- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Heart rate estimation from photoplethysmography signal for wearable health monitoring devices
摘要： Wearable wrist type health monitoring devices use photoplethysmography (PPG) signal to estimate heart rate (HR). The HR estimation from these devices becomes difficult due to the existence of strong motion artifacts (MA) in PPG signal thereby leading to inaccurate HR estimation. The objective is to develop a novel de-noising algorithm that reduces the MA present in PPG signal, resulting in an accurate HR estimation. A novel de-noising technique using the hierarchical structure of cascade and parallel combinations of two different pairs of adaptive filters which reduces MA from the PPG signal and improves HR estimation is proposed. The first pair combines normalized least mean squares (NLMS) and recursive least squares (RLS) adaptive filters and the second pair combines recursive least squares (RLS) and least mean squares (LMS) adaptive filters. The de-noised signals obtained from the first and second pairs are combined to form a single de-noised PPG signal by means of convex combination. The HR of the de-noised PPG signal is estimated in the frequency domain using a Fast Fourier transform (FFT). Performance of the proposed technique is evaluated using a dataset of 12 individuals performing running activity in Treadmill. It resulted in an average absolute error of 0.92 beats per minute (BPM), standard deviation of the absolute error of 1.17 beats per minute (BPM), average relative error of 0.72 and Pearson correlation coefficient of 0.9973.
关键词： Photoplethysmography,Convex combination,Heart rate estimation,Motion artifact,Wearable devices,Combination of adaptive filters
更新于2025-09-23 15:22:29
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Lower Power, Better Uniformity, and Stability CBRAM Enabled by Graphene Nanohole Interface Engineering
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： image reconstruction,image representations,Adaptive filters,image edge analysis,image enhancement,synthetic aperture radar (SAR),image analysis,digital filters
更新于2025-09-23 15:21:01
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A Phase Calibration Method for Millimeter-Wave Up-Converter Using Electro-Optic Sampling
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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Dual-Core Photonic Crystal Fiber-Based Plasmonic RI Sensor in the Visible to Near-IR Operating Band
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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Advances in reduction of total harmonic distortion in solar photovoltaic systems: A literature review
摘要： The use of photovoltaic (PV) systems has increased in recent years due to the high demand for clean energy sources. PV systems can utilize abundant and free energy from the sun, which is a substantial advantage. However, compared with other renewable technologies, the PV system still faces major obstacles such as high cost and low efficiency. In addition, fluctuating incident energy from the sun creates harmonics in the generated power that might lead to undesirable system performance. Total harmonic distortion (THD) is the ratio of distorted power to the main power of the signal, and is most commonly used to indicate the amount of signal distortion. THD has become a serious concern as more PV systems are integrated into grid systems. Previous research and reviews have attempted to reduce THD and its effect, but unfortunately focused on reducing THD at individual parts of the PV system. For the first time, this study holistically and systematically reviews the advances in THD reduction techniques for the entire PV system. The causes of harmonics, current solutions, and research gaps for further investigation are described in detail. Moreover, the current THD reduction techniques used in each stage of the PV system are compared, including their main benefits and drawbacks. Finally, this study recommends the use of adaptive filters as a possible solution for THD reduction because these filters have effectively reduced noise and disturbance in other systems.
关键词： THD reduction,DC-DC converter,harmonics,THD,photovoltaic,inverters,PV,adaptive filters,renewable energy,MPPT
更新于2025-09-12 10:27:22
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Hyperspectral Imaging Classification based on a Convolutional Neural Network with Adaptive Windows and Filters Sizes
摘要： Image classification by the Convolutional Neural Networks (CNN) has shown its great performances in recent years, in several areas, such as image processing and pattern recognition; However, there is still some improvement to do. The main problem in CNN is the initialization of the number and size of the filters, which can obviously change the results. In this article, we assign three major contributions, based on the CNN model; (1) adaptive selection of the number of filters. (2) using an adaptive size of the windows. (3) using an adaptive size of the filters. The tests results, applied to different hyperspectral datasets (SalinasA, Pavia University, and Indian Pines), have proven that this framework is able to improve the accuracy of the HSI classification.
关键词： Adaptive Filters,Convolutional Neural Networks,Image Classification,Hyperspectral Imaging
更新于2025-09-09 09:28:46