Wireless and Satellite Systems

Images obtained by Intravascular ultrasound (IVUS) technology play a key role in detecting the lining of blood vessels. However, IVUS images are not clear enough usually, it is difficult to detect the inner wall of the blood vessels. It further affects the diagnosis results. In view of this situation, we first applies the pseudo-color enhancement algorithm to enhance the image; Second, the images were dichotomized by Support Vector Classification (SVC), and the images were divided into internal and external parts; then Hough gradient transform based on Canny operator is applied to detect the inner wall of blood vessels. The proposed method was applied to detect 100 frames of IVUS images and compared with the actual judgment results of doctors. The detection results showed that the detection results of blood vessel lining in 97 frames were consistent with the doctors’ judgment results, and the detection accuracy could reach 97%. Experimental results show that the method can effectively highlight the characteristics of the inner wall of blood vessels and detect the inner wall of blood vessels. It can greatly improve the diagnostic accuracy in the actual medical process.

In the foreseen future, connected autonomous vehicles (CAVs) are expected to improve driving safety and experience considerably; however, cybersecurity remains a critical issue. CAN protocol, the de-facto standard for in-vehicle networks, provides no security mechanism, which makes it one of the most attack-prone parts. The lack of security mechanisms in CAN messages allows intruders to conduct devastating attacks, putting drivers’ and passengers’ lives at risk. An Intrusion Detection System (IDS) can monitor CAN network activities and detect suspicious behaviors resulting from an attack to help safeguard CAVs. The destructive behavior of an intruder is reflected as point and group anomalies in the sequence of CAN messages. Our study proposes an LSTM-based IDS for the CAN bus by exploiting the temporal correlations of the messages on the bus to detect anomalies. Specifically, it is a one-class classifier trained with attack-free data to predict the upcoming value of CAN messages. Then a Gaussian Naïve Bayes classifier is used to classify messages as normal and attack according to the resulting prediction errors. The proposed IDS is evaluated in terms of detection performance and compared with state-of-the-art one-class classifiers, including OCSVM, Isolation Forest, and Autoencoder, using two real-world datasets (Car Hacking Dataset and Survival Analysis Dataset). The proposed method outperforms baselines and achieves detection accuracy and F-score by nearly 100%.

Satellite cognitive network is currently facing a lot of complex spectrum environment with a lot of interference, and the required user signal strength will change with a variety of external factors, which directly affects the above series of results obtained through the decision mechanism and it can not be well applied to satellite cognitive network. So a new blind detection algorithm based on maximum and minimum eigenvalues of sampling covariance matrix is proposed. In this algorithm, the ratio of the difference and sum of the maximum and minimum eigenvalues of the sampling covariance matrix is used as the perceptual decision quantity. Then, by introducing the latest results of the distribution of the maximum and minimum eigenvalues of the sampling covariance matrix in large dimensional random matrix, an effective decision threshold calculation method is designed. Compared with the classical eigenvalue detection algorithm, the new algorithm has the advantage of accurate calculation of perceptual decision threshold, and can effectively improve the detection performance and the reliability of decision results.

The hybrid beamforming (HBF) design for terahertz (THz) massive multiple-input multiple-output (mMIMO) communications is an essential but challenging problem in future wireless communications. In this paper, we propose an efficient HBF design framework for downlink multi-user multi-stream transmission in broadband THz mMIMO systems. First, the analytic solutions for digital precoder are derived to minimize the sum-mean-square error between the transmitted and received symbols. Then, we propose a tractable criterion for the codebook-based fully-connected analog precoder design, and further extend it to the case where the dynamic partially-connected structure is considered. A low-complexity energy-based greedy antenna grouping scheme is proposed for the dynamic hybrid structure. Simulation results demonstrate the effectiveness and the superiority of the proposed scheme in terms of sum rate and bit error rate over its counterparts.

In the Internet of Things (IoT) applications based on visible light communication (VLC) systems, such as outdoor intelligent transportation and indoor intelligent home, a large amount of light energy is scattered. Therefore, this paper studies a visible light receiving circuit based on VLC, which is used to collect energy and receive data at the same time to solve such problems, which is called the simultaneous visible-light information and power transfer (SVIPT) circuit. At present, most of the circuits with similar functions to SVIPT are based on two branches, where they are using capacitors to filter direct current (DC) in the data reception branch and inductors to block alternating current (AC) signals at the energy collection end, but these circuits are inefficient and have significant drawbacks that cannot be truly used in practical applications. The SVIPT circuits proposed in this paper discard the previous concept and we have analyzed the main functions of the two branches specifically. Finally, the SVIPT circuit architecture that can be used in the actual scene is designed, and some simulations are carried out to prove its feasibility.

Wireless communication is becoming a trend in today's environment because it facilitates access to a wider range than non-wireless communication. In entrepreneurship, the product marketing process will greatly benefit if it is carried out with the right STP strategy. In addition to creating efficiencies in marketing mix activities, it also expands the reach of consumers and becomes more sophisticated. With this research study, we review the literature and relevant research data on marketing in wireless communication. In particular, we report a systematic literature review applying the PRISMA guidelines. There were 21 relevant articles on wireless communications and marketing published based on a systematic search of the Scopus database. The results show that publications on marketing in wireless communication have not been widely analyzed with various levels, quantitative, industries, and perspectives. The top research country was the United States and the most widely used stratified analysis was the network level. Industry analysis contains mostly information technology topics using a positioning and process perspective. Further research is possible, such as research into marketing in wireless communications using team levels with energy or food industry topics and a marketing element perspective, targeting, price, and place.

The triangular lattice is preferred in the case when the available estate is limited, since it entails a higher element density than that obtainable with a square lattice of identical inter-element distance. However, the antenna pattern of the triangular lattice is very sensitive to the change of the beam pointing direction due to its nonuniform distribution of beam gain, which probably results in a large SNR decrease with a minor beam pointing error. To address this issue, in this paper, we focus on the two dimensions, i.e., the elevation angle and the azimuth angle, and propose an adaptive azimuth adjustment algorithm to overcome the performance loss caused by the unpredictable elevation angle’s variation. Simulation results reveal that the total SNR reduction is less than 0.18dB when the elevation angle changes up to \(\pm {5^\circ }\), which demonstrates the robustness of our proposed algorithm.

Age of incorrect information (AoII) has been proposed recently to overcome the shortcomings of age of information (AoI) in internet of things (IoT) systems. AoII takes into account the content of the information by penalizing the sink only when it has an incorrect perception of the monitored source. This is of paramount importance for scenarios where actuations are taken based on the current data sample. On the other hand, random access (RA) has been identified as a promising solution for supporting next-generation IoT systems. Therefore, a thorough understanding of the behaviors of RA policies from the perspective of AoII is key for the design of IoT systems. In this paper, we study two representative RA schemes, namely slotted ALOHA (SA) and irregular repetition slotted ALOHA (IRSA), with Markov sources. We track the AoII evolution for both schemes through a Markovian analysis, where state transition probabilities are derived and closed form expressions for the average AoII are obtained. Simulation results are provided to validate our analysis. The study reveals the influences of the Markov source on the system performance as well as the design trade-offs for IRSA. Furthermore, the performance of SA and IRSA are compared under various settings, showing the cases where IRSA can largely outperform SA in terms of average AoII.