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UNF VS Embry-Riddle [Neg# 12A]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1155/thumbnail.jp
UNF VS Embry-Riddle [Neg# 34A]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1176/thumbnail.jp
UNF VS Embry-Riddle_Roll2 [Neg # 5]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Roll 2, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1182/thumbnail.jp
UNF VS Embry-Riddle_Roll2 [Neg # 9]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Roll 2, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1186/thumbnail.jp
UNF VS Embry-Riddle_Roll2 [Neg # 26]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Roll 2, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1203/thumbnail.jp
Enhancing manufacturing efficiency through advanced vision system and RFID technology integration
No full textMedical device manufacturing faces several difficulties, including stringent regulatory requirements, the need for unparalleled precision, and a constant need to boost productivity. These demands are usually beyond the capabilities of conventional production techniques, particularly as devices get more complex and miniaturized. This thesis addresses these problems by integrating advanced vision systems and radio-frequency identification (RFID) technologies into a single-use sterile medical device assembly line.
This study examines the application of RFID technology for real-time product tracking, recording pass/fail outcomes at each station, and preventing the reuse of single-use products, thereby ensuring thorough traceability in the production process. Simultaneously, advanced vision systems are deployed to verify part correctness, detect missing components, and confirm proper assembly, enhancing quality control by identifying defects at the point of occurrence.
Two pilot studies were conducted to evaluate the effectiveness of the integrated system compared to a traditional assembly line. The results showed a considerable reduction in scrap rates, with the advanced system achieving a scrap reduction efficiency of around 53%, resulting in substantial cost savings. The findings demonstrate that merging advanced vision systems and RFID technology improves production efficiency and product quality while delivering reliable data for regulatory compliance and ongoing process improvement.
The necessity of integrating these technologies is highlighted by their long-term benefits, which include reduced waste, enhanced traceability, and higher compliance with regulatory standards. Despite the challenges of high initial investment costs and operator training, this study offers empirical evidence of the advantages of integrating cutting-edge technologies into medical device manufacturing. This research advances the field of smart manufacturing and helps manufacturers stay competitive in an increasingly automated market
Media haze classification in retinal images using deep learning
Get PDFMedia Haze (MH) is a condition that affects an individual’s quality of life by affecting their eyes. Current practice is to detect MH by manually examining retinal fundus (retinal) images. The analysis of images being used as the prevalent technique for identifying the MH condition strongly suggests that automation of this process may be possible. In recent years, machine learning, specifically computer vision, has allowed for the automation of tasks relating to image analysis. This ability to automate has also recently been shown in the medical field for some eye conditions and diseases. This thesis centers around the problem of the detection of MH not being effectively automated. Therefore, this thesis develops a system that can effectively and satisfactorily detect whether MH is present within a retinal image. Furthermore, this thesis accomplishes this by proposing a method of preprocessing retinal images, and examines the effectiveness of 10 deep learning models. LeNet-5, the modified form of LeNet, AlexNet, and Vision Transformer were trained from scratch, and MobileNet, ResNet152v2, DenseNet, NASNetMobile, EfficientNetV2 B0, and VGG-16 were all pretrained and fine-tuned on our dataset. Then, an ensemble of the 6 fine-tuned models was also examined. EfficientNetV2 B0, MobileNet, and ResNet, being the most effective independent models in testing, were then added, along with an ensemble, to the web application created for this thesis, which demonstrates a user-friendly potential real-world application of this work. When combined with the proposed preprocessing, MobileNet can achieve 96.1% accuracy, and EfficientNetV2 B0 can achieve 96.6% accuracy, both of which are better than the current state-of-the-art attempts at automating the detection of MH. The ensemble of fine-tuned models also has a 96.6% accuracy. The precision, recall, F1 score, and AUC results further evidence this improved performance
Record of Copy of The State of Florida vs. Joseph Jackson
Get PDFDocument: Certification of the copy record of the State of Florida vs. Joseph Jackson, signed by D. W. Parfitt, Clerk, Criminal Court, Duval County. (Associated with item H1-364) Date: March 6, 193