In this paper, an approach for object tracking that is inspired from human oculomotor system is proposed and verified experimentally. The developed approach divided into two phases, fast tracking or saccadic phase and smooth pursuit phase. In the first phase, the field of the view is segmented into four regions that are analogue to retinal periphery in the oculomotor system. When the object of interest is entering these regions, the developed vision system responds by changing the values of the pan and tilt angles to allow the object lies in the fovea area and then the second phase will activate. A fuzzy logic method is implemented in the saccadic phase as an intelligent decision maker to select the values of the pan and tilt angle based on suggested logical rules. In smooth pursuit phase, the object is kept in the center area of the field of the view by smooth adjusting the values of the pan and tilt angles where this stage is analogue to putting the observing object in the fovea centralis of the oculomotor system. The proposed approach was implemented by using (Camera-Pan / Tilt) configuration and showed good results to improve the vision capability of the humanoid robots.
RMK Al-Zaid, AT Al-Musawi, SJ Mohammad
In this paper, for the first time we introduce a new four-parameter model called the Gumbel- Pareto distribution by using the T-X method. We obtain some of its mathematical properties. Some structural properties of the new distribution are studied. The method of maximum likelihood is used for estimating the model parameters. Numerical illustration and an application to a real data set are given to show the flexibility and potentiality of the new model.
المقدمة ومنهجية البحث
تتفق أغلب الأدبيات ومنها المحاسبية, بأن الخطوة الأولى في وضع وتقييم أي نظام إداري أو محاسبي هي في صياغة أهداف ذلك النظام لما لذلك من أهمية في توفير معيار للحكم على كفاءة التطبيق من عدمه.
وتعرف الأهداف بأنها (النهايات التي تعمل أو توجه النشاطات باتجاهها أو هي النهايات التي تطمح النشاطات تحقيقها). ( 1 ) ..,
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... Show MoreGround-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holl
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