Search

A method for Underdetermined Blind Source Separation is proposed using third‐order cumulants and tensor compression. To effectively suppress symmetrical distributed noise, the third‐order cumulant is considered. Additionally, the complexity of high‐dimensional tensors can be reduced through high order singular value decomposition (HOSVD) for compression purposes. The method begins by calculating the third‐order cumulant tensor for whitening signals at different time delays, and then stacks several cumulants into a fourth‐order tensor. The HOSVD decomposition is applied to the fourth‐order tensor, compressing the high‐dimensional tensor into a low‐dimensional core tensor. Next, the core tensor is further decomposed using the canonical polyadic decomposition, and the resulting factor matrices are fused to obtain an estimation of the mixed matrix. Finally, leveraging the signal independence, a matrix diagonalisation method is employed to recover the source signals. Theoretical analysis and simulation results demonstrate that the proposed method effectively suppresses the influence of Gaussian noise, reduces computational complexity, and saves computational time. Moreover, compared with five representative approaches, the proposed method achieves superior separation results. Specifically, for the 3 × 4 mixed model with a signal‐to‐noise ratio of 20 dB, the average relative error of speech signal and radio signal are −11.02 and −6.8 dB respectively. [ABSTRACT FROM AUTHOR]

Blind Source Separation (BSS) of complex signals composed of radar, communication and jamming signals is the first step in an integrated electronic system, which requires higher accuracy of separation. However, the traditional Single‐Channel Blind Source Separation (SCBSS) method has low separation accuracy and poor robustness. Aiming at this problem, this paper proposes a SCBSS method based on spatial‐temporal fusion deep learning model. This is a deep neural network model, which realizes spatial‐temporal of mixed signals by integrating Convolutional Neural Network (CNN) and Bidirectional Long Short‐Term Memory Network (BiLSTM). Convolutional Neural Network is used to extract spatial features from input sequences, and BiLSTM is used to mine timing rules of signals. A batch normalisation layer and a dropout layer are added to improve stability and prevent overfitting. The experiments show that the average similarity coefficient of the separated signals is above 0.99 and the Signal‐Distortion Ratio (SDR) is up to 27 dB without noise. When the Signal‐Noise Ratio is 0–20 dB and Jamming‐Signal Ratio is 15 dB, the SDR is 5–30 dB higher than the traditional methods and the single network structure deep learning methods. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional underdetermined blind source separation (UBSS) algorithm does not have high accuracy in estimating the mixing matrix and the number of source signals cannot be determined, a genetic annealing UBSS method with source number estimation function is proposed. This method first performs a short‐time Fourier transform on the observed signal, and then performs single‐source point detection and normalization processing to transform linear clusters into dense clusters. In view of the strong global search ability of the genetic algorithm (GA) and the good local optimization ability of the simulated annealing algorithm, the GA and simulated annealing algorithm are combined, and the number of source signals is determined according to the BWP (Between‐Within Proportion, BWP) index. After estimating the mixing matrix, a modified subspace projection method is used to reconstruct the source signal. The simulation results of four kinds of radar signals show that in the case of dual receiving channels and three receiving channels, compared with the traditional method, the similarity coefficients of the proposed algorithm are greater, which can effectively improve the mixing matrix estimation accuracy, and can accurately determine the number of source signals. [ABSTRACT FROM AUTHOR]

Obstacle avoidance path planning capability, as one of the key capabilities of UAV (Unmanned Aerial Vehicle) to achieve safe autonomous flight, has always been a hot research topic in UAV research filed. As a commonly used obstacle avoidance path planning algorithm, RRT (Rapid-exploration Random Tree) algorithm can carry out obstacle avoidance path planning in real time and online. In addition, it can obtain the asymptotically optimal obstacle avoidance path on the premise of ensuring the completeness of probability. However, it has some problems, such as high randomness, slow convergence speed, long transit time, and curved flight trajectory, so that it cannot meet the flight conditions of the actual UAV. To solve these problems, the paper proposes an improved RRT algorithm. In the process of extending the random tree, ACO (ant colony optimization) is introduced to make the planning path asymptotically optimal. The optimized algorithm can set pheromones on the path obtained by RRT and select the next extension point according to the pheromone concentration. And then through a certain number of iterations, it converges to an ideal path scheme. In addition, this paper also uses MATLAB to verify the effectiveness and superiority of the algorithm: Although RRT is easy to fall into local optimization, since the optimization method in this paper can almost certainly converge to the optimal solution, when it is necessary to preplan the path before UAV takeoff, it can be used. [ABSTRACT FROM AUTHOR]

Abstract: A novel method for preparation of a dense diffusion barrier limiting current oxygen sensor is presented. The dense thin‐layer with the nominal composition Sr‐, Mg‐ and Fe‐doped lanthanum gallate (LSGMF) was prepared over La0.8Sr0.2Ga0.8Mg0.2O3–δ (LSGM) electrolyte by a thermal diffusion method. The crystal structure, morphology, elemental distribution, conductivity, and sensing performance of the prepared sample were investigated. X‐ray diffraction (XRD) analysis shows a highly crystalline perovskite. SEM‐EDS analysis shows that the dense thin‐layer with about 40 μm thickness is well attached to the solid electrolyte. Electrical conductivity measurement shows that the dense thin‐layer has high conductivity of 8.73 S·cm−1 at 800°C. The oxygen sensor exhibits good sensing performance. The variations of the limiting current (IL) vs oxygen content (x(O2)) can be obtained as follows: I L ( mA ) = 96.65 x ( O ) 2 ( mol ) % + 28.62 [ABSTRACT FROM AUTHOR]

Cancer incidence disparities exist among specific Asian American populations. However, the existing reports exclude data from large metropoles like Chicago, Houston and New York. Moreover, incidence rates by subgroup have been underestimated due to the exclusion of Asians with unknown subgroup. Cancer incidence data for 2009 to 2011 for eight states accounting for 68% of the Asian American population were analyzed. Race for cases with unknown subgroup was imputed using stratified proportion models by sex, age, cancer site and geographic regions. Age-standardized incidence rates were calculated for 17 cancer sites for the six largest Asian subgroups. Our analysis comprised 90,709 Asian and 1,327,727 non-Hispanic white cancer cases. Asian Americans had significantly lower overall cancer incidence rates than non-Hispanic whites (336.5 per 100,000 and 541.9 for men, 299.6 and 449.3 for women, respectively). Among specific Asian subgroups, Filipino men (377.4) and Japanese women (342.7) had the highest overall incidence rates while South Asian men (297.7) and Korean women (275.9) had the lowest. In comparison to non-Hispanic whites and other Asian subgroups, significantly higher risks were observed for colorectal cancer among Japanese, stomach cancer among Koreans, nasopharyngeal cancer among Chinese, thyroid cancer among Filipinos, and liver cancer among Vietnamese. South Asians had remarkably low lung cancer risk. Overall, Asian Americans have a lower cancer risk than non-Hispanic whites, except for nasopharyngeal, liver and stomach cancers. The unique portrayal of cancer incidence patterns among specific Asian subgroups in this study provides a new baseline for future cancer surveillance research and health policy. [ABSTRACT FROM AUTHOR]

A review of the article "Uncovering nativity disparities in cancer patterns: Multiple imputation strategy to handle missing nativity data in the Surveillance, Epidemiology, and End Results data file" by Jane R. Montealegre and Renke Zhou which appeared on April 15, 2014 in the journal is presented.