A multi-biometric iris recognition system based on a deep learning approach

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris-V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.     

Data fusion of multi-source imagery based on linear features registration

Image fusion is the process of combining images captured by different sensors under different conditions. These images usually have different geometric and radiometric properties. The enormous increase in the volume of remotely sensed data has created the need for robust data processing techniques that can fuse data observed by different acquisition systems. This need is motivated by the fact that collected data by these sensors are complementary in nature. Therefore, simultaneous utilization of the collected data would guarantee a full understanding of the object/phenomenon under consideration. In this regard, a data-fusion procedure can be defined as being concerned with the problem of how to combine data and/or information from multiple sensors in order to achieve improved accuracies and better inference about the environment than could be attained through the use of a single sensor. Fusion of multi-source imagery captured under different conditions is a challenging problem. The difficulty is attributed to the varying radiometric and geometric resolutions of the acquired imagery. Image registration is considered as one of the most critical requirement for accurate data fusion. The most appropriate primitives, transformation function and similarity measure have been incorporated in a matching strategy to solve the registration problem. Experimental results using real data proved the feasibility and the robustness of the suggested method.     

Impact of the Syrian conflict on irrigated agriculture in the Orontes Basin

The impact of conflict on irrigated agriculture and consequently summer crop production within conflict-affected agricultural lands was observed in the Orontes Basin. Water and energy use were reconfigured through a transition from rainfed to irrigated agricultural production over the past 20 years, but have been disrupted as the Syrian war has unfolded since 2011. Remotely sensed vegetation indices were used to determine irrigated summer crop yields during the year 2013. Findings suggest that irrigated agricultural production dropped between 15% and 30% in the Syrian portion of the basin in 2000–2013, with hotspots identifiable in Idleb, Homs, Hama, Daraa and Aleppo. The developed approach demonstrated effectiveness in quantifying and geolocating hotspots where conflicts have the strongest impact on agricultural water use, agricultural production, and eventually support relief and regional agricultural reconstruction in this and other conflict regions.     

Electrical–structural characterisation of smart carbon-based textile reinforced concrete beams by integrative gauge factors

The goal of this study is to characterise the piezoresistive capabilities of self-sensory carbon roving reinforcements by means of integrative gauge factors (GFs). The correlation between the measured integrative electrical resistance of the carbon rovings and the distributed strain is experimentally investigated by two different textile reinforced concrete beams under monotonic flexural loading. Because the microstructural mechanism of the rovings within the concrete matrix affects the electrical resistance, the GF is a function of the structural health. Two approaches to explore the GF are suggested: the first is by considering the design and damaged states separately and accordingly defining a constant GF for each state, and the second is by considering the entire structural response which leads to a continuous non-linear correlation. The potential of the two representations of GFs is presented by investigating the two beams. It is demonstrated that similar ranges of GFs are obtained for both beams, which further demonstrates the potential of using the proposed methodology for quantitative monitoring purposes.     

Convergence analysis of the Q1-finite element method for elliptic problems with non-boundary-fitted meshes

The aim of this paper is to derive a priori error estimates when the mesh does not fit the original domain's boundary. This problematic of the last century (e.g. the finite difference methodology) returns to topical studies with the huge development of domain embedding, fictitious domain or Cartesian-grid methods. These methods use regular structured meshes (most often Cartesian) for non-aligned domains. Although non-boundary-fitted approaches become more and more applied, very few studies are devoted to theoretical error estimates. In this paper, the convergence of a Q₁-non-conforming finite element method is analyzed for second-order elliptic problems with Dirichlet, Robin or Neumann boundary conditions. The finite element method uses standard Q₁-rectangular finite elements. As the finite element approximate space is not contained in the original solution space, this method is referred to as non-conforming. A stair-step boundary defined from the Cartesian mesh approximates the original domain's boundary. The convergence analysis of the finite element method for such a kind of non-boundary-fitted stair-stepped approximation is not treated in the literature. The study of Dirichlet problems is based on similar techniques as those classically used with boundary-fitted linear triangular finite elements. The estimates obtained for Robin problems are novel and use some more technical arguments. The rate of convergence is proved to be in 𝒪(h^1/2) for the H¹-norm for all general boundary conditions, and classical duality arguments allow one to obtain an 𝒪(h) error estimate in the L²-norm for Dirichlet problems. Numerical results obtained with fictitious domain techniques, which impose original boundary conditions on a non-boundary-fitted approximate immersed interface, are presented. These results confirm the theoretical rates of convergence. Copyright © 2008 John Wiley & Sons, Ltd.     

Uptake of NPK and yield of rainfed groundnut (Arachis hypogaea L.)

Experiments were conducted on sandy loam soils of Tirupati campus of Andhra Pradesh Agricultural University for two rainy seaons of 1980 and 1981 to study the effect of split application of NPK fertilizers on Spanish bunch groundnut. The fertilizer doses were 40 N, 20 P and 40 K kg ha^–1 in 1980 and 30 N, 10 P and 25 K kg ha^–1 in 1981.In 1980, uptake of N (48 kg ha^–1), P (7 kg ha^–1) and K (37 kg ha^–1) was maximum with the application of 10 N, 5 P and entire 40 K kg ha^–1 as basal and 30 N and 15 P kg ha^–1 at 30 days after sowing, leading to highest pod yield (0.76 t ha^–1). In 1981, application of 20 N, 10 P and 25 K kg ha^–1 as basal dose and 20 N kg ha^–1 at 30 days after seeding resulted in highest uptake of N (114 kg ha^–1), P (17 kg ha^–1) and K (58 kg ha^–1) and hence the pod yield (2.36 t ha^–1).Differences in the uptake of NPK and pod yield in 1980 and 1981 was due to variation in total rainfall and its distribution during the crop period. Rainfall was equally distributed throughout the crop period in 1981, whereas there were two prolonged dry spells of more than 40 days in 1980.     

Influence of substitutional doping on the electronic properties of carbon nanotubes with Stone Wales defects: density functional calculations

Abstract

In this work, we implemented density function theory to investigate the structural and the electronic properties of nitrogen doped single walled carbon nanotube under different orientations of Stone Wales defect. We have found that, the doped defected structures are more stable than the non-doped defected structures. Furthermore, doping defected carbon nanotubes with a nitrogen atom has significantly narrowed the band gap and slightly shifted the Fermi level toward the conduction band. Moreover, nitrogen substitution creates new band levels just above the Fermi level which exemplifies an n-type doping. However, the induced band gap is indirect band gap compared to direct band gap as in pristine carbon nanotubes. Furthermore, the electronic and structural properties of nitrogen doped carbon nanotube with Stone Wales defects is crucially affected by the dopant site as well as the orientations of Stone Wales defects.     

Solvability and asymptotic behavior of generalized Riccatiequations arising in indefinite stochastic LQ controls

The optimal control problem in a finite time horizon with an indefinite quadratic cost function for a linear system subject to multiplicative noise on both the state and control can be solved via a constrained matrix differential Riccati equation. In this paper, we provide general necessary and sufficient conditions for the solvability of this generalized differential Riccati equation. Furthermore, its asymptotic behavior is investigated along with its connection to the generalized algebraic Riccati equation associated with the linear quadratic control problem in finite time horizon. Examples are presented to illustrate the results established     

Theoretical analysis of residence time distribution functions and strain distribution functions in plasticating screw extruders

The residence time distribution (RTD) function in a single screw plasticating extruder was theoretically calculated. The calculation is based on the solids conveying, melting, and melt conveying models in extruders. The screw channel is divided into small axial increments and the path of each exiting fluid particle is followed from hopper to die. In addition to the residence times the total shear deformation or strain imposed on the fluid particles was also calculated. This together with the RTD function has led to the definition and calculation of the strain distribution function (SDF). This function is proposed for quantitative characterization of the mixing performance of screw extruders as well as other laminar mixers. Some simple idealized batch and continuous laminar mixers are analyzed in terms of the SDF. Finally, the effect of extruder operating conditions and screw design on the RTD and SDF were investigated by computer simulations.