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• A novel model, ROSE-Net, has been trained on clinical PPG data that estimates SpO2 accurately within the clinical standard. • ROSE-Net has been tested on two external rPPG datasets where SpO2 has been estimated within clinical standards. • Robust error analysis has been conducted to validate the accuracy of SpO2 estimation. • The study has demonstrated the potential of using rPPG as a viable alternative to clinical PPG for vital health measurements such as SpO2. A method for accurately estimating physiological signals from video streams at a minimal cost holds immense value, particularly in pre-clinical health monitoring applications. This technique is particularly indispensable in scenarios where traditional sensors, such as finger photoplethysmography (PPG), are not viable, such as in cases involving burn victims, premature infants, or individuals with sensitive skin. Remote photoplethysmography (rPPG) is a process of estimating PPG signals using video streams instead of traditional sensors. rPPG has thus been seen as a promising alternative to traditional PPG. As an alternative to using PPG for estimating oxygen saturation (SpO2), we propose ROSE-Net. ROSE-Net, trained on clinical PPG, was tested on an external rPPG dataset, PURE. The model achieved a mean absolute error (MAE) of 1.20 and a root mean square error (RMSE) of 1.86 on clinical PPG. When tested on rPPG, it exhibited an MAE of 1.95 and an RMSE of 2.46 in PURE, an MAE of 0.77, and an RMSE of 0.96 in ARPOS. These results demonstrate the model's ability to estimate SpO2 levels within acceptable margins when applied to rPPG data. Consequently, rPPG presents a viable approach for estimating SpO2 levels, paving the way for non-contact health tracking applications. [ABSTRACT FROM AUTHOR]

One of the most common and successful biometric frameworks is iris recognition, which has yielded promising results in systems of access control and identity authentication. Recent systems of iris recognition have concentrated on photos taken in non-ideal circumstances. Non-ideal imaging environments involve the capture of iris pictures in motion, at a distance, or under near-infrared (NIR) or visible wavelength illumination, which introduces noise factors such as gaze deviation, lack of focus, and obstruction by hair, eyelids, lighting, specular reflections, and eyeglasses. Segmenting irises is still a challenging task for eye images taken under non-ideal circumstances for iris recognition, and is strongly dependent on pupil segmentation; to estimate a line of sight using the pupil centre method, pupil segmentation is essential. The quality of eye pictures often changes due to noise effects and the individual variance between human eyes, making segmentation of the pupil a tricky problem. To address this issue, we propose an efficacious, swift, and robust iris recognition method that is convenient for eye pictures under non-ideal conditions. In this paper, we present a novel pupil segmentation method based on a fusion of the multi-scale gray-level co-occurrence matrix (MSGLCM) and the multi-range circle Hough transform (MRRCHT) methods. Using this segmentation approach, the pupil texture region is extracted correctly and a Hough transform is then used for the outer iris region. To support the improvements in segmentation accuracy, we also apply pre- and post-processing operations. An evaluation of the proposed method is conducted in this paper using the CASIA v4.0 (Distance) and MMU V2 databases, which reflect the challenges of iris recognition from non-ideal pictures. Extensive experimental findings clarify the effectiveness of the suggested method for non-ideal iris pictures. [ABSTRACT FROM AUTHOR]

One of the most common and successful biometric frameworks is iris recognition, which has yielded promising results in systems of access control and identity authentication. Recent systems of iris recognition have concentrated on photos taken in non-ideal circumstances. Non-ideal imaging environments involve the capture of iris pictures in motion, at a distance, or under near-infrared (NIR) or visible wavelength illumination, which introduces noise factors such as gaze deviation, lack of focus, and obstruction by hair, eyelids, lighting, specular reflections, and eyeglasses. Segmenting irises is still a challenging task for eye images taken under non-ideal circumstances for iris recognition, and is strongly dependent on pupil segmentation; to estimate a line of sight using the pupil centre method, pupil segmentation is essential. The quality of eye pictures often changes due to noise effects and the individual variance between human eyes, making segmentation of the pupil a tricky problem. To address this issue, we propose an efficacious, swift, and robust iris recognition method that is convenient for eye pictures under non-ideal conditions. In this paper, we present a novel pupil segmentation method based on a fusion of the multi-scale gray-level co-occurrence matrix (MSGLCM) and the multi-range circle Hough transform (MRRCHT) methods. Using this segmentation approach, the pupil texture region is extracted correctly and a Hough transform is then used for the outer iris region. To support the improvements in segmentation accuracy, we also apply pre- and post-processing operations. An evaluation of the proposed method is conducted in this paper using the CASIA v4.0 (Distance) and MMU V2 databases, which reflect the challenges of iris recognition from non-ideal pictures. Extensive experimental findings clarify the effectiveness of the suggested method for non-ideal iris pictures. [ABSTRACT FROM AUTHOR]

We examine the magneto-optical and image state rotation (MOR) of left and right circularly plane-polarized light pulse through a double Λ -type four-level configuration of Rubidium atom via Kerr field and Doppler broadening in the presence of the static magnetic field. The medium's behavior is different for both orthogonal components of the probe beam, causing linear dichroism or circular birefringence. Enhanced and lossless MOR is obtained by applying the control field through a closed-loop birefringent system. We observe a magnified angle of rotation of probe field in Doppler broadened medium. It is shown that due to the Kerr nonlinearity, the rotation angle of the probe beam is generated and modified through cold and Doppler broadened medium. Moreover, almost complete polarization rotation is observed for a particular set of parameters. The MOR can be effectively utilized in designing new TM/TE polarization modes, optical communication, polarization spectroscopy, lider with backscattering depolarization, precision measurements, and magnetometry. • We theoretically examine the magneto-optical rotation (MOR) of left and right circularly plane-polarized light pulse through a double Λ -type four-level configuration of birefringent Rubidium atom. • The effects of the Kerr field and Doppler broadening in the presence of the static magnetic field are investigated on the MOR and image state rotation of the medium. • The medium's behavior is different for both orthogonal components of the probe beam, causing linear dichroism or circular birefringence. • Enhancement in MOR is obtained by applying the control field through a closed-loop system and observing a magnified angle of rotation of the probe field in a Doppler broadened medium. • It is shown that due to the Kerr nonlinearity, the rotation angle of the probe beam is generated and modified through a cold and Doppler broadened medium. [ABSTRACT FROM AUTHOR]