Hyper-spectral characteristics and classification of farmland soil in northeast of China

The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter（SOM） based on pre-classification. This experiment was conducted under a controllable environment, and different soil samples from northeast of China were measured using ASD2500 hyperspectral instrument. The results showed that there are different reflectances in different soil types. There are statistically significant correlation between SOM and reflectence at 0.05 and 0.01 levels in 550–850 nm, and all soil types get significant at 0.01 level in 650–750 nm. The results indicated that soil types of the northeast can be divided into three categories： The first category shows relatively flat and low reflectance in the entire band; the second shows that the spectral reflectance curve raises fastest in 460–610 nm band, the sharp increase in the slope, but uneven slope changes; the third category slowly uplifts in the visible band, and its slope in the visible band is obviously higher than the first category. Except for the classification by curve shapes of reflectance, principal component analysis is one more effective method to classify soil types. The first principal component includes 62.13–97.19% of spectral information and it mainly relates to the information in 560–600, 630–690 and 690–760 nm. The second mainly represents spectral information in 1 640–1 740, 2 050–2 120 and 2 200–2 300 nm. The samples with high OM are often in the left, and the others with low OM are in the right of the scatter plot（the first principal component is the horizontal axis and the second is the longitudinal axis）. Soil types in northeast of China can be classified effectively by those two principles; it is also a valuable reference to other soil in other areas.

Morphologic characteristic and classification description of one species Octopodidae in Antarctic waters

Morphologic characteristic and classification description of female octopus Pareledone turqueti collected by R/V Xuelong during the 22nd Expedition in the Antarctic waters is discussed.The results indicate that this species belongs to suborder Incirrina,Family Octopodidae,Subfamily Eledoninae,Genus Pareledone,Pareledone turqueti.It is characterized by having soft and smooth skin without papillae,funnel organ VV-shaped,crop,ink sac,anterior and posterior salivary gland present,developed radulae with 7 small heterodont teeth,gills with 7 lamellae on the inner and outer demibranch respectively.Arms moderate with uniserial suckers no enlarged,arm formula is Ⅱ=III.I=IV,web formula is c=d.e.b.a.

The pedogenic groups and diagnostic characteristics in the Fildes Peninsula of King George Island， Antarctica

ased on the analysis of the soil-forming factors, pedogenic processes and the soil properties in the study area, this paper is to propose the soil geographico-genetic classification of maritime-climatic Sub-Antarctic regions. The soils of the Fildes Peninsula were classified into 3 soil-orders,4 suborders and 13 soil groups, and then the relationship between the soil distribution and the environment was discussed.

The ARMA model’s pole characteristics of Doppler signals fromthe carotid artery and their classification application

In order to diagnose the cerebral infarction, a classification system based on the ARMA model and BP (Back-Propagation) neural network is presented to analyze blood flow Doppler signals from the carotid artery. In this system, an ARMA model is first used to analyze the audio Doppler blood flow signals from the carotid artery. Then several characteristic parameters of the pole's distribution are estimated. After studies of these characteristic parameters' sensitivity to the textcolor cerebral infarction diagnosis, a BP neural network using sensitive parameters is established to classify the normal or abnormal state of the cerebral vessel. With 474 cases used to establish the appropriate neural network, and 52 cases used to test the network, the results show that the correct classification rate of both training and testing are over 94%. Thus this system is useful to diagnose the cerebral infarction.

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope（SEM）micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found（1） Rosa xanthina Lindl（shrub species） had greater retention efficiency than Broussonetia papyrifera（broadleaf species） and Pinus bungeana Zucc.（coniferous species）, in terms of particle number and particle area cover.（2） 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.（3） 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.

Speed Planning Algorithm Based on Improved S-Type Acceleration and Deceleration Model

The acceleration saltation of the traditional S-type acceleration model in the speed planning of the NURBS curve will result in the vibration and flexible impact of the machine tool.It will affect the surface quality of the components.The high speed smooth S-type acceleration and deceleration model deals with flexible impact,but the calculation is tedious.Aimed at the above problems,the traditional S-type acceleration and deceleration model is improved to make the jerk change linearly at a certain slope to reduce the flexible impact.Before the speed planning,it is needed to find the arc length and curvature of each point on the NURBS curve with a tiny step,and to determine the speed sensitivity point on the curve accordingly.According to the speed sensitive point,the NURBS curve is segmented.The attribute parameters of each section are determined by adaptive speed planning.Then,the speed planning can be performed on the NURBS curve according to the speed characteristics classification.The simulation results show that the algorithm can effectively reduce the flexible impact,improve the machining precision and efficiency,and simplify the classification of speed characteristics.