• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.529-531
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• 2005

A block-based fast motion estimation algorithm is proposed in this paper to perform motion estimation based on the efficiently reduced search ranges in MPEG-4(ERS). This algorithm divides the search areas into several small search areas and the candidate small search area that has the lowest average of sum norm difference between current macroblock and candidate macroblock is chosen to perform block motion estimation using the Nobel Successive Elimination Algorithm (NSEA). Experimental results of the proposed algorithm show that the averaging PSNR improvement is better maximum 0.125 dB than other tested algorithms and bit saving effect is maximum 20kbps for some tested sequences in low-bit rate circumstance.

• ETRI Journal
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• v.35 no.5
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• pp.757-766
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• 2013

This paper deals with the estimation of the time of arrival (TOA) of ultra-wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses a threshold that is derived analytically according to a preset false alarm probability. To highlight the effectiveness of the developed approach, simulation setups as well as real data experiments are conducted to assess the performance of the new TOA estimation algorithm. Thereby, the proposed method is compared with the cell averaging constant false alarm rate technique, the threshold comparison algorithm, and the technique based on maximum energy selection with search back. The obtained results are promising, considering both simulations and collected real-life data.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.4043-4048
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• 2016

Background: Head and neck cancer (HNC) is the ninth most common cancer worldwide, and has a poor 5-year survival rate averaging 50%, which has not changed for decades. A high prevalence of HNC has been reported in the southwestern region of Saudi Arabia, as compared to other areas of the country. However, data in regards to HNC are scattered and not well documented. Thus, the aim of this systematic review was to gather all available and updated important information regarding HNC in Saudi Arabia, and highlight the gaps of knowledge in our country with regard to this disease. In addition, suggestions of solutions to overcome the current status and improve our future standard of care to fight HNC are also highlighted. Materials and Methods: The electronic databases PubMed and Google Scholar using English-language literature were used for this systematic review, using specific inclusion and exclusion criteria and keywords. The search was performed in April 2016 and updated in June 2016. Results: Our search revealed twenty-one studies that fulfilled our inclusion and exclusion criteria and that were conducted in Saudi Arabia. These studies investigated different aspects of HNC, including prevalence, risk factors, biomarkers, and assessed knowledge and awareness of both public and practitioners with regard to HNC. Conclusions: This review uncovered a big gap in our epidemiological data in cancer information in general, and head and neck cancer in particular. In addition, a lack of knowledge and awareness of both the public and health care practitioners hinders the early diagnosis of disease and negatively impact the prognosis, treatment and outcome. The Ministry of Health in Saudi Arabia should develop a more systematic way and adapt policies to gather cancer information in general, and head and neck cancer in particular, from all governmental and private sectors from all over the kingdom, and develop educational programs to raise the knowledge and awareness of HNC in the country.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.