Nonlinearly Frequency-Adaptive, Self-Powered, Proton-Driven Somatosensor Inspired by a Human Mechanoreceptor.

In the human skin, it has been well known that several mechanoreceptors uniquely sense external stimuli with specific frequencies and magnitudes. With regard to sensitivity, the output response shows nonlinearity depending on the frequency magnitude of the stimulus. We demonstrate a self-powered proton-driven solid-state somatosensor, which mimics a unique nonlinear response and intensity behavior of human mechanoreceptors. For this, a solid-state sensor is fabricated by combining a piezoelectric film and a proton generation device. The proton injection electrode and the Nafion layer conjugated with sulfonated graphene oxide are used for proton generation and transport. Two types of nonlinear signals from the sensor are similar to the Merkel/Ruffini (low deviation of threshold intensity), and in contrast, the behavior of Pacinian/Meissner (high deviation of threshold intensity) is simultaneously shown. The region of the most responsive frequency is also discriminated according to proton conduction. Moreover, it is asserted that unique signal patterns are obtained from the stimuli of various frequencies, such as respiration, radial artery pulse, and neck vibration, which naturally occur in the human body.

An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU.

Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.