Analysis of random laser scattering pulse signals with lognormal distribution

The statistical distribution of natural phenomena is of great significance in studying the laws of nature. In order to study the statistical characteristics of a random pulse signal, a random process model is proposed theoretically for better studying of the random law of measured results. Moreover, a simple random pulse signal generation and testing system is designed for studying the counting distributions of three typical objects including particles suspended in the air, standard particles, and background noise. Both normal and lognormal distribution fittings are used for analyzing the experimental results and testified by chi-square distribution fit test and correlation coefficient for comparison. In addition, the statistical laws of three typical objects and the relations between them are discussed in detail. The relation is also the non-integral dimension fractal relation of statistical distributions of different random laser scattering pulse signal groups.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Influence of maximum decking charge on intensity of blasting vibration

Based on the character of short-time non-stationary random signal, the relationship between the maximum decking charge and energy distribution of blasting vibration signals was investigated by means of the wavelet packet method. Firstly, the characteristics of wavelet transform and wavelet packet analysis were described. Secondly, the blasting vibration signals were analyzed by wavelet packet based on software MATLAB, and the change of energy distribution curve at different frequency bands were obtained. Finally, the law of energy distribution of blasting vibration signals changing with the maximum decking charge was analyzed. The results show that with the increase of decking charge, the ratio of the energy of high frequency to total energy decreases, the dominant frequency hands of blasting vibration signals tend towards low frequency and hlasting vibration does not depend on the maximum decking charge.

Fast ISAR imaging method based on scene segmentation

Although compressed sensing inverse synthetic aperture radar(ISAR) imaging methods are widely used in radar signal processing, its reconstructing time and memory storage space requirements are very high. The main reason is that large scene reconstruction needs a higher dimension of the sensing matrix. To reduce this limitation, a fast high resolution ISAR imaging method,which is based on scene segmentation for random chirp frequencystepped signals, is proposed. The idea of scene segmentation is used to solve the problems aforementioned. In the method,firstly, the observed scene is divided into multiple sub-scenes and then the sub-scenes are reconstructed respectively. Secondly, the whole image scene can be obtained through the stitching of the sub-scenes. Due to the reduction of the dimension of the sensing matrix, the requirement of the memory storage space is reduced substantially. In addition, due to the nonlinear superposition of the reconstructed time of the segmented sub-scenes, the reconstruction time is reduced, and the purpose of fast imaging is achieved.Meanwhile, the feasibility and the related factors which affect the performance of the proposed method are also analyzed, and the selection criterion of the scene segmentation is afforded. Finally,theoretical analysis and simulation results demonstrate the feasibility and effectiveness of the proposed method.

Propagation of quasi-periodic random sound pulse sequence signals in shallow-water waveguides

It is an efficient method to model ship radiated noise as quasi-periodic random sound pulse sequences. Based on the model, this paper discusses the characteristics change of ship noise after through shallow-water waveguides. Theoretical analysis and numerical simulation show that random waveguides and multi-path effects can bring much additional transmission loss for the noise line spectra, demonstrating the instability of ship radiated noise line spectra to a certain extent, and providing some theoretical support for advanced studies of ship radiated noise.

A NEW SYSTEM DYNAMIC EXTREMUM SELF-SEARCHING METHOD BASED ON CORRELATION ANALYSIS

Objective To propose a new dynamic extremum self searching method, which can be used in industrial processes extremum optimum control systems, to overcome the disadvantages of traditional method. Methods This algorithm is based on correlation analysis. A pseudo random binary signal m sequence u(t) is added as probe signal in system input, construct cross correlation function between system input and output, the next step hunting direction is judged by the differential sign. Results Compared with traditional algorithm such as step forward hunting method, the iterative efficient, hunting precision and anti interference ability of the correlation analysis method is obvious over the traditional algorithm. The computer simulation experimental given illustrate these viewpoints. Conclusion The correlation analysis method can settle the optimum state point of device operating process. It has the advantage of easy condition , simple calculate process.

Effect of charge trapping on electrical characteristics of silicon junctionless nanowire transistor

We investigated the effect of charge trapping on electrical characteristics of silicon junctionless nanowire transistors which are fabricated on heavily n-type doped silicon-on-insulator substrate. The obvious random telegraph noise and current hysteresis observed at the temperature of 10 K indicate the existence of acceptor-like traps. The position depth of the traps in the oxide from Si/SiO_(2) interface is 0.35 nm, calculated by utilizing the dependence of the capture and emission time on the gate voltage. Moreover, by constructing a three-dimensional model of tri-gate device structure in COMSOL Multiphysics simulation software, we achieved the trap density of 1.9 × 10^(12) cm^(–2) and the energy level position of traps at 0.18 eV below the intrinsic Fermi level.

Correlation between dark current RTS noise and defects for AlGaInP multiple-quantum-well laser diode

The correlation model between dark current RTS noise and defects for A1GalnP multiple-quantum-well laser diode is derived. Experimental results show that dark current RTS noise caused carrier number fluctuations at the interface of the heterojunction in the active region. According to this correlation model, the defect types are determined, and the defects＇ energy levels are quantitatively determined. The comer frequency of RTS noise power spectral density is analyzed. The experimental results are in good agreement with the theoretical. This result provided an effective method for estimating the deep-level traps in the active region of A1GaInP multiple quantum well laser diode.

Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction

Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.