CORTEXA
← Browse
crossrefMicromachines2022-12-01Cited by 5

Vertical Cavity Surface Emitting Laser Performance Maturing through Machine Learning for High-Yield Optical Wireless Network

Ammar Armghan, Khaled Aliqab, Farman Ali, Fayadh Alenezi, Meshari Alsharari

The high-yield optical wireless network (OWN) is a promising framework to strengthen 5G and 6G mobility. In addition, high direction and narrow bandwidth-based laser beams are enormously noteworthy for high data transmission over standard optical fibers. Therefore, in this paper, the performance of a vertical cavity surface emitting laser (VCSEL) is evaluated using the machine learning (ML) technique, aiming to purify the optical beam and enable OWN to support high-speed, multi-user data transmission. The ML technique is applied on a designed VCSEL array to optimize paths for DC injection, AC signal modulation, and multiple-user transmission. The mathematical model of VCSEL narrow beam, OWN, and energy loss through nonlinear interference in an optical wireless network is studied. In addition, the mathematical model is then affirmed with a simulation model following the bit error rate (BER), the laser power, the current, and the fiber-length performance matrices. The results estimations declare that the presented methodology offers a narrow beam of VCSEL, mitigating nonlinear interference in OWN and increasing energy efficiency.

View free PDFSource page

Related papers

crossrefMicromachines2024-12-26Cited by 14

A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance

Na Xiao, Yonghui Li, Peiyan Sun, Peihua Zhu, Hongyan Wang, Yin Wu, et al.

In recent years, metal nanomaterials and nanoproducts have been developed intensively, and they are now widely applied across various sectors, including energy, aerospace, agriculture, industry, and biomedicine. However, nanomaterials have been identified as potentially toxic, wi…

View free PDFSource page
crossrefMicromachines2026-05-27

Explainable Ensemble Machine Learning for Predicting Deposition Characteristics in Advanced Additive Manufacturing

Sandeep Jain, Pradyumn Kumar Arya

In advanced manufacturing processes, precise deposition behavior prediction is crucial for process parameter optimization. In order to forecast significant deposition responses such as bead width (w), bead height (h), energy input (EI), and volumetric input (VI) based on process…

View free PDFSource page
crossrefMicromachines2023-05-29Cited by 27

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Ahmed Fadlelmoula, Susana O. Catarino, Graça Minas, Vítor Carvalho

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient–provider decision-making. This paper presents a review of artic…

View free PDFSource page
crossrefMicromachines2023-07-13Cited by 9

Flexible Pressure Sensors and Machine Learning Algorithms for Human Walking Phase Monitoring

Thanh-Hai Nguyen, Ba-Viet Ngo, Thanh-Nghia Nguyen, Chi Cuong Vu

Soft sensors are attracting much attention from researchers worldwide due to their versatility in practical projects. There are already many applications of soft sensors in aspects of life, consisting of human-robot interfaces, flexible electronics, medical monitoring, and health…

View free PDFSource page
crossrefMicromachines2023-12-22Cited by 39

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies

Wei Wang, Peiren Wang, Hanzhong Zhang, Xiaoyi Chen, Guoqi Wang, Yang Lu, et al.

Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the reali…

View free PDFSource page
crossrefMicromachines2023-07-30Cited by 14

An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning

Xiaodong Wu, Zhizhen Wang, Shenglin Ma, Xianglong Chu, Chunlei Li, Wei Wang, et al.

The decreasing-width, increasing-aspect-ratio RDL presents significant challenges to the design for reliability (DFR) of an advanced package. Therefore, this paper proposes an ML-based RDL modeling and simulation method. In the method, RDL was divided into blocks and subdivided i…

View free PDFSource page