Raouf Hamzaoui

Professor Raouf Hamzaoui

Job: Professor in Media Technology

Faculty: Computing, Engineering and Media

School/department: School of Engineering and Sustainable Development

Research group(s): Institute of Engineering Sciences

Address: 黑料网, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 207 8096

E: rhamzaoui@dmu.ac.uk

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Personal profile

Raouf Hamzaoui received the MSc degree in mathematics from the University of Montreal, Canada, in 1993, the Dr.rer.nat. degree from the University of Freiburg, Germany, in 1997 and the Habilitation degree in computer science from the University of Konstanz, Germany, in 2004. He was an Assistant Professor with the Department of Computer Science of the University of Leipzig, Germany and with the Department of Computer and Information Science of the University of Konstanz. In September 2006, he joined 黑料网 where he is a Professor in Media Technology and Head of the Signal Processing and Communications Systems Group in the Institute of Engineering Sciences. Raouf Hamzaoui is an IEEE Senior member. He was a member of the Editorial Board of the IEEE Transactions on Multimedia and IEEE Transactions on Circuits and Systems for Video Technology. He has published more than 120 research papers in books, journals, and conferences. His research has been funded by the EU, DFG, Royal Society, Chinese Academy of Sciences, China Ministry of Science and Technology, and industry and received best paper awards (ICME 2002, PV’07, CONTENT 2010, MESM’2012, UIC-2019, CCF Transactions on Pervasive Computing and Interaction 2020).

Research group affiliations

Institute of Engineering Sciences (IES)

Signal Processing and Communications Systems (SPCS)

Publications and outputs

dc.title: Air Traffic Management and Communication over ATN/IPS for Future Datalink Communication dc.contributor.author: Aydo臒an, Emre; 脰zmen, Sergun; Cetek, Fulya Aybek; Arnaldo Vald茅s, Rosa Mar铆a; Delgado-Aguilera Jurado, Raquel; Carmona Fern谩ndez, 脕ngel Ernesto; Mart铆nez Miralles, Adri谩n; Vendruscolo, Tommaso; Bonelli, Stefano; Delahaye, Daniel; Chaimatanan, Supatcha; Chen, Feng; Hamzaoui, Raouf dc.description.abstract: The growing demand for air traffic presents challenges in air traffic management, making seamless gate-to-gate communication essential. Traditional radio frequency communication faces limitations such as weather dependency and frequency restrictions. To address these issues, data link communications have gained importance, using VHF channels, satellite systems, and ATN/IPS-based networks. This study introduces the ATMACA (Air Traffic Management and Communication Over ATN/IPS) protocol, an advanced context management framework for ATN/IPS, designed to enhance aviation communications. ATMACA integrates instant messaging and software-defined nodes to improve connectivity, session continuity, and mobility management across networks and devices. It ensures seamless user interaction, reduces pilot workload, and enhances flight safety through automated Air Traffic Control (ATC) sector handoff in Controller鈥揚ilot Data Link Communications (CPDLC) and Data Link Initiation Capability (DLIC) applications. Another key innovation of the ATMACA framework is Green Route Operations (GRO), which enables real-time trajectory prediction and optimization.
dc.title: Social Media Narratives: Addressing Extremism in Middle Age (SMIDGE) dc.contributor.author: Lee, Jason; Wilford, Sara; Hamzaoui, Raouf; Bhalla, Nitika dc.description.abstract: This paper examines the ongoing work of a three-year Horizon Europe project titled 鈥楽ocial Media Narratives: Addressing Extremism in Middle Age鈥� (SMIDGE). The project will cover aspects of the following areas: ethical dimensions, review of the literature (including conspiracy theories, misinformation and extremism online), co-designing of quantitative surveys, stakeholder engagement through qualitative focus groups, national nuances, changing technological issues, platform use and regulations. We take this analysis as a case study template that we believe will be useful to researchers in this field and potentially policy makers, especially from a multidisciplinary and transnational perspective. The project is split into four phases; Phase 1 - Understanding the landscape, profiling content and users, Phase 2 - Understanding the 鈥榓ttractiveness鈥� of the narrative, Phase 3 - Creating counter narratives and Phase 4 - Guidelines and policy briefs: spreading the word. We will unpack the challenges and opportunities of this approach for social media analysis and its real-world impact on democracy. Once the initial phase is completed in year one, we will start to construct counter-narratives to combat extremism in this context. This will take the form of creating counter videos and a documentary, as well as producing a series of podcasts and webinars. Furthermore, the outputs of the empirical research will inform and feed into the development of educational and training materials, guidelines and recommendations, as well as policy briefs that can be useful to policy makers, researchers, security professionals, journalists and beyond. The outputs from the SMIDGE project will provide evidence-based content, tools and resources that will directly help to counter extremist narratives from multiple perspectives. This will enable a greater understanding of the specificities and characteristics of those in the middle-age category, specifically those aged 45-65 years, and their vulnerability to extremism online. dc.description: open access article
dc.title: Progressive Knowledge Transfer Network Based on Human Visual Perception Mechanism for No-Reference Point Cloud Quality Assessment dc.contributor.author: Su, Honglei; Liu, Yiyun; Liu, Qi; Yuan, Hui; Hamzaoui, Raouf dc.description.abstract: Point cloud perceptual quality assessment plays a critical role in many applications, including compression and communication. We propose PKT-PCQA, a point-based no-reference point cloud quality assessment deep learning network that emulates the human visual system by using progressive knowledge transfer to convert coarse-grained quality classification knowledge into a fine-grained quality prediction task. PKTPCQA exploits local and global features, as well as an attention mechanism based on spatial and channel attention modules. Experiments on three large and independent point cloud assessment datasets show that PKT-PCQA outperforms existing no-reference and reduced-reference point cloud quality assessment methods and achieves better or similar performance compared to several state-of-the-art full-reference methods. The code will be available for download at https://github.com/sdqi/PKT-PCQA. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution dc.contributor.author: Fu, Congrui; Yuan, Hui; Jiang, Shiqi; Zhang, Guanghui; Shen, Liquan; Hamzaoui, Raouf dc.description.abstract: By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information disseminat