ALBERT

Description: We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.

Description: Radar-based remote sensing for measurement of ocean surface waves presents advantages over conventional point sensors such as wave buoys. As its use becomes more widespread, it is important to understand the sensitivity of the extracted wave parameters to the characteristics of the radar and the scatterers. To examine such issues, experiments were performed offshore of the Scripps Institution of Oceanography pier in July 2010. Radar measurements in low wind speeds were performed with a dual-polarized high-resolution X-band pulse-Doppler radar at low grazing angles along with two independent measurements of the surface waves using conventional sensors, a GPS-based buoy, and an ultrasonic array. Comparison between radar cross section (RCS) and Doppler modulations shows peak values occurring nearly in-phase, in contrast with tilt modulation theory. Spectral comparisons between Doppler-based and RCS-based spectra show that Doppler-based spectra demonstrate greater sensitivity to swell-induced modulations, whereas RCS-based spectra show greater sensitivity to small-scale modulations (or generally have more noise at high frequency), and they equally capture energy at the wind wave peak. Doppler estimates of peak period were consistent with the conventional sensors, whereas the RCS differed in assignment of peak period to wind seas rather than swell in a couple of cases. Higher order period statistics of both RCS and Doppler were consistent with the conventional sensors. Radar-based significant wave heights are lower than buoy-based values and contain nontrivial variability of ∼33%. Comparisons between HH and VV polarization data show that VV data more accurately represent the wave field, particularly as the wind speeds decrease.

Description: Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model.

Description: The on-orbit radiometric calibration of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite is carried out primarily through observations of a fully illuminated solar diffuser (SD) panel. Accurate knowledge of the solar spectral radiance scattered from the SD is available. The sensor aperture spectral radiance is assumed to be a quadratic polynomial function of a VIIRS detector's background-subtracted response in digital number. The coefficients of the polynomial were initially determined prelaunch. Once on orbit, we assume that these coefficients change uniformly by a common calibration factor, which is referred to as the $F$ -factor. The known solar spectral radiance scattered from a fully illuminated SD allows for the determination of these $F$ -factors. We describe the methodology and the associated algorithms used in the calculation of the RSB $F$ -factors. Our results show that the $F$ -factors change over time, with the largest change occurring at a wavelength of 862 nm (with a value of about 1.55 on day 950 after the satellite launch, relative to its value at the beginning of the launch) . In addition, we estimate the relative error standard deviations of the computed top-of-the-atmosphere reflectance at the detector pixel level. On day 950 of the mission, the relative error standard deviations are all less or equal to 0.016, except for the M11 band (band central wavelength of 2257 nm) , which has a relative error standard deviation of about 0.049 due to a very low signal-to-noise ratio.

Description: Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40times $ magnification.

Description: This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.

Description: In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

Description: Feature point matching is a fundamental and challenging problem in many computer vision applications. In this paper, a robust feature point matching algorithm named spatial order constraints bilateral-neighbor vote (SOCBV) is proposed to remove outliers for a set of matches (including outliers) between two images. A directed ${k}$ nearest neighbor ( knn ) graph of match sets is generated, and the problem of feature point matching is formulated as a binary discrimination problem. In the discrimination process, the class labeled matrix is built via the spatial order constraints defined on the edges that connect a point to its knn . Then, the posterior inlier class probability of each match is estimated with the knn density estimation and spatial order constraints. The vote of each match is determined by averaging all posterior class probabilities that originate from its associative inliers set and is used for removing outliers. The algorithm iteratively removes outliers from the directed graph and recomputes the votes until the stopping condition is satisfied. Compared with other popular algorithms, such as RANSAC, RSOC, GTM, SOC and WGTM, experiments under various testing data sets demonstrate strong robustness for the proposed algorithm.

Description: Acoustic localization is an essential technique in speech capturing, speech enhancement, video conferencing, and human–robot interaction. However, in practical situations, localization has to be performed in abominable environments, where the presence of reverberation and noise degrades the performance of available position estimates. Besides, the designed systems should be adaptive to locomotion of targets with low computational complexity. In the context, this paper introduces a robust hierarchical acoustic localization method via time-delay compensation (TDC) and interaural matching filter (IMF). Firstly, interaural time-delay (ITD) and interaural level difference (ILD), which are cues involved in first two layers, respectively, are yielded by TDC all at once. Then, a novel feature named IMF, which can eliminate the difference between binaural signals, is proposed in the third layer. The final decision making is based on a Bayesian rule. The relationships among the three layers are that the former layer provides candidate directions for later ones such that the searching space becomes gradually smaller to reduce matching time. Experiments using both a public database and a real scenario verify that TDC and IMF are robust for acoustic localization, and hierarchical system has less consumption time.

Description: Canonical correlation analysis (CCA) is a widely used data analysis tool that allows to assess the correlation between two distinct sets of signals. It computes optimal linear combinations of the signals in both sets such that the resulting signals are maximally correlated. The weight vectors defining these optimal linear combinations are referred to as “principal CCA directions”. In addition to this particular type of data analysis, CCA is also often used as a blind source separation (BSS) technique, i.e., under certain assumptions, the principal CCA directions have certain demixing properties. In this paper, we propose a distributed CCA (DCCA) algorithm that can operate in wireless sensor networks (WSNs) with a fully connected or a tree topology. The algorithm estimates the $Q$ principal CCA directions from the sensor signal observations collected by the different nodes in the WSN and extracts the corresponding sources. These network-wide principal CCA directions are estimated in a time-recursive fashion without explicitly constructing the corresponding network-wide correlation matrices, i.e., without the need for data centralization. Instead, each node locally computes smaller CCA problems and only transmits compressed sensor signal observations (of dimension $Q$ ), which significantly reduces the bit rate over the wireless links of the WSN. We prove convergence and optimality of the DCCA algorithm, and we demonstrate its performance by means of numerical simulations in a blind source separation scenario.

Description: This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics. The Web extra at http://youtu.be/E1PVTitj7h0 is a video demonstration of a novel solution to multivariate data visualization that helps users interactively explore data by combining standard presentations, from detailed views to high-level overviews.

Description: The data rearrangement engine (DRE) performs in-memory data restructuring to accelerate irregular, data-intensive applications. An emulation on a field-programmable gate array shows how the DRE could improve speedup, memory bandwidth, and energy consumption on three representative benchmarks.

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Description: This paper proposes the improved design of an ultraviolet (UV)- and-blue-light-inhibited white light-emitting diode for use as a safe and practical light source. Covered with a glass substrate coated with a photocatalyst resin on one side and a reflectance film on the other side, wavelengths below 400 nm are reflected back to re-excite the red–green–blue phosphors and, consequently, enhance luminous efficiency. The absorption spectrum of bismuth oxide photocatalysts is below 521 nm, and the leaked UV and blue light can be absorbed, thereby exciting electron–hole pairs and producing the photocatalytic effect. Thus, blue light and UV leakage can be suppressed appreciably, and the luminous efficiency can be increased markedly. Experimental results showed a UV suppression ratio of 88.43% and a visible light increasing ratio of 21.66%. The Commission International de L'Eclairage chromaticity coordinates $(x,y)$ were (0.343, 0.404), the correlated color temperature and the deviation from de Blackbody locus were (5201 K, 0.0250), and the color rendering index was 93.16. In addition, the photocatalyst coating layer can act as a diffuser to provide a comfortable visual experience and facilitate environmental purification.

Description: Metamaterial-based perfect absorbers have attracted considerable attention due to their potential for practical applications. The existing absorbers, however, are mostly polarization insensitive or only sensitive to one direction, which is inapplicable in some areas. Polarization tunable or high absorption in two orthogonal directions is very useful and necessary. Herein, we present a polarization tunable absorber formed by an asymmetric patch and a dielectric layer on top of a metallic board. With this structure, the frequency of the absorber can be tuned by merely changing the polarization of the incident. The tunable mechanism originates from the different length of the patch along the two orthogonal directions. The concept is rather general and applicable to various absorbers, as long as the asymmetric design is valid. The absorber can find practical applications in manipulation of the polarization of the light and detecting waves with specific polarization.

Description: The major activity during speech production is glottal activity and is earlier detected using strength of excitation (SoE). This work uses the normalized autocorrelation peak strength (NAPS) and higher order statistics (HOS) as additional features for detecting glottal activity. The three features, namely, SoE, NAPS, and HOS, are, respectively indicators of different attributes of glottal activity, namely, energy, periodicity, and asymmetrical nature of the resulting source signal. The effectiveness of these features is analyzed using the differential electroglottograph signal, zero-frequency filtered signal, and integrated linear prediction residual, as representatives of source signal. The combination of glottal activity information from the three features outperforms any single of them, demonstrating different information represented by each of these features.

Description: This letter addresses the texture classification problem through a pixel-based local binary pattern (LBP) statistics aggregation mechanism. Real-world texture images often present challenges for classification algorithms in terms of intra-class variability due, among others, to variable illumination. The LBP operator, a state-of-the-art texture descriptor, possesses key properties for tackling real-world texture images: discriminative power and invariance against monotonic gray level changes. We propose a novel texture classification approach that increases the robustness of LBP-based methods with respect to any type of intra-class variations. The method locally characterizes each pixel with an LBP code histogram and globally computes the label of a textured image by aggregating pixel labels through a voting process. Our approach can be in principle applied to any LBP version, as it focuses on how statistics are computed from LBP codes. We show that the proposed pixel-based approach improves upon traditional LBP block-based approaches in terms of classification accuracy by up to 5.1 p.p. on the public Outex database for the classic LBP with various neighborhoods as well as for various LBP extensions.

Description: The atmospheric condition parameters used in the radiative transfer-based atmospheric correction (AC) are often uncertain. This uncertainty propagates to the estimated reflectance. The reflectance, is, however, not equally sensitive to all the parameters. A sensitivity analysis (SA) helps in prioritizing the parameters. The objective of this study was to perform an SA of reflectance to water vapor concentration ( $wv$ ) and aerosol optical thickness ( $AOT$ ). SA was performed using the Fourier amplitude sensitivity test (FAST) method, which computes sensitivity indices ( $text{SI}$ ) of these parameters. Besides variation in the two parameters, we also studied the effect of surface albedo on the $text{SI}$ by quantifying $text{SI}$ for three target surfaces (in the spectral range $text{0.44{-}0.96};upmu$ m): 1) a dark target (water); 2) a bright target (bare soil); and 3) a target having low albedo in the visible and high albedo in near-infrared range (forest). For $AOT$ , high ( $approx!text{0.9}$ ) $text{SI}$ values were observed at the nonwater absorption wavelengths. For $wv$ , high $text{SI}$ values were observed at wavelengths, where strong absorption features are loca- ed and when the surface albedo was high. For the dark target, the effect of $AOT$ was prominent throughout the spectral range. We found that the sensitivity of reflectance to $wv$ and $AOT$ is a function of wavelength, strength of the absorption features, and surface albedo. We conclude that $AOT$ is a more important parameter for dark targets than $wv$ even at the principal absorption feature. For bright targets, the importance of $wv$ and $AOT$ depends on the strength of the absorption feature.

Description: The feasibility study of the HALESIS (High-Altitude Luminous Events Studied by Infrared Spectro-imagery) project is presented. The purpose of this experiment is to measure the atmospheric perturbation in the minutes following the occurrence of transient luminous events (TLEs) from a stratospheric balloon in the altitude range of 20–40 km. The instrumentation will include a spectro-imager embedded in a pointing gondola. Infrared signatures of a single blue jet were simulated under the assumption of local thermodynamic equilibrium (LTE), and were then compared with a panel of commercially available instrument specifications. The sensitivity of the signatures with a local perturbation of the main vibrational energy level populations of ${mathbf{rm CO}_{2}}$ , CO, NO, ${mathbf{rm O}_{3}}$ , and ${mathbf{rm H}_{2}}{rm O}$ was measured and the infrared signatures of a single blue jet taking into account non-LTE hypotheses were compared with the same panel of commercially available instrument specifications. Lastly, the feasibility of the study is discussed.

Description: To improve accuracy and efficiency of object detection and classification with hyperspectral imagery (HSI), we propose a novel smoothing algorithm by coupling of a Laplacian-based reaction term to a classical divergence-based anisotropic diffusion partial differential equation (PDE). In addition, an adaptive parameter is introduced to regularize this nonlinear reaction-diffusion PDE by explicitly integrating the interband correlations with the noise level of each band in HSI. It is also well-known that the interband correlations can be implicitly embedded into the diffusion coefficient of the divergence-based PDE, to allow a selective smoothing that reduces the local homogeneous area variability while preventing smoothing across edges. Therefore, the interband correlations in HSI are exploited in the proposed method in both explicit and implicit ways. As a result, our algorithm is more effective at controlling the behavior of the diffusion evolution when compared to previous multi/hyperspectral diffusion algorithms. The simulations based on both synthetic data and real hyperspectral remote sensing data show that our algorithm can improve the hyperspectral data quality in terms of both visual inspection and image quality indices.

Description: The goal of cross-domain matching (CDM) is to find correspondences between two sets of objects in different domains in an unsupervised way. CDM has various interesting applications, including photo album summarization where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system, and temporal alignment which aligns sequences such as videos that are potentially expressed using different features. In this paper, we propose an information-theoretic CDM framework based on squared-loss mutual information (SMI). The proposed approach can directly handle non-linearly related objects/sequences with different dimensions, with the ability that hyper-parameters can be objectively optimized by cross-validation. We apply the proposed method to several real-world problems including image matching, unpaired voice conversion, photo album summarization, cross-feature video and cross-domain video-to-mocap alignment, and Kinect -based action recognition, and experimentally demonstrate that the proposed method is a promising alternative to state-of-the-art CDM methods.

Description: We present a new trigonometric basis function that is capable of perfectly reproducing circles, spheres and ellipsoids while at the same time being interpolatory. Such basis functions have the advantage that they allow to construct shapes through a sequence of control points that lie on their contour (2-D) or surface (3-D) which facilitates user-interaction, especially in 3-D. Our piecewise exponential basis function has finite support, which enables local control for shape modification. We derive and prove all the necessary properties of the kernel to represent shapes that can be smoothly deformed and show how idealized shapes such as ellipses and spheres can be constructed.

Description: The skeleton of a 2D shape is an important geometric structure in pattern analysis and computer vision. In this paper we study the skeleton of a 2D shape in a two-manifold $mathcal {M}$ , based on a geodesic metric. We present a formal definition of the skeleton $S(Omega )$ for a shape $Omega$ in $mathcal {M}$ and show several properties that make $S(Omega )$ distinct from its Euclidean counterpart in $mathbb {R}^2$ . We further prove that for a shape sequence $lbrace Omega _irbrace$ that converge to a shape $Omega$ in $mathcal {M}$ , the mapping $Omega righta- row overline{S}(Omega )$ is lower semi-continuous. A direct application of this result is that we can use a set $P$ of sample points to approximate the boundary of a 2D shape $Omega$ in $mathcal {M}$ , and the Voronoi diagram of $P$ inside $Omega subset mathcal {M}$ gives a good approximation to the skeleton $S(Omega )$ . Examples of skeleton computation in topography and brain morphometry are illustrated.

Description: A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.

Description: We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and colour, coupled with relevance vector machines for classification and regression. We first show that using hand-labelled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image.

Description: Multiple view segmentation consists in segmenting objects simultaneously in several views. A key issue in that respect and compared to monocular settings is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. Second, we show how other modalities such as depth may be seamlessly integrated in the model and benefit the segmentation. The paper exposes a detailed set of experiments used to validate the algorithm, showing results comparable with the state of art, with reduced computational complexity. We also discuss the use of different modalities for specific situations, such as dealing with a low number of viewpoints or a scene with color ambiguities between foreground and background.

Description: This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information. It first consists of a supervised spectral dimension reduction step that transforms the HS image into a score image that has fewer channels. These channels are chosen so as to enhance distances between classes to be discriminated and to reduce background variability, thus leading to edges that correspond to actual class borders. In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered background variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved. We implement this approach using the partial least squares (PLS) method for spectral dimension reduction and the anisotropic diffusion for spatial regularization. We then compare linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) for the class decision. The effectiveness of our method was evaluated with three remotely sensed HS images. Its robustness was also assessed for different training sets, since the latter has a crucial influence on classification performance. On average, our method gave better results in terms of classification accuracy and was more robust than other classification methods tested with the same images.

Description: Due to its simple, fast, and good generalization ability, extreme learning machine (ELM) has recently drawn increasing attention in the pattern recognition and machine learning fields. To investigate the performance of ELM on the hyperspectral images (HSIs), this paper proposes two spatial–spectral composite kernel (CK) ELM classification methods. In the proposed CK framework, the single spatial or spectral kernel consists of activation–function-based kernel and general Gaussian kernel, respectively. The proposed methods inherit the advantages of ELM and have an analytic solution to directly implement the multiclass classification. Experimental results on three benchmark hyperspectral datasets demonstrate that the proposed ELM with CK methods outperform the general ELM, SVM, and SVM with CK methods.

Description: This paper proposes a framework for hyperspectral images (HSIs) classification with composite kernels discriminant analysis (CKDA). The CKDA uses the spectral and spatial information extracted by Gaussian weighted local mean operator (GWLM) and is suitable to solve few labeled samples classification problem of HSI, which has very important practical significance for the case that training samples are insufficient due to high cost. Experimental results show that the spatial information extracted by GWLM can greatly improve the performance, and demonstrate the superiority of CKDA for HSI classification in the case of few labeled samples. Compared with other state-of-the-art spectral–spatial kernel methods, the proposed methods also show very good advantages, especially the parallel kernel method.

Description: Accurate generation of a land cover map using hyperspectral data is an important application of remote sensing. Multiple classifier system (MCS) is an effective tool for hyperspectral image classification. However, most of the research in MCS addressed the problem of classifier combination, while the potential of selecting classifiers dynamically is least explored for hyperspectral image classification. The goal of this paper is to assess the potential of dynamic classifier selection/dynamic ensemble selection (DCS/DES) for classification of hyperspectral images, which consists in selecting the best (subset of) optimal classifier(s) relative to each input pixel by exploiting the local information content of the image pixel. In order to have an accurate as well as computationally fast DCS/DES, we proposed a new DCS/DES framework based on extreme learning machine (ELM) regression and a new spectral–spatial classification model, which incorporates the spatial contextual information by using the Markov random field (MRF) with the proposed DES method. The proposed classification framework can be considered as a unified model to exploit the full spectral and spatial information. Classification experiments carried out on two different airborne hyperspectral images demonstrate that the proposed method yields a significant increase in the accuracy when compared to the state-of-the-art approaches.

Description: Advanced classifiers, e.g., partial least squares discriminant analysis (PLS-DA) and random forests (RF), have been recently used to model reflectance spectral data in general, and of soil properties in particular, since their spectra are multivariate and highly collinear. Preprocessing transformations (PPTs) can improve the classification accuracy by increasing the variability between classes while decreasing the variability within classes. Such PPTs are common practice prior to a PLS-DA, but are rarely used for RF. The objectives of this paper are twofold: to compare the performances of PLS-DA and RF for modeling the spectral reflectance of soil in changed land-uses with different treatments and to compare the effects of nine different PPTs on the prediction accuracy of each of these classification methods. Differences in six physical, biological, and chemical soil properties of changed land-uses from the northern Negev Desert in Israel were evaluated. Significant differences were found between soil properties, which are used to classify land-uses and treatments. Depending on the dataset, different PPTs improved the classification accuracy by 11%–24% and 32%–42% for PLS-DA and RF, respectively, in comparison to the spectra without PPT. Out of the PPTs tested, the generalized least squares weighting (GLSW)-based transformations were found to be the most effective for most classifications using both PLS-DA and RF. Our results show that both PLS-DA and RF are suitable classifiers for spectral data, provided that an appropriate PPT is applied.

Description: This paper contributes the concept of spectral–spatial kernel-based multivariate analysis (KMVSSA) based on the statistical principle of multivariate statistics. The essence of proposed framework is to expose the inherent structure and meaning revealed within spectral and spatial features through various statistical methods in hyperspectral remotely sensed data. This kernel-based framework is investigated to incorporate the spectral and spatial information simultaneously for dimension reduction and classification of hyperdimensional datasets. The method uses multivariate analysis to choose and apply a transform matrix that the transformed components are as orthogonal as possible. This nonlinear framework is derived by means of the theory of complete orthonormal systems. KMVSSA exhibits great flexibility by the combination of spectral and spatial features. We investigate the possibility of using KMVSSA for the classification of hyperspectral images and dimension reduction. The proposed framework is examined and compared in different merits with several hyperspectral images in different conditions (urban/agricultural area and size of the training set). Experimental results show that the proposed framework can meaningfully enhance the dimensionality reduction and also it greatly improves the overall as well as per class classification accuracies. We demonstrate a comprehensive comparison of some state of the art hyperspectral image classification methods.

Description: Development of intelligent decision-making systems for complex problems, such as land covers classification of hyperspectral remote sensing (HSRS) images, requires efficient interpretation of available information through conceptual rather than numerical level. Granular neural network (GNN) in combination with the granular representation of information using linguistic terms is one such system. GNN takes the fuzzified input information and processes them with neural network (NN) architecture, where the network structure is transparent enough to interpret the processing steps. Further, knowledge encoding has been considered as one of the principal elements of intelligent decision-making systems. This paper proposes a new model of knowledge-encoded GNNs for land cover classification of HSRS images. Knowledge encoding is done using neighborhood rough sets (NRSs) that explore the local/contextual information. The encoded knowledge using NRS is obtained in the form of dependency rules with respect to the output class labels of land covers and these rules determine appropriate number of hidden nodes of GNNs. The dependency factors obtained during rule generation are used for initializing the connecting weights of GNNs. NRS is also used here in the selection of a subset of features for reducing the burden of high-dimensional fuzzy-granulated feature space of HSRS image. The proposed model thus explores jointly the advantages of fuzzy granulation, GNNs, and feature selection and knowledge encoding using NRS. Superiority of the model to similar other methods are justified in land covers classification of two HSRS images acquired by different remotely placed sensors.

Description: Several popular endmember extraction and unmixing algorithms are based on the geometrical interpretation of the linear mixing model, and assume the presence of pure pixels in the data. These endmembers can be identified by maximizing a simplex volume, or finding maximal distances in subsequent subspace projections, while unmixing can be considered a simplex projection problem. Since many of these algorithms can be written in terms of distance geometry, where mutual distances are the properties of interest instead of Euclidean coordinates, one can design an unmixing chain where other distance metrics are used. Many preprocessing steps such as (nonlinear) dimensionality reduction or data whitening, and several nonlinear unmixing models such as the Hapke and bilinear models, can be considered as transformations to a different data space, with a corresponding metric. In this paper, we show how one can use different metrics in geometry-based endmember extraction and unmixing algorithms, and demonstrate the results for some well-known metrics, such as the Mahalanobis distance, the Hapke model for intimate mixing, the polynomial post-nonlinear model, and graph-geodesic distances. This offers a flexible processing chain, where many models and preprocessing steps can be transparently incorporated through the use of the proper distance function.

Description: Progress in mapping plant species remotely with imaging spectroscopy data is limited by the traditional classification framework, which carries the requirement of exhaustively defining all classes (species) encountered in a landscape. As the research objective may be to map only one or a few species of interest, we need to explore alternative classification methods that may be used to more efficiently detect a single species. We compared the performance of three support vector machine (SVM) methods designed for single-class detection—binary (one-against-all) SVM, one-class SVM, and biased SVM—in detecting five focal tree and shrub species using data collected by the Carnegie Airborne Observatory over an African savanna. Prior to this comparison, we investigated the effects of training data amount and balance on binary SVM and evaluated alternative methods for tuning one-class and biased SVMs. A key finding was that biased SVM was generally best parameterized by crown-level cross validation paired with the tuning criterion proposed by Lee and Liu [1] . Among the different single-class methods, binary SVM showed the best overall performance (average F-scores 0.43–0.78 among species), whereas one-class SVM showed very poor performance (F-scores 0.09–0.46). However, biased SVM produced results similar to those obtained with binary SVM (F-scores 0.40–0.72), despite using labeled training data from only the focal class. Our results indicate that both binary and biased SVMs can work well for remote single-species detection, while both methods, particularly biased SVM, greatly reduce the amount of training data required compared with traditional multispecies classification.

Description: Hundreds of narrow contiguous spectral bands collected by a hyperspectral sensor have provided the opportunity to identify the various materials present on the surface. Moreover, spatial information, enforcing the assumption that the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, two predominant approaches have been developed to exploit the spatial information. First, by decomposing each pixel and the spatial neighborhood into a low-rank form, the spatial information can be efficiently integrated into the spectral signatures. Meanwhile, in order to describe the low-rank structure of the decomposed data more precisely, an $ell_{1/2}$ norm regularization is introduced and a discrete algorithm is proposed to solve the combined optimization problem by the augmented Lagrange multiplier (ALM) and a half-threshold operator. Second, a graph cuts segmentation algorithm has been applied on the sparse-representation-based probability estimates of the hyperspectral data to further improve the spatial homogeneity of the material distribution. Experimental results on four real hyperspectral data with different spectral and spatial resolutions have demonstrated the effectiveness and versatility of the proposed spatial information-fused approaches for hyperspectral image classification.

Description: Classification of hyperspectral imagery using few labeled samples is a challenging problem, considering the high dimensionality of hyperspectral imagery. Classifiers trained on limited samples with abundant spectral bands tend to overfit, leading to weak generalization capability. To address this problem, we have developed an enhanced ensemble method called multiclass boosted rotation forest (MBRF), which combines the rotation forest algorithm and a multiclass AdaBoost algorithm. The benefit of this combination can be explained by bias-variance analysis, especially in the situation of inadequate training samples and high dimensionality. Furthermore, MBRF innately produces posterior probabilities inherited from AdaBoost, which are served as the unary potentials of the conditional random field (CRF) model to incorporate spatial context information. Experimental results show that the classification accuracy by MBRF as well as its integration with CRF consistently outperforms the other referenced state-of-the-art classification methods when limited labeled samples are available for training.

Description: Curse of dimensionality is a major disadvantage for classification of hyperspectral imagery since a large number of bands need to be dealt with. Band selection is a task to reduce the number of bands. An unsupervised band selection method is proposed in this article. It is a three-step procedure. In the first step, characteristics (attributes) of the bands are found out. Next, redundancy among the bands is removed by executing clustering operation. At last, the remaining bands, which are nonredundant among themselves, are ranked according to their discriminating capability. Discriminating capability is calculated by measuring the capacitory discrimination of the bands. Results are compared with four state-of-the-art methods: a band elimination method, a ranking-based, and two clustering-based band selection methods to demonstrate the effectiveness of the proposed method. Four evaluation measures, namely: 1) classification accuracy; 2)  Kappa coefficient; 3) class separability, and 4) entropy, are calculated over the selected bands to assess the efficiency of the selected bands. The proposed method shows promising results compared to them.

Description: A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation (k-CV). In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, submodels can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels.

Description: Band selection is an essential step toward effective and efficient hyperspectral image classification. Traditional supervised band selection methods are often hindered by the problem of lacking enough training samples. To address this problem, we propose a semisupervised band selection method that allows contribution from both labeled and unlabeled hyperspectral pixels. This method first builds a hypergraph model from all hyperspectral samples to measure the similarity among pixels. We show that hypergraph can capture relationship among pixels in both spectral and spatial domain. In the second step, a semisupervised learning method is introduced to propagate class labels to unlabeled samples. Then a linear regression model with group sparsity constraint is used for band selection. Finally, hyperspectral pixels with selected bands are used to train a support vector machine (SVM) classifier. The proposed method is tested on three benchmark datasets. Experimental results demonstrate its advantages over several other band selection methods.

Description: Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It amounts the identification of pure spectral signatures ( endmembers ) in the data, and the estimation of the abundance of each endmember in each (possibly mixed) pixel. A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular and widely used techniques for this purpose is the HySime algorithm but, due to the complexity and high dimensionality of hyperspectral scenes, this technique is computational expensive. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present the first FPGA design for the HySime algorithm. Our system includes a direct memory access (DMA) module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASAs airborne visible infra-red imaging spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World trade center (WTC) in New York. Experimental results demonstrate that our hardware version of the HySime algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.

Description: Spatial–spectral classification is a very important topic in the field of remotely sensed hyperspectral imaging. In this work, we develop a parallel implementation of a novel supervised spectral–spatial classifier, which models the likelihood probability via ${bm{l}_{mathbf{1}}} - {bm{l}_{mathbf{2}}}$ sparse representation and the spatial prior as a Gibbs distribution. This classifier takes advantage of the spatial piecewise smoothness and correlation of neighboring pixels in the spatial domain, but its computational complexity is very high which makes its application to time-critical scenarios quite limited. In order to improve the computational efficiency of the algorithm, we optimized its serial version and developed a parallel implementation for commodity graphics processing units (GPUs). Our parallel spatial–spectral classifier with sparse representation and Markov random fields (SSC-SRMRF-P) exploits the low-level architecture of GPUs. The parallel optimization of the proposed method has been carried out using the compute unified device architecture (CUDA). The performance of the parallel implementation is evaluated and compared with the serial and multicore implementations on central processing units (CPUs). In fact, the proposed method has been designed to adequately exploit the massive data parallel capacities of GPUs together with the control and logic capacities of CPUs, thus resorting to a heterogeneous CPU–GPU framework in the design of the parallel algorithm. Experimental results using real hyperpsectral images demonstrate very high performance for the proposed CPU–GPU parallel method, both in terms of classification accuracy and computational performance.

Description: Classification is one of the most important analysis techniques for hyperspectral image analysis. Sparse representation is an extremely powerful tool for this purpose, but the high computational complexity of sparse representation-based classification techniques limits their application in time-critical scenarios. To improve the efficiency and performance of sparse representation classification techniques for hyperspectral image analysis, this paper develops a new parallel implementation on graphics processing units (GPUs). First, an optimized sparse representation model based on spatial correlation regularization and a spectral fidelity term is introduced. Then, we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The first GPU optimization algorithm for sparse representation classification (SRCSC_P) of hyperspectral images is proposed in this paper, and a parallel implementation of the proposed method is developed using compute unified device architecture (CUDA) on GPUs. The GPU parallel implementation is compared with the serial and multicore implementations on CPUs. Experimental results based on real hyperspectral datasets show that the average speedup of SRCSC_P is more than $mathbf{130} times$ , and the proposed approach is able to provide results accurately and fast, which is appealing for computationally efficient hyperspectral data processing.

Description: This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.

Description: As current and future satellite systems provide both hyperspectral and multispectral images, a need has arisen for image fusion using hyperspectral and multispectral images to improve the fusion quality. This study introduces a hyperspectral image fusion algorithm using multispectral images with a higher spatial resolution and partially different wavelength range compared with the corresponding hyperspectral images. This study focuses on an image fusion technique that enhances the spatial quality and preserves the spectral information of hyperspectral images. The proposed algorithm generates a simulated multispectral band via a spectral unmixing technique and extracts high-frequency information based on blocks of associated bands. The algorithm was applied to Compact Airborne Spectrographic Imager (CASI) datasets acquired in two modes and was compared with two existing methods. Although the wavelength range of the multispectral image did not coincide with that of the hyperspectral image, the proposed algorithm efficiently improved the spatial details and preserved the spectral information of the fused results.

Description: Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.

Description: Extreme learning machine (ELM) is an efficient learning algorithm that has been recently applied to hyperspectral image classification. In this paper, the first implementation of the ELM algorithm fully developed for graphical processing unit (GPU) is presented. ELM can be expressed in terms of matrix operations so as to take advantage of the single instruction multiple data (SIMD) computing paradigm of the GPU architecture. Additionally, several techniques like the use of ensembles, a spatial regularization algorithm, and a spectral–spatial classification scheme are applied and projected to GPU in order to improve the accuracy results of the ELM classifier. In the last case, the spatial processing is based on the segmentation of the hyperspectral image through a watershed transform. The experiments are performed on remote sensing data for land cover applications achieving competitive accuracy results compared to analogous support vector machine (SVM) strategies with significantly lower execution times. The best accuracy results are obtained with the spectral–spatial scheme based on applying watershed and a spatially regularized ELM.

Description: Leaf area index (LAI) is a basic quantity indicating crop growth situation and plays a significant role in ecological model and interaction model between earth surface and atmosphere. However, nonlinear estimation processes of LAI from heterogeneous remote sensing data would induce a scaling bias. The purpose of this study is to provide a method to evaluate and correct the scaling bias. For the effectiveness of the method, first both statistical and physical models were built to estimate LAI directly from modified soil-adjusted vegetation index (MSAVI) as a function with univariate and also from red and near infrared reflectances as a bivariate model. The analysis of wavelet transform and fractal theory revealed that the scaling bias and the high-frequency coefficient from LAI at fine resolution decomposed by Haar wavelet were fractal relation. Based on the wavelet–fractal method, scaling bias could be well denoted by high-frequency coefficient in log–log coordinate for both univariate model and bivariate model, and the root-mean-square error (RMSE) and relative error (RE) of estimated LAI caused by the scaling bias could be greatly reduced after scaling correction. Additionally, to analyze the influence of spatial heterogeneity and nonlinearity of the retrieval model, the scaling bias was investigated on horizontal comparison of LAI retrieval models with univariate and bivariate at a certain resolution and was longitudinally discussed in a retrieval model at different aggregation scales. This study suggests that it is feasible to successfully correct and analyze the scaling bias using the wavelet–fractal method.

Description: Hyperspectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. Linear spectral unmixing is frequently used to characterize mixed pixels in hyperspectral data. Over the last few years, many techniques have been proposed for identifying pure spectral signatures (endmembers) in hyperspectral images. The iterated constrained endmembers (ICE) algorithm is an iterative method that uses the linear model to extract endmembers and abundances simultaneously from the data set. This approach does not necessarily require the presence of pixels in the hyperspectral image as it can automatically derive the signatures of endmembers even if these signatures are not present in the data. As it is the case with other endmember identification algorithms, ICE suffers from high computational complexity. In this paper, a complete and scalable adaptation of the ICE algorithm is implemented using the parallel nature of commodity graphics processing units (GPUs). This gives significant speed increase over the traditional ICE method and allows for processing of larger data set with an increased number of endmembers.

Description: Detecting and mapping plant invaders using hyperspectral remote sensing is necessary in mitigating the extensive ecologic and economic damage these alien plants induce on our forest ecosystems. Using AISA Eagle image data, this study investigated the capability of two unsupervised classification methods for the detection and mapping of Solanum mauritianum located within commercial forestry ecosystems. The existing random forest (RF) outlier detection method when used in conjunction with Anselins Moran’s I produced a detection rate (DR) of 89% with a false positive rate (FPR) of 9.26%. In comparison, the newly developed methodology which is based on the decomposition of the RF proximity matrix using principal component analysis (PCA) resulted in a DR of 95% with a lower FPR (6.39%). Overall, this research has demonstrated the potential of utilizing an unsupervised and accurate RF framework for the detection and mapping of alien invasive plants.

Description: Hyperspectral image classification has been an active topic of research. In recent years, it has been found that light detection and ranging (LiDAR) data provide a source of complementary information that can greatly assist in the classification of hyperspectral data, in particular when it is difficult to separate complex classes. This is because, in addition to the spatial and the spectral information provided by hyperspectral data, LiDAR can provide very valuable information about the height of the surveyed area that can help with the discrimination of classes and their separability. In the past, several efforts have been investigated for fusion of hyperspectral and LiDAR data, with some efforts driven by the morphological information that can be derived from both data sources. However, a main challenge for the learning approaches is how to exploit the information coming from multiple features. Specifically, it has been found that simple concatenation or stacking of features such as morphological attribute profiles (APs) may contain redundant information. In addition, a significant increase in the number of features may lead to very high-dimensional input features. This is in contrast with the limited number of training samples often available in remote-sensing applications, which may lead to the Hughes effect. In this work, we develop a new efficient strategy for fusion and classification of hyperspectral and LiDAR data. Our approach has been designed to integrate multiple types of features extracted from these data. An important characteristic of the presented approach is that it does not require any regularization parameters, so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a hyperspectral image and a LiDAR-derived digital surface model (DSM) collected over the University of Houston campus and the neighboring urban area, indicate that the proposed fram- work for multiple feature learning provides state-of-the-art classification results.

Description: The availability of graphics processing units (GPUs) provides a low-cost solution to real-time processing, which may benefit many remote sensing applications. In this paper, a spectral–spatial classification scheme for hyperspectral images is specifically adapted for computing on GPUs. It is based on wavelets, extended morphological profiles (EMPs), and support vector machine (SVM). Additionally, a preprocessing stage is used to remove noise in the original hyperspectral image. The local computation of the techniques used in the proposed scheme makes them particularly suitable for parallel processing by blocks of threads in the GPU. Moreover, a block-asynchronous updating process is applied to the EMP to speedup the morphological reconstruction. The results over different hyperspectral images show that the execution can be speeded up to $8.2times$ compared to an efficient OpenMP parallel implementation, achieving real-time hyperspectral image classification while maintaining the high classification accuracy values of the original classification scheme.

Description: This study is an application of the hybrid regression method to improve the accuracy of retrieved humidity profile from infrared hyperspectral sounding observations. In hybrid regression method, a weighted average of two regression products that are derived using two different forms of predictand is computed. Regression coefficients for each form of predictand are computed using principal components of MetOp-IASI radiance spectra. First regression product uses logarithm of specific humidity as predictand, whereas second regression product uses only specific humidity as predictand. The weights used in hybrid regression are computed at different pressure levels based on error statistics of humidity retrieval from different predictands. The hybrid regression-based method shows improvement over the state-of-the-art regression method. Humidity profiles retrieved from different regression methods are validated with collocated ECMWF humidity profiles and radiosonde observations for dry, wet, and combined atmospheric conditions. For all cases, humidity retrieved from hybrid regression method is found to be the most accurate at all pressure levels.

Description: Target and anomaly detection are important techniques for remotely sensed hyperspectral data interpretation. Due to the high dimensionality of hyperspectral data and the large computational complexity associated to processing algorithms, developing fast techniques for target and anomaly detection has received considerable attention in recent years. Although several high-performance architectures have been evaluated for this purpose, field programmable gate arrays (FPGAs) offer the possibility of onboard hyperspectral data processing with low-power consumption, reconfigurability and radiation tolerance, which make FPGAs a relevant platform for hyperspectral processing. In this paper, we develop a novel FPGA-based technique for efficient target detection in hyperspectral images. The proposed method uses a streaming background statistics (SBS) approach for optimizing the constrained energy minimization (CEM) and Reed-Xiaoli (RX) algorithms, which are widely used techniques for target and anomaly detection, respectively. Specifically, these two algorithms are implemented in streaming fashion on FPGAs. Most importantly, we present a dual mode that implements a flexible datapath to decide in real time which one among these two algorithms should be used, thus allowing for the dynamic adaptation of the hardware to either target detection or anomaly detection scenarios. Our experiments, conducted with several well-known hyperspectral scenes, indicate the effectiveness of the proposed implementations.

Description: A sensor aboard an unmanned aerial vehicle (UAV) is vulnerable to vibration and natural conditions such as erratic winds. Considering the difference between laboratory and vicarious environments of calibration, a vicarious calibration is closer to the real environment and is a complement to laboratory calibrations for remote sensors. The existing vicarious calibration of UAVs only uses a reflectance-based method, rather than irradiance-based method. Therefore, the error caused by aerosol-type assumptions, which is the largest uncertainty for reflectance-based method, is not considered sufficiently during vicarious calibration of UAVs. Considering the difference in the upward radiative transfer path between satellites and UAVs, we propose an improved irradiance-based method. A simulation experiment was designed to compare the relative differences between two aerosol types under different aerosol optical thicknesses (AOTs) and heights for both the reflectance-based and the improved methods. Additionally, two field-calibration campaigns, under different weather conditions, were performed to calibrate a Headwall hyperspectral imager payload on a UAV, with the help of calibration tarps and MODTRAN5 radiative transfer code. When weather conditions were unsatisfactory, the total uncertainties of the original and improved methods were c. 5.9%–6.7% and c. 2.3%–3.5%, respectively, and the uncertainties caused by aerosol-type assumption were c. 15.8%–18.7% and c. 3.5%–8.0%, respectively. The results of the simulation and field experiments verified that the improved method has higher accuracy and lower uncertainty and is more suitable for the vicarious calibration of UAV hyperspectral remote sensors.

Description: Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks, while most current research efforts only focus on horizontal or near horizontal scene text. In this paper, first we present a unified distance metric learning framework for adaptive hierarchical clustering, which can simultaneously learn similarity weights (to adaptively combine different feature similarities) and the clustering threshold (to automatically determine the number of clusters). Then, we propose an effective multi-orientation scene text detection system, which constructs text candidates by grouping characters based on this adaptive clustering. Our text candidates construction method consists of several sequential coarse-to-fine grouping steps: morphology-based grouping via single-link clustering, orientation-based grouping via divisive hierarchical clustering, and projection-based grouping also via divisive clustering. The effectiveness of our proposed system is evaluated on several public scene text databases, e.g., ICDAR Robust Reading Competition data sets (2011 and 2013), MSRA-TD500 and NEOCR. Specifically, on the multi-orientation text data set MSRA-TD500, the $f$ measure of our system is $71$ percent, much better than the state-of-the-art performance. We also construct and release a practical challenging multi-orientation scene text data set (USTB-SV1K), which is available at http://prir.ustb.edu.cn/TexStar/MOMV-text-detection/.

Description: We demonstrate low-voltage waveguide-coupled germanium avalanche photodetectors (APDs) with a (wafer-scale mean) gain $times$ bandwidth product of 140 GHz at $-$ 5 V by utilizing a 185-nm-thick Ge layer. An optical receiver based on such an APD operating up to 25 Gb/s is demonstrated.

Description: The realization of high-efficiency organic small molecule: Fullerene solar cells are challenging but become more and more feasible due to the rapid development in donor materials and device fabrication techniques. In the past several years, the optimization in the processing techniques, such as, solvent vapor annealing (SVA), the use of solvent additives had led to superior improvement in the device performance of organic solar cells from different donor systems, but at an expense of reduction in the open-circuit voltage ( $V_{{rm OC}}$ ). In this paper, we report that the overall device performance of the organic small molecule solar cells (SMSCs) can be significantly enhanced through a two-step process consisting of SVA and thermal annealing (TA) (short for SVA+TA), especially the reduction in $V_{{rm OC}}$ can be effectively avoided in case of exclusive use of SVA. The carrier dynamics is determined by transient photovoltage and transient photocurrent measurements, which can provide information on the origin of enhanced device performance upon the two-step annealing. The observed $V_{{rm OC}}$ recovery is attributed to the preferable change in charge dynamic, thus, leading to a superior overall device performance. Furthermore, the SVA and the consequential TA are complementary to each other; thus, the two-step annealing method represents a feasible route to simultaneously improve the $V_{{rm OC}}$ , $J_{{rm SC}}$ , FF, and PCE of SMSCs.

Description: We report a fast and efficient method for permanently correcting fabrication-induced phase errors in silicon photonic circuits. The method uses femtosecond laser pulses at 400-nm wavelength to amorphize a thin layer of crystalline silicon near the waveguide surface, thereby inducing a change in the effective index of the waveguide. Using a single femtosecond laser pulse, we reduced the polarization-dependent frequency shift between the two interferometers of a polarization diversity differential phase shift keying silicon demodulator from 11 GHz to less than 0.5 GHz, thereby restoring the polarization diversity operation of the circuit with little degradation to the circuit performance.

Description: A refined model of a mid-IR amplifier, constituted by a tapered chalcogenide fiber coupled to an erbium-doped chalcogenide microsphere, is integrated with a global solution search procedure based on particle swarm optimization approach. It is implemented in a computer code in order to obtain an inversion algorithm useful to evaluate the spectroscopic parameters of rare-earth-doped glass microspheres. The rare earth parameters can be recovered by means of the optical gain measurement. The error in evaluation of the erbium lifetime $tau _{41}$ is <3.5%. It is <0.5% for the other lifetimes and ion–ion interaction parameters. These excellent results are obtained since whispering gallery mode electromagnetic field interacts with rare earth for long effective distances.

Description: We demonstrate an integrated spectral shaper based on a Sagnac loop incorporating a chirped Bragg grating in silicon photonics for the photonic generation of chirped microwave pulses. The technique is based on optical spectral shaping combined with linear frequency-to-time mapping. By tuning the central wavelength of the input optical pulse, we obtain chirped microwave pulses with central frequencies ranging from $sim 10$ GHz to $sim 30$ GHz and an RF chirp rate of $sim 20$ GHz/ns with both positive and negative signs.

Description: Internet of Things (IoTs) is a technology to connect machines, objects, and people with electronic devices. Many researchers have focused their works on architecture design from different perspectives. The deployment of IoT applications contains a variety of things that are attached with end-point devices, such as sensors, actuators, and radio frequency identification tags to perform sensing, controlling, and/or identifying things. The GS1 EPCglobal Architecture and IEEE 1451 standards are adopted as the cornerstone of our IoT architecture framework. In this paper, we focus on how to resolve the heterogeneity and dynamic characteristics of this variety of things. A uniform expression mechanism based on electronic product coding (EPC) scheme is introduced. The practical facilities are presented and examples of how to generate EPC scheme for transducers are illustrated. By integrating the IEEE 1451 compatible transducers, based on transducer electronic data sheet, to the identify layer of the extended GS1 EPCglobal Architecture, we can process more versatile data of different things in IoT applications. The uniform EPC scheme of heterogeneous things in the identify layer will make a consistent expression of things and compatible with the upper layer. This mechanism plays a fundamental role to support transducer capability in GS1 EPCglobal Architecture.

Description: Digital circuits are expected to increasingly suffer from more hard faults due to technology scaling. Especially, a single hard fault in ALU (Arithmetic Logic Unit) might lead to a total failure in processors or significantly reduce their performance. To address these increasingly important problems, we propose a novel cost-efficient fault-tolerant mechanism for the ALU, called LIZARD. LIZARD employs two half-word ALUs, instead of a single full-word ALU, to perform computations with concurrent fault detection. When a fault is detected, the two ALUs are partitioned into four quarter-word ALUs. After diagnosing and isolating a faulty quarter-word ALU, LIZARD continues its operation using the remaining ones, which can detect and isolate another fault. Even though LIZARD uses narrow ALUs for computations, it adds negligible performance overhead through exploiting predictability of the results in the arithmetic computations. We also present the architectural modifications when employing LIZARD for scalar as well as superscalar processors. Through comparative evaluation, we demonstrate that LIZARD outperforms other competitive fault-tolerant mechanisms in terms of area, energy consumption, performance and reliability.

Description: Information searches are the most common application within social networks. Normally, the social network is modeled as a network graph, consisting of nodes (In the rest of the paper, unless otherwise specified, we will use the terms “user” and “node” interchangeably.) representing users within the network and edges representing relationships between users. Choosing the appropriate nodes to form an auxiliary structure for supporting the effective query message spreading can reduce the troublesome repeated queries. To accomplish this, a hybrid search (HS) scheme is proposed. If the query message is received by a node belonging the auxiliary structure constructed by dynamic weighted distributed label clustering (DW-DLC), it would be flooded to all neighbors of the visited node; otherwise, it would be forwarded to one neighbor of the visited node. The DW-DLC based auxiliary structure can accelerate the process of obtaining required information within the network. The simulation results show that the HS+DW-DLC scheme can reduce the average searching delay time, even in a required-information-scarce social network. In addition, the proposed scheme can generate a relatively low amount of repeated messages to lower repeatedly asking social network users.

Description: This paper presents a derivation of four radix-2 division algorithms by digit recurrence. Each division algorithm selects a quotient digit from the over-redundant digit set {−2, −1, 0, 1, 2}, and the selection of each quotient digit depends only on the two most-significant digits of the partial remainder in a redundant representation. Two algorithms use a two’s complement representation for the partial remainder and carry-save additions, and the other two algorithms use a binary signed-digit representation for the partial remainder and carry-free additions. Three algorithms are novel. The fourth algorithm has been presented before. Results from the synthesized netlists show that two of our fastest algorithms achieve an improvement of 10 percent in latency per iteration over a standard radix-2 SRT algorithm at the cost of 36 percent more power and 50 percent more area.

Description: We present WaFS, a user-level file system, and a related scheduling algorithm for scientific workflow computation in the cloud. WaFS’s primary design goal is to automatically detect and gather the explicit and implicit data dependencies between workflow jobs, rather than high-performance file access. Using WaFS’s data, a workflow scheduler can either make effective cost-performance tradeoffs or improve storage utilization. Proper resource provisioning and storage utilization on pay-as-you-go clouds can be more cost effective than the uses of resources in traditional HPC systems. WaFS and the scheduler controls the number of concurrent workflow instances at runtime so that the storage is well used, while the total makespan (i.e., turnaround time for a workload) is not severely compromised. We describe the design and implementation of WaFS and the new workflow scheduling algorithm based on our previous work. We present empirical evidence of the acceptable overheads of our prototype WaFS and describe a simulation-based study, using representative workflows, to show the makespan benefits of our WaFS-enabled scheduling algorithm.

Description: Given a database table with records that can be ranked, an interesting problem is to identify selection conditions for the table, which are qualified by an input record and render its ranking as high as possible among the qualifying tuples. In this paper, we study this standing maximization problem, which finds application in object promotion and characterization. After showing the hardness of the problem, we propose greedy methods, which are experimentally shown to achieve high accuracy compared to exhaustive enumeration, while scaling very well to the problem input size. Our contributions include a linear-time algorithm for determining the optimal selection range for an ordinal attribute and techniques for choosing and prioritizing the most promising selection predicates to apply. Experiments on real datasets confirm the effectiveness and efficiency of our techniques.

Description: Some fairly recent research has focused on providing XACML-based solutions for dynamic privacy policy management. In this regard, a number of works have provided enhancements to the performance of XACML policy enforcement point (PEP) component, but very few have focused on enhancing the accuracy of that component. This paper improves the accuracy of an XACML PEP by filling some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide an XACML-based implementation of a dynamic privacy policy management framework and an evaluation of the applicability of our system in comparison to some of the existing approaches.

Description: This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships , which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.

Description: Provides a listing of current staff, committee members and society officers.

Description: A novel planar multilayered epsilon-near-zero (ENZ) tunnel sensor based on fully laminated surface integrated waveguide (SIW) technology is proposed for the microwave measurement of dispersive materials. The proposed sensor is designed and optimized using parametric analysis to obtain the multilayered ENZ tunnel dimensions. It is observed that the width of the upper tunnel of the designed two-tunnel sensor should be at least half of the SIW width of the actual SIW structure for the multiband operation. The complex permittivity measurement using the proposed sensor is possible at two frequencies with a single set of measurement data. The proposed method is based on perturbation of the squeezed electric field having constant magnitude and high intensity inside the multilayered ENZ tunnel, which eventually increases the sensitivity of the proposed sensor. The sensitivity and accuracy of the proposed sensor are tested using both the simulated and the experimented data. It is found that the proposed sensor is highly sensitive, and typically demonstrates 6% error under ideal conditions, thus making it a good candidate for the microwave measurement of dispersive materials.

Description: A system based on phase-sensitive optical time domain reflectometry is proposed for simultaneously strain and vibration sensing. The strain of fiber is detected by comparing the patterns of signal for different laser frequencies, and the vibration of fiber is detected simultaneously from the signals for any certain laser frequency. During the measurement, frequencies of the probe optical pulses are modulated sequentially in ascending or descending order. Using the signals generated by optical pulses with the same frequency, the vibration of fiber is detected with fast response speed; using that with different frequencies, the strain of fiber is detected with high resolution. In our experiment, a sensing system with 2-m spatial resolution, up to 1-kHz frequency measurement range and 10- $text{n}{{varepsilon }}$ strain resolution is realized for a 9-km sensing fiber length.

Description: The following is included in this newsletter: IEEE Young Professionals, Lahore Section Win World Class Award "Hall of Fame 2015"; Distinguished Lecturer Tour of Nei Kato in China, May 2015; Distinguished Lecturer Tour of Tom Hou to Beijing and Nanjing, China; European Wireless 2015 in Budapest; Novel In-Band Full-Duplex Communication Prototype for 5G Systems, Finland, May 2015

Description: Wireless vehicular networks offer the promise of connectivity to vehicles that could provide a myriad of safety and driving-enhancing services to drivers and passengers. With wireless technology available in each car, it is expected that huge amounts of information will be exchanged between vehicles or between vehicles and roadside infrastructure. Due to defective sensors, software viruses, or even malicious intent, legitimate vehicles might inject untrustworthy information into the network. Besides relying on the public key infrastructure, this article proposes a social network approach to study trustworthy information sharing in a vehicular network. We first cover recent research progress in measuring direct trust and modeling indirect trust in online social networks, and then discuss how to apply them to vehicular social networks despite several pressing research challenges.

Description: Presents the front cover for this issue of the publication.

Description: The mobile crowdsourcing network (MCN) is a promising network architecture that applies the principles of crowdsourcing to perform tasks with human involvement and powerful mobile devices. However, it also raises some critical security and privacy issues that impede the application of MCNs. In this article, in order to better understand these critical security and privacy challenges, we first propose a general architecture for a mobile crowdsourcing network comprising both crowdsourcing sensing and crowdsourcing computing. After that, we set forth several critical security and privacy challenges that essentially capture the characteristics of MCNs. We also formulate some research problems leading to possible research directions. We expect this work will bring more attention to further investigation on security and privacy solutions for mobile crowdsourcing networks.

Description: Presents the ComSoc conference calendar of events.

Description: Energy harvesting technologies have gained widespread attention for their perpetual energy supply for sensor nodes. However, the energy resources are still insufficient while the harvesting module is added on the node. To prolong the network lifetime and meet the demand of a green wireless network, a dynamic gradient-aware hierarchical packet forwarding mechanism is designed. According to the relative positions of nodes, gradient-aware clusters are established. Consequently, considering the energy conversion efficiency and relative distance, cluster heads are selected reasonably. Furthermore, by exploiting the available energy and the number of cluster members, packets can be forwarded to the sink in an energy-efficient manner. Results show that the network lifetime can be noticeably improved.

Description: Recently, there has been substantial research interest in the subject of simultaneous wireless information and power transfer (SWIPT) due to its cross-disciplinary appeal and its wide-ranging application potential, which motivates this overview. More explicitly, we provide a brief survey of the state of the art and introduce several practical transceiver architectures that may facilitate its implementation. Moreover, the most important link-level as well as system-level design aspects are elaborated on, along with a variety of potential solutions and research ideas. We envision that the dual interpretation of RF signals creates new opportunities as well as challenges requiring substantial research, innovation and engineering efforts.

Description: The emerging trends of 2014 have continued to dominate the developments in the first half of 2015: 1. The packet optical transport system (P-OTS) concept supports the service provider goal to build a common network infrastructure by integrating Ethernet, IP/MPLS, and DWDM 2. There has been intensifying focus and attention on network functions virtualization (NFV) and softwaredefined networking (SDN) to provide service providers with the tools for more effective operation and management of communications networks in general, and optical communications networks in particular.

Description: In June 2015, the ComSoc Board of Governors approved a revision of ComSoc Constitution. The IEEE approved the revision in July 2015, and ComSoc Membership approval is currently pending.

Description: The directivity factor (DF) of a beamformer describes its spatial selectivity and ability to suppress diffuse noise which arrives from all directions. For a given array constellation, it is possible to select beamforming weights which maximize the DF for a particular look-direction, while enforcing nulls for a set of undesired directions. In general, the resulting DF is dependent upon the specific look- and null directions. Using the same array, one may apply a different set of weights designed for any other feasible set of look- and null directions. In this contribution, we show that when the optimal DF is averaged over all look directions, the result equals the number of sensors minus the number of null constraints. This result holds regardless of the positions and spatial responses of the individual sensors and regardless of the null directions. The result generalizes to more complex wave-propagation domains (e.g., reverberation).

Description: This paper presents an anomaly detection model that is granular and distributed to accurately and efficiently identify sensed data anomalies within wireless sensor networks. A more decentralised mechanism is introduced with wider use of in-network processing on a hierarchical sensor node topology resulting in a robust framework for dynamic data domains. This efficiently addresses the big data issue that is encountered in large scale industrial sensor network applications. Data vectors on each node’s observation domain is first partitioned using an unsupervised approach that is adaptive regarding dynamic data streams using cumulative point-wise entropy and average relative density . Second order statistical analysis applied on average relative densities and mean entropy values is then used to differentiate anomalies through robust and adaptive thresholds that are responsive to a dynamic environment. Anomaly detection is then performed in a non-parametric and non-probabilistic manner over the different network tiers in the hierarchical topology in offering increased granularity for evaluation. Experiments were performed extensively using both real and artificial data distributions representative of different dynamic and multi-density observation domains. Results demonstrate higher accuracies in detection as more than 94 percent accompanied by a desirable reduction of more than 85 percent in communication costs when compared to existing centralized methods.

Description: We report on the design, fabrication, and optical characterization of InN-based optical waveguides aiming at their application as all-optical limiters at 1.55 $mu text{m}$ . The InN guiding layers are grown by radio frequency (RF) sputtering on sapphire substrates. Experimental cutback method and nonlinear optical transmittance measurements were performed for the developed devices. The waveguides present nonlinear behavior associated with two photon absorption process. A nonlinear absorption coefficient ranging from $sim 43$ to 114 cm/GW is estimated from optical measurements. These results open the possibility of using RF sputtering as a low cost and thermally harmless technique for the development and overgrowth of InN-based optical waveguides in future III-nitride all-optical integrated circuits working at telecom wavelengths.

Description: A new simple structure of ultrahigh birefringent and ultrahigh nonlinear slot silicon microfiber with highly efficient dispersion reduction is proposed and numerically simulated using the full-vector finite-element method with anisotropic perfectly matched layers. Benefiting from the slot effect-induced sub-wavelength mode confinement, ultrahigh birefringence up to the order of $10^{mathrm {{-1}}}$ can be realized within a wide wavelength range from 1.4 to 1.7 $mu text{m}$ , which is comparable with the results in these ultrahigh birefringent photonic crystal fibers, but cannot be achieved in traditional microfibers. Meanwhile, the nonlinear coefficients of quasi-TE mode and quasi-TM mode at the wavelength of 1.55 $mu text{m}$ are as high as 969.58 $text{W}^{mathrm {{-1}}}text{m}^{mathrm {{-1}}}$ and 156.74 $text{W}^{mathrm {{-1}}}text{m}^{mathrm {{-1}}}$ , respectively. Furthermore, the dispersion value of quasi-TE mode at 1.55 $mu text{m}$ can be decreased from $1.2358times 10^{mathrm {{3}}}$ ps/(nm $cdot $ km) to 0 ps/(nm $cdot $ km) simply by modifying the slot size. Owing to its excellent performance, the proposed slot silicon microfiber will have great potential for polarization maintaining nonlinear signal processing applications.

Description: Hierarchical routing in wireless sensor networks (WSNs) is a very important topic that has been attracting the research community in the last decade. Typical hierarchical routing is called clustering routing, in which the network is divided into multiple clusters. Recently, some types of atypical hierarchical routing arise, including chain-based, tree-based, grid-based routing, and area-based routing. There are several survey papers that present and compare the hierarchical routing protocols from various perspectives, but a survey on atypical hierarchical routing is still missing. This paper makes a first attempt to provide a comprehensive review on atypical hierarchical routing. We offer a classification of atypical hierarchical routing of WSNs, and give detailed analysis of different logical topologies. The most representative atypical hierarchical routing protocols are described, discussed, and qualitatively compared. In particular, the advantages and disadvantages of different atypical hierarchical routing protocols are analyzed with respect to their significant performances and application scenarios. Finally, we put forward some open issues concerning the design of hierarchical WSNs. This survey aims to provide useful guidance for system designers on how to evaluate and select appropriate logical topologies and hierarchical routing protocols for specific applications.

Description: A novel fiber-optic relative humidity (RH) sensor with thermal compensation based on a fiber Bragg grating (FBG) is presented. Changes in the RH are transduced to the FBG properties by an ORMOCER coating. The cross sensitivity to temperature is compensated by inscribing the Bragg grating in a highly birefringent fiber and using the individual Bragg peaks of each polarization direction for separation (discrimination) of temperature and humidity influences.

Description: This paper focuses on the nondestructive dielectric measurement for thin dielectric material using open-ended coaxial probe. The probe calibration procedure requires only a measurement of a half-space air and three open standard kits. The measured reflection coefficient for thin sample, which is backed by metal plate, is taken with a vector network analyzer up to 7 GHz and the reflection coefficient is converted to relative dielectric constant and tangent loss via closed form capacitance model and simple calibration process.

Description: We present a physical model to study the dark current and noise characteristics of quantum-dot infrared photodetectors (QDIPs). This model considers both carrier’s thermoexcitaion and carrier’s field-assisted tunneling, which are the main origin of dark current and noise in QDIPs. We use this model to analyze the effect of different structural parameters, such as QDs lateral size and density of QD, and environmental parameters, such as bias voltage and operating temperature on the dark current and the noise of QDIP. The theoretical results obtained from our model are in excellent agreement with reported experimental data. Therefore, the validity of the model is proven for these reasons. This model can be extended to other quantum structure detectors to evaluate the noise and dark current characteristics.

Description: We analyze models for predicting the probability of a strikeout for a batter/pitcher matchup in baseball using player descriptors that can be estimated accurately from small samples. We start with the log5 model which has been used extensively for describing matchups in sports. Log5 is a special case of a logit model and we use constrained logistic regression over nearly one million matchup observations to assess the use of the log5 explanatory variables for this application. We also show that a batter/pitcher ground ball rate interaction variable is significant for the prediction of strikeout probability and we provide physical justification for the inclusion of this variable in the model. We quantify the differences among the models and show that batters control the majority of the variance in predicted strikeout rate.

Description: This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low energy of P2. To identify such split, HVD method is used to decompose the S2 into a number of components while preserving the phase information. Further, A2s and P2s are localized using smoothed pseudo Wigner-Ville distribution followed by reassignment method. Finally, the split iscalculated by taking the differences between the means of time indices of A2s and P2s. Experiments on total 33 clips of S2 signals are performed for evaluation of the method. The mean ± standard deviation of the split is 34.7 ± 4.6 ms. The method measures the splitefficiently, even when A2-P2 overlap is ≤ 20 ms and the normalized peak temporal ratio of P2 to A2 is low (≥ 0.22). This proposed method thus, demonstrates its robustness by defining split detectability (SDT), the split detection aptness through detecting P2s, by measuring upto 96 percent. Such findings reveal the effectiveness of the method as competent against the other baselines, especially for A2-P2 overlaps and low energy P2.

Description: Post-acquisition denoising of magnetic resonance (MR) images is an important step to improve any quantitative measurement of the acquired data. In this paper, assuming a Rician noise model, a new filtering method based on the linear minimum mean square error (LMMSE) estimation is introduced, which employs the self-similarity property of the MR data to restore the noise-less signal. This method takes into account the structural characteristics of images and the Bayesian mean square error (Bmse) of the estimator to address the denoising problem. In general, a twofold data processing approach is developed; first, the noisy MR data is processed using a patch-based L 2 -norm similarity measure to provide the primary set of samples required for the estimation process. Afterwards, the Bmse of the estimator is derived as the optimization function to analyze the pre-selected samples and minimize the error between the estimated and the underlying signal. Compared to the LMMSE method and also its recently proposed SNR-adapted realization (SNLMMSE), the optimized way of choosing the samples together with the automatic adjustment of the filtering parameters lead to a more robust estimation performance with our approach. Experimental results show the competitive performance of the proposed method in comparison with related state-of-the-art methods.

Description: Large-scale ad hoc analytics of genomic data is popular using the R-programming language supported by over 700 software packages provided by Bioconductor. More recently, analytical jobs are benefitting from on-demand computing and storage, their scalability and their low maintenance cost, all of which are offered by the cloud. While biologists and bioinformaticists can take an analytical job and execute it on their personal workstations, it remains challenging to seamlessly execute the job on the cloud infrastructure without extensive knowledge of the cloud dashboard. How analytical jobs can not only with minimum effort be executed on the cloud, but also how both the resources and data required by the job can be managed is explored in this paper. An open-source light-weight framework for executing R-scripts using Bioconductor packages, referred to as ‘RBioCloud’, is designed and developed. RBioCloud offers a set of simple command-line tools for managing the cloud resources, the data and the execution of the job. Three biological test cases validate the feasibility of RBioCloud. The framework is available from http://www.rbiocloud.com .

Description: Of major interest to translational genomics is the intervention in gene regulatory networks (GRNs) to affect cell behavior; in particular, to alter pathological phenotypes. Owing to the complexity of GRNs, accurate network inference is practically challenging and GRN models often contain considerable amounts of uncertainty. Considering the cost and time required for conducting biological experiments, it is desirable to have a systematic method for prioritizing potential experiments so that an experiment can be chosen to optimally reduce network uncertainty. Moreover, from a translational perspective it is crucial that GRN uncertainty be quantified and reduced in a manner that pertains to the operational cost that it induces, such as the cost of network intervention. In this work, we utilize the concept of mean objective cost of uncertainty (MOCU) to propose a novel framework for optimal experimental design. In the proposed framework, potential experiments are prioritized based on the MOCU expected to remain after conducting the experiment. Based on this prioritization, one can select an optimal experiment with the largest potential to reduce the pertinent uncertainty present in the current network model. We demonstrate the effectiveness of the proposed method via extensive simulations based on synthetic and real gene regulatory networks.

Description: This paper presents a new approach for the definition and identification of a transistor model suitable for low-noise amplifier (LNA) design. The resulting model is very robust to layout modifications (i.e., source degeneration) providing accurate predictions of device noise-performance and small-signal parameters. Moreover, the described procedure is very robust since it does not require any numerical optimization, with possibly related problems like local minima and unphysical model parameters. The adopted model topology is based on a lumped element parasitic network and a black-box intrinsic device, which are both identified on the basis of full-wave electromagnetic simulations, as well as noise and ${ S}$ -parameter measurements. The procedure has been applied to three GaN HEMTs having different peripheries and a Ku-band LNA has been designed, demonstrating a very good agreement between measurements and predicted results.

Description: Novel design of a shielded vertically stacked ring resonator (VSRR) is presented in this paper. The use of a shielded VSRR with a layer of the low-loss liquid that fills the partial space between the fed patch and the parasitic patch have been investigated. Dependencies of the resonating frequency and input impedance of the shielded VSRR on structure size and material properties of the test liquid layer are discussed. The method, of finding the complex permittivity (CP), particularly of petroleum liquids, is verified using electromagnetic modeling with full wave simulation software ANSYS HFSS-15 and confirmed experimentally. The proposed new design of the resonator will improve the sensitivity of single ring boxed resonator in terms of the quality factor, and in turn, increase the CP measurement sensitivity.

Description: This paper proposes a design scheme for extending the bandwidth of a three-stage Doherty power amplifier (DPA) based on symmetric devices for broadband applications. The proposed bandwidth enhancement scheme provides an optimized solution for the load combiner parameters while operating the auxiliary power amplifier (PA) at lower current values as compared to the main PA at saturation. The proposed scheme promises 30.3% fractional bandwidth in terms of efficiency enhancement up to 9.54-dB back-off. The proposed design methodology is validated with the design of a broadband three-stage DPA using three 10-W packaged GaN HEMT devices. Measurement results show more than 51.6% drain efficiency at 6-dB output power back-off (OPBO) over the entire frequency range from 700 to 950 MHz. At 9.54-dB OPBO, the drain efficiency is better than 50.2% over this 250-MHz band. The peak drain efficiency at saturation is better than 60.04% over the entire band of operation. Measurement with 5-MHz WCDMA modulated signal shows the average drain efficiency of about 57.6% at 33.97-dBm average output power at the center frequency of operation. The corresponding adjacent channel power ratio is better than ${-}{hbox{45.6}}$ dBc after applying digital predistortion. The circuit is realized with microstrip technology, which can be easily fabricated using conventional printed circuit processes.