Freestanding Multi-gate IZO-based Neuromorphic Transistors on Composite Electrolyte Membranes

Brain-inspired neuromorphic computing would bring a breakthrough to the classical computing paradigm through its massive parallelism and potential low power consumption advantages. Introduction of flexibility may bring vitality to this area by expanding its application areas to such as wearable and implantable electronics. At present, the development of flexible neuromorphic devices makes it a choice with wide prospect for next-generation wearable artificial neuromorphic computing. In this study, a freestanding graphene oxide (GO)/polyvinyl alcohol (PVA) composite solid electrolyte membrane is utilized as the gate dielectric and support material, and indium–zinc-oxide (IZO) neuromorphic transistors are fabricated on such membrane. Based on the in-plane gate modulation, many key synaptic plasticity behaviors have been successfully emulated, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), high-pass filtering, and spatiotemporal signal processing. Moreover, transition of the spiking logic and the superlinear and sublinear dendritic integration function are realized. Our results indicate that these freestanding IZO-based neuromorphic transistors may of great significance for future flexible anthropomorphic robots, wearable bionic perception.

Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks

A number of mathematical methods have been developed to make temporal signal analyses based on time series. However, no effective method for spatial signal analysis, which are as important as temporal signal analyses for geographical systems, has been devised. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring spatial complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to developing an approach for analyzing spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform (FFT) and ordinary least squares (OLS) regression methods are employed to calculate spectral exponents. The results show that the wave-spectrum density distribution of all these urban traffic networks follows scaling law, and that the spectral scaling exponents can be converted into fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world. This study has implications for the further development of fractal-based spatiotemporal signal analysis in the future.

Dynamic Memristor-based Reservoir Computing for High-Efficiency Spatiotemporal Signal Processing

Reservoir computing (RC) is a highly efficient network for processing spatiotemporal signals due to its low training cost compared to standard recurrent neural networks. The design of different reservoir states plays a very important role in the hardware implementation of RC system. Recent studies have used the device-to-device variation to generate different reservoir states; however, this method is not well controllable and reproducible. To solve this problem, we report a dynamic memristor-based RC system. By applying a controllable mask process, we reveal that even a single dynamic memristor can generate rich reservoir states and realize the complete reservoir function. We further build a parallel RC system that can efficiently handle spatiotemporal tasks including spoken-digit and handwritten-digit recognitions, in which high classification accuracies of 99.6% and 97.6% have been achieved, respectively. The performance of dynamic memristor-based RC system is almost equivalent to the software-based one. Besides, our RC system does not require additional read operations, which can make full use of the device nonlinearity and further improve the system efficiency. Our work could pave the road towards high-efficiency memristor-based RC systems to handle more complex spatiotemporal tasks in the future.

Synthesis of interference resistant spatiotemporal filter for navigation parameters high precision measurements using global navigation satellite systems

The article considers problems of modern global navigation satellite systems high-precision user terminals performance in jamming and spoofing environment. The solution to the low interference resistance problem is the use of digital antenna arrays with spatiotemporal signal processing algorithms. The well-known, most studied and brought to practice algorithms for the spatiotemporal signal processing are described, reasons that do not allow the use of these algorithms in global navigation satellite systems high-precision user terminals are given. This article proposes a spatiotemporal signal processing algorithm based on a spatiotemporal filter of finite length with a special, theoretically justified requirement for the Hermitian symmetry of the matrix impulse response, which guarantees the absence of signal distortion in any interference environment. In this case, the spatiotemporal filter impulse response is calculated by the criterion of optimal interference suppression. The proposed spatiotemporal filter characteristics, as well as other spatiotemporal signal processing algorithms characteristics, were studied by mathematical simulation with random enumeration of interference environment parameters (directions to signals, to numerous interferences and their reflections, range of interference reflectors, reflection phases, interference and reflections levels, etc.). Simulation results are presented in the form of distribution functions of signal-to-noise ratios at the output of spatiotemporal signal processing algorithms and distribution functions of phase and signal time biases. The obtained dependencies confirm the absence of phase and signal time biases in the spatiotemporal filter in absolutely any interference environment with interfering multipath, while the spatiotemporal filter provides greater interference resistance than compensating spatiotemporal signal processing algorithms.