Publications

2025

• Decoding the Hierarchy: A Hybrid Approach to Hierarchical Multi-label Text Classification
  
  • Torba Fatos
  • Gravier Christophe
  • Laclau Charlotte
  • Kammoun Abderrhammen
  • Subercaze Julien
  , 2025, 15572, pp.405-420. Hierarchical multi-label text classification (HMTC) aims to predict multiple labels from a tree-like hierarchy for a given input text. Recent approaches frame HMTC as a seq2seq problem, where the objective is to predict the sequence of associated labels, regardless of their order or position in the hierarchy. Despite promising results, these approaches rely solely on attention mechanisms from previously generated tokens. This limit prevents them from acquiring information about the global hierarchy and may lead to the accumulation of errors as the model learns hierarchical cues among labels. We propose a novel HMTC model based on a hybrid version of the encoder-decoder architecture where the decoder is pre-populated with the entire label embeddings. By leveraging the decoder’s Cross-Attention and Hierarchical Self-Attention mechanisms, we achieve a label representation that benefits from instance and global label-wise information. Empirical experiments on four HMTC benchmark datasets demonstrated the effectiveness of our approach by settling new state-of-the-art results. Code (https://github.com/FatosTorba/HLPD) and datasets are made available to facilitate the reproducibility and future work. (10.1007/978-3-031-88708-6_26)
  DOI : 10.1007/978-3-031-88708-6_26
• A Hybrid Model for Weakly-Supervised Speech Dereverberation
  
  • Bahrman Louis
  • Fontaine Mathieu
  • Richard Gael
  , 2025. This paper introduces a new training strategy to improve speech dereverberation systems using minimal acoustic information and reverberant (wet) speech. Most existing algorithms rely on paired dry/wet data, which is difficult to obtain, or on target metrics that may not adequately capture reverberation characteristics and can lead to poor results on non-target metrics. Our approach uses limited acoustic information, like the reverberation time (RT60), to train a dereverberation system. The system's output is resynthesized using a generated room impulse response and compared with the original reverberant speech, providing a novel reverberation matching loss replacing the standard target metrics. During inference, only the trained dereverberation model is used. Experimental results demonstrate that our method achieves more consistent performance across various objective metrics used in speech dereverberation than the state-of-the-art.
• Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping
  
  • Berger Clémentine
  • Badeau Roland
  • Essid Slim
  , 2025. People often listen to music in noisy environments, seeking to isolate themselves from ambient sounds. Indeed, a music signal can mask some of the noise's frequency components due to the effect of simultaneous masking. In this article, we propose a neural network based on a psychoacoustic masking model, designed to enhance the music's ability to mask ambient noise by reshaping its spectral envelope with predicted filter frequency responses. The model is trained with a perceptual loss function that balances two constraints: effectively masking the noise while preserving the original music mix and the user's chosen listening level. We evaluate our approach on simulated data replicating a user's experience of listening to music with headphones in a noisy environment. The results, based on defined objective metrics, demonstrate that our system improves the state of the art.
• Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
  
  • Quelennec Aurian
  • Chouteau Pierre
  • Peeters Geoffroy
  • Essid Slim
  , 2025, pp.1-5. <div><p>Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned latent space can be further transformed to extract higher-level information that could be more suited for downstream classification tasks. Therefore, we propose a new method: MAsked latenT Prediction And Classification (MATPAC), which is trained with two pretext tasks solved jointly. As in previous work, the first pretext task is a masked latent prediction task, ensuring a robust input representation in the latent space. The second one is unsupervised classification, which utilises the latent representations of the first pretext task to match probability distributions between a teacher and a student. We validate the MATPAC method by comparing it to other state-of-the-art proposals and conducting ablations studies. MATPAC reaches state-of-the-art self-supervised learning results on reference audio classification datasets such as OpenMIC, GTZAN, ESC-50 and US8K and outperforms comparable supervised methods' results for musical auto-tagging on Magna-tag-a-tune.</p></div> (10.1109/ICASSP49660.2025.10887666)
  DOI : 10.1109/ICASSP49660.2025.10887666
• Multiple Choice Learning for Efficient Speech Separation with Many Speakers
  
  • Perera David
  • Derrida Francois
  • Mariotte Théo
  • Richard Gael
  • Essid Slim
  , 2025. <div><p>Training speech separation models in the supervised setting raises a permutation problem: finding the best assignation between the model predictions and the ground truth separated signals. This inherently ambiguous task is customarily solved using Permutation Invariant Training (PIT). In this article, we instead consider using the Multiple Choice Learning (MCL) framework, which was originally introduced to tackle ambiguous tasks. We demonstrate experimentally on the popular WSJ0-mix and LibriMix benchmarks that MCL matches the performances of PIT, while being computationally advantageous. This opens the door to a promising research direction, as MCL can be naturally extended to handle a variable number of speakers, or to tackle speech separation in the unsupervised setting.</p></div> (10.1109/ICASSP49660.2025.10888528)
  DOI : 10.1109/ICASSP49660.2025.10888528
• Learning Source Disentanglement in Neural Audio Codec
  
  • Bie Xiaoyu
  • Liu Xubo
  • Richard Gaël
  , 2024. Neural audio codecs have significantly advanced audio compression by efficiently converting continuous audio signals into discrete tokens. These codecs preserve high-quality sound and enable sophisticated sound generation through generative models trained on these tokens. However, existing neural codec models are typically trained on large, undifferentiated audio datasets, neglecting the essential discrepancies between sound domains like speech, music, and environmental sound effects. This oversight complicates data modeling and poses additional challenges to the controllability of sound generation. To tackle these issues, we introduce the Source-Disentangled Neural Audio Codec (SD-Codec), a novel approach that combines audio coding and source separation. By jointly learning audio resynthesis and separation, SD-Codec explicitly assigns audio signals from different domains to distinct codebooks, sets of discrete representations. Experimental results indicate that SD-Codec not only maintains competitive resynthesis quality but also, supported by the separation results, demonstrates successful disentanglement of different sources in the latent space, thereby enhancing interpretability in audio codec and providing potential finer control over the audio generation process. (10.1109/ICASSP49660.2025.10888065)
  DOI : 10.1109/ICASSP49660.2025.10888065
• Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges
  
  • Peeters Geoffroy
  • Rafii Zafar
  • Fuentes Magdalena
  • Duan Zhiyao
  • Benetos Emmanouil
  • Nam Juhan
  • Mitsufuji Yuki
  , 2025, pp.1-5. In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large part of it focuses on signal processing techniques for music data, fostering a close relationship with the IEEE Audio and Acoustic Signal Processing Technical Committee. In this paper, we reflect the main research achievements of MIR along the three EDICS related to music analysis, processing and generation. We then review a set of successful practices that fuel the rapid development of MIR research. One practice is the annual research benchmark, the Music Information Retrieval Evaluation eXchange, where participants compete on a set of research tasks. Another practice is the pursuit of reproducible and open research. The active engagement with industry research and products is another key factor for achieving large societal impacts and motivating younger generations of students to join the field. Last but not the least, the commitment to diversity, equity and inclusion ensures MIR to be a vibrant and open community where various ideas, methodologies, and career pathways collide. We finish by providing future challenges MIR will have to face. (10.1109/ICASSP49660.2025.10888947)
  DOI : 10.1109/ICASSP49660.2025.10888947
• O-EENC-SD: Efficient Online End-to-End Neural Clustering for Speaker Diarization
  
  • Gruttadauria Elio
  • Fontaine Mathieu
  • Le Roux Jonathan
  • Essid Slim
  , 2025. We introduce O-EENC-SD: an end-to-end online speaker diarization system based on EEND-EDA, featuring a novel RNN-based stitching mechanism for online prediction. In particular, we develop a novel centroid refinement decoder whose usefulness is assessed through a rigorous ablation study. Our system provides key advantages over existing methods: a hyperparameter-free solution compared to unsupervised clustering approaches, and a more efficient alternative to current online end-to-end methods, which are computationally costly. We demonstrate that O-EENC-SD is competitive with the state of the art in the two-speaker conversational telephone speech domain, as tested on the CallHome dataset. Our results show that O-EENC-SD provides a great trade-off between DER and complexity, even when working on independent chunks with no overlap, making the system extremely efficient.
• Contrastive Knowledge Distillation for Embedding Refinement in Personalized Speech Enhancement
  
  • Serre Thomas
  • Fontaine Mathieu
  • Benhaim Éric
  • Essid Slim
  , 2025, pp.1-5. Personalized speech enhancement (PSE) has shown convincing results when it comes to extracting a known target voice among interfering ones. The corresponding systems usually incorporate a representation of the target voice within the enhancement system, which is extracted from an enrollment clip of the target voice with upstream models. Those models are generally heavy as the speaker embedding's quality directly affects PSE performances. Yet, embeddings generated beforehand cannot account for the variations of the target voice during inference time. In this paper, we propose to perform on-thefly refinement of the speaker embedding using a tiny speaker encoder. We first introduce a novel contrastive knowledge distillation methodology in order to train a 150k-parameter encoder from complex embeddings. We then use this encoder within the enhancement system during inference and show that the proposed method greatly improves PSE performances while maintaining a low computational load. (10.1109/icassp49660.2025.10887609)
  DOI : 10.1109/icassp49660.2025.10887609
• AnCoGen: Analysis, Control and Generation of Speech with a Masked Autoencoder
  
  • Sadok Samir
  • Leglaive Simon
  • Girin Laurent
  • Richard Gaël
  • Alameda-Pineda Xavier
  , 2025, pp.1-5. This article introduces AnCoGen, a novel method that leverages a masked autoencoder to unify the analysis, control, and generation of speech signals within a single model. AnCoGen can analyze speech by estimating key attributes, such as speaker identity, pitch, content, loudness, signal-to-noise ratio, and clarity index. In addition, it can generate speech from these attributes and allow precise control of the synthesized speech by modifying them. Extensive experiments demonstrated the effectiveness of AnCoGen across speech analysisresynthesis, pitch estimation, pitch modification, and speech enhancement. Code and audio examples are available online.
• Convex Quartic Problems: Homogenized Gradient Method and Preconditioning
  
  • Dragomir Radu-Alexandru
  • Nesterov Yurii
  SIAM Journal on Optimization, Society for Industrial and Applied Mathematics, 2025, 35 (2), pp.651-677. <div><p>We consider a convex minimization problem for which the objective is the sum of a homogeneous polynomial of degree four and a linear term. Such task arises as a subproblem in algorithms for quadratic inverse problems with a difference-of-convex structure. We design a first-order method called Homogenized Gradient, along with an accelerated version, which enjoy fast convergence rates of respectively O(κ 2 /K 2 ) and O(κ 2 /K 4 ) in relative accuracy, where K is the iteration counter. The constant κ is the quartic condition number of the problem.</p><p>Then, we show that for a certain class of problems, it is possible to compute a preconditioner for which this condition number is √ n, where n is the problem dimension. To establish this, we study the more general problem of finding the best quadratic approximation of an ℓ p norm composed with a quadratic map. Our construction involves a generalization of the so-called Lewis weights.</p></div> (10.1137/23M1583363)
  DOI : 10.1137/23M1583363
• On the compressibility of large-scale source code datasets
  
  • Boffa Antonio
  • Di Cosmo Roberto
  • Ferragina Paolo
  • Guerra Andrea
  • Manzini Giovanni
  • Vinciguerra Giorgio
  • Zacchiroli Stefano
  Journal of Systems and Software, Elsevier, 2025, 227, pp.112429. Storing ultra-large amounts of unstructured data (often called objects or blobs) is a fundamental task for several object-based storage engines, data warehouses, data-lake systems, and key-value stores. These systems cannot currently leverage similarities between objects, which could be vital in improving their space and time performance. An important use case in which we can expect the objects to be highly similar is the storage of large-scale versioned source code datasets, such as the Software Heritage Archive (Di Cosmo and Zacchiroli, 2017). This use case is particularly interesting given the extraordinary size (1.5 PiB), the variegated nature, and the high repetitiveness of the at-issue corpus. In this paper we discuss and experiment with content-and context-based compression techniques for source-code collections that tailor known and novel tools to this setting in combination with state-of-the-art general-purpose compressors and the information coming from the Software Heritage Graph. We experiment with our compressors over a random sample of the entire corpus, and four large samples of source code files written in different popular languages: C/C++, Java, JavaScript, and Python. We also consider two scenarios of usage for our compressors, called Backup and File-Access scenario, where the latter adds to the former the support for single file retrieval. As a net result, our experiments show (i) how much ''compressible'' each language is, (ii) which content-or context-based techniques compress better and are faster to (de)compress by possibly supporting individual file access, and (iii) the ultimate compressed size that, according to our estimate, our best solution could achieve in storing all the source code written in these languages and available in the Software Heritage Archive: namely, in 3 TiB (down from their original 78 TiB total size, with an average compression ratio of 4%). (10.1016/j.jss.2025.112429)
  DOI : 10.1016/j.jss.2025.112429
• Memory Distortions Induced by Reality-Altering Media
  
  • Bonnail Elise
  , 2025. Memory, particularly autobiographical and episodic memory, is a central cognitive function, essential for constructing a sense of identity, supporting social bonds, and making decisions. However, our memory is not always reliable, as we are prone to forgetting and distortions. Memory distortions can arise from source confusion, when memories are attributed to an incorrect source, for example when imagined events are confused for real ones. This phenomenon makes us vulnerable to external suggestions that can lead to the creation of false memories.While research has focused on the impact of fabricated narratives and edited photos on memory, newer forms of media now expose us to an altered perception of reality. In this thesis I introduce the term “reality-altering media” to describe media that add virtual elements to our perception of reality, either retrospectively through altered re-representations, such as edited photos, or in real-time through co-temporal media, such as Extended Reality (XR), including Augmented Reality (AR) and Virtual Reality (VR). Co-temporal media such as XR are becoming increasingly popular and are used in various contexts. However, there is limited understanding of the phenomenon of source confusion between events experienced through XR and those from reality, as well as how this technology might be exploited to manipulate users' memories. Regarding altered re-representations, the evolution of photo-editing, driven by the advent of generative Artificial Intelligence (AI) tools, allows individuals to effortlessly edit their own photos with just a few clicks. Yet, the effects of self-photo editing on memory remain unexplored.This thesis explores the impacts of new forms of reality-altering media on memory. Specifically, I investigate how XR could be leveraged to manipulate memories in a future where such technology would be ubiquitous (RQ1), whether new forms of reality-altering media (VR and AI-edited photos) can lead to memory distortions (RQ2), and how users distinguish between real and virtual events (RQ3).To address these questions, I use a combination of speculative design and empirical lab studies. For RQ1, I conducted three speculative design workshops (n=12) involving XR and memory experts who created and discussed scenarios of memory manipulations. Through qualitative analysis, I define XR Memory Manipulations (XRMMs) and categorize them into three classes (at encoding, pre-retrieval, at retrieval), which differ in terms of technology used (AR, VR) and impact on memory (influencing quality of memories, inducing forgetting, distorting memories). These scenarios allow to anticipate potential benefits but also future risks associated with the development of new forms of immersive reality-altering media. For RQ2 and RQ3, I conducted two mixed-method lab studies. The first study (n=29) confirms the occurrence of source confusion between real and VR experiences with current technology, providing quantitative and qualitative insights into the factors influencing this confusion. The second study (n=34) evaluates the impact of self-editing photos using generative AI (adding and removing elements) compared to photo editing by an external person and no editing, showing that editing one's own photo increases the chances of forgetting elements from the original event. In both studies, I collected qualitative data to identify the cues and mechanisms participants used to distinguish between memories from virtual and real events. This analysis builds on the Source Monitoring Framework, providing insights for designing technologies that minimize risks of memory distortions.From these results, I discuss the need for ethical considerations around the loss of perceptual integrity (the trust that what we perceive and remember accurately reflects reality), to ensure a responsible use of reality-altering media.
• Sécurité des canaux cachés : de la théorie à la pratique
  
  • Guilley Sylvain
  • Rioul Olivier
  , 2025. La sécurité des implémentations cryptographiques s'appuie sur la confidentialité des clés secrètes et privées, ainsi que sur l'intégrité des clés publiques. Ainsi, des protocoles de confiance peuvent être construits, pour établir des applications de sécurité, tels que l'authentification (en terme d'identité) ou l'attestation (vis-à-vis des firmware). Maintenant, les attaques sur les canaux cachés permettent de remonter aux clés sans cryptanalyse, simplement en espionnant une émanation fortuite de l'équipement considéré. Or de nombreux travaux académiques ont été réalisés pour caractériser de telles attaques. Par exemple, le livre "Mathematical Foundations for Side-Channel Analysis of Cryptographic Systems", publié en 2024 par les auteurs, démontre différents chemins d'attaque. La communauté scientifique a aussi proposé des bornes de sécurité, c'est-à-dire une limite inférieure sur le nombre de traces espionnées pour retirer suffisamment d'information permettant de remonter à la clé. Maintenant, ces bornes s'appuient sur des notions et des métriques de théorie de l'information. Or l'estimation de ces grandeurs n'est pas triviale. Dans cette exposé de conférence, nous démontrons comment relier la complexité des attaques à un nombre de mesure, i.e., à un effort tel que décrit par les Critères Communs (CC). Cette mise en pratique est utile par les laboratoires réalisant les certifications. Notre présentation vise à rendre didactique et application des travaux publiés à des conférences de cryptographie de haut niveau. Une traduction dans le langage de la normalisation (notamment le standard ISO/IEC 17825) sera proposée.
• F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music Generation
  
  • Agarwal Manvi
  • Wang Changhong
  • Richard Gael
  , 2025. While music remains a challenging domain for generative models like Transformers, recent progress has been made by exploiting suitable musically-informed priors. One technique to leverage information about musical structure in Transformers is inserting such knowledge into the positional encoding (PE) module. However, Transformers carry a quadratic cost in sequence length. In this paper, we propose F-StrIPE, a structure-informed PE scheme that works in linear complexity. Using existing kernel approximation techniques based on random features, we show that F-StrIPE is a generalization of Stochastic Positional Encoding (SPE). We illustrate the empirical merits of F-StrIPE using melody harmonization for symbolic music.
• Analog Neural Networks Trained by Equilibrium Propagation
  
  • Kiraz Zulal
  , 2025. Contribution to “Visions of Research, PhD-Driven Breakthroughs - Harnessing AI: PhD Research to Advance and Adapt Artificial Intelligence” Title: Analog Neural Networks Trained by Equilibrium Propagation This contribution is published in the Hi! PARIS white book: Hi! PARIS (2025). Visions of Research: PhD-Driven Breakthroughs. ● Full white book: https://www.hi-paris.fr/visions-of-research-phd-driven-breakthroughs/ ● Direct link to my contribution (page 39): https://heyzine.com/flip-book/0c61e8e85b.html#page/38
• Piecewise Constant Spectral Graph Neural Network
  
  • Martirosyan Vahan
  • Giraldo Jhony H
  • Malliaros Fragkiskos D
  Transactions on Machine Learning Research Journal, [Amherst Massachusetts]: OpenReview.net, 2022, 2025. Graph Neural Networks (GNNs) have achieved significant success across various domains by leveraging graph structures in data. Existing spectral GNNs, which use low-degree polynomial filters to capture graph spectral properties, may not fully identify the graph's spectral characteristics because of the polynomial's small degree. However, increasing the polynomial degree is computationally expensive and, beyond certain thresholds, leads to performance plateaus or degradation. In this paper, we introduce the Piecewise Constant Spectral Graph Neural Network (PieCoN) to address these challenges. PieCoN combines constant spectral filters with polynomial filters to provide a more flexible way to leverage the graph structure. By adaptively partitioning the spectrum into intervals, our approach increases the range of spectral properties that can be effectively learned. Experiments on nine benchmark datasets, including both homophilic and heterophilic graphs, demonstrate that PieCoN is particularly effective on heterophilic datasets, highlighting its potential for a wide range of applications. The implementation of PieCoN is available at https://github.com/vmart20/PieCoN.
• Multi-Sensor Data Fusion for Enhanced Detection of Laser Fault Injection Attacks in Cryptographic Hardware: Practical Results
  
  • Ebrahimabadi Mohammad
  • Viera Raphael
  • Guilley Sylvain
  • Danger Jean-Luc
  • Dutertre Jean-Max
  • Karimi Naghmeh
  , 2025, pp.1 - 2. Though considered secure the cryptographic hardware can be compromised by fault injection attack, especially laser illumination due to its precision in targeting specific areas and its fine temporal control. To address this threat, this paper presents a low-cost detection scheme that utilizes Time-to-Digital Converters (TDCs) to sense the IR drops induced by laser illumination. To achieve a high detection rate while minimizing false alarms, the proposed approach incorporates multiple sensors, with as few as two sensors demonstrated in the study. The effectiveness of the scheme is validated using a real laser setup to illuminate a targeted AES module implemented on an AMD/Xilinx Artix-7 FPGA. (10.23919/date64628.2025.10992902)
  DOI : 10.23919/date64628.2025.10992902
• ASML-REG: Automated Machine Learning for Data Stream Regression
  
  • Verma Nilesh
  • Bifet Albert
  • Pfahringer Bernhard
  • Bahri Maroua
  , 2025, pp.440-447. Online learning scenarios present a significant challenge for Au-toML techniques due to the dynamic nature of data distributions, where the optimal model and configuration may change over time. While most research in machine learning for data streams has primarily focused on classification algorithms, regression methods have received significantly less attention. To address this gap, we propose ASML-REG, an Automated Streaming Machine Learning framework designed specifically for regression tasks on data streams. ASML-REG continuously explores a vast and diverse space of pipeline configurations, adapting to evolving data by focusing on the current best design, performing adaptive random searches in promising areas, and maintaining an ensemble of top-performing pipelines. Our experiments with real and synthetic datasets demonstrate that ASML-REG significantly outperforms current state-ofthe-art data stream regression algorithms. (10.1145/3672608.3707742)
  DOI : 10.1145/3672608.3707742
• An Efficient Compact Dual-Band Metasurface RF Energy Harvester
  
  • Sharifi Raziyeh
  • Lepage Anne Claire
  • Niotaki Kyriaki
  • Begaud Xavier
  , 2025. This paper presents a dual-band metasurface-based RF energy harvester to efficiently collect the ambient radio frequency energy at 2.45 GHz and 5.2 GHz (Wi-Fi bands). The methodology to design the dual-band metasurface by employing the EM simulation software CST Studio Suite is presented. The dual-band metasurface is designed based on a proposed single-band metasurface harvester, with both unit cell designs achieving high absorption characteristics at their operating frequencies. An array of 4×5 unit cells for both the single-band and dual-band designs were fabricated to validate the concept. The first results for the single band design demonstrate a good agreement between the simulated and the measured results.
• Millimeter Wave Rectifiers: A Review of Technology and Performance
  
  • Wang Yibo
  • Niotaki Kyriaki
  • Lepage Anne Claire
  • Begaud Xavier
  , 2025. Rectifiers operating at the millimeter wave (mmWave) frequencies have recently attracted interest for a wide range of applications, including wireless power transmission and energy harvesting. This work presents an overview of the rectifiers operating in the mmWave band, and more specifically from 24 GHz to 94 GHz. It also highlights the main mmWave rectifier's design challenges along with some novel techniques used to optimize their performance.
• Assessment of the representativeness of numerical subjects for the WBAN indoor channel modeling
  
  • Youssef Badre
  • Roblin Christophe
  , 2025, pp.1-5. <div><p>Wireless Body Area Networks peculiarities can be summed up in two points: firstly, the high number of sources of variability, i.e. the subject, the antennas, the frequency, the environment, etc.; secondly the strong electromagnetic disturbance of the human body (due to its composition, around 70% of water). Concerning the subject, there are two aspects that can be considered: the morphology and the dynamic. Here, we are interested only on the first one. When this source of variability is considered in the channel modeling, the problem is the difficulty of obtaining a representative statistical sample. One solution would be to use numerical subject models, which would give us a much wider range of subjects than is currently proposed in the literature. Then the purpose of this article is to assess the representativeness of this approach in the context of WBAN channel modeling, by comparing key channel parameters (the mean Path Loss of the on-body cluster and the surrounded antenna pattern) obtained from simulations results for different numerical subjects and experimentations performed with a whole body phantom. In this study, the scenario based approach is preferred and simulations and experimentations are performed in the 1 st UWB band ([3.1, 4.8] GHz). The results obtained are satisfactory, and validate the use of this approach in view of the assumptions exposed for modeling the WBAN channel.</p></div> (10.23919/EuCAP63536.2025.10999677)
  DOI : 10.23919/EuCAP63536.2025.10999677
• Impact of Rough Building Material Surfaces on Reflection Coefficients from 5 GHz to 260 GHz
  
  • Conrat Jean-Marc
  • Cousin Jean-Christophe
  • Begaud Xavier
  , 2025. When an electromagnetic wave is reflected by a smooth and planar surface, its amplitude is decreased by the Fresnel reflection coefficients depending on the material permittivity, incidence angle and polarization. Recent work showed that the permittivity does not depend on the frequency from 2 to 260 GHz implying that the reflection gain is constant from 2 to 260 GHz for a given incidence angle and polarization. But Fresnel coefficients are only valid for materials with a smooth surface which is not always the case for building materials. Materials such as concrete or patterned glass may be rough or have a relief surface. This paper analyzes the reflection gain deviation from the Fresnel coefficients when material surfaces are not smooth and proposed a simplified model based on the Rayleigh-Rice or Beckmann-Kirchhoff theoretical approaches. The investigated frequencies ranged from 5 to 260 GHz.
• Wavelength and Code Orthogonality Based Distributed Acoustic Sensing over a Passive Optical Network
  
  • Kumar Choudhury Pallab
  • Awwad Élie
  , 2025. We present a wavelength and code orthogonality based DFOS enabling simultaneous sensing of all paths of a splitter-based passive optical network. Strain sensitivity of 80nmpp is measured with no penalty on coexisting 10Gb/s downstream transmission.
• Reconfigurable intelligent surfaces (RIS) using NOMA with thermal energy harvesting
  
  • Boujemaa Hatem
  • Alhussein Musaed
  • Rekaya Ghaya
  Signal, Image and Video Processing, Springer Verlag, 2025, 19 (5), pp.419:1-419:8. The integration of Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) and thermal energy harvesting presents a novel approach to enhancing wireless communication networks. RIS technology optimizes signal propagation and improves network efficiency through programmable surface elements, while NOMA increases spectral efficiency by allowing multiple users to share the same frequency resource. When combined with thermal energy harvesting, which captures ambient heat and converts it into electrical power, this integration offers a sustainable solution to power the RIS infrastructure. This paper explores the synergistic benefits of RIS using NOMA with thermal energy harvesting, examining its impact on network performance, energy efficiency, and sustainability. Through a review of recent advancements and research, we discuss how this combined approach can address key challenges in modern wireless communications and contribute to the development of greener, more efficient networks. (10.1007/s11760-025-03996-x)
  DOI : 10.1007/s11760-025-03996-x