Mengxin Li

Single-Walled Carbon Nanotube-Based Gas Sensors for NO2 Detection

An efficient method for nitrogen dioxide (NO2) gas detection in single-walled carbon nanotubes (SWCNTs) ordered using dielectrophoretical (DEP) technology after dispersion in sodium dodecyl sulfate (SDS) surfactant solution. Atom force microscopy (AFM) and scanning electron microscopy (SEM) images revealed that SWCNTs were assembled between the microelectrodes. SWCNTs were affected by the electrophoretic force which was carried out by the related theoretical analysis in a nonuniform electric field. SWCNT field effect transistors (SWCNT-FETs) geometry was obtained. The electrical performance of NO2 gas sensor with SWCNT-FETs structure was tested before and after NO2 at room temperature. Experimental results that the efficient assembly of SWCNTs were obtained when the applied alternating current voltage has a frequency of 2 MHz and an amplitude of 10 V. SWCNTs-based gas sensor had high sensitivity to NO2, and the electrical conductance of NO2 gas sensor reduced two times. SWCNTs surface gas molecules were washed out by means of ultraviolet ray irradiation in 10 minutes. NO2 gas sensor could be duplicated. Meanwhile, it also provided an effective method of assembly and manufacture for other one-dimensional nanomaterials assembly of nanoelectronic devices.

A Vision-Based Inspection System using Fuzzy Rough Neural Network Method

A vision-based inspection method based on rough set theory, fuzzy set and neural network algorithm is presented. The rough set method is proposed to remove redundant features for its data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable form for input to a BP network classifier. The BP neural classifier is considered the most popular, effective and easy-to-learn model for complex, multi-layered network. By the experiment research, the hybrid method shows good classification accuracy and short running time, which are better than the results using BP network and neural network with fuzzy input

An Improved BP Network Classifier Based on VPRS Feature Reduction

Variable precision rough sets (VPRS), as a extension of rough sets (RS) is adopted to reduce the redundant features for its ability of more useful information adopted compared with RS. The reduced features after VPRS are fed into the improved BP network proposed to inspect the defects of surface quality, which results in short training time and a high classification accuracy with a typical application in defect inspection of wood veneer

A cluster-based PMVS algorithm with geometric constraint

Patch-based Multi-view Stereo (PMVS) is a simple and effective algorithm for generating colorful 3D points with a set of pictures and camera parameters but without any initialization. Still, there are some problems when large image sets are reconstructed. On the one hand the reconstructed surface is not smooth, continuous enough, and the problem becomes more serious under certain image capturing configuration such as downward-shooting or upward-shooting. On the other hand, time and space complexity are too high to reconstruct. This paper proposes a novel algorithm for 3D reconstruction based on clustering. Firstly, decomposing the images into a set of overlapping subsets of pictures to reduce the running time and solve the problem that system memory consumption is too large, then reconstruct each cluster through PMVS algorithm with geometric constraint to improve the accuracy and smoothness, finally merge all resulting reconstructions. Experimental results show that the proposed method is available and practical.

Overview of 3D Reconstruction Methods Based on Multi-view

3D reconstruction based on multi-view is a hot research topic in computer vision due to more information and wide scope of the view of multiple views. Feature detection and matching, fundamental matrix estimation, camera self-calibration, 3D reconstruction and dense surface reconstruction are key proportions of 3D reconstruction. This paper summarizes the main algorithms in these parts, analyses and compares merits and drawbacks of the methods. The aim is to provide a concise, complete understanding of the subject so that the researchers in this field can have a holistic view of the problem and provide more efficient and unique solutions in the future.

High Yield Assembly of Cu/CuO Nanowires Device

The high yield assembly and fabrication method for Cu/CuO nanowires for nanoelectronic devices was implemented. Assembly of Cu/CuO nanowires nano-electronic devices were realized by floating potential and dielectrophoresis approach. The simulation of floating potential distribution of the chip was performed by comsol multiphysics coupling software. Six hundred devices were assembled on the area of less than one square centimeter. The assembled devices were characterized by scanning electron microscopy. The experimental results showed that high yield assembly had been realized, and the success rate of Cu/CuO nanowires ideal assembly for nanoelectronic devices had been assessed.

Study on Assembly Method for GaAs Nanowires Device

The assembly and fabrication method for Gallium arsenide (GaAs) nanowires nano devices was implemented. Assembly of GaAs nanowires field effect transistor (FET) was realized by dielectrophoresis approach. Before deposition of the contacts, GaAs nanowires were treated wet etching in an ammonium polysulfide ((NH4)2S) solution to remove a surface oxide layer. The assembled devices were characterized by atomic force microscopy. The experimental results showed that the efficient assembly of GaAs nanowires was obtained when the applied alternating current voltage has a frequency of 1.5MHz and an amplitude of 10 V, and the success rate of ideal assembly for GaAs nanowires FET had been assessed. Meanwhile, it also provided an effective assembly method for other one-dimensional nanomaterials assembly of nano devices.

A new path planning method for a shape-shifting robot

A shape-shifting robot with changeable configurations can accomplish search and rescue tasks which could not be achieved by manpower sometimes. The accessibility of this robot to uneven environment was efficiently enlarged by changing its configuration. In this paper, a path planning method is presented that integrates the reconfigurable ability of the robot with the potential field law. An adaptive genetic algorithm is applied to solve effectively the local minimum problem. The experiments show that the robots configurations can be changed to perform the path planning with the environmental variation. Moreover, the path has been shortened effectively.