[2024 - 2025]

@inproceedings{Orsholits2025,

title = {Context-Rich Interactions in Mixed Reality through Edge AI Co-Processing},

author = {Alex Orsholits and Manabu Tsukada},

url = {https://link.springer.com/chapter/10.1007/978-3-031-87772-8_3},

doi = {10.1007/978-3-031-87772-8_3},

isbn = {978-3-031-87771-1},

year  = {2025},

date = {2025-04-09},

urldate = {2025-04-09},

booktitle = {The 39-th International Conference on Advanced Information Networking and Applications (AINA 2025)},

address = {Barcelona, Spain},

abstract = {Spatial computing is evolving towards leveraging data streaming for computationally demanding applications, facilitating a shift to lightweight, untethered, and standalone devices. These devices are therefore ideal candidates for co-processing, where real-time context understanding and low-latency data streaming are fundamental for seamless, general-purpose Mixed Reality (MR) experiences. This paper demonstrates and evaluates a scalable approach to augmented contextual understanding in MR by implementing multi-modal edge AI co-processing through a Hailo-8 AI accelerator, a low-power ARM-based single board computer (SBC), and the Magic Leap 2 AR headset. The proposed system utilises the native WebRTC streaming capabilities of the Magic Leap 2 to continuously stream camera data to the edge co-processor, where a collection of vision AI models-object detection, pose estimation, face recognition, and depth estimation-are executed. The resulting inferences are then streamed back to the headset for spatial re-projection and transmitted to cloud-based systems for further integration with large-scale AI models, such as LLMs and VLMs. This seamless integration enhances real-time contextual understanding in MR while facilitating advanced multi-modal, multi-device collaboration, supporting richer, scalable spatial cognition across distributed systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhu2025,

title = {A Distributed Content Subscription Mechanism with Revision Discovery to Decouple Content Sharing Platform and Creator ID},

author = {Zhihai Zhu and Ye Tao and Manabu Tsukada and Hiroshi Esaki},

year  = {2025},

date = {2025-02-18},

urldate = {2025-02-18},

booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2025) },

address = {Fukuoka, Japan},

abstract = {Only the chairs can edit This paper proposes a distributed content subscription mechanism that enables content creators to share updates with their audience while maintaining platform independence and anonymity. The mechanism extends the Kademlia distributed hash table (DHT) protocol by incorporating revision numbers and republication timestamps into the DHT key computation, allowing subscribers to discover content updates through heuristic revision queries. It leverages public key cryptography for creator identification and content authenticity, while integrating with established peer-to-peer protocols like BitTorrent for efficient content distribution. Preliminary testing with 200 simulated nodes demonstrates the mechanism's ability to maintain content availability and update discovery even when content creators are offline. This approach particularly benefits creators operating under strict content controls or surveillance, offering them greater creative freedom and distribution autonomy compared to existing centralized and decentralized solutions.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}