Decidable subsets of open logic and an algorithm for R-calculus

Open logic is an attractive logic theory that can describe the growth and evolution of knowledge.However, related studies show that open logic is undecidable in first-order logic and thus is hard to be programmed. This paper proposes that open logic should be implemented with constraints, and provides a set of syntax constraints under which open logic is decidable. Furthermore, it is shown that the constraints are necessary and sufficient for the decidable formulas of open logic. A Domino problem-based algorithm R-CP that implements the R-calculus of the constrained open logic is presented and its reachability is proved.     

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.     

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.     

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.     

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.     

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.     

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Neural Computing and Applications - Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples...     

Implementation of enhanced canny recognition algorithm and non – natural neural system based speech fusion for sightless persons

Along with the rapid development of information technology, the sightless persons have numerous visual difficulties in doing their day by day activities. In this technological world, we have many resources for the human to live their life, still blind peoples suffers a lot for their survival in this hi-tech world. Through computing the solution can be obtained for their independent survival to the blind peoples. This paper implements a powerful speech fusion system with to support the sightless persons. This system produces the text image documents as voice.

     

A micro amperometric immunosensor for detection of human immunoglobulin

1 Introduction In recent years, electrochemical immunosensors have gained considerable interests as bioanalytical devices[1―3]. They have many attractive features, such as convenience for manipulate, being easy to achieve high sensitivity and excellent d…     

Fully coupled algorithm for heat and water transport – Estimation of non-linear parameters based on the experimental data

We solve the problem of coupled heat conduction and water transport in bentonite with water present in vapour and adsorbed form in a non-equilibrium state. The problem is governed by a system of two parabolic PDE and one ODE. Most of the coefficients are non-linear functions, defined either by underlying physical phenomena or empirically. We present a numerical scheme using FEM with linear base functions, implicit time discretization, and simple iterations for the non-linear terms. The model is verified against experiments (one 1D and one 3D) and we demonstrate the use of optimization algorithm for parameter calibration. Some of the parameters could be estimated successfully while others with limited confidence, which is explained by the particular character of the non-linear parameter dependence and resulting small sensitivity of the model on some parameters.     

Computing the Duquenne–Guigues basis: an algorithm for choosing the order

This paper presents an algorithm for choosing the order in which pseudo-intents are enumerated when computing the Duquenne–Guigues basis of a formal context. Sets are constructed through the use of a spanning tree to ensure they are all found once. The time and space complexities of the algorithm are empirically evaluated using, respectively, the number of logical closures and the number of sets in memory as measures. It is found that only the space complexity depends on the enumeration order.     

A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs

Face detection is a key component in applications such as security surveillance and human–computer interaction systems, and real-time recognition is essential in many scenarios. The Viola–Jones algorithm is an attractive means of meeting the real time requirement, and has been widely implemented on custom hardware, FPGAs and GPUs. We demonstrate a GPU implementation that achieves competitive performance, but with low development costs. Our solution treats the irregularity inherent to the algorithm using a novel dynamic warp scheduling approach that eliminates thread divergence. This new scheme also employs a thread pool mechanism, which significantly alleviates the cost of creating, switching, and terminating threads. Compared to static thread scheduling, our dynamic warp scheduling approach reduces the execution time by a factor of 3. To maximize detection throughput, we also run on multiple GPUs, realizing 95.6 FPS on 5 Fermi GPUs.