Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

Flame Imaging Technology Based on 64-Pixel Area Array Sensor

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

Flame Edge Detection Method Based on a Convolutional Neural Network

In this study, an improved flame edge detector based on convolutional neural network (CNN) was proposed. The proposed method can generate edge graphs and extract edge graphs relatively effectively. Our network architecture was based on VGG16 primarily, the last two max-pooling operators and all full connection layers of the VGG16 network were deleted, and the rest was taken as the basic network. The images output by the five convolution layers were upsampled to the size of the input images and finally fused to the edge image. Error calculation and back propagation of the fusion image and label image are carried out to form a weakly supervised model. Using the open datasets BSDS500 to train the network, the ODS F-measure can reach 0.810. Various experiments were carried out on different flame and fire images, including butane-air flame, oxygen-ethanol flame, energetic material flame, and oxygen-acetylene premixed jet flame, and the infrared thermogram was also verified by our method. The results demonstrate the effectiveness and robustness of the proposed algorithm.