Integration of biological data resources using image object keying.

This paper proposes a novel concept of ‘image object keying'. The work builds on earlier research in this area and shows how the 3D structure of a protein can be retrieved interactively from a gel electrophoresis protein spot. It uses intelligent image matching operations like the Hough Transform and Edge Detection techniques. Unique aspects are that searches may be initiated from multiple biological resources but with the results being integrated into a single page. A significant outcome of this work is that it enables researchers to search the database without the need to write and complex script

Integration of biological data resources using image object keying.

This paper proposes a novel concept of ‘image object keying'. The work builds on earlier research in this area and shows how the 3D structure of a protein can be retrieved interactively from a gel electrophoresis protein spot. It uses intelligent image matching operations like the Hough Transform and Edge Detection techniques. Unique aspects are that searches may be initiated from multiple biological resources but with the results being integrated into a single page. A significant outcome of this work is that it enables researchers to search the database without the need to write and complex script

A cooperative framework for molecular biology database integration using image object selection

The theme and the concept of 'Molecular Biology Database Integration' and the problems associated with this concept initiated the idea for this Ph.D research. The available technologies facilitate to analyse the data independently and discretely but it fails to integrate the data resources for more meaningful information. This along with the integration issues created the scope for this Ph.D research. The research has reviewed the 'database interoperability' problems and it has suggested a framework for integrating the molecular biology databases. The framework has proposed to develop a cooperative environment to share information on the basis of common purpose for the molecular biology databases. The research has also reviewed other implementation and interoperability issues for laboratory based, dedicated and target specific database. The research has addressed the following issues: diversity of molecular biology databases schemas, schema constructs and schema implementation multi-database query using image object keying, database integration technologies using context graph, automated navigation among these databases. This thesis has introduced a new approach for database implementation. It has introduced an interoperable component database concept to initiate multidatabase query on gene mutation data. A number of data models have been proposed for gene mutation data which is the basis for integrating the target specific component database to be integrated with the federated information system. The proposed data models are: data models for genetic trait analysis, classification of gene mutation data, pathological lesion data and laboratory data. The main feature of this component database is non-overlapping attributes and it will follow non-redundant integration approach as explained in the thesis. This will be achieved by storing attributes which will not have the union or intersection of any attributes that exist in public domain molecular biology databases. Unlike data warehousing technique, this feature is quite unique and novel. The component database will be integrated with other biological data sources for sharing information in a cooperative environment. This involves developing new tools. The thesis explains the role of these new tools which are: meta data extractor, mapping linker, query generator and result interpreter. These tools are used for a transparent integration without creating any global schema of the participating databases. The thesis has also established the concept of image object keying for multidatabase query and it has proposed a relevant algorithm for matching protein spot in gel electrophoresis image. An object spot in gel electrophoresis image will initiate the query when it is selected by the user. It matches the selected spot with other similar spots in other resource databases. This image object keying method is an alternative to conventional multidatabase query which requires writing complex SQL scripts. This method also resolve the semantic conflicts that exist among molecular biology databases. The research has proposed a new framework based on the context of the web data for interactions with different biological data resources. A formal description of the resource context is described in the thesis. The implementation of the context into Resource Document Framework (RDF) will be able to increase the interoperability by providing the description of the resources and the navigation plan for accessing the web based databases. A higher level construct is developed (has, provide and access) to implement the context into RDF for web interactions. The interactions within the resources are achieved by utilising an integration domain to extract the required information with a single instance and without writing any query scripts. The integration domain allows to navigate and to execute the query plan within the resource databases. An extractor module collects elements from different target webs and unify them as a whole object in a single page. The proposed framework is tested to find specific information e.g., information on Alzheimer's disease, from public domain biology resources, such as, Protein Data Bank, Genome Data Bank, Online Mendalian Inheritance in Man and local database. Finally, the thesis proposes further propositions and plans for future work

A cooperative framework for molecular biology database integration using image object selection.

The theme and the concept of 'Molecular Biology Database Integration’ and the problems associated with this concept initiated the idea for this Ph.D research. The available technologies facilitate to analyse the data independently and discretely but it fails to integrate the data resources for more meaningful information. This along with the integration issues created the scope for this Ph.D research. The research has reviewed the 'database interoperability' problems and it has suggested a framework for integrating the molecular biology databases. The framework has proposed to develop a cooperative environment to share information on the basis of common purpose for the molecular biology databases. The research has also reviewed other implementation and interoperability issues for laboratory based, dedicated and target specific database. The research has addressed the following issues: - diversity of molecular biology databases schemas, schema constructs and schema implementation -multi-database query using image object keying -database integration technologies using context graph - automated navigation among these databases This thesis has introduced a new approach for database implementation. It has introduced an interoperable component database concept to initiate multidatabase query on gene mutation data. A number of data models have been proposed for gene mutation data which is the basis for integrating the target specific component database to be integrated with the federated information system. The proposed data models are: data models for genetic trait analysis, classification of gene mutation data, pathological lesion data and laboratory data. The main feature of this component database is non-overlapping attributes and it will follow non-redundant integration approach as explained in the thesis. This will be achieved by storing attributes which will not have the union or intersection of any attributes that exist in public domain molecular biology databases. Unlike data warehousing technique, this feature is quite unique and novel. The component database will be integrated with other biological data sources for sharing information in a cooperative environment. This/involves developing new tools. The thesis explains the role of these new tools which are: meta data extractor, mapping linker, query generator and result interpreter. These tools are used for a transparent integration without creating any global schema of the participating databases. The thesis has also established the concept of image object keying for multidatabase query and it has proposed a relevant algorithm for matching protein spot in gel electrophoresis image. An object spot in gel electrophoresis image will initiate the query when it is selected by the user. It matches the selected spot with other similar spots in other resource databases. This image object keying method is an alternative to conventional multidatabase query which requires writing complex SQL scripts. This method also resolve the semantic conflicts that exist among molecular biology databases. The research has proposed a new framework based on the context of the web data for interactions with different biological data resources. A formal description of the resource context is described in the thesis. The implementation of the context into Resource Document Framework (RDF) will be able to increase the interoperability by providing the description of the resources and the navigation plan for accessing the web based databases. A higher level construct is developed (has, provide and access) to implement the context into RDF for web interactions. The interactions within the resources are achieved by utilising an integration domain to extract the required information with a single instance and without writing any query scripts. The integration domain allows to navigate and to execute the query plan within the resource databases. An extractor module collects elements from different target webs and unify them as a whole object in a single page. The proposed framework is tested to find specific information e.g., information on Alzheimer's disease, from public domain biology resources, such as, Protein Data Bank, Genome Data Bank, Online Mendalian Inheritance in Man and local database. Finally, the thesis proposes further propositions and plans for future work

A cooperative framework for molecular biology database integration using image object selection

The theme and the concept of 'Molecular Biology Database Integration' and the problems associated with this concept initiated the idea for this Ph.D research. The available technologies facilitate to analyse the data independently and discretely but it fails to integrate the data resources for more meaningful information. This along with the integration issues created the scope for this Ph.D research. The research has reviewed the 'database interoperability' problems and it has suggested a framework for integrating the molecular biology databases. The framework has proposed to develop a cooperative environment to share information on the basis of common purpose for the molecular biology databases. The research has also reviewed other implementation and interoperability issues for laboratory based, dedicated and target specific database. The research has addressed the following issues: - diversity of molecular biology databases schemas, schema constructs and schema implementation -multi-database query using image object keying -database integration technologies using context graph - automated navigation among these databases This thesis has introduced a new approach for database implementation. It has introduced an interoperable component database concept to initiate multidatabase query on gene mutation data. A number of data models have been proposed for gene mutation data which is the basis for integrating the target specific component database to be integrated with the federated information system. The proposed data models are: data models for genetic trait analysis, classification of gene mutation data, pathological lesion data and laboratory data. The main feature of this component database is non-overlapping attributes and it will follow non-redundant integration approach as explained in the thesis. This will be achieved by storing attributes which will not have the union or intersection of any attributes that exist in public domain molecular biology databases. Unlike data warehousing technique, this feature is quite unique and novel. The component database will be integrated with other biological data sources for sharing information in a cooperative environment. This/involves developing new tools. The thesis explains the role of these new tools which are: meta data extractor, mapping linker, query generator and result interpreter. These tools are used for a transparent integration without creating any global schema of the participating databases. The thesis has also established the concept of image object keying for multidatabase query and it has proposed a relevant algorithm for matching protein spot in gel electrophoresis image. An object spot in gel electrophoresis image will initiate the query when it is selected by the user. It matches the selected spot with other similar spots in other resource databases. This image object keying method is an alternative to conventional multidatabase query which requires writing complex SQL scripts. This method also resolve the semantic conflicts that exist among molecular biology databases. The research has proposed a new framework based on the context of the web data for interactions with different biological data resources. A formal description of the resource context is described in the thesis. The implementation of the context into Resource Document Framework (RDF) will be able to increase the interoperability by providing the description of the resources and the navigation plan for accessing the web based databases. A higher level construct is developed (has, provide and access) to implement the context into RDF for web interactions. The interactions within the resources are achieved by utilising an integration domain to extract the required information with a single instance and without writing any query scripts. The integration domain allows to navigate and to execute the query plan within the resource databases. An extractor module collects elements from different target webs and unify them as a whole object in a single page. The proposed framework is tested to find specific information e.g., information on Alzheimer's disease, from public domain biology resources, such as, Protein Data Bank, Genome Data Bank, Online Mendalian Inheritance in Man and local database. Finally, the thesis proposes further propositions and plans for future work.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

A Cognitive Radio (CR) is an intelligent wireless communication system, which is able to improve the utilization of the spectral environment. Spectrum sensing (SS) is one of the most important phases in the cognitive radio cycle, this operation consists in detecting signals presence in a particular frequency band. In order to detect primary user (PU) existence, this paper proposes a low cost and low power consumption spectrum sensing implementation. Our proposed platform is tested based on real world signals. Those signals are generated by a Raspberry Pi card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type).  RTL-SDR dongle is used as a reception interface. In this work, we compare the performance of three methods for SS operation: The energy detection technique, the Artificial neural network (ANN) and the support vector machine (SVM). So, the received data could be classified as a PU or not (noise) by the ED method, and by training and testing on a proposed ANN and SVM classification model. The proposed algorithms are implemented under MATLAB software. In order to determine the best architecture, in the case of ANN, two different training algorithms are compared. Furthermore, we have investigated the effect of several SVM functions. The main objective is to find out the best method for signal detection between the three methods. The performance evaluation of our proposed system are the probability of detection and the false alarm probability . This Comparative work has shown that the SS operation by SVM can be more accurate than ANN and ED