Novel passive localization algorithm based on double side matrix-restricted total least squares

AbstractIn order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares (DSMRTLS) is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms

A Downward-looking Three-dimensional Imaging Method for Airborne FMCW SAR Based on Array Antennas

AbstractWith regard to problems in conventional synthetic aperture radar (SAR), such as imaging distortion, beam limitation and failure in acquiring three-dimensional (3-D) information, a downward-looking 3-D imaging method based on frequency modulated continuous wave (FMCW) and digital beamforming (DBF) technology for airborne SAR is presented in this study. Downward-looking 3-D SAR signal model is established first, followed by introduction of virtual antenna optimization factor and discussion of equivalent-phase-center compensation. Then, compensation method is provided according to reside video phase (RVP) and slope term for FMCW SAR. As multiple receiving antennas are applied to downward-looking 3-D imaging SAR, range cell migration correction (RCMC) turns to be more complex, and corrective measures are proposed. In addition, DBF technology is applied in realizing cross-track resolution. Finally, to validate the proposed method, magnitude of slice, peak sidelobe ratio (PSLR), integrated sidelobe ratio (ISLR) and two-dimensional (2-D) contour plot of impulse response function (IRF) of point target in three dimensions are demonstrated. Satisfactory performances are shown by simulation results

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

<p>Abstract</p> <p>Background</p> <p>Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.</p> <p>Results</p> <p>In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.</p> <p>Conclusion</p> <p>Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.</p

CdSe Quantum Dot (QD)-Induced Morphological and Functional Impairments to Liver in Mice

Quantum dots (QDs), as unique nanoparticle probes, have been used in in vivo fluorescence imaging such as cancers. Due to the novel characteristics in fluorescence, QDs represent a family of promising substances to be used in experimental and clinical imaging. Thus far, the toxicity and harmful health effects from exposure (including environmental exposure) to QDs are not recognized, but are largely concerned by the public. To assess the biological effects of QDs, we established a mouse model of acute and chronic exposure to QDs. Results from the present study suggested that QD particles could readily spread into various organs, and liver was the major organ for QD accumulation in mice from both the acute and chronic exposure. QDs caused significant impairments to livers from mice with both acute and chronic QD exposure as reflected by morphological alternation to the hepatic lobules and increased oxidative stress. Moreover, QDs remarkably induced the production of intracellular reactive oxygen species (ROS) along with cytotoxicity, as characterized by a significant increase of the malondialdehyde (MDA) level within hepatocytes. However, the increase of the MDA level in response to QD treatment could be partially blunted by the pre-treatment of cells with beta-mercaptoethanol (β-ME). These data suggested ROS played a crucial role in causing oxidative stress-associated cellular damage from QD exposure; nevertheless other unidentified mediators might also be involved in QD-mediated cellular impairments. Importantly, we demonstrated that the hepatoxicity caused by QDs in vivo and in vitro was much greater than that induced by cadmium ions at a similar or even a higher dose. Taken together, the mechanism underlying QD-mediated biological influences might derive from the toxicity of QD particles themselves, and from free cadmium ions liberated from QDs as well

A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification

With the continuous development of the convolutional neural network (CNN) concept and other deep learning technologies, target recognition in Synthetic Aperture Radar (SAR) images has entered a new stage. At present, shallow CNNs with simple structure are mostly applied in SAR image target recognition, even though their feature extraction ability is limited to a large extent. What&rsquo;s more, research on improving SAR image target recognition efficiency and imbalanced data processing is relatively scarce. Thus, a lightweight CNN model for target recognition in SAR image is designed in this paper. First, based on visual attention mechanism, the channel attention by-pass and spatial attention by-pass are introduced to the network to enhance the feature extraction ability. Then, the depthwise separable convolution is used to replace the standard convolution to reduce the computation cost and heighten the recognition efficiency. Finally, a new weighted distance measure loss function is introduced to weaken the adverse effect of data imbalance on the recognition accuracy of minority class. A series of recognition experiments based on two open data sets of MSTAR and OpenSARShip are implemented. Experimental results show that compared with four advanced networks recently proposed, our network can greatly diminish the model size and iteration time while guaranteeing the recognition accuracy, and it can effectively alleviate the adverse effects of data imbalance on recognition results

IL4 (interleukin 4) induces autophagy in B cells leading to exacerbated asthma

<p>Allergic asthma is a common airway inflammatory disease in which B cells play important roles through IgE production and antigen presentation. SNP (single nucleotide polymorphism) analysis showed that <i>Atg</i> (autophagy-related) allele mutations are involved in asthma. It has been demonstrated that macroautophagy/autophagy is essential for B cell survival, plasma cell differentiation and immunological memory maintenance. However, whether B cell autophagy participates in asthma pathogenesis remains to be investigated. In this report, we found that autophagy was enhanced in pulmonary B cells from asthma-prone mice. Autophagy deficiency in B cells led to attenuated immunopathological symptoms in asthma-prone mice. Further investigation showed that IL4 (interleukin 4), a key effector Th2 cytokine in allergic asthma, was critical for autophagy induction in B cells both in vivo and in vitro, which further sustained B cell survival and enhanced antigen presentation by B cells. Moreover, IL4-induced autophagy depended on JAK signaling via an MTOR-independent, PtdIns3K-dependent pathway. Together, our data indicate that B cell autophagy aggravates experimental asthma through multiple mechanisms.</p