Phylogeny of SARS-CoV based on high-dimensional information geometry

We proposed a novel methodology, which firstly, extracting features from species' complete genome data, using k-tuple, followed by studying the evolutionary relationship between SARS-CoV and other coronavirus species using the method, called "High-dimensional information geometry". We also used the mothod, namely "caculating of Minimum Spanning Tree", to construct the Phyligenetic tree of the coronavirus. From construction of the unrooted phylogenetic tree, we found out that the evolution distance between SARS-CoV and other coronavirus species is comparatively far. The tree accurately rebuilt the three groups of other coronavirus. We also validated the assertion from other literatures that SARS-CoV is similar to the coronavirus species in Group I.

Study on text-dependent speaker recognition based on Biomimetic Pattern Recognition

We studied the application of Biomimetic Pattern Recognition to speaker recognition. A speaker recognition neural network using network matching degree as criterion is proposed. It has been used in the system of text-dependent speaker recognition. Experimental results show that good effect could be obtained even with lesser samples. Furthermore, the misrecognition caused by untrained speakers occurring in testing could be controlled effectively. In addition, the basic idea "cognition" of Biomimetic Pattern Recognition results in no requirement of retraining the old system for enrolling new speakers.

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.

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

Dynamic power management approaches based on neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system. by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level policies. We proposed two PAY policies-Back propagation Power Management (BPPM) and Radial Basis Function Power management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79,145,1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

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.

Object-recognition with oblique observation directions based on biomimetic pattern recognition

In this paper, we propose a new scheme for omnidirectional object-recognition in free space. The proposed scheme divides above problem into several onmidirectional object-recognition with different depression angles. An onmidirectional object-recognition system with oblique observation directions based on a new recognition theory-Biomimetic Pattern Recognition (BPR) is discussed in detail. Based on it, we can get the size of training samples in the onmidirectional object-recognition system in free space. Omnidirection ally cognitive tests were done on various kinds of animal models of rather similar shapes. For the total 8400 tests, the correct recognition rate is 99.89%. The rejection rate is 0.11% and on the condition of zero error rates. Experimental results are presented to show that the proposed approach outperforms three types of SVMs with either a three degree polynomial kernel or a radial basis function kernel.

A novel image restoration approach based on point location in high-dimensional space geometry

The goal of image restoration is to restore the original clear image from the existing blurred image without distortion as possible. A novel approach based on point location in high-dimensional space geometry method is proposed, which is quite different from the thought ways of existing traditional image restoration approaches. It is based on the high-dimensional space geometry method, which derives from the fact of the Principle of Homology-Continuity (PHC). Begin with the original blurred image, we get two further blurred images. Through the regressive deducing curve fitted by these three images, the first iterative deblured image could be obtained. This iterative "blurring-debluring-blurring" process is performed till reach the deblured image. Experiments have proved the availability of the proposed approach and achieved not only common image restoration but also blind image restoration which represents the majority of real problems.

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.

A DSWN-based specific-purpose neural computing system

The Double Synapse Weighted Neuron (DSWN) is a kind of general-purpose neuron model, which with the ability of configuring Hyper-sausage neuron (HSN). After introducing the design method of hardware DSWN synapse, this paper proposed a DSWN-based specific purpose neural computing device-CASSANN-IIspr. As its application, a rigid body recognition system was developed on CASSANN-IIspr, which achieved better performance than RIBF-SVMs system.

Face recognition based on PCA and multi-degree of freedom neurons

An algorithm of PCA face recognition based on Multi-degree of Freedom Neurons theory is proposed, which based on the sample sets' topological character in the feature space which is different from "classification". Compare with the traditional PCA+NN algorithm, experiments prove its efficiency.

A power-saving scheme for IEEE 802.11 Ad hoc networks

In the Wireless Local Area Networks (WLANs), the terminals are often powered by battery, so the power-saving performance of the wireless network card is a very important issue. For IEEE 802.11 Ad hoc networks, a power-saving scheme is presented in Medium Access Control (MAC) layer to reduce the power consumption by allowing the nodes enter into the sleep mode, but the scheme is based on Time-Drive Scheme (TDS) whose power-saving efficiency becomes lower and lower with the network load increasing. This paper presented a novel energy-saving mechanism, called as Hybrid-Drive Scheme (HDS), which introduces into a Message.-Drive Scheme (MDS) and combines MDS with the conventional TDS. The MDS, could obtain high efficiency when the load is heavy; meanwhile the TDS has high efficiency when the network load is small. The analysis shows that the proposed HDS could obtain high energy-efficiency whether the network load is light or heavy and have higher energy-saving efficiency than conventional scheme in the IEEE 802.11 standard.

Study on text-dependent speaker recognition based on Biomimetic Pattern Recognition

We studied the application of Biomimetic Pattern Recognition to speaker recognition. A speaker recognition neural network using network matching degree as criterion is proposed. It has been used in the system of text-dependent speaker recognition. Experimental results show that good effect could be obtained even with lesser samples. Furthermore, the misrecognition caused by untrained speakers occurring in testing could be controlled effectively. In addition, the basic idea "cognition" of Biomimetic Pattern Recognition results in no requirement of retraining the old system for enrolling new speakers.

When is `partial' adequate? A logic-based proof technique using partial specifications

A technique is presented for ascertaining when a (finite-state) partial process specification is adequate, in the sense of being specified enough, for contexts in which it is to be used. The method relies on the automatic generation of a modal formula from the partial specification; if the remainder of the network satisfies this formula, then any process that meets the specification is guaranteed to ensure correct behavior of the overall system. Using the results, the authors develop compositional proof rules for establishing the correctness of networks of parallel processes and illustrate their use with several examples