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Considering tag sensitivity and in-phase and quadrature-phase imbalance (IQI), this paper investigates the reliability and security of a cognitive radio (CR)-aided ambient backscatter communication (AmBC) non-orthogonal multiple access (NOMA) system. Herein, the source communicates with its NOMA users in the presence of an eavesdropper, and to bolster security, artificial noise (AN) injection is introduced. The effects of non-ideal successive interference cancellation (SIC) and AN removal on system performance are also examined. Subsequently, outage probabilities (OPs) and interception probabilities (IPs) of the tag and NOMA signals are derived, with asymptotic results for further insights. Finally, simulation results are provided to validate the theoretical analysis. It is observed that both reliability and security remain stable even as interference or maximum transmission power increases up to a certain threshold. Furthermore, the reliability under NOMA mechanism outperforms that of orthogonal multiple access. Notably, IQI can consistently enhance the confidentiality of NOMA but selectively strengthen backscatter transmission, albeit at the expense of reliability. This paper comprehensively underscores the potential for real-world implementation by incorporating sensitivity constraints, IQI, AN, and imperfect SIC into the system model.

With the rapid emergence of connected vehicle (CV) technologies, there is a shortage of research to understand CV technology’s effect on drivers’ risk-taking behavioral intentions. This article aims to analyze driver responses to the real-time information by comparing their reactions to driving intentions between the CV and non-CV environments. A multi-group structural equation model (SEM) is employed to explore the heterogeneity in the relationships between behavioral intentions, attitudes, subjective norms, perceived behavioral control, and risk perceptions under the two different environments. This study reveals two key findings: 1) regarding driver responses to the theory of planned behavior (TPB) model, there are significant differences in attitudes and risk perceptions between the CV and non-CV environments; 2) irrespective of driving environments, risk-taking behavioral intentions are directly related to perceived behavioral control and risk perceptions. While intentions are directly related to attitudes but not associated with subjective norms under the non-CV environment. In contrast, intentions are directly related to subjective norms but not associated with attitude under the CV environment. The findings provide a theoretical basis for using TPB to evaluate CV technology’s effects and understanding the differences between the CV and non-CV environments.

A parameterized controller design approach is proposed to solve the problem of multi-objective control for vehicle active suspension systems by using symbolic computation. The considered model is a quarter-vehicle model of the active suspension system. The multi-objective robust control performances include the sprung mass acceleration, suspension deflection, and tire deflection. Based on dissipative Hamiltonian systems and Lyapunov function, a multi-objective $H_{\infty }$ controller design approach is developed, which can avoid solving Hamilton-Jacobi-Issacs equations. Then, an algorithm of solving semi-positive definite polynomial with tuning parameters is proposed by using symbolic computation. Furthermore, a method of parameter optimization is proposed. Simulations and comparation show that the control performance is significantly improved comparing with passive controlled systems and existing other control systems for active suspension systems.

The deep learning methods in the field of computer vision and big data are becoming more and more mature. Through the application of big data and deep learning technology, the diagnosis of artificial intelligence medical image can be realized, which provides a new opportunity for the automatic analysis of obstetrics medical image and the assistance of doctors to realize high-precision intelligent diagnosis of diseases. The current medical obstetric image diagnosis platform mainly targets low-resolution medical obstetric image files, and does not consider the data-sharing problem of the distributed file system in different storage nodes, which greatly reduces the efficiency of obstetric image storage and diagnosis. Based on this, this article designs an obstetric image diagnostic platform based on cloud computing technology. First, a medical imaging platform was designed by combining cloud computing technology, caching technology, and a distributed file system. Secondly, the use of contrast-enhanced ultrasound technology provides a more accurate ultrasound image for assessing the structure, size, location, and developmental abnormalities of the placenta. Finally, the effectiveness of the obstetric imaging diagnostic platform proposed in this paper is verified by experiments. The results show that the platform has fast data processing speed and convenient use, which greatly reduces the cost of medical equipment and improves efficiency. The hospital only needs to collect the obstetric image of the patient at the front end, transfer it to the cloud for image processing, and finally diagnose the disease.

This paper considers the problem of spectrum sensing in multi-antenna cognitive radio networks. Energy detection (ED) method for spectrum sensing does not require any information of the source signal and channel, as well as it is suitable for detecting independent identically distributed signals. Since covariance matrix catches the signal correlations well, the maximum eigenvalue detection (MED) method is more competitive than the ED method for correlated signals. Under the framework of random matrix theory, this paper firstly proposes two enhanced detection algorithms based on the maximum eigenvalue and energy of the signal to achieve performance improvement while preserving the advantages of the two algorithms. The proposed algorithms are a generalization of the ED and MED methods. To render the proposed algorithms more practical, we propose two other new blind spectrum sensing algorithms based on the maximum likelihood estimate of unknown noise variance. Using random matrix theory, the theoretical analysis on detection probability, false alarm probability and threshold are given. Finally, simulation results show the effectiveness and robustness of the proposed algorithms.

For the pulse Doppler radar system with short pulse sequence, maximum eigenvalue-based matrix CFAR detector (MEMD) provides an effective solution to the problem of detecting moving targets in the background of K distribution sea clutter when the target Doppler frequency is within clutter spectrum. However, its performance is inferior to adaptive normalized matched filter method (ANMF) when the target Doppler frequency heavily deviates from the clutter spectrum. In essence, the reason for the better performance of ANMF method is the clutter is effectively suppressed by whitening and matched filter. Motivated by this, the fusion idea of eigenvalue detection and pre-processing via fast Fourier transform is considered taking advantage of the priori information of target steering vector. A pre-processing-based maximum eigenvalue matrix CFAR detection (P-MEMD) method is proposed to suppress the influence of clutter on detection performance. Finally, the numerical experimental results based on simulated data and real sea clutter data show that the proposed method achieves better detection performance and robustness than some conventional methods.

Ambient backscatter is an emerging green communication technology that exploits the environmental radio frequency (RF) signals to enable passive devices to communicate with each other. This paper investigates channel capacity and outage performance of ambient backscatter communication systems. Specifically, a calculation method is proposed to facilitate capacity analysis, and the ambient backscatter system capacities are derived in the case of four different RF signals. It is surprisingly found that the channel capacity is obtained when the RF signals are not equiprobably backscattered by tag, and that the capacity with complex Gaussian RF signals is not exactly twice that with real ones for the ambient backscatter communication systems. Then, the outage probability and its asymptotic value in the high signal-to-noise ratio regime are obtained. Since the exact outage expression consists of an infinite number of terms, a tight truncation error bound is derived to reasonably estimate the number of effective terms for numerical simulation. Finally, simulation results are provided to corroborate theoretical analysis.