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RISS 인기검색어
- 검색키워드
- 저자 : Othman O. Khalifa (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Human Posture Recognition: Methodology and Implementation
Kyaw Kyaw Htike,Othman O. Khalifa 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, humancomputer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.
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Human Posture Recognition: Methodology and Implementation
Htike, Kyaw Kyaw,Khalifa, Othman O. The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.
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Position-Based Multicast Routing in Mobile Ad hoc Networks: An Analytical Study
( Mohammad M. Qabajeh ),( Aisha H. Adballa ),( Othman O. Khalifa ),( Liana K. Qabajeh ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.6
With the prevalence of multimedia applications and the potential commercial usage of Mobile Ad hoc Networks (MANETs) in group communications, Quality of Service (QoS) support became a key requirement. Recently, some researchers studied QoS multicast issues in MANETs. Most of the existing QoS multicast routing protocols are designed with flat topology and small networks in mind. In this paper, we investigate the scalability problem of these routing protocols. In particular, a Position-Based QoS Multicast Routing Protocol (PBQMRP) has been developed. PBQMRP builds a source multicast tree guided by the geographic information of the mobile nodes, which helps in achieving more efficient multicast delivery. This protocol depends on the location information of the multicast members which is obtained using a location service algorithm. A virtual backbone structure has been proposed to perform this location service with minimum overhead and this structure is utilized to provide efficient packet transmissions in a dynamic mobile Ad hoc network environment. The performance of PBQMRP is evaluated by performing both quantitative analysis and extensive simulations. The results show that the used virtual clustering is very useful in improving scalability and outperforms other clustering schemes. Compared to On-Demand Multicast Routing Protocol (ODMRP), PBQMRP achieves competing packet delivery ratio and significantly lower control overhead.
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A Cluster-based QoS Multicast Routing Protocol for Scalable MANETs
( Mohammad M. Qabajeh ),( Aisha H. Adballa ),( Othman O. Khalifa ),( Liana K. Qabajeh ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.4
Recently, multicast routing protocols become increasingly important aspect in Mobile Ad hoc Networks (MANETs), as they effectively manage group communications. Meanwhile, multimedia and real-time applications are becoming essential need for users of MANET. Thus it is necessary to design efficient and effective Quality of Service (QoS) multicast routing strategies. In this paper, we address the scalability problem of multicast routing protocols to support QoS over MANETs. In particular, we introduce a Position-Based QoS Multicast Routing Protocol (PBQMRP). Basically, the protocol based on dividing the network area into virtual hexagonal cells. Then, the location information is exploited to perform efficient and scalable route discovery. In comparison with other existing QoS multicast routing protocols, PBQMRP incurs less control packets by eliminating network flooding behavior. Through simulation, the efficiency and scalability of PBQMRP are evaluated and compared with the well-known On-Demand Multicast Routing Protocol (ODMRP). Simulation results justify that our protocol has better performance, less control overhead and higher scalability.
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