[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Peng et al., 2023 - Google Patents

Supervised contrastive learning for RFF identification with limited samples

Peng et al., 2023

View PDF
Document ID
11531342340345639162
Author
Peng Y
Hou C
Zhang Y
Lin Y
Gui G
Gacanin H
Mao S
Adachi F
Publication year
Publication venue
IEEE Internet of Things Journal

External Links

Snippet

Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6261Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/38Quantising the analogue image signal, e.g. histogram thresholding for discrimination between background and foreground patterns
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00006Acquiring or recognising fingerprints or palmprints
    • G06K9/00067Preprocessing; Feature extraction (minutiae)
    • G06K9/00073Extracting features related to minutiae and pores
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00006Acquiring or recognising fingerprints or palmprints
    • G06K9/00087Matching; Classification

Similar Documents

Publication Publication Date Title
Peng et al. Supervised contrastive learning for RFF identification with limited samples
He et al. Cooperative specific emitter identification via multiple distorted receivers
Zhang et al. An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation
Tu et al. Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification.
Xie et al. A generalizable model-and-data driven approach for open-set RFF authentication
Tao et al. Symbol detection of ambient backscatter systems with Manchester coding
Liu et al. Overcoming data limitations: a few-shot specific emitter identification method using self-supervised learning and adversarial augmentation
CN105119862B (en) A kind of identification of signal modulation method and system
Wang et al. A convolutional neural network-based RF fingerprinting identification scheme for mobile phones
Zhang et al. Variable-modulation specific emitter identification with domain adaptation
Li et al. RadioNet: Robust deep-learning based radio fingerprinting
Li et al. Design and evaluation of a graphical deep learning approach for RF fingerprinting
He et al. Radio frequency fingerprint identification with hybrid time-varying distortions
Zeng et al. Multi-channel attentive feature fusion for radio frequency fingerprinting
Tian et al. Transfer learning-based radio frequency fingerprint identification using ConvMixer network
Shi et al. Fedrfid: Federated learning for radio frequency fingerprint identification of wifi signals
Yang et al. Deep learning based RFF recognition with differential constellation trace figure towards closed and open set
US11184783B1 (en) Real-time channel-resilient optimization of radio fingerprinting
Zhang et al. Data augmentation aided few-shot learning for specific emitter identification
Zhao et al. Gan-rxa: A practical scalable solution to receiver-agnostic transmitter fingerprinting
He et al. Specific emitter identification via sparse Bayesian learning versus model-agnostic meta-learning
Liu et al. A robust few-shot sei method using class-reconstruction and adversarial training
He et al. Channel-agnostic radio frequency fingerprint identification using spectral quotient constellation errors
Ying et al. Differential complex-valued convolutional neural network-based individual recognition of communication radiation sources
Morehouse et al. RF device identification using CNN based PUF