Two-Mode Biomedical Sensor Build-up: Characterization of Optical Amplifier

Masud, Usman; Jeribi, Fathe; Alhameed, Mohammed; Akram, Faraz; Tahir, Ali; Naudhani, Mohammad Yousaf

doi:10.32604/cmc.2022.020417

Cited by 13 publications

(10 citation statements)

References 55 publications

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“…A comparison of RIN results with earlier outcomes provides a close contrast between the values which is quite encouraging [17,72]. We now attempt to check the values of RIN at different values of SOA injection current and temperature.…”

Section: Discussionsupporting

confidence: 53%

Design of Two-Mode Spectroscopic Sensor for Biomedical Applications: Analysis and Measurement of Relative Intensity Noise through Control Mechanism

et al. 2022

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The design of an intracavity spectroscopy based two-mode biomedical sensor involves a thorough investigation of the system. For this purpose, the individual components that are present in the system must be examined. This work describes the principle of two very important gadgets, namely the Fibre Bragg Grating (FBG), and the tunable coupler. We adhere to a Petri network scheme to model and analyze the performance of the FBG, and the results mirror strikingly low difference in the values of Bragg Wavelength during its ascending and descending operational principle, thereby maintaining the accuracy of the sensor’s results. Next, a pseudocode is developed and implemented for the investigation of the optical coupler in LabView. The values of its maximum output power are determined, and the coupling ratio for various values of controlling voltage is determined at three different wavelengths. The hysteresis results mirror an extremely low difference between the forward and reverse values in the results. Both the results of the FBG and the coupler are thereby extremely reliable to use them in the laser system, as evident from the respective intensity noise outcomes, as well as the experimentation on substances of interest (Dichloro Methane and Propofol).

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Section: Discussionsupporting

confidence: 53%

Design of Two-Mode Spectroscopic Sensor for Biomedical Applications: Analysis and Measurement of Relative Intensity Noise through Control Mechanism

et al. 2022

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“…In order to improve the detection speed, 1-stage detector that integrates the region proposal and classification steps have been proposed. The representative technologies are YOLO [20], SSD [21], and Retina-Net [22], and they are widely used in real-time applications such as a biomedical service [23] and assistive system for visually impaired people [24].…”

Section: Object Detectionmentioning

confidence: 99%

Effective Denoising Architecture for Handling Multiple Noises

Kim¹,

Kim²

2023

Computer Systems Science and Engineering

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Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities. Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images. In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality. Thus, an appropriate process for removing noise must precede to improve object detection performance. Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously. In this paper, we propose a convolutional denoising autoencoder (CDAE)-based architecture trained on various types of noises. We also present various composing modules for each noise to improve object detection performance for night images. Using the exclusively dark (ExDark) Image dataset, experimental results show that the Sequential filtering architecture showed superior mean average precision(mAP) compared to other architectures.

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“…The process of continuously combining many low-resolution points of view (POVs) to create a higher-resolution image is called super-resolution [31][32][33][34][35][36]. The original assessment of the highresolution image, f 1 (0) , inside this Irani and Peleg concept of a super-resolution algorithm can be derived from the average of the input images scanned and relocated to a similar stationary frame:…”

Section: Preprocessing Of Blood Smear Imagesmentioning

confidence: 99%

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Veeraiah¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person's tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization techniques have already been introduced, although they still have certain drawbacks. In order to enhance feature extraction and classification in such a practical way, Mayfly optimization with Generative Adversarial Network (MayGAN) is introduced in this research. Furthermore, Generative Adversarial System (GAS) is integrated with Principal Component Analysis (PCA) in the feature-extracted model to classify the type of blood cancer in the data. The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia. Acute lymphocytic Leukaemia (ALL), acute myelogenous Leukaemia (AML), chronic lymphocytic Leukaemia (CLL), chronic myelogenous Leukaemia (CML), and aberrant White Blood Cancers (WBCs) are all successfully classified by the proposed MayGAN model. The proposed MayGAN identifies the abnormal activity in the WBC, considering the geometric features. Compared with the state-of-the-art methods, the proposed MayGAN achieves 99.8% accuracy, 98.5% precision, 99.7% recall, 97.4% F1-score, and 98.5% Dice similarity coefficient (DSC).

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Two-Mode Biomedical Sensor Build-up: Characterization of Optical Amplifier

Cited by 13 publications

References 55 publications

Design of Two-Mode Spectroscopic Sensor for Biomedical Applications: Analysis and Measurement of Relative Intensity Noise through Control Mechanism

Design of Two-Mode Spectroscopic Sensor for Biomedical Applications: Analysis and Measurement of Relative Intensity Noise through Control Mechanism

Effective Denoising Architecture for Handling Multiple Noises

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

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