Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Human Activity Recognition Through Augmented WiFi CSI Signals by Lightweight Attention-GRU.

Sensors (Basel, Switzerland)·2025

Same author

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

International journal of neural systems·2024

Same author

Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering.

Sensors (Basel, Switzerland)·2023

Same author

Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals.

Sensors (Basel, Switzerland)·2023

Same author

Unambiguous and High-Fidelity Backdoor Watermarking for Deep Neural Networks.

IEEE transactions on neural networks and learning systems·2023

Same author

Divergent Angular Representation for Open Set Image Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021

Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026

Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026

Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026

Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026

Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026

Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 17, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

Meng-Hui Lim¹, Andrew Beng Jin Teoh, Kar-Ann Toh

¹Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong. menghui.lim@gmail.com

IEEE Transactions on Cybernetics

|October 11, 2012

Summary

This summary is machine-generated.

This study enhances biometric discretization for cryptographic key generation by improving the detection rate optimized bit allocation (DROBA) scheme. The new method reduces feature misdetection and underdiscretization, boosting classification accuracy.

Related Experiment Videos

Last Updated: May 17, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

Computer Science
Biometrics
Cryptography

Background:

Biometric discretization is crucial for generating cryptographic keys from biometric data.
Current methods like DROBA face challenges with feature misdetection and underdiscretization.

Purpose of the Study:

To address the limitations of the DROBA scheme in biometric discretization.
To improve the accuracy and security of biometric cryptographic key generation.

Main Methods:

A novel two-stage algorithm was developed to improve DROBA.
Stage 1: Dynamic search for recapturing misdetected features and optimizing bit allocation.
Stage 2: Genuine interval concealment to mitigate information leakage.

Main Results:

The proposed algorithm effectively recaptures misdetected features and optimizes bit allocation.
Information leakage from dynamic search is significantly alleviated.
Demonstrated improvements in classification accuracy on face datasets compared to DROBA.

Conclusions:

The enhanced biometric discretization approach offers superior performance over DROBA.
The method provides a more robust and secure solution for biometric cryptographic key generation.