Characterization of geometrical detection-system properties for two-dimensional tomography

2000 ◽  
Vol 71 (3) ◽  
pp. 1370-1378 ◽  
Author(s):  
L. C. Ingesson ◽  
C. F. Maggi ◽  
R. Reichle
Keyword(s):  
Detection System ◽  
Two Dimensional ◽  
System Properties

Characterization of compounds in crude oils by gas purge micro-syringe extraction coupled to comprehensive two-dimensional gas chromatography

2014 ◽  
Vol 32 (10) ◽  
pp. 1144
Author(s):  
Ting TONG ◽  
Wanfeng ZHANG ◽  
Donghao LI ◽  
Jinhua ZHAO ◽  
Zhenyang CHANG ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Crude Oils ◽  
Two Dimensional ◽  
Gas Purge

Mass Spectrometry and Integrated Virus Detection System Characterization of MS2 Bacteriophage

2005 ◽  
Author(s):  
Rabih E. Jabbour ◽  
Deborah Kuzmanovic ◽  
Patrick E. McCubbin ◽  
Ilya Elashvili ◽  
Charles H. Wick
Keyword(s):  
Mass Spectrometry ◽  
Detection System ◽  
Virus Detection ◽  
Ms2 Bacteriophage ◽  
System Characterization

scholarly journals Terahertz characterization of two-dimensional low-conductive layers enabled by metal gratings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prashanth Gopalan ◽  
Yunshan Wang ◽  
Berardi Sensale-Rodriguez
Keyword(s):  
Optical Transmission ◽  
Terahertz Spectroscopy ◽  
Design Guidelines ◽  
Two Dimensional ◽  
Highly Sensitive ◽  
Transmission Measurements ◽  
Metal Gratings ◽  
Low Sensitivity ◽  
Non Destructive

AbstractWhile terahertz spectroscopy can provide valuable information regarding the charge transport properties in semiconductors, its application for the characterization of low-conductive two-dimensional layers, i.e., σs <  < 1 mS, remains elusive. This is primarily due to the low sensitivity of direct transmission measurements to such small sheet conductivity levels. In this work, we discuss harnessing the extraordinary optical transmission through gratings consisting of metallic stripes to characterize such low-conductive two-dimensional layers. We analyze the geometric tradeoffs in these structures and provide physical insights, ultimately leading to general design guidelines for experiments enabling non-contact, non-destructive, highly sensitive characterization of such layers.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

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