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Harmanjeet Singh

Researcher at Panjab University, Chandigarh

Publications -  6
Citations -  61

Harmanjeet Singh is an academic researcher from Panjab University, Chandigarh. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & Frequency offset. The author has an hindex of 3, co-authored 3 publications receiving 52 citations.

Papers
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Journal ArticleDOI

Design of a novel hybrid intercarrier interference mitigation technique through wavelet implication in an OFDM system

TL;DR: This paper proposes an approach that devises a new hybrid technique, which is a combination of Maximum Likelihood Estimation (MLE) and Self Cancellation (SC) techniques through wavelet implication, to enhance BER performance of the OFDM system.
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A novel energy-efficient ICI cancellation technique for bandwidth improvements through cyclic prefix reuse in an OFDM system

TL;DR: A new energy-efficient, bandwidth-effective technique is proposed to mitigate ICI through cyclic prefix (CP) reuse at the receiver end, which outperforms the legacy ICI cancellation schemes under consideration.
Proceedings ArticleDOI

A novel hybrid ICI cancellation technique for OFDM optimization

TL;DR: In this paper, a new proposed technique is presented to further improve OFDM performance with varying Doppler shift error, which has significant improvements in the error performance and comparative analysis suggest that proposed technique has outperformed legacy Self Cancellation and Maximum Likelihood ICI cancellation scheme with huge gain.
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Effects of irrigation water quality and NPK-fertigation levels on plant growth, yield and tuber size of potatoes in a sandy loam alluvial soil of semi-arid region of Indian Punjab

TL;DR: In this paper , a 4-year study was conducted to investigate the effects of four water qualities (canal water, desalinated water (DSW), saline ground water (GW) and mixed water (MW) (CW + GW) on potato yields.
Proceedings ArticleDOI

An Efficient Approach for Sentiment Analysis using Data Mining Algorithms

TL;DR: This research work design a feature selection technique with Bagged Random Forest classification to predict the sentiments of news articles to prove that the proposed technique is having better results than the previous one.