Aging effects on early-stage face perception: An ERP study

We compared early stages of face processing in young and older participants as indexed by ERPs elicited by faces and non-face stimuli presented in upright and inverted orientations. The P1 and N170 components were larger in older than in young participant

THE PHYLOGENETIC-RELATIONSHIPS AND CLASSIFICATION OF THE DOUCS AND SNUB-NOSED LANGURS OF CHINA AND VIETNAM

The taxonomy of the douc and snub-nosed langurs has changed several times during the 20th century. The controversy over the systematic position of these animals has been due in part to difficulties in studying them: both the doucs and the snub-nosed langurs are rare in the wild and are generally poorly represented in institutional collections. This review is based on a detailed examination of relatively large numbers of specimens of most of the species of langurs concerned. An attempt was made to draw upon as many types of information as were available in order to make an assessment of the phyletic relationships between the langur species under discussion. Toward this end, quantitative and qualitative features of the skeleton, specific features of visceral anatomy and characteristics of the pelage were utilized. The final data matrix comprised 178 characters. The matrix was analyzed using the program Hennig86. The results of the analysis support the following conclusions: (1) that the douc and snub-nosed langurs are generically distinct and should be referred to as species of Pygathrix and Rhinopithecus, respectively; (2) that the Tonkin snub-nosed langur be placed in its own subgenus as Rhinopithecus (Presbytiscus) avunculus and that the Chinese snub-nosed langur thus be placed in the subgenus Rhinopithecus (Rhinopithecus); (3) that four extant species of Rhinopithecus be recognized: R. (Rhinopithecus) roxellana Milne Edwards, 1870; R. (Rhinopithecus) bieti Milne Edwards, 1897; R. (Rhinopithecus) brelichi Thomas, 1903, and R. (Presbytiscus) avunculus Dollman, 1912; (4) that the Chinese snub-nosed langurs fall into northern and southern subgroups divided by the Yangtze river; (5) that R. lantianensis Hu and Qi, 1978, is a valid fossil species, and (6) the precise affinities and taxonomic status of the fossil species R. tingianus Matthew and Granger, 1923, are unclear because the type specimen is a subadult.

CLASSIFICATION AND EVOLUTION OF ASIAN COLOBINES

In order to study the differentiation of Asian colobines, 14 variables measured on 123 skulls, including Rhinopithecus, Presbytis, Presbytiscus (Rhinopithecus avunculus), Pygathrix and Nasalis were analyzed by one-way, cluster and discriminant function analyses. Information on paleoenvironmental changes in China and southeast Asia since the late Tertiary was used to examine the influences of migratory routes and range of distribution in Asian colobines. A cladogram for 6 genera of Asian colobines was constructed from the results of various analyses. Some new points or revisions were suggested: (1) Following one of two migratory routes, ancient species of Asian colobines perhaps passed through Xizang (Tibet) along the northern bank of the Tethys sea and through the Heng Duan Shan regions of Yunnan into Vietnam. An ancient landmass linking Yunnan and Xizang was already present on the east bank of the Tethys sea. Accordingly, Asian colobines would have two centers of evolutionary origin: Sundaland and the Heng Duan Shan regions of China. (2) Pygathrix shares more cranial features with Presbytiscus than with Rhinopithecus. This differs somewhat from the conclusion reached by Groves. (3) Nasalis (karyotype: 2n = 48) may be the most primitive genus among Asian colobines. Certain features shared with Rhinopithecus, e.g. large body size, terrestrial activity and limb proportions, can be interpreted as symple-siomorphic characters. (4) Rhinopithecus, with respect to craniofacial features, is a special case among Asian colobines. It combines a high degree of evolutionary specialization with retention of some primitive features thought to have been present in the ancestral Asian colobine.

An Automatic Classification Method of Metabolic Cycle Based on Biomimetic Pattern Recognition

Correct classification of different metabolic cycle stages to identification cell cycle is significant in both human development and clinical diagnostics. However, it has no perfect method has been reached in classification of metabolic cycle yet. This paper exploringly puts forward an automatic classification method of metabolic cycle based on Biomimetic pattern recognition (BPR). As to the three phases of yeast metabolic cycle, the correct classification rate reaches 90%, 100% and 100% respectively.

Method for improving classification performance of neural network based on fuzzy input and network inversion

In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.

Viruses' complete genomes classification based on geometrical learning

In this paper, we proposed a method of classification for viruses' complete genomes based on graph geometrical theory in order to viruses classification. Firstly, a model of triangular geometrical graph was put forward, and then constructed feature-space-samples-graphs for classes of viruses' complete genomes in feature space after feature extraction and normalization. Finally, we studied an algorithm for classification of viruses' complete genomes based on feature-space-samples-graphs. Compared with the BLAST algorithm, experiments prove its efficiency.

A novel face recognition method based on hypersausage neural networks

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

Homoepitaxial growth and device characteristics of SiC on Si-face (0001) 6H-SiC

Homoepitaxial growth of SiC on a Si-face (0 0 0 1) GH-SIC substrate has been performed in a modified gas-source molecular beam epitaxy system with Si2H6 and C2H4 at temperatures ranging 1000 1450 degreesC while keeping a constant SiC ratio (0.7) in the gas phase. X-ray diffraction patterns, Raman scattering measurements. and low-temperature photoluminescence spectra showed single-crystalline SiC. Mesa-type SiC p-n junctions were obtained on these epitaxial layers, and their I-V characteristics are presented. (C) 2001 Elsevier Science B.V. All rights reserved.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

Research of Face Detection System Applied for Detecting Faces with Complex Background and Illumination

An effective face detection system used for detecting multi pose frontal face in gray images is presented. Image preprocessing approaches are applied to reduce the influence of the complex illumination. Eye-analog pairing and improved multiple related template matching are used to glancing and accurate face detecting, respectively. To shorten the time cost of detecting process, we employ prejudge rules in checking candidate image segments before template matching. Test by our own face database with complicated illumination and background, the system has high calculation speed and illumination independency, and obtains good experimental results.

Research of Face Location System Based on Human Vision Simulations

In this paper we present a robust face location system based on human vision simulations to automatically locate faces in color static images. Our method is divided into four stages. In the first stage we use a gauss low-pass filter to remove the fine information of images, which is useless in the initial stage of human vision. During the second and the third stages, our technique approximately detects the image regions, which may contain faces. During the fourth stage, the existence of faces in the selected regions is verified. Having combined the advantages of Bottom-Up Feature Based Methods and Appearance-Based Methods, our algorithm performs well in various images, including those with highly complex backgrounds.

A face detection algorithm based on high dimensional space geometry

In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean Distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean Distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises.