A quorum sensing inspired algorithm for dynamic clustering

Quorum sensing is a decentralized biological process, through which a community of cells with no global awareness coordinate their functional behaviors based only on cell-medium interactions and local decisions. This paper draws inspiration from quorum sensing and colony competition to derive a new algorithm for data clustering. The algorithm treats each data as a single cell, and uses knowledge of local connectivity to cluster cells into multiple colonies simultaneously. It simulates auto-inducers secretion in quorum sensing to tune the influence radius for each cell. At the same time, sparsely distributed core cells spread their influences to form colonies, and interactions between colonies eventually determine each cell's identity. The algorithm has the flexibility to analyze both static and time-varying data, and its stability and convergence properties are established. The algorithm is tested on several applications, including both synthetic and real benchmarks datasets, alleles clustering, dynamic systems grouping and model identification. Although the algorithm is originally motivated by curiosity about biology-inspired computation, the results suggests that in parallel implementation it performs as well as state-of-the art methods on static data, while showing promising performance on time-varying data such as e.g. clustering robotic swarms.Boeing Compan

Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented

Processing of Byzantine Neume Notation in Ancient Historical Manuscripts

This article presents the principal results of the doctoral thesis “Recognition of neume notation in historical documents” by Lasko Laskov (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), successfully defended before the Specialized Academic Council for Informatics and Mathematical Modelling on 07 June 2010.Byzantine neume notation is a specific form of note script, used by the Orthodox Christian Church since ancient times until nowadays for writing music and musical forms in sacred documents. Such documents are an object of extensive scientific research and naturally with the development of computer and information technologies the need of a software tool which can assist these efforts is needed. In this paper a set of algorithms for processing and analysis of Byzantine neume notation are presented which include document image segmentation, character feature vector extraction, classifier learning and character recognition. The described algorithms are implemented as an integrated scientific software system.* This work has been partly supported by Grant No. DTK 02/54, Bulgarian Science Fund, Ministry of Education, Youth and Science

Implementation of an Automated Image Processing System for Observing the Activities of Honey Bees

This research designed and implemented an automated system to collect data on honey bees using computer science techniques. This system utilizes image processing techniques to extract data from the videos taken in front or at the top of the hive’s entrance. Several web-based applications are used to obtain temperature and humidity data from National weather Service to supplement the data that are collected at the hive locally. All the weather data and those extracted from the images are stored in a MySQL database for analysis and accessed by an iPhone App that is designed as part of this research

An Improved GA Based Modified Dynamic Neural Network for Cantonese-Digit Speech Recognition

Author name used in this publication: F. H. F. Leung2007-2008 > Academic research: refereed > Chapter in an edited book (author)published_fina

Feature Extraction Methods for Character Recognition

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