13572740
Image Texture Segmentation with Ant Colony Systems
A new scheme for texture segmentation based on ant colony systems (ACS) is proposed in this paper. Texture segmentation is one of the important branches in image pattern recognition, which provides usefulness in many applications. Until now, how to find an effective way for accomplishing texture segmentation in practical applications is still a major task. In this paper, we employ wavelet coefficients and characteristics of different subbands to serve as the basis of characteristic vectors, and we use three feature-extraction elements, namely, the extrema, entropy, and energy, to compose the characteristic vector. To alleviate segmentation fragments caused from the information in high frequency bands of texture images, we integrate the fourth element, the mean variance, into the characteristic vector. Finally, we use ACS to find a trade-off between texture segmentation and fragments. Simulation results demonstrate the effectiveness and practicability of the proposed algorithm
41865724
Improved Image Coding with Classified VQ and Side-Match VQ
A new scheme for vector quantization (VQ) is proposed in this paper. We employ side-match and classified criteria for designing VQ codebooks to combat blocking effects induced from high compression rates, and then we use the proposed algorithm to improve the reconstructed image quality. Simulation results demonstrate the better image quality to compare with that produced from conventional schemes objectively and subjectively, at the cost of reasonable encoding complexity.
14288907
RgS-Miner: A Biological Data Warehousing, Analyzing and Mining System for Identifying Transcriptional Regulatory Sites in Human Genome
Recently, biological databases and analytical methods have become available for analyzing gene expression and transcriptional regulatory sequences. However, users must make the complicated analyses to query the data in various databases, and then they must analyze the gene upstreams using various predictive tools, before finally converting date among formats. Beyond methods for predicting transcriptional regulatory sites, new automated and integrated methods for analyzing gene upstream sequences on a higher level are urgently required. Efficient and integrated data management methods are essential, too. We present an integrated system, namely RgS-Miner, to predict transcriptional regulatory sites and detect co-occurrence of these regulatory sites. RgS-Miner comprises a biological data warehousing system, pattern discovery programs, pattern occurrence association detectors and user interfaces. The system is available at http://rgsminer.csie.ncu.edu.tw/.
8641332
Magnetic diffusion: Scalability, reliability, and QoS of data dissemination mechanisms for wireless sensor networks
Envisioning a new generation of sensor network applications in healthcare and workplace safety, we seek mechanisms that provide timely and reliable transmissions of mission-critical data. Inspired by the physics in magnetism, we propose a simple diffusion-based data dissemination mechanism, referred to as the magnetic diffusion (MD). In that, the data sink, functioning like the magnet, propagates the magnetic charge to set up the magnetic field. Under the influence of the magnetic field, the sensor data, functioning like the metallic nails, are attracted towards the sink. We compare MD to the state-of-the-art mechanisms and find that MD: (1) performs the best in timely delivery of data, (2) achieves high data reliability in the presence of network dynamics, and yet (3) works as energy efficiently as the state of the art. These suggest that MD is an effective data dissemination solution to the mission-critical applications.
7795322
The Protein Information Resource
The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.
10114656
Efficient runtime generation of association rules
Mining frequent patterns in transaction databases has been a popular subject in data mining research. Common activities include finding patterns in database transactions, times-series, and exceptions. The Apriori algorithm is a widely accepted method of generating frequent patterns. The algorithm can require many scans of the database and can seriously tax resources. New methods of finding association rules, such as the Frequent Pattern Tree (FP-Tree) have improved performance, but still have problems when new data becomes available and require two scans of the database.This paper proposes a new method, which requires only one scan of the database and supports update of patterns when new data becomes available. We design a new structure called Pattern Repository (PR), which stores all of the relevant information in a highly compact form and allows direct derivation of the FP-Tree and association rules quickly with a minimum of resources. In addition, it supports run-time generation of association rules by considering only those patterns that meet on-line data requirements.
6353106
The Universal Protein Resource (UniProt): an expanding universe of protein information
The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at or downloaded at .