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This paper proposes a HV (high-voltage) level shifter to shield the dV/dt noise. The main mechanism is to determine whether there is dV/dt noise through signal changes. When there is the dV/dt noise, the proposed level shifter’s output is locked by the dV/dt noise shielding circuit. So, the proposed level shifter has infinite dV/dt immunity, which is not affected by the supply voltage, and processes. In normal operation, the proposed level shifter can still maintain high response speed by using the auxiliary circuit. Therefore, the proposed HV level shifter cannot only realize dV/dt noise shielding, but also have a fast response speed. In the 0.5µm BCD process, the proposed level shifter is simulated, realizing the ±250V/ns dV/dt noises shielding function and less than 1.5ns delay time under the 400V HV power supply.

The joint of the donor and acceptor moieties allows a facile and effective strategy to develop novel organic conjugated materials. However, few pieces of work report the understanding of the donor-acceptor interactions at the molecular level. Herein, we developed three small molecules containing one acceptor motif with different amounts of the donor unit. By combining theoretical calculations and energy level characterization, the lowest unoccupied molecular orbital (LUMO) levels of the three molecules were proven to be almost identical. The molecular packing modes were evaluated from crystal structure prediction. Owing to the donor-acceptor interactions, the packing mode can be tuned from a 1D slipped stacking to a 2D brick layer. The 2D molecular packing and charge transport channel endowed the materials a higher electron mobility of 3.29 cm2 V-1 s-1 in the single-crystal field-effect transistors after such modulation.

To improve the predictive ability of computational fluid dynamics (CFD) on the transonic buffet phenomenon, NASA SC(2)-0714 supercritical airfoil is numerically investigated by noninstructive probabilistic collocation method for uncertainty quantification. Distributions of uncertain parameters are established according to the NASA wind tunnel report. The effects of the uncertainties on lift, drag, mean pressure and root-mean square pressure are discussed. To represent the stochastic solution, the mean and standard deviation of variation of flow quantities such as lift and drag coefficients are computed. Furthermore, mean pressure distribution and root-mean square pressure distribution from the upper surface are displayed with uncertainty bounds containing 95% of all possible values. It is shown that the most sensitive part of flow to uncertain parameters is near the shock wave motion region. Comparing uncertainty bounds with experimental data, numerical results are reliable to predict the reduced frequency and mean pressure distribution. However, for root-mean square pressure distribution, numerical results are higher than the experimental data in the trailing edge region.

In this paper, a new approach to enhance the humidity sensitivity of quartz crystal microbalance (QCM) humidity sensor was proposed. By the asymmetric treatment of QCM electrode, the as-fabricated QCM humidity sensor produces an additional frequency shifts associated with the change of dielectric constant of sensitive material besides mass sensitivity. Renewable cellulose nanocrystals (CNCs) as humidity sensing material was deposited on the sensing electrode of QCM. The material characteristics of the synthesized CNCs were examined by TEM, AFM, FTIR and XRD, respectively. The humidity sensing performances of CNCs based QCM humidity sensor, including humidity sensitivity, stability, humidity hysteresis and dynamic response and recovery, were comprehensively studied by using an oscillating circuit method. The results proved that the humidity sensitivity of CNCs based QCM humidity sensor can be effectively enhanced by the asymmetric treatment of QCM electrode structure. Further, the underlying sensitivity enhancement mechanism was analyzed and discussed using an equivalent circuit analysis method. This work highlights that QCM transducer with an asymmetric sensitive electrode structure is a promising approach to improve the humidity sensitivity, and CNCs is also a promising material for humidity sensing.

An ultra-low-power fast-transient output-capacitor-less low-dropout regulator (LDO) with advanced adaptive biasing (AAB) circuit is presented in this study. At light load, the AAB circuit only delivers 0.1 μA bias current to the amplifier to maintain the stability and reduce the quiescent current. At medium load to heavy load, the AAB circuit increases bias current to 2.5 μA for performance enhancement. A simple but effective hysteresis current comparator is proposed to eliminate the metastable region between the bias current transitions. When output voltage recovers from overshoot, the settling time at minimum load current of 1 μA is too long because of the 100-pF load capacitor. Hence, a gradually descending load current is delivered by AAB circuit for regulating output voltage from overshoot to the nominal value promptly. The proposed circuit has been implemented in a mixed-signal 0.13-μm CMOS process. From the measurement results, the proposed LDO regulates the output voltage at 0.8 V from a 1-V input with 2.9 μA quiescent current at minimum load. Output voltage could be fully recovered within 1.7 μs at a voltage spike

As a simple and effective feedforward neural network, extreme learning machine (ELM) can randomly generate the connection weight between input layer and hidden layer and the hidden layer neuron threshold. Extreme learning machine can be used to solve the classification problem, but its classification accuracy is not good enough. In this paper, we proposed an improved firefly algorithm called IFA and use it to select the parameters in ELM. Experimental results showed that the IFA can solve the premature problem and the classification ability of ELM can be improved by the use of IFA.

This paper investigates on the problem of parameter estimation in statistical model when observations are intervals assumed to be related to underlying crisp realizations of a random sample. The proposed approach relies on the extension of likelihood function in interval setting. A maximum likelihood estimate of the parameter of interest may then be defined as a crisp value maximizing the generalized likelihood function. Using the expectation-maximization (EM) to solve such maximizing problem therefore derives the so-called interval-valued EM algorithm (IEM), which makes it possible to solve a wide range of statistical problems involving interval-valued data. To show the performance of IEM, the following two classical problems are illustrated: univariate normal mean and variance estimation from interval-valued samples, and multiple linear/nonlinear regression with crisp inputs and interval output.