Many theories have sought to explain the evolution of sex, but the question remains unanswered owing to the scarcity of compelling empirical tests. Here we summarize the results of two of our published studies investi...
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This article was originally published online on 10 May 2012 with an incorrect affiliation for co-author D. L. Fan and an incorrect footnote designation for co-a
This article was originally published online on 10 May 2012 with an incorrect affiliation for co-author D. L. Fan and an incorrect footnote designation for co-a
In this paper, an approach of integrating skin color segmentation and self-adapted template matching for the robust face detection is proposed. According to the clustering properties of the skin color of human faces i...
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Sharing resources and information on Internet has become an important activity for education. The learning object repository has been developed to achieve efficient management of learning objects. Following usage expe...
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Sharing resources and information on Internet has become an important activity for education. The learning object repository has been developed to achieve efficient management of learning objects. Following usage experiences of learning objects collected in the past, this study concentrates on investigating implicit information between learning objects. We define a social structure for identifying relationship between learning objects and define a set of metrics to evaluate the interdependency. The structure identifies usage experiences and can be graphed in terms of implicit and explicit relations among learning objects. As a practical contribution, an adaptive algorithm is proposed to mine the social structure. The algorithm generates adaptive learning sequence by identifying possible interactive search input and assists them in completing self-paced learning situation.
This paper presents a fully complex-valued functional link network (CFLN). The CFLN is a single-layered neural network, which introduces nonlinearity in the input layer using nonlinear functions of the original input ...
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This paper presents a fully complex-valued functional link network (CFLN). The CFLN is a single-layered neural network, which introduces nonlinearity in the input layer using nonlinear functions of the original input variables. In this study, we consider multivariate polynomials as the nonlinear functions. Unlike multilayer neural networks, the CFLN is free from local minima problem, and it offers very fast learning in parameters because of its linear structure. In the complex domain, polynomial based CFLN has an additional advantage of not requiring activation functions, which is a major concern in the complex-valued neural networks. However, it is important to select a smaller subset of polynomial terms (monomials) for faster and better performance, since the number of all possible monomials may be quite large. In this paper, we use the orthogonal least squares method in a constructive fashion (starting from lower degree to higher) for the selection of a parsimonious subset of monomials. Simulation results demonstrate that computing CFLN in purely complex domain is advantageous than in double-dimensional real domain, in terms of number of connection parameters, faster design, and possibly generalization performance. Moreover, our proposed CFLN compares favorably with several other multilayer networks in the complex domain.
An FIR filter is implemented in this work. Enhancing the arithmetic operations of the filter is considered. For the addition operation, the signed-digit number system is utilized. For the multiplication operation, Boo...
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An FIR filter is implemented in this work. Enhancing the arithmetic operations of the filter is considered. For the addition operation, the signed-digit number system is utilized. For the multiplication operation, Booth-3 algorithm is used to reduce the number of partial products. Then a 1D filter is used to construct a 2D filter that is deployed on real hardware in an image processing application.
We introduce some modifications to the widely deployed Kerberos authentication protocol. The principle's secret-key will be independent of the user password to overcome the weak passwords chosen by the network pri...
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We introduce some modifications to the widely deployed Kerberos authentication protocol. The principle's secret-key will be independent of the user password to overcome the weak passwords chosen by the network principal that are susceptible to password guessing attacks, the main drawback of the Kerberos protocol. Instead, the Kerberos Distribution Center saves a profile for every instance in its realm to generate the principle's secret-key by hashing the profile, and encrypting the output digest. Besides, the lifetime of the secret-key is controlled using the sys-tem clock. Triple-Des is used for encryption, SHA-256 for hashing, and Blum Blum Shub for random number gen-eration.
Background/Purpose. Limited spatial resolution of positron emission tomography (PET) requires partial volume correction (PVC). Region-based PVC methods are based on geometric transfer matrix implemented either in imag...
Background/Purpose. Limited spatial resolution of positron emission tomography (PET) requires partial volume correction (PVC). Region-based PVC methods are based on geometric transfer matrix implemented either in image-space (GTM) or sinogram-space (GTMo), both with similar performance. Although GTMo is slower, it more closely simulates the 3D PET image acquisition, accounts for local variations of point spread function, and can be implemented for iterative reconstructions. A recent image-based symmetric GTM (sGTM) has shown improvement in noise characteristics and robustness to misregistration over GTM. This study implements the sGTM method in sinogram space (sGTMo), validates it, and evaluates its performance. Methods. Two 3D sphere and brain digital phantoms and a physical sphere phantom were used. All four region-based PVC methods (GTM, GTMo, sGTM, and sGTMo) were implemented and their performance was evaluated. Results. All four PVC methods had similar accuracies. Both noise propagation and robustness of the sGTMo method were similar to those of sGTM method while they were better than those of GTMo method especially for smaller objects. Conclusion. The sGTMo was implemented and validated. The performance of the sGTMo in terms of noise characteristics and robustness to misregistration is similar to that of the sGTM method and improved compared to the GTMo method.
This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, ...
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This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, and transparent fashion with a web-based education environment, in this case Module Object-Oriented Dynamic Learning Environment (Moodle), using Android mobile devices. In order to provide a strong and lasting architecture, the Service Oriented Architecture (SOA) methodology is used given that it allows easy software re-utilization as well as integration of heterogeneous services. The architecture is based on web services implemented with Representational State Transfer (REST), as it has been demonstrated to be lighter and less consuming than other protocols, for devices with limited resources such as mobile devices. Web services provide the communication means between the server side and the client side of the architecture, whereas agents are used to deliver the services itself. The authors propose the development of an environment that facilitates the integration of various educational resources to support m-learning. An important aspect of the proposal is the offering of a tool to provide customized alerts for students and teachers, enabling them to remain updated about activities taking place in the courses.
We report results of the experimental investigation of the low-frequency noise in graphene transistors. The graphene devices were measured in three-terminal configuration. The measurements revealed low flicker noise l...
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We report results of the experimental investigation of the low-frequency noise in graphene transistors. The graphene devices were measured in three-terminal configuration. The measurements revealed low flicker noise levels with the normalized noise spectral density close to 1/f (f is the frequency) and the Hooge parameter α H ~10 -3 . Both top-gate and back-gate devices were studied. The analysis of the noise spectral-density dependence on the gate biases helped us to elucidate the noise sources in these devices. We compared the noise performance of graphene devices with that of carbon nanotube devices. It was determined that graphene devices works better than carbon nanotube devices in terms of the low-frequency noise. The obtained results are important for graphene electronic, communication and sensor applications.
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