A Lightweight Method for Detecting and Correcting Errors in Low-Frequency Measurements for In-Orbit Demonstrators.

In the pursuit of enhancing the technological maturity of innovative magnetic sensing techniques, opportunities presented by in-orbit platforms (IOD/IOV experiments) provide a means to evaluate their in-flight capabilities. The Magnetic Experiments for the Laser Interferometer Space Antenna (MELISA) represent a set of in-flight demonstrators designed to characterize the low-frequency noise performance of a magnetic measurement system within a challenging space environment. In Low Earth Orbit (LEO) satellites, electronic circuits are exposed to high levels of radiation coming from energetic particles trapped by the Earth's magnetic field, solar flares, and galactic cosmic rays. A significant effect is the accidental bit-flipping in memory registers. This work presents an analysis of memory data redundancy resources using auxiliary second flash memory and exposes recovery options to retain critical data utilizing a duplicated data structure. A new and lightweight technique, CCM (Cross-Checking and Mirroring), is proposed to verify the proper performance of these techniques. Four alternative algorithms included in the original version of the MELISA software (Version v0.0) are presented. All the versions have been validated and evaluated according to various merit indicators. The evaluations showed similar performances for the proposed techniques, and they are valid for situations in which the flash memory suffers from more than one bit-flip. The overhead due to the introduction of additional instructions to the main code is negligible, even in the target experiment based on an 8-bit microcontroller.

Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons.

Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.