High Speed Regular Expression Matching Engine with Fast Pre-Processing

Regular expression matching is playing an important role in deep inspection. The rapid development of SDN and NFV makes the network more dynamic, bringing serious challenges to traditional deep inspection matching engines. However, state-of-theart matching methods often require a significant amount of pre-processing time and hence are not suitable for this fast updating scenario. In this paper, a novel matching engine called BFA is proposed to achieve high-speed regular expression matching with fast pre-processing. Experiments demonstrate that BFA obtains 5 to 20 times more update abilities compared to existing regular expression matching methods, and scales well on multi-core platforms.

Pre-processing filter design at transmitters for IBI mitigation in an OFDM system

In order to meet the demands for high transmission rates and high service quality in broadband wireless communication systems, orthogonal frequency division multiplexing （OFDM） has been adopted in some standards. However, the inter-block interference （IBI） and inter-carrier interference （ICI） in an OFDM system affect the performance. To mitigate IBI and ICI, some pre-processing approaches have been proposed based on full channel state information （CSI）, which improved the system performance. A pre-processing filter based on partial CSI at the transmitter is designed and investigated. The filter coefficient is given by the optimization processing, the symbol error rate （SER） is tested, and the computation complexity of the proposed scheme is analyzed. Computer simulation results show that the proposed pre-processing filter can effectively mitigate IBI and ICI and the performance can be improved. Compared with pre-processing approaches at the transmitter based on full CSI, the proposed scheme has high spectral efficiency, limited CSI feedback and low computation complexity.

Pre-processing and post-processing method for geostress simulation using seismic interpretation results

In order to carry out numerical simulation using geologic structural data obtained from Landmark(seismic interpretation system), underground geological structures are abstracted into mechanical models which can reflect actual situations and facilitate their computation and analyses.Given the importance of model building, further processing methods about traditional seismic interpretation results from Landmark should be studied and the processed result can then be directly used in numerical simulation computations.Through this data conversion procedure, Landmark and FLAC(the international general stress software) are seamlessly connected.Thus, the format conversion between the two systems and the pre-and post-processing in simulation computation is realized.A practical application indicates that this method has many advantages such as simple operation, high accuracy of the element subdivision and high speed, which may definitely satisfy the actual needs of floor grid cutting.

Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.

Review of pre-processing technologies for ice cores

High-resolution ice core records covering long time spans enable reconstruction of the past climatic and environmental conditions allowing the investigation of the earth system's evolution. Preprocessing of ice cores has direct impacts on the data quality control for further analysis since the conventional ice core processing is time-consuming, produces qualitative data, leads to ice mass loss, and leads to risks of potential secondary pollution. However, over the past several decades, preprocessing of ice cores has received less attention than the improvement of ice drilling, the analytical methodology of various indices, and the researches on the climatic and environmental significance of ice core records. Therefore, this papers reviews the development of the processing for ice cores including framework, design as well as materials, analyzes the technical advantages and disadvantages of the different systems. In the past, continuous flowanalysis(CFA) has been successfully applied to process the polar ice cores. However, it is not suitable for ice cores outside polar region because of high level of particles, the memory effect between samples, and the filtration before injection. Ice core processing is a subtle and professional operation due to the fragility of the nonmetallic materials and the random distribution of particles and air bubbles in ice cores, which aggravates uncertainty in the measurements. The future developments of CFA are discussed in preprocessing, memory effect, challenge for brittle ice, coupling with real-time analysis and optimization of CFA in the field. Furthermore, non-polluting cutters with many different configurations could be designed to cut and scrape in multiple directions and to separate inner and outer portions of the core. This system also needs to be coupled with streamlined operation of packaging, coding, and stacking that can be implemented at high resolution and rate, avoiding manual intervention. At the same time, information of the longitudinal sections could be scanned andidentified, and then classified to obtain quantitative data. In addition, irregular ice volume and weight can also be obtained accurately. These improvements are recorded automatically via user-friendly interfaces. These innovations may be applied to other paleomedias with similar features and needs.

Image pre-processing research of coal level in underground coal pocket

Mathematical morphology is widely applicated in digital image procesing.Vari- ary morphology construction and algorithm being developed are used in deferent digital image processing.The basic idea of mathematical morphology is to use construction ele- ment measure image morphology for solving understand problem.The article presented advanced cellular neural network that forms mathematical morphological cellular neural network (MMCNN) equation to be suit for mathematical morphology filter.It gave the theo- ries of MMCNN dynamic extent and stable state.It is evidenced that arrived mathematical morphology filter through steady of dynamic process in definite condition.

Signal pre-processing method and application design of edge nodes for distributed electromechanical system

A signal pre-processing method based on optimal variational mode decomposition(OVMD)is proposed to improve the efficiency and accuracy of local data filtering and analysis of edge nodes in distributed electromechanical systems.Firstly,the singular points of original signals are eliminated effectively by using the first-order difference method.Then the OVMD method is applied for signal modal decomposition.Furthermore,correlation analysis is conducted to determine the degree of correlation between each mode and the original signal,so as to accurately separate the real operating signal from noise signal.On the basis of theoretical analysis and simulation,an edge node pre-processing system for distributed electromechanical system is designed.Finally,by virtue of the signal-to-noise ratio(SNR)and root-mean-square error(RMSE)indicators,the signal pre-processing effect is evaluated.The experimental results show that the OVMD-based edge node pre-processing system can extract signals with different characteristics and improve the SNR of reconstructed signals.Due to its high fidelity and reliability,this system can also provide data quality assurance for subsequent system health monitoring and fault diagnosis.

Intelligent Data Pre-processing Model in Integrated Ocean Observing Network System

There are a number of dirty data in observation data set derived from integrated ocean observing network system. Thus, the data must be carefully and reasonably processed before they are used for forecasting or analysis. This paper proposes a data pre-processing model based on intelligent algorithms. Firstly, we introduce the integrated network platform of ocean observation. Next, the preprocessing model of data is presemed, and an imelligent cleaning model of data is proposed. Based on fuzzy clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means clustering. The proposed dynamic algorithm can automatically f＇md the new clustering center with the updated sample data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results through observation data analysis.

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.

The Pre-processing Parallel Algorithm of A Sparse Linear Equation Group

The solution of linear equation group can be applied to the oil exploration, the structure vibration analysis, the computational fluid dynamics, and other fields. When we make the in-depth analysis of some large or very large complicated structures, we must use the parallel algorithm with the aid of high-performance computers to solve complex problems. This paper introduces the implementation process having the parallel with sparse linear equations from the perspective of sparse linear equation group.

Pre-process algorithm for satellite laser ranging data based on curve recognition from points cloud

The satellite laser ranging （SLR） data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was discussed data based on curve recognition from points cloud is proposed. The results obtained by the new algorithm are 85 % （or even higher） consistent with that of the screen displaying method, furthermore, the new method can process SLR data automatically, which makes it possible to be used in the development of the COMPASS navigation system.

Research of Image pre-processing Algorithm in Automation Fingerprint Identification

In the paper, a valid method of fingerprint Image pre- processing is introduced. Experiment results show that this kind of algorithm can availably wipe off yawp imported by the incom- plete leave fingerprint - marking of sensor surface when finger- print sensor record fingerprint. Meanwhile, it can extract the ef- fective and uneffective zone of fingerprint effectively, and also further enhance ridge line and vale line of fingerprint so that make the lines of fingerprint clear, continuum, lubricity and has better contrast, at the same time, has quite quick speed, this fingerprint Image pre- processing time can be shorten greatly.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

A Survey on Pre-Processing in Image Matting

Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask （Trimap）. This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories： static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.

PRE-Processing for Video Coduing with Rate-Distortion Optimization Decision

This paper proposes an adaptive video pre-processing algorithm for video coding. This algorithm works on the original image before intraor inter-prediction. It adopts Gaussian filter to remove noise and insignificant features existing in images of video. Detection and restoration of edges are followed to restore the edges which are excessively filtered out in filtered images. Rate-Distortion Optimization （RDO） is employed to decide adaptively whether a processed block or a unprocessed block is coded into bit-streams doe more efficient coding. Our experiment results show that the algorithm achieves good coding performances on both subjective and objective aspects. In addition, the proposed pre-processing algorithm is transparent to decoder, and thus can be compliant with any video coding standards without modifying the decoder.

Optimization scheme with pre-processing for cooperative relay multicast networks in cellular system

For cooperative relay multicast networks, the general cross-layer optimization approaches converge to the global optimal value slowly because of the large quantity of relay terminals. However, the mobility of relay terminals requires quick converging optimization strategies to refresh the relay links frequently. Based on the capacity analysis of multiple relay channels, an improved cross-layer optimization scheme is proposed to resolve this problem, in which the bound of the relay selecting region is determined as a pre-processing. Utilizing the primal-dual algorithm, a cross-layer framework with pre-processing optimizes both the relay terminal selection and power allocation with quick convergence. The simulation results prove the effectiveness of the proposed algorithm.

Concurrent multi-task pre-processing method for LEO mega-constellation based on dynamic spatio-temporal grids

The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.

Hardware pre-processing for data of SBL underwater positioning system

The synchro double pulse signal mode is freuqently used in Short Base Line (SBL)underwater positioning system so as to obtain the information of both distance and depth of a target simultaneously. Howerer, this signal mode also brings about ranging indistinctness resulting in a shorter positioning distance much less than that limited by the period of the synchro signal. This paper presents a hardware distance-gate data acquiring scheme. It puts the original data sent to the computer in order of ' direct first pulse- depth information pulse (or first pulse reflected by water surface )…' to guarantee the effective positioning distance of the system. It has the advantage of reducing the processing time of the computer thus ensuring the realtime functioning of the system. A figure of the orbit of an underwater moving target measured in practice is attached to the end of the paper.

白酒中风味成分的检测分析技术研究进展

白酒历史悠久,是中国的国酒。白酒中的风味成分虽然含量较低,但其类别、组成及比例对产品口感、风味及香气极为重要。近年来,对风味成分的研究已成为白酒领域的热点,本文综述了白酒风味成分检测分析方面的研究进展,介绍了风味成分检测前处理技术与分析方法,总结了各大香型白酒中的风味挖掘及分析的最新成果,探讨了研究中存在的局限与不足,并展望了未来技术发展的方向和趋势。