Enhanced Room Temperature Coefficient of Resistance and Magneto-resistance of Ag-added La0.7Ca0.3-xBaxMnO3 Composites

In this paper we report an enhanced temperature coefficient of resistance (TCR) close to room temperature in La0.7Ca0.3-xBaxMnO3 + Agy (x = 0.10, 0.15 and y = 0.0 to 0.40) (LCBMO+Ag) composite manganites. The observed enhancement of TCR is attributed to the grain growth and opening of new conducting channels in the composites. Ag addition has also been found to enhance intra-granular magneto-resistance. Inter-granular MR, however, is seen to decrease with Ag addition. The enhanced TCR and MR at / near room temperature open up the possibility of the use of such materials as infrared bolometric and magnetic field sensors respectively.Comment: 22 pages of Text + Figs:comments/suggestions([email protected]

Soil Invertebrates of \u3cem\u3eLasiurus sindicus\u3c/em\u3e Based Grazing Lands: Impact of Management and Grazing Intensity

Arid Western plains of India are dominated by pasture and grazing lands. Lasiurus sindicus (LS) is the dominant na-tive grass species growing on sandy plains and low dunes under the low rainfall extreme desert climate. Palatability and higher crude protein (8-14% in early vegetative growth, 4-6% in 80-120 days of growth) make this grass a highly preferred grazing species. Since drought is frequent (47%) in this part of the country the LS grasslands are under tremendous grazing pressures and classified under poor or very poor condition for livestock. It is imperative to re-store the natural resources on which this grassland depends. Soil invertebrates especially soil collembola and mites are an integral part of this grassland ecosystem. Their community structure changes in response to the changes in management and other factors, and may serve as a tool for rapid impact assessment of restoration measures. With this background, Lasiurus sindicus grazing lands in Jaisalmer District of Western Rajasthan of India were evaluated, to understand the impact of grazing intensity and management practices on the community structure of the soil invertebrates

Design of Optimal Hybrid Position/Force Controller for a Robot Manipulator Using Neural Networks

The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed into a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The optimal feedback control law is shown to be globally exponentially stable using Lyapunov function approach. The dynamic model uncertainties are compensated with a feedforward neural network. The neural network requires no preliminary offline training and is trained with online weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a 4-DOF robot manipulator to track an elliptical planar constrained surface while applying the desired force on the surface

Variability of young stellar objects in the star-forming region Pelican Nebula

We observed a field of $16'\times 16'$ in the star-forming region Pelican Nebula (IC 5070) at $BVRI$ wavelengths for 90 nights spread over one year in 2012-2013. More than 250 epochs in $VRI$-bands are used to identify and classify variables up to $V\sim 21$~mag. We present a catalogue of optical time-series photometry with periods, mean-magnitudes and classifications for 95 variable stars including 67 pre-main-sequence variables towards star-forming region IC 5070. The pre-main-sequence variables are further classified as candidate classical T Tauri and weak-line T Tauri stars based on their light curve variations and the locations on the color-color and color-magnitude diagrams using optical and infrared data together with Gaia DR2 astrometry. Classical T Tauri stars display variability amplitudes up to three times the maximum fluctuation in disk-free weak-line T Tauri stars, which show strong periodic variations. Short-term variability is missed in our photometry within single nights. Several classical T Tauri stars display long-lasting ($\geq 10$ days) single or multiple fading and brightening events up to a couple of magnitudes at optical wavelengths. The typical mass and age of the pre-main-sequence variables from the isochrone-fitting and spectral energy distributions are estimated to be $\le 1~M_\odot$ and $\sim 2$ Myr, respectively. We do not find any correlation between the optical amplitudes or periods with the physical parameters (mass and age) of pre-main-sequence stars.Comment: 17 pages, 14 figures, accepted for publication in Astronomy and Astrophysic

KNN-Based ML Model for the Symbol Prediction in TCM Trellis Coded Modulation TCM Decoder

Machine Learning is a booming technology today. In a machine learning set of training, data is to be provided to the model for training and that model predicts the output. Machine Learning models are trained using a computer program known as ML algorithms.The new machine learning-based Transition Metric Unit (TMU) of 4D- 8PSK Trellis coded Modulation TCM Decoder is presented in this work. The classic Viterbi decoder's branch metric unit, or TMU, takes on a complex structure. Trellis coded Modulation (TCM) is a combination of 8 PSK modulations and Error Correcting Code (ECC). TMU is one of the complex units of the TCM decoder, which is essentially a Viterbi decoder. Similar to how the first Branch metric is determined in the straightforward Viterbi decoder, the TCM decoder performs this BM computation via the TMU unit. The TMU becomes challenging and uses more dynamic power as a result of the enormous constraint length and the vast number of encoder states.In the proposed algorithm innovative KNN (K nearest neighbours) based ML model is developed. It is a supervised learning model in which input and output both are provided to the model, training data also called the labels, when a new set of data will come the model will give output based on its previous set experience and data.Here we are using this ML model for the symbol prediction at the receiver end of the TCM decoder based on the previous learning. Using the proposed innovation, the paper perceives the optimization of the TCM Decoder which will further reduce the H/W requirements and low latency which results in less power consumption

Growth of dense CNT on the multilayer graphene film by the microwave plasma enhanced chemical vapor deposition technique and their field emission properties

Catalyst assisted carbon nanotubes (CNTs) were grown on multilayer graphene (MLG) on copper and silicon substrates by the microwave plasma enhanced chemical vapor deposition technique. The transmission of the MLG was found to vary between 82 to 91.8% with the increase of deposition time. Scanning electron microscopy depicted that the MLG film survived at the deposition condition of CNTs with the appearance of the damaged structure due to the plasma. Growth of CNTs was controlled by adjusting the flow rates of methane gas. The density of carbon nanotubes was observed to increase with a higher supply of methane gas. It was observed that the field emission properties were improved with the increased density of CNTs on MLG. The lowest turn-on field was found to be 1.6 V mu m(-1) 1 accompanied with the highest current density of 2.8 mA cm(-2) for the CNTs with the highest density. The findings suggested that the field emission properties can be tuned by changing the density of CNTs

Electrical Properties of lead free ceremics Na1−XKxNbO3, at x=0.305.

By solid state reaction method, ceramic pellets of Na0.695K0.305NbO3 have been prepared. X-ray- diffraction, Piezo properties, scanning electron microscope, and temperature dependence of dielectric constant and loss tangent of the prepared samples have been studied. It has been observed that, at the transition temperature, dielectric constant peak shifts to lower temperature, and the dielectric constant and loss tangent peak heights decrease, with  increasing frequency, and show three structural phase transitions