Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

It is estimated that 70% or more of broadband bandwidth is consumed by transmitting music, games, video and other content through Peer-to-Peer (P2P) clients. In order to detect, identify, and manage P2P traffic, some port, payload and transport layer feature based methods were proposed. Most of them were applied to offline traffic classification mainly due to the performance reason. In this paper, a network processors (NPs) based online hybrid traffic classifier is proposed. The designed hardware classifier is able to classify P2P traffic based on the static characteristic namely on line speed, and the Flexible Neural Tree(FNT) based software classifier helps learning and selecting P2P traffic attributes from the statistical characteristics of the P2P traffic. Experiment results illustrate that the hybrid classifier performs well for online classification of P2P traffic from gigabit network. The proposed framework also depicts good expansion capabilities to add new P2P features and to adapt to new P2P applications online.     

Delimiting the urban growth boundaries with a modified ant colony optimization model

Delimiting urban growth boundaries (UGBs) has been generally regarded as a regulatory measure for controlling chaotic urban expansion. There are increasing demands for delimiting urban growth boundaries in fast growing regions in China. However, existing methods for delimiting UGBs mainly focus on intrinsic dynamic processes of urban growth and ignore external planning interventions. Delimiting UGBs to restrain chaotic expansion and conserve ecological areas is actually a spatial optimization problem. This study aims to develop an optimization-based framework for delimiting optimal UGBs by incorporating dynamic processes and planning interventions into an ant colony optimization (ACO) algorithm. Local connectivity, total utility values and quantity assignment were integrated into the exchange mechanism to make ACO adaptive for the delimitation of UGBs. The core area of Changsha-Zhuzhou-Xiangtan urban agglomeration, a very fast growing area in Central China was selected as the case study area to validate the proposed model. UGBs under multi planning scenarios with given combinations of weights for urban suitability, high-quality farmland protection, and landscape compactness were efficiently derived from the ACO model. Hypothetic datasets were initially used to test the performance of ACO on global optimum and its ability to optimize complex landscape patterns. Compared with experts' planning scenario, the optimal UGBs delimited by ACO model is practical. Results indicate that spatial optimization methods are plausible for delimiting optimal UGBs.     

A maximum entropy method to extract urban land by combining MODIS reflectance,MODIS NDVI,and DMSP-OLS data

Researchers often encounter difficulties in obtaining timely and detailed information on urban growth. Modern remote-sensing techniques can address such difficulties. With desirable spectral resolution and temporal resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) products have significant advantages in tackling land-use and land-cover change issues at regional and global scales. However, simply based on spectral information, traditional methods of remote-sensing image classification are barely satisfactory. For example, it is quite difficult to distinguish urban and bare lands. Moreover, training samples of all land-cover types are needed, which means that traditional classification methods are inefficient in one-class classification. Even support vector machine, a current state-of-the-art method, still has several drawbacks. To address the aforementioned problems, this study proposes extracting urban land by combining MODIS surface reflectance, MODIS normalized difference vegetation index (NDVI), and Defense Meteorological Satellite Program Operational Linescan System data based on the maximum entropy model (MAXENT). This model has been proved successful in solving one-class problems in many other fields. But the application of MAXENT in remote sensing remains rare. A combination of NDVI and Defense Meteorological Satellite Program Operational Linescan System data can provide more information to facilitate the one-class classification of MODIS images. A multi-temporal case study of China in 2000, 2005, and 2010 shows that this novel method performs effectively. Several validations demonstrate that the urban land extraction results are comparable to classified Landsat TM (Thematic Mapper) images. These results are also more reliable than those of MODIS land-cover type product (MCD12Q1). Thus, this study presents an innovative and practical method to extract urban land at large scale using multiple source data, which can be further applied to other periods and regions.     

一种精确求解Van Genuchten方程参数的有效方法

Van Genuchten方程参数的求解属于复杂的非线性拟合问题,传统方法解决此类问题存在着诸多弊端.本文试图找到一种精确求解方程参数的有效方法,枚举法由于列出所有可能的组合,不存在陷入局部最优值,因此被本文采用求解方程参数.首先,通过实验对比,确定目标函数指数k的取值;然后,讨论有效数字位数对方程参数及拟合精度的影响,确定方程参数的有效数字位数;最后,将枚举法与其它方法通过实验进行比较.结果表明:在目标函数、有效数字位数等相同条件下,枚举法具有最高的拟合精度,是一种精确求解Van Genuchten方程参数的有效方法.     

Estimation of Sea Surface Velocities from SAR Images Using the Doppler Shift

The sea surface velocities field plays an important role in seawater exchange and substance transportation.In this paper,the Wide Swath Mode(WSM) data derived from the ENVISAT Advanced Synthetic Aperture Radar(ASAR) are used to retrieve the high-resolution sea surface velocities field.based on the theoretical model of SAR Doppler shift,the errors caused by the relative motion between Earth and satellite are removed.Then we use the C Band Doppler Frequency Model(CDOP) and the Bragg scattering model to remove the errors caused by the sea surface wind and Bragg scattering,respectively.The data used to verify the accuracy of the retrieval results are AVISO(Archiving Validation and Interpretation of Satellite Oceanographic) velocities and the GLD(Global Drifting Buoy) velocities.Our method is applied both in the coastal area with land cover(Agulhas) and the open sea(Kuroshio) without any land.Results show that in Agulhas,the velocity ranges from -1.8 m/s to 1.8 m/s,and their directions agree very well.In the Kuroshio,the ASAR current can clearly reveal the flow path and direction of the Kuroshio,and it matches well with the AVISO current.The comprehensive results shows,the Root Mean Square Error(RMSE) of ASAR and AVISO is 0.17 m/s,and the RMSE of ASAR and GLD is 0.11 m/s.This implies that the methods used here not only simplified the processes but also has high accuracy to retrieve sea surface velocities both in the coastal area and in the open sea.     

Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts.     

Quantifying the spatial and temporal relationship between air and land surface temperatures of different land-cover types in Southeastern China

Near surface air temperature (T_a) is an important variable utilized for various fields of research such as agriculture, epidemic forecasting, and climate change. It is usually obtained from a limited number of weather stations that are unevenly distributed within a region. Thus, the spatial information of T_a is rarely provided especially in complex topographical and underdeveloped regions. To compensate for this shortcoming, T_a can be estimated by using the land surface temperature (T_s) from satellite data because remote sensing has an advantage of describing the spatial heterogeneity over a large geographical area. In previous research, authors focused on analysing the effect of limited factors on T_a observations by using a small number of weather stations. However, this study explores a thorough sensitivity analysis of several factors on the relationship between T_a (obtained from a dense network of automatic weather stations) and T_s of different land-cover types using statistical analysis. This article discovered the relationships between T_a and T_s. First, comparisons between daytime and night-time Moderate Resolution Imaging Spectroradiometer (MODIS) T_s data with T_a data showed that a better agreement was achieved during the night than the day. Second, when comparing the results of different land-cover types, the correlation coefficient of the vegetated area was higher than that of water and impervious surface. Third, a comparison of statistical results for different seasons indicated that the correlation between T_a and T_s was weakest in a hot season. Fourth, altitude seemed to have no significant effect on the T_a–T_s relationship. Finally, the relationship between T_a and T_s strengthened with increasing window size except for the vegetated area but tended to saturate as the T_s window increased to the optimal size. These results will be helpful for building an appropriate model to derive air temperature directly from the remotely sensed data in the future.     

How does the new-type urbanisation affect CO2 emissions in China? An empirical analysis from the perspective of technological progress

The development of traditional urbanisation has generated environmental problems, so the Chinese Government has proposed a new-type of urbanisation path with uniquely Chinese characteristics. How does this new-type of urbanisation affect CO₂ emissions? Based on panel data from 29 provinces in China (2005 to 2016), we apply an exploratory spatial data analysis model, a spatial econometric model, and a threshold model to analyse the spatial autocorrelation of CO₂ emissions, the direct and indirect effects of new-type urbanisation on CO₂ emissions, and the threshold characteristics produced by technological progress, respectively. The key results are: (1) CO₂ emissions show significant positive autocorrelation in China, and the spatial distribution of CO₂ emissions is HH (High-High) or LL (Low-Low) clustered in most provinces; (2) new-type urbanisation has a paradoxical effect on CO₂ emissions. Energy-saving technology has a rebound effect on CO₂ emissions, but environmental technology inhibits CO₂ emissions; (3) by eliminating the rebound effect of energy-saving technology on CO₂ emissions and promoting environmental technology, new-type urbanisation indirectly inhibits CO₂ emissions; (4) new-type urbanisation exhibits a threshold effect on CO₂ emissions due to the different levels of energy-saving technology and environmental technology. Finally, policy recommendations for CO₂ emissions reduction are proposed from the perspective of new-type urbanisation, energy-saving technology, and environmental technology.     

The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises

The relationship between location and land use patterns is one of the classic theoretical issues in urban studies. Classic models based on the monocentricity hypothesis have limitations in the interpretation of modern urban structure. China has experienced institutional transformation in recent decades, and the interaction of national government policy, market forces and the natural environment has influenced urban planning in Chinese metropolises, resulting in urban structures with special characteristics. This paper examines the distribution of location and land use intensity, and tested the Alonso model by the relationship between them in five Chinese metropolises using Point of Interest data, space syntax methodology, the grid weighted statistical method and the Geographically Weighted Regression (GWR) model. Universal patterns about the scaling relation between intensity of land use types and the centrality of location are revealed. The elasticity of land use types to location, from high to low sensitivity, is commercial, residential then industrial land in most of the five metropolises studied. The sensitivity sequence of land use studied suggests that the hypothetical model based on the classical Alonso model can explain the spatial structure of modern metropolises in China to some extent, especially for the commercial land. But the order of sensitivity of residential land and industrial land to location does not conform to the model. The spatial heterogeneity in land use intensity and centrality were explored and the factors embedded were discussed. It can be found that the relation between centrality and land use intensity conforms to power law. In most of the metropolises studied, when the scaling relation between land use intensity and centrality is super linear, the sequence of the frequency value from high to low are commercial, residential and industrial land; when the scaling relation is sublinear, the sequence of the frequency value is industrial, residential and commercial land.     

Where will the next emergency event occur? Predicting ambulance demand in emergency medical services using artificial intelligence

Predicting demand in emergency medical services is crucial for saving people's lives. Most studies aggregate demand prediction within a zone, failing to offer insights at a more detailed level. This study aspires to fill this gap by introducing a novel, three-level, spatial-based approach that identifies the geographical location of expected emergency events. First, the proposed methodology introduces new concepts and notions to model emergency events, as sets of interconnected points in space, that create paths over time. Second, based on these paths, an artificial neural network, optimized using a new evolutionary algorithm, predicts the location of future demand (emergencies). Third, based on the predicted demand, a location-allocation model is applied to site ambulances prior to actual emergencies occurrence, enhancing thus location planning and decision making. This method is applied to a dataset comprising 2851 emergency events in Athens, Greece, and the outcomes are evaluated based on the actual emergency events occurred. Results show that the mean distance, between an actual emergency event and the nearest ambulance, located based on the expected demand as estimated by our approach, deviates by 110 m relative to the optimal solution. This deviation, adds only a few seconds of delay to the response time of an ambulance relative to the theoretically optimal solution (post hoc location). In addition, it improves the current solution (in which ambulances are waiting in a set of fixed location throughout the year), by >1 km, decreasing significantly response time. From a policy perspective, these results indicate that assessing expected emergency events through the proposed method, would allow medical services to optimally locate ambulances in advance, reducing response time and thus increasing survival rates and public safety.