International Journal of Optical Science and Optical Engineering

Advancements in Laser Technology: A Comprehensive Review and Future Perspectives

Dr. Satya Sukumar Makkapati

This article explores the evolution of laser technology and its diverse applications across various industries. Beginning with the foundational work of Einstein and the pioneering efforts of Maiman and Dr. Leon Goldman, it delves into the development of laser diode chips and their widespread utilization in industrial, commercial, and telecommunications sectors. The article also discusses the application of lasers in automotive lightweight construction, military operations, photochemistry, and photobiology. Furthermore, it examines the emerging technology of Lidar remote sensing, especially in space, and the utilization of diode laser-based systems in atomic and molecular spectroscopy. Additionally, the article highlights specific applications such as laser-assisted cataract surgery, photodynamic therapy for cancer treatment, laser beam welding, and pigment-specific laser technology in dermatology.

Comparative Performance Analysis of Optical Detectors Using The GRA Method

Gaurav Saxena

Optical science is a fundamental and broad branch of physics and engineering that focuses on the study of light, its properties, and its interaction with matter in a wide range of applications and technical areas. This broad field also includes the study of other areas such as photonics, laser physics, fiber optics, spectroscopy, and imaging science. Essentially, it is the study of the properties and behavior of electromagnetic radiation, especially in the visible, infrared, and ultraviolet regions. Modern research in optical science has greatly contributed to the development and creation of cutting-edge technologies in high-precision microscopy, optical communication networks, and other areas. Alternatives: Optical Fiber Sensor, Laser Interferometer, Photonic Crystal Sensor, Optical Coherence Tomography Device, Fibber Bragg Grating Sensor, Raman Spectroscopy System, Holographic Imaging System. Evaluation criteria: Sensitivity, Accuracy, Cost Efficiency, Response Time. According to the results, Laser Interferometer, ranked highest, while Fibber Bragg Grating Sensor, to Users ranked lowest. According to the GRA method approach, Wheat has the highest value for optical science. Key Words: Light propagation, reflection, refraction, edge effect, absorption, optical imaging, spectroscopy, fiber optics, laser technology, optical analysis.

Comparative Study of Optical Imaging Methods Using the Weighted Product Model

Varaha Venkata Nagabhushan Rao Singampalli

The functioning of contemporary optical devices relies on the accurate control and manipulation of These parameters influence the behaviour of optical signals and support essential operations such as signal transmission, modulation, filtering, and detection. Recent progress in optical technologies, including laser-assisted microscopy, photonic crystal fibres, graphene-based modulators, and meta surfaces, has greatly enhanced the capabilities of optical systems in communication, sensing, and imaging applications. These developments have also contributed to significant advancements in scientific disciplines such as biology, chemistry, and astronomy by enabling the observation of microscopic structures and complex cosmic events. Furthermore, modern computational methods, particularly artificial intelligence and artificial neural networks, have strengthened the analysis of complex high-dimensional data and improved the efficiency of optical and communication systems. Challenges in optical science, such as phase retrieval problems and the observation of short-lived astrophysical phenomena like gamma-ray bursts, emphasize the need for advanced analytical and computational techniques. In addition, decision-making, along with optimization strategies, facilitate the effective evaluation and selection of materials, technologies, and system designs. Overall, the integration of optical science with intelligent algorithms and modern decision-making methodologies continues to promote innovation and progress in information and communication technologies, engineering systems, and scientific research.

Predicting Optical Transmittance in Thin Films Using Machine Learning

Kiran Kumar Mandula Samuel

A The prediction of optical transmittance is important in the design and analysis of thin film optical materials. This study investigates the relationship between key optical parameters such as wavelength, refractive index, incident angle, and film thickness in predicting the transmittance percentage. The association between was modeled using machine learning techniques like Linear Regression and Random Forest Regression, the input parameters and the output parameter. Research Significance: Understanding and accurately predicting optical transmittance is essential in fields such as optical coating design, photonic devices, and material science. The interaction between light and thin film materials depends on several physical parameters, making analytical prediction complex. By applying machine learning models, this research provides an efficient computational approach to examine how optical characteristics affect transmittance. Methodology: The study follows a machine learning–based predictive modeling approach. Initially, a dataset containing optical parameters such as wavelength, refractive index, incident angle, and film thickness was analyzed using statistical and visualization techniques. Alternative (Input Parameters): The study's input parameters include wavelength (nm), refractive index, incident angle (degrees), and film thickness (nm). These parameters represent the key physical characteristics that influence the behavior of light when interacting with thin film materials. Variations in these factors have a major impact on the optical transmission characteristics of the material. Evaluation Parameter (Output Parameter): The output parameter taken into account in this investigation is the transmittance percentage. Transmittance represents the proportion of incident light that passes through the thin film material. It serves as the primary indicator for evaluating the optical performance of the material and is predicted using the selected machine learning models. Result: The experimental results indicate that both Linear Regression and Random Forest Regression are capable of predicting transmittance values. The analysis also reveals that incident angle, refractive index, and film thickness show stronger relationships with transmittance compared to wavelength. Conclusion: The results highlight the potential of data-driven methods in optical material analysis and design. Future research may explore additional parameters and advanced machine learning algorithms to further enhance prediction accuracy. Keywords: Optical Transmittance, Machine Learning, Linear Regression, Random Forest Regression, Thin Film Optics, Optical Parameters, Predictive Modeling.

Predicting Q Factor Performance in Next-Generation Optical Fiber Networks Using Machine Learning Regression Paradigms

Rajendar Dommeti

As channel speeds approach 100Gb/s and beyond, networks face increased vulnerability to imperfections including pigment dispersion, polarization mode dispersion, nonlinear Kerr effects, and OSNR degradation. This study explores machine learningbased signal processing to predict and optimize Q factor, a key network performance metric. Using a dataset of 109 observations across five signal parameters – OSNR, output power, fiber length, dispersion and nonlinear effects – the analyses included descriptive statistics, correlation and supervised. In contrast, random forest regression achieved a high training R² of 0.9732, but dropped to 0.5318 in testing, indicating overfitting. Correlation analysis identified OSNR as the most influential factor in Q factor performance. The results indicate that linear regression provides a more reliable and general approach to Q factor prediction, while ensemble models such as RFR require regularization to improve robustness in high-dimensional optical signal processing tasks. Keywords: Optical fiber networks, signal processing, Q factor prediction, machine learning regression, dense wavelength division multiplexing (DWDM)