A genetic map of large yellow croaker Pseudosciaena crocea

Genetic linkage maps were constructed for large yellow croaker Pseudosciaena crocea (Richardson, 1846) using AFLP and microsatellite markers in an F-1 family. Five hundred and twenty-three AFLP markers and 36 microsatellites were genotyped in the parents and 94 F-1 progeny. Among these, 362 AFLP markers and 13 SSR markers followed the 1:1 Mendelian segregation ratio (P > 0.05). The female genetic map contained 181 AFLP and 7 microsatellite markers forming 24 linkage groups spanning 2959.1 cM, while the male map consisted of 153 AFLP and 8 microsatellite markers in 23 linkage groups covering 2205.7 cM. One sex linked marker was mapped to the male map and co-segregated with the AFLP marker agacta355, suggesting an XY-male determination mechanism and this may be useful in the breeding of monosex populations. (c) 2007 Elsevier B.V. All rights reserved.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

A novel extremely broadband superluminescent diode based on symmetric graded tensile-strained bulk InGaAs

A novel broadband superluminescent diode (SLD), which has a symmetric graded tensile-strained bulk InGaAs active region, is developed. The symmetric-graded tensile-strained bulk InGaAs is achieved by changing the group III TMGa source flow only during its growth process by low-pressure metalorganic vapor-phase epitaxy (LP-MOVPE), in which the much different tensile strain is introduced simultaneously. At 200mA injection current, the full width at half maximum (FWHM) of the emission spectrum of the SLID can be up to 122nm, covering the range of 1508-1630nm, and the output power is 11.5mW.

Influence of Mg content on molecular-beam-epitaxy-grown ZnMgS ultraviolet photodetectors

Zn1-xMgxS-based Schottky barrier ultraviolet (UV) photodetectors were fabricated using the molecular-beam-epitaxy (MBE) technique. The influence of Mg content on MBE-grown Zn1-xMgxS-based UV photodetectors has been investigated in details with a variety of experimental techniques, including photoresponse (PR), capacitance-voltage, deep level transient Fourier spectroscopy (DLTFS) and photoluminescence (PL). The room-temperature PR results show that the abrupt long-wavelength cutoffs covering 325, 305 295. and 270 nm with Mg contents of 16%, 44%, 57%, and 75% in the Zn1-xMgxS active layers, respectively, were achieved. But the responsivity and the external quantum efficiency exhibited a slight decrease with the Mg content increasing. In good agreement with the PR results, both of the integrated intensity of the PL spectra obtained from Zn1-xMgxS thin films with different Mg compositions (x = 31% and 52%, respectively) and the DLTFS spectra obtained from Zn1-xMgxS-based (x = 5% and 45%, respectively) UV photodetector samples clearly revealed a significant concentration increase of the non-radiative deep traps with increasing Mg containing in the ZnMgS active layers. Our experimental results also indicate that the MBE-grown ZnMgS-based photodetectors can offer the promising characteristics for the detection of short-wavelength UV radiation. (C) 2004 Elsevier B.V. All rights reserved.

The application of DBF neural networks for object recognition

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively. (C) 2003 Elsevier Inc. All rights reserved.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

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.

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.

Cognitive models in biomimetic pattern cognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite k-mean covering, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Porous InP array-directed assembly of InAs nanostructure

Fascinating features of porous InP array-directed assembly of InAs nanostructures are presented. Strained InAs nanostructures are grown by molecular-beam epitaxy on electrochemical etched porous InP substrate. Identical porous substrate with different pore depths defines different growth modes. Shallow pores direct the formation of closely spaced InAs dots at the bottom. Deep pores lead to progressive covering of the internal surface of pores by epitaxial material followed by pore mouth shrinking. For any depth an obvious dot depletion feature occurs on top of the pore framework. This growth method presages a pathway to engineer quantum-dot molecules and other nanoelements for fancy physical phenomena. (c) 2006 American Institute of Physics.

Modification of emission wavelength of self-assembled In(Ga)As/GaAs quantum dots covered by InxGa1-xAs(0 <= x <= 0.3) layer

Red shifts of emission wavelength of self-organized In(Cla)As/GaAs quantum dots (QDs) covered by 3 nm thick InxGa1-xAs layer with three different In mole fractions (x = 0.1, 0.2 and 0.3, respectively) have been observed. Transmission electron microscopy images demonstrate that the stress along growth direction in the InAs dots was reduced due to introducing the InxGa1-xAs (x = 0.1, 0.2 and 0.3) covering layer instead of GaAs layer. Atomic force microscopy pictures show a smoother surface of InAs islands covered by an In0.2Ga0.8As layer. It is explained by the calculations that the redshifts of the photoluminescence (PL) spectra from the QDs covered by the InxGa1-xAs (x greater than or equal to 0.1) layers were mainly due to the reducing of the strain other than the InAs/GaAs intermixing in the InAs QDs. The temperature dependent PL spectra further confirm that the InGaAs covering layer can effectively suppress the temperature sensitivity of PL emissions. 1.3 mum emission wavelength with a very narrow linewidth of 19.2 mcV at room temperature has been obtained successfully from In,In0.5Ga0.5As/GaAs self-assembled QDs covered by a 3-nm In0.2Ga0.2As strain reducing layer. (C) 2001 Elsevier Science B.V. All rights reserved.

Influence of InxGa1-xAs (0 <= x <= 0.3) cap layer on structural and optical properties of self-assembled InAs/GaAs quantum dots

Optical and structural properties of self-organized InAs/GaAs quantum dots (QDs) with InxGa1-xAs or GaAs cover layers grown by molecular beam epitaxy (MBE) have been characterized by transmission electron microscopy (TEM), atomic force microscopy (AFM) and photoluminescence (PL) measurements. The TEM and AFM images show that the surface stress of the InAs QDs was suppressed by overgrowth of a InxGa1-xAs covering layer on the top of the QDs and the uniformity of the QDs preserved. PL measurements reveal that red shifts of the PL emission due to the reduction of the surface strain of the InAs islands was observed and the temperature sensitivity of the PL emission energy was suppressed by overgrowth of InxGa1-xAs layers compared to that by overgrowth of GaAs layers.