Tootfinder

CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks
Aathira G Menon (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka), Prabu Krishnan (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka), Shyam Lal (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka)

CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks
Heterogeneous Vehicular Networks (HetVNets) play a key role by stacking different communication technologies such as sub-6GHz, mm-wave and DSRC to meet diverse connectivity needs of 5G/B5G vehicular networks. HetVNet helps address the humongous user demands-but maintaining a steady connection in a highly mobile, real-world conditions remain a challenge. Though there has been ample of studies on beam prediction models a dedicated solution for HetVNets is sparsely explored. Hence, it is the need …

Me cuesta entender lo que quiere decir este titular. ¿El hijo de Chadwick logró recaudar mas de 50 millones como consecuencia de la fiscalización vehicular que le hicieron? ¿Después de la fiscalización? O, lo mšs probable, solo intentaron poner un titular sensacionalista que llame la atención para capturas mas clicks. 🤷🏻‍♂️
@…

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Tian-Yu Xiang, Rui-Ze Ma, Nu-Fang Xiao, Zeng-Guang Hou
https://arxiv.org/abs/2508.10794

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
Accurate vessel segmentation in X-ray angiograms is crucial for numerous clinical applications. However, the scarcity of annotated data presents a significant challenge, which has driven the adoption of self-supervised learning (SSL) methods such as masked image modeling (MIM) to leverage large-scale unlabeled data for learning transferable representations. Unfortunately, conventional MIM often fails to capture vascular anatomy because of the severe class imbalance between vessel and background…

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Crosslisted article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation
Xin Xing, Irmak Karaca, Samira Badrloo, Quan Dong Nguyen, Mahadevan Subramaniam

Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Vascular Networks
Timo Jakumeit, Bastian Heinlein, Leonie Richter, Sebastian Lotter, Robert Schober, Maximilian Sch\"afer
https://arxiv.org/abs/2510.11743

Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Vascular Networks
Synthetic molecular communication (MC) in the cardiovascular system (CVS) is a key enabler for many envisioned medical applications in the human body, such as targeted drug delivery, early cancer detection, and continuous health monitoring. The design of MC systems for such applications requires suitable models for the signaling molecule propagation through complex vessel networks (VNs). Existing theoretical models offer limited analytical tractability and lack closed-form solutions, making the…

MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation
Xin Xing, Irmak Karaca, Samira Badrloo, Quan Dong Nguyen, Mahadevan Subramaniam
https://arxiv.org/abs/2509.10554

MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation
We propose MAE-SAM2, a novel foundation model for retinal vascular leakage segmentation on fluorescein angiography images. Due to the small size and dense distribution of the leakage areas, along with the limited availability of labeled clinical data, this presents a significant challenge for segmentation tasks. Our approach integrates a Self-Supervised learning (SSL) strategy, Masked Autoencoder (MAE), with SAM2. In our implementation, we explore different loss functions and conclude a task-sp…

Visible Light Communication for Vehicular Networks: A Tutorial
Pedro E. G\'oria Silva, Eduardo S. Lima, Jules M. Moualeu, Mohamed Korium, Pedro H. J. Nardelli
https://arxiv.org/abs/2510.11123

Visible Light Communication for Vehicular Networks: A Tutorial
The advent of the fifth-generation technology promises to bring about more vertical applications and emerging services that include vehicular networks and intelligent transportation systems (ITSs). To achieve their vision of real-time and safetyapplications, vehicular networks rely on short-range to medium-range communications. One emerging technology that aims to provide reliability and high-data rate in short-range communications is the visible light communications (VLC). Due to its remarkabl…

Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks
Seyed Hossein Ahmadpanah
https://arxiv.org/abs/2508.09149 https:…

Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks
Next-generation automotive applications require vehicular edge computing (VEC), but current management systems are essentially fixed and reactive. They are suboptimal in extremely dynamic vehicular environments because they are constrained to static optimization objectives and base their decisions on the current network states. This paper presents a novel Semantic-Aware Proactive LLM Orchestration (SP-LLM) framework to address these issues. Our method transforms the traditional Digital Twin (DT…

Multi-Modal Sensing Aided mmWave Beamforming for V2V Communications with Transformers
Muhammad Baqer Mollah, Honggang Wang, Hua Fang
https://arxiv.org/abs/2509.11112 https://

Multi-Modal Sensing Aided mmWave Beamforming for V2V Communications with Transformers
Beamforming techniques are utilized in millimeter wave (mmWave) communication to address the inherent path loss limitation, thereby establishing and maintaining reliable connections. However, adopting standard defined beamforming approach in highly dynamic vehicular environments often incurs high beam training overheads and reduces the available airtime for communications, which is mainly due to exchanging pilot signals and exhaustive beam measurements. To this end, we present a multi-modal sen…