No abstract

Resorting to power accurate delivery for wireless power transfer (WPT) based on microwaves, a theory and method based on time reversal (TR) for WPT in 3D indoor environments are presented in this paper. Conventional WPT methods have the limitations of requiring that the elements on power source array (PSA) must have the same structure and regular arrangement. Unlike those conventional methods, the PSA used in this paper can be constructed by any omnidirectional antenna elements with any structure and any arrangement, whose principles are based on the auto-match of antenna radiation and environment adaptability of TR. Moreover, compared with those conventional methods, which perform complex multi-objective optimizations of beam pattern cost-functions at each frequency for getting excitation of PSA, the proposed algorithm can obtain PSA's excitation over a wide frequency range only by a single run of TR and Fourier transform operations. In addition, by weighting the time reversed charging request signal from powered devices, the interference caused by different observing elevation angles and amplitude difference among each channel can be suppressed effectively. At last, multi-powered devices placed arbitrarily can be charged simultaneously, and get similar power from PSA without power unfairness by our proposed algorithm. INDEX TERMS 3D indoor environment, auto-match of antenna radiation and channel, microwave, time reversal, wireless power transfer.

Inspired by the successful experience of convolutional neural networks (CNN) in image classification, encoding vibration signals to images and then using deep learning for image analysis to obtain better performance in bearing fault diagnosis has become a highly promising approach. Based on this, we propose a novel approach to identify bearing faults in this study, which includes image-interpreted signals and integrating machine learning. In our method, each vibration signal is first encoded into two Gramian angular fields (GAF) matrices. Next, the encoded results are used to train a CNN to obtain the initial decision results. Finally, we introduce the random forest regression method to learn the distribution of the initial decision results to make the final decisions for bearing faults. To verify the effectiveness of the proposed method, we designed two case analyses using Case Western Reserve University (CWRU) bearing data. One is to verify the effectiveness of mapping the vibration signal to the GAFs, and the other is to demonstrate that integrated deep learning can improve the performance of bearing fault detection. The experimental results show that our method can effectively identify different faults and significantly outperform the comparative approach.

Feature-based (FB) algorithms for automatic modulation recognition of radar signals have received much attention since they are usually simple to realize. However, existing FB approaches usually focus on several specific modulations and fail when applied to various modulations. To overcome this issue, we propose a simple and effective FB algorithm based on Manhattan distance-based features (MDBFs) in this paper. MDBFs are new features for radar signals that can be applied for recognition of different modulations. The main contributions of this paper are as follows. First, radar signals are represented as wavelet ridges, which includes important information that can distinguish different modulations, and the piecewise aggregate approximation algorithm is introduced to reduce signal dimensions. Then, the dynamic time warping averaging is employed instead of the traditional k-means algorithm to extract realistic centroids for each class. Finally, the Manhattan distances between each data sample and each centroid are used to construct MDBFs, and decisions are made using the k-nearest neighbor. In addition, we prove that MDBFs have better class separability power than the Euclidean-based features. MDBFs contain information about the correlations between different classes, which means that these features suitable for discriminating various modulations when their class distributions do not overlap badly in representation space. The extensive experiments on a synthetic dataset demonstrate the outstanding performance of our proposed method and are hardly affected by the pulse width of the signal. Thus, the proposed method with the effectiveness and robustness could be a promising modulation recognition method of the radar signal. INDEX TERMS Modulation recognition, Manhattan distance-based feature, wavelet ridge, dynamic time warping averaging, class centroid.

Quantifying the abnormal degree of each instance within data sets to detect outlying instances, is an issue in unsupervised anomaly detection research. In this paper, we propose a robust anomaly detection method based on principal component analysis (PCA). Traditional PCA-based detection algorithms commonly obtain a high false alarm for the outliers. The main reason is that ignores the difference of location and scale to each component of the outlier score, this leads to the cumulated outlier score deviates from the true values. To address the issue, we introduce the median and the Median Absolute Deviation (MAD) to rescale each outlier score that mapped onto the corresponding principal direction. And then, the true outlier scores of instances can be obtained as the sum of weighted squares of the rescaled scores. Also, the issue that the assignment of the weight for each outlier score will be solved. The main advantage of our new approach is easy to build with unsupervised data and the recognition performance is better than the classical PCA-based methods. We compare our method to the five different anomaly detection techniques, including two traditional PCA-based methods, in our experiment analysis. The experimental results show that the proposed method has a good performance for effectiveness, efficiency, and robustness.