Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification

Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification. However, Deep learning also present risks and vulnerabilities. Despite their outstanding classification performance, they exhibit fragility when confronted with meticulously crafted adversarial examples, posing potential risks to the reliability of modulation recognition results. Addressing this issue, this letter pioneers the development of an intelligent modulation classification framework based on conformal theory, named the Conformal Shield, aimed at detecting the presence of adversarial examples in unknown signals and assessing the reliability of recognition results. Utilizing conformal mapping from statistical learning theory, introduces a custom-designed Inconsistency Soft-solution Set, enabling multiple validity assessments of the recognition outcomes. Experimental results demonstrate that the Conformal Shield maintains robust detection performance against a variety of typical adversarial sample attacks in the received signals under different perturbation-to-signal power ratio conditions

Fast acquisition method using modified PCA with a sparse factor for burst DS spread-spectrum transmission

To improve the acquisition speed and inbound capacity of the ground station in a burst direct-sequence (DS) spread-spectrum transmission system, an acquisition method based on a modified parallel code-phase acquisition (PCA) scheme is proposed. By taking advantage of the sparsity of the acquisition result with PCA in the time domain, we introduce the sparse factor to handle the signals via sparsification and apply the sparse recovery algorithm to search and estimate the acquisition result. The computational complexity, mean acquisition time, and relationship between the inbound capacity and acquisition performance are provided. We theoretically analyse the effect of the sparse factor on the acquisition performance. The estimation errors verify our analysis, and simulations show that the acquisition time of our proposed method outperforms that of advanced PCA by 1.2–4.0 times; additionally, the inbound capacity increases by 6.2–36.7%

Evolution of Phase Transformation on Microwave Dielectric Properties of BaSi<i>_x</i>O_1+2<i>x</i> Ceramics and Their Temperature-Stable LTCC Materials

BaSixO1+2x (1.61 ≤ x ≤ 1.90) and LiF-doped BaSi1.63O4.26 ceramics were prepared by using a traditional solid-state method at the optimal sintering temperatures. The evolution of phase compositions of BaSixO1+2x (1.61 ≤ x ≤ 1.9) ceramics was revealed. The coexistence of Ba5Si8O21 and Ba3Si5O13 phases was obtained in BaSixO1+2x (1.61 ≤ x ≤ 1.67) ceramics. The BaSi2O5 phase appeared inBaSixO1+2x (1.68 ≤ x ≤ 1.90) ceramics. At 1.68 ≤ x ≤ 1.69, only BaSi2O5 and Ba3Si5O13 phases existed. With the further increase in x, the Ba5Si8O21 phase appeared, and BaSi2O5, Ba5Si8O21 and Ba3Si5O13 phases coexisted in BaSixO1+2x (1.70 ≤ x ≤ 1.90) ceramics. The phase compositions of BaSixO1+2x (1.61 ≤ x ≤ 1.90) ceramics were controlled by the ratio of Ba:Si. The BaSixO1+2x (x = 1.68) ceramics with 98.15 wt% Ba3Si5O13 and 1.85 wt% BaSi2O5 phases exhibited a negative τf value (−37.53 ppm/°C), and the good microwave dielectric properties of εr = 7.51, Q × f = 13,038 GHz and τf = +3.95 ppm/°C were obtained for BaSi1.63O4.26 ceramics with 70.05 wt% Ba5Si8O21 and 29.95 wt% Ba3Si5O13 phases. The addition of LiF sintering aids were able to reduce the sintering temperatures of BaSi1.63O4.26 ceramics to 800 °C. The phase composition of BaSi1.63O4.26 ceramics was affected by the sintering temperature, and the coexistence of Ba5Si8O21, Ba2Si3O8, BaSi2O5 and SiO2 phases was achieved in BaSi1.63O4.26-3 wt% LiF ceramics. The BaSi1.63O4.26-3 wt% LiF ceramics sintered at 800 °C exhibited dense microstructures and excellent microwave dielectric properties (εr = 7.10, Q × f = 12,463 GHz and τf = +5.75 ppm/°C), and no chemical reaction occurred between BaSi1.63O4.26-3 wt% LiF ceramics and the Ag electrodes, which indicates their potential for low-temperature co-fired ceramic (LTCC) applications

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification