Low-complexity three-dimensional AOA-cross geometric center localization methods via multi-UAV network

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE

Secure and Precise Wireless Transmission for Random-Subcarrier-Selection-Based Directional Modulation Transmit Antenna Array

In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques, including artificial noise (AN) projection, phase alignment/beamforming, and random subcarrier selection (RSCS) based on orthogonal frequency division multiplexing (OFDM), and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structure for the desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depends intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of the SINR and clarifying the wiretap region. Simulation and analysis show that the proposed scheme actually can achieve a secure and precise wireless transmission of confidential messages in line-of-propagation channel, and the derived theoretical formula of average secrecy rate is verified to coincide with the exact results well for medium and large scale transmit antenna array or in the low and medium SNR regions

Effects of Al addition on the structure and mechanical properties of Zn alloys

The noticeable improvement of hardness, elongation and yield stress in the cast zinc alloys was achieved using aluminium inoculation. Through varying the addition level of this eutectic-forming solute (aluminium), the mechanism of such property improvement of cast Zn alloys was investigated. The increase of hardness, elongation and yield stress was very sensitive to the aluminium content due to grain-refinement and solid-solution strengthening. Beyond the maximum solubility of aluminium in zinc, a three-dimensional eutectic network was developed to form a “eutectic-skeleton”, which produced further reinforcement in yield stress and elongation, but only marginal enhancement in hardness. These improved mechanical properties are found to be closely associated with significant microstructural refinement. The microstructural refinement, i.e. the columnar-to-equiaxed transition and the reduction in grain sizes, was mainly elucidated in terms of the Interdependence theory

CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation

Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for cell nucleus segmentation. Additionally, the shortcomings of background noise, highly overlapping between cell nucleus, and blurred edges often lead to poor performance. To address these challenges, we propose a novel framework termed CausalCellSegmenter, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) techniques. The DAC module is designed which incorporates diverse downsampling features through a simple, parameter-free attention module (SimAM), aiming to overcome the problems of false-positive identification and edge blurring. Furthermore, we introduce CIM to leverage sample weighting by directly removing the spurious correlations between features for every input sample and concentrating more on the correlation between features and labels. Extensive experiments on the MoNuSeg-2018 dataset achieves promising results, outperforming other state-of-the-art methods, where the mIoU and DSC scores growing by 3.6% and 2.65%.Comment: 10 pages, 5 figures, 2 tables, MICCA