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Note: The ATM design described in this 1990s article (adapted from ID magazine) no longer exists. PeakForce Tapping, like TappingMode, avoids lateral forces by intermittent contacting of the sample. The controlled interaction force can be lower due to the higher force sensitivity in a soft cantilever. The underlying calculations enabling SA happens on the fast FPGA chips implemented in Bruker controllers. The built-in flexibility of SA allows users to fully or partially control the PFT operation. The issue of height data in TappingMode is resolved in PFT, since it responds only to short-range interaction as long-range interactions are ignored for height control, a key to high-resolution imaging. The force curves are also available to the user to extract additional material specific information.
A variety of modes, traditionally carried out in contact mode operation, can greatly benefit from combination with PFT. Advantage of AFM dominated Tapping is the lack of lateral forces inherent to contact imaging. This information has been sourced, reviewed and adapted from materials provided by Bruker Nano Surfaces. Perkin Elmer DMA 8000 – Dynamic Mechanical Analysis for Advanced R&D and QCPerkin Elmer’s DMA 8000 Dynamic Mechanical Analyzer is a highly flexible and cost effective analyzer with a sophisticated design, offering flexible operation and high functionality.
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I'm building an application for Android devices that requires it to recognize, by accelerometer data, the difference between walking noise and double tapping it.
I've choosen a 25 input 1 output multi-layer perceptron, which I am training with backpropagation.

You should probably perform some type of preprocessing so your inputs are in a more acceptable range such as (-1, 1), or (0, 1). In my experience, you will not get good results when you have very unbalanced classes (50 tap, 3000 no tap) if you just naively apply some machine learning algorithm. Are single hidden-layered neural networks at least as good as multi hidden-layered neural networks? In the case of Disney, maybe this doesn't matter--DisneyWorld and DisneyLand are supposed to be magical places that you enjoy exploring.
Many banks have switched to talking ATMs instead, and online banking makes it easier for blind and partially-sighted users to do most of their banking using their own computers and screenreaders. The crucial step in AFM is the time-consuming actual adjustment of AFM feedback parameters by the user. This gives high-quality images without the user adjusting imaging parameters and the problematic AFM user interfaces. The noise threshold, the key parameter, is automatically adjusted by the AFM after it has completed a full frame as not all scans require the ultra-low noise that Bruker AFMs can achieve.
By consistently controlling the short-range interaction forces, PFT enables image quality control with fewer artifacts. Bruker uses the capability of obtaining multiple force–distance curves at each image location in its PeakForce QNM package.
The NANOS measuring head, which is part of all our instruments, employs a unique fiber-optic interferometer for measuring the cantilever deflection, which makes the setup so compact that it is no larger than a standard research microscope objective. But when it came to normal walking it never seemed to learn even though I fed it with a large proportion of noise data. The input is the changes in acceleration every 20ms and output ranges from -1 (for no-tap) to 1 (for tap). When the magnitude of the input values are large, the the hidden units are likely to saturate (i.e. For example, a classifier which always outputs -1 will achieve approximately 98% accuracy on your dataset, while being completely useless for your task.

Unfortunately, getting ML algorithms to work in the world is more art than science, and involves a lot of guess and check work in my experience.
For example, on a chart, use a dashed line and a solid line; they can be red and green if you like, but the difference between them is reinforced by the style of line. The benefits of contact and TappingMode imaging are combined in the oscillating system in PeakForce Tapping and unwanted resonances at turnaround points are avoided. The lever is driven by a sinusoidal wave and the curves are displayed as force versus time and force versus distance.
By selecting a discrete threshold, the AFM can adjust feedback and imaging speed to get a certain result instead of manipulating the feedback loop.
The force curves that enable the imaging can be extracted and also be used for further analysis.
I've tried pretty much every constallation of hidden inputs there are, but had most luck with 3 - 10.
Neural networks are notorious for overfitting and if you use the same data to train and assess accuracy, you almost surely will. We are doing cross validation now and are seeing better results compared to the amount of data.
This causes the gradients to be nearly zero which can result in backpropagation performing poorly. I'm curious thought - do you think that a set of maybe 100 taps and noise data would be sufficient or way to low?
There is also an option to set the AutoControl field by individual and choose the parameters.

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