Gas Chromatography - Ion Mobility Spectrometry (GC-IMS) serves as a valuable analytical tool for enhancing non-targeted screening of volatile organic compounds in a wide range of different applications. Nowadays, authenticating various food samples such as honey and olive oil, as well as identifying microorganism cultures, is essential for practical applications and can be effectively achieved using GC-IMS alongside various classification methods. However, limited resources and the time-intensive data collection process have led to a scarcity of datasets with a relatively low amount of data. As a result, training of generalized classification models becomes more challenging and complex. To address issues, such as effectively learning essential spectral features, compressing the data into a more compact, representative latent space of less than 1% of the input parameters, and classifying unseen spectra with limited training data available, we propose utilizing various configurations of CNN-based Deep Auto-Encoders. Moreover, we propose a detailed pre-processing pipeline along with three different denoising protocols for GC-IMS spectra to achieve a better generalization of our models. Through the evaluation of the proposed models utilizing three publicly available datasets, the experimental results demonstrate that these models outperform state-of-the-art methods reported in the literature across various tasks, including the classification of complex food samples and the identification of microorganism cultures.
Accurate tool wear measurement is crucial for optimizing tool life and product quality. Therefore, our research aim is to realize an optomechatronic sensor solution for in-situ tool wear measurements, using a chromatic confocal sensor and a robotic arm integrated into a turning machine. To enable tool wear measurements with maximal repeatability, i. e. to enable precise sensor positioning despite the robotic arm's position uncertainty of 50 mu m, a tool coordinate system (TCS) is defined based on the tool's unworn edge. Validation experiments on a reference sphere show enhanced repositioning capabilities of the optomechatronic sensor system with the proposed TCS-based registration method in the order of 6 mu m.
Oxygen saturation (SpO2) is a critical indicator of peripheral circulatory health, particularly in conditions such as peripheral artery disease. This study focuses on developing an innovative approach to quantify SpO2 at varying skin depths using multispectral illumination. By employing light sources emitting multiple wavelengths (e.g., 660 nm, 850 nm, 890 nm, and 940 nm) and capturing reflected skin images, the method analyzes SpO2 at different depths and constructs three-dimensional SpO2 distribution maps, enabling visualization and quantification of oxygen saturation across tissue layers. The study further examines the enhancement and retention effects of near-infrared (NIR) light exposure (600-1700 nm) on SpO2 at various depths. Results demonstrate that after 10 minutes of NIR illumination, SpO2 in superficial vascular regions increases by 7%. For retention analysis, the most effective layers for vascular and tissue SpO2 were identified as 940 nm and 890 nm, respectively. Notably, following 15 minutes of NIR exposure, SpO2 in vascular regions decreased by less than 2% within 15 minutes after the light source was turned off. This innovative, non-invasive, and quantitative approach offers insights into oxygen dynamics in tissues and blood vessels. It provides a promising tool for clinical diagnostics and therapeutic monitoring, showcasing the efficacy of NIR in enhancing blood oxygenation.
The temperature field of falling film is crucial of the heat transfer characteristics. However, falling film interface topology and temperature distribution are constantly changing, which is challenging for the analysis of heat transfer characteristics. The Planar Laser-Induced Fluorescence (PLIF) measurement method enables highly accurate and non-intrusive. The PLIF40 method effectively minimizes the influence of total reflection, which ensures the true film thickness more accurately and reliably from complex interfacial waves. In this paper, the super resolution reconstruction algorithm is designed for the falling film by PLIF40. It is utilized to improve the resolution of the falling film images. Moreover, noise is eliminated and the edge of the liquid film is smoothed. The temperature information is accurately extracted, and the heat transfer coefficient and Nusselt number of the temperature field are quantified. The effectiveness of the method is verified by numerical calculations and experiments. Finally, the heat transfer characteristics of the falling film are further investigated.
Inversion of sea surface wind direction using nautical radar images has significant practical value in engineering applications. While traditional single-curve fitting methods effectively address the issue of comprehensive data provision, the method is prone to substantial errors under unstable sea surface condition. This paper proposes a one-dimensional discrete wavelet transform-based algorithm for inverting wind direction from nautical radar images. The algorithm first extracts static low-frequency wind signals from radar images by applying the one-dimensional discrete wavelet transform (1D-DWT) to the one-dimensional radial echo intensity distance distribution data. This step effectively reduces the interference of high-frequency noise and wave signals on echo intensity, while preserving the relationship between echo intensity and azimuth. Secondly, prior to curve fitting, 1D-DWT is applied to extract the smoothed mean value of the echo intensity along each azimuthal line, thereby reducing the influence of echo intensity anomalies caused by breaking waves on the sea surface. Finally, the wind direction is extracted by fitting a cosine squared function using the least squares method. In this paper, real X-band nautical radar image data are utilized, compared to the traditional single-curve fitting algorithm, and the results demonstrate that the correlation coefficient between the inverted wind direction and the sensor wind direction improves by 0.07, the mean bias is reduced by 1.45 degrees, and the root mean square error decreases by 1.64 degrees.
