An Overview of Literature on Primates in China

把1870-2001年来自美国"现代灵长类文献题录数据库"、"维普中文数据库"及"中国灵长类研究文献题录"的资料按4个时期(I:1870-1949;Ⅱ:1950-1965;Ⅲ:1966-1977;Ⅳ:1978-2001)分类整理.此外,使用了"科学引文数据库(扩展版)".结果如下:(1)1870-2001年共有20 52篇文献,2个文献数量高峰分别出现于1950-1965和1978-2001年;科研部门所发表的论文占54.2%,并随时间而增加;而国外部门则随之减少.(2)2 052篇文献中,超过9%的文献为SCI所收录,其中1966-1977年被SCI收录的文献百分比最高;在全部被SCI收录的文献中又以科研部门的占优势(59.1%);但国外部门则以其30.1%的文献被SCI收录而领先.(3)统计了灵长类研究9个领域文献百分比及其变化,其中1978-2001间生态学和行为、神经生物学、繁殖和饲养快速发展;化石灵长类、形态学和解剖学减少;分类及分布、细胞及分子进化显得不甚突出;在第Ⅱ时期和第Ⅳ时期疾病防治研究相当多;保护生物学得到越来越多的关注.第Ⅳ时期作者数量最多,但每个作者的文献平均数却不如第Ⅰ时期.一般地说,在SCI收录的作者中以中国作者居多,但在第I时期唯一被SCI收录的作者则是一名外国学者;在第Ⅲ时期外国作者占被收录作者数的60%.

Heavy metals in plants and phytoremediation - A state-of-the-art report with special reference to literature published in Chinese journals

Goal, Scope and Background. In some cases, soil, water and food are heavily polluted by heavy metals in China. To use plants to remediate heavy metal pollution would be an effective technique in pollution control. The accumulation of heavy metals in plants and the role of plants in removing pollutants should be understood in order to implement phytoremediation, which makes use of plants to extract, transfer and stabilize heavy metals from soil and water. Methods. The information has been compiled from Chinese publications stemming mostly from the last decade, to show the research results on heavy metals in plants and the role of plants in controlling heavy metal pollution, and to provide a general outlook of phytoremediation in China. Related references from scientific journals and university journals are searched and summarized in sections concerning the accumulation of heavy metals in plants, plants for heavy metal purification and phytoremediation techniques. Results and Discussion. Plants can take up heavy metals by their roots, or even via their stems and leaves, and accumulate them in their organs. Plants take up elements selectively. Accumulation and distribution of heavy metals in the plant depends on the plant species, element species, chemical and bioavailiability, redox, pH, cation exchange capacity, dissolved oxygen, temperature and secretion of roots. Plants are employed in the decontamination of heavy metals from polluted water and have demonstrated high performances in treating mineral tailing water and industrial effluents. The purification capacity of heavy metals by plants are affected by several factors, such as the concentration of the heavy metals, species of elements, plant species, exposure duration, temperature and pH. Conclusions. Phytoremediation, which makes use of vegetation to remove, detoxify, or stabilize persistent pollutants, is a green and environmentally-friendly tool for cleaning polluted soil and water. The advantage of high biomass productive and easy disposal makes plants most useful to remediate heavy metals on site. Recommendations and Outlook. Based on knowledge of the heavy metal accumulation in plants, it is possible to select those species of crops and pasturage herbs, which accumulate fewer heavy metals, for food cultivation and fodder for animals; and to select those hyperaccumulation species for extracting heavy metals from soil and water. Studies on the mechanisms and application of hyperaccumulation are necessary in China for developing phytoremediation.

Digits speech recognition based on geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems.We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions via the Hilbert transform. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy, Experiments show method based on ICA and geometrical learning outperforms HMM in different number of train samples.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

Mandarin speech emotion recognition based on high dimensional geometry theory

In this paper, a novel approach for mandarin speech emotion recognition, that is mandarin speech emotion recognition based on high dimensional geometry theory, is proposed. The human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. According to the characteristics of these emotional speech signals, the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. The new method called high dimensional geometry theory is applied for recognition. Compared with traditional GSVM model, the new method has some advantages. It is noted that this method has significant values for researches and applications henceforth.

Research on continuous speech recognition with small vocabulary based on biomimetic pattern recognition

Based on biomimetic pattern recognition theory, we proposed a novel speaker-independent continuous speech keyword-spotting algorithm. Without endpoint detection and division, we can get the minimum distance curve between continuous speech samples and every keyword-training net through the dynamic searching to the feature-extracted continuous speech. Then we can count the number of the keywords by investigating the vale-value and the numbers of the vales in the curve. Experiments of small vocabulary continuous speech with various speaking rate have got good recognition results and proved the validity of the algorithm.

A speaker-independent continuous speech recognition system using biomimetic pattern recognition

In speaker-independent speech recognition, the disadvantage of the most diffused technology (HMMs, or Hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of Biomimetic pattern recognition (BPR) in recognizing some mandarin continuous speech in a speaker-independent manner. A speech database was developed for the course of study. The vocabulary of the database consists of 15 Chinese dish's names, the length of each name is 4 Chinese words. Neural networks (NNs) based on Multi-weight neuron (MWN) model are used to train and recognize the speech sounds. The number of MWN was investigated to achieve the optimal performance of the NNs-based BPR. This system, which is based on BPR and can carry out real time recognition reaches a recognition rate of 98.14% for the first option and 99.81% for the first two options to the persons from different provinces of China speaking common Chinese speech. Experiments were also carried on to evaluate Continuous density hidden Markov models (CDHMM), Dynamic time warping (DTW) and BPR for speech recognition. The Experiment results show that BPR outperforms CDHMM and DTW especially in the cases of samples of a finite size.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

Biomimetic pattern recognition for speaker-independent speech recognition

In speaker-independent speech recognition, the disadvantage of the most diffused technology ( Hidden Markov Models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of Biomimetic Pattern Recognition (BPR) in recognizing some Mandarin Speech in a speaker-independent manner. The vocabulary of the system consists of 15 Chinese dish's names. Neural networks based on Multi-Weight Neuron (MWN) model are used to train and recognize the speech sounds. Experimental results are presented to show that the system, which can carry out real time recognition of the persons from different provinces speaking common Chinese speech, outperforms HMMs especially in the cases of samples of a finite size.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.