Research on Chromium Retrieval of Black Soil with Hyperspectral Imagery in Northeast of China#br#

According to the definition of spectral integral，a new spectral characteristic parameter，with the name Reversed Spectral Absorption Integral(RSAI)，is proposed and used to retrieve the chromium content based on the Partial Least Squares Regression(PLSR) model.The contrastive study with other traditional spectral characteristic parameters，including differential transformation，inverse transformation，absorption area，etc.indicates that(1) the first derivation of square root transformed model can predict the chromium content quantitatively in terms of spectral transformations.(2) the stability of the absorption area model is slightly poor，and the chromium content of samples can only be estimated roughly.(3) However，as to the inversed spectral absorption integral model，the adjustment determination coefficient(Ad-R²) of the modeling and verification is 0.73 and 0.77，while the Root Mean Squared Error(RMSE) is 2.63 mg/kg and 2.36 mg/kg respectively with Relative Percent Deviation(RPD) being 3.21，which shows that the RSAI model has excellent prediction ability.So，the inversed spectral absorption integral new model can improve the accuracy and stability used to retrieve the chromium content，which provides a new idea for monitoring the chromium contamination in soil.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.     

Trajectory-Shaping Guidance with final speed and load factor constraints

This paper describes the design of a guidance law used for guiding a hypersonic gliding vehicle against a ground target from a near-vertical orientation with a specified final speed and a near-zero final load factor. The guidance law consists of two terms: one is Trajectory-Shaping Guidance (TSG) used for steering the vehicle to the target from the specified orientation; the other is Final-Speed-Control Scheme (FSCS) used for controlling the vehicle to perform lateral maneuver to adjust the final speed. Further, the generalized closed form solutions of TSG are obtained from a more general linearized engagement model, where the speed of the vehicle can be an arbitrary positive function of time. By analyzing these solutions, the stability domain of the guidance coefficients is obtained such that the final load factor is zero. This domain is not affected by the change rate of the speed. Thus, according to this analysis, the proposed guidance law can achieve a near zero final load factor by properly selecting the guidance coefficients in the stability domain.     

融入局部几何特征的流形谱聚类图像分割

为了改善谱聚类图像分割的精准性和时效性,文中提出融入局部几何特征的流形谱聚类图像分割算法.首先,考虑图像数据的流形结构,在数据点的K近邻域内执行局部PCA,得到数据间本征维数的关系.然后,引入流形学习中的局部线性重构技术,通过混合线性分析器得到数据间局部切空间的相似性,结合二者构造含有局部几何特征的相似性矩阵.再利用Nystr m技术逼近待分割图像的特征向量,对构造的k个主特征向量执行谱聚类.最后,在Berkeley数据集上的对比实验验证文中算法的准确性和时效性优势.     

Effects of hydrogen enhancement on mesoscale burner array flame stability under acoustic perturbations

Partial substitution of hydrocarbon fuel with hydrogen can effectively improve small-scale combustion system stability and performance, potentially opening the way for novel compact power generation and/or propulsion systems in the future. In this study, the effects of hydrogen enhancement between 0% and 40% hydrogen volumetric fractions in methane fuel were experimentally observed in a mesoscale burner array subjected to external acoustic perturbations. The mesoscale burner array utilizes an array of swirl-stabilized burner elements and their interactions with neighboring elements to improve the overall flame stability and simultaneously reduces the combustor length scale. OH1 chemiluminescence and OH planar laser-induced fluorescence (OH-PLIF) were used to image various hydrogen-enriched flames at an equivalence ratio of 0.7, subjected to transverse acoustic perturbations at 320 Hz. Two acoustic modes were imposed by controlling the phase difference between two speakers perturbing the flow. OH1 chemiluminescence images exhibited flame length scale reduction, leading to a denser flame array. Also, flame arrays with higher hydrogen enrichment were found to be more robust against transverse acoustic perturbations, demonstrated by reduced fluctuations in the global heat release rate. OH-PLIF images showed that flames with higher hydrogen enrichment initiated V- to M-shaped flame shape transition even under fuel lean conditions, thereby improving the combustion stability. OH-PLIF images were also used for flame stability analysis through spectral proper orthogonal decomposition (SPOD). The SPOD analysis showed hydrogen enrichment diminished flame fluctuation structures under fuel lean operation.     

DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

Reducing over- and under-estimation of the a priori SNR in speech enhancement techniques

Most speech enhancement methods based on short-time spectral modification are generally expressed as a spectral gain depending on the estimate of the local signal-to-noise ratio (SNR) on each frequency bin. Several studies have analyzed the performance of a priori SNR estimation algorithms to improve speech quality and to reduce speech distortions. In this paper, we concentrate on the analysis of over- and under estimation of the a priori SNR in speech enhancement and noise reduction systems. We first show that conventional approaches such as the decision-directed approach proposed by Ephraïm and Malah lead to a biased estimator for the a priori SNR. To reduce this bias, our strategy relies on the introduction of a correction term in the a priori SNR estimate depending on the current state of both the available a posteriori SNR and the estimated a priori one. The proposed solution leads to a bias-compensated a priori SNR estimate, and allows to finely estimating the output speech signal to be very close to the original one on each frequency bin. Such refinement procedure in the a priori SNR estimate can be inserted in any type of spectral gain function to improve the output speech quality. Objective tests under various environments in terms of the Normalized Covariance Metric (NCM) criterion, the Coherence Speech Intelligibility Index (CSII) criterion, the segmental SNR criterion and the Perceptual Evaluation of Speech Quality (PESQ) measure are presented showing the superiority of the proposed method compared to competitive algorithms.     

Synthesis,characterization, electrochemical behavior,thermal study and antibacterial/antifungal properties of some new zinc(II) coordination compounds

A novel symmetrical Schiff base ligand was prepared by condensation reaction of 2,2-dimethyl-1.3-diaminopropane and (E)3-(2nitrophenyl)acrylaldehyde. The ligand and its Zn(II) coordination compounds were well characterized by the elemental analysis, FTIR, ¹H, ¹³C NMR, UV–vis spectra and molar conductance. Thermal behaviors of all compounds were investigated from the room temperature to 600 °C with a heating rate of 10 °C/min. Furthermore some decomposition thermo-kinetic parameters were evaluated by Coats–Redfern equation at each decomposition step. Electrochemical properties of ligand and its complexes were studied by cyclic voltammetry technique. Also antibacterial/antifungal activities of the ligand and its complexes were tested against three Gram-negative bacteria Escherichia coli (ATCC 25922), Salmonella spp. and Pseudomonas aeruginosa (ATCC 9027) and two Gram-positive bacteria Staphylococcus aureus (ATCC 6538) and Corynebacterium renale and also three fungi (Aspergillus niger, Penicillium chrysogenum and Candida albicans). The results exhibited suitable antibacterial/antifungal properties for ligand and Zn(II) complexes. The study has shown that the complexation of ligand to zinc center lead to enhancement of antibacterial/antifungal activity.     

Quantifying spectral changes experienced by plasmonic nanoparticles in a cellular environment to inform biomedical nanoparticle design

Metal nanoparticles (NPs) scatter and absorb light in precise, designable ways, making them agile candidates for a variety of biomedical applications. When NPs are introduced to a physiological environment and interact with cells, their physicochemical properties can change as proteins adsorb on their surface and they agglomerate within intracellular endosomal vesicles. Since the plasmonic properties of metal NPs are dependent on their geometry and local environment, these physicochemical changes may alter the NPs'' plasmonic properties, on which applications such as plasmonic photothermal therapy and photonic gene circuits are based. Here we systematically study and quantify how metal NPs'' optical spectra change upon introduction to a cellular environment in which NPs agglomerate within endosomal vesicles. Using darkfield hyperspectral imaging, we measure changes in the peak wavelength, broadening, and distribution of 100-nm spherical gold NPs'' optical spectra following introduction to human breast adenocarcinoma Sk-Br-3 cells as a function of NP exposure dose and time. On a cellular level, spectra shift up to 78.6 ± 23.5 nm after 24 h of NP exposure. Importantly, spectra broaden with time, achieving a spectral width of 105.9 ± 11.7 nm at 95% of the spectrum''s maximum intensity after 24 h. On an individual intracellular NP cluster (NPC) level, spectra also show significant shifting, broadening, and heterogeneity after 24 h. Cellular transmission electron microscopy (TEM) and electromagnetic simulations of NPCs support the trends in spectral changes we measured. These quantitative data can help guide the design of metal NPs introduced to cellular environments in plasmonic NP-mediated biomedical technologies.     

Study on dynamic characteristics of natural and mechanical wind in built environment using spectral analysis

What are the differences and similarities between natural and mechanical wind in built environment? This is an interesting question that has not been well answered yet. In the paper, spectral analysis is applied to study the dynamic characteristics of natural and mechanical wind in different conditions. The results show that in the frequency region sensitive to human sensation, the power spectrum characteristics of natural wind and mechanical wind have obvious differences as well as interesting connections. The power spectrum exponent (β$\beta$ value) of natural wind is between 1.1 and 2.0 in the human sensitive frequency region, while the value of mechanical wind is between 0 and 0.5 around the air supply outlet. With the diffusion of mechanical wind, β$\beta$ value will increase gradually and reach the value of the typical natural wind when the mean velocity is lower than 0.25 m/s. Finally, the influence of spectral characteristics on human sensation for airflow is discussed.