Real Time Automated Machinery Threat Detection and Identification System for Pipeline Infrastructure Protection

2016 ◽  
Vol 2016 (11) ◽  
pp. 1-5 ◽  
Author(s):  
Almabrok Essa ◽  
Paheding Sidike ◽  
Vijayan K Asari
Keyword(s):  
Real Time ◽  
Identification System ◽  
Threat Detection ◽  
Infrastructure Protection ◽  
Detection And Identification

Robust Model Reference Fault Detection and Identification System for Fixed Wing Aircrafts

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
R. Jaganraj ◽  
R. Velu
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Fault Estimation ◽  
Identification System ◽  
Model Reference ◽  
Robust Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Time Parameters ◽  
Implantable Sensor

Fault Detection and Identification system (FDI) and Fault Tolerant Flight Control (FTFC) system are used to correct the faulty operation of an aircraft. Both FDIs and FTFCs have operational disadvantages due to their inherent limitation of fault source identification. This paper presents the design and implementation of a robust model reference fault detection and identification (MRFDI) system on a fixed-wing aircraft for identifying actuator fault, instrument fault and presence of any uncertainties. The proposed MRDFI fuses the real-time parameters and actuator feedback to combine the advantages of data driven and model reference FDI that makes robust fault estimation. The MRFDI system is implemented on a typical aircraft altitude hold autopilot simulation environment with a predefined fault scenario. The fault scenario includes a faulty elevator, a faulty skin-implantable sensor and wind gust as environmental uncertainty. The MRFDI performs logical analysis to detect fault using state-dependent real-time parameters and state-independent skin implantable sensor. This two-step fault detection method makes MRFDI robust to any type of fault identification. The results show that the MRFDI detects and distinguishes faults in actuator, instrument and any of the listed uncertainties thrown by the environment accurately.

scholarly journals Raman Spectroscopic and Advanced Signal Processing analyses for Real Time Standoff Detection and Identification of Explosives

2021 ◽  
Author(s):  
Adel Abdallah ◽  
Alaaeldin Mahmoud ◽  
Mohamed Mokhtar ◽  
Aiman Mousa ◽  
Yahia Elbashar ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Signal Processing ◽  
Real Time ◽  
Laser Raman Spectroscopy ◽  
Identification System ◽  
Second Phase ◽  
Accurate Identification ◽  
Laser Raman ◽  
Detection And Identification ◽  
Two Phases

Abstract Laser Raman spectroscopy is a powerful instrument commonly used for detection of bulk and trace amounts of explosives. The work carried out in this paper is divided into two phases; the first phase is to propose a real time standoff explosive detection and identification system based on Raman spectroscopy that can be deployed in static checkpoints. The measurement is performed for samples placed in contact and at distances up to 1 meter in ambient light conditions. The second phase is to propose a novel sophisticated signal processing and pattern recognition techniques for accurate identification and classification of the investigated materials.

scholarly journals Detection of Fake Indian Currency Notes using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 1353-1357
Author(s):  
Soha K Deshpande
Keyword(s):  
Real Time ◽  
Identification System ◽  
Detection Accuracy ◽  
Test Results ◽  
Test Dataset ◽  
Model Based ◽  
Detection And Identification ◽  
Commercial Transaction ◽  
Normal Human ◽  
Visually Challenged

A normal human being can easily see and distinguish any banknote, however doing the same job is extremely difficult for someone who is visually challenged or blind. Because money plays such an essential part in our everyday lives and is required for any commercial transaction, real-time detection and recognition of banknotes is a must for anyone who is blind or visually impaired. The mobilenet based CNN model-based Indian currency detection and identification system is presented for this purpose, and it is quick and accurate. To make the system more resilient, pictures of various denominations and situations were collected first, and then these images were supplemented with various geometric and image modifications. These augmented pictures are then manually tagged, and training and validation image sets are created from them. Later, the trained model's performance was assessed on a real-time scenario as well as a test dataset. The suggested mobile net model-based technique exhibits detection accuracy of 91.33% according to the test results. This standalone system operates in real-time.

Systematic Government Access to Private-Sector Data in the United States I

2017 ◽  
Author(s):  
Stephanie K. Pell
Keyword(s):  
Private Sector ◽  
Public Information ◽  
The United States ◽  
Third Party ◽  
Threat Detection ◽  
Detection And Identification ◽  
Potential Applications ◽  
September 11 Attacks ◽  
Access To Data ◽  
Sector Data

After the September 11 attacks, law enforcement's mission expanded to include, at times even prioritize, the general “prevention, deterrence and disruption” of terrorist attacks, which presumed a new emphasis upon threat detection and identification by analyzing patterns in larger, less specific bodies of information. Indeed, the unprecedented level of “third-party” possession of information inevitably makes the private sector the most reliable and comprehensive source of information available to law enforcement and intelligence agencies alike. This chapter explores the potential applications of systematic government access to data held by third-party private-sector intermediaries that would not be considered public information sources but, rather, data generated based on the role these intermediaries play in facilitating economic and business transactions (including personal business, such as buying groceries or staying at a hotel on vacation).

scholarly journals A machine learning approach for accurate and real-time DNA sequence identification

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiren Wang ◽  
Mashari Alangari ◽  
Joshua Hihath ◽  
Arindam K. Das ◽  
M. P. Anantram
Keyword(s):  
Real Time ◽  
Dna Sequence ◽  
Single Molecule ◽  
Identification Accuracy ◽  
Measuring System ◽  
Identification System ◽  
Genetic Sequencing ◽  
Sequence Identification ◽  
Tree Classifier ◽  
Single Mismatch

Abstract Background The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. Results This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. Conclusions A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

scholarly journals A single-run, real-time PCR for detection and identification ofBorrelia burgdorferi sensu latospecies, based on thehbbgene sequence

2006 ◽  
Vol 259 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Denis Portnoï ◽  
Natacha Sertour ◽  
Elisabeth Ferquel ◽  
Martine Garnier ◽  
Guy Baranton ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Detection And Identification

scholarly journals Real-time detection and identification of nematode eggs genus and species through optical imaging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Farah Qazi ◽  
Asma Khalid ◽  
Arpita Poddar ◽  
Jean-Philippe Tetienne ◽  
Athavan Nadarajah ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Real Time ◽  
Detection And Identification ◽  
Nematode Eggs ◽  
Real Time Detection
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