Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Smart hydrogel-based optical fiber SPR sensor for pH measurements

Abstract: A hydrogel-coated optical fiber surface plasmon resonance (SPR) sensor based on MMF-SMF-MMF structure has been fabricated for pH measurements. When the solutions with difference pH values contacting the sensing probe, the amount of the dissociated carboxylic ions in the hydrogel will change according to the pH value of the solution. This process will modify the volume and refractive index of the hydrogel, which results in a shift of the SPR wavelength. The experimental sensor is able to indicate pH variations in a wide pH range from 1 to 12, and the maximum pH sensitivity of the proposed sensor can reach 13 nm/pH at higher pH region (8–10 pH). The influence of temperature (in the range of 20 °C–40 °C) on SPR wavelength is found to be weak. Further, the sensor possesses remarkable repeatability and excellent stability. Due to the wide measuring range and simple fabrication process, the sensor is promising to be used for the pH measurements.

A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction

Abstract: Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.