Purva Halarnkar (purvahalarnkar)

purvahalarnkar

Geek Repo

Company:Syracuse University

Location:Syracuse

Home Page:www.linkedin.com/in/purva-halarnkar-8009b6162/

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Purva Halarnkar's repositories

RADAR-Signal-Processing

Performed matched filter, windowing, and doppler processing on a radar data cube (9000 range bins x 256 pulses) to detect moving targets.

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Denoising-EMG-signals-via-TKEO

Inclusion of the Teager–Kaiser energy operator (TKEO) in signal conditioning increases the accuracy of popular electromyography (EMG) onset detection methods.

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Narrowband-Filtering

Used narrowband filtering to separate out the different sources of noise from different sources in the audio signal.

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Signal-strength-on-Urban-Street

Simulated the signal strength of a mobile receiver at various locations on a street between two buildings. Observed that the total path loss and signal strength is different at each locations.

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Welch-s-method

Welch's method (also called the periodogram method) for estimating power spectra is carried out by dividing the time signal into successive blocks, forming the periodogram for each block, and averaging.

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Avoid-Edge-Effects-with-Reflection

The contamination of a signal at the edges while passing through a zero phase-shift filter can be avoided by reflection.

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Buy-and-Sell-Signals

The closing prices of a financial data is passed through two SMA filters of different order to create crude "Buy" and "Sell" signals. The point where the output of two SMA filters intersect and the curve of first SMA filter is increasing, this is the “Sell” signal and if the curve is decreasing then it is “Buy” signal.

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SMA-on-Music-Files

As SMA filter is a simple low pass FIR filter, the high frequency content of the audio signal is removed. Higher the order of the filter, more high frequency components are removed.

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Cognitive-Radio-System

A cognitive radio system is a tranceiver which automatically detects available channels in a wireless spectrum and accordingly changes its transmissiona and reception parameters. This project is a basic simulation of a simple cognitive radio environment.

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Finite-word-length-effects

Q1) Designed a 6th order butterworth LPF with infinite precision and 24 bit precision. Q2) Designed a 2nd order recursive filter with infinite precision and 2's complement 4-bit precision

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Inverse-Filter-on-Audio

The output of inverse filter is corrupted by noise. The quality of audio has decreased. Noise amplification is a major problem for the inverse filter.

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Low-pass-filter

Applied low pass filter (firls) to a randomly generated signal containing brownian noise, white noise and a large spike.

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Moving-Average-

Calculated moving average of 5 random numbers

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Remove-Artifact

The goal is to find some pattern in the data channel that looks like the artifact and remove it from the data channel.

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Resource-allocation-in-Cognitive-Radio-

Resource allocation for underlay DSA Cognitive Radio networks using reinforcement learning (Q-Learning))

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