Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in realistic communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

As computer vision advances, synthetic aperture radar (SAR) ship detection increasingly incorporates deep learning techniques based on convolutional neural networks (CNNs). Presently, these approaches predominantly depend on large network architectures and anchor-based object representation methods. Although such networks can yield superior results, they are often inefficient in detection. For SAR ship detection, this letter proposes a novel network structure, the Contour Key Points Network (CPoints-Net), which utilizes a compact backbone ResNet18 and Feature Pyramid Structure (FPN) to enhance network efficiency. The Contour Key Points object representation employed in CPoints-Net is an anchor-free method that directly locates and represents ship objects, circumventing the complex post-processing associated with anchor-based techniques. Furthermore, this anchor-free approach preserves a higher degree of object feature information, and enables direct object prediction. To address the distribution of contour key points, CPoints-Net applies pointwise feature grouping (PFG) part and dynamic feature grouping (DyFG) part to refine features and employs Deformable Convolution (DCN) to achieve a more precise fit of key points to contours. On the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID), CPoints-Net achieves AP ₅₀ scores of 96.3% and 90.5%, respectively. This network has 18.64M parameters (Params), with Frames Per Second (FPS) rates of 43.8 img/s and 32.6 img/s respectively, demonstrating the optimal performance and efficiency of this network. Its source code is located at https://github.com/Daniel-tech307/CPoints_net.git.

This paper proposes a phase current reconstruction strategy based on a dc bus using a single current sensor for a surface permanent magnet synchronous motor (SPMSM). The method of a single current sensor reduces the number of mechanical hall sensors and shunt resistors by using a modified current reconstruction algorithm. The information of rotor position is estimated by the sliding mode observer for its rapid response and strong anti-interference ability, and the observer needs to detect voltage and current components from α − β coordinate system. In order to reduce the buffeting problem of sliding mode observers, an adaptive neural network is introduced, by the way of extracting angle speed estimated values from sliding mode observers, and these values are trained to obtain the compensate angular velocity and minus index value to suppress speed value. The performance of this sensorless speed regulation strategy in the high-speed region using a single current sensor with an optimized adaptive neural network is verified and evaluated by PSIM simulation and experiments.

With the rapid development of wireless communications technology, long term evolution (LTE) technology in unlicensed bands (LTE-U) can effectively solve the lack of spectrum resources. However, the competition in LTE-U and wireless fidelity (WiFi) will seriously interfere their communication quality, which making the friendly coexistence of LTE-U and WiFi becomes a hot research area. In this paper, we propose a classification algorithm based on deep learning to realize the identification of LTE-U and WiFi signal. Experiment results use mixed data at different signal to noise ratios (SNRs) and compare the classification results within two data forms. Experimental results show that our proposed deep learning-aided method can effectively distinguish LTE-U and WiFi signals and further achieve their friendly coexistence.

The aim of this paper is to show the problems of implementing the wireless adaptive networks with the free space optical (FSO) technology. Implementing adaptive networks with the wireless optical communication technology has several benefits and also some hindering problems. The thermal optical noise modeled with Gaussian distribution and link turbulence is two of the major problems of this implementation. In this paper, the theoretical analysis of the FSO link effects that are modeled with K-distribution and Negative exponential distributions are considered on the estimation performance of the adaptive incremental networks. These distributions arise when the FSO link is contaminated with strong optical turbulence. Experiments are designed to cover these conditions and the analysis is based on the steady state mean square deviation (MSD) and excess mean square error (EMSE) values for the incremental LMS (ILMS) algorithm and these are the metrics that show how well the adaptive network performs. Simulation results are presented for different parameters of K-distribution and negative exponential distribution and the results show perfect match with the theoretical outcomes. Based on these results, we show that implementing the incremental adaptive networks in the strong turbulence conditions is not feasible and we must think of some countermeasures for these cases.