Radiometric Identification Based on Low-Rank Representation and Minimum Prediction Error Regularization

1D-local binary pattern based feature extraction for classification of epileptic EEG signals

Discriminative feature extraction based on sparse and low-rank representation

Feature extraction based on Low-rank affinity matrix for biological recognition

This paper demonstrates that highly accurate radiometric identification is possible using CAPoNeF feature engineering method. We tested basic ML classification algorithms on experimental data gathered by SDR. The statistical and correlational properties of suggested features were analyzed first with the help of Point Biserial and Pearson Correlation Coefficients and then using P-values. The most relevant features were highlighted. Random Forest provided 99% accuracy. We give LIME description of model behavior. It turns out that even if the dimension of the feature space is reduced to 1, it is still possible to classify devices with 99% accuracy.

Specific emitter identification (SEI) can distinguish communication radio emitters with the fingerprint features carried by the received signal, and this technology has been widely used in military and civilian fields. However, in the real electromagnetic environment, the number of communication radio emitters is large and the signal‐to‐noise ratio (SNR) is low, which leads to poor nonlinear fingerprint analysis of SEI in a single domain. Therefore, combining the exploration of multiple domains of electromagnetic spatial information resources, this paper proposed a radio frequency (RF) fingerprint extraction method based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multidomain joint entropy. The proposed method is an attempt and exploration further extraction of nonlinear fingerprint features in multiple domains. Firstly, considering the nonstationarity of the communication signal, this article adopts the CEEMDAN method to decompose the signal to multiple intrinsic mode functions (IMF). Then, the decomposed signal is represented in multiple spaces by a multidimensional phase space reconstruction technique. Nonlinear analysis of the original signal is performed in multiple spaces: multidimensional differential approximate entropy space, singular spectral entropy space, and power spectral entropy space. Finally, the support vector machine (SVM) is adopted in the classification stage. To demonstrate the robustness of the method, the method is verified on the universal software radio peripheral (USRP) dataset and the Northeastern University public dataset. In terms of the identification accuracy, the proposed method performs with 98.5% accuracy on the 5‐class USRP dataset. It also performs with 94.7% accuracy on the 16‐class public dataset. The experimental results show that the proposed method has a stable identification performance and has a more than 85% recognition rate in the SNR above 5dB.

Feature extraction (FE) methods based on low-rank representation (LRR) have become important topics in hyperspectral images (HSIs) data analysis. In this paper, a supervised FE method for HSIs data based on LRR with the ability to preserve the local pairwise constraints information (LRLPC) is proposed. LRLPC does not change the data dimensionality and only employs a technique to enrich the original feature space (OFS) and to obtain enriched feature space, which results in features richer than OFS. To overcome the problem of LRR in lacking the local structure information (LSI) of data, a local discriminative regularization term is imposed on the fitness function of LRR to keep the LSI of data. For nonlinear structure of data, LRLPC is extended to kernel LRLPC (KLRLPC) using kernel trick. Utilization of existing information in the pairwise constraints is useful for limited labeled samples situations as a common problem in HSI data analysis. The obtained experimental results using two well-known HSI data sets confirm the effectiveness of LRLPC and KLRLPC for dimension reduction and classification of HSIs.

Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal

With the development of cross-modal image fusion in multi-sensor systems, current fusion technologies have made significant progress in feature extraction, facilitating more effective image analysis. However, insufficient fusion information may degrade the correlation between the source and fused images, often resulting in the omission of critical features from the original modalities. Therefore, in order to preserve as much information as possible, especially for the complete extraction of effective feature information in source images, this paper proposes a new cross-modal image fusion method based on low-rank representation and convolutional sparse learning named LRCFuse. Firstly, the learned low-rank representation (LLRR) blocks are employed to perform dimensionality reduction on the source images while simultaneously extracting their low-rank and sparse feature components. Nevertheless, considering that the low-rank representation has insufficient modeling ability for different modal images, we introduce common feature preservation module (CFPM) blocks based on convolutional sparse coding. By leveraging the CFPM module, LRCFuse recovers common features from both source images to mitigate the loss caused by the imperfect assumptions of low-rank representation. Based on this, a multi-level optimization strategy incorporating pixel loss, shallow-level loss, mid-level loss, deep-level loss, and sobel loss is proposed to hierarchically learn and refine diverse image features. Quantitative and qualitative evaluations are conducted across various datasets, revealing that LRCFuse can effectively detect targets infrared salient targets, preserve additional details in visible images, and achieve better fusion results for subsequent downstream tasks.

Fingerprint recognition is a method of biometric authentication that uses pattern recognition techniques [1] based on high-resolution fingerprints images of the individual. Fingerprints have been used in forensic as well as commercial applications for identification as well as verification. The fingerprint surface is made up of a system of ridges and valleys. The steps for Fingerprint recognition include image acquisition, preprocessing, feature extraction and matching [2]. In the present work, a new fingerprint feature detection algorithm has been proposed. It has been found that presence of noise in fingerprint images leads to spurious minutiae. To overcome this problem, feature extraction has been done which efficiently determine the minutiae points in fingerprint [1]. The proposed method can be used in matching the template for finding bifurcation and termination. The new smoothing algorithm is proposed for the detection of the features of fingerprints. A method has been introduced for finding ridges in the fingerprint image with the help of eight different masks. It is a process of making a binary image of ridges from the grayscale fingerprint image. The experimental results showed the accuracy of the algorithm in terms of genuine acceptance rate, false rejection rate, false acceptance rate.

Structure-Constrained Low-Rank and Partial Sparse Representation with Sample Selection for image classification

In the object recognition based on the bag of visual words, we focus on several main feature extraction algorithms. The Scale Invariant Feature Transform (SIFT) that based on local features with good significance and robustness has became a popular feature extraction method. But we take into consideration that the SIFT will generate a large number of high-dimensional feature vectors, which will increase the computational cost. In this paper, a novel feature extraction algorithm by means of SIFT to fuse region information entropy (SIFT-entropy) is proposed. The algorithm can improve the quality of the feature extracted from the image by cluster similar key points to removing the noisy key points. So a more discriminative high quality “visual word” codebook could be generated. We made a comprehensive comparison between the proposed method and the original SIFT method on Caltech-101 database. The experimental results show that this improvement can reduce the dimension of the feature space, and has higher classification accuracy.

In quality detection, it is important to classify and predict the unbalanced data sets with a high proportion of qualified and unqualified products. There exist already some machine learning methods available. However, these existing methods assume that the samples are evenly distributed among the different classes and ignore the unbalanced characteristics of data. In addition, existing methods cannot be directly applied to high‐dimensional data and cannot accurately express the relationship between data features and the quality of industrial engineering products. In this paper, we propose a new quality detection method of the unbalanced data by establishing a non‐parameter Log–Log classification model. The principal component analysis (PCA) is used to extract the features and reduce the dimension of the original data sets. We develop a sieve maximum likelihood algorithm to obtain the non‐parameter function classifier. The proposed method is applied to the product quality detection of industrial semiconductor manufacturing. The results show the proposed method has high detection performance and classification ability. Compared with traditional machine learning methods, the proposed method has a higher classification accuracy can better describe the relationship between product characteristics and product quality and has a strong generalization ability for different data sets.

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

The low-rank property of hyperspectral imagery is well exploited with low-rank decomposition methods recently. In our approach, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. Further, a dictionary learned from the whole image with a random selection process, which can be viewed as the spectra of the background only, is introduced into the decomposition process. The adoption of LD improves the robustness of LRR to its parameters with a less computational cost. Experimental results demonstrate that the proposed method has a satisfactory result.

Low-rank representation (LRR) efficiently performs the subspace segmentation and feature extraction from corrupted data. However, there are three disadvantages in existing LRR techniques. First, the inference algorithm of LRR (as a generative model) is computationally expensive. Second, LRR ignores the discriminative information for image classification. Third, although the robust representation is implemented by recovering the low-rank components and the sparse noises, it has been limited due to the constrained assumption that noises is sparse. To solve these problems, and inspired by Denoising Autoencoders (DAE) and Contractive Autoencoders (CAE), this paper proposes a discriminative low-rank representations framework (DLRR) for image classification. We directly learn a discriminative projection dictionary that results in fast inference. Simultaneously, DLRR can obtain a robust representation from any corrupted input. Our implementation of DLRR achieves state-of-the-art results on artificial dataset and dataset of Olivetti Face Patches.