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An approach is described for spectrally parallel hyperspectral mid-infrared imaging with spatial resolution dictated by fluorescence imaging. Quantum cascade laser (QCL)-based dual-comb mid-infrared spectroscopy enables the acquisition of infrared spectra at high speed (<1 millisecond) through the generation of optical beat patterns and radio-frequency detection. The high-speed nature of the spectral acquisition is shown to support spectral mapping in microscopy measurements. Direct detection of the transmitted infrared beam yields high signal-to-noise spectral information, but long infrared wavelengths impose low diffraction-limited spatial resolution. The use of fluorescence detected photothermal infrared (F-PTIR) imaging provides high spatial resolution tied directly to the integrated IR absorption. Computational imaging using a multi-agent consensus equilibrium (MACE) approach combines the high spatial resolution of F-PTIR and the high spectral information of dual-comb infrared transmission in a single optimized equilibrium hyperspectral data cube.
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This content will become publicly available on August 1, 2026
Dual-GRE: Dual-Phase Enhancement in Radiomap Estimation Based on Graph Attention
Radiomap characterizes geographical radio spectrum coverage and can facilitate resource allocation and management of wireless networks. One practical radiomap estimation (RME) task is to form a full radiomap from sparse samples collected by sensors or mobile devices. Often, traditional RME approaches focus on statistical data distributions without exploiting the underlying spatial correlations among sparse observations. Utilizing geometric/geographical path correlation, this letter proposes a novel dual-phase RME method based on graph neural networks. In this Dual-phase Graph-based Radiomap Estimation (Dual-GRE) framework, the first phase integrates graph attention (GAT) networks with radio propagation models to construct a coarse-resolution (CR) radiomap to embed the spatial information and physical principles. Phase 2 utilizes a deep convolution neural network that uses the CR radiomap and landscape information to derive fine-resolution radiomaps. Our experimental results demonstrate the power of physics-integrated GAT in capturing the spatial spectrum information, together with the efficiency of the proposed Dual-GRE in radiomap estimation.
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- PAR ID:
- 10628796
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Wireless Communications Letters
- Volume:
- 14
- Issue:
- 8
- ISSN:
- 2162-2337
- Page Range / eLocation ID:
- 2646 to 2650
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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