Mankhush Singh

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Human pose recognition is an active topic of vision research that has applications in diverse fields such as collaborative virtual environments and robot teleoperation. We propose a novel method for the recognition of human pose using the Radon transform. A binary skeleton representation of the human subject is computed and information concerning the pose(More)
In this paper design and fabrication of microstrip line fed aperture coupled patch antenna is presented at 10 GHz using 0.762 mm and .508 mm thick (epsiv<sub>r</sub>=3.2) substrates. The maximum size of proposed antenna is 35.4 mm by 35.4 mm. The design is optimized by means of parameter-variation studies that have been performed using a 3D electromagnetic(More)
In this paper, design of a quarter wave transformer-fed circular patch antenna is presented. The maximum size of proposed antenna is 25.4 mm by 25.4 mm. The substrate material used for this antenna has thickness of substrate 0.762 mm and relative permittivity epsiv<sub>r </sub> 3.2. The design frequency of the antenna is 10 GHz. By selecting optimum(More)
In this paper, we present a novel coplanar waveguide (CPW) fed printed monopole antenna for ultra wideband communications. The antenna was fabricated on a 0.762 mm thick GML (relative permittivity of 3.2) substrate. The antenna structure was analyzed using a transmission line matrix (TLM) method based flomerics micro-stripes (version 7). The measured 10 dB(More)
In this Paper, the WiMAX Traffic Forecasting on Day basis is done. The traffic time series is decomposed with Stationary Wavelet Transform (SWT). Further these coefficients will be trained and predicted with the Trainable Cascade-Forward Backpropagation Neural Networks. The quality of forecasting obtained is shown in terms of the four parameters. Keywords—(More)
In this Paper, the WiMAX Traffic Forecasting on Week basis is done. The traffic time series is decomposed with Stationary Wavelet Transform (SWT). Further these coefficients will be trained and predicted with the Trainable Cascade-Forward Backpropagation Neural Networks. The quality of forecasting obtained is shown in terms of the four parameters.
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