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There is a growing demand for hand-held and/or field-grade sensors for biochemical analysis of fluids. These systems have applications in monitoring of nitrogen-based compounds (such as nitrate and ammonia) in the wastewater treatment industry; bacterial detection in drinking water; analysis of biofluids, such as urine or blood; and in many other areas. Mid-infrared (midIR) spectroscopy is a powerful tool for identification and quantification of a wide range of common organic and inorganic compounds. Although IR radiation is strongly absorbed in water, this technology can be adapted for analysis of fluids by utilizing the principles of attenuated total reflection (ATR). In this contribution we highlight the application of IR spectroscopy in wastewater analysis as well as for metabolomic analysis in bioreactors. We discuss the requirements for IR signal stability that are necessary for biochemical analysis of fluids and provide examples of challenges encountered during transition from FTIR to a QCL-based platform. Overall, our stepwise efforts target eventual integration of a QCL light source, waveguide sensor, and IR detector onto a single photonic integrated circuit (PIC) for applications in the defense sector as well as for a broad consumer market. 

Fabric density and distortion offer important information on fabric attributes and quality during the manufacturing process. However, most current procedures require human effort, which is often inefficient, time-consuming, and imprecise. In this paper, we propose to use an automatic method using the 2D Fast Fourier Transform (2D-FFT) to count the number of yarns and determine the angle rotation of weft yarns in fabric images. First, we explain the mathematical background of Fourier Transform and 2D-FFT. Then, we use a customized and optimized software package to apply a 2D- FFT to extract image magnitude, phase, and power spectrum. We apply the inverse 2D Fast Fourier Transform (2D-iFFT) on selected frequencies corresponding to periodic structures – basic weave patterns – to reconstruct the original image and extract warp and weft yarns separately. Finally, we use a local adaptive threshold process to convert reconstructed images into binary images for the counting and calculating process. For the weft rotation, we apply a mathematical calculation on the frequency domain to collect the angular distribution and then figure out the major rotation of weft yarns. Our experiments show that the proposed method is highly accurate and capable of inspecting different patterns of fabric. We also observe that the processing time of our proposal method is practical and time-efficient.