《统计教育》“统计图”版式要求

1.图的宽度大致以16开刊物的分栏宽度为准,长宽比例以美观适度为宜。 2.图不取外边框。 3.图内外文字或数字取6-8号为宜,宋体。 4.图底色为白色。

Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.

Unification and Harmonization in True Color Image City Map

True color image city map is a sort of new-style map which combines the high resolution image and map symbols and shows both advantages in visualization. At the same time, the map unification and harmonization should be taken into account dur-ing the design process, since some visual conflicts appear when map symbols overlaid on the true color image. The objective of this research is to explore the rules in the process of true color image city map design based on chromatic and aesthetic knowledge. At the end, taking the Image Atlas of Guangzhou as an example, image color adjustment, road network presentation, and symbol de-signing issues will be discussed in the application.

Computation of Average Run Length for Residual-Based T^2 Control Chart for Multivariate Autocorrelated Processes

The expression of residual is obtained according to its dynamic response to mean shift, then the distribu- tion of T2 statistic applied to the residual is derived, thus the probability of the 7a statistic lying outside the control limit is calculated. The above-mentioned results are substituted into the infinite definition expression of the average run length （ARL）, and then the final finite ARL expression is obtained. An example is used to demonstrate the procedures of the proposed method. In the comparative study, eight autocorrelated processes and four different mean shifts are performed, and the ARL values of the proposed method are compared with those obtained by simulation method with 50 000 replications. The accuracy of the proposed method can be illustrated through the comparative results.

Hardware Optimization Technique of Full-Customized HW/SW Co-Design

The hardware optimization technique of mono similarity system generation is presented based on hardware/software(HW/SW) co design.First,the coarse structure of sub graphs' matching based on full customized HW/SW co design is put forward.Then,a universal sub graphs' combination method is discussed.Next,a more advanced vertexes' compression algorithm based on sub graphs' combination method is discussed with great emphasis.Experiments are done successfully with perfect results verifying all the formulas and the methods above.

Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998～2001

Objective: To estimate the prevalence rates of neural tube defects (NTDs) in Heshun County, Shanxi Province, China by Bayesian smoothing technique. Methods: A total of 80 infants in the study area who were diagnosed with NTDs were analyzed. Two mapping techniques were then used. Firstly, the GIS software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. Results: The classical statistical approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable. The maps produced by the Bayesian technique indicate the tendency of villages in the southeastern region to produce higher prevalence or risk values. Conclusions: The Bayesian smoothing technique addresses the issue of heterogeneity in the population at risk and it is therefore recommended for use in explorative mapping of birth defects. This approach provides procedures to identify spatial health risk levels and assists in generating hypothesis that will be investigated in further detail.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.

A psychophysical-based fuzzy image interpolation algorithm

To produce a smoother and more natural interpolated image, and to preserve and enhance original image details, we defined three perception-based local statistic parameters, namely contrast, noise visibility, and edge strength based on three psychophysical principles, including Weber’s Law, Fechner’s Law, and Stevens’ Power Law, and integrated these parameters into a fuzzy logic system to set up an advanced image interpolation algorithm. Application of this algorithm to detect edge behaviors and local statistical information of images demonstrated better noise removal ability and obtained sharper edges than traditional image interpolation algorithems such as nearest neighbor, bilinear and bicubic interpolation methods.

Quantitative Detection of Screening for Cervical Lesions with ThinPrep Cytology Test

OBJECTIVE To investigate the available parameters in gynecological screening for cervical lesions by liquid-based cytology technology （ThinPrep Cytology Test, TCT） and The Bethesda System （TBS）, also with computer image analysis. METHODS With application of the image analysis system, all grades of cervical lesion cells were detected quantitatively and sorted in atypical squamous cells of undetermined significance （ASCUS）, atypical squamous cells-cannot exclude HSIL （ASC-H）, low-grade squamous intraepithelial lesion （LSIL）, high-grade squamous intraepithelial lesion （HSIL） and cervical squamous cell carcinoma （SCC） with the mean optical density （MOD）, average grey （AG）, positive units （PU）, and nucleus to cytoplasmic ratio （N： C）. Differences between each group of cells were compared and analyzed statistically. RESULTS Apart from four stereologic parameters in LSIL and HSIL groups there were no differences among them, in the other groups, there was statistically significant in differences between MOD, AG and PU values. Differences between them in the ratio of nucleus to cytoplasm were highly statistically significant. CONCLUSION Stereological indexes may serve as a screening tool for cervical lesions. The image analysis system is expected to become a new means of cytological assisted diagnosis.

PROJECTION BASED STATISTICAL FEATURE EXTRACTION WITH MULTISPECTRAL IMAGES AND ITS APPLICATIONS ON THE YELLOW RIVER MAINSTREAM LINE DETECTION

Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.

BFA BASED NEURAL NETWORK FOR IMAGE COMPRESSION

A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are firstly introduced into neural network in the proposed algorithm. Extensive experiments are conducted on standard testing images and the results show that the pro- posed method can improve the quality of the reconstructed images significantly.

Probability and Statistical Error of Soil Sampling on Patches Assimilated during Delineation of Precision Farming Management Zones

When precision fanning management zones （MZs） are delineated in an agricultural field for precision nutrient management, unsupervised classification and cluster analysis procedures using remote sensing image analysis software are performed. These unsupervised classification and cluster analysis procedures are performed on the basis of the assumption that grouping of data points into naturally occurring clusters reduces within zone variability. The problem is that, there are small patches of different soil types within each management zone that are regarded as insignificant by the farmer, and are assimilated within larger MZs. These will consequently make soils within a management zone to be inhomogeneous. The objective of this study was to determine the probability of soil sampling occurrences on patches assimilated during delineation of MZs after a cluster analysis was performed. The study was conducted on a 5.0 ha （25°05′34.46″ S and 28°18′30.01″ E） and a 24.4 ha （23°59′04.61″ S and 28°52′29.43″ E） fields in the Waterberg District of the Limpopo Province in South Africa. A bare-soil high resolution Quickbird satellite imagery of a conventionally tilled agricultural field was used to develop MZs in the field. Soils were sampled using systematic unaligned sampling on a 35.0 m and 30.0 m grids for the 24.4 ha and 5.0 ha fields, respectively. Probabilities were calculated based on percentage area assimilated during the cluster analysis procedure that was performed using remote sensing image analysis software. The results indicated that in the 24.4 ha field there were 2.5 ha patches of high and medium zones that were assimilated within the low zone, and thus making low zones non-homogeneous. After cluster analysis and assimilation of patches, the low zone in the 24.4 ha field increased by 45.5% （2.5 ha） while the high zone was 16.4% （2.4 ha） smaller in size. In the smaller field of 5.0 ha, the high zone, which was originally 3.20 ha, lost 0.37 ha （11.6%）, which was assimilated in either low or medium zone. The study indicates that unequal probability proportional to size sampling could be used to minimize error when sampling across precision farming MZs because typically the low, medium and high MZs are not of equal size and do not contribute equally towards the mean values of soil samples.

The optimal slice thickness of CT in revealing lobulation of malignant solitary pulmonary nodules

Objective： The aim of this study was to determine an optimal slice thickness that was efficient in revealing Iobulation of malignant solitary pulmonary nodules （SPNs） on multi-slice spiral computed tomography （MSCT） images preliminarily. Methods： Fifty patients with malignant SPNs （diameter -〈 3 cm） underwent multidetector-row computed tomography of the chest in a single-breath-hold technique. The raw data were acquired with a collimation of 0.625 mm. Three sets of contiguous images were reconstructed with 1-, 2-, and 5-ram slice thickness, respectively. The Iobulation sign of SPNs on the computed tomography （CT） images presented in 1-, 2-, and 5-ram slice thickness was compared. Using the 1-mm sections as the gold standard, an optimal slice thickness in revealing Iobulation sign of SPNs was determined. Results： The 1-mm-thick images CT revealed 98 Iobulations （25 with chord distance 〈 1 ram; 30 with chord distance 1-2 ram; 43 with chord distance 〉 2 mm） of 45 malignant SPNs. 18 Iobulations with chord distance 〈 1 mm presented in 2-mm-thick sections were as same as those in I-ram-thick sections. Statistically significant difference in Iobulations number was found between that revealed in 2-ram-thick images and that in I-ram-thick images （P = 0.023 〈 0.05）. 16 Iobulations with chord distance 〈 1 mm presented in 5-mm-thick sections were as same as that in I-ram-thick sections. There was statistically significant difference in Iobulations number between that revealed in 5-mm-thick images and that in I-ram-thick images （P = 0.004 〈 0.05）. The 24 Iobulations with chord distance 1-2 mm presented in 2-ram-thick sections were as same as that in 1-mm-thick sections. No statistically significant difference in Iobulations number were found between that revealed in 2-mm-thick images and that in 1-mm-thick images （P = 0.261 〉 0.05）. 13 Iobulations with chord distance 1-2 mm presented in 5-ram-thick sections were as same as that in 1- mm-thick sections. There was statistically significant difference in Iobulations number between that revealed in 5-ram-thick images and that in I-ram-thick images （P = 0.003 〈 0.05）. 40 Iobulations with chord distance 〉 2 mm presented in 2-ram-thick sections were as same as that in I-ram-thick sections. No statistically significant difference in Iobulations number was found between that revealed in 2-ram-thick images and that in I-ram-thick images （P = 0.631 〉 0.05）. 36 Iobulations with chord distance 〉 2 mm presented in 5-mm-thick sections were as same as that in I-ram-thick sections. There was no statistically significant difference in Iobulations number between that revealed in 5-ram-thick images and that in I-ram-thick images （P = 0.264 〉 0.05）. Conclusion： It is suggested that the use of 1-mm slice thickness is suitable in revealing Iobulations with chord distance 〈 1 ram. A 2-mm slice thickness is suggested to be used in revealing Iobulafions with chord distance 1-2 mm and 5-mm slice thickness to be used in revealing Iobulations with chord distance 〉 2 mm.

AUDIO STEGANALYSIS OF DSSS BASED ON STATISTICAL MOMENTS OF HISTOGRAM

Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.

Estimation of Antropogenenic Pressure on Landscapes of Oasises （on Example of Mid and Lower Part of Zarafshan River）

The paper discusses about anthropogenic pressure classification system on landscape oasises. As a result, based on designed criteria rates a thematic map titled ＂Anthropogenic pressure and its consequences on landscapes oasises of mid and lower part of Zarafshan River Basin＂ was developed. The thematic map depicts realthionship between ecological conditions and population diseases along various landscape oasises of the Zarafshan river valley.

Detecting and Tracking Moving Targets on Omnidirectional Vision

A method was presented to implement the detecting and tracking of moving targets through omnidirec-tional vision. The method combined optical flow with particle filter arithmetic, in which optical flow field was used to detect and locate moving targets and particle filter was used to track the detected moving objects. According to the circular image character of omnidirectional vision, the calculation equation of optical flow field and the tracking arithmetic of particle filter were improved based on the polar coordinates at the omnidirectional center. The edge of a randomly moving object could be detected by optical flow field and was surrounded by a reference region in the particle filter. A dynamic motion model was established to predict particle state. Histograms were used as the fea-tures in the reference region and candidate regions. The mutual information (MI) and Gaussian function were com-bined to calculate particle weights. Finally, the state of tracked object was computed by the total particle states with weights. Experiment results show that the proposed method could detect and track moving objects with better real-time performance and accuracy.

Statistics and Its Applications in Identification of Irregular Particles

Phase Doppler anemometry(PDA) is very sensitive to the shape of testing particles, which is based on sphericity assumption and Mie’s theory. In practice, there exists effectiveness of non sphericity and the response of PDA system deviates from the theoretical prediction. In this paper, the statistic characteristics of PDA signal are analyzed and a method of identifying and quantifying irregular particles is proposed. It is concluded that phase difference of PDA signal for irregular particles is an unbiased estimation for spherical particles.

Collaborative task planning for an internet based multi-operator multi-robot system

In an Internet based multi-operator and multi-robot system (IMOMR), operators have to work collaboratively to overcome the constraints of space and time. Inherently, the activities among them can be defined as a computer-supported cooperative work (CSCW). As a practical application of CSCW, a collaborative task planning system (CTPS) for IMOMR is proposed in this paper on the basis of Petri nets. Its definition, components design, and concrete implementation are given in detail, respectively. As a result, a clear collaboration mechanism of multiple operators in an IMOMR is obtained to guarantee their task planning.