Fine Tuning of Narrow-Band SAW Devices Using Dielectric Overlays

1977 ◽  
Author(s):  
C.N. Helmick ◽  
D.J. White ◽  
R.L. King
Keyword(s):  
Narrow Band ◽  
Fine Tuning ◽  
Saw Devices ◽  
Band Saw

Study on the Online Detection Technology of the Band Saw Equipment

2013 ◽  
Vol 753-755 ◽  
pp. 2329-2333
Author(s):  
Guo Jin Chen ◽  
Jing Ni ◽  
Ting Ting Liu ◽  
Hui Peng Chen ◽  
Ming Xu
Keyword(s):  
Control System ◽  
Real Time ◽  
Digital Control ◽  
Surface Profile ◽  
Fine Tuning ◽  
Digital Control System ◽  
The Real ◽  
Detection Technology ◽  
Real Time Detection ◽  
Band Saw

Aiming at the lower automation, accuracy and efficiency of the domestic band sawing machine, this paper studies the real-time detection technology based on the sawing load, develops the digital control system of the constant power sawing with the micro-feed performance to improve the load imbalance of the band saw blades in the sawing process. The real-time detection technology based on the micro-deviation of the band saws trajectory is studied. The digitized deviation-correction control system of the band saws trajectory is developed with the fine-tuning performance of the saw stiffness to correct automatically the band saws trajectory. The weight-detection technology based on the scan reconstruction of the surface profile size is researched. The digital control system of the fixed weight sawing is developed to meet that the weight error of the sawed workpiece is fewer than 3%. That can improve the accuracy and efficiency of the band sawing machine and provide the foundation for the realization of the digital control of the band sawing machine.

Numerical simulation of evaluation of surface breaking cracks by array-lasers generated narrow-band SAW

2011 ◽  
Vol 57 (5) ◽  
pp. 730-736 ◽  
Author(s):  
Li-ming Dong ◽  
Chen-yin Ni ◽  
Zhong-hua Shen ◽  
Xiao-wu Ni
Keyword(s):  
Numerical Simulation ◽  
Narrow Band ◽  
Band Saw

AlN epitaxial film on 6H-SiC(0001) using MOCVD for GHz-band SAW devices

2004 ◽  
Author(s):  
K. Uehara ◽  
C.-M. Yang ◽  
T. Furusho ◽  
S.-K. Kim ◽  
S. Kameda ◽  
...  
Keyword(s):  
Epitaxial Film ◽  
Saw Devices ◽  
Band Saw

Ultrawide-Band SAW Devices Using SH0 Mode Wave with Increased Velocity for 5G Front-Ends

2021 ◽  
Author(s):  
Hongyan Zhou ◽  
Shibin Zhang ◽  
Jinbo Wu ◽  
Pengcheng Zheng ◽  
Liping Zhang ◽  
...  
Keyword(s):  
Mode Wave ◽  
Saw Devices ◽  
Band Saw

scholarly journals Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8157
Author(s):  
Nazila Esmaeili ◽  
Esam Sharaf ◽  
Elmer Jeto Gomes Ataide ◽  
Alfredo Illanes ◽  
Axel Boese ◽  
...  
Keyword(s):  
Laryngeal Cancer ◽  
High Performance ◽  
Performance Metrics ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Visual Assessment ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Laryngeal Mucosa ◽  
Unseen Data

(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.

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