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Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function
These histograms are influenced by the intrinsic properties of the fluorophore, instrument parameters, time-of-flight distributions associated with pixel-wise variations in the topographic and optical characteristics of the sample.
Unlocking Real-Time Fluorescence Lifetime Imaging: Multi-Pixel Parallelism for FPGA-Accelerated Processing
The limited memory and computational resources on the FPGA require efficient scheduling of operations and memory allocation to deploy deep learning models for low-latency applications.
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging
Fluorescence lifetime imaging (FLI) is an important technique for studying cellular environments and molecular interactions, but its real-time application is limited by slow data acquisition, which requires capturing large time-resolved images and complex post-processing using iterative fitting algorithms.
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