Ni Vision Acquisition Software 2011 Crack [WORK] 💖

Ni Vision Acquisition Software 2011 Crack

in the machine vision-based structural monitoring, the displacement, strain/stress, rotation, vibration, crack, and spalling should be captured by optical, infrared, or microwave sensors. most of these methods are based on the laser, which has been widely used in the measurement of displacement, strain/stress, and vibration response [ 16 ]. however, the laser device is not a perfect point source and the displacement measurement by using the conventional laser interferometric method is subjected to a number of limitations including lack of reliability, susceptibility to environmental disturbances, and accuracy [ 14 18 ]. to overcome these problems, a significant number of research works have been carried out [ 15 17 ].

the optical sensors have been developed in the early days. they are mainly classified into two categories: (1) the single-channel devices, including the ccd camera, charge-coupled device (ccd) camera, and camera phone, and (2) the multi-channel devices, such as the two-dimensional charge-coupled device (ccd) camera, three-dimensional ccd camera, and infrared image-intensified (i2) camera. the ccd camera is the first image sensor in the market and it is widely used in machine vision-based sensing and monitoring [ 19 ]. the ccd camera is mainly composed of an array of photosensitive sites, which can detect the light intensity variation, and an electronic circuit board which converts the variation into an electric signal. the quality of a ccd camera is closely related to its pixel, which is defined as the smallest unit of an image. the quality of ccd camera is measured by the number of pixels in the image and the resolution of the image. according to the type of the signal conversion circuit board and the lens, the ccd camera can be classified into two categories: the serial-parallel converter-type and the parallel-serial converter-type. in the parallel-serial converter-type, an array of photosensitive sites is treated as one sensor unit, which is connected to the input of a parallel-serial converter and then output to the electronic circuit board. in the serial-parallel converter-type, each sensor unit is independent, and the electronic circuit board is connected to the outputs of the array of sensors. the overall performance of the serial-parallel converter-type is better than that of the parallel-serial converter-type due to its simple construction.

a software-integrated system for 3d visual inspection of bridge components, called the steel structure inspection system (ssis) is developed. this system is demonstrated and validated by a practical bridge inspection case. the ssis consists of an image acquisition unit, a dedicated image processing unit, and a 3d visual inspection unit. the image acquisition unit consists of a high resolution camera, a line-scan camera, and a floating head scanner. the image processing unit is implemented on a personal computer (pc) with a dedicated graphics board. the 3d visual inspection unit has a stereo vision system and an optical measuring system for the height measurement. the feasibility of the proposed ssis is verified by conducting a practical bridge inspection case. the effectiveness of the proposed ssis is validated by comparing the defect results with the results of the conventional 3d visual inspection technique.
to estimate the performance of the vision-based system for inspection of the road bearing capacity of bridge foundation, the system is applied to inspect the visual images of the bridge. it is found that the vision-based system can effectively detect the cracks on the bridge foundation.
in recent years, with the rapid development of image processing technology, digital image processing has played an important role in the field of nondestructive testing (ndt) and structural health monitoring (shm). the main purpose of this paper is to review the applications of the machine vision-based techniques for bridge inspection and system fault diagnosis, and the authors hope that this paper is helpful for those who are interested in the practical applications of the machine vision-based ndt techniques.
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