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Summary: The state‐of‐charge (SOC) of the battery is the most critical parameter for an electric vehicle (EV) dashboard. Generally, the Coulomb counting (CC) method is used for SOC estimation, but the conventional CC method has some errors due to initial SOC determination. This work uses battery modeling and parameter estimation of a lithium‐iron‐phosphate (LFP) battery to determine the initial SOC. The equivalent circuit models (ECMs) have been used for battery parameters estimation by the least‐squares estimation (LSE) algorithm. This algorithm uses only experimental terminal voltage and current data from Arbin battery testing system. The ECM parameters vary with battery SOC, and the differential capacity plot has explained the variation. SOC can be estimated using estimated parameters and the relationship between SOC and open‐circuit voltage (OCV). Additionally, a modified CC method is proposed to solve the problem of estimating initial SOC by determining an OCV‐SOC relation using experimental data. The modeling of the battery is validated by comparing ECMs with physical battery terminal voltage profiles. [ABSTRACT FROM AUTHOR]

The pressing demand for workload along with social media interaction leads to diminished alertness during work hours. Researchers attempted to measure alertness level from various cues like EEG, EOG, video‐based eye movement analysis, etc. Among these, video‐based eyelid and iris motion tracking gained much attention in recent years. However, most of these implementations are tested on video data of subjects without spectacles. These videos do not pose a challenge for eye detection and tracking. In this work, the authors have designed an experiment to yield a video database of 58 human subjects wearing spectacles and are at different levels of alertness. Along with spectacles, they introduced variation in session, recording frame rate (fps), illumination, and time of the experiment. They carried out an analysis to detect the reliableness of facial and ocular features like yawning and eye‐blinks in the context of alertness level detection capability. Also, they observe the influence of spectacles on ocular feature detection performance under spectacles and propose a simple preprocessing step to alleviate the specular reflection problem. Extensive experiments on real‐world images demonstrate that the authors' approach achieves desirable reflection suppression results within minimum execution time compared to the state‐of‐the‐art. [ABSTRACT FROM AUTHOR]

This study presents an image denoising technique using multiscale non‐local means (NLM) filtering combined with hard thresholding in curvelet domain. The inevitable ringing artefacts in the reconstructed image – due to thresholding – is further processed using a guided image filter for better preservation of local structures like edges, textures and small details. The authors decomposed the image into three different curvelet scales including the approximation and the fine scale. The low‐frequency noise in the approximation sub‐band and the edges with small textural details in the fine scale are processed independently using a multiscale NLM filter. On the other hand, the hard thresholding in the remaining coarser scale is applied to separate the signal and the noise subspace. Experimental results on both greyscale and colour images indicate that the proposed approach is competitive at lower noise strength with respect to peak signal to noise ratio and structural similarity index measure and excels in performance at higher noise strength compared with several state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]