29,361 research outputs found
Automated detection and classification of concealed objects using infrared thermography and convolutional neural networks
No full textAbstract This paper presents a study on the effectiveness of a convolutional neural network (CNN) in classifying infrared images for security scanning. Infrared thermography was explored as a non-invasive security scanner for stand-off and walk-through concealed object detection. Heat generated by human subjects radiates off the clothing surface, allowing detection by an infrared camera. However, infrared lacks in penetration capability compared to longer electromagnetic waves, leading to less obvious visuals on the clothing surface. ResNet-50 was used as the CNN model to automate the classification process of thermal images. The ImageNet database was used to pre-train the model, which was further fine-tuned using infrared images obtained from experiments. Four image pre-processing approaches were explored, i.e., raw infrared image, subject cropped region-of-interest (ROI) image, K-means, and Fuzzy-c clustered images. All these approaches were evaluated using the receiver operating characteristic curve on an internal holdout set, with an area-under-the-curve of 0.8923, 0.9256, 0.9485, and 0.9669 for the raw image, ROI cropped, K-means, and Fuzzy-c models, respectively. The CNN models trained using various image pre-processing approaches suggest that the prediction performance can be improved by the removal of non-decision relevant information and the visual highlighting of features
Non-contact, portable, and stand-off infrared thermal imager for security scanning applications
No full textIn this article, we demonstrated the physical application of a portable infrared (IR) security scanning system for the non-contact and stand-off detection of target objects concealed underneath clothing. Such a system combines IR imaging and transfer learning with convolutional neural networks (CNNs) to enhance the detection of weak thermal signals and automate the classification of IR images. A mid-wavelength IR detector was used to record the real-time heat emitted from the clothing surface by human subjects. Concealed objects reduce the transmissivity of IR radiation from the body to the clothing surface, generally showing lower IR intensity compared to regions without objects. Due to limited resources for training data, the transfer learning approach was applied by fine-tuning a pre-trained CNN ResNet-50 model using the ImageNet database. Two image types were investigated here, i.e., raw thermal and Fuzzy-c clustered images. Receiver operating characteristic curves were built using a holdout set, showing an area-under-the-curve of 0.8934 and 0.9681 for the raw and Fuzzy-c clustered image models, respectively. The gradient-weighted class activation mapping visualization method was used to improve target identification, showing an accurate prediction of the object area. It was also found that complex clothing, such as those composed of materials of different transmissivity, could mislead the model in classification. The proposed IR-based detector has shown potential as a non-contact, stand-off security scanning system that can be deployed in diverse locations and ensure the safety of civilians
Towards a framework for automatic firewalls configuration via argumentation reasoning
No full textFirewalls have been widely used to protect not only small and local networks but also large enterprise networks. The configuration of firewalls is mainly done by network administrators, thus, it suffers from human errors. This paper aims to solve the network administrators’ problem by introducing a formal approach that helps to configure centralized and distributed firewalls and automatically generate conflict-free firewall rules. We propose a novel framework, called ArgoFiCo, which is based on argumentation reasoning. Our framework automatically populates the firewalls of a network, given the network topology and the high-level requirements that represent how the network should behave. ArgoFiCo provides two strategies for firewall rules distribution
Chen Chen, 42nd Annual ODU Literary Festival
No full textChen Chen is the author of When I Grow Up I Want to Be a List of Further Possibilities (BOA Editions, 2017), which was long-listed for the National Book Award and won the Thom Gunn Award, among other honors. Bloodaxe Books published a UK edition in June. He is also the author of four chapbooks, most recently You MUST Use the Word Smoothie (Sundress Publications, 2019) and Gesundheit! (in collaboration with Sam Herschel Wein and forthcoming from Glass Poetry Press, fall 2019). His work appears in many publications, including Poem-a-Day, The Massachusetts Review, The Best American Poetry, and The Best American Nonrequired Reading. He has received a Pushcart Prize and fellowships from Kundiman and the National Endowment for the Arts. He holds an MFA from Syracuse University and a PhD from Texas Tech University. He teaches at Brandeis University as the Jacob Ziskind Poet-in-Residence and co-runs the journal, Underblong. He lives in Waltham, Massachusetts, with his partner, Jeff Gilbert, and their pug, Mr. Rupert Gile
Infrared thermography as a non-invasive scanner for concealed weapon detection
No full textNon-invasive scanning techniques are vital for threat detection in areas of heavy human traffic to ensure civilian safety. Longer waves in the electromagnetic spectrum, such as millimetre waves and terahertz, have been successfully deployed in commercial personnel scanning systems. However, these waves suffer from lower image resolution due to their longer wavelengths.
Infrared has a shorter wavelength compared to millimetre waves and terahertz. Infrared has a lower penetration potential compared to its counterparts but boosts higher image resolution due to its shorter wavelength. Machine learning techniques, i.e., principal component analysis, active contour, and Fuzzy-c, were applied to the infrared images to improve the visualization of concealed objects.
Convolutional neural networks, i.e., ResNet-50, were explored as an automatic classifier for the presence of concealed objects. A transfer learning approach was applied to an ImageNet pre-trained ResNet-50 model. After preprocessing the IR images using Fuzzy-c, two models were trained, using 900 and 3082 images, respectively. Evaluating the models using a confusion matrix and receiver operating characteristic curve, an area-under-curve of 0.869 and 0.922 was obtained. An optimization procedure was used to determine the model threshold, resulting in a prediction error of 19.9% and 14.9%, respectively.</p
Supplemental Table3 - Supplemental material for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1
No full textSupplemental material, Supplemental Table3 for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1 by Hanwei Jiao, Zonglin Zheng, Xuehong Shuai, Li Wu, Jixuan Chen, Yichen Luo, Yu Zhao, Hongjun Wang and Qingzhou Huang in Innate Immunity</p
Supplemental Table2 - Supplemental material for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1
No full textSupplemental material, Supplemental Table2 for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1 by Hanwei Jiao, Zonglin Zheng, Xuehong Shuai, Li Wu, Jixuan Chen, Yichen Luo, Yu Zhao, Hongjun Wang and Qingzhou Huang in Innate Immunity</p
Supplemental Figure - Supplemental material for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1
No full textSupplemental material, Supplemental Figure for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1 by Hanwei Jiao, Zonglin Zheng, Xuehong Shuai, Li Wu, Jixuan Chen, Yichen Luo, Yu Zhao, Hongjun Wang and Qingzhou Huang in Innate Immunity</p
Supplemental Table1 - Supplemental material for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1
No full textSupplemental material, Supplemental Table1 for MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1 by Hanwei Jiao, Zonglin Zheng, Xuehong Shuai, Li Wu, Jixuan Chen, Yichen Luo, Yu Zhao, Hongjun Wang and Qingzhou Huang in Innate Immunity</p
- …
