Measurement performance of self-mixing interferometric (SMI) laser sensor can be significantly affected due to the presence of noise. In this case, conventional signal enhancement techniques yield compromised performance due to several limitations which include processing signals in frequency domains only, relying mainly on first order statistics, loss of important information present in higher frequency band and handling limited number of noise types. To address these issues, we propose a solution based on using generative adversarial network, a popular deep learning scheme, to enhance SMI signal corrupted with different noise types. Thus, taking advantage of the deep networks that can learn arbitrary noise distribution from large example set, our proposed method trains the deep network model end-to-end, able to process raw waveforms directly, learn 51 different noise conditions including white noise and amplitude modulation noise for 1,140 different types of SMI waveforms made up of 285 different optical feedback coupling factor (C) values and 4 different line-width enhancement factor α values. The results show that the proposed method is able to significantly improve the SNR of noisy SM signals on average of 19.49, 16.29, 10.34 dB for weak-, moderate-, and strong-optical feedback regime signals, respectively. For amplitude modulated SMI signals, the proposed method has corrected the amplitude modulation with maximum error (using area-under-the-curve based quantitative analysis) of 0.73% for SMI signals belonging to all optical feedback regimes. Thus, our proposed method can effectively reduce the noise without distorting the original signal. We believe that such a unified and precise method leads to enhancement of performance of SMI laser sensors operating under real-world, noisy conditions.

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry–clear, humid–hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

The measurement of Morphometric parameters of Fish Erythrocytes (MFE) has been recognized as a valuable tool for ecological biomonitoring. Conventional methods for measuring MFE greatly depend on manual measurements, leading to a labor-intensive and time-consuming process that relies on human expertise. To overcome these issues, in this paper, a solution based on image processing and ellipse fitting to provide an automated framework for the measurement of MFE is presented. The performance of the proposed method is assessed quantitatively by comparing its results with manual measurements performed by an expert biologist. Additionally, a comparison of the proposed method with three state-of-the-art methods is presented by providing the statistical analysis of their mean absolute errors. The analysis of results demonstrates the proposed method significantly outperforms existing methods for both cell and nucleus measurement. It notably achieves a lower absolute mean error value of 78 pixels for cell area and 36 pixels for nucleus area, confirming the better performance of the proposed method compared to the others. The experimental results demonstrate that the proposed method can correctly fit the ellipse on both fish blood cells and their nuclei.

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry.