FIRST RECORD OF AN EYE DISEASE IN THE BOWHEAD WHALE (BALAENA MYSTICETUS)

Bowheadwhale(Balaenamysticetus)isahighlyvaluedspecies,andofimportancetotheAlaskancoastEskimoswhoharvestitastheirtraditionalfoodresource.Itisknownastheicewhalebecauseitspendsmuchofitslifeintheice.ItwassurprisingtofindacaseofeyediseaseintheeyespecimensfromthebowheadwhalesharvestedinthecoldenvironmentatBarrow,Alaska,USA.Somebasicinformationispasentedbelow.Bowheadwhale96B14washarvestedbywhalingcaptainJacobAdamsonSeptember16,1996.ThisWhalewasafemalewithbodylengthof8.33m.Thecorneaofbotheyeswas…     

PRECIPITATION PATTERNS IN FLOOD SEASON OVER CHINA ASSOCIATED WITH THE EL NI■O/SOUTHERN OSCILIATION

I.I~IOXENds(EINabs/SouthernOscillation)eventsaretheintensivesignalsofair-seainteractionintropicalarea.PlentyOfresultsfromdifferentresearchersshoWthattheENdssignalcanbedetectedoninterannualvallationsoflargescaleatmOSPhericcirculationandclimateregimeSovermostpartsoftheworld.SeveralkindsofrelationShipbetweenENdsandprecipitationoverChinahavebeenrevealedbydifferentreSearcherS,buttherearemanydifferencesamongthepublishedreSults.Itmayberesulted*fromthatdifferentreSearchersuseddifferentindic…     

双成分星际介质的折射闪烁

本文用比较严格的理论讨论了由连续介质和散射薄屏组成的星际介质对河外射电源的折射闪烁．对折射闪烁引起的流量变化的结构函数Ｄ（τ）（τ为迟后时间）的计算表明，结构函数的幂律部份的指数Ｐ（Ｄ（τ）（∝τＰ）依赖于上述两个成份的相对散射强度，即在连续介质散射为主的情况下；而在薄屏散射为主的情况下；若两个成份的散射可相比较时，１＜Ｐ＜２．因此本模型可以用于解释在河外射电源和脉冲星中观测到的折射闪烁的结构函数具有不同幂指数的情形，特别是Ｐ～１．５的情形．     

古环境研究中深海沉积物粒度测试的预处理方法

对深海沉积物样品进行粒度测试前,应根据研究目的,在预处理过程中有效去除碳酸盐和生物硅,同时完好保留陆源碎屑组分,才能使测试结果准确反映古气候和古环境变化。对采于南海的样品分别加入不同剂量浓度为10％的盐酸或浓度为25％的醋酸,以及不同剂量的NaOH或Na2CO3,同时利用有机元素分析仪、硅钼蓝比色法、环境扫描电子显微镜分析、观察不同方法的预处理效果,并结合粒度测试的结果认为,对于碳酸盐,盐酸和醋酸均可将其有效去除,但由于少量盐酸仍会对陆源矿物组分造成破坏,因此应使用酸性较弱的醋酸;对于生物硅,大剂量的Na2CO3仍难将其有效去除,而使用小剂量的NaOH即可去除干净,但需控制剂量以避免破坏矿物组分。针对南海沉积物中的各种生物组分含量,确定了醋酸和NaOH的使用剂量并建立了预处理流程。     

基于双线性内插规则格网DEM地形误差模型

双线性DEM表面模型的精度包括:使用双线性建模方法从格网量测数据传递过来的误差和地形表面的线性表达导致的精度损失.从理论上推导并证明了地形线性表达的误差极值是格网分辨率与地形坡度乘积的常数倍.并根据地形说明了,在不同地形时常数c的不同取值.     

Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations

With the improvement in resolution, more and more useful information is contained in the space of remote sensing images, which makes the processing of remote sensing data become more complex, and it is easy to cause the curse of dimensionality and the poor recognition effect. In this paper, a remote target recognition approach named AJRC is proposed, which uses joint feature dictionary for sparse representation based on different feature information for adaptive weighting. Firstly, the features of the images are extracted to calculate the contribution weight of each eigenvalue in sparse representation, and each eigenvalue contribution weight is calculated in sparse representation. Through the adaptive method, the contribution ability of each feature value in sparse representation is strengthened, and new atoms are formed to construct feature dictionary, which makes the dictionary more discriminative. Then, the common features of each category image and the private features of a single image are extracted from the feature vector, and a joint dictionary is formed to represent the test image sparse and recognize the output of the target. Aiming at the problem that the target visual contrast difference, the low resolution and the rotation of the target with different angles, the experiment is carried out by different feature extraction methods. At the same time, we use the PCA method to reduce the feature dictionary in order to avoid dimensionality. Experiments show that compared with the existing SRC method and JSM method, this method has better recognition rate.     

Inversion of Vegetation Components Based on the Spectral Mixture Analysis Using Hyperion Data

Hyper spectral remote sensing is widely used to identify ground objects as a result of the advantages of ground radiation intensity characteristics and spectral position characteristics, in which inversion of vegetation components is the difficult point and hotspot. In this study, Huma county of Heilongjiang Province was selected as the study area, the canopy spectra of four types of typical vegetation were measured in situ firstly, including mongolian oak, cotton grass, lespedeza and white birch. Then, on the basis of analyzing the canopy spectral characteristics and their parameterization, the spectral differences of different vegetations were located, and the parameterization method of characteristics identification was determined. Finally, Hyperion data were used to calculate the canopy albedos based on the bidirectional reflectance model of vegetation canopies, and to map the vegetation components in the study area by use of linear spectral mixture model. The results showed that inversion of vegetation components in high vegetation-covered area was accurate using the canopy albedos and liner spectral mixture model, and was identical with the field sampling, which validated the feasibility of canopy albedos and liner spectral mixture model for the inversion of vegetation components.