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.     

Electricity Generation and Wastewater Treatment of Oil Refinery in Microbial Fuel Cells Using Pseudomonas putida

Microbial fuel cells (MFCs) represent a novel platform for treating wastewater and at the same time generating electricity. Using Pseudomonas putida (BCRC 1059), a wild-type bacterium, we demonstrated that the refinery wastewater could be treated and also generate electric current in an air-cathode chamber over four-batch cycles for 63 cumulative days. Our study indicated that the oil refinery wastewater containing 2213 mg/L (ppm) chemical oxygen demand (COD) could be used as a substrate for electricity generation in the reactor of the MFC. A maximum voltage of 355 mV was obtained with the highest power density of 0.005 mW/cm² in the third cycle with a maximum current density of 0.015 mA/cm² in regard to the external resistor of 1000 Ω. A maximum coulombic efficiency of 6 × 10⁻²% was obtained in the fourth cycle. The removal efficiency of the COD reached 30% as a function of time. Electron transfer mechanism was studied using cyclic voltammetry, which indicated the presence of a soluble electron shuttle in the reactor. Our study demonstrated that oil refinery wastewater could be used as a substrate for electricity generation.     

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.     

Fabrication of electrically conductive membrane electrode of gelatin-tin (IV) phosphate nanocomposite for the detection of cobalt (II) ions

Gelatin-tin (IV) phosphate nanocomposite (GT/TPNC) ion exchanger was synthesized by mixing gelatin gel into the precipitates of tin (IV) phosphate using sol–gel method. GT/TPNC was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The ion exchange capacity of GT/TPNC was reported to be1.44?meq/g. The material was found monofunctional as indicated from pH titration curves. The distribution coefficient of different metal ions such as Zn²⁺ (42.10), Cd²⁺ (37.93), Mg²⁺ (33.33), Cu²⁺ (33.21), Al³⁺ (14.28), Pb²⁺ (6.06), Ni²⁺ (12.50) and Co²⁺ (50.0) was studied using GT/TPNC ion exchanger. The distribution studies confirmed the selectivity of GT/TPNC for Co (II). The photocatalytical degradation of MB was found to be 78% within 5 h of solar illumination using GT/TPNC. Some binary separations such as Co²⁺–Pb²⁺, Cd²⁺–Ni²⁺, Co²⁺–Mg²⁺, Mg²⁺–Zn²⁺, Pb²⁺–Zn²⁺, Cu²⁺–Al³⁺, Al³⁺–Cd²⁺, Ni²⁺–Cu²⁺ were attempted using GT/TPNC ion exchanger. GT/TPNC was explored for the fabrication of ion-sensitive membrane electrode for the determination of Co (II) in the water system. The membrane electrode was found mechanically more stable with quick response time (30?s) and a wide pH working range (4.0–7.0).     

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.     

Adaptive recurrent nonnegative matrix factorization with phase compensation for Single-Channel speech enhancement

The speech signals are affected by the background noise distortion that is unfavorable to both the intelligibility as well as the speech quality. Most of the speech processing algorithms function with the spectral magnitude without consideration of the spectral phase by leaving them unexplored and unstructured. The proposed single channel speech enhancement model called the Adaptive Recurrent Nonnegative Matrix Factorization (AR-NMF) is designed based on the phase compensation strategy with deep learning. The two major phases considered here are the training phase and the testing phase. During the process of training, the noisy speech signal is decomposed by the Hurst exponent-based Empirical Mode Decomposition (HEMD) and is converted into the frequency domain using Short Time Fourier Transform. Further, the new AR-NMF is used for denoising, where the tuning factor is optimally generated by the optimized RNN. Here, the hidden neurons are optimized using the proposed Adaptive Attack Power-based Sail Fish Optimization (AAP-SFO) with consideration of minimizing the Mean Absolute Error between the actual value and the predicted value. Finally, this phase compensated speech signal is given to the ISTFT that results in the final denoised clean speech signal. From the analysis, the CSED of AAP-SFO-AR-NMF for the street noise is 58.24%, 57.34%, 56.72%, and 77.37% more than RNMF, esHRNR, esTSNR, and Vuvuzela respectively. The performance of the proposed deep enhancement method is extensively evaluated and compared to diverse adverse noisy environments that describe the superiority of the proposed method.

     

An accurate recognition of facial expression by extended wavelet deep convolutional neural network

Facial expressions are essential in community based interactions and in the analysis of emotions behaviour. The automatic identification of face is a motivating topic for the researchers because of its numerous applications like health care, video conferencing, cognitive science etc. In the computer vision with the facial images, the automatic detection of facial expression is a very challenging issue to be resolved. An innovative methodology is introduced in the presented work for the recognition of facial expressions. The presented methodology is described in subsequent stages. At first, input image is taken from the facial expression database and pre-processed with high frequency emphasis (HFE) filtering and modified histogram equalization (MHE). After the process of image enhancement, Viola Jones (VJ) framework is utilized to detect the face in the images and also the face region is cropped by finding the face coordinates. Afterwards, different effective features such as shape information is extracted from enhanced histogram of gradient (EHOG feature), intensity variation is extracted with mean, standard deviation and skewness, facial movement variation is extracted with facial action coding (FAC),texture is extracted using weighted patch based local binary pattern (WLBP) and spatial information is extracted byentropy based Spatial feature. Subsequently, dimensionality of the features are reduced by attaining the most relevant features using Residual Network (ResNet). Finally, extended wavelet deep convolutional neural network (EWDCNN) classifier uses the extracted features and accurately detects the face expressions as sad, happy, anger, fear disgust, surprise and neutral classes. The implementation platform used in the work is PYTHON. The presented technique is tested with the three datasets such as JAFFE, CK+ and Oulu-CASIA.

     

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.