Back to Basics: AMR sensors

Today, let’s discuss AMR sensors. These sensors are still relatively new to many embedded builders because you mostly find them in motors, steering systems, actuators and industrial encoders. I thought it is worth covering for newer folks.

AMR means Anisotropic Magneto-Resistance. A thin magnetic film changes its resistance based on the direction of the magnetic field around it. So an AMR angle sensor is mostly asking which way the field is pointing, rather than only asking how strong the magnet is. Inside the chip, that resistance change is arranged as Wheatstone bridges. Think of them as tiny resistor networks that turn a small resistance shift into a voltage. With a rotating diametric magnet above the sensor, the outputs become sine and cosine signals. The MCU or the chip itself then calculates angle from those signals.

A basic AMR angle sensor repeats every 180°. It cannot naturally tell whether the magnet is at 30° or 210°. For full 360°, modern ICs they add Hall effect information. TI’s TMAG6180-Q1 mixes AMR with Hall quadrant outputs. Analog Devices’ ADMT4000 combines AMR with GMR(Giant magnetoresistance) for 360° and multiturn tracking.

Accuracy is where AMR becomes useful. Some have around 0.25°-0.5° angular error. These results depends on magnet centering, air gap, temperature, ADC quality and calibration though so expect that result always.

You will see AMR as angle sensors, linear position sensors and magnetic switches. Its usually used for motor rotor position, robotic joints, valves, e-bike motors, steering angle and compact encoders. AMR sensors can offer very good signal-to-noise performance and fast response times, making them attractive for high-speed control loops and precision motion systems where reliability and repeatability matter.

AMR sensors definitely have their place, especially when accurate angular position sensing is required. Just don’t confuse them with Hall effect sensors. While both use magnets, they operate on different physical principles and are often chosen for different design goals. We’ll dive deeper into that in future if needed.

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Advanced Tech: Image SynthID Reversal

Last week we discussed SynthID and how it watermarks AI-generated content so specific detectors can identify it. Today I wanted to discuss a GitHub project by aloshdenny that claims to confuse Gemini’s SynthID image detector. I think its an interesting piece of akin to cracking software security by reverse engineering. Lets discuss how something like this can be done.

SynthID has an invisible image watermark that’s fundamentally a weak signal hidden inside pixels. In a normal mountain or portrait image, that signal is buried under texture, edges and color variation. So the method’s first move is to simplify the input. It uses plain Gemini images, like near-white or near-black frames. When the image content is almost flat, repeated hidden structure becomes easier to isolate by enhancing contrast and saturation. You can then amplify the residual, which means studying tiny pixel variation that should ideally stay invisible.

So now you have look at the image from the frequency domain PoV. An FFT views an image as slow and fast spatial patterns instead of only pixel values. If the watermark has repeated structure, it may show up as carrier-like energy at certain frequencies. Based on experimentation, these patterns change with resolution of created, and that phase consistency across simple images helps separate watermark-like signal from normal image content. That is the core idea.

The removal side is based on the same idea. Once they have isolated watermark-related frequency patterns, they apply small image transformations and spectral adjustments designed to weaken those patterns while keeping the image visually similar. This can reduce detector confidence. Its like adding noise to a pattern so that detector cant detect it accurately. These are for image creations, similar breaking patterns emerge for AI generated text too.

Google has disputed these claims, so I would not call this a full crack. Still, it is an interesting example of how AI watermarking is becoming a signal-processing challenge as much as an AI one. I think watermarking and watermark stress-testing will become an hot research area.

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