Ultraviolet-induced damage in fluorocarbon plasma and its reduction by pulse-time-modulated plasma in charge coupled device image sensor wafer processes

2004 ◽  
Vol 22 (6) ◽  
pp. 2818 ◽  
Author(s):  
Mitsuru Okigawa ◽  
Yasushi Ishikawa ◽  
Yoshinari Ichihashi ◽  
Seiji Samukawa
Keyword(s):  
Image Sensor ◽  
Pulse Time ◽  
Charge Coupled Device ◽  
Fluorocarbon Plasma

800 X 800 Charge-Coupled Device Image Sensor

1983 ◽  
Vol 22 (5) ◽  
Author(s):  
Morley M. Blouke ◽  
James R. Janesick ◽  
Joseph E. Hall ◽  
Marvin W. Cowens ◽  
Patrick J. May
Keyword(s):  
Image Sensor ◽  
Charge Coupled Device

scholarly journals Microtubules of the kinetochore fiber turn over in metaphase but not in anaphase.

1989 ◽  
Vol 109 (2) ◽  
pp. 653-662 ◽  
Author(s):  
G J Gorbsky ◽  
G G Borisy
Keyword(s):  
Quantitative Analysis ◽  
Control Level ◽  
Apparent Movement ◽  
Image Sensor ◽  
Fluorescence Recovery ◽  
Charge Coupled Device ◽  
Spindle Microtubules ◽  
A Charge ◽  
Almost All ◽  
Turn Over

In previous work we injected mitotic cells with fluorescent tubulin and photobleached them to mark domains on the spindle microtubules. We concluded that chromosomes move poleward along kinetochore fiber microtubules that remain stationary with respect to the pole while depolymerizing at the kinetochore. In those experiments, bleached zones in anaphase spindles showed some recovery of fluorescence with time. We wished to determine the nature of this recovery. Was it due to turnover of kinetochore fiber microtubules or of nonkinetochore microtubules or both? We also wished to investigate the question of turnover of kinetochore microtubules in metaphase. We microinjected cells with x-rhodamine tubulin (x-rh tubulin) and photobleached spindles in anaphase and metaphase. At various times after photobleaching, cells were detergent lysed in a cold buffer containing 80 microM calcium, conditions that led to the disassembly of almost all nonkinetochore microtubules. Quantitative analysis with a charge coupled device image sensor revealed that the bleached zones in anaphase cells showed no fluorescence recovery, suggesting that these kinetochore fiber microtubules do not turn over. Thus, the partial fluorescence recovery seen in our earlier anaphase experiments was likely due to turnover of nonkinetochore microtubules. In contrast fluorescence in metaphase cells recovered to approximately 70% the control level within 7 min suggesting that many, but perhaps not all, kinetochore fiber microtubules of metaphase cells do turn over. Analysis of the movements of metaphase bleached zones suggested that a slow poleward translocation of kinetochore microtubules occurred. However, within the variation of the data (0.12 +/- 0.24 micron/min), it could not be determined whether the apparent movement was real or artifactual.

scholarly journals The Inspection of Raw- Silk Defects Using Image Vision

2018 ◽  
Vol 13 (3) ◽  
pp. 155892501801300 ◽  
Author(s):  
Qingtian Pan ◽  
Miao Chen ◽  
Baoqi Zuo ◽  
Yucai Hu
Keyword(s):  
Detection System ◽  
Image Sensor ◽  
Support Vector ◽  
Technological Parameters ◽  
Image Capture ◽  
Charge Coupled Device ◽  
The Moment ◽  
A Charge ◽  
Ccd Image

The inspection of defects is one of the most important aspects in the quality inspection of raw silk. We introduce a raw-silk defect detection system based on image vision and image analysis that is accurate and objective. In the experimental phase, we develop an image-acquisition section—which includes a charge-coupled device (CCD) image sensor, a telecentric lens, a light source, and a raw-silk winding device to capture the raw silk images steadily. After the image capture stage, an image-processing section tasked with threshold segmentation and morphology operations is carried out to obtain the defects of raw silk. To classify the raw-silk defects accurately and quickly, we propose an area method for the classification of raw-silk defects into five categories: larger defects, large defects, common defects, small defects, and smaller defects. Meanwhile, in order to recognize the common raw-silk defects—e.g., Bavella silk, nodes, and loose ends—that cannot be detected by the Uster evenness tester, the moment invariants of each segmented region of the images are extracted and used as the input of support vector machine(SVM).A SVM is designed as a classifier to recognize the samples. The experimental results show that the proposed method can recognize these common raw-silk defects effectively. According to the new classification and accurate recognition of raw-silk defects using the proposed method, we can improve the inspection standards for raw silk and advise raw-silk reeling enterprises seeking to optimize the technological parameters.

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