Catálogo de publicaciones - libros

This paper investigates the similarity of two sequences, one of the main issues for fragments clustering and classification when sequencing the genomes of microbial communities directly sampled from natural environment. In this paper, we use the relative entropy as a criterion of similarity of two sequences and discuss its characteristics in DNA sequences. A method for evaluating the relative entropy is presented and applied to the comparison between two sequences. With combination of the relative entropy and the length of variables defined in this paper, the similarity of sequences is easily obtained. The SOM and PCA are applied to cluster subsequences from different genomes. Computer simulations verify that the method works well.

In whole genome shotgun sequencing when DNA fragments are derived from thousands of microorganisms in the environment sample, traditional alignment methods are impractical to use because of their high computation complexity. In this paper, we take the divergence vector which is consist of Kullback-Leibler divergences of different word lengths as the feature vector. Based on this, we use BP neural network to identify whether two fragments are from the same microorganism and obtain the similarity between fragments. Finally, we develop a new novel method to cluster DNA fragments from different microorganisms into different groups. Experiments show that it performs well.

After the Chinese National Neuroinformatics Working Group was formed in 2001, neuroinformatics research has progressed rapidly in China. This paper reviews the history of neuroinformatics in China, reports current researches and discusses recent trends of neuroinformatics in China.

The Biomedical Imaging Lab in Singapore has been involved in neuroinformatics research for more than a decade. We are focused on clinical neuroinformatics, developing suitable models, tools, and databases. We report here our work on construction of anatomical, vascular, and functional brain atlases as well as development of atlas-assisted neuroscience education, research, and clinical applications. We also present future research activities.

In this paper, we propose a novel approach called the gradual noisy chaotic neural network (G-NCNN) to find a collision-free time slot schedule in a time division multiple access (TDMA) frame in packet radio network (PRN). In order to find a minimal average time delay of the network, we aim to find an optimal schedule which has the minimum frame length and provides the maximum channel utilization. The proposed two-phase neural network approach uses two different energy functions, with which the G-NCNN finds the minimal TDMA frame length in the first phase and the NCNN maximizes the node transmissions in the second phase. Numerical examples and comparisons with the previous methods show that the proposed method finds better solutions than previous algorithms. Furthermore, in order to show the difference between the proposed method and the hybrid method of the Hopfield neural network and genetic algorithms, we perform a paired t-test between two of them and show that G-NCNN can make significantly improvements.

This paper presents a fast online support vector machine (FOSVM) algorithm for variable-step CDMA power control. The FOSVM algorithm distinguishes new added samples and constructs current training sample set using K.K.T. condition in order to reduce the size of training samples. As a result, the training speed is effectively increased. We classify the received signals into two classes with FOSVM algorithm, then according to the output label of FOSVM and the distance from the data points to the SIR decision boundary, variable-step power control command is determined. Simulation results illustrate that the algorithm has a fast training speed and less support vectors. Its convergence performance is better than the fixed-step power control algorithm.

This study addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple reordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, with fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.

A new equalization method for a wireless ATM communication channel using a simplified version of the complex bilinear recurrent neural network (S-CBLRNN) is proposed in this paper. The S-BLRNN is then applied to the equalization of a wireless ATM channel for 8PSK and 16QAM. The results show that the proposed S-CBLRNN converges about 40 % faster than the CBLRNN and gives very favorable results in both of the MSE and SER criteria over the other equalizers.

Recently, interacting neural network has been studied out coming a novel result that the two neural networks can synchronize to a stationary weight state with the same initial inputs. In this paper, a simple but novel interacting neural network based authentication scheme is proposed, which can provide a full dynamic and security remote user authentication over a completely insecure communication channel.

MANET (Mobile Ad Hoc Network) is a collection of wireless mobile nodes forming a temporary computer communication network without the aid of any established infrastructure or centralized administration. MANET is characterized by both highly dynamic network topology and limited energy. This makes the efficiency of MANET depending not only on its control protocol, but also on its topology management and energy management. Clustering Strategy can improve the flexibility and scalability in network management. With graph theory model and genetic annealing hybrid optimization algorithm, this paper proposes a new clustering strategy named GASA (Genetic Algorithm Simulated Annealing). Simulation indicates that this strategy can with lower clustering cost and obtain dynamic balance of topology and load inside the whole network, so as to prolong the network lifetime.