Contours extraction from two dimensional echocardiographic images has been a challenge in digital image processing. This is essentially due to the heavy noise, poor quality of these images and some artifacts like papillary muscles, intra-cavity structures as chordate, and valves that can interfere with the endocardial border tracking. In this paper, we will present a technique to extract the contours of heart boundaries from a sequence of echocardiographic images, where it started with pre-processing to reduce noise and produce better image quality. By pre-processing the images, the unclear edges are avoided, and we can get an accurate detection of both heart boundary and movement of heart valves.
This manuscript presents several applications for solving special kinds of ordinary and partial differential equations using iteration methods such as Adomian decomposition method (ADM), Variation iterative method (VIM) and Taylor series method. These methods can be applied as well as to solve nonperturbed problems and 3rd order parabolic PDEs with variable coefficient. Moreover, we compare the results using ADM, VIM and Taylor series method. These methods are a commination of the two initial conditions.
: In this study, a linear synchronous machine is compared with a linear transverse flux machine. Both machines have been designed and built with the intention of being used as the power take off in a free piston engine. As both topologies are cylindrical, it is not possible to construct either using just flat laminations and so alternative methods are described and demonstrated. Despite the difference in topology and specification, the machines are compared on a common base in terms of rated force and suitability for use as a generator. Experience gained during the manufacture of two prototypes is described.
BMMAM Saleh, EUROPEAN ACADEMIC RESEARCH, 2016
The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreIt is known that life is as series of variety of difficult problems that individual looks
forward to overcome so as to achieve adaptation and to reach the desired aims .The transition
of the students from the school stage to the stage of the university is actually regarded a
dramatic change where students face when they enter university life that differs from what
they lived in secondary school.
The executive functions are considered the main element that participate in solving the
problems of high orders , because it involves the mental abilities that assist individual to
think and initiative as well as solving problems .
These functions include operational planning and the activated memory and inhibition of
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In The Bluest Eye (1970), the American-African writer, Toni Morrison explores how
Western standards of ideal beauty are created and propagated with and among the black
community. The novel not only portrays the lives of those whose dark skinned and Negroid
features blight their lives; it also shows how the standard of white beauty, when imposed on
black youth, can drastically damage one’s self-love and esteem which usually occurs when
beauty goes unrecognized. Morrison in this novel focuses on the damage that the black
women characters suffer through the construction of femininity in a racialised society where
whiteness is used as a standard of beauty.
Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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