Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

Coexistence of negative differential resistance (NDR) and resistive switching (RS) memory is observed using a Ag|TiOx|F‐doped‐SnO2 memory cell at room temperature. Unlike other reports, the coexistence of NDR and RS strongly depends on the relative humidity levels at room temperature. The NDR disappears when the cells are placed in a dry air ambient (H2O < 5 ppm) or in vacuum, but the coexistence emerges and gradually becomes obvious after the cells are exposed to ambient air with relative humidity of 35%, and then becomes dramatically enhanced as the relative humidity becomes higher. Due to the excellent stability and reversibility of the coexistence of NDR and RS, a multilevel RS memory is developed at room temperature. Hydroxide ion (OH−) is induced by gas‐phase water‐molecule splitting on the surface and interface of the memory cell. The OH− interacts with oxygen vacancies and transports in the bulk of memory cell to facilitate the migration of Ag ions and oxygen vacancies along grain boundaries. These processes are responsible for the moisture‐modulated and room‐temperature coexistence. This work demonstrates moisture‐modulated coexistence of NDR and RS for the first time and gives an insight into the influence of water molecules on transition‐metal‐oxide‐based RS memory systems.

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.

(5 of 33)www.advelectronicmat.de state after operation an external stimulation (Figure 2f). As the increasing stimulation, the transition state entering to the metallic state leads to the Mott layer with low resistance state (LRS) (Figure 2g). The insulator to metal transition is in a timescale of femtosecond and picosecond, [69] while from the opposite transition from the metal state to the insulator state Figure 3. Second-order memristor for temporal information simulation. a) Conception of the second-order memristor. Adapted with permission. [51] Copyright 2015, American Chemical Society. b) Schematic of an artificial neuron consisting of dendrites, soma, and axon constructed by the secondorder memristor circuit. c) The temporal summation of excitatory postsynaptic currents (EPSCs) for the frequency-dependency conductance evolution of 2nd memristor. Adapted with permission. [52] Copyright 2018, Wiley. d) The second-order memristor networks consist of 128 inputs and 7 outputs for temporal learning simulation, from up to bottom denotes before training state with random weights and different learned motion speeds. Adapted with permission. [49] Copyright 2017, IEEE. e) Transient temperature evolution with Δt = 1 µs and Δt = 100 ns. Adapted with permission.

Ultra-flexible egg albumen protein paper with a permittivity of 15–21, which is an improvement of nearly 300% compared with native egg albumen, and the protein-based memristor arrays and photoelectric logic gates are developed.