Assessment and improvement of the Cloud Emission and Scattering Index (CESI) – an algorithm for cirrus detection

This study evaluates and improves the cirrus detection of the middle and low peak Weighting Function (WF) Cloud Emission and Scattering Index (CESI) by using the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask product. Results show that CESI-3 (peak WF about 890 hPa) accurately detect cirrus with the Probability of detection (POD) value reaching 0.69 during both at day and nighttimes. The CESI-1 (peak WF about 550 hPa) falsely detects cirrus in the Sahara Desert, south of the Saudi Arabia, north of Tibetan Plateau and in Australia during the ascending process. More false alarm rates (FARs) are detected in the northern mid-latitude around 60° N because of the surface type and seasonal effects. Furthermore, the thresholds of middle and low peak WF CESIs for cirrus detection are determined and the performance is satisfactory. In addition, Linear Discriminant Analysis (LDA) is employed to train four combination types (combining CESI-1 and CESI-2, combining CESI-1 and CESI-3, combining CESI-2 and CESI-3, and combining CESI-1, CESI-2, and CESI-3). Accordingly, results elucidate that Comb-1-3 (combining CESI-1 and CESI-3) is appropriate to detect cirrus for the ascending process, increasing the POD by 11.2% as compared to CESI-3. However, Comb-1-2-3 (combining CESI-1, CESI-2 with peak WF around 790 hPa, and CESI-3) is most suitable for cirrus detection in the descending process, increasing the PODs by 8.5%. We also find that the POD of cirrus detection over the land increases relatively higher than over the sea. The most significant improvement of cirrus detection occurs over the land in the summer in the daytime with a 35% POD increase from 0.57 to 0.76.     

KSpheres – an efficient algorithm for joining skinning surfaces

Besides classical point based surface design, sphere based creation of characters and other surfaces has been introduced by some of the recently developed modeling tools in computer graphics. ZSpheres^® by Pixologic, or Spore™ by Electronic Arts are just two prominent examples of these softwares. In this paper we introduce a new sphere based modeling tool, which allows us to create smooth, tubular-like surfaces by skinning a user-defined set of spheres. The main advantage of the new method is to provide a parametric surface with more natural and smoother shape, especially at the connection of branches than the surfaces provided by the existing softwares and methods.     

Reaction–diffusion algorithm for stereo disparity detection

The present paper proposes a novel stereo algorithm utilizing multi-sets of reaction–diffusion equations. The problem of detecting a stereo disparity map becomes the segmentation problem, in which the uniqueness assumption and the continuity assumption on disparity distribution are taken into account. A set of reaction–diffusion equations realizes the continuity assumption, while a mutual-inhibition mechanism among the multi-sets realizes the uniqueness one. In addition, each set of equations has a self-inhibition mechanism, which is necessary for the reaction-diffusion equations applied to stereo disparity detection. Performance of the proposed algorithm is evaluated for well-known test stereo images.     

Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Wheat plants were experimentally infested with wheat stem sawflies, and hyperspectral images (reflectance range from 402.8–838.7 nm) were collected from leaves of infested and non‐infested plants. Mean and variance reflectance per leaf were calculated in five of 213 spectral bands (452, 553, 657, 725, and 760 nm) and compared with vegetation indices (NDVI, SI and PRI), and standard variogram parameters (nugget, sill and range values). Mean reflectance values and their variance values and vegetation indices showed significant effects of sawfly infestation in one dataset but not in another. Based on directional variogram analyses, we showed that: (1) better separation of leaf type and infested/non‐infested wheat plants was seen in variograms in longitudinal direction compared to transverse; (2) mainly spectral bands in the red edge and NIR showed consistent effect of sawfly infestation; (3) range values were not affected significantly by either sawfly infestation or leaf type; and (4) sawfly‐induced stress was most likely to be detected about three weeks after infestation. Variogram analysis is one of the key standards in quantitative spatial ecology, and this study supports further research into its use in remote sensing with particular emphasis on detection of biotic stress.     

CubeDMA – Optimizing three-dimensional DMA transfers for hyperspectral imaging applications

Onboard computing is one of the principal needs in space-related technology in the recent years. In particular, onboard hyperspectral imaging (HSI) processing has advanced significantly. Due to advances in sensor technology, onboard HSI processing continuously meets new challenges related to increasing dataset size, limited processing time and limited communication links. High throughput and data reduction are crucial for satisfying real-time constraint and for preserving transmission bandwidth. For systems capable of accommodating a wide range of processing algorithms, there is a need for a flexible communication infrastructure that can provide fast access to/from memory in different access patterns. In this paper, existing FPGA-related Direct Memory Access (DMA) solutions have been evaluated, and a new DMA solution tailored for hyperspectral images has been proposed. Results show that the proposed DMA core, CubeDMA, handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient.     

A multidisciplinary user acceptability study of hyperspectral data compressed using an on‐board near lossless vector quantization algorithm

To deal with the extremely high data rate and huge data volume generated on‐board a hyperspectral satellite, the Canadian Space Agency (CSA) has developed two fast on‐board data compression techniques for hyperspectral imagery. The CSA is planning to place a data compressor on‐board a proposed Canadian hyperspectral satellite using these techniques to reduce the requirement for on‐board storage and provide a better match to available downlink capacity. Since the compression techniques are lossy, it is essential to assess the usability of the compressed data and the impact on remote sensing applications. In this paper, 11 hyperspectral data users covering a wide range of application areas and a variety of hyperspectral sensors assessed the usability of the compressed data using their well understood datasets and predefined evaluation criteria. Double blind testing was adopted to eliminate bias in the evaluation. Four users had ground truth available. They qualitatively and quantitatively compared the products derived from the compressed data to the ground truth at compression ratios from 10?:?1 to 50?:?1 to examine whether the compressed data provided the same amount of information as the original for their applications. They accepted all the compressed data. The users who did not have ground truths available evaluated the compression impact by comparing the products derived from the compressed data with those derived from the original data. They accepted most of the compressed data.     

Iteration complexity of an interior-point algorithm for nonlinear p∗-complementarity problems

This paper provides an analysis of the iterative complexity of a predictor-corrector type interior-point algorithm for a class of non-monotone nonlinear complementarity problems, i.e., the nonlinear P?-complementarity problems, which is quite general because it includes as a special case the monotone complementarity problem. At each corrector step, one has to compute an approximate solution of a nonlinear system such that a certain accuracy requirement is satisfied. The proof of the iterative complexity of the proposed algorithm requires that the mapping associated the problem satisfies a scaled Lipschitz condition.     

Experimental application and enhancement of the XFEM–GA algorithm for the detection of flaws in structures

The extended finite element formulation (XFEM) combined with genetic algorithms (GAs) have previously been shown to be very effective in the detection of flaws in structures. By this approach, the XFEM is used to model the forward problem and a GA is used as the optimization scheme, converging to the true flaw. The convergence is obtained by minimizing the error between sensor measurements and data obtained by solving the forward problem.The current study proposes several advances of this XFEM–GA algorithm, more specifically: (i) a novel genetic algorithm that accelerates the convergence of the scheme and alleviates entrapment in local optima, (ii) a generic XFEM formulation of an elliptical hole which is utilized to detect any type of flaw (cracks or holes) of any shape, and (iii) experimental verification of the approach for an arbitrary crack in a 2D plate.Convergence studies on various benchmark problems including the experimental verification clearly show the potential of this approach to detection of arbitrary flaws.     

Modification of a two-dimensional fast Fourier transform algorithm with an analog of the Cooley–Tukey algorithm for image processing

Two-dimensional fast Fourier transform (FFT) for image processing and filtering is widely used in modern digital image processing systems. This paper concerns the possibility of using a modification of two-dimensional FFT with an analog of the Cooley–Tukey algorithm, which requires a smaller number of complex addition and multiplication operations than the standard method of calculation by rows and columns.     

Performance analysis of an integrated piezoelectric ZnO sensor for detection of head–disk contact

Integrated capability for detection of head–disk contact is desired for magnetic sliders with near-contact flying height. At the same time, fabrication of added features should be compatible with the existing slider micromachining process which is highly specialized and cost sensitive. Aimed at meeting the two requirements, a novel sensor configuration is explored in the present study. The new sensor configuration consists of a piezoelectric zinc oxide (ZnO) thin-film sensor sandwiched in the magnetic slider on its trailing side. Coupled structural and piezoelectric finite-element analysis for a sensor–slider–suspension assembly was performed to investigate the dynamic sensing performance. Output voltages on the millivolt level were obtained under typical head–disk interactions. The second in-plane bending mode of the slider was found to be the major contributor to the output voltage. Parametric study further showed that a thicker ZnO layer generally generated a larger output, while the thickness of the slider overcoat had only minimal effect. Simulation results from harmonic and transient analyses demonstrated that the piezoelectric thin-film ZnO sensor was sufficiently sensitive for detection of head–disk contact.     

EDXY – A low cost congestion-aware routing algorithm for network-on-chips

In this paper, an adaptive routing algorithm for two-dimensional mesh network-on-chips (NoCs) is presented. The algorithm, which is based on Dynamic XY (DyXY), is called Enhanced Dynamic XY (EDXY). It is congestion-aware and more link failure tolerant compared to the DyXY algorithm. On contrary to the DyXY algorithm, it can avoid the congestion when routing from the current switch to the destination whose X position (Y position) is exactly one unit apart from the switch X position (Y position). This is achieved by adding two congestion wires (one in each direction) between each two cores which indicate the existence of congestion in a row (column). The same wires may be used to alarm a link failure in a row (column). These signals enable the routing algorithm to avoid these paths when there are other paths between the source and destination pair. To assess the latency of the proposed algorithm, uniform, transpose, hotspot, and realistic traffic profiles for packet injection are used. The simulation results reveal that EDXY can achieve lower latency compared to those of other adaptive routing algorithms across all workloads examined, with a 20% average and 30% maximum latency reduction on SPLASH-2 benchmarks running on a 49-core CMP. The area of the technique is about the same as those of the other routing algorithms.     

Comments on the applicability of “An improved weighted recursive PCA algorithm for adaptive fault detection”

This paper discusses some limitations of the weighted recursive PCA algorithm (WARP) proposed by Portnoy, Melendez, Pinzon, and Sanjuan (2016) which is used for fault detection (FD) by arguing that it can reduce false alarms. The motivation of these comments is the lack of a clear criterion in the WARP algorithm to distinguish between process deviations and faults' scenarios, and as a consequence, the applicability of this algorithm is questionable from the FD point of view. Moreover, we address the absence of a formal justification why the computational complexity achieved by using the WARP algorithm is reduced in comparison with methods discussed in the paper.     

A novel algorithm of cloud detection for water quality studies using 250 m downscaled MODIS imagery

ABSTRACT

This study is part of a project aimed at developing an automated algorithm for algal bloom detection and quantification in inland water bodies using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. An important step is to adequately detect and exclude clouds and haze because their presence affects chlorophyll-a (chl-a) estimations. Currently available cloud masking products appear to be ineffective in turbid coastal waters. The purpose of this study is to develop a cloud masking algorithm based on a probabilistic algorithm (Linear Discriminant Analysis) and designed for water bodies by using MODIS images downscaled at a 250 m spatial resolution (MODIS-D-250). Confusion matrix shows that the new cloud mask algorithm yields very satisfactory results, enabling water classification for heavy turbid conditions with a mean kappa coefficient (κ) of 0.993 and a 95% confidence interval ranging from 0.990 to 0.997. The model also shows a very low commission error (sensitive to the presence of haze), which is essential for accurate water quality monitoring, knowing that the presence of clouds/haze/aerosols leads to major issues in the estimation of water quality parameters. The cloud mask model applied on MODIS-D-250 images improves the sensitivity to haze and the classification of turbid waters located at the edge of urban areas better than the operational MODIS products, and it clearly shows an improvement of the spatial resolution (250 m spatial resolution) compared to other cloud mask algorithms (500 m or 1 km spatial resolution), leading to an increase in exploitable data for water quality studies.     

Analogue algorithm for parallel factorization of an exponential number of large integers: II—optical implementation

Quantum Information Processing - We report a detailed analysis of the optical realization of the analogue algorithm described in the first paper of this series (Tamma in Quantum Inf Process...     

New Index for Quantifying an Individual＇s Scientific Research Output

Classifying researchers＇ work according to the quality of their publications rather than the quantity of their publications is an important issue. To this end we introduce a new measure, the ＂percentage range＂ or A-index, which provides a qualitative evaluation of a researchers＇ productivity. The percentage range depends to a great extent on the number of single-author published papers and their citations. It is to be a new index to be considered along with the h-index. The combined factors have the advantage of making clearer the innovation of the individual authors. The resultant percentage range gives a reduced impact on its numerical value for authors who gain citations by adding their names on multi-author papers. It is shown that various dimensions of ethical integrity and originality are clarified by the new index. The important scenarios arising from this analysis are demonstrated with examples, The great differences between the new percentage range and old h-index come from the percentage range＇s emphasis on considering the whole work of an author, including the significance of the author＇s single-author papers as opposed to multiple-author contributions. This emphasis is demonstrated.     

SAR-based detection of flooded vegetation – a review of characteristics and approaches

The ability of synthetic aperture radar (SAR) to detect flooded vegetation (FV) (the temporary or permanent occurrence of waterbodies underneath vegetated areas) offers a great benefit in the research fields of flood and wetland monitoring. The growing demand for near real-time information in flood monitoring and an increased awareness of the importance of wetland ecosystems are strong drivers for the ongoing research in these fields, where FV constitutes an essential part. This study reviewed 128 publications summarizing the knowledge about the relationships between the SAR parameters and the environmental conditions for the detection of FV. An advanced review of 83 studies was carried out to gain insights about applied classification techniques and SAR data for the extraction of FV. Although some trends emerged about which wavelengths, polarisations, or incidence angles to use, there is variation in the application of different classification techniques or using SAR-derived information depending on the data sets and the study area. Notable throughout the analysed articles is the growing demand for unsupervised and computationally efficient methods of higher accuracy for the extraction of FV. Based on the advances in SAR with regard to spatial and temporal resolution, the development of robust approaches for the extraction of FV from various and complex environments has to be further pursued.