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.

Liquid Structure of Bis(trifluoromethylsulfonyl)imide-Based Ionic Liquids Assessed by FT-IR Spectroscopy.

Abstract: Ionic liquids are a fertile and active area of research, in part, due to the unique properties these solvents offer over traditional molecular solvents. Because these properties are rooted in the fundamental ion–ion interactions that govern their liquid structure, there is a strong motivation to characterize the liquid structure of ionic liquids. Infrared spectroscopy is a standard analytical tool for assessing liquid structures, for the intramolecular vibrational modes of the ions composing the materials are often quite sensitive to their local potential energy environment. In this work, we demonstrate that the band asymmetry for the νa(SNS) anion mode of N(Tf)2–-based ionic liquids originates from the dynamic coupling of vibrationally induced dipole moments of anions across a quasilattice. The magnitude of TO–LO splitting is linearly correlated with the number densities of the ionic liquids; an observation that is in accord with the predictions of dipolar coupling theory. Dipole moment derivatives of νa...

Defining undergraduate research in computer science: a survey of computer science faculty

Abstract: What are the objectives of undergraduate research by computer science students: to prepare select students for graduate school or to enhance the undergraduate experience? What should be the criteria for classifying a student's work as "undergraduate research?" To address these questions, a comprehensive questionnaire was developed and distributed to faculty at the SIGCSE Technical Symposium, 2008 and the CCSC Central Plains conference, 2008. This paper presents key results from analyzing the responses to many of the questions that were included in the questionnaire. The study comprises a large body of useful information for the academic computer science community in evaluating existing implementations of undergraduate research and as a guide in developing new programs.

Variational Regularized Tree-Structured Wavelet Sparsity for CS-SENSE Parallel Imaging

Abstract: Both compressed sensing magnetic resonance imaging (MRI) and parallel MRI have emerged as effective techniques to accelerate MRI data acquisition in various clinical applications. The hybrid parallel imaging reconstruction methods by combining these two techniques have been developed for providing further acceleration. However, the widely used $L_{1}$ -norm of wavelet coefficients and total variation (TV) regularizer in traditional hybrid imaging methods limited further improvement in image quality. To further enhance imaging quality and reduce acquisition time, we proposed a regularized parallel imaging reconstruction method by incorporating sparsity-promoting wavelet prior and total generalized variation (TGV) regularizer. Specifically, the wavelet sparsity is effectively promoted through the $L_{0}$ quasi-norm of wavelet coefficients and tree-structured wavelet representation. This sparsity-promoting wavelet prior is capable of representing a better measure of sparseness to guarantee high-quality reconstruction even for high degrees of undersampling. Unlike TV regularizer, which preserves sharp edges but suffers from staircaselike artifacts, TGV regularizer can balance the tradeoff between edges preservation and artifacts suppression. Numerous experiments have been conducted on both simulated and in vivo MRI data sets to compare our proposed method with some state-of-the-art reconstruction methods. Experimental results have demonstrated its superior imaging performance in terms of both quantitative evaluation and visual quality.

Reliability of rotary prism fusional vergence ranges.

Abstract: Background The measurement of fusional vergence ranges is an important clinical test in the assessment of binocular vision status. Fusional vergence ranges are typically measured by recording a patient's reports of blur, break, and recovery to base-in (BI) and base-out (BO) prism. Published reliability data on fusional vergence ranges are very limited. Methods Eight subjects underwent four testing sessions, at which repeated measurements of fusional vergence ranges were taken. Near ranges were tested at the first session only Distance ranges were tested at all four sessions. Intra-examiner standard deviations were calculated for each fusional vergence test result (BI and BO; blur, break, and recovery) for each session. Intra-examiner standard deviations were averaged. These values were used to determine 95% limits of agreement. Results The 95% limits of agreement were between 2 delta and 2.5 delta for the distance BI break and recovery and for the near BI recovery; between 3 and 4 delta for near BI break and near BO break; between 4 and 5 delta for distance BO blur and recovery and for near BI blur; and between 5 and 5.5 delta for distance BO break and near BO blur and recovery.

FSLM: An Intelligent Few-Shot Learning Model Based on Siamese Networks for IoT Technology

Abstract: As an important application of the Internet of Things (IoT) devices, sentiment analysis has been paid more attention with the rapid development of artificial intelligence. As a widely used method in artificial intelligence applications, traditional deep learning methods need massive data for training. However, due to the limitations of hardware, IoT devices have deficiencies in processing big data. In the case of insufficient sample size, how to carry out a machine learning method for IoT devices has become a common concern of the industry. In order to perform sentiment analysis on text with few data samples from the IoT devices, we propose FSLM, which is an intelligent few-shot learning model based on Siamese networks. The FSLM model consists of two self-attention models with the same parameters, which are divided into two parts. First, for two input texts, a self-attention model is used to extract sentiment features, and then the Mahalanobis distance is adopted to measure the similarity between two feature vectors to determine whether they belong to the same category. The FSLM is tested on the Amazon Review Sentiment Classification (ARSC) data set. The extensive experimental results on this data set demonstrate that the FSLM model has better accuracy and robustness for text sentiment analysis than other main existing models with a small number of samples.