Nothing Special   »   [go: up one dir, main page]

Bai et al., 2024 - Google Patents

An Interpretable Explanation Approach for Signal Modulation Classification

Bai et al., 2024

Document ID
10798362110686231794
Author
Bai J
Lian Y
Wang Y
Ren J
Xiao Z
Zhou H
Jiao L
Publication year
Publication venue
IEEE Transactions on Instrumentation and Measurement

External Links

Snippet

Signal modulation classification (SMC) has attracted extensive attention for its wide application in the military and civil fields. The current direction of combining deep-learning (DL) technology with wireless communication technology is developing hotly. DL models are …
Continue reading at ieeexplore.ieee.org (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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • G06F17/30595Relational databases
    • G06F17/30598Clustering or classification
    • G06F17/30601Clustering or classification including cluster or class visualization or browsing
    • 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
    • G06K9/4604Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G06K9/4609Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • G06K9/4619Biologically-inspired filters, e.g. receptive fields
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30477Query execution
    • G06F17/30507Applying rules; deductive queries
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods

Similar Documents

Publication Publication Date Title
Wang et al. Multi-task learning for generalized automatic modulation classification under non-Gaussian noise with varying SNR conditions
Zheng et al. Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification
Imran et al. An intelligent and efficient network intrusion detection system using deep learning
Dong et al. SSRCNN: A semi-supervised learning framework for signal recognition
Zhang et al. A novel automatic modulation classification scheme based on multi-scale networks
Huang et al. Visualizing deep learning-based radio modulation classifier
Hao et al. Automatic modulation classification via meta-learning
Li et al. Automatic modulation classification using ResNeXt-GRU with deep feature fusion
Wang et al. Adversarial unsupervised domain adaptation for cross scenario waveform recognition
Bai et al. An Interpretable Explanation Approach for Signal Modulation Classification
Zhang et al. A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition
Liu et al. Learning multiple gaussian prototypes for open-set recognition
Han et al. Radar specific emitter identification based on open-selective kernel residual network
Bai et al. RffAe-S: Autoencoder based on random fourier feature with separable loss for unsupervised signal modulation clustering
Wei et al. Automatic modulation recognition using neural architecture search
Bai et al. Achieving efficient feature representation for modulation signal: A cooperative contrast learning approach
Zhou et al. Deep radio signal clustering with interpretability analysis based on saliency map
Chen et al. FEM: Feature extraction and mapping for radio modulation classification
Liu et al. Unknown radar waveform recognition system via triplet convolution network and support vector machine
Feng et al. FCGCN: Feature Correlation Graph Convolution Network for Few-Shot Individual Identification
Zha et al. Intelligent identification technology for high‐order digital modulation signals under low signal‐to‐noise ratio conditions
Mobini et al. Deep chaos synchronization
Hosseinzadeh et al. A self training approach to automatic modulation classification based on semi-supervised online passive aggressive algorithm
Wang et al. SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification
Zhang et al. Modulation recognition of communication signals based on SCHKS-SSVM