Walid Saad

Some great news! Our paper "Toward Modeling and Assessing the Disorientation and Misalignment Effect in Optical Wireless Nano-Networks" has been published in IEEE Journal on Selected Areas in Communications ( Volume: 42, Issue: 8, August 2024). The paper was authored by Alexandros-Apostolos A. Boulogeorgos (University of Western Macedonia), Stylianos E. Trevlakis (InnoCube P.C.), Theodoros Tsiftsis (University of Thessaly), and Angeliki ALEXIOU (University of Piraeus). This work was supported by the Project-MINOAS within the H.F.R.I call “Basic Research Financing (Horizontal Support of all Sciences)” through the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union-NextGenerationEU (H.F.R.I.) under Project 15857. Abstract: It is true that medical diagnosis and treatment have always been at the top of research activities. Recently, a very promising area that combines the operation of human body with the principles of wireless communications has arisen and given birth to the field of bio-photonics. Although the deployment of nano-networks inside the human body seems to be an achievable goal, the implementation of optical wireless nano-networks remains a very challenging field due to the lack of accurate channel modeling. Therefore, in this paper, we comprehensively investigate the optical wireless nano-communication channels by considering the deterministic geometric losses (e.g., optical absorption and scattering in human tissues), and modeling the impact of nano-node disorientation and misalignment in in-body nano-networks. To this end, we analytically study the stochastic nature of disorientation and misalignment coefficient in terms of the position and orientation of the nano-node with reference to the out-of-body optical receiver. In particular, we study the statistics of the joint disorientation and misalignment coefficient in terms of the probability density function, which is given in closed-form expression, whereas its cumulative distribution function is introduced as a semi-infinite series expression. Building upon them, we extract novel analytical expressions for the average signal-to-noise-ratio and the outage probability, and a thorough study on the impact of the human tissues on the above performance metrics is conducted. The analytical results are corroborated and cross-compared by means of Monte Carlo simulations, which reveal the importance of accounting for the joint impact of disorientation and misalignment of nano-node when designing optical wireless nano-networks. Keywords: #6G, #Optical #wireless #communications, #modeling, #misalignment, #disorientation, #performance #analysis, #MonteCarlo, #nanotechnology, #nanonetworks, #human-tissue, #biophotonics, #photonics. You can read the full paper in the following link.

41

1 Comment

📢 Our new paper "Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks" has been accepted in IEEE Transactions on Signal and Information Processing over Networks! This work introduces sparse Sobolev graph neural networks (S2-GNN) to address a key challenge in scaling up GNNs. While traditional GNNs typically rely on immediate node neighbors, limiting their effectiveness in capturing complex graph relationships, S2-GNN employs higher-order, sparse filters to efficiently handle large graphs. Our key contributions: Spectral insights: We reveal a spectral similarity between higher-order Laplacians and their sparse approximations, which supports the design of high-performing yet efficient GNNs. Architecture: S2-GNN leverages cascades of sparse filters to enable accurate updates in node embeddings without excessive computational costs. Robustness: We provide stability analysis of S2-GNN, demonstrating robustness to graph perturbations. Extensive benchmarking: S2-GNN consistently outperforms or competes with leading GNN methods across diverse datasets and tasks. We hope this work contributes to advancing scalable, high-efficiency GNN architectures for real-world applications. 🔗 Preprint: https://lnkd.in/eAJJ9SAD 🔗 Code: https://lnkd.in/eWK7XZM2 Thanks to my co-authors Aref Einizade, Andjela Todorovic, Jhon Castro-Correa, Mohsen Badiey, Thierry Bouwmans, and Fragkiskos Malliaros. This work was supported by ANR, the French National Research Agency, Office of Naval Research, Hi! PARIS Center - AI for Science, Business & Society, Télécom Paris, Institut Polytechnique de Paris, University of Delaware, La Rochelle Université, CentraleSupélec, Université Paris-Saclay, and Inria. #GraphNeuralNetworks #SparseGNN

64

9 Comments

Mehmet Savasci presented our paper titled "PADS: Power Budgeting with Diagonal Scaling for Performance-Aware Cloud Workloads" at the recently held IEEE Conference on Green and Sustainable Computing (IGSC) conference in Austin, TX. This paper presents a new hardware-agnostic diagonal scaling technique for power management and power capping in compute clusters. Read it here: https://lnkd.in/eyeKsrz8 Abel S. Ahmed Ali El Din David Irwin #energyefficiency #cloudcomputing

26

3 Comments

I am thrilled to announce that my solo research paper, titled "A Novel Generic Autonomous Synchronization Method for Microgrids," has been published in IEEE Access, which has an Impact Factor of 3.4! https://lnkd.in/e9_CgkeD This paper presents a novel synchronization approach that effectively synchronizes islanded microgrids (IMs) with grid-connected microgrids (GCMs). It enables seamless reconnection of distributed generation (DG)-based converters or isolated microgrids after intentional disconnections and facilitates re-synchronization during abnormal conditions, such as short circuits. Key Contributions: Generic Synchronization Technique: Operates effectively across various scenarios without the need for a phase-locked loop (PLL). Robust Performance: Works under short-circuit conditions while maintaining system stability. Autonomous Functionality: Enables synchronization of both balanced and unbalanced utilities, allowing for autonomous disconnection and resynchronization of Microgrids.

107

17 Comments

Pleased to share that our new research paper from my PhD student Furkan Hasan SAKACI's thesis, entitled “Conducted Emission signal-based identification and real-time hardware security with deep learning” has been published in Engineering Applications of Artificial Intelligence. 50 days' free access to the paper. Anyone clicking on this link before September 9, 2024 will be taken directly to the final version of this paper on ScienceDirect, which they are welcome to read or download. No sign up, registration or fees are required. https://lnkd.in/dh53PRpF Abstract: In advancing electronic systems, the replicable nature of devices used in critical environments raises concerns regarding system security. It is of great importance to establish security by identifying electronic devices and computers based on their unique electrical signals, rather than assigning an artificial identity number for security purposes. This paper presents a methodology for identifying electronic devices by capturing their Conducted Emission signal over cables using a Line Impedance Stabilization Network (LISN), followed by applying deep learning techniques. After the extraction of the identities specific to each device based on their electronic characteristics, the establishment of security has been accomplished through these characteristics. The identification process was analyzed through theoretical approaches, and signals were examined by acquiring simulation-based data. After performing the analyses, the data was converted into spectrograms for training the deep learning model using the Conducted Emission signals, and a Convolutional Neural Network (CNN) was used due to its recognized efficacy in handling two-dimensional data formats like spectrogram for the deep learning algorithm. It was observed that with an accuracy of 99.9%, identification was achieved in deep learning results utilizing the data obtained through simulations. Real-time data from 15 devices was collected after training the system with simulation-based data, demonstrating a 99.8% accuracy in accurately identifying the devices. Furthermore, the developed deep learning model was deployed on an Embedded Linux edge device, tested within the system for one day, and achieved an average accuracy of 99.54% in accurately identifying registered devices.

93

Well worth knowing about this. Thanks Yiran Chen!

3

🚀 Excited to Share Our Latest Research! 🌟 Our article, "Preamble Design and Noncoherent ToA Estimation for Pulse-Based Wireless Networks-on-Chip Communications in the Terahertz Band," has been published in the journal Micromachines! 📝 🔬 Key Highlights: 1. Explores innovative preamble-aided synchronization for Time-of-Arrival (ToA) estimation in Terahertz (THz) band Wireless Networks-on-Chip (WiNoC). 2. Introduces an energy-detection-based noncoherent receiver design for ultra-reliable synchronization. 3. Demonstrates synchronization reliability of over 98% for distances under 10 mm, even in challenging environments. 💡 This work addresses critical challenges in enabling ultrafast, energy-efficient communications for next-gen multicore chip architectures. The proposed approach is a step forward in supporting the future of high-performance computing with THz-WiNoCs. 🔗 Read the full open-access article here: https://lnkd.in/gxzR3ejG A huge thank you to my co-author and collaborator for their incredible contributions. 🎉 #Research #Innovation #Terahertz #WirelessCommunication #Micromachines #WiNoC #Synchronization #MDPI #Micromachines

5

Happy about our latest paper titled "Deduplicator: When Computation Reuse Meets Load Balancing at the Network Edge", which was recently accepted for publication by IFIP Networking 2024. All the props go to my senior Ph.D. student, Md. Washik Al Azad. This is the 9th (!) publication during his Ph.D., which is a substantial accomplishment for a systems student. The rest of his papers have been published at IEEE PerCom (best paper runner up award), the IEEE Internet of Things Journal (impact factor > 10), IEEE CLOUD, and the IEEE Wireless Communications Magazine (impact factor > 10), among other venues. In this paper, we explore whether we can achieve the contradictory in nature functions of load balancing and computation reuse at the network edge. To this end, we propose an edge middlebox, called the Deduplicator. Our experimental results demonstrate that we can facilitate computation reuse and distribute the load among edge servers at the same time. You can find a preprint of our paper on arXiv: https://lnkd.in/dkQqq9XM Our code is also available on GitHub: https://lnkd.in/dq3HXJSZ #research #cloudcomputing #edgecomputing #loadbalancing #distributedsystems #systems #computernetworks #middleboxes

45

6 Comments

📚 One of our Hybrid DC-DC Converter research, done by our PhD student Xiongjie Zhang, has been published in the early access of IEEE Journal of Solid-State Circuits (JSSC)! 🔍 This work has undergone a review and revision process over one year, and we are thrilled to share our outcomes with the community finally. 📝 Our paper explores [Interleaved-inductor SC hybrid DC-DC topology design for high-step-down scenarios, and on-chip gate-driver techniques]. 🌐 Welcome to read our paper and engage in discussions. Feel free to reach out with any questions or comments! Let's drive innovation forward!🚀💡 #Research #DCDCConverter #Innovation #IEEEJSSC #SolidStateCircuits #Technology #Engineering

24

Machine learning has changed the PHY-layer security landscape. See our survey paper published in IEEE Communications Surveys & Tutorials: https://lnkd.in/esrjQ3TR

12

Useful guidelines from the IEEE TSE board to support discussion about what constitutes software engineering for AI. Special thanks to Seb for championing and seeing this piece through.

8

📢 Call for Papers 📢 I am honored to be a Guest Editor for the special issue titled "Advances in Flexible Electronics, Wearable Sensors, and Terahertz Communications for Smart Systems and 6G Networks" 💻 📶 . This special issue will focus on how advanced wearable sensing, wireless communication, and internet-of-medical-things (IoMT) can reshape the landscape of modern healthcare. 📖 Potential topics include but are not limited to the following: 💡 Flexible Electronics: Wearable Technology, Advanced Manufacturing Techniques for Producing Flexible Electronic Devices and Their Applications 💡 Wearable Sensors: Health Monitoring, Environmental Sensing, and Smart Textiles 💡 Terahertz Communications: High-Speed Data Transfer, Imaging and Sensing, and Integration with 6G Networks 💡 Smart Systems: Internet of Things (IoT), Automation and AI 6G Networks: Advanced Network Architectures and Integration with Emerging Technologies for Improved Performance and Capabilities 📅 Submission deadline: 06 June 2025 Dr. Shonak Bansal Dr. Krishna Prakash Dr. Mohammad Aljaidi

17

2 Comments

I am proud to share that our paper, "Greedy Recursive Spectral Bisection for Modularity-Bound Hierarchical Divisive Community Detection," has been published in Statistics and Computing. Thanks to an agreement between Springer and FCT - Portugal, you can access the full article for free! Dive into our innovative approach for community detection and explore the detailed methodology and results. Check it out here: https://rdcu.be/dL8bM #StatisticsAndComputing #CommunityDetection #Research #OpenAccess #Springer #PPCIC #PPPRO #CEFETRJ #DataScience

30

Our paper, "DRAMScope: Uncovering DRAM Microarchitecture and Characteristics by Issuing Memory Commands," was accepted at ISCA 2024! We had an intriguing journey to figure out the sizes of DRAM MATs, common pitfalls regarding address and data mapping in the computer architecture community, coupled-row activation, and RowHammer/RowPress characteristics induced by the 6F^2 DRAM cell structure, to list a few, by issuing ordinary commands to DRAM. We look forward to presenting our paper in Buenos Aires, Argentina. I am really proud of my students and colleagues, without whom I could not imagine to publish this work in a one-and-done manner. Pdf: https://lnkd.in/gnnQS_XA Authors: Hwayong Nam, Seungmin Baek, Minbok Wi, Michael Jaemin Kim, Jaehyun Park, Chihun Song, Nam Sung Kim, and Jung Ho Ahn Abstract: The demand for precise information on DRAM microarchitectures and error characteristics has surged, driven by the need to explore processing in memory, enhance reliability, and mitigate security vulnerability. Nonetheless, DRAM manufacturers have disclosed only a limited amount of information, making it difficult to find specific information on their DRAM microarchitectures. This paper addresses this gap by presenting more rigorous findings on the microarchitectures of commodity DRAM chips and their impacts on the characteristics of activate-induced bitflips (AIBs), such as RowHammer and RowPress. The previous studies have also attempted to understand the DRAM microarchitectures and associated behaviors, but we have found some of their results to be misled by inaccurate address mapping and internal data swizzling or lack of a deeper understanding of the modern DRAM cell structure. For accurate and efficient reverse engineering, we use three tools: AIBs, retention time test, and RowCopy, which can be cross-validated. With these three tools, we first take a macroscopic view of modern DRAM chips to uncover the size, structure, and operation of their subarrays, memory array tiles (MATs), and rows. Then, we analyze AIB characteristics based on the microscopic view of the DRAM microarchitecture, such as 6F^2 cell layout, through which we rectify misunderstandings regarding AIBs and discover a new data pattern that accelerates AIBs. Lastly, based on our findings at both macroscopic and microscopic levels, we identify previously unknown AIB vulnerabilities and propose a simple yet effective protection solution.

154

3 Comments

Excited to share that our (Syed Waqas Haider Shah, PhD, Saud Althunibat, Joerg Widmer) latest research paper has been accepted for publication in IEEE Transactions on Vehicular Technology! In this work, we explore the exciting potential of Reconfigurable Intelligent Surfaces (RISs) in securing mmWave communications. The paper focuses on optimizing the Quality of Service (QoS) in a RIS-assisted mmWave network under the challenging scenario of channel aging. Acquiring perfect channel information is resource-intensive; and thus our research sheds light on how secrecy performance can still be optimized using outdated channel estimates. This research opens new avenues for secure, reliable, and energy-efficient RIS-assisted networks. **Many thanks to my co-authors and collaborators for their invaluable contributions! https://lnkd.in/dzfEFJXw

86

9 Comments

In our new paper with Airat Kamaletdinov entitled "Coupling Light with Matter for Identifying Dominant Subnetworks" (arXiv:2405.17296), we present a novel light-matter platform that uses complex-valued oscillator networks, a form of physical neural networks, to identify dominant subnetworks and uncover indirect correlations within larger networks. This approach offers potential advantages, including low energy consumption, high processing speed, and the immediate identification of co- and counter-regulated nodes without post-processing. The effectiveness of this approach is demonstrated through its application to biological networks, and we also propose its applicability to a wide range of other network types. https://lnkd.in/ePmYmNX4 Research is funded by HeIsinberg EU Horizon under GA No: 101114978: https://lnkd.in/e_i_9eba

34

Call for Papers: Special Session on Integrating Machine Learning Methods into Evolutionary Optimization Event: IEEE Congress on Evolutionary Computation (CEC) Location: Hangzhou, China Dates: June 8–12, 2025 Overview and Scope Evolutionary Algorithms (EAs) are highly effective for optimization challenges but depend on parameter tuning, algorithm selection, and computational resources. Integrating Machine Learning (ML) enhances EAs' adaptability, enabling automated parameter tuning, intelligent algorithm selection, surrogate modeling, and adaptive search strategies. These innovations address large-scale and real-world problems. This session seeks contributions presenting novel methods, empirical validations, or theoretical insights into ML-enhanced EAs. Topics of Interest Submissions are encouraged (but not limited to) the following topics: ✅ Machine learning for dynamic parameter tuning in evolutionary algorithms ✅ Automated algorithm/operator selection using ML techniques ✅ Surrogate-assisted optimization for computationally expensive problems ✅ Reinforcement learning and deep learning for guiding search strategies ✅ Adaptive evolutionary approaches for large-scale or real-world optimization ✅ Data-driven approaches to enhance exploration and exploitation balance ✅ ML-driven hybridization of EAs with other optimization techniques ✅ Empirical studies demonstrating ML-enhanced EAs on benchmark problems Submission Guidelines All submissions must follow the general guidelines of the IEEE CEC 2025 Submission Website (https://www.cec2025.org/). Authors should explicitly mention that their paper is being submitted to this special issue. Accepted papers will appear in IEEE Xplore. Important Dates Paper Submission Deadline: January 15, 2025 Paper Acceptance Notification: March 15, 2025 Final Paper Submission and Early Registration Deadline: May 1, 2025 Conference Dates: June 8–12, 2025 Session Organizers Prof. Lhassane Idoumghar, Université de Haute-Alsace, France Prof. Amir H Gandomi, University of Technology Sydney, Australia Dr. Mahmoud Golabi (Ph.D.), Université de Haute-Alsace, France Dr. Abdennour Azerine, Université de Haute-Alsace, France For full details, see the attached document. #MachineLearning #EvolutionaryOptimization #IEEECEC2025 #CallForPapers

29

Just presented our paper on "Schrodinger Equation for Mobility Problem in a N-Channel MOSFET using GNU Octave" at 2024 IEEE Latin American Electron Devices Conference (LAEDC)📝💻 Our research delves into advanced solutions for understanding transistor behavior. By employing the Schrödinger equation, we're able to model quantum effects, such as mobility and quantum tunneling, which can significantly impact device performance.

37