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DR. Saeid Hosseini Sohar University |
Dr. Abdolreza Abhari Toronto Metropolitan University |
DR. Nematollaah Shiri Concordia Unversity |
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From Value-wise ConvNets to Signal-wise Transformers: Toward LLM-inspired Architectures for Vectorized Data The study explores adapting Large Language Models (LLMs), particularly transformer-based architectures, to process continuous signal data. It proposes representing signals as vectorized tokens input into transformers, redesigning core components like embeddings, self-attention, and feedforward layers for efficiency and effectiveness, and using the model’s output head to generate meaningful vector representations for signal processing applications. |
Empowering Digital Twins with AI Generative Models Digital twins are virtual representations of physical systems that reflect real-time behavior, performance, and status. Powered by sensor networks, these twins receive continuous data streams that mirror real-world conditions. Integrating AI generative models enables the creation of synthetic data and simulation of complex scenarios, enhancing prediction, optimization, and decision-making capabilities within the digital twin framework.This work explores the integration of AI generative models with digital twins—virtual counterparts of physical systems that rely on sensor networks for real-time data input. |
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- Important Notice for Authors
- Important Notice on Compliance with the Official Template and Paper Submission
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- Call for Papers – Final Deadline Extension
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Paper Submission Deadline: October 22, 2025
Extention Paper Submission Deadline: November 11, 2025
Extention Paper Submission Deadline: November 21, 2025
Notification of Acceptance: December 1, 2025
Final Version Submission: December 7, 2025
Registration Start Time: December 10, 2025
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June 2, 2025






