The cognitive radio communication is taking the attentions because the development of the technique came to be possible to analyze wireless signals In the IEEE 80222 WRAN Systems[1], how to detect a spectrum and signals is continuously studied In this paper, we propose the efficient signal detection method using SCF (Spectral Correlation Function) It is easy to detect the signal feature when we are using the SCF Because most modulated signals have the cyclo-stationarity which is unique for each signal But the fading channel effected serious influence even though it detects the feature of the signal We applied LMS(Least Mean Square) filter for the compensation of the signal which is effected the serious influence in the fading channel And we analyze some signal patterns through the SCF And we show the unique signal feature of each signal through the SCF method It is robust for low SNR(Signal to Noise Ratio) environment and we can distinguish it in the fading channel using LMS Filter

/pdf/efficient-signal-feature-detection-method-using-spectral-12487tkd20.pdf

Efficient Signal Feature Detection method using Spectral Correlation Function in the Fading channel

In this paper, we discuss the mesh segmentation problem which divides a given 3D mesh into several disjoint sets. To solve the problem, we propose a greedy method based on the merging priority metric defined for representing the geometric properties of meaningful parts. The proposed priority metric is a weighted function using five geometric parameters, those are, a distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. In special, we can control by setting up the weight values of the proposed geometric parameters to obtain visually better mesh segmentation. Finally, we carry out an experiment on several 3D mesh models using the proposed methods and visualize the results.

A Greedy Merging Method for User-Steered Mesh Segmentation

This paper concerns with the application of e-based education methodology for people with disabilities targeting to empower them. Instead of the classic educational support such as extended time to take exams, a reader and/or scribe to assist with exams and note taking services, we suggest the use of new pedagogy-the science of educations integrating the state-of-the art. In this paper, we introduce the definition of disabilities for the people who does not fully understand what they means, first, and then possible implementing tools which can empower them with accomplishments. Most of the research in the field of pedagogy has tended to concentrate on the behavioral aspects of instructional sciences. Therefore we would like to point out that we concentrate on the aspects of instructional science particularly related with people with disabilities.

/pdf/the-e-based-pedagogy-for-people-with-disabilities-23s8fhyoge.pdf

The E-based Pedagogy for People with Disabilities

Emerging modern database applications require multi-dimensional index structures to provide high performance for data retrieval. In order for a multi-dimensional index structure to be integrated into a commercial database system, efficient techniques that provide transactional access to data through this index structure are necessary. The techniques must support all degrees of isolation offered by the database system. Especially degree 3 isolation, called “no phantom read,” protects search ranges from concurrent insertions and the rollbacks of deletions. In this paper, we propose a new phantom protection method for multi-dimensional index structures that uses a multi-level grid technique. The proposed mechanism is independent of the type of the multi-dimensional index structure, i.e., it can be applied to all types of index structures such as tree-based, file-based, and hash-based index structures. In addition, it has a low development cost and achieves high concurrency with a low lock overhead. It is shown through various experiments that the proposed method outperforms existing phantom protection methods for multi-dimensional index structures.

/pdf/phantom-protection-method-for-multi-dimensional-index-1ono1txtv3.pdf

Phantom Protection Method for Multi-dimensional Index Structures

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

Efficient Face Recognition using Low-Dimensional PCA

This paper proposed a new modular inverse algorithm based on the right-shifting binary Euclidean algorithm. For an n-bit numbers, the number of operations for the proposed algorithm is reduced about 61.3% less than the classical binary extended Euclidean algorithm. The proposed algorithm implementation shows substantial reduction in computation time over Galois field GF(p).

Improved Modular Inversion over GF(p)

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Improved Modular Inversion over GF(p)