Image signal-to-noise ratio estimation using Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average model

We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.     

Collision Detection and Safe Reaction Algorithm for Non-backdrivable Manipulator with Single Force/Torque Sensor

The paper presents a robust control law for homing of an autonomous robot. The proposed work aims to solve this problem for practical conditions such as random errors in commanded velocities and unknown distance sensor characteristics. The proposed steering control aligns the robot’s orientation with homing vector using arbitrary real valued distance function providing the capability to work in changing environment conditions. Finite time convergence to the equilibrium using proposed control law is achieved in the presence of bounded random velocity errors regardless of the initial position and orientation. Just the sign information as feedback supports applicability of proposed control law with any distance function. A matching parameter between panoramic images obtained at home and current positions is a function of distance between home and current positions. However, explicit relation between distance and image matching parameter is unknown. This work demonstrates the application of proposed method for visual homing based on image distance function rendering the benefit of minimal image processing. Various simulation and experimental results are presented for visual homing to support the theory presented in this paper. Advantage of proposed visual homing is also explored in changing environment conditions.     

Detecting people in dense crowds

We propose a scheme to detect individuals in any image frame of a video sequence showing densely crowded scenes against cluttered backgrounds. The method uses only spatial information, and in an initial pass through the image a trained Viola–Jones-type local detector is used to locate individuals in the densely crowded scene. This yields a large number of false alarms. Hence, in a second step, we seek to reduce the false alarms, and propose two methods for this. In the first, color information from the initially detected windows is passed to a classifier to reduce the false alarms. This classifier consists of a cascade of boosted classifiers with Haar-like features as input and is trained with color information from local windows. In the second method, a weak perspective model of an uncalibrated camera is used to further reduce the false alarm rate while maintaining the detection rate. This is based on the size and locations of the detections in the image frame, without the use of any 3D world information. Results are presented in the form of receiver operating characteristic curves. For instance, at a 79.0% detection accuracy, the false alarm rate is 20.3%.     

Metabolomics of Genetically Modified Crops

Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade.     

Nonwoven Ni–NiO/carbon fibers for electrochemical water oxidation

The development of technologically efficient anodes for water oxidation is crucial to improve hydrogen production via water splitting. Electrodes based on metallic active sites dispersed in carbon matrices have been shown to be an attractive way to attain this goal. However, challenges remain to prevent catalyst agglomeration that otherwise can result in a decrease of performance over time.In this work, we report an alternative and efficient method to produce nickel-nickel oxide nanoparticles-embedded in carbon nanofibers (Ni–NiO/C), by the solution blow spinning (SBS) process. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analyses show the carbon nanofibrillar matrix as a robust support, with well-dispersed nickel nanoparticles on the surface. The responses of the linear scanning voltammetry, cyclic voltammetry and electrochemical impedance spectroscopy demonstrate how a small fraction of nickel on the fiber surface (≈1.2–5.3%) is enough to promote substantial improvement in performance (η = 278 and 309 mV vs RHE for 10 mA cm^?2) and a significant turnover frequency (TOF) values of 1.38 (η = 278) and 1.30 s^?1 (η = 309). These promising results are correlated with a large amount of Ni³⁺ present on the fiber surfaces, as identified by X-ray Photoelectron Spectroscopy (XPS). This work provides a low-cost and rapid preparation technique that can be extended for the manufacture of a wide variety of electrodes based on metals supported on carbon nanofibers.     

A simultaneous sample-and-filter strategy for robust multi-structure model fitting

In many robust model fitting methods, obtaining promising hypotheses is critical to the fitting process. However the sampling process unavoidably generates many irrelevant hypotheses, which can be an obstacle for accurate model fitting. In particular, the mode seeking based fitting methods are very sensitive to the proportion of good/bad hypotheses for fitting multi-structure data. To improve hypothesis generation for the mode seeking based fitting methods, we propose a novel sample-and-filter strategy to (1) identify and filter out bad hypotheses on-the-fly, and (2) use the remaining good hypotheses to guide the sampling to further expand the set of good hypotheses. The outcome is a small set of hypotheses with a high concentration of good hypotheses. Compared to other sampling methods, our method yields a significantly large proportion of good hypotheses, which greatly improves the accuracy of the mode seeking-based fitting methods.     

How much nitrogen is fixed by biological symbiosis in tropical dry forests? 2. Herbs

Although biological nitrogen fixation (BNF) is considered the main input of N in mature and regenerating native tropical vegetation, it has seldom been quantified. Biomass and N accumulation and fixation were determined for spontaneously occurring herbaceous species in caatinga areas in four regeneration stages (2, 17, 39 and >50?years after abandonment from agricultural use). BNF was estimated using the ¹⁵N natural-abundance method. The 2-year regeneration area had the highest total herb (6,355?kg?ha^?1) and legume (262?kg?ha^?1) biomass production, N stocks (82?kg?ha^?1) and fixed N (5.0?kg?ha^?1). N₂-fixing legumes (nine species in the sampled area) contributed over 97?% of legume biomass in all areas. Macroptilium gracile added the largest amount of N (3.9?kg?ha^?1 in the 2-year regeneration area) because of its large biomass production (205?kg?ha^?1), although it was not the species with the highest proportion of fixed N (76?%). All of the N₂-fixing species obtained large proportions of their N from symbiosis, most of them more than 50?%.However, the amounts of fixed N per unit area were relatively low (0.22?C5.00?kg?ha^?1) because the biomass of N₂-fixing species was always less than 5?% of the total herb biomass.     

Microstructure of Reaction Zone Formed During Diffusion Bonding of TiAl with Ni/Al Multilayer

In this article, the characterization of the interfacial structure of diffusion bonding a TiAl alloy is presented. The joining surfaces were modified by Ni/Al reactive multilayer deposition as an alternative approach to conventional diffusion bonding. TiAl substrates were coated with alternated Ni and Al nanolayers. The nanolayers were deposited by dc magnetron sputtering with 14 nm of period (bilayer thickness). Joining experiments were performed at 900 °C for 30 and 60 min with a pressure of 5 MPa. Cross sections of the joints were prepared for characterization of their interfaces by scanning electron microscopy (SEM), transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM), high resolution TEM (HRTEM), energy dispersive x-ray spectroscopy (EDS), and electron backscatter diffraction (EBSD). Several intermetallic compounds form at the interface, assuring the bonding of the TiAl. The interface can be divided into three distinct zones: zone 1 exhibits elongated nanograins, very small equiaxed grains are observed in zone 2, while zone 3 has larger equiaxed grains. EBSD analysis reveals that zone 1 corresponds to the intermetallic Al₂NiTi and AlNiTi, and zones 2 and 3 to NiAl.     

Relationship between solar radiation and surface distribution of vegetation in Fildes Peninsula and Ardley Island,Maritime Antarctica

The aim of this work is to produce a simplified vegetation map of ice-free areas of the Fildes Peninsula (FP) and Ardley Island (AI) thought object-oriented classification using a QuickBird satellite image and to evaluate the influence of the global solar radiation (GSR) over the vegetation distribution. The vegetation data were generated from multiresolution segmentation using the panchromatic and infrared layers, and for the classification we calculated the normalized vegetative difference index (NVDI) and the green NVDI. Two classes were created – Lichen and Moss Cushion SubFormation and Moss Subformation – with 48 vegetation samples collected on surveys during the austral summers of 2008 and 2009. We used a kappa index to evaluate the classification efficiency using 100 sampled points obtained in austral summer of 2013. The GSR was estimated, and in order to evaluate the effect of meteorological phenomena and cloudless, we measured the GSR using a net radiometer model CNR4 installed in FP between 2014 and 2016. The estimate of GSR was done for seasons of 2015, in order to estimate the light compensation point and the saturation point for the plant communities in FP and AI. The kappa index was 0.73 and the global accuracy was 0.78, showing consistency between the classification and ground truth. The area was covered by vegetation in FP was 16.7% and in AI is 59.1%. The vegetation cover is distributed differently at FP and AI, and our results suggest GSR plays an important role in vegetation distribution and these tendencies could be related to greater GSR demand by mosses when compared to lichens.