scholarly journals Towards Fingerprint Spoofing Detection in the Terahertz Range

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3379
Author(s):  
Norbert Pałka ◽  
Marcin Kowalski
Keyword(s):  
Theoretical Calculations ◽  
Complex Refractive Index ◽  
Detection Methods ◽  
Terahertz Time Domain Spectroscopy ◽  
Detection Rates ◽  
Time Frequency ◽  
Spoofing Detection ◽  
True Detection ◽  
Reflection Configuration ◽  
Frequency Features

Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time–frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability.

scholarly journals Enabling Noninvasive Physical Assault Monitoring in Smart School with Commercial Wi-Fi Devices

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Qizhen Zhou ◽  
Chenshu Wu ◽  
Jianchun Xing ◽  
Shuo Zhao ◽  
Qiliang Yang
Keyword(s):  
School Violence ◽  
Domain Knowledge ◽  
Global Perspective ◽  
Detection Methods ◽  
Physical Assault ◽  
Indoor Environments ◽  
Time Frequency ◽  
True Detection ◽  
Body Sensors ◽  
Signal Processing Methods

Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited surveillance area, which are privacy-invasive and cannot provide ubiquitous assault monitoring. In this paper, we present Wi-Dog, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances. Wi-Dog is based on widely deployed commodity Wi-Fi infrastructures. The key intuition is that Wi-Fi signals are easily distorted by human motions, and motion-induced signals could convey informative characteristics, such as intensity, regularity, and continuity. Specifically, to explicitly reveal the substantive properties of physical assault, we innovatively propose a set of signal processing methods for informative components extraction by selecting sensitive antenna pairs and subcarriers. Then a novel signal-complexity-based segmentation method is developed as a location-independent indicator to monitor targeted movement transitions. Finally, holistic analysis is employed based on domain knowledge, and we distinguish the violence process from both local and global perspective using time-frequency features. We implement Wi-Dog on commercial Wi-Fi devices and evaluate it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog which consistently outperforms the advanced abnormal detection methods with a higher true detection rate of 94% and a lower false alarm rate of 8%.

Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

2021 ◽  
Author(s):  
Matthew B. Creasy ◽  
Wade Travis Tinkham ◽  
Chad M. Hoffman ◽  
Jody C. Vogeler
Keyword(s):  
Forest Structure ◽  
Ponderosa Pine ◽  
Laser Scanning ◽  
Point Clouds ◽  
System Structure ◽  
Detection Methods ◽  
Unmanned Aerial System ◽  
Individual Tree ◽  
Detection Rates ◽  
Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

scholarly journals Active case detection methods for crusted scabies and leprosy: A systematic review

2021 ◽  
Vol 15 (7) ◽  
pp. e0009577
Author(s):  
Miriam Glennie ◽  
Karen Gardner ◽  
Michelle Dowden ◽  
Bart J. Currie
Keyword(s):  
Systematic Review ◽  
Disease Control ◽  
Detection Methods ◽  
Contact Tracing ◽  
Case Detection ◽  
Community Screening ◽  
Detection Rates ◽  
Active Case Detection ◽  
Active Case ◽  
Crusted Scabies

Background Crusted scabies is endemic in some remote Aboriginal communities in the Northern Territory (NT) of Australia and carries a high mortality risk. Improvement in active case detection (ACD) for crusted scabies is hampered by a lack of evidence about best practice. We therefore conducted a systematic review of ACD methods for leprosy, a condition with similar ACD requirements, to consider how findings could be informative to crusted scabies detection. Methods and principle findings We conducted systematic searches in MEDLINE, CINAHL, Scopus and the Cochrane Database for Systematic Reviews for studies published since 1999 that reported at least one comparison rate (detection or prevalence rate) against which the yield of the ACD method could be assessed. The search yielded 15 eligible studies from 511. Study heterogeneity precluded meta-analysis. Contact tracing and community screening of marginalised ethnic groups yielded the highest new case detection rates. Rapid community screening campaigns, and those using less experienced screening personnel, were associated with lower suspect confirmation rates. There is insufficient data to assess whether ACD campaigns improve treatment outcomes or disease control. Conclusion This review demonstrates the importance of ACD campaigns in communities facing the highest barriers to healthcare access and within neighbourhoods of index cases. The potential benefit of ACD for crusted scabies is not quantified, however, lessons from leprosy suggest value in follow-up with previously identified cases and their close contacts to support for scabies control and to reduce the likelihood of reinfection in the crusted scabies case. Skilled screening personnel and appropriate community engagement strategies are needed to maximise screening uptake. More research is needed to assess ACD cost effectiveness, impact on disease control, and to explore ACD methods capable of capturing the homeless and highly mobile who may be missed in household centric models.

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

2018 ◽  
Vol 29 (4) ◽  
pp. 045004 ◽  
Author(s):  
Wei He ◽  
Yigang He ◽  
Qiwu Luo ◽  
Chaolong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Time Frequency ◽  
Frequency Features

scholarly journals Molecular Fingerprints of Hemoglobin on a Nanofilm Chip

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3016 ◽  
Author(s):  
Yeşeren Saylan ◽  
Adil Denizli
Keyword(s):  
Real Time ◽  
Theoretical Calculations ◽  
Essential Element ◽  
Hemoglobin Concentration ◽  
Ammonium Persulfate ◽  
Detection Methods ◽  
Molecular Fingerprints ◽  
Molecularly Imprinted ◽  
On Chip ◽  
High Level

Hemoglobin is an iron carrying protein in erythrocytes and also an essential element to transfer oxygen from the lungs to the tissues. Abnormalities in hemoglobin concentration are closely correlated with health status and many diseases, including thalassemia, anemia, leukemia, heart disease, and excessive loss of blood. Particularly in resource-constrained settings existing blood analyzers are not readily applicable due to the need for high-level instrumentation and skilled personnel, thereby inexpensive, easy-to-use, and reliable detection methods are needed. Herein, a molecular fingerprints of hemoglobin on a nanofilm chip was obtained for real-time, sensitive, and selective hemoglobin detection using a surface plasmon resonance system. Briefly, through the photopolymerization technique, a template (hemoglobin) was imprinted on a monomeric (acrylamide) nanofilm on-chip using a cross-linker (methylenebisacrylamide) and an initiator-activator pair (ammonium persulfate-tetramethylethylenediamine). The molecularly imprinted nanofilm on-chip was characterized by atomic force microscopy and ellipsometry, followed by benchmarking detection performance of hemoglobin concentrations from 0.0005 mg mL−1 to 1.0 mg mL−1. Theoretical calculations and real-time detection implied that the molecularly imprinted nanofilm on-chip was able to detect as little as 0.00035 mg mL−1 of hemoglobin. In addition, the experimental results of hemoglobin detection on the chip well-fitted with the Langmuir adsorption isotherm model with high correlation coefficient (0.99) and association and dissociation coefficients (39.1 mL mg−1 and 0.03 mg mL−1) suggesting a monolayer binding characteristic. Assessments on selectivity, reusability and storage stability indicated that the presented chip is an alternative approach to current hemoglobin-targeted assays in low-resource regions, as well as antibody-based detection procedures in the field. In the future, this molecularly imprinted nanofilm on-chip can easily be integrated with portable plasmonic detectors, improving its access to these regions, as well as it can be tailored to detect other proteins and biomarkers.

VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

2019 ◽  
Vol 19 (03) ◽  
pp. 1950008
Author(s):  
MONALISA MOHANTY ◽  
PRADYUT BISWAL ◽  
SUKANTA SABUT
Keyword(s):  
Ventricular Tachycardia ◽  
Decision Tree ◽  
Ventricular Arrhythmias ◽  
Ventricular Tachyarrhythmia ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Tree Classifier ◽  
Frequency Features

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

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