Automatic modulation classification (AMC) plays a critical role in both civilian and military applications. In this letter, we propose a multi-scale radio transformer (Ms-RaT) with dual-channel representation for fine-grained modulation classification (FMC). In Ms-RaT, a dual-channel representation (DcR) of radio signals is designed to help the model learn discriminative features by converging the multi-modality information, including frequency, amplitude, and phase. During the learning process, multi-scale analysis is introduced into the model to form the tighter decision boundary. Finally, extensive simulation results demonstrate that Ms-RaT achieves superior modulation classification accuracy with similar or lower computational complexity than existing state-of-the-art deep learning methods. Through ablation studies, we also validate the effectiveness of DcR and multi-scale analysis in Ms-RaT.

Fine-Grained Modulation Classification Using Multi-Scale Radio Transformer With Dual-Channel Representation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal-Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

https://www.mdpi.com/1424-8220/21/9/2910/pdf

Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.

https://www.mdpi.com/1424-8220/21/9/3155/pdf

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.

Automatic modulation classification (AMC) plays an important role in wireless spectrum monitoring. Motivated by the success of deep learning in various informatics domains, many AMC methods based on deep learning have been proposed. However, they usually require a large amount of labeled training samples for each category of modulation, which is hardly applicable in real-world AMC tasks. To tackle this issue, this paper proposes a novel few-shot learning framework, namely the spatial-temporal hybrid feature extraction network (STHFEN). In STHFEN, two feature extraction networks are designed to map the wireless communication signals into the spatial feature space and the temporal feature space, respectively. Then, a hybrid inference classifier is designed to combine the classification results in the two feature spaces. To train STHFEN more effectively, a hybrid loss function which promotes better inter-class separability of signals in the two feature spaces is proposed. Experimental results have demonstrated the effectiveness and robustness of STHFEN in few-shot AMC tasks. 

Spatial-Temporal Hybrid Feature Extraction Network for Few-Shot Automatic Modulation Classification

In this paper, we propose a method of identifying human motions, such as standing, walking, running, and crawling, using a millimeter wave radar sensor. In our method, two signal processing is performed in parallel to identify the human motions. First, the moment at which a person’s motion changes is determined based on the statistical characteristics of the radar signal. Second, a deep learning-based classification algorithm is applied to determine what actions a person is taking. In each of the two signal processing, radar spectrograms containing the characteristics of the distance change over time are used as input. Finally, we evaluate the performance of the proposed method with radar sensor data acquired in an indoor environment. The proposed method can find the moment when the motion changes with an error rate of 3%, and also can classify the action that a person is taking with more than 95% accuracy.

https://www.mdpi.com/1424-8220/21/7/2305/pdf

Identification of Human Motion Using Radar Sensor in an Indoor Environment.

Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method.

Yang Li

Papers

Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification