eess.SP · 2026-08-27 · No. 97

Signal Processing, 2026-08-27.

3 new papers in eess.SP. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Adaptive Peer Clustering with Hierarchical Random Linear Network Coding for Resilient Decentralized Wireless Networks

    Navaneetha Krishnan Kamalakannan, Harinisri Velmurugan

    eess.SP · cs.DC · cs.IT · cs.NI

    Decentralized wireless collectives including vehicular swarms, IoT clusters, and edge AI networks require communication protocols that maintain robustness under dynamic topologies and heterogeneous link quality. While Random Linear Network Coding (RLNC) provides algebraic resilience against packet erasures, its performance degrades significantly when peers exhibit diverse channel conditions. This paper presents Adaptive Peer Clustering with...

    arxiv.org/abs/2608.26040 · PDF

  2. 02

    CardioFusion-AI: Robust ECG--PPG Fusion for Multimodal Physiological Monitoring Under Signal Degradation

    Navaneetha Krishnan Kamalakannan, Janakiraman Kamalakannan

    eess.SP · cs.LG · q-bio.QM

    Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fusion strategies that assume both modalities are equally trustworthy can become less reliable than a single clean modality under degradation. We present CardioFusion-AI, a framework whose signal-processing front end, including R-peak and...

    arxiv.org/abs/2608.26000 · PDF

  3. 03

    Token-Oriented Semantic Communication with Pretrained Vision Transformers

    Jiwoong Im, Minwoo Kim, Jaeho Lee, Yo-Seb Jeon, Yongjune Kim

    eess.SP · cs.AI · cs.CV · cs.LG

    Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client--server collaborative inference in resource-constrained edge systems. However, directly transmitting token embeddings presents two practical challenges: substantial communication cost and limited interoperability across model-specific token embedding spaces. To address these challenges, we...

    arxiv.org/abs/2608.25410 · PDF

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