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Article Citation - WoS: 33Citation - Scopus: 41Visual and Auditory Data Fusion for Energy-Efficient and Improved Object Recognition in Wireless Multimedia Sensor Networks(Ieee-inst Electrical Electronics Engineers inc, 2019) Koyuncu, Murat; Yazici, Adnan; Civelek, Muhsin; Cosar, Ahmet; Sert, MustafaAutomatic threat classification without human intervention is a popular research topic in wireless multimedia sensor networks (WMSNs) especially within the context of surveillance applications. This paper explores the effect of fusing audio-visual multimedia and scalar data collected by the sensor nodes in a WMSN for the purpose of energy-efficient and accurate object detection and classification. In order to do that, we implemented a wireless multimedia sensor node with video and audio capturing and processing capabilities in addition to traditional/ordinary scalar sensors. The multimedia sensors are kept in sleep mode in order to save energy until they are activated by the scalar sensors which are always active. The object recognition results obtained from video and audio applications are fused to increase the object recognition performance of the sensor node. Final results are forwarded to the sink in text format, and this greatly reduces the size of data transmitted in network. Performance test results of the implemented prototype system show that the fusing audio data with visual data improves automatic object recognition capability of a sensor node significantly. Since auditory data requires less processing power compared to visual data, the overhead of processing the auditory data is not high, and it helps to extend network lifetime of WMSNs.Article Citation - WoS: 16Citation - Scopus: 25An Intelligent Fuzzy Object-Oriented Database Framework for Video Database Applications(Elsevier, 2009) Ozgur, Nezihe Burcu; Koyuncu, Murat; Yazici, AdnanVideo database applications call for flexible and powerful modeling and querying facilities, which require an integration or interaction between database and knowledge-based technologies. It is also necessary for many real life video database applications to incorporate uncertainty, which naturally occurs due to the complex and subjective semantic content of video data. In this study, firstly, we introduce a fuzzy conceptual data model to represent the semantic content of video data. For that purpose, UML (unified modeling language) is utilized and extended to represent uncertain information along with video specific properties. Secondly, we present an intelligent fuzzy object-oriented database framework for video database applications. The introduced fuzzy conceptual model is used in this framework, which provides modeling of complex and rich semantic content and knowledge of video data including uncertainty. Moreover, it supports various flexible queries including (fuzzy) semantic, temporal and (fuzzy) spatial queries, based on the video data model. We think that the presented conceptual data model and the framework can be used for any video database application. (C) 2009 Elsevier B.V. All rights reserved.Article Citation - WoS: 9Citation - Scopus: 11Food Index: a Multidimensional Index Structure for Similarity-Based Fuzzy Object Oriented Database Models(Ieee-inst Electrical Electronics Engineers inc, 2008) Yazici, Adnan; Ince, Cagri; Koyuncu, MuratA fuzzy object-oriented data model is a fuzzy logic-based extension to an object-oriented database model that permits uncertain data to be explicitly represented. The fuzzy object-oriented database (FOOD) model is one of the proposed models in the literature to handle uncertainty in object-oriented databases. Several kinds of fuzziness are dealt with in the FOOD model, including fuzziness at attribute level and between object and class and between class and superclass relations. The traditional index structures do not allow efficient access to both crisp and fuzzy objects for fuzzy object-oriented databases since they are not efficient enough in processing both crisp and fuzzy queries. In this study, we propose a new index structure, namely a FOOD index (FI), to deal with different kinds of fuzziness in fuzzy object-oriented databases and to support multidimensional indexing. In this paper, we describe this proposed index structure and show how it supports various types of flexible queries, and evaluate its performance for exact, range, and fuzzy queries.
