Northeastern State University

About: Northeastern State University is a education organization based out in Tahlequah, Oklahoma, United States. It is known for research contribution in the topics: Wireless sensor network & Computer science. The organization has 477 authors who have published 831 publications receiving 21482 citations. The organization is also known as: NSU.

The Internal Other: Exploring the Dialectical Relationship Between Regional Exclusion and the Construction of National Identity

Abstract: Societies have historically sought to spatialize difference—to other—even within the boundaries of supposedly unified polities. Drawing on previous scholarship on the spatialization of difference in published case studies, we examine the dialectical relationship between the formation and institutionalization of regions, on the one hand, and the nation-building process more broadly on the Other. Certain regions become repositories for undesirable national traits as part of a dialectical process of nation and region building. The processes of othering are rarely as linear and tidy as proposed in some current formulations of the theory; rather, othering involves a host of concomitant processes that work together to produce economically and culturally differentiated regions. The processes by which particular places or regions become “othered” are not only interesting in the abstract but also carry with them enduring material consequences. To demonstrate this effect, we visit two historical case studies that e...

A Greedy Deep Learning Method for Medical Disease Analysis

Abstract: This paper proposes a new deep learning method, the greedy deep weighted dictionary learning for mobile multimedia for medical diseases analysis. Based on the traditional dictionary learning methods, which neglects the relationship between the sample and the dictionary atom, we propose the weighted mechanism to connect the sample with the dictionary atom in this paper. Meanwhile, the traditional dictionary learning method is prone to cause over-fitting for patient classification of the limited training data set. Therefore, this paper adopts $\mathrm {l}_{2}$ -norm regularization constraint, which realizes the limitation of the model space, and enhances the generalization ability of the model and avoids over-fitting to some extent. Compared with the previous shallow dictionary learning, this paper proposed the greedy deep dictionary learning. We adopt the thinking of layer by layer training to increase the hidden layer, so that the local information between the layer and the layer can be trained to maintain their own characteristics, reduce the risk of over-fitting and make sure that each layer of the network is convergent, which improves the accuracy of training and learning. With the development of Internet of Things and the soundness of healthcare monitoring system, the method proposed have better reliability in the field of mobile multimedia for healthcare. The results show that the learning method has a good effect on the classification of mobile multimedia for medical diseases, and the accuracy, sensitivity, and specificity of the classification have good performance, which may provide guidance for the diagnosis of disease in wisdom medical.

A Robust Watermarking Scheme in YCbCr Color Space Based on Channel Coding

Abstract: The rapid development of big data and cloud computing technologies greatly accelerate the spreading and utilization of images and videos. The copyright protection for images and videos is becoming increasingly serious. In this paper, we proposed the robust non-blind watermarking schemes in YCbCr color space based on channel coding. The source watermark image is encoded and singular value decomposed. Subsequently, the singular value matrixes are embedded into the Y, Cb, and Cr components of the host image after four-level discrete wavelet transform (DWT). The embedding factor for each component is calculated based on the just-noticeable distortion and the singular vectors of HL subband of DWT. The peak signal-to-noise ratio of the watermarked image and the normalized correlation coefficient of the extracted watermark are investigated. It is shown that the proposed channel coding-based schemes can achieve near exact watermark recovery against all kinds of attacks. Considering both robustness and transparency, the convolutional code-based additive embedding scheme is optimal, which can also achieve good performance for video watermarking after extension.