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Hyperspectral target detection (HTD) methods aim to exploit the abundant hyperspectral information to distinguish the key target pixels from multifarious background pixels. However, the performances of existing HTD methods are limited by the dilemmas of scarcity of target prior spectra, imprecise estimation of background spectra, as well as noise pollution. For the issues, this article proposes a novel target prior augmentation and background suppression-based multidetector fusion (TBMF) method for HTD, based on the joint optimization of the target prior spectra augmentation, low-rank pure background spectra separation, and nontarget nonbackground noise component removal. Specifically, a constrained linear spectral mixture model is seamlessly incorporated to implicitly augment the target's prior spectra. Also, the nontarget nonbackground components of HSI, i.e., noise with complex distribution are removed by a noise-robust l1,1-norm-based regularization. Subsequently, multiple basic constrained energy minimization detectors are trained using the augmented diverse target spectra in the background-suppression subspace derived by the separated background spectra. The detection results of these basic detectors are fused with a winner-take-all strategy to acquire the final detection result. Plenty of experimental results on four HSI datasets show that the proposed TBMF method performs promisingly when compared with several classical and recently proposed HTD methods.

An ultra-wideband absorber with an absorption rate of near unit is studied and numerically simulated in the terahertz frequency band. The proposed absorber is a stacked compact structure consisting of a double-layer patterned graphene embedded between two separated dielectric. Under normal incidence, the absorption rate exceeds 99% at 2.31–3.92 THz. The absorption bandwidth can reach 2.54 THz for absorptions greater than 90% (1.91–4.45 THz), and the relative bandwidth achieves 80%. Furthermore, the absorber is tunable, with absorption ranging from 14% to almost 99.9% by altering the Fermi energy of graphene from 0 to 1 eV. The phenomenon of ultra-wideband absorption is analyzed in relation to impedance matching and field distribution. Moreover, the absorber has high polarization-independence and can operate across a broad range of incidence angles. This tunable ultra-wideband absorber has promising functions in modulation, stealth, switching, and imaging technologies.

Representation learning-based hyperspectral target detection (HTD) methods generally follow a learning paradigm of single-layer or one-step representation residual learning and the target detection on original full spectral bands, which, in some cases, cannot accurately distinguish the target pixels from variable background pixels via one-round of the detection process. To alleviate the problem and make full use of the latent discriminate characteristics in different spectral bands and the representation residual, this paper proposes a level-wise band-partition-based hierarchical representation residual feature (LBHRF) learning method for HTD with a parallel and cascaded hybrid structure. Specifically, the LBHRR method proposes to partition and fuse different levels of sub-band spectra combinations, and take full advantages of the discriminate information in representation residuals from different levels of band-partition. The highlights of this work include three aspects. First, the original full spectral bands are partitioned in a parallel level-wise manner to obtain the augmented representation residual feature through level-wise band-partition-based representation residual learning, such that the global spectral integrity and contextual information of local adjacent sub-bands are flexibly fused. Second, the SoftMax transformation, pooling operation, and augmented representation residual feature reuse among different layers are equipped in cascade to enhance the learning ability of the nonlinear and discriminant hierarchical representation residual features of the method. Third, a hierarchical representation residual feature-based HTD method is developed in an efficient stepwise learning manner instead of back-propagation optimization. Experimental results on several HSI datasets demonstrate that the proposed model can yield promising detection performance in comparison to some state-of-the-art counterparts.

In this article, we designed an adaptive residual convolutional neural network (ARCNN) that takes raw hyperspectral image (HSI) cubes as input data for land-cover classification. In this network, spectral and spatial feature extraction blocks are explored to learn discriminative features from abundant spectral information and spatial contexts in HSIs. The proposed ARCNN is an end-to-end deep learning framework that alleviates the declining-accuracy phenomenon of deep learning models, and it also ranks the correlation and importance of each band in HSIs. Furthermore, the residual blocks connect every other 3-D convolutional layer by using an identity mapping, which facilitates backpropagation of gradients. In order to address the common issue of imbalance between high dimensionality and limited availability of training samples for HSI classification, an attention mechanism and a feature fusion block are investigated to improve the performance of the ARCNN. Finally, some strategies, batch normalization and dropout, are imposed on every convolutional layer to regularize the learning process. Therefore, the ARCNN method brings benefits to extract discriminative features, and it is easier to avoid overfitting. Experimental results on three public HSI datasets demonstrate the effectiveness of the ARCNN over some state-of-the-art methods.

The ability of planktivorous fishes to exert top-down control on Bythotrephes potentially has far-reaching impacts on aquatic food-webs, given previously described effects of Bythotrephes on zooplankton communities. We estimated consumption of Bythotrephes by planktivorous and benthivorous fishes, using bioenergetics and daily ration models at nearshore (18 m), intermediate (46 m), and offshore (110 m) depths along one western Lake Superior transect (April, and September-November) and two northern Lake Michigan transects (April, July, and September). In Lake Superior, consumption (primarily by cisco Coregonus artedi) exceeded Bythotrephes production at all offshore sites in September-November (up to 396% of production consumed) and at the intermediate site in November (842%) with no evidence of consumption nearshore. By comparing Bythotrephes biomass following months of excessive consumption, we conservatively concluded that top-down control was evident only at the offshore site during September-October. In Lake Michigan, consumption by fishes (primarily alewife Alosa pseudoharengus) exceeded production at nearshore sites (up to 178%), but not in deeper sites (< 15%). Evidence for top-down control in the nearshore was not supported, however, as Bythotrephes never subsequently declined. Using generalized additive models, temperature, and not fish consumption nor zooplankton prey density, best explained variability in Bythotrephes biomass. The non-linear pattern revealed Bythotrephes to increase with temperature up to 16 °C, and then decline between 16 and 23 °C. We discuss how temperature likely has direct negative impacts on Bythotrephes when temperatures near 23 °C, but speculate that predation also contributes to declining biomass when temperatures exceed 16 °C. [ABSTRACT FROM AUTHOR]

Patch-clamp and single cell [Ca2+]i measurements have been used to investigate the effects of the potassium channel modulators cromakalim, diazoxide and tolbutamide on the insulin-secreting cell line RINm5F. In intact cells, with an average cellular transmembrane potential of -62 +/- 2 mV (n = 42) and an average basal [Ca2+]i of 102 +/- 6 nM (n = 37), glucose (2.5-10 mM): (i) depolarized the membrane, through a decrease in the outward KATP current, (ii) evoked Ca2+ spike potentials, and (iii) caused a sharp rise in [Ca2+]i. In the continued presence of glucose both cromakalim (100-200 microM) and diazoxide (100 microM) repolarized the membrane, terminated Ca2+ spike potentials and attenuated the secretagogue-induced rise in [Ca2+]i. In whole cells (voltage-clamp records) and excised outside-out membrane patches, both cromakalim and diazoxide enhanced the current by opening ATP-sensitive K+ channels. Diazoxide was consistently found to be more potent than cromakalim. Tolbutamide, a specific inhibitor of ATP-sensitive K+ channels, reversed the effects of cromakalim on membrane potential and KATP currents. [ABSTRACT FROM AUTHOR]

Conventional classifiers treat hyperspectral image (HSI) as a list of spectral measurements without considering the spatial relationship between pixels. Therefore, these methods discard the information associated with spatial correlations among distinct pixels in the image, and they are sensitive to the noise pixels in HSI. To address these drawbacks, a spatial-spectral combined classification method termed weighted mean reconstruction-based support vector machine with composite kernels (WMR-SVMCK) is proposed for HSI classification. At first, WMR-SVMCK tries to reconstruct pixels by means of spatial neighbor pixels with reconstruction weights to fuse spatial and spectral information. The reconstructed pixels include the spatial-spectral combined features that enhance the separability of the samples from different classes and reduce the influence of noise pixels in HSI. Then, the proposed method utilizes SVMCK classifier to classify the reconstructed pixels. WMR-SVMCK effectively extracts the discriminating spatial-spectral features and enhances the robustness by reconstructing each pixel with spatial neighbor pixels, which significantly improves the classification performance. Experiments on two real hyperspectral data sets (Indian Pines and PaviaU) are performed to demonstrate the effectiveness of the proposed WMR-SVMCK method.