An Automatic Voice Query System for Bank Based on Telephone Network
Automatic voice query system for bank by phone was a hi-tech product based on computer technology, electronic communication technology and speech synthesis technology. The hardware design of this system adopted modularization design scheme, which centered on SCM AT89C51, and controlled the phone transceiver chip MT8870 and voice chip ISD4004, realizing voice query for bank account by phone remote control. This paper mainly analyzed and designed several circuits such as ringing detection circuit, on-hook and off-hook control circuit, amplified-filter circuit, receiving circuit, voice circuit and the connection circuit between PC and SCM; it finally gave the realization process of the systempsilas software.

title: Topic Distributions over Links on Web
abstract: It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a web page on another if there is a link between the two linked web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to build a model, based on a training link-labeled and link-weighted graph where a link-label represents the category of a link and a link-weight represents the influence of one web page on the other in a link. Based on the model built, we categorize links and quantify the influence of web pages on the others in a large graph in the same application domain. We discuss our proposed approach, namely Pairwise Restricted Boltzmann Machines (PRBMs), and conduct extensive experimental studies to demonstrate the effectiveness of our approach using large real datasets.

title: Computing Exact p-Value for Structured Motif
abstract: Extracting motifs from a set of DNA sequences is important in computational biology. Occurrence probability is a common used statistics to evaluate the statistical significance of a motif. A main problem is how to calculate the occurrence probability of the motif on the random model of DNA sequence efficiently and accurately. In this paper, we are interested in a particular motif model which is useful in transcription process. This motif, which is called structured motif, is composed two motif words on single nucleotide alphabet and with fixed spacers between them. We present an efficient algorithm to calculate the exact occurrence probability of a structured motif on a given sequence. It is the first nontrivial algorithm to calculate the exact p-value for such kind of motifs.

title: Extraction and mining of an academic social network
abstract: This paper addresses several key issues in extraction and mining of an academic social network: 1) extraction of a researcher social network from the existing Web; 2) integration of the publications from existing digital libraries; 3) expertise search on a given topic; and 4) association search between researchers. We developed a social network system, called ArnetMiner, based on proposed methods to the above problems. In total, 448,470 researcher profiles and 981,599 publications were extracted/integrated after the system having been in operation for two years. The paper describes the architecture and main features of the system. It also briefly presents the experimental results of the proposed methods.

title: MicroRNA identification based on sequence and structure alignment
abstract: MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes.



In this paper, we present a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. We applied this method on Anopheles gambiae and detected 59 new miRNA genes.



This program is available at http://bioinfo.au.tsinghua.edu.cn/miralign.



Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm.

title: htSNPer1.0: software for haplotype block partition and htSNPs selection
abstract: BackgroundThere is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. heuristic algorithm, generalized branch-and-bound algorithm, is applied to the searching of minimal set of haplotype tagging SNPs (htSNPs) according to different htSNPs performance criteria. We develop a software htSNPer1.0 to implement the algorithm, and integrate three htSNPs performance criteria and four haplotype block definitions for haplotype block partitioning. It is a software with powerful Graphical User Interface (GUI), which can be used to characterize the haplotype block structure and select htSNPs in the candidate gene or interested genomic regions. It can find the global optimization with only a fraction of the computing time consumed by exhaustive searching algorithm.ConclusionhtSNPer1.0 allows molecular geneticists to perform haplotype block analysis and htSNPs selection using different definitions and performance criteria. The software is a powerful tool for those focusing on association mapping based on strategy of haplotype block and htSNPs.

title: Toward Protein Structure Analysis with Self-Organizing Maps
abstract: Establishing structure-function relationships on the proteomic scale is a unique challenge faced by bioinformatics and molecular biosciences. Large protein families represent natural libraries of analogues of a given catalytic or protein function, thus making them ideal targets for the investigation of structure-function relationships in proteins. To this end, we have developed a new technique for analyzing large amounts of detailed molecular structure information focusing on the functional centers of homologous proteins. Our approach uses unsupervised machine learning, in particular, self-organizing maps. The information captured by a self-organizing map and stored in its reference models highlights the essential structure of the proteins under investigation and can be effectively used to study detailed structural differences and similarities among homologous proteins. Our preliminary results obtained with a prototype based on these techniques demonstrate that we can classify proteins and identify common and unique structures within a family and, more importantly, identify common and unique structural features of different conformations of the same protein. The approach developed here outperforms many of today’s structure analysis tools. These tools are usually either limited by the number of proteins they can process at the same time or they are limited by the structural resolution they can accommodate, that is, many of the structural analysis tools that can handle multiple proteins at the same time limit themselves to secondary structure analysis and therefore miss fine structural nuances within proteins.

title: The Predictive Control of Sintering Temperature in Rotary Kiln Based on Image Feedback and Soft Computing
abstract: The sintering temperature in rotary kiln is measured through image processing algorithm as feedback of its predictive control. The predictive model based on radial basis function (RBF) network is designed by the image and other process data optimized by genetic algorithm. The final results of application at an alumina sintering kiln show that this intelligent predictive control strategy can achieve higher stability in normal condition.

title: Design of an Automatic Control System for Coal Preparation Process
abstract: In this paper, an automatic control system based on industrial control computers (IPC) and a programmable logic controller is designed by using field bus, PLC and configuration techniques to solve the automatic control problem of coal preparation process. The cross complex process control and fault processing are implanted by PLC, and configuration program provides friendly interface to user. Compared with traditional relay control system, this system has advantages of simplified operation, convenient maintenance and good adaptability. Practical production shows that this system has the characteristics of steady performance, high efficiency and economical benefit.

title: Watermarking for Authentication of LZ-77 Compressed Documents
abstract: Fragile Watermarking by exploiting the multiplicity of encoding of a file using LZ77- based compressed method (we called"LZS-77"), has been proposed recently as a new method of document authentication. It allows one to hide enough information within the compressed document to warrant its authenticity. However, the watermark embedding will degrade the compression performance. In this paper, some variations have been made to the LZS-77 algorithm to reduce the compression degradation. The experimental results show that our algorithm (we called"LZSC-77"), compared to the LZS-77 algorithm, can improve compression rates on many files of the Cargary corpus and Canterbury corpus.

title: Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS
abstract: The idea of multisensor integration is to use multiple sensors for measuring the same variables, where each sensor has its own accuracy, reliability and drawbacks. The sensor information is integrated by some data integration algorithms. In this paper, Radial Basis Function (RBF) neural networks and multisensor data fusion technology are combined and used in the fault detection and diagnosis of sensors hardware faults in the Satellite Attitude Measurement System (SAMS). The fusion method of the RBF neural networks is adopted. By using the combination method the outputs of the system are more accurate and reliable than each individual sensor. Research results show that this method for the detection and diagnosis of the sensors hardware faults in the SAMS is feasible and more effective, and for the sensors which measure the same attitude angle, using the method of firstly integration, then faults diagnosis, finally connection with the measurement system, the systematic measurement precision and performance-price-ratio can be improved.