scholarly journals QUALITY ASSESSMENT TECHNIQUES IN FRP COMPOSITES – A REVIEW

2017 ◽  
Vol 4 (09) ◽  
Keyword(s):  
Quality Assessment ◽  
Frp Composites ◽  
Assessment Techniques

Pharmacy Considerations Related to Radiolabeled Monoclonal Antibodies

1989 ◽  
Vol 2 (3) ◽  
pp. 177-184 ◽  
Author(s):  
George H. Hinkle ◽  
Robert W. Beightol ◽  
Ned Gregorio ◽  
Steven R. Lefevre
Keyword(s):  
Monoclonal Antibodies ◽  
Quality Assessment ◽  
Tumor Cells ◽  
Cell Cultures ◽  
Antibody Production ◽  
Selection Criteria ◽  
Radioimmunoguided Surgery ◽  
Assessment Techniques

Current modalities used for the detection and treatment of cancer suffer from the lack of specificity for tumor cells. Radiolabeled monoclonal antibodies have been, and continue to be, actively investigated for use in this regard. Various radionuclides, cell cultures, and radiolabeling processes have been examined, aimed at producing a product with sufficient immunoreactivity to enhance the use of these agents in various radioimmunodetection, radioimmunotherapy and radioimmunoguided surgery capacities. Antibody production, radiolabeling and radionuclide selection criteria, and quality assessment techniques for radiolabeled monoclonal antibodies are reviewed.

scholarly journals A comparison of resistance spot weld quality assessment techniques

2019 ◽  
Vol 29 ◽  
pp. 305-312 ◽  
Author(s):  
Cameron Summerville ◽  
Paul Compston ◽  
Matthew Doolan
Keyword(s):  
Quality Assessment ◽  
Spot Weld ◽  
Weld Quality ◽  
Resistance Spot ◽  
Resistance Spot Weld ◽  
Assessment Techniques

scholarly journals NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

2018 ◽  
Vol 37 (2) ◽  
pp. 105 ◽  
Author(s):  
Kanjar De ◽  
Masilamani V
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Multiple Sources ◽  
Noise Image ◽  
Assessment Techniques ◽  
Image Quality Measure ◽  
Channel Errors

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available. 

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