arXiv subjects

Omar Ibrahim

Publications and source records attributed to Omar Ibrahim.

6 recordsLinked to original sources

arXiv2026

LiDAR-Derived Surface Priors for Multimodal Sensing-Assisted NLoS Beam Search in Indoor 60-GHz Networks

Highly directional 60-GHz Internet-of-Things (IoT) links can exploit naturally occurring indoor surfaces to sustain connectivity under blockage. Identifying viable non-line-of-sight (NLoS) paths, however, can require extensive RF beam training. This paper investigates whether LiDAR can reduce this search overhead by providing a surface-aware prior without assuming a direct mapping between optical return and mmWave reflection. The proposed framework uses LiDAR-derived geometry and return statistics to rank candidate propagation directions, while RF measurements remain responsible for final beam selection. The experimental validation is organized in three stages to separate descriptor robustness, cross-modal association, and beam-search performance. Controlled LiDAR measurements first quantify how geometric and radiometric surface descriptors vary with acquisition geometry. Matched LiDAR and 60-GHz measurements in an L-shaped corridor then determine whether these descriptors are associated with the measured surface-mediated RF response under a prescribed NLoS interaction. Finally, a separate room-scale campaign evaluates the resulting prior using exhaustive TX-RX beam maps without prescribing the underlying propagation mechanism. The measurements show that descriptor reliability depends on acquisition geometry and point-cloud representation, and that LiDAR and RF surface responses exhibit cross-modal association without supporting deterministic RF-power prediction. In the room experiment, local three-ring 3-D planarity retains a beam within 3 dB of exhaustive search at 74.5% of the measured locations while reducing RF beam-pair probing by 72% relative to exhaustive probing over the candidate search region. These results establish LiDAR-derived local surface structure as a communication-oriented prior for concentrating RF probing and reducing mmWave beam-search uncertainty.

eess.SY↗

arXiv2025

Benchmarking and Adapting On-Device LLMs for Clinical Decision Support

Large language models (LLMs) have rapidly advanced in clinical decision-making, yet the deployment of proprietary systems is hindered by privacy concerns and reliance on cloud-based infrastructure. Open-source alternatives allow local inference but often have large model sizes that limit their use in resource-constrained clinical settings. Here, we benchmark on-device LLMs from the gpt-oss (20b, 120b), Qwen3.5 (9B, 27B, 35B), and Gemma 4 (31B) families across three representative clinical tasks: general disease diagnosis, specialty-specific (ophthalmology) diagnosis and management, and simulation of human expert grading and evaluation. We compare their performance with state-of-the-art proprietary models (GPT-5.1, GPT-5-mini, and Gemini 3.1 Pro) and a leading open-source model (DeepSeek-R1), and we further evaluate the adaptability of on-device systems by fine-tuning gpt-oss-20b and Qwen3.5-35B on general diagnostic data. Across tasks, on-device models achieve performance comparable to or exceeding DeepSeek-R1 and GPT-5-mini despite being substantially smaller. In addition, fine-tuning remarkably improves diagnostic accuracy, with the fine-tuned Qwen3.5-35B reaching 87.9% and approaching the proprietary GPT-5.1 (89.4%). Among base on-device models, Gemma 4 31B achieved the strongest general diagnostic accuracy at 86.5%, exceeding GPT-5-mini and approaching the fine-tuned Qwen3.5-35B. Error characterization revealed that 87.2% of diagnostic errors across all models were clinically plausible differentials rather than off-topic predictions, and upper-bound analysis showed up to 93.2% attainable accuracy through improved answer selection. These findings highlight the potential of on-device LLMs to deliver accurate, adaptable, and privacy-preserving clinical decision support, offering a practical pathway for broader integration of LLMs into routine clinical practice.

cs.CL↗

arXiv2025

See and Beam: Leveraging LiDAR Sensing and Specular Surfaces for Indoor mmWave Connectivity

Millimeter-wave (mmWave) communication enables multi-gigabit-per-second data rates but is highly susceptible to path loss and blockage, especially indoors. Many indoor settings, however, include naturally occurring specular surfaces such as glass, glossy metal panels, and signage, that reflect both light and mmWave signals. Exploiting this dual reflectivity, we propose See and Beam, a low-cost framework that combines LiDAR sensing with passive specular reflectors to enhance mmWave connectivity under non-line-of-sight (NLoS) conditions. In this paper, as a proof of concept, we deploy three types of reflectors, glossy, smooth, and matte (non-specular), to evaluate joint LiDAR/mmWave reflection in an indoor scenario. We demonstrate that using LiDAR-mmWave co-reflective surfaces enables a co-located LiDAR sensor to map the NLoS environment, localize NLoS users, and identify viable communication reflection points. Experimental results at 60 GHz show that LiDAR-guided beam steering with co-reflective surfaces improves the minimum received signal strength by over 20 dB in deep NLoS regions. Moreover, LiDAR-derived angle-of-departure steering achieves performance comparable to exhaustive NLoS beam search. This low cost, and scalable framework serves as an effective alternative to configurable reflecting surfaces and enables robust mmWave connectivity in future 6G and beyond networks.

cs.NI↗

arXiv2025

BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model

Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explanations. BioReason addresses this by tightly integrating a DNA foundation model with a large language model (LLM), enabling the LLM to directly interpret and reason over genomic information. Through supervised fine-tuning and reinforcement learning, BioReason learns to produce logical, biologically coherent deductions. It achieves major performance gains, boosting KEGG-based disease pathway prediction accuracy from 86% to 98% and improving variant effect prediction by an average of 15% over strong baselines. BioReason can reason over unseen biological entities and explain its decisions step by step, offering a transformative framework for interpretable, mechanistic AI in biology. All data, code, and checkpoints are available at https://github.com/bowang-lab/BioReason

cs.LG↗

arXiv2024

LiDAR-Aided Millimeter-Wave Range Extension using a Passive Mirror Reflector

Passive reflectors mitigate millimeter-wave (mmwave) link blockages by extending coverage to non-line-ofsight (NLoS) regions. However, their deployment often leads to irregular reflected beam patterns and coverage gaps. This results in rapid channel fluctuations and potential outages. In this paper, we propose two LiDAR-aided link enhancement techniques to address these challenges. Leveraging user position information, we introduce a location-dependent link control strategy and a user selection technique to improve NLoS link reliability and coverage. Experimental results validate the efficacy of the proposed techniques in reducing outages and enhancing NLoS signal strength.

eess.SY↗

arXiv2021

Optical Magnetic Multipolar Resonances in Large Dynamic Metamolecules

Dynamic metamolecules (DMMs) are composed of a dielectric core made of hydrogel surrounded by randomly-packed plasmonic beads that can display magnetic resonances when excited by light at optical frequencies. Their optical properties can be controlled by controlling their core diameter through temperature variations. We have recently shown that DMMs display strong optical magnetism, including magnetic dipole and magnetic quadrupole resonances, offering significant potential for novel applications. Here, we use a T-matrix approach to characterize the magnetic multipole resonance modes of model metamolecules and explore their presence in experimental data. We show that high-order multipole resonances become prominent as the bead size and the overall structure sizes are increased, and when the the inter-bead gap is decreased. In this limit, mode mixing among high-order magnetic multipole modes also become significant, particularly in the directional scattering spectra. We discuss trends in magnetic scattering observed in both experiments and simulations, and provide suggestions for experimental design and verification of high-order optical magnetic resonances in the forward or backward scattering spectra. In addition, angular scattering of higher-order magnetic modes can display Fano-like interference patterns that should be experimentally detectable.

physics.optics↗