When using Laguerre and Hermite spectral methods to numerically solve PDEs in unbounded domains, the number of collocation points assigned inside the region of interest is often insufficient, particularly when the region is expanded or translated in order to safely capture the unknown solution. Simply increasing the number of collocation points cannot ensure a fast convergence to spectral accuracy. In this paper, we propose a scaling technique and a moving technique to adaptively cluster enough collocation points in a region of interest in order to achieve fast spectral convergence. Our scaling algorithm employs an indicator in the frequency domain that both is used to determine when scaling is needed and informs the tuning of a scaling factor to redistribute collocation points in order to adapt to the diffusive behavior of the solution. Our moving technique adopts an exterior-error indicator and moves the collocation points to capture the translation. Both frequency and exterior-error indicators are defined using only the numerical solutions. We apply our methods to a number of different models, including diffusive and moving Fermi--Dirac distributions and nonlinear Dirac solitary waves, and demonstrate recovery of spectral convergence for time-dependent simulations. A performance comparison in solving a linear parabolic problem shows that our frequency scaling algorithm outperforms the existing scaling approaches. We also show our frequency scaling technique is able to track the blowup of average cell sizes in a model for cell proliferation. In addition to the Laguerre and Hermite basis functions with exponential decay at infinity, we also successfully apply the frequency-dependent scaling technique into rational basis functions with algebraic decay at infinity.
Particulate composites are one of the widely used materials in producing numerous state-of-the-art components in biomedical, automobile, aerospace including defence technology. Variety of modelling techniques have been adopted in the past to model mechanical behaviour of particulate composites. Due to their favourable properties, particle-based methods provide a convenient platform to model failure or fracture of these composites. Smooth particle hydrodynamics (SPH) is one of such methods which demonstrate excellent potential for modelling failure or fracture of particulate composites in a Lagrangian setting. One of the major challenges in using SPH method for modelling composite materials depends on accurate and efficient way to treat interface and boundary conditions. In this paper, a master-slave method based multi-freedom constraints is proposed to impose essential boundary conditions and interfacial displacement constraints in modelling mechanical behaviour of composite materials using SPH method. The proposed methodology enforces the above constraints more accurately and requires only smaller condition number for system stiffness matrix than the procedures based on typical penalty function approach. A minimum cut-off value-based error criteria is employed to improve the computational efficiency of the proposed methodology. In addition, the proposed method is further enhanced by adopting a modified numerical interpolation scheme along the boundary to increase the accuracy and computational efficiency. The numerical examples demonstrate that the proposed master-slave approach yields better accuracy in enforcing displacement constraints and requires approximately the same computational time as that of penalty method. (C) 2020 China Ordnance Society. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co.
Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.
Abstract Recent studies indicate that synaptic scaling is a vital mechanism to solve instability risks brought by the positive feedback of synaptic weight change related with standalone Hebbian plasticity. There are two kinds of synaptic scaling in the neural network, including local scaling and global scaling, both important for stabilizing the neural function. In this paper, for the first time, local synaptic scaling is emulated based on the MoS2 neuristor. The first-principle calculation reveals that synaptic scaling achieved by the neuristor is associated with an internal residual Li+-related weak dynamical process. Experimental results show the potential of achieving global synaptic scaling by the same device. Moreover, inspired by the synaptic scaling in the human brain, a new method of weight mapping called weight scaling mapping (WSM) is proposed to improve the stability of an artificial neural network (ANN). The simulation results indicate that WSM can improve the accuracy and anti-noise ability of the network compared with the traditional mapping method. These findings provide new insight into bionic research and help advance the construction of stable neuromorphic systems.
This article presents an adaptive zoom-capacitance-to-digital converter (CDC)-based CMOS humidity sensor. The humidity sensor is realized by means of two differential capacitors whose dielectrics are sensitive to humidity. The sensing capacitors are interfaced with a zoom CDC, which consists of a successive-approximation-register (SAR) analog-to-digital converter (ADC) and a 3rd-order delta–sigma modulator ( $Δ Σ \textM$ ). The SAR ADC eliminates the influence of the baseline capacitance to reduce the input range of the $Δ Σ \textM$ . To improve the energy efficiency of the CDC across the full input range, a power-aware floating inverter amplifier (FIA) array is proposed, which is configured based on the conversion results of the SAR logic. In addition, an adaptive range-shift (ARS) zoom CDC is proposed to: 1) resist off-chip parasitics and interference and 2) allow low redundancy and a more energy-efficient FIA-based comparator, thus reducing power consumption. The proposed CMOS humidity sensor is implemented in a 0.11- $μ \textm$ CMOS process. Measurement results show a capacitance resolution of 17.9 aF and an effective number of bits (ENOB) of 14.0 within a conversion time of 1.01 ms. The proposed humidity sensor consumes 1.5 $μ \textW$ of power and exhibits a 0.0094 % relative humidity (RH) resolution and a ±1.5 %RH peak-to-peak accuracy (3 $\sigma $ error of 5.5 %RH) among 12 chips from 20 to 85 %RH, and it achieves a figure of merit (FoM) of 0.135 pJ $\cdot $ %RH2, which is more than six times better than the state of the art.
Potassium periodate (PI, KIO4) was readily activated by Fe(II) under acidic conditions, resulting in the enhanced abatement of organic contaminants in 2 min, with the decay ratios of the selected pollutants even outnumbered those in the Fe(II)/peroxymonosulfate and Fe(II)/peroxydisulfate processes under identical conditions. Both 18O isotope labeling techniques using methyl phenyl sulfoxide (PMSO) as the substrate and X-ray absorption near-edge structure spectroscopy provided conclusive evidences for the generation of high-valent iron–oxo species (Fe(IV)) in the Fe(II)/PI process. Density functional theory calculations determined that the reaction of Fe(II) with PI followed the formation of a hydrogen bonding complex between Fe(H2O)62+ and IO4(H2O)−, ligand exchange, and oxygen atom transfer, consequently generating Fe(IV) species. More interestingly, the unexpected detection of 18O-labeled hydroxylated PMSO not only favored the simultaneous generation of ·OH but also demonstrated that ·OH was indirectly produced through the self-decay of Fe(IV) to form H2O2 and the subsequent Fenton reaction. In addition, IO4– was not transformed into the undesired iodine species (i.e., HOI, I2, and I3–) but was converted to nontoxic iodate (IO3–). This study proposed an efficient and environmental friendly process for the rapid removal of emerging contaminants and enriched the understandings on the evolution mechanism of ·OH in Fe(IV)-mediated processes.