This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels.

Flying ad hoc networks (FANETs) or drone technologies have attracted great focus recently because of their crucial implementations. Hence, diverse research has been performed on establishing FANET implementations in disparate disciplines. Indeed, civil airspaces have progressively embraced FANET technology in their systems. Nevertheless, the FANETs’ distinct characteristics can be tuned and reinforced for evolving security threats (STs), specifically for intrusion detection (ID). In this study, we introduce a deep learning approach to detect botnet threats in FANET. The proposed approach uses a hybrid shark and bear smell optimization algorithm (HSBSOA) to extract the essential features. This hybrid algorithm allows for searching different feature solutions within the search space regions to guarantee a superior solution. Then, a dilated convolutional autoencoder classifier is used to detect and classify the security threats. Some of the most common botnet attacks use the N-BaIoT dataset, which automatically learns features from raw data to capture a malicious file. The proposed framework is named the hybrid shark and bear smell optimized dilated convolutional autoencoder (HSBSOpt_DCA). The experiments show that the proposed approach outperforms existing models such as CNN-SSDI, BI-LSTM, ODNN, and RPCO-BCNN. The proposed HSBSOpt_DCA can achieve improvements of 97% accuracy, 89% precision, 98% recall, and 98% F1-score as compared with those existing models.

Calibration on multi-camera imaging system in circular arrangement is a challenge task, because the preliminary shared feature points are absent among opposite viewpoints in the calibration procedure. In this paper, we propose a scheme of group geometric calibration and ideal rectification for circular multi-camera imaging system. In this scheme, a three-dimensional (3D) calibration object with new patterns is firstly designed to extract the extrinsic parameters. After that, the ideal camera projection matrix is estimated based on these parameters. Finally, we extend our scheme from static to dynamic scenario. In the experiments, our method has a superior performance over state-of-the-art calibration methods. Furthermore, we verify our scheme by application of 3D modeling and improvements can also be found. These results demonstrate that our method performance on such imaging system is applicable for future interactive visual applications.

In this paper, a coplanar waveguide (CPW)-fed patch antenna is fabricated on a layer of metasurface to increase gain. The antenna is fabrication on Roger substrate with a thickness of 0.25mm, with the overall dimension of the proposed design being 45×30×0.25mm³. The size of the patch antenna is 24×14×0.25mm³, and the AMC unit cell is 22×22×0.25mm³. This metasurface is designed based on the split-ring resonator unit cells forming an array of the artificial magnetic conductor (AMC). Meanwhile, the antenna operation on 3.5GHz is enabled by etching a split-ring resonator slot on the ground plane with a small gap to enhance antenna gain and improve impedance bandwidth when integrated with a metasurface. This simulation planer monopole antenna is applied for 5G application. The experimenter test is applied for the antenna performance in terms of return loss, gain, and radiation patterns. The operating frequency range with and without MTM is from 3.41 to 3.68GHz (270MHz) and 3.37 to 3.55GHz (180MHz), respectively, with gain improvements of about 2.7dB (without MTM) to 6.0dB (with MTM) at 3.5GHz. The maximum improvement of the gain is about 42 when integrated with the AMC. The AMC has solved several issues to overcome the typical limitation in conventional antenna design. A circuit model is also proposed to simplify the estimation of the performance of this antenna at the desired frequency band. The proposed design is simulated by CST microwave studio. Finally, the antenna is fabricated and measured. Result comparison between simulations and measurements indicates a good agreement between them.

With the booming integration of IoT technology in our daily life applications such as smart industrial, smart city, smart home, smart grid, and healthcare, it is essential to ensure the security and privacy challenges of these systems. Furthermore, time‐critical IoT applications in healthcare require access from external parties (users) to their real‐time private information via wireless communication devices. Therefore, challenges such as user authentication must be addressed in IoT wireless sensor networks (WSNs). In this paper, we propose a secure and lightweight three‐factor (3FA) user authentication protocol based on feature extraction of user biometrics for future IoT WSN applications. The proposed protocol is based on the hash and XOR operations, including (i) a 3‐factor authentication (i.e., smart device, biometrics, and user password); (ii) shared session key; (iii) mutual authentication; and (iv) key freshness. We demonstrate the proposed protocol’s security using the widely accepted Burrows–Abadi–Needham (BAN) logic, Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool, and the informal security analysis that demonstrates its other features. In addition, our simulations prove that the proposed protocol is superior to the existing related authentication protocols, in terms of security and functionality features, along with communication and computation overheads. Moreover, the proposed protocol can be utilized efficiently in most of IoT’s WSN applications, such as wireless healthcare sensor networks.