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.

Hypersausage neural networks and its application in face recognition

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.

Architecture research and hardware implementation on simplified neural computing system for face identification

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.

A method of Biomimetic Pattern Recognition for face recognition

A new method of face recognition, based on Biomimetic Pattern Recognition and Multi-Weights Neuron Network, had been proposed. A model for face recognition that is based on Biomimetic Pattern Recognition had been discussed, and a new method of facial feature extraction also had been introduced. The results of experiments with BPR and K-Nearest Neighbor Rules showed that the method based on BPR can eliminate the error recognition of the samples of the types that not be trained, the correct rate is also enhanced.

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.

FACE稻田中与C、N转化相关土壤微生物的响应

利用江都市小记镇的稻-麦轮作FACE平台，在水稻生长季研究了不同施肥（施常规氮量和低氮量）、不同秸秆还田（秸秆全还田、秸秆半还田、秸秆不还田）处理土壤中的硝化、反硝化、产甲烷和甲烷氧化菌数量变化，借助气相色谱测定了土壤的反硝化潜力、产甲烷潜力和甲烷氧化潜力。并对水稻土中的硝酸还原酶、脲酶、蔗糖酶和过氧化氢酶活性及有效C、N含量也进行了研究，目的是评估FACE稻田土壤反硝化活性和甲烷产生能力。 结果表明：与对照相比，FACE稻田土中的有效N含量呈降低趋势；土壤反硝化潜势明显受到抑制；水稻生长各时期土壤反硝化菌群数量也趋于减少，这种现象在常规氮肥施用及秸秆不还田情形下表现最为显著（P<0.01）；在水稻的大多数生育期土壤中的硝酸还原酶和脲酶活性也受到抑制；总体表现为FACE稻田土壤反硝化活性受到抑制。FACE既促进土壤的甲烷产生潜力，也促进甲烷氧化能力；对产甲烷菌在分蘖期具有促进作用，而在抽穗与收获期具有抑制作用，这种作用在低氮条件下达到显著水平（P<0.05）。同样，在低氮条件下，FACE促进了水稻生长前3个时期土壤甲烷氧化菌群数量增长，仅在收获期表现为抑制作用，因而，FACE稻田甲烷产生是产甲烷和氧化综合作用的结果。