The development of advanced signal processing algorithms specifically for Micro/Nano Electro Mechanical Systems (MEMS/NEMS) based sensors has been largely unexplored and can be regarded as the single most important area for improving the performance of these devices. In this paper we present three classes of algorithms that were created to extract weak signals from devices operating in different sensing modalities. The first, stochastic resonance approach, named Active Signal Processing, depends on improving signal-to-noise ratio (SNR) by injecting noise into the measurement. ViaLogy invented and successfully demonstrated the Quantum Resonance Interferometry (QRI) algorithm, a quantum stochastic resonance (QSR)-based technique for improving SNR. QRI processing involves the QSR-based generation of a Quantum Expressor Function (QEF) for the sensor, encoding within it the noise environment, minimum level of detection, and the precision of measurement. Signal detection is achieved by the destruction of the resonance condition responsible for generating the system QEF. The next algorithm, also known as the "swept window" Maximum Aposteriori Probability algorithm, was developed for the case of signals with discrete statistics such as low analyte ion fluxes in mass spectrometers. Finally, a novel, ViaLogy-developed "Multi Scale Estimator" algorithm showed significant improvement over the Allan Deviation behavior of a state-of-the-art MEMS microgyroscope.