654 research outputs found
Sort by
Blackout mitigation during space vehicle re-entry
No full textSpace vehicle re-entering the earth's atmosphere is surrounded by a layer of plasma, preventing radio communication from the vehicle, leading to a phenomena called ‘radio blackout’. In this study, the concept of guiding high power laser through plasma has been discussed for establishing communication with the space vehicle during re-entry. It is found that, with increase in relativistic electron mass the refractive index of the plasma medium increases, displaying properties similar to that of converging lens. It is also shown that the power of the laser, if maintained above a critical limit, will assist in focussing of waves. The self-focusing property of plasma is therefore explored for achieving this target and the theoretical proof has been provided
Methods of denoising of electroencephalogram signal: a review
No full textElectroencephalogram (EEG) is obtained as a result of electrical activity of neurons in the brain. These signals have very small amplitudes and hence are quite prone to contamination by different artefacts. The major types of artefacts that affect the EEG are baseline wandering, power line noise, eye movements, Electromyogram (EMG) disturbance, and Electrocardiogram (ECG) disturbance. The presence of artefacts makes the analysis of EEG difficult for clinical evaluation and information. To deal with these artefacts, numerous methods and techniques have been evolved by different researchers. These methods include regression, blind source separation, wavelet and empirical mode decomposition etc. This paper provides a review of these methods for denoising of EEG signal
Effect of Sampling Frequency on Acoustic Emission Onset Determination using Fractal Dimension
No full textAcoustic emission is the generation of transient stress waves by rapid release of energy from localized sources giving rise to a versatile non-destructive testing technique. Numerous researchers have tried to exploit the fractal nature of acoustic emission for analysis purposes. The analysis of acoustic emission signature typically consists of four stages namely damage detection, damage localization, damage characterization and health prediction. The determination of arrival of
first energy of particular phase at sensor, called onset determination, is an important prerequisite for
source localization and source mechanism analysis because the accuracy in determination of onset time directly translates to accuracy in source localization and source mechanism analysis. However, the effect of acquisition parameters on the analysis is not well understood. In this present work, the objective is to understand the effect of one of the important acquisition parameters, sampling frequency. Choice of low sampling frequency might lead to loss of signal information while a high
sampling frequency increases storage and computation, hence identifying the appropriate sampling frequency is a critical factor for onset determination. In the present work, the effect of sampling frequency on onset determination using fractal dimension has been assessed. Two open source datasets available at Acoustic emission portal set-up by Muravin have been employed for the
research work. The first dataset pertains to a concrete beam while the second data is generated from a carbon fiber woven fabric plate. The acoustic emission events have been generated by using pencil lead break tests. The sampling frequency has been varied synthetically from 0.8 to 2.0 MHz in the steps of 100 kHz. The onset is determined for each individual value of sampling acquisition, by detecting change of fractal dimension estimated using Higuchi’s method. It has been observed
by analyzing the onset values determined at different sampling frequencies that the onset does not always converge to the onset having high accuracy with increase of sampling frequency. This observation has been further verified by calculating Spearman Rank correlation values. Also, the results depict the dependence of technique on material in which emissions are generated. The
results may be useful for the design of instrumentation for acoustic emission as well as development of computational methods for analysis
Fiber Bragg Grating Based Technique for Sensing Current
No full textIn this article, investigative study for measuring electric current using temperature sensitivity of fiber Bragg grating (FBG) is discussed. An FBG is kept inside a nichrome coil, which was pre-calibrated with respect to current and temperature by applying known value of current and measuring corresponding temperature. Two types of setups were used for the experiment: with and without housing. We found that the results were quite consistent and stable with the housing setup. The proposed current sensor has shown good response, repeatability, and very low hysteresis
Amperometric sensing of urea using edge activated graphene nanoplatelets
No full textSensing of urea is the key component in the diagnosis of kidney related diseases and milk adulteration. Until now, the methods developed for urea sensing are not easy to perform, and very little attention has been paid to commercialization of such sensors. Herein, for the first time we report the low cost graphene nanoplatelets (GNPlts) based sensing platform for urea. Specifically edge functionalized GNPlts are used for keeping graphitic activity of graphene planes intact. We have successfully sensed variable ranges of urea concentrations from 0.1–0.8 mg ml−1. The amperometeric characterization showed a linear variation in current as a function of urea concentration. The developed platform has a rapid response time of 15 s with good sensitivity (33 μA (mg ml−1)−1) and specificity. This developed nanoplatform could be highly beneficial for the development of an ultrasensitive, disposable, routine use sensor for urea
Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification
No full textThe paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis
Green synthesis of multi-shaped silver nanoparticles: optical, morphological and antibacterial properties
No full textThe manuscript deals with the green synthesis of anisotropic silver nanoparticles (AgNPs). For synthesis, the maltose has been used as reducing and polyvinyl pyrrolidone (PVP) as capping agent and the reaction has been initiated using microwave heating. A strong SPR band at 427 nm and a tail around 590 nm in UV–Vis spectrum of AgNPs, and TEM imaging confirmed the synthesis of anisotropic nanoparticles (NPs). Microwave irradiation time, silver precursor concentration and capping agent concentration affected the particle size as well as particle size distribution. Antibacterial behaviour of anisotropic AgNPs was better than their spherical counterparts
Reduced Order Modeling & Controller Design for Mass Transfer in a Grain Storage System
No full textThis paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations (PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization, the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed. Numerical results are presented to show the validity of the reduced model and possible further extensions are identified
Investigations on postural stability and spatiotemporal parameters of human gait using developed wearable smart insole.
No full textMeasurement of spatiotemporal parameters of human gait is important for designing new, intelligent and efficient prosthetic and orthotic devices. The paper presents a novel application of smart insole for measuring force generated at various pressure points during dynamic gait on a human foot. Besides recording and analysing the spatiotemporal parameters during stance phase, the developed sensor is also used for development of active orthotic devices. Data from the sensors is analysed in LabVIEW software for detection of plantar force and temporal gait parameters. The smart instrumentation allows processing, display and storage of gait parameters and gait events in real time. Variations of pressure pattern reported by gait experiments can also be used in identifying an accidental fall. This information will be used as a feedback signal for controlling the motion of an indigenously developed gait assistive device, i.e. an active orthotic device. Pressure at the heel and great toe points is higher than the metatarsal heads during dynamic walk. It is higher at the heel and metatarsals points than the toe point during standing position
Determination of Chemical Properties of Desi Chickpea Flour (Besan) Using Near Infrared Spectroscopy and Chemometrics
No full textA method was developed to determine the protein, carbohydrate, fat and moisture content of desi chickpea flour (besan) using Near Infrared Spectrometer [NIRS] and multivariate regression namely, Principal Component Regression and Partial Least Square Regression Analysis . Spectra of the samples was collected in reflectance mode using lab built pre dispersive filter based NIRS in the wavelength range of 700-2500 nm. Reference analysis was collected using the Association of official Analytical Chemists (AOAC) methods. NIR spectral data and reference data was used to develop regression models using Partial Least Square Regression and Principal Component Regression. Prediction performance of the models
was compared on the basis of the coefficient of correlation [R2] and Root Mean Square Error [RMSE] for calibration and validation sets. The R2 c values for prediction of moisture, fat, protein and carbohydrate content from PLSR model were 0.9858, 0.9863, 0.9888, 0.9915 respectively. PCR model resulted in R2 c 0.9739 for protein, 0.9833 for carbohydrate,0.9795 for fat and 0.9655 for moisture content. PLSR and PCR models results were accurate enough for prediction of the parameters. This study showed that NIR can be used to determine the chemical parameters of food material