Assembly and Characterization of Nanodevice using Carbon Nanotubes and Nanowires
ABSTRACT We report the assembly and characterization of three types of Ag, SnO2, and Ga2O3 nanowires (NWs) and two types of carbon nanotubes (NTs) using dielectrophoresis. The NWs and NTs were individually assembled using an experimental approach based on the dielectrophoretic force equation. After depositing a Pt top electrode using a focused ion beam, we investigated the I-V curves of NW and NT devices.
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ABSTRACT: We report the assembly and characterization of individually suspended Ag, GaN, SnO2, and Ga2O3 nanowires (NWs) using dielectrophoresis. The four kinds of NWs were individually assembled using an experimental approach based on the dielectrophoretic force equation. To freely suspend the individual NWs, we controlled the thickness of the bottom electrode. After depositing a Pt top electrode using a focused ion beam, we investigated the I–V curves of NW devices according to the change in the bottom electrode metal as well as the free suspension height from the insulator. We found that their conductivity for four kinds of NWs was remarkably increased along with the increase in the suspension height, while the gate effect in GaN was reduced.Nanotechnology 06/2006; 17(14):3388. DOI:10.1088/0957-4484/17/14/008 · 3.67 Impact Factor
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ABSTRACT: W<sup>4</sup> is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W<sup>4</sup> employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them. W<sup>4</sup> can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames. W<sup>4</sup> can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320×240 resolution images on a 400 MHz dual-Pentium II PCIEEE Transactions on Pattern Analysis and Machine Intelligence 09/2000; DOI:10.1109/34.868683 · 5.69 Impact Factor
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ABSTRACT: This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the userIEEE Transactions on Intelligent Transportation Systems 04/2002; 3(1-3):37 - 47. DOI:10.1109/6979.994794 · 2.47 Impact Factor