A Low-Power, Single-Chip Electronic Skin Interface for Prosthetic Applications.

A low-power, single-chip electronic skin interface is presented. The system on chip (SoC) implementation significantly reduces the physical footprint and power requirements compared to commercial interfaces, which enables the creation nimble prosthetic limbs. Its small size and reduced battery requirements are ideal for advanced prosthetics that utilize electronic skin to provide their user tactile feedback.

Low-power analog processing for sensing applications: low-frequency harmonic signal classification.

A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouriér Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution.

A novel power amplification scheme for nuclear magnetic resonance/nuclear quadrupole resonance systems.

Nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR)-based chemical analysis systems have been widely utilized in various areas such as medicine, security, and academic research. In these applications, the power amplifier stage plays a key role in generating the required oscillating magnetic fields within a radio frequency coil that serves as the probe. However, the bulky size and relatively low efficiency of the traditional power amplification schemes employed present a bottleneck for the realization of compact sized and portable NMR and NQR systems.

ANNSyS: an Analog Neural Network Synthesis System.

A synthesis system based on a circuit simulator and a silicon assembler for analog neural networks to be implemented in MOS technology is presented. The system approximates on-chip training of the neural network under consideration and provides the best starting point for 'chip-in-the-loop training'. Behaviour of the analog neural network circuitry is modeled according to its SPICE simulations and those models are used in the initial training of the analog neural networks prior to the fine tuning stage.