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Your search keyword '"Ahmed, Hasmath Farhana Thariq"' showing total 7 results
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7 results on '"Ahmed, Hasmath Farhana Thariq"'

5. Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture.

Author

Punithavathi, R., Rani, A. Delphin Carolina, Sughashini, K. R., Kurangi, Chinnarao, Nirmala, M., Ahmed, Hasmath Farhana Thariq, and Balamurugan, S. P.

Subjects
DEEP learning, COMPUTER vision, PRECISION farming, AGRICULTURAL productivity, SPRAYING & dusting in agriculture
Abstract

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures. [ABSTRACT FROM AUTHOR]

Published
2023
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7. A Survey on MIMO-OFDM Systems

Author

Harkat, Houda, Monteiro, Paulo, Gameiro, Atilio, Guiomar, Fernando, Ahmed, Hasmath Farhana Thariq, and CTS - Centro de Tecnologia e Sistemas

Abstract

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified. publishersversion published

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