Synthesis of transfer functions by nonsymmetrical lattice networks using lossy coils

A method for realising a v.t.f.by a nonsymmetrical lattice in which all coils can be made lossy is given.     

Microwave Studies on Strontium Ferrite Based Absorbers

Single layer microwave absorbers based on strontium ferrite-epoxy composites have been fabricated and their reflection loss characteristics studied in the X-band (8–12.4 GHz) of microwave frequencies. Permittivity (_r – j_r) and permeability (_r – j_r) of Co and Ti added strontium ferrite SrCo _x Ti _x Fe_{12 – 2x} O₁₉ (x = 0.1 to 0.9 in steps of 0.2), have been measured. Thickness of the absorber is an important criterion influencing the absorption characteristics. Composites of 3 mm thickness are found to absorb over a reasonable range of X-band frequencies. A minimum reflection loss of –36.5 dB is observed for the composite with x = 0.3.     

Modeling of the refractive index and extinction coefficient of binary composite films

Southwell's analysis of optical multilayers within the limits of very thin films has been extended to include absorption in the multilayer for predicting the effective values of the refractive index n(e) and extinction coefficient k(e) of mixed-composition binary homogeneous films over a wide spectral region, including the high-absorption (k > 10(-2)) region. It has been found that n(e) in general is a complicated function of the optical parameters (n(1), k(1), n(2), k(2)) and volume fractions (f(1), f(2)) of the component materials in a homogeneous layer, and the expression for n(e) becomes the same as that predicted by the Drude model in the spectral region where the layers are transparent. Moreover, according to the present analysis, the volume fractions of the product of the refractive index and the extinction coefficient of the component materials of a binary composite film are additive and the sum equals the product of the effective refractive index and extinction coefficient of the composite film.     

Probabilistic neural-network-based protection of power transformer

An optimal probabilistic neural network (PNN) as a core classifier for fault detection and status indication of a power transformer has been presented. In this scheme, various operating conditions of a transformer are distinguished using signatures of the differential currents. The proposed differential protection scheme is implemented through two different structures of PNN, that is, one having one output and the other having five outputs. The developed algorithm is found to be stable against external fault, magnetising inrush, sympathetic inrush and over-excitation conditions for which relay operation is not required. For the test data of fault, it is found to operate successfully. The performance of proposed PNN and classical artificial neural network (ANN) has been compared. For evaluation of the developed algorithm, relaying signals for various operating conditions of a transformer are obtained by modelling the transformer in PSCAD/EMTDC. The algorithms are implemented using MATLAB. The results show the capability of PNN in terms of classification accuracy and speed in comparison to classical ANNs.     

Characterization and photoluminescence studies on hydrothermally synthesized Mn-doped barium titanate nano powders

Manganese doped cubic barium titanate nanocrystals with [Mn/Ti] mole percent varying from 0.1 to 3 were prepared through hydrothermal route at 150 °C. The mean crystallite diameters obtained for different Mn concentrations varied within 26 to 30 nm. TEM of a typical sample showed most of the particles as single crystallites (particle size 15-40 nm) with some weakly agglomerated particles. The photo luminescent (PL) spectra of each sample showed a sharp peak in the blue band centered approximately at 464 nm and a weaker peak in the green band centered nearly at 494 nm. The luminescent efficiency passed through maxima for the sample with 2% [Mn/Ti] molar ratio. Effect of calcination temperature in the range of 200 to 600 °C on PL spectra showed that the intensity of peak corresponding to blue region decreased with the increase in calcination temperature from 200 to 600 °C and for the sample calcined at 600 °C, only a broad peak corresponding to green luminescence region was observed.     

Coverage Modeling and Reliability Analysis Using Multi-state Function

Fault tree analysis is an effective method for predicting the reliability of a system.It gives a pictorial representation and logical framework for analyzing the reliability.Also,it has been used for a long time as an effective method for the quantitative and qualitative analysis of the failure modes of critical systems.In this paper,we propose a new general coverage model (GCM) based on hardware independent faults.Using this model,an effective software tool can be constructed to detect,locate and recover fault from the faulty system.This model can be applied to identify the key component that can cause the failure of the system using failure mode effect analysis (FMEA).     

Active mode leakage reduction using fine-grained forward body biasing strategy

Leakage power minimization has become an important issue with technology scaling. Variable threshold voltage schemes have become popular for standby power reduction. In this work we look at another emerging aspect of this potent problem which is leakage power reduction in active mode of operation. In gate level circuits, a large number of gates are not switching in active mode at any given point in time but nevertheless are consuming leakage power. We propose a fine-grained forward body biasing (FBB) scheme for active mode leakage power reduction in gate level circuits without any delay penalty. Our results show that our optimal polynomial time FBB allocation algorithm results in 70.2% reduction in leakage currents. We also present an exact standard-cell placement driven FBB allocation algorithm that effectively reduces the area penalty using the post-placement area slack and results in 56.5%, 62.8% and 66.1% reduction in leakage currents for 0%, 4% and 8% area slack, respectively. Furthermore, we present a heuristic to solve the standard-cell placement driven FBB allocation problem that is computationally efficient and results in leakage within 2% of that from the exact formulation.     

Ascorbic acid-mediated synthesis and characterisation of iron oxide/gold core–shell nanoparticles.

The current article reports on providing surface modification of magnetic nanoparticles with gold to provide stability against aggregation. Gold-coated magnetite nanoparticles were synthesised to combine both magnetic as well as surface plasma resonance (SPR) properties in a single moiety. The nanocomposites were produced by reduction (using ascorbic acid) of gold chloride on to the surface of iron oxide nanoparticles. Ascorbic acid not only acts as a reducing agent, but also the oxidised form of ascorbic acid i.e. Dehydro-ascorbic acid acts as a capping agent to impart stability to as synthesised gold-coated iron oxide nanocomposites. The synthesised nanocomposite was monodispersed with a mean particle size of around 16 nm and polydispersity index of 0.190. X-ray diffraction analysis confirms presence of gold on the surface of magnetite nanoparticles. The synthesised nanocomposites had a total organic content of around 3.2% w/w and also showed a shifted SPR peak at 546 nm as compared to gold nanoparticles (528 nm). Both uncoated and gold-coated magnetite exhibited superparamagnetic behaviour at room temperature. Upon coating with gold shell, saturation magnetisation of iron oxide nanoparticles decreases from 42.806 to 3.54 emu/gram.     

A neuro-fuzzy approach for prediction of longitudinal wave velocity

Adaptive neuro-fuzzy inference system (ANFIS) is rapidly gaining popularity in the area of geophysics and geomechanics. This paper discusses the importance of ANFIS to prediction of p-wave velocity and its advantages over other conventional methods of computing. This paper deals with the application of a ANFIS to predict longitudinal wave velocity. P-wave measurement, which is also an indicator of peak particle velocity during blasting in a mine, is an important parameter to be determined to minimize the damage caused by ground vibrations. A number of previous researchers have tried to use different empirical methods to predict p-wave. But these empirical methods have their limitations due to its less versatile application. The fracture propagation is not only influenced by the physico-mechanical parameters of rock but also on the dynamic wave velocity of rock (e.g., compressional wave velocity). It has wide application in the different field of geophysics. An ANFIS model is designed to predict the compressional wave velocity of different rocks. The fracture roughness coefficient and physico-mechanical properties are taken as input parameters and compressional wave velocity as output parameters. The error for the predicted values is found to be negligible (0.5%) and generalization capability of the neuro-fuzzy model is found to be very useful for such type of geophysical problems.