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In response to the problem of limited performance and low spectral efficiency in complex multipath environments of traditional direct sequence spread spectrum communication, a cyclic prefix (CP) block transmission structure spread spectrum system with a time-frequency hybrid direct sequence spread spectrum receiver structure was proposed.The system adopted maximum ratio combination (MRC) in the frequency domain to achieve equalization and de-spreading processes simultaneously, while achieving optimal reception performance and omitting the IFFT process to reduce computational complexity significantly.On this basis, it was proposed to replace the m-sequence with the constant amplitude zero auto correlation (CAZAC) sequence as the spread spectrum sequence, which significantly improved the system performance without increasing computational complexity.Finally, a high-speed spread spectrum communication method based on cyclic code shift keying (CCSK) was proposed to solve the problems of low spectrum utilization efficiency and limited transmission rate in traditional direct sequence spread spectrum.By utilizing the property of Fourier transform time-domain cyclic shift, a low implementation complexity CCSK decoding method was designed, which reduced the multiplication number required for CCSK de-spreading by O(N2) reduced to O (Nlog N), greatly reducing resource consume.The simulation results show that compared to existing direct sequence spread spectrum methods, the proposed method achieves approximately 3 dB gain in both Gaussian and complex multipath channel environments.

BiCuSeO compound is a promising thermoelectric material, which has attracted many experimental studies through trial-and-error approaches to improve its thermoelectric performance by element doping, such that a fast and efficient prediction of thermoelectric property for unexplored and rarely explored doped-BiCuSeO is highly desired. In this work, a machine learning (ML) model for predicting the ZT value of M element doped-BiCuSeO (Bi1-xMxCuSeO) has been established via the correlation analysis for descriptors and the comparison among different ML approaches. The results show that Gradient Boosting Regressor is the most appropriate approach for our ML model, which is well validated by comparing the predicted and experimental ZT values for the cases in the dataset. The ML model is also used to predict the ZT values of Bi1-xMxCuSeO with unexplored and rarely explored doping element M, and the optimal doping elements as well as their doping contents are screened out. The results indicate that the ZT of Bi0.86Po0.14CuSeO (Po-doped) and Bi0.88Cs0.12CuSeO (Cs-doped) are higher than that of pure BiCuSeO, and are improved by 104 % and 98 % at the 923 K, respectively. The enhancement is well explained by the first-principles calculations. The findings offer a guideline for exploring superior thermoelectric performance in BiCuSeO.

Realizing the distributed adaptive network construction of multi-UAV networks is an urgent challenge, as they lack a reliable common control channel and can only maintain a limited sensing range in crowded electromagnetic environments. Multi-unmanned aerial vehicle (UAV) networks are gaining popularity in many fields. In order to address these issues, this paper proposes a multi-UAV network channel rendezvous algorithm based on average consistency. The goal of the algorithm is to adjust the communication channels of each UAV to converge on the same channel, since the communication link of the multi-UAV network is broken due to interference. The proposed memory-based average consistency (MAC) algorithm utilizes the network adjacency matrix as prior information. Furthermore, for the case where the adjacency matrix is unknown, this paper also proposes the Multi-Radio Average Consensus (MRAC) algorithm, which achieves a beneficial trade-off between rendezvous performance and hardware cost. Simulation results demonstrate that the proposed MAC and MRAC algorithms provide superior network convergence time and scalability in networks of different densities. Finally, a hardware simulation platform based on a multi-UAV network was designed using a software-defined radio platform, and experimental simulations were performed to prove the effectiveness of the MAC algorithm in a real environment.

Environmentally friendly lead-free K1-xNaxNbO3 (KNN) ceramics possess electromechanical properties comparable to lead-based ferroelectric materials but cannot meet the needs of device miniaturization, and the corresponding thin films lack theoretical and experimental studies. To this end, we developed the nonlinear phenomenological theory for ferroelectric materials to study the effects of non-equiaxed misfit strain on the phase structure, electromechanical properties, and electrical response of K0.5Na0.5NbO3 epitaxial films. We constructed in-plane misfit strain (u1−u2) phase diagrams. The results show that K0.5Na0.5NbO3 epitaxial film under non-equiaxed in-plane strain can exhibit abundant phase structures, including orthorhombic a1c, a2c, and a1a2 phases, tetragonal a1, a2, and c phases, and monoclinic r12 phases. Moreover, in the vicinity of a2c−r12, a1c−c, and a1a2−a2 phase boundaries, K0.5Na0.5NbO3 epitaxial films exhibit excellent dielectric constant ε11, while at a2c−r12 and a1c−c phase boundaries, a significant piezoelectric coefficient d15 is observed. It was also found that high permittivity ε33 and piezoelectric coefficients d33 exist near the a2c−a2, a1a2−r12, and a1c−a1 phase boundaries due to the existence of polymorphic phase boundary (PPB) in the KNN system, which makes it easy to polarize near the phase boundaries, and the polarizability changes suddenly, leading to electromechanical enhancement. In addition, the results show that the K0.5Na0.5NbO3 thin films possess a large electrocaloric response at the phase boundary at the a1a2−r12 and a1c−a1 phase boundaries. The maximum adiabatic temperature change ΔT is about 3.62 K when the electric field change is 30 MV/m at room temperature, which is significantly enhanced compared with equiaxed strain. This study provides theoretical guidance for obtaining K1−xNaxNbO3 epitaxial thin films with excellent properties.

The rapid growth in the scale of the intelligent connected vehicles (ICVs) brings about the need for novel random access schemes that realize collision avoidance and reliable communication. The coded random access (CRA) scheme utilizes successive interference cancellation (SIC) across slots to enable the extraction of multiple users' packets from the collision slots and proves to be an efficient solution to collision resolution. In this paper, we propose a random backoff-based CRA scheme to achieve a tradeoff between the access success probability and access delay for CRA. Specifically, we partition the traditional CRA frame into subframes, so that the user access delay is reduced to the same scale of the subframe length. In addition, the backoff operation among subframes is introduced so that access failures originated from the subframe length limit can be reduced with the retransmission in subsequent subframes. In particular, we consider practical Nakagami-m fading channel in the CRA performance analysis and take into account the capture effect in the iterative interference cancellation process. Simulation results show that backoff-based CRA can significantly reduce the average access delay without severe negative effect on the access success probability for moderate system load compared with traditional CRA. Comparisons with the state-of-the-art non-orthogonal random access (NORA) scheme proposed for 5G indicates that backoff-based CRA provides a feasible solution for random access of large-scale ICVs.

The distributed scheduling mode possesses numerous advantages over the centralized scheduling mode in scenarios where the topology changes rapidly, such as low operation and maintenance costs, high robustness and improved delay performance, and thus has broad application prospects in robot swarms, device-to-device networks, industrial sensor networks, etc. The distributed election mechanism can effectively reduce collisions and improve the success probability of random access, which is one of the most commonly used channel access methods in distributed networks. The size of election interval directly affects the probability of successful election and the network performance. Specifically, with the expansion of the network scale, smaller election intervals will lead to more election nodes and reduce the probability of successful elections. Although expanding the election interval can reduce collisions to a certain extent, the number of legal election nodes will increase, which reduces the probability of successful elections. Therefore, the choice of the election interval needs to be explored in detail. This paper analyzes the relationship between the election interval and the number of effective election nodes, and establishes the analytical model of the election success probability, scheduling delay and effective throughput. In addition, the election interval size and the number of control slots are optimized to improve effective throughput and delay performance. Finally, the impact of the election interval on the network performance is verified by simulation, which provides guidance for parameter selection in realistic scenarios.

A combination of Fresnel law and machine learning method is proposed to identify the layer counts of 2D materials. Three indexes, which are optical contrast, red-green-blue, total color difference, are presented to illustrate and simulate the visibility of 2D materials on Si/SiO2 substrate, and the machine learning algorithms, which are k-mean clustering and k-nearest neighbors, are employed to obtain thickness database of 2D material and test the optical images of 2D materials via red-green-blue index. The results show that this method can provide fast, accurate and large-area property of 2D material. With the combination of artificial intelligence and nanoscience, this machine learning assisted method eases the workload and promotes fundamental research of 2D materials. Keywords: Two-dimensional materials, Optical contrast, Total color difference, Red-green-blue, K-means clustering, K-nearest neighbors (k-NN)

To enhance the network coverage and capacity, unmanned aerial vehicle (UAV) communication has become a promising technology thanks to the high mobility and the flexible deployment of UAVs. However, the consistency of the service is not guaranteed due to the limitation single UAV base station (BS), leading to the research for the user-centric UAV swarm networks. This paper derives the semi-closed-form expressions of coverage probability and average achievable rate for the user-centric UAV swarm networks to characterize the performance gain obtained from the increased UAV swarm diversity. Especially, the user-centric UAV swarm networks organize a dynamic UAV swarm for each user consisting of potential serving UAVs in user's vicinity, which gives the design of boundaryless cells to serve each user seamlessly without user's involvement. By modeling the locations of the UAVs as Poisson point process (PPP) and modeling the channel fading as Nakagami-$m$ distribution, the analytical results are calculated under two transmission modes, depending on whether cell coordination is considered or not. The results show that the coverage performance can be improved by 30% without cell coordination and 50% with cell coordination in comparison with traditional cell-centric networks when the UAV altitude is 100 m and SINR threshold is 0 dB.

Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.

Compared with a monostatic radar, airborne distributed coherent radar (ADCR) has been widely applied thanks to its flexibility, greater degree of freedom, stronger detection, and anti-jamming ability. Unlike distributed ground-based radar, the precondition for ADCR to perform tasks is maintenance of stable wireless communication links among the unmanned aerial vehicles (UAVs). Therefore, the communication channel modeling of ADCR is very important. This paper mainly analyzes the performance of a communication system composed of radar UAVs, communication UAV (relay), and ground base station. The probability density function (PDF) and outage probability (OP) of signal-to-noise ratio (SNR) at the ground terminal are derived analytically in the cases of transmission power error, UAV position error, and multi-path fading. Numerical simulation shows the validity of the derived results.