Direct data domain approach to space-time adaptive processing

In non-homogeneous environment, traditional space-time adaptive processing doesn＇t effectively suppress interference and detect target, because the secondary data don＇ t exactly reflect the statistical characteristic of the range cell under test. A ravel methodology utilizing the direct data domain approach to space-time adaptive processing （ STAP ） in airbome radar non-homogeneous environments is presented. The deterministic least squares adaptive signal processing technique operates on a ＂snapshot-by-snapshot＂ basis to dethrone the adaptive adaptive weights for nulling interferences and estimating signal of interest （SOI）. Furthermore, this approach eliminates the requirement for estimating the covariance through the data of neighboring range cell, which eliminates calculating the inverse of covariance, and can be implemented to operate in real-time. Simulation results illustrate the efficiency of interference suppression in non-homogeneous environment.

A Novel Receiving Method for Baseband DSSS Signal based on Phase Domain Processing

In this paper, the phase characteristic disturbance model for baseband direct sequence spread spectrum (DSSS) signal in additive white Gaussian noise (AWGN) environment is established, and the probability density function (PDF) of phase characteristic disturbance is obtained. Then a novel receiver model for baseband DSSS signal based on maximum likelihood (ML) criterion is proposed. The simulation results show that, comparing with correlation scheme, the performance of the proposed method for baseband DSSS signal is 1dB worse in AWGN environment. However, if there is narrow interference in the AWGN environment, the proposed method will show better performance up to 2.5dB, and it has good adaptive resistance to narrowband interference located in different frequency points. This method could be used as an alterative communication scheme for military circumstance when existing strong narrowband interference of various frequencies.

TIME DOMAIN PROCESSING MODE SPREAD SPECTRUM SYSTEM

The construction and specifications of a surface acoustic wave storage correlator are described. A time domain processing mode spread spectrum system is presented. An analysis of the interference rejection for this system is provided. The formula for calculating the probability of error of the system is given. The experimental results agree with the theoretical analysis.

Human Brain Microwave Imaging Signal Processing: Frequency Domain (S-parameters) to Time Domain Conversion

The paper presents the microwave signal processing method using MATLAB based on the result of microwave imaging system simulation developed using Computer Simulation Technology (CST). The simulation system contains a transmitting/receiving antenna, human brain and a tumor inside the brain model. The source signal, microwave signal operates from 1 to 10 GHz. The generated scattering parameters (S-parameters) are in frequency domain form. This paper describes in detail regarding the signal conversion from frequency domain to time domain through proposed Inverse Fast Fourier Transform (IFFT) method as well as the noise filtering process. Peaks detection process was performed in order to identify the time delay of the reflection points at different Y-axis

Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.

CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

Phase synchronization processing method for alternating bistatic mode in distributed SAR

In the distributed synthetic aperture radar （SAR）, the alternating bistatic mode can perform phase reference without a synchronization link between two satellites compared with the pulsed alternate synchronization method. The key of the phase synchronization processing is to extract the oscillator phase differences from the bistatic echoes. A signal model of phase synchronization in the alternating bistatic mode is presented. The phase synchronization processing method is then studied. To reduce the phase errors introduced by SAR imaging, a sub-aperture processing method is proposed. To generalize the sub-aperture processing method, an echo-domain processing method using correlation of bistatic echoes is proposed. Finally, the residual phase errors of the both proposed processing methods are analyzed. Simulation experiments validate the proposed phase synchronization processing method and its phase error analysis results.

Physiological signal processing in heart rate variability measurement:A focus on spectral analysis

Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.

The Role of Combined OSR and SDF Method for Pre-Processing of Microarray Data that Accounts for Effective Denoising and Quantification

Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.

Simulation of pavement roughness based on time domain model

In order to describe pavement roughness more intuitively and effectively, a method of pavement roughness simulation, i.e., the stochastic sinusoidal wave, is introduced. The method is based on the primary idea that pavement roughness is denoted as the sum of numerous sines or cosines with stochastic phases, and uses the discrete spectrum to approach the target stochastic process. It is a discrete numerical method used to simulate pavement roughness. According to a given pavement power spectral density （PSD） coefficient, under the condition that the character of displacement frequency based on the time domain model is in accordance with the given pavement surface spectrum, the pavement roughness is optimized to stochastic equivalent vibrations by computer simulation, and the curves that describe pavement roughness under each grade are obtained. The results show that the stochastic sinusoidal wave is suitable for simulation of measured pavement surface spectra based on the time domain model. The method of the stochastic sinusoidal wave is important to the research on vehicle ride comfort due to its rigorous mathematical derivation, extensive application range and intuitive simulation curve. Finally, a roughness index defined as the nominal roughness index （NRI） is introduced, and it has correlation with the PSD coefficient.

Argonaute protein as a linker to command center of physiological processes

MicroRNAs （miRNAs） post-transcriptionally regulate gene expression by binding to target mRNAs with perfect or imperfect complementarity, recruiting an Argonaute （AGO） protein complex that usually results in degradation or translational repression of the target mRNA. AGO proteins function as the Slicer enzyme in miRNA and small interfering RNA （siRNA） pathways involved in human physiological and pathophysiological processes, such as antiviral responses and disease formation. Although the past decade has witnessed rapid advancement in studies of AGO protein functions, to further elucidate the molecular mechanism of AGO proteins in cellular function and biochemical process is really a challenging area for researchers. In order to understand the molecular causes underlying the pathological processes, we mainly focus on five fundamental problems of AGO proteins, including evolution, functional domain, subcellular location, post-translational modification and protein-protein interactions. Our discussion highlight their roles in early diagnosis, disease prevention, drug target identification, drug response, etc.

Hierarchical Domain Assignment BaseC on Word-Gloss

This paper proposes a hierarchical word domain assignment algorithm to automatically build domain dictionaries from Machine-Readable Dictionary(MRD).The process for word domain assignment can be divided into three steps:1) Hierarchical structure constructing;2) Classifier training;3) Word domain assigning.Compared with the traditional methods,the hierarchical word domain assignment algorithm enhances the accuracy of word domain assignment while reducing human efforts on collecting corpus.Experiments on WordNet 2.0 show that 62.53% of the first domain labels are matched with the WordNet Domains 3.0 by using gloss-based word domain assignment,and the performance can be further improved by utilizing the hierarchical relationships among the domain sets.

TN型产品Zara Domain理论研究与分析改善

Zara Domain是高PPI TN型TFT-LCD制成中较为常见的一种不良,为了提高产品品质,急需改善Zara Domain。以3.97in TN产品(PPI 235)为例,Zara Domain发生率30%,对产品良率影响较大,文章结合生产情况对Zara Domain发生的原因进行理论分析和实验验证,得出改善Zara Domain的方法。首先,产品设计上可以通过增加平坦膜改善彩膜侧平坦度。其次,材料上可以选择高预倾角的配向膜以及低Pitch的液晶分子。最后工艺上优化摩擦强度和对盒后冷却工艺。实验结果表明:通过设计,材料,工艺三方面改善,最终生产过程中Zara Domain发生率<1%,提高了产品品质。

Temperature dependence of multi-jump magnetic switching process in epitaxial Fe/MgO(001) films

Temperature dependence of magnetic switching processes with multiple jumps in Fe/MgO（001） films is investigated by magnetoresistance measurements. When the temperature decreases from 300K to 80K, the measured three-jump hysteresis loops turn into two-jump loops. The temperature dependence of the fourfold in-plane magnetic anisotropy constant K1, domain wall pinning energy, and an additional uniaxial magnetic anisotropy constant KUare responsible for this transformation. The strengths of K1 and domain wall pinning energy increase with decreasing temperature, but KU remains unchanged. Moreover, magnetization reversal mechanisms, with either two successive or two separate 90°domain wall propagation, are introduced to explain the multi-jump magnetic switching process in epitaxial Fe/MgO（001） films at different temperatures.

Asymptotic Behavior for A Class of Non-autonomous Nonclassical Parabolic Equations with Delay on Unbounded Domain

In this article,we investigate the longtime behavior for the following nonautonomous nonclassical parabolic equations on unbounded domain ut−∆ut−∆u+λu=f(x,u(x,t−ρ(t)))+g(x,t).Under some suitable conditions on the delay term f and the non-autonomous forcing term g,we prove the existence of uniform attractors in Banach space CH1(RN)for the multivalued process generated by non-autonomous nonclassical parabolic equations with delays in unbounded domain.

Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and nonstationary.As the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering techniques.To address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario.The proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and error.This normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)algorithms.Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals.The ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts.Hence,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.

Empirical Likelihood Statistical Inference for Compound Poisson Vector Processes under Infinite Covariance Matrix

The paper discusses the statistical inference problem of the compound Poisson vector process(CPVP)in the domain of attraction of normal law but with infinite covariance matrix.The empirical likelihood(EL)method to construct confidence regions for the mean vector has been proposed.It is a generalization from the finite second-order moments to the infinite second-order moments in the domain of attraction of normal law.The log-empirical likelihood ratio statistic for the average number of the CPVP converges to F distribution in distribution when the population is in the domain of attraction of normal law but has infinite covariance matrix.Some simulation results are proposed to illustrate the method of the paper.

数据产权法律构造论

在数字经济环境中,数据已成为一种新型生产要素,需要更好地挖掘和开发其经济价值。数据不同于传统有体物以及知识产权客体的特征和属性使其在法律制度构建和运行上也具有特殊性。数据产权制度是数据基础制度的核心内容,其应涵盖保护数据主体合法权益、协调和平衡数据利益关系、促进数据有序流动和分享、促进数据要素利益公平分配等内容。数据产权的法律构造旨在建立数据资源持有权、数据加工使用权和数据产品经营权的数据动态流转和价值实现机制,最终服务于数字经济发展目标。