White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.

Tunable Diode Laser Absorption Spectroscopy (TDLAS), a trace gas sensing technology based on infrared absorption spectroscopy, has been developed rapidly in the past few decades. The advantages of low cost and easy miniaturization could be applied in real-time monitoring. As an important factor, the detection sensitivity of TDLAS has been improved by a variety of methods. In this review paper, the important advances in TDLAS detection sensitivity are discussed, including the selection of absorption lines, the improvement of diode lasers, the design of effective optical paths, data demodulation, and the suppression of background interference. For gases with high application values, such as CH₄, CO₂, and NO, we summarize the detection sensitivity that the existing TDLAS system has been achieved, combined with the above-improved process. However, considering the principle of infrared absorption, the increase in detection sensitivity could reach an ultra-limit. Therefore, the hypothesis of the sensitivity limit of TDLAS is proposed at the end of the paper, through the quantization analysis.

We propose a novel denoising method based on empirical mode decomposition (EMD) to improve the signal-to-noise ratio (SNR) for vibration sensing in phase-sensitive optical time domain reflectometry (φ-OTDR) systems. Raw Rayleigh backscattering traces are decomposed into a series of intrinsic mode functions (IMFs) and a residual component using an EMD algorithm. High frequency noise is eliminated by removing several IMFs at the position without vibration selected by the Pearson correlation coefficient (PCC). When the pulse width is 50 ns, the SNR of location information for the vibration events of 100 Hz and 1.2 kHz is increased to as high as 42.52 dB and 39.58 dB, respectively, with a 2 km sensing fiber, which demonstrates the excellent performance of this new method.

This paper presents a novel method for segmentation of white blood cells (WBCs) in peripheral blood and bone marrow images under different lights through mean shift clustering, color space conversion and nucleus mark watershed operation (NMWO). The proposed method focuses on obtaining seed points. First, color space transformation and image enhancement techniques are used to obtain nucleus groups as inside seeds. Second, mean shift clustering, selection of the C channel component in the CMYK model, and illumination intensity adjustment are employed to acquire WBCs as outside seeds. Third, the seeds and NMWO are employed to precisely determine WBCs and solve the cell adhesion problem. Morphological operations are further used to improve segmentation accuracy. Experimental results demonstrate that the algorithm exhibits higher segmentation accuracy and robustness compared with traditional methods.

We propose an adaptive algorithm that is a Variational Mode Decomposition (VMD) optimized by the particle swarm optimization (PSO) algorithm, named PSO-VMD. The method selects the envelope entropy of the last intrinsic mode function (IMF) in the VMD as the fitness function of the PSO and 1/10 of the maximum value of the correlation coefficient between the IMFs and the standard signal as the threshold of the correlation coefficient. In the processing of simulated and experimental second harmonic signals, a series of standards, including the same correlation coefficient threshold and standard signal, are used to adaptively achieve noise reduction processing. After processing a simulated signal using PSO-VMD, the signal-to-noise ratio (SNR) was improved by 4.03877 dB and the correlation coefficient (R²) between the gas concentration and the second harmonic maximum was improved from 0.97743 to 0.99782. In the processing of an experimental signal, the correlation coefficient (R²) was 0.99733. The mean value and standard deviation of the second harmonic signal of multiple cycles processed by PSO-VMD were improved compared to the unprocessed experimental signal. This demonstrated that the method has the advantage of being reliable and stable.