Pearson＇s Correlation Coefficient： A More Realistic Threshold for Applications on Autonomous Robotics

Many applications for control of autonomous platform are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing. Taking into account the temporal coherence between consecutive frames, the PCC （Pearson＇s Correlation Coefficient） was proposed and applied as： discarding criteria methodology, dynamic power management solution, environment observer method which selects automatically only the regions-of-interest; and taking place in the obstacle avoidance context, as a method for collision risk estimation for vehicles in dynamic and unknown environments. Even if the PCC is a great tool to help the autonomous or semi-autonomous navigation, distortions in the imaging system, pixel noise, slight variations in the object＇s position relative to the camera, and other factors produce a false PCC threshold. Whereas there are homogeneous regions in the image, in order to obtain a more realistic Pearson＇s correlation, we propose to use some prior known environment information.

Agricultural Market Name Geo-Locating System Based on an Administrative Ontology and Web Search Engine

The problem of associating the agricultural market names on web sites with their locations is essential for geographical analysis of the agricultural products. In this paper, an algorithm which employs the administrative ontology and the statistics from the search results were proposed. The experiments with 100 market names collected from web sites were conducted. The experimental results demonstrate that the algorithm proposed obtains satisfactory performance in resolving the problem above, thus the effectiveness of the method is verified.

Impact of environmental factors on water quality at multiple spatial scales and its spatial variation in Huai River Basin, China

Quantitative assessment of water quality and its spatial variation identification, as well as the discernment of primary factors affecting water quality are in its urgent in water environment management. In this study, four key water quality indicators,namely, ammonia nitrogen(NH_4^+-N), permanganate index(COD_(Mn)), total phosphorus(TP) and total nitrogen(TN) at 71 sampling sites were selected to evaluate water quality and its spatial variation identification. More concerns were emphasized on the anthropogenic factors(land use pattern) and natural factors(river density, elevation and precipitation) to quantify the overall water quality variations at different spatial scales. Results showed that the Yi-Shu-Si River sub-basin had a better water quality status than the Huai River sub-basin. The moderate polluted area nearly distributed in the upper and middle reaches of the Shaying River and Guo River. The high cluster centers which were surrounded with COD_(Mn), NH_4^+-N, TN and TP mainly also distributed in the upper and middle reaches of the Shaying River and Guo River. Redundancy analysis showed that the 200 m buffer area acted as the most sensitive area, which was easily subjected to pollution. The precipitation was identified as the most important variables among all the studied hydrological units, followed by farmland, urban land or elevation. The point source pollution was still existed although the non-point source pollution was also identified. The urban surface runoff pollution was severer than farmland fertilizer loss at the sub-basin scale in flood season, while the farmland showed "small-scale" effects for explaining overall water quality variations. This research is helpful for identifying the overall water quality variations from the scale-process interactions and providing a scientific basis for pollution control and decision making for the Huai River Basin.

A Modified Chi-Squared Goodness-of-Fit Test

In goodness-of-fit tests, Pearson＇s chi-squared test is one of most widely used tools of formal statistical analysis. However, Pearson＇s chi-squared test depends on the partition of the sample space. Different constructions of the partition of the sample space may lead to different conclusions. Based on an equiprobable partition of sample space, a modified chi^quared test is proposed. A method for constructing the modified chi-squared test is proposed. As an application, the proposed test is used to test whether vectorial data come from an uniformity distribution defined on the hypersphere. Some simulation studies show that the modified chisquared test against different alternative is robust.

Concentrations and classification of HCHs and DDTs in soil from the lower reaches of the Jiulong River, China

Soil is an important source to other environ- mental media and organisms for organochlorine pesticides （OCPs） bioaccumulation. Twenty-four representative sur- face soil samples were collected from the lower reaches of the Jiulong River, China, in 2009. The concentrations of hexachlorocyclohexane isomers （HCHs） ranged from 0.38 to 39.52 ng.g~, with a mean value of 9.51 ng.g-~. The concentrations of dichlorodiphenyltrichloroethanes （DDTs） and their metabolites were within the ranges of 0.94-700.99 ng. gl, with a mean value of 71.17 ng. g-1. The concentrations of HCHs and DDTs in the soil were lower than the first grade level （50 ng. g-a） of the Chinese Environmental Quality Standard （GB 15618-1995）. Hier- archical Cluster Analysis （HCA） and Pearson＇s bivariate Correlations Analysis （PCA） were used to analyse the distribution and contamination levels of OCPs in this region. The results showed that DDTs were the major contaminants and there were no significant correlations between various OCPs concentrations and the total organic carbon （TOC） contents. A significant positive correlation was observed between HCHs and DDTs （p 〈 0.01）, which indicates that HCHs and DDTs may have similar sources and fate in the study area.