CNN trained on practical surveilance targets.

A stationary surveilance camera is position opposite the exit to a busy station its goal is to locate and track individuals matching a target group and behaving suspicously.

Each individual is given the task of leaving the station carrying a concealed item without being seen by a a set of cameras placed throughout the station.

Using an advanced form of deep learning a Convolutional Neural Network dynamically picks out those from the group who are trying to evade detection.

The group is instructed not to act in anyway to betray their location to surveilance.

The CNN correclty identify's isolates and tracks each individual using advance image processing and deep learning.

The algorithm first works by reading the body language of any individual moving differently to the other members of the public. It then uses a rule of thumb based on its previous training to determine whether they are a valid target.

Using a purpose built database it then matches the individual using rudimentary face recognition.

Source : Reuters

Will the Convolutor be a GAN - Generative Adversarial Network

My CNN Condor has been coming on leaps and bounds trained on MINST data performs admireably only after 205 iterations of training.

Fig 1 These are the Minst images after successive discriminating filters with preloaded weights.

However I cannot get it to perform as a GAN - Generative Adversarial Network.

I have created purpose built functions that assemble the GAN's elements -

Generator - A series of RGB Convolution layers in the literature they are of varying size and donot have a Pool layer or an MLP layer.

Discriminator - Well this is just the CNN pretrained with its preloaded weights which takes as its input the output of the generator - and feeds back the error.

I have tested it with an MLP layer and without but I have yet to vary the size of each layer.

What I get with training is simply an image that looks like an untuned TV with the contrast on high. Its dark mush but it may yet deliver some results after I vary the

size of the layers and perhaps play around with their transfer functions.

Interestingly the MLP included produces the same result even after training.

Random Code -> MLP1 -> Layer 1...3 -> Output Image 100x100

->Input to CNN -> MLP2 Generate Error -> Feedback Error to MLP1 and Layer 1..3

Nice try.

Making some Noise

A new and exciting project is now underway. Negative noise.

I had some time back been experimenting with combining oscilators to produce a chaotic signal and recalled being able to produce a signal that had a negative voltage.

I am not sure how I did it and frustrated having dismantled and forgotten how the original circuit worked. All I recall was that there were two oscillators and the output was passed through my sound card to the oscilloscope.

The soundcard (not being designed for this purpose) although revealling some interesting waveforms eventually caused the soudcard to develop a glitch as the current was too high even though it had passed through a filter.

I then went and brought an oscilloscope which uses the ps2302 usb to serial this works lovely.

I then spent some time trying to build a circuit that could replicate these previous attempts and after many failures I suddenly hit upon the right combination of components. Now I can use a variety of different transistor types to create a noise signal that has both a positive and a negative component. After passing it through another filter I can reveal more of the signal and change its underlying form as a high pass filter its really interesting that you can do so much with just a few components and this circuit design is flexible to use different  transistor types in order to create the noise and then amplify the signal using an op-amp.

I would now like to take it to the next stage and make the signal
audio with more amplification and create an A2D convertor so that the noisy signal can be read by a really LITE version of my Mlp-on-a-chip.

I'd like the Mlp-on-a-chip to read the noise and perform a time series prediction. It may be a lot to ask and it would be enough to get it to distinguish between the different noise types produced by each transistor. An ambitious project that I will hopefully begin with making the noise audible through a pair of headphones and an amplifier circuit addon that I must build.

I shall upload the circuit for my Negative Noise here for anyone interested.

For ages I could only build a working Multivibrator and still cannot get a good Sine Wave. But now I have Negative Noise!


Notes to self use Noisey Hyperparameters as-per Noisey transfer function.