An opinion on imaging challenges in phenotyping field crops

Almost all the world’s food is grown in open fields, where plant phenotypes can be very different from those observed in greenhouses. Geneticists and agronomists studying food crops routinely detect, measure, and classify a wide variety of phenotypes in fields that contain many visually distinct types of a single crop. Augmenting humans in these tasks by automatically interpreting images raises some important and nontrivial challenges for research in computer vision. Nonetheless, the rewards for overcoming these obstacles could be exceptionally high for today’s 7 billion people, let alone the 9.6 billion projected by 2050 (United Nations Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision). To stimulate dialog between researchers in computer vision and those in genetics and agronomy, we offer our views on three computational challenges that are central to many phenotyping tasks. These are disambiguating one plant from another; assigning an individual plant’s organs to it; and identifying field phenotypes from those shown in archival images. We illustrate these challenges with annotated photographs of maize highlighting the regions of interest. We also describe some of the experimental, logistical, and photographic constraints on image collection and processing. While collecting the data sets needed for algorithmic experiments requires sustained collaboration and funding, the images we show and have posted should allow one to consider the problems, think of possible approaches, and decide on the next steps.     

Structural and Luminescence Studies on Barium Sodium Borosilicate Glasses Containing Uranium Oxides

Barium sodium borosilicate glasses containing different amounts of uranium oxides were prepared by conventional melt quench method and investigated for their structural aspects by ²⁹Si and ¹¹B MAS NMR technique combined with steady‐state luminescence and lifetime measurements. Based on MAS NMR studies, it is confirmed that uranium ions act as network modifier up to 15 wt% and beyond which a separate uranium containing phase is formed. From the luminescence studies, it is inferred that uranyl species is in a highly distorted environment. For more than 15 wt% uranium oxide incorporation, weaker U–O–U linkages are formed at the expense stronger U–O–Si/B linkages, as suggested by the excited state lifetime value of the uranyl species as well as red shift in emission peak maximum. For glass samples containing more than 25 wt% uranium oxides, crystalline barium uranium silicate gets phase separated from glass matrix as confirmed by XRD studies.     

Fourth-Order Runge–Kutta Schemes for Fluid Mechanics Applications

Multiple high-order time-integration schemes are used to solve stiff test problems related to the Navier–Stokes (NS) equations. The primary objective is to determine whether high-order schemes can displace currently used second-order schemes on stiff NS and Reynolds averaged NS (RANS) problems, for a meaningful portion of the work-precision spectrum. Implicit–Explicit (IMEX) schemes are used on separable problems that naturally partition into stiff and nonstiff components. Non-separable problems are solved with fully implicit schemes, oftentimes the implicit portion of an IMEX scheme. The convection–diffusion-reaction (CDR) equations allow a term by term stiff/nonstiff partition that is often well suited for IMEX methods. Major variables in CDR converge at near design-order rates with all formulations, including the fourth-order IMEX additive Runge–Kutta (ARK₂) schemes that are susceptible to order reduction. The semi-implicit backward differentiation formulae and IMEX ARK₂ schemes are of comparable efficiency. Laminar and turbulent aerodynamic applications require fully implicit schemes, as they are not profitably partitioned. All schemes achieve design-order convergence rates on the laminar problem. The fourth-order explicit singly diagonally implicit Runge–Kutta (ESDIRK4) scheme is more efficient than the popular second-order backward differentiation formulae (BDF2) method. The BDF2 and fourth-order modified extended backward differentiation formulae (MEBDF4) schemes are of comparable efficiency on the turbulent problem. High precision requirements slightly favor the MEBDF4 scheme (greater than three significant digits). Significant order reduction plagues the ESDIRK4 scheme in the turbulent case. The magnitude of the order reduction varies with Reynolds number. Poor performance of the high-order methods can partially be attributed to poor solver performance. Huge time steps allowed by high-order formulations challenge the capabilities of algebraic solver technology.     

Investigations of Ce3+ co-doped SbPO4:Tb3+ nano-ribbons and nanoparticles by vibrational and photoluminescence spectroscopy

Nano-ribbons and very small nanoparticles (size 2-5 nm) of SbPO4 doped with lanthanide ions (Ce3+ and Tb3+) are prepared at a relatively low temperature of 120 degrees C based on a solution method. Detailed vibrational and luminescence studies on these samples establish that these lanthanide ions are incorporated at Sb3+ site of the SbPO4 lattice. The excitation spectrum corresponding to the Tb3+ emission and the excited state lifetime of the 5D4 level of Tb3+ ions in the sample confirm the energy transfer from Ce3+ to Tb3+ ions in the SbPO4 host. The extent of energy transfer from Ce3+ to Tb3+ in these samples is found to be around 60%. Dispersion of these nanomaterials in silica matrix effectively shields the lanthanide ions at the surface of the nano-ribbons/nanoparticles from the stabilizing ligands resulting in the reduction in the vibronic quenching of the excited state. Our results show significant reduction in the surface contribution in the decay curve corresponding to the 5D4 level of the Tb3+ ions after incorporating the nano-ribbons/nanoparticles in silica. These nanomaterials incorporated in silica matrix can have potential applications in bio-assays and bio-imaging.     

Textural feature based face recognition for single training images

A novel face recognition algorithm using single training face image is proposed. The algorithm is based on textural features extracted using the 2D log Gabor wavelet. These features are encoded into a binary pattern to form a face template which is used for matching. Experimental results show that on the colour FERET database the accuracy of the proposed algorithm is higher than the local feature analysis (LFA) and correlation filter (CF) based face recognition algorithms even when the number of training images is reduced to one. In comparison with recent single training image based face recognition algorithms, the proposed 2D log Gabor wavelet based algorithm shows an improvement of more than 3% in accuracy.     

Biometric classifier update using online learning: A case study in near infrared face verification

The performance of a large scale biometric system may deteriorate over time as new individuals are continually enrolled. To maintain an acceptable level of performance, the classifier has to be re-trained offline in batch mode using both existing and new data. The process of re-training can be computationally expensive and time consuming. This paper presents a new biometric classifier update algorithm that incrementally re-trains the classifier using online learning and progressively establishes a decision hyperplane for improved classification. The proposed algorithm incorporates soft labels and granular computing in the formulation of a 2ν$\nu$-Online Granular Soft Support Vector Machine (SVM) to re-train the classifier using only the new data. Granular computing makes it adaptive to local and global variations in data distribution, while soft labels provide resilience to noise. Each time data is acquired, new support vectors that are linearly independent are added and existing support vectors that do not improve the classifier performance are removed. This constrains the size of the support vectors and significantly reduces the training time without compromising the classification accuracy. The efficacy of the proposed online learning strategy is validated in a near infrared face verification application involving different covariates. The results obtained on a heterogeneous near infrared face database of 328 subjects show that in all experiments using different feature extraction and classification algorithms the proposed online 2ν$\nu$-Granular Soft Support Vector Machine learning approach is 2–3 times faster while achieving a high level of accuracy similar to offline training using all data.     

Improved luminescence from Y2Sn2O7:Tb nanoparticles co-doped with Sb ions

Very small nanoparticles (size 3-5 nm) of Y₂Sn₂O₇, Y₂Sn₂O₇:Tb³⁺ and Sb³⁺ co-doped Y₂Sn₂O₇:Tb³⁺ were prepared at a relatively low temperature of 700 °C. Y₂Sn₂O₇ host is characterised by an emission around 436 nm, which is arising from the oxygen vacancies present in the lattice. Tb³⁺ emission improves significantly when Sb³⁺ ions are co-doped with Y₂Sn₂O₇:Tb³⁺ nanoparticles. Incorporation of Sb³⁺ ions at the Y³⁺ site of Y₂Sn₂O₇ lattice and associated lattice distortion around Tb³⁺/Y³⁺ ions brought about by the difference in the stable coordination number of Sb³⁺ and Y³⁺ ions are responsible for the improved Tb³⁺ emission from the co-doped samples.     

Corrosion of Borosilicate Glasses Subjected to Aggressive Test Conditions: Structural Investigations

Sodium borosilicate (NBS) and barium sodium borosilicate (BBS) glasses, used for immobilization of high‐level nuclear waste with compositions (SiO₂)_0.477(B₂O₃)_0.239(Na₂O)_0.170(TiO₂)_0.023(CaO)_0.068(Al₂O₃)_0.023 and (SiO₂)_0.482(B₂O₃)_0.244(Na₂O)_0.220(BaO)_0.054 were subjected leaching experiments under hydrothermal conditions in an autoclave at 200°C for different time durations. Morphological and structural transformations associated with leaching, have been monitored with techniques like XRD, SEM, solid‐state nuclear magnetic resonance. XRD and SEM along with NMR studies have confirmed that, upon leaching, formation of an aluminosilicate phase, Zeolite‐P (Na₆Al₆Si₁₀O₃₂·12H₂O), occurs with NBS glass. BBS glass upon subjecting to the same conditions leads to formation of multiple amorphous phases having Q⁴ (silica rich phase) and Q³ structural units of Silicon along with structurally modified residual glass. Upon leaching BO₃ structural units preferentially get released from BBS glass. Comparison of results with international simple glass confirmed that, for the latter, mass loss rates are one order of magnitude lower.