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27 results on '"Harkat, Houda"'

2. Towards the Automation of Wildfire Monitoring with Aerial Vehicles: The FIREFRONT Project

Author

Ribeiro, Ricardo, Bernardino, Alexandre, Cruz, Gonçalo, Silva, Diogo, Felix, Luís, Caetano, João, Folgado, Duarte, Francisco, João, Simões, Nuno, Viegas, Carlos Xavier, Viegas, Domingos Xavier, Harkat, Houda, Nascimento, Jose, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rousseau, Jean-Jacques, editor, and Kapralos, Bill, editor

Published
2023
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4. Towards the Automation of Wildfire Monitoring with Aerial Vehicles: The FIREFRONT Project

Author

Ribeiro, Ricardo, primary, Bernardino, Alexandre, additional, Cruz, Gonçalo, additional, Silva, Diogo, additional, Felix, Luís, additional, Caetano, João, additional, Folgado, Duarte, additional, Francisco, João, additional, Simões, Nuno, additional, Viegas, Carlos Xavier, additional, Viegas, Domingos Xavier, additional, Harkat, Houda, additional, and Nascimento, Jose, additional

Published
2023
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20. Sign Language Gesture Recognition with Bispectrum Features using SVM.

Author

Thariq Ahmed, Hasmath Farhana, Ahmad, Hafisoh, Phang, Swee King, Vaithilingam, Chockalingam Aravind, Harkat, Houda, and Narasingamurthi, Kulasekharan

Subjects
SPEECH & gesture, SIGN language, SUPPORT vector machines, FEATURE extraction, GESTURE
Abstract

Wi-Fi based sensing system captures the signal reflections due to human gestures as Channel State Information (CSI) values in subcarrier level for accurately predicting the fine-grained gestures. The proposed work explores the Higher Order Statistical (HOS) method by deriving bispectrum features (BF) from raw signal by adopting a Conditional Informative Feature Extraction (CIFE) technique from information theory to form a subset of informative and best features. Support Vector Machine (SVM) classifier is adopted in the present work for classifying the gesture and to measure the prediction accuracy. The present work is validated on a secondary dataset, SignFi, having data collected from two different environments with varying number of users and sign gestures. SVM reports an overall accuracy of 83.8%, 94.1%, 74.9% and 75.6% in different environments/scenarios. [ABSTRACT FROM AUTHOR]

Published
2020
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27. 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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