ABHILASH SINGH (@abhilash12iec002)
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Top repositories

1

Penetration-depth-evaluation-of-L-and-S-band-SAR-Signals

We study the functional relationship between the dielectric constant of soil-water mixture and penetration depth of microwave signals into the ground at different frequency (L&S) band and incidence angles. Penetration depth of microwave signals into the ground depends on the incidence angle and wavelength of radar pulses and also on the soil properties such as moisture content and textural composition. It has been observed that the longer wavelengths have higher penetration in the soil but the penetration capability decreases with increasing dielectric behaviour of the soil. Moisture content in the soil can significantly increase its dielectric constant. Various empirical models have been proposed that evaluate the dielectric behaviour of soil-water mixture as a function of moisture content and texture of the soil. In this analysis we have used two such empirical models, the Dobson model and the Hallikainen model, to calculate the penetration depth at L- and C-band in soil and compared their results. We found that both of these models give different penetration depth and show different sensitivity towards the soil composition. Hallikainen model is more sensitive to soil composition as compared to Dobson model. Finally, we explore the penetration depth at different incidence angle for the proposed L- and S-band sensor of upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission by using Hallikainen empirical model. We found that the soil penetration depth of SAR signals into the ground decreases with the increase in soil moisture content, incident angle and frequency. References [1] A. Singh, G. K. Meena, S. Kumar and K. Gaurav, "Evaluation of the Penetration Depth of L- and S-Band (NISAR mission) Microwave SAR Signals into Ground," 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India, 2019, pp. 1-1. doi: 10.23919/URSIAP-RASC.2019.8738217 keywords: {Synthetic aperture radar;Dielectrics;Moisture;Soil moisture;Sensors;Remote sensing}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8738217&isnumber=8738126 [2] Singh, A., Meena, G. K., Kumar, S., and Gaurav, K.: ANALYSIS OF THE EFFECT OF INCIDENCE ANGLE AND MOISTURE CONTENT ON THE PENETRATION DEPTH OF L- AND S-BAND SAR SIGNALS INTO THE GROUND SURFACE, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 197-202, https://doi.org/10.5194/isprs-annals-IV-5-197-2018, 2018. [3] ABHILASH SINGH (2019). Penetration depth evaluation at L-and S-band SAR signals (https://www.mathworks.com/matlabcentral/fileexchange/73040-penetration-depth-evaluation-at-l-and-s-band-sar-signals), MATLAB Central File Exchange. Retrieved October 19, 2019.
MATLAB
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Modelling-and-Analysis-of-Polarization-Noise-in-VCSEL

Previous researches have provided mathematical models of vertical cavity surface emitting LASER’s (VCSEL’s) that evaluated the effect of spontaneous noise on the Laser’s performance but completely neglected the effect of polarization noise. Also, the results presented were in terms of RIN variation with respect to frequency. In this paper, a model is proposed to analyse the effect of spontaneous and polarization noise on VCSEL’s output power. Spontaneous noise is included by augmenting feedback in photon rate equations. Polarization noise is incorporated in the dynamics with the help of self-stabilizing and gain recovering coefficients. Characteristics of VCSEL’s output power are studied under continuous wave (CW) and sinusoidal modulation. VCSEL dynamics are simulated for both the cases with and without noise, firstly without noise and then with the effect of noise. The output light’s power curves depict the effect of spontaneous and polarization noise in VCSEL. For high-frequency modulation, the outputs have fluctuations throughout the simulation time but noise amplifies these fluctuations, affecting the performance of LASER. For CW modulation, the output light shows exponential behaviour with respect to bias.
MATLAB
9
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3

Real-Time-Object-Detection-using-Deep-Learning.

The smartphone is used as a webcam device. We can use it by installing the IP Webcam app. Make sure that the Laptop and your smartphone must be connected to the same network using WiFi.
M
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Hybrid-models-for-ClimSim-dataset

This includes two hybrid regression algorithms that couple the nature-inspired algorithms Teaching-Learning-Based Optimization (TLBO) and Invasive Weed Optimization (IWO) with fuzzy theory. These algorithms can be applied to any regression task across various application domains, beyond just earth and environmental sciences.
MATLAB
3
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intrusion_detection

Intrusion Detection Using WSNs
MATLAB
2
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Principal-Component-Analysis-PCA-on-images-in-MATLAB-GUI-

Principal Component Analysis (PCA) on images in MATLAB (GUI)
MATLAB
1
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