Practical Issues in Manual Demining: Implications for New Detection Technologies

2002 ◽  
pp. 155-164
Author(s):  
James Trevelyan
Keyword(s):  
Detection Technologies

An AHP-Based Evaluating Model to Evaluate Intelligent Vehicle Detection Technologies in Street Parking Scenario

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Xuejin Wan ◽  
Shangfo Huang ◽  
Bowen Du ◽  
Rui Sun ◽  
Jiong Wang ◽  
...  
Keyword(s):  
Vehicle Detection ◽  
Intelligent Vehicle ◽  
Detection Technologies

scholarly journals Recent Advancements in Enzyme-Based Lateral Flow Immunoassays

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3358
Author(s):  
Donato Calabria ◽  
Maria Maddalena Calabretta ◽  
Martina Zangheri ◽  
Elisa Marchegiani ◽  
Ilaria Trozzi ◽  
...  
Keyword(s):  
Lateral Flow ◽  
Qualitative Information ◽  
Eye Color ◽  
Commercial Success ◽  
Color Changes ◽  
Naked Eye ◽  
Future Developments ◽  
Highly Sensitive ◽  
Critical Overview ◽  
Detection Technologies

Paper-based lateral-flow immunoassays (LFIAs) have achieved considerable commercial success and their impact in diagnostics is continuously growing. LFIA results are often obtained by visualizing by the naked eye color changes in given areas, providing a qualitative information about the presence/absence of the target analyte in the sample. However, this platform has the potential to provide ultrasensitive quantitative analysis for several applications. Indeed, LFIA is based on well-established immunological techniques, which have known in the last year great advances due to the combination of highly sensitive tracers, innovative signal amplification strategies and last-generation instrumental detectors. All these available progresses can be applied also to the LFIA platform by adapting them to a portable and miniaturized format. This possibility opens countless strategies for definitively turning the LFIA technique into an ultrasensitive quantitative method. Among the different proposals for achieving this goal, the use of enzyme-based immunoassay is very well known and widespread for routine analysis and it can represent a valid approach for improving LFIA performances. Several examples have been recently reported in literature exploiting enzymes properties and features for obtaining significative advances in this field. In this review, we aim to provide a critical overview of the recent progresses in highly sensitive LFIA detection technologies, involving the exploitation of enzyme-based amplification strategies. The features and applications of the technologies, along with future developments and challenges, are also discussed.

scholarly journals Chinese Character Image Completion Using a Generative Latent Variable Model

2021 ◽  
Vol 11 (2) ◽  
pp. 624
Author(s):  
In-su Jo ◽  
Dong-bin Choi ◽  
Young B. Park
Keyword(s):  
Latent Variable ◽  
Chinese Character ◽  
Image Inpainting ◽  
Latent Variable Model ◽  
Chinese Characters ◽  
Image Completion ◽  
Variable Model ◽  
Variational Autoencoder ◽  
Convolution Model ◽  
Detection Technologies

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

scholarly journals Recent advances in radiation detection technologies enabled by metal-halide perovskites

2021 ◽  
Author(s):  
Tiebin Yang ◽  
Feng Li ◽  
Rongkun Zheng
Keyword(s):  
Gamma Rays ◽  
Performance Optimization ◽  
Radiation Detection ◽  
High Energy ◽  
Device Performance ◽  
High Energy Radiation ◽  
Energy Radiation ◽  
Halide Perovskites ◽  
Alpha Beta ◽  
Detection Technologies

Perovskite halides hold great potential for high-energy radiation detection. Recent advancements in detecting alpha-, beta-, X-, and gamma-rays by perovskite halides are reviewed and an outlook on the device performance optimization is provided.

Specific antigen‐based and emerging detection technologies of mycotoxins

2019 ◽  
Vol 99 (11) ◽  
pp. 4869-4877 ◽  
Author(s):  
Hamid Ur Rahman ◽  
Xiaofeng Yue ◽  
Qiuyu Yu ◽  
Huali Xie ◽  
Wen Zhang ◽  
...  
Keyword(s):  
Specific Antigen ◽  
Detection Technologies

scholarly journals Mid-infrared photoacoustic gas monitoring driven by a gas-filled hollow-core fiber laser

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yazhou Wang ◽  
Yuyang Feng ◽  
Abubakar I. Adamu ◽  
Manoj K. Dasa ◽  
J. E. Antonio-Lopez ◽  
...  
Keyword(s):  
Fiber Laser ◽  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
Laser Source ◽  
High Signal ◽  
Mid Infrared ◽  
Raman Fiber Laser ◽  
Custom Made ◽  
Core Fiber ◽  
Detection Technologies

AbstractDevelopment of novel mid-infrared (MIR) lasers could ultimately boost emerging detection technologies towards innovative spectroscopic and imaging solutions. Photoacoustic (PA) modality has been heralded for years as one of the most powerful detection tools enabling high signal-to-noise ratio analysis. Here, we demonstrate a novel, compact and sensitive MIR-PA system for carbon dioxide (CO2) monitoring at its strongest absorption band by combining a gas-filled fiber laser and PA technology. Specifically, the PA signals were excited by a custom-made hydrogen (H2) based MIR Raman fiber laser source with a pulse energy of ⁓ 18 μJ, quantum efficiency of ⁓ 80% and peak power of ⁓ 3.9 kW. A CO2 detection limit of 605 ppbv was attained from the Allan deviation. This work constitutes an alternative method for advanced high-sensitivity gas detection.

Deepfakes, misinformation and disinformation and authenticity infrastructure responses: Impacts on frontline witnessing, distant witnessing, and civic journalism

Journalism ◽  
2021 ◽  
pp. 146488492110606
Author(s):  
Sam Gregory
Keyword(s):  
Fake News ◽  
Technology Infrastructure ◽  
Civic Journalism ◽  
Global Context ◽  
Synthetic Media ◽  
Media Manipulation ◽  
Societal Dynamics ◽  
Implementation Data ◽  
Detection Technologies ◽  
Key Questions

Frontline witnessing and civic journalism are impacted by the rhetoric and the reality of misinformation and disinformation. This essay highlights key insights from activities of the human rights and civic journalism network WITNESS, as they seek to prepare for new forms of media manipulation, such as deepfakes, and to ensure that an emergent “authenticity infrastructure” is in place to respond to global needs for reliable information without creating additional harms. Based on global consultations on perceived threats and prioritized solutions, their efforts are primarily targeted towards synthetic media and deepfakes, which not only facilitate audiovisual falsification (including non-consensual sexual images) but also, by being embedded in societal dynamics of surveillance and civil society suppression, they challenge real footage and so undermine the credibility of civic media and frontline witnessing (also known as “liar’s dividend”). They do this within a global context where journalists and some distant witness investigators self-identify as lacking relevant skills and capacity, and face inequity in access to detection technologies. Within this context, “authenticity infrastructure” tracks media provenance, integrity, and manipulation from camera to edit to distribution, and so comes to provide “verification subsidies” that enable distant witnesses to properly interpret eye-witness footage. This “authenticity infrastructure” and related tools are rapidly moving from niche to mainstream in the form of initiatives the Content Authenticity Initiative and Coalition for Content Authenticity and Provenance, raising key questions about who participates in the production and dissemination of audiovisual information, under what circumstances and to which effect for whom. Provenance risks being weaponized unless key concerns are integrated into infrastructure proposals and implementation. Data may be used against vulnerable witnesses, or the absence of a trail, for legitimate privacy and technological access reasons, used to undermine credibility. Regulatory and extra-legal co-option are also a fear as securitized “fake news” laws proliferate. The investigation of both phenomena, deepfakes and emergent authenticity infrastructure(s), this paper argues, is important as it highlights the risks related  both to the “information disorder” of deepfakes as they challenge the credibility and safety of frontline witnesses  and to responses to such “disorder,” as they risk worsening inequities in access to tools for mitigation or increasing exposure to harms from technology infrastructure.

scholarly journals Calculating screen to face distance

2020 ◽  
Author(s):  
Ivan Ludvig Tereshko
Keyword(s):  
Face Detection ◽  
Average Distance ◽  
Android Application ◽  
Detection Technologies

A method of calculating screen to face distance is presented. The method relies on average distance between the user’s eyes (pupillary distance) and does not require calibration. The algorithm is implemented as an Android application using face detection technologies provided by Android.<br><br>

scholarly journals Aptamer Based Point of Care Diagnostic for the Detection of Food Allergens

2021 ◽  
Author(s):  
Sarah Stidham ◽  
Valerie Villareal ◽  
Vasant Chellappa ◽  
Lucas Yoder ◽  
Olivia Alley ◽  
...  
Keyword(s):  
Molecular Detection ◽  
Detection Method ◽  
Point Of Care ◽  
Protein Detection ◽  
Peanut Protein ◽  
The Novel ◽  
Children And Families ◽  
Peanut Allergens ◽  
Consumer Device ◽  
Detection Technologies

Abstract Aptamers, due to their small size, strong target affinity, and ease of chemical modification, are ideally suited for molecular detection technologies. Here, we describe successful use of aptamer technology in a consumer device for the detection of peanut antigen in food. The novel aptamer-based protein detection method is robust across a wide variety of food matrices and sensitive to peanut protein at concentrations as low as 12.5 ppm (37.5 µg peanut protein in the sample). Integration of the assay into a sensitive, stable, and consumer friendly portable device will empower users to easily and quickly assess the presence of peanut allergens in foods before eating. With most food reactions occurring outside the home, the type of technology described here has significant potential to improve lives for children and families.

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