Magnetic Hard Disk Serial Number Recognition Method Based on Machine Vision

2021 ◽  
Author(s):  
Zhe Xu ◽  
Xiaoge Liu ◽  
Jian Tang ◽  
Pengsheng Li ◽  
Ziying Zhang
Keyword(s):  
Machine Vision ◽  
Hard Disk ◽  
Recognition Method ◽  
Serial Number ◽  
Number Recognition ◽  
Magnetic Hard Disk

Experimental Observation of Fractal-Regular Surfaces and a Transformation Scheme for Extracting Fractal Parameters

2005 ◽  
Author(s):  
Shao Wang ◽  
Wai Kin Chan
Keyword(s):  
Silicon Wafer ◽  
Roughness Parameter ◽  
Hard Disk ◽  
Spectral Density Function ◽  
Power Spectral ◽  
Fractal Parameters ◽  
Asperity Contacts ◽  
Fractal Roughness ◽  
Magnetic Hard Disk ◽  
Wafer Surfaces

To account for the effects of asperity contacts at various length scales, it is appropriate to characterize an engineering surface as a fractal-regular surface. In spite of significant theoretical advancement, there is a desperate need for experimental verification of the theory of fractal-regular surfaces and a consistent scheme of obtaining the fractal parameters. In the present study, the existence of a fractal region and a regular-shape region in the power spectral density function for fractal-regular surfaces was confirmed experimentally, for the first time, with data obtained from magnetic hard disk and silicon wafer surfaces. A novel scheme involving a variable transformation was developed to extract fractal parameters. This scheme was validated by accurate recovery of fractal parameters from simulated surfaces. The fractal dimension, the fractal roughness parameter and the fractal domain length were found for magnetic hard disk and silicon wafer surfaces.

Analog Frontend for Tribo-Current-Based Fly-Height Sensor for Magnetic Hard Disk Drive

2018 ◽  
Vol 65 (2) ◽  
pp. 556-566
Author(s):  
Arup Polley ◽  
Pankaj Pandey ◽  
Bryan E. Bloodworth ◽  
Costin Cazana
Keyword(s):  
Hard Disk Drive ◽  
Hard Disk ◽  
Disk Drive ◽  
Magnetic Hard Disk ◽  
Fly Height

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

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