Comparative Evaluation of Two Neural Network Based Techniques for the Classification of Microcalcifications in Digital Mammograms

This paper investigates two neural network based techniques for the classification of microcalcifications in digital mammograms. Both techniques extract suspicious areas containing microcalcifications from digital mammograms and classify them into two categories: whether they contain benign or malignant clusters. The centroids and radii provided by expert radiologists are being used to locate and extract suspicious areas. Two neural networks based on iterative and non-iterative training methods are used to classify them into benign or malignant. The proposed techniques have been implemented in C++ on the SP2 supercomputer. The database from the Department of Radiology at the University of Nijmegen has been used for the experiments. The comparative results are very interesting and promising. Some of them are included in this paper.     

Multiperson interaction recognition in images: A body keypoint based feature image analysis

Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.     

HSIC-based affinity measure for learning on graphs

Pattern Analysis and Applications - The performance of graph-based learning techniques largely relies on the edges defined between the vertices of the graph. These edges that represent the affinity...     

Policy-based Diffserv on Internet servers: the AIX approach

The Internet infrastructure must evolve from best-effort service to meet the needs of different customers and applications. With Internet traffic differentiation, service providers can support a range of offerings, such as loss or delay bounds and network bandwidth allocation, to meet different performance requirements. The differentiated services (Diffserv) architecture provides a scalable approach, in which network access (or edge) devices aggregate traffic flows onto provisioned pipes that traverse a streamlined network core. We have identified the key requirements for provisioning Diffserv functions on Internet servers. Based on these requirements, we have implemented, and deployed, a policy-based architecture on IBM's AIX operating system that provides Diffserv services to both QoS-aware and -unaware applications     

A neuro-fuzzy approach for prediction of longitudinal wave velocity

Adaptive neuro-fuzzy inference system (ANFIS) is rapidly gaining popularity in the area of geophysics and geomechanics. This paper discusses the importance of ANFIS to prediction of p-wave velocity and its advantages over other conventional methods of computing. This paper deals with the application of a ANFIS to predict longitudinal wave velocity. P-wave measurement, which is also an indicator of peak particle velocity during blasting in a mine, is an important parameter to be determined to minimize the damage caused by ground vibrations. A number of previous researchers have tried to use different empirical methods to predict p-wave. But these empirical methods have their limitations due to its less versatile application. The fracture propagation is not only influenced by the physico-mechanical parameters of rock but also on the dynamic wave velocity of rock (e.g., compressional wave velocity). It has wide application in the different field of geophysics. An ANFIS model is designed to predict the compressional wave velocity of different rocks. The fracture roughness coefficient and physico-mechanical properties are taken as input parameters and compressional wave velocity as output parameters. The error for the predicted values is found to be negligible (0.5%) and generalization capability of the neuro-fuzzy model is found to be very useful for such type of geophysical problems.     

Practical Equivalent Continuum Model for Simulation of Jointed Rock Mass Using FLAC3D

A simple practical equivalent continuum numerical model for simulating the behavior of jointed rock mass has been extended to three-dimensional using FLAC3D. This model estimates the properties of jointed rock mass from the properties of intact rock and a joint factor (Jf), which is the integration of the properties of joints to take care of the effects of frequency, orientation, and strength of joint. A new FISH function has been written in FLAC3D specifically for modeling jointed rocks using the Duncan and Chang hyperbolic model. This model has been validated first with simple element tests at different confining pressures for different rocks with different joint configurations. Explicit modeling of the joints has also been done in element tests and results obtained compare well with the results of equivalent continuum model and also with experimental results. Further, this has been applied for a case study of a large underground power house cavern in the Himalayas. The analysis has been done under various stages of excavation, assigning a null model available in FLAC3D for simulating the excavation.     

An efficient neural-network model for real-time fault detection in industrial machine

Neural Computing and Applications - Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical...     

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000-2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.     

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO₂) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.     

Reconnaissance mapping and management of salt-affected soils using satellite images

The Indo-Gangetic alluvial plain is subjected to large scale soil alkalization. In order to map and characterize salt-affected soils, with the aim of applying management techniques, Etah district in Uttar Pradesh, located between 26o^o45' to 28^o02' N and 78^o15' to 79^o20' E was selected. Multidate, high resolution, IRS-LISS II, geocoded FCC images on 1550 000 scale were used. Integrating visual image interpretation, physiographic analysis, ground data and laboratory analysis of soil samples, a legend for mapping salt-affected soils (SAS) was formulated. Based on variations in physicochemical properties: nature, intensity and depth wise distribution of salts, five categories of SAS requiring specific reclamation measures were identified. Soil categories S2, S3 and S4 have a gypsum requirement (GR) of 20, 12 and 4t/ha^-1 respectively. Reclamation of medium to heavy textured highly alkali soils requires the addition of amendments and a rice-wheat rotation for the initial 3-4 years. Under resource constraints, pit planting of Prosopis juliflora can bring about slow but effective reclamation. The soil category S5 is slightly alkali in the substratum, needing only biological reclamation by growing salt tolerant varieties of rice and wheat crops. Soils of category S1 are saline and need management by hydrological treatments. Incorporation of village boundaries on a map showing SAS would facilitate decision taking in planning reclamation projects and accelarate management operations directly at village level.