The Experts below are selected from a list of 8550 Experts worldwide ranked by ideXlab platform

Teresa K Attwood - One of the best experts on this subject based on the ideXlab platform.

  • the prints protein Fingerprint Database in its fifth year
    Nucleic Acids Research, 1998
    Co-Authors: Teresa K Attwood, Michael E Beck, Darren R Flower, Philip Scordis, Julian N Selley
    Abstract:

    PRINTS is a Database of protein family 'Fingerprints' offering a diagnostic resource for newly-determined sequences. By contrast with PROSITE, which uses single consensus expressions to characterise particular families, PRINTS exploits groups of motifs to build characteristic signatures. These signatures offer improved diagnostic reliability by virtue of the mutual context provided by motif neighbours. To date, 800 Fingerprints have been constructed and stored in PRINTS. The current version, 17.0, encodes approximately 4500 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. The Database is accessible via the UCL Bioinformatics World Wide Web (WWW) Server at http://www. biochem.ucl.ac.uk/bsm/dbbrowser/ . We have recently enhanced the usefulness of PRINTS by making available new, intuitive search software. This allows both individual query sequence and bulk data submission, permitting easy analysis of single sequences or complete genomes. Preliminary results indicate that use of the PRINTS system is able to assign additional functions not found by other methods, and hence offers a useful adjunct to current genome analysis protocols.

  • novel developments with the prints protein Fingerprint Database
    Nucleic Acids Research, 1997
    Co-Authors: Teresa K Attwood, Michael E Beck, Alan J Bleasby, Kirill Degtyarenko, A D Michie, David J Parrysmith
    Abstract:

    The PRINTS Database of protein family 'Fingerprints' is a diagnostic resource that complements the PROSITE dictionary of sites and patterns. Unlike regular expressions, Fingerprints exploit groups of conserved motifs within sequence alignments to build characteristic signatures of family membership. Thus Fingerprints inherently offer improved diagnostic reliability by virtue of the mutual context provided by motif neighbours. To date, 600 Fingerprints have been constructed and stored in PRINTS, representing a 50% increase in the size of the Database in the last year. The current version, 13.0, encodes approximately 3000 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. The Database is accessible via UCL's Bioinformatics World Wide Web (WWW) server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser / . We describe here progress with the Database, its Web interface, and a recent exciting development: the integration of a novel colour alignment editor (http://www.biochem.ucl.ac.uk/bsm/dbbrowser++ +/CINEMA ), which allows visualisation and interactive manipulation of PRINTS alignments over the Internet.

  • progress with the prints protein Fingerprint Database
    Nucleic Acids Research, 1996
    Co-Authors: Teresa K Attwood, Michael E Beck, Alan J Bleasby, Kirill Degtyarenko, D Parry J Smith
    Abstract:

    PRINTS is a compendium of protein motif 'Fingerprints' derived from the OWL composite sequence Database. Fingerprints are groups of motifs within sequence alignments whose conserved nature allows them to be used as signatures of family membership. To date, 400 Fingerprints have been constructed and stored in Prints, the size of which has doubled in the last year. The current version, 9.0, encodes approximately 2000 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. Fingerprints inherently offer improved diagnostic reliability over single motif methods by virtue of the mutual context provided by motif neighbours. PRINTS thus provides a useful adjunct to the widely used PROSITE dictionary of patterns. The Database is now accessible via the Database Browser on the UCL Bioinformatics server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser .

  • prints a protein motif Fingerprint Database
    Protein Engineering, 1994
    Co-Authors: Teresa K Attwood, Michael E Beck
    Abstract:

    The PRINTS Database of protein 'Fingerprints' is described. Fingerprints comprise sets of motifs excised from conserved regions of sequence alignments, their diagnostic power or potency being refined by iterative Database scanning (in this case the OWL composite sequence Database). Generally, the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3-D space. The use of groups of independent, linearly or spatially separate motifs allows particular protein folds and functionalities to be characterized more flexibly and powerfully than conventional single-component patterns or regular expressions. The current version of the Database (4.0) contains 150 entries (encoding > 700 motifs), covering a wide range of globular and membrane proteins, modular polypeptides and so on. The growth of the Database is influenced by a number of factors, e.g. the use of multiple motifs, the maximization of sequence information through iterative Database scanning and the fact that the Database searched is a large composite. The information contained within PRINTS is distinct from but complementary to the single consensus expressions stored in the widely used PROSITE dictionary of patterns.

Hongji Cao - One of the best experts on this subject based on the ideXlab platform.

  • an adaptive weighted knn positioning method based on omnidirectional Fingerprint Database and twice affinity propagation clustering
    Sensors, 2018
    Co-Authors: Yunjia Wang, Hongji Cao
    Abstract:

    The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional Fingerprint Database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional Fingerprinting methods.

  • UPINLBS - A novel adaptive weighted K-nearest neighbor positioning method based on omnidirectional Fingerprint Database and twice affinity propagation clustering
    2018 Ubiquitous Positioning Indoor Navigation and Location-Based Services (UPINLBS), 2018
    Co-Authors: Yunjia Wang, Hongji Cao
    Abstract:

    Human body has a great influence on Wi-Fi signal propagation. Therefore, we present a novel adaptive weighted K-nearest neighbor (KNN) positioning method based on omnidirectional Fingerprint and twice affinity propagation clustering considering user’s orientation. Firstly, an improved Fingerprint Database model named omnidirectional Fingerprint Database (ODFD) is proposed, which includes the position, orientation and the sequence of mean received signal strength indicator at each reference point. Secondly, affinity propagation clustering (APC) algorithm is introduced for clustering on the offline stage based on the hybrid distance, which is the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed. KNN method is exploited to obtain K initial reference points (RPs), then all of them are clustered by APC algorithm based on RPs’ position-domain distances. The most probable cluster is reserved by the comparison of RPs’ number and signal-domain distance between cluster center and test point. The weighted average coordinate value of residual RPs in the remaining cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 meters, the root mean square error of 1.5 meters. Experimental results show that our proposed method outperforms traditional Fingerprinting methods.

Michael E Beck - One of the best experts on this subject based on the ideXlab platform.

  • the prints protein Fingerprint Database in its fifth year
    Nucleic Acids Research, 1998
    Co-Authors: Teresa K Attwood, Michael E Beck, Darren R Flower, Philip Scordis, Julian N Selley
    Abstract:

    PRINTS is a Database of protein family 'Fingerprints' offering a diagnostic resource for newly-determined sequences. By contrast with PROSITE, which uses single consensus expressions to characterise particular families, PRINTS exploits groups of motifs to build characteristic signatures. These signatures offer improved diagnostic reliability by virtue of the mutual context provided by motif neighbours. To date, 800 Fingerprints have been constructed and stored in PRINTS. The current version, 17.0, encodes approximately 4500 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. The Database is accessible via the UCL Bioinformatics World Wide Web (WWW) Server at http://www. biochem.ucl.ac.uk/bsm/dbbrowser/ . We have recently enhanced the usefulness of PRINTS by making available new, intuitive search software. This allows both individual query sequence and bulk data submission, permitting easy analysis of single sequences or complete genomes. Preliminary results indicate that use of the PRINTS system is able to assign additional functions not found by other methods, and hence offers a useful adjunct to current genome analysis protocols.

  • novel developments with the prints protein Fingerprint Database
    Nucleic Acids Research, 1997
    Co-Authors: Teresa K Attwood, Michael E Beck, Alan J Bleasby, Kirill Degtyarenko, A D Michie, David J Parrysmith
    Abstract:

    The PRINTS Database of protein family 'Fingerprints' is a diagnostic resource that complements the PROSITE dictionary of sites and patterns. Unlike regular expressions, Fingerprints exploit groups of conserved motifs within sequence alignments to build characteristic signatures of family membership. Thus Fingerprints inherently offer improved diagnostic reliability by virtue of the mutual context provided by motif neighbours. To date, 600 Fingerprints have been constructed and stored in PRINTS, representing a 50% increase in the size of the Database in the last year. The current version, 13.0, encodes approximately 3000 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. The Database is accessible via UCL's Bioinformatics World Wide Web (WWW) server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser / . We describe here progress with the Database, its Web interface, and a recent exciting development: the integration of a novel colour alignment editor (http://www.biochem.ucl.ac.uk/bsm/dbbrowser++ +/CINEMA ), which allows visualisation and interactive manipulation of PRINTS alignments over the Internet.

  • progress with the prints protein Fingerprint Database
    Nucleic Acids Research, 1996
    Co-Authors: Teresa K Attwood, Michael E Beck, Alan J Bleasby, Kirill Degtyarenko, D Parry J Smith
    Abstract:

    PRINTS is a compendium of protein motif 'Fingerprints' derived from the OWL composite sequence Database. Fingerprints are groups of motifs within sequence alignments whose conserved nature allows them to be used as signatures of family membership. To date, 400 Fingerprints have been constructed and stored in Prints, the size of which has doubled in the last year. The current version, 9.0, encodes approximately 2000 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. Fingerprints inherently offer improved diagnostic reliability over single motif methods by virtue of the mutual context provided by motif neighbours. PRINTS thus provides a useful adjunct to the widely used PROSITE dictionary of patterns. The Database is now accessible via the Database Browser on the UCL Bioinformatics server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser .

  • prints a protein motif Fingerprint Database
    Protein Engineering, 1994
    Co-Authors: Teresa K Attwood, Michael E Beck
    Abstract:

    The PRINTS Database of protein 'Fingerprints' is described. Fingerprints comprise sets of motifs excised from conserved regions of sequence alignments, their diagnostic power or potency being refined by iterative Database scanning (in this case the OWL composite sequence Database). Generally, the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3-D space. The use of groups of independent, linearly or spatially separate motifs allows particular protein folds and functionalities to be characterized more flexibly and powerfully than conventional single-component patterns or regular expressions. The current version of the Database (4.0) contains 150 entries (encoding > 700 motifs), covering a wide range of globular and membrane proteins, modular polypeptides and so on. The growth of the Database is influenced by a number of factors, e.g. the use of multiple motifs, the maximization of sequence information through iterative Database scanning and the fact that the Database searched is a large composite. The information contained within PRINTS is distinct from but complementary to the single consensus expressions stored in the widely used PROSITE dictionary of patterns.

Yunjia Wang - One of the best experts on this subject based on the ideXlab platform.

  • an adaptive weighted knn positioning method based on omnidirectional Fingerprint Database and twice affinity propagation clustering
    Sensors, 2018
    Co-Authors: Yunjia Wang, Hongji Cao
    Abstract:

    The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional Fingerprint Database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional Fingerprinting methods.

  • UPINLBS - A novel adaptive weighted K-nearest neighbor positioning method based on omnidirectional Fingerprint Database and twice affinity propagation clustering
    2018 Ubiquitous Positioning Indoor Navigation and Location-Based Services (UPINLBS), 2018
    Co-Authors: Yunjia Wang, Hongji Cao
    Abstract:

    Human body has a great influence on Wi-Fi signal propagation. Therefore, we present a novel adaptive weighted K-nearest neighbor (KNN) positioning method based on omnidirectional Fingerprint and twice affinity propagation clustering considering user’s orientation. Firstly, an improved Fingerprint Database model named omnidirectional Fingerprint Database (ODFD) is proposed, which includes the position, orientation and the sequence of mean received signal strength indicator at each reference point. Secondly, affinity propagation clustering (APC) algorithm is introduced for clustering on the offline stage based on the hybrid distance, which is the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed. KNN method is exploited to obtain K initial reference points (RPs), then all of them are clustered by APC algorithm based on RPs’ position-domain distances. The most probable cluster is reserved by the comparison of RPs’ number and signal-domain distance between cluster center and test point. The weighted average coordinate value of residual RPs in the remaining cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 meters, the root mean square error of 1.5 meters. Experimental results show that our proposed method outperforms traditional Fingerprinting methods.

Mu Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Fingerprint Database Updating Using Crowdsourcing in Indoor Bluetooth Positioning System
    2020
    Co-Authors: Zengshan Tian, Haifeng Cong, Mu Zhou
    Abstract:

    Fingerprint-based Bluetooth positioning is a popular indoor positioning technology. However, the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution, which will result in the decline of positioning accuracy. The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the Fingerprints with position labels for Fingerprint Database construction and updating. To address this problem, this paper presents an adaptive Fingerprint Database updating approach. First, the crowdsourced data including the Bluetooth Received Signal Strength (RSS) sequences and the speed and heading of the pedestrian were recorded. Second, the recorded crowdsourced data were fused by the Kalman Filtering (KF), and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate Fingerprints. Third, after enough candidate Fingerprints were obtained at each Reference Point (RP), the Density-based Spatial Clustering of Applications with Noise (DBSCAN) approach was conducted on both the original and the candidate Fingerprints to filter out the Fingerprints which had been identified as the noise, and then the mean of Fingerprints in the cluster with the largest data volume was selected as the updated Fingerprint of the corresponding RP. Finally, the extensive experimental results show that with the increase of the number of candidate Fingerprints and update iterations, the Fingerprint-based Bluetooth positioning accuracy can be effectively improved.

  • WISATS (1) - An Adaptive Fingerprint Database Updating Scheme for Indoor Bluetooth Positioning
    Wireless and Satellite Systems, 2019
    Co-Authors: Haifeng Cong, Liangbo Xie, Mu Zhou
    Abstract:

    The accuracy of Fingerprint based Bluetooth positioning technology depends on the Fingerprint Database established in offline phase. However, the change of environment and Access Point (AP) locations has significant impact on wireless signal distribution, resulting a decline in indoor Bluetooth positioning accuracy. In order to solve this problem, this paper presents a Fingerprint Database updating algorithm. Firstly, RSSI sequence, head, and speed information are extracted from crowdsourcing date. Secondly, the extracted information is used in Pedestrian Dead Reckoning Modification (PDRM) algorithm to get candidate Fingerprint. Finally, we propose concepts of standard Fingerprint, negative exponential time model, and similarity filtering to update original Fingerprint Database. The experimental results show that after the proposed Fingerprint Database updating, Fingerprint Database positioning accuracy is improved by 0.5 m.

  • Cost-efficient BLE Fingerprint Database construction approach via multi-quadric RBF interpolation
    EURASIP Journal on Wireless Communications and Networking, 2019
    Co-Authors: Liangbo Xie, Mu Zhou, Xiaoxiao Jin, Yue Wang, Zengshan Tian
    Abstract:

    The demand for indoor localization is becoming urgent, but the traditional location Fingerprint approach takes a lot of manpower and time to construct a fine-grained location Fingerprint Database. To address this problem, we propose to use the approach of combining dynamic collection of Fingerprint samples with Radial Basis Function (RBF) interpolation. Specifically, the raw sparse Fingerprint Database is constructed from a small number of Fingerprints collected on a few paths, in which the pedestrian track correction algorithm improves the validity and accuracy of the sparse Fingerprint Database. Then, the RBF interpolation approach is applied to enrich the sparse Fingerprint Database, in which the Genetic Algorithm (GA) is used to optimize the free shape parameter and the cut-off radius is determined according to the experimental results. Extensive experiments show that the proposed approach guarantees high interpolation and localization accuracy and also significantly reduces the effort of manual collection of Fingerprint samples.

  • MLICOM (1) - An Effective BLE Fingerprint Database Construction Method Based on MEMS
    Machine Learning and Intelligent Communications, 2018
    Co-Authors: Mu Zhou, Zengshan Tian, Xiaoxiao Jin, Cong Haifeng, Ren Haoliang
    Abstract:

    In indoor positioning system based on Fingerprint, the traditional Fingerprint Database construction method consumes much manpower and time cost. To solve this problem, we propose an effective method for constructing Fingerprint Database by using Microelectro Mechanical System (MEMS) to assist Bluetooth Low Energy (BLE), which overcomes the low efficiency of traditional methods. Meanwhile, the method achieves the comparable positioning accuracy and reduces workload more than 70%. In the optimization procedure, we use affine propagation clustering, outlier detection and filtering of Received Signal Strength Indication (RSSI) to optimize Fingerprint Database. Finally, the BLE positioning error conducted by the effective Database is about 2 m.

  • A Novel Method to Generate Wi-Fi Fingerprint Database Based on MEMS
    Communications Signal Processing and Systems, 2018
    Co-Authors: Zengshan Tian, Zipeng Wu, Mu Zhou, Ze Li, Yue Jin
    Abstract:

    Fingerprint-based positioning in Wi-Fi environment has caught much attention recently. One key issue is about the radio map construction, which generally requires significant effort to collect enough Wi-Fi Received Signal Strength (RSS) measurements. Based on the observation that the Micro Electromechanical System (MEMS) can automatically calibrate the target locations without complex equipment, we propose an efficient radio map construction method based on the technology of multi-sensor. Different from the conventional methods, the proposed one first relies on the gait detection approach and quaternion-based extend Kalman filter algorithm to estimate the velocity and heading of the target. Second, the Pedestrian Dead Reckoning (PDR) algorithm is used to calculate the current location of the target in a real-time manner, and meanwhile the data from Wi-Fi module are collected to generate the Fingerprint Database. The experimental results show that the proposed method is effective in positioning accuracy and efficient by saving the time and energy.