R
Raouf Hamzaoui
Researcher at De Montfort University
Publications - 120
Citations - 2072
Raouf Hamzaoui is an academic researcher from De Montfort University. The author has contributed to research in topics: Fractal compression & Decoding methods. The author has an hindex of 23, co-authored 111 publications receiving 1762 citations. Previous affiliations of Raouf Hamzaoui include Université libre de Bruxelles & University of Konstanz.
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Unequal Error Protection Using Fountain Codes With Applications to Video Communication
TL;DR: Simulations for the scalable video coding (SVC) extension of the H.264/AVC standard showed that the proposed method for unequal error protection with a Fountain code required a smaller transmission bit budget to achieve high-quality video.
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Optimized error protection of scalable image bit streams [advances in joint source-channel coding for images]
TL;DR: This article focuses on FEC for scalable image coders, and considers JSCC (joint source-channel coding) at the application layer only, which can be readily extended to transmit scalable compressed bit streams of video sequences and 3-D meshes.
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Feature learning for Human Activity Recognition using Convolutional Neural Networks: A case study for Inertial Measurement Unit and Audio data
Federico Cruciani,Anastasios Vafeiadis,Chris D. Nugent,Ian Cleland,Paul McCullagh,Konstantinos Votis,Dimitrios Giakoumis,Dimitrios Tzovaras,Liming Chen,Raouf Hamzaoui +9 more
TL;DR: A case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions evaluating its use with a large scale real-world dataset.
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Fast algorithm for rate-based optimal error protection of embedded codes
TL;DR: An algorithm is given that accelerates the computation of the optimal strategy of Chande and Farvardin by finding an explicit formula for the number of occurrences of the same channel code.
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Real-time error protection of embedded codes for packet erasure and fading channels
TL;DR: Experimental results for the two-dimensional and three-dimensional set partitioning in hierarchical trees coders showed that the proposed algorithms provide close to optimal average peak signal-to-noise ratio performance, and that their running time is significantly lower than that of all previously proposed solutions.