a matrix it will return the derivative of the reduce_sum of the matrix:

with tf.GradientTape() as t:
    M = tf.concat([tf.stack([[a**2,a**2/2]]),tf.stack([[1,b**2]])],0)
    gradients = t.gradient(M,[a,b])
[<tf.Tensor: shape=(), dtype=float32, numpy=3.0>, <tf.Tensor: shape=(), dtype=float32, numpy=4.0>]

Which one can see is returning the derivative of the sum of M.