arXiv subjects

Jiangchuan Mu

Publications and source records attributed to Jiangchuan Mu.

2 recordsLinked to original sources

arXiv2025

Integrated Sensing and Communication System Based on Radio Frequency Resonance Beam

To address the complex beam control in traditional multiple-input multiple-output (MIMO) systems, researchers have proposed adaptive beam alignment using retro-directive antenna (RDA) arrays. This approach creates echo resonance between the base station (BS) and user equipment (UE), significantly reducing computational load. However, conventional resonant beam systems (RBS) suffer from echo interference due to the shared uplink and downlink frequency. Therefore, this paper proposes an innovative resonance beam-based integrated sensing and communication (RB-ISAC) system designed for efficient passive sensing and bidirectional communication. In this system, the UE operates passively, with both the BS and UE utilizing a phase conjugation and frequency conversion structure to decouple uplink and downlink carrier frequencies, ensuring continuous electromagnetic wave oscillation between the two ends. Effective compensation for signal propagation loss enables resonance after multiple oscillations. At this point, the beam's field forms a low-diffraction-loss, highly focused pattern, automatically aligning the transmitter and receiver. This enables high-precision passive positioning alongside robust uplink and downlink communication. Simulation results demonstrate the proposed system achieves resonance within multiple iterations, supporting uplink and downlink communication up to 5 m, and enabling passive direction of arrival (DOA) estimation with an error under 2$^\circ$ .

eess.SP↗

arXiv2024

Can video generation replace cinematographers? Research on the cinematic language of generated video

Recent advancements in text-to-video (T2V) generation have leveraged diffusion models to enhance visual coherence in videos synthesized from textual descriptions. However, existing research primarily focuses on object motion, often overlooking cinematic language, which is crucial for conveying emotion and narrative pacing in cinematography. To address this, we propose a threefold approach to improve cinematic control in T2V models. First, we introduce a meticulously annotated cinematic language dataset with twenty subcategories, covering shot framing, shot angles, and camera movements, enabling models to learn diverse cinematic styles. Second, we present CameraDiff, which employs LoRA for precise and stable cinematic control, ensuring flexible shot generation. Third, we propose CameraCLIP, designed to evaluate cinematic alignment and guide multi-shot composition. Building on CameraCLIP, we introduce CLIPLoRA, a CLIP-guided dynamic LoRA composition method that adaptively fuses multiple pre-trained cinematic LoRAs, enabling smooth transitions and seamless style blending. Experimental results demonstrate that CameraDiff ensures stable and precise cinematic control, CameraCLIP achieves an R@1 score of 0.83, and CLIPLoRA significantly enhances multi-shot composition within a single video, bridging the gap between automated video generation and professional cinematography.\textsuperscript{1}

cs.CV↗