Event-based visual-inertial odometry (VIO) is often considered a promising sensing solution for low-power micro-robots because event cameras naturally produce sparse, low-latency measurements. However, an efficient sensor does not automatically lead to an energy-efficient system. This paper adopts a system-level view, treating the energy consumption of event-based VIO as an outcome of the interaction between event representation and state-estimation strategy. it examines how information density and computational demand jointly shape system power. From this analysis, it introduces the principle of representation–estimation coupling consistency. Evidence from a broad range of systems supports this principle. The framework also suggests where future low-power VIO systems are likely to converge: event-driven, semi-continuous, and jointly adaptive architectures under strict power budgets. Finally, it provides design guidance for three power ranges and discuss open problems. This study aims to offer a unified perspective for building energy-efficient perception systems for resource-constrained micro-robots.

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To address background noise that reduces speech intelligibility in voice communications, this paper compares the noise-reduction performance of FIR (window function), Butterworth IIR, and adaptive LMS filters. Experiments using speech signals with white noise, pink noise, and real-world environmental noise under input SNRs of 0–10 dB show that at 0 dB input SNR, the LMS filter achieves an output SNR of 8 dB and MSE of 0.01, significantly outperforming FIR (6 dB, 0.07) and IIR (5 dB, 0.08). At an input SNR of 10 dB, the performance gap narrows to 1 dB. The IIR filter has the lowest order (5th) and minimal computational cost; the FIR filter (65th) offers the best linear phase; the LMS filter (32nd) exhibits the strongest adaptability at low SNRs. Overall, LMS prioritizes adaptability, IIR prioritizes efficiency, and FIR suits phase-sensitive applications. This study provides quantitative references for noise suppression in voice communication systems.

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As 6G networks advance toward higher levels of autonomy and intelligence, the demand for sophisticated multimodal data processing in communication systems is growing exponentially. Conventional localized AI models encounter significant generalization bottlenecks when handling cross-layer network operations and dynamic resource allocation. To overcome these limitations, this paper systematically investigates the application frameworks of large language models (LLMs) in wireless communication systems—spanning from physical-layer protocols to high-layer network management—while critically evaluating the associated deployment challenges. Drawing on a comprehensive review of prominent literature published over the past three years, this study empirically assesses the performance of diverse LLM architectures across three key domains: physical-layer protocol parsing, network-layer resource allocation, and service orchestration. Results demonstrate that LLMs yield substantial improvements in end-to-end semantic communication, standardized protocol interpretation, and intelligent network resource scheduling. Nevertheless, practical deployment remains severely hindered by the computational constraints of edge devices and prohibitively high inference latency. We conclude that the co-design of lightweight, telecom-specific large language models (Telecom-LLMs) and distributed inference mechanisms constitutes a pivotal evolutionary pathway toward realizing endogenous intelligence in future wireless communication systems.

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Neural scene reconstruction has become an important tool for building digital environments used in robotics, autonomous systems, and physical AI training. However, NeRF-based reconstruction often requires high computational cost and does not directly produce simulation-ready mesh assets. 3D Gaussian Splatting offers a faster alternative by representing a captured scene as an explicit cloud of Gaussian primitives that can be rendered in real time. This paper proposes a practical pipeline that converts 3D Gaussian Splatting outputs into mesh-based assets and exports them into NVIDIA Omniverse and Isaac Sim workflows via OpenUSD. The proposed method extracts geometry from the Gaussian primitive cloud, reconstructs a watertight or simulation-usable mesh, bakes appearance information into textures and materials, and exports the result as an OpenUSD-compatible asset with physics and semantic metadata. The goal is to lower the cost of building realistic training environments while preserving visual and geometric fidelity sufficient for simulation, synthetic data generation, and robot learning. The paper also outlines a future extension toward live conversion, where streamed captures are incrementally transformed into simulation-ready scene updates.

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