[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

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/38—Quantising the analogue image signal, e.g. histogram thresholding for discrimination between background and foreground patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/68—Methods 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00006—Acquiring or recognising fingerprints or palmprints
    • G06K9/00067—Preprocessing; Feature extraction (minutiae)
    • G06K9/00073—Extracting features related to minutiae and pores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00006—Acquiring or recognising fingerprints or palmprints
    • G06K9/00087—Matching; Classification

Similar Documents

Publication Publication Date Title
Peng et al. Supervised contrastive learning for RFF identification with limited samples
Shen et al. Towards scalable and channel-robust radio frequency fingerprint identification for LoRa
Xie et al. A generalizable model-and-data driven approach for open-set RFF authentication
Liu et al. Overcoming data limitations: A few-shot specific emitter identification method using self-supervised learning and adversarial augmentation
CN112312457B (en) A Method for Individual Identification of Communication Radiation Sources Based on Complex Deep Residual Networks
Wang et al. A convolutional neural network-based RF fingerprinting identification scheme for mobile phones
Wang et al. Radio frequency fingerprint identification based on deep complex residual network
Perenda et al. Learning the unknown: Improving modulation classification performance in unseen scenarios
Zhang et al. Variable-modulation specific emitter identification with domain adaptation
He et al. Radio frequency fingerprint identification with hybrid time-varying distortions
Zeng et al. Multi-channel attentive feature fusion for radio frequency fingerprinting
Fadul et al. Improving RF-DNA fingerprinting performance in an indoor multipath environment using semi-supervised learning
Tian et al. Transfer learning-based radio frequency fingerprint identification using ConvMixer network
Yang et al. Deep learning based RFF recognition with differential constellation trace figure towards closed and open set
Shi et al. FedRFID: federated learning for radio frequency fingerprint identification of WiFi signals
CN111245821B (en) Radiation source identification method and device, and radiation source identification model creation method and device
He et al. Channel-agnostic radio frequency fingerprint identification using spectral quotient constellation errors
Zhang et al. Data augmentation aided few-shot learning for specific emitter identification
Liu et al. A robust few-shot SEI method using class-reconstruction and adversarial training
Tang et al. Causal learning for robust specific emitter identification over unknown channel statistics
Qi et al. Data-and-channel-independent radio frequency fingerprint extraction for LTE-V2X
Zhang et al. Wi-Fi device identification based on multi-domain physical layer fingerprint
Zhang et al. FWSResNet: An edge device fingerprinting framework based on scattering and convolutional networks
Ponnaluru et al. A software‐defined radio testbed for deep learning‐based automatic modulation classification
Feng et al. Waveform Reconstruction of DSSS Signal Based on VAE‐GAN