Search Results for: jalal

Logical Foundations for Reasoning in Cyber – Physical Systems

T V Gopal*

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This paper aims at building the causal relations and event structures [13] to study the complex and evolving cyber – physical systems with illustrations of the reasoning based on Robotics [24] and Policy Analysis [25] for Communication Systems. An empirical analysis points to the realism that network security is also a geometric theory with safety and authentication tending to geometric formulae that make the larger structures. Security is very much a matter of perception too. The proposed approach also factors the perceptual aspects of the human mind. This paper includes several interesting possibilities for using linear algebra, discrete mathematics, analysis, and topology in the domains such as economics, game theory, robotics and biology to mention a few.

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Comparative Evaluation of WOFOST and CERES-Rice Models in Simulating Yield of Rice Cultivars at Navsari

Nilesh J. Hadiya1, Neeraj Kumar1, B. M. Mote2, Chiragkumar. M. Thumar1 and D. D. Patil2

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A field experiment was conducted during kharif season of 2015 to assess the prediction performance of CERES-Rice and WOFOST model for grain and straw yield of three rice cultivars viz., (V1:Jaya, V2: Gurjari and V3: GNR-2) sown under four different environments viz., (D1: 10/07/2015, D2: 25/07/2015, D3: 09/08/2015 and D4: 24/08/2015) with two nitrogen levels N1:75 and N2:100 kg NPK/ha-1.Results showed that the prediction of WOFOST model forgrain yield of rice cultivars under different treatments more close to the corresponding observed value with percent error PE between (18.66%)as camper to CERES-rice model with PE (28.56%), but for straw yield CERES-rice model give more close prediction than WOFOST model with PE (20.99%) and (27.33%) between predicted and observed value.

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Speeding Up Edge Segment Based Moving Object Detection Using Background Subtraction in Video Surveillance System

Amir S. Almallah and Jalal H. Awad

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Automatic real time video monitoring and object detection is indeed a challenge since there are many criteria that should be taken In mind in designing and implementing algorithms for this sake. The criteria that should be considered for example are processing speed, scene illumination variation and dynamic outdoor environment. In this study we propose a fast, flexible and immune against illumination variation approach for moving object detection based on the combination of edge segment based background modeling and background subtraction techniques. The first technique is used for building robust and flexible statistical background model, while the other technique is used for the prime detection of moving object to be compared later withthe flexible background. Thus this combination leads to computational reduction due to the second technique, and then flexible matching and precise detection due to the first technique.

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Background Construction of Video Frames in Video Surveillance System Using Pixel Frequency Accumulation

Jalal H. Awad and Amir S. Almallah   

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Moving object detection has been widely used in diverse discipline such as intelligent transportation systems, airport security systems, video monitoring systems, and so on. In this paper we proposed an edge segment based statistical background modeling algorithm, which can be implemented for moving edge detection in video surveillance system using static camera. The proposed method is an edge segment based, so it can help to exceed some of the difficulties that face traditional pixel based methods in updating background model or bringing out ghosts while a sudden change occurs in the background.As an edge segment based method it is robust to illumination variation and noise, it is also robust against the traditional difficulties that faces existing pixel based methods like the scattering of the moving edge pixels. Therefore they can’t utilize edge shape information. Some other edge segment based methods treat every edge segment equally creating edge mismatch due to non stationary background. The proposed method found elegant solution to this lake by using a model that uses the statistics of each background edge segment, so that it can model both the static and partially moving background edges using ordinary training images that may even contain moving objects.

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