Correction to: Innovations in Electronics and Communication Engineering

Saini, H. S.; Singh, Rahul; Beg, Mirza Tariq; Sahambi, J. S.

doi:10.1007/978-981-15-3172-9_75

Cited by 3 publications

(1 citation statement)

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“…When comparing measured and reference signals, similarity methods based on Manhattan metrics can be used to minimize the distances between signals [68,69]. Besides the better accuracy, Manhattan metrics are more preferable than Euclidean in terms of resource consumption, as Euclidean requires square-root operations, and Manhattan needs only the absolute value of a subtraction [70].…”

Section: Rocchio and K-nn With Different Distance Metricsmentioning

confidence: 99%

Fiber-Optic Hydrophone Based on Michelson’s Interferometer with Active Stabilization for Liquid Volume Measurement

Duque

Díaz

Leal‐Junior

et al. 2022

Sensors

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Sensing technologies using optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophone (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. This work presents an FOH system composed of two optical fiber coils made with commercial single mode fiber (SMF) working in the sensor head of a Michelson’s interferometer (MI) supported by an active stabilization mechanism that drives another optical coil wound around a piezoelectric actuator (PZT) in the reference arm to mitigate external mechanical and thermal noise from the environment. A 1000 mL glass graduated cylinder filled with water is used as a test tank, inside which the sensor head and an ultrasound source are placed. For detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 mL and sensitivity of 340 mrad/mL and 70 mvolts/mL. The nonlinear behavior of both measurands requires classification, distance metrics, and regression algorithms to define an adequate model. The results show the system can determine liquid volumes with an accuracy of 99.4% using a k-nearest neighbors (k-NN) classification with one neighbor and Manhattan’s distance. Moreover, Gaussian process regression using rational quadratic metrics presented a root mean squared error (RMSE) of 0.211 mL.

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Section: Rocchio and K-nn With Different Distance Metricsmentioning

confidence: 99%

Fiber-Optic Hydrophone Based on Michelson’s Interferometer with Active Stabilization for Liquid Volume Measurement

Duque

Díaz

Leal‐Junior

et al. 2022

Sensors

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Evaluation of Free Space Optics Uplink Availability to LEO Satellite Using Climatic Data in Cairo

Zaghloul¹,

Shaalan²,

Kasban³

et al. 2021

jcm

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Free space optics uplink to Low Earth Orbit (LEO) satellite with 500Km distance and 1.55μm wavelength is evaluated with 10Gbps data rate and 70W transmitted power. The equivalent effects for all weather parameters are given by the measured visibility used to determine the weather attenuations by using Mie-Scattering, in this proposed. These high attenuations reduce the availability performance of the FSO uplink. The scintillation (Cn2) had estimated using a compatible model of Cairo weather, and the turbulence effect (beam divergence) becomes very weak by using the aperture diameter of the receiver higher than the maximum optical spot size due to turbulence and divergence. During the year 2019, the visibility must be greater than 5Km, and weak scintillation turbulence is Cn2<0.00566∗10−16 m-2/3at distance 500Km. The availability evaluations based on predicted attenuation due to weather and turbulence using data measured in Cairo and the effects of all parameters on the performance of FSO are discussed. The required transmitted power must be related to the expected visibility. In year 2019, the availability of link becomes 99.9945%.

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Developing a Cloud-Based Mobile Learning Adoption Model to Promote Sustainable Education

2020

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Education plays a very significant role in the context of sustainability. As the world population is growing, providing education through the traditional classroom setting is not sufficient and not feasible to extend learning in professional life. Therefore, modern technology-mediated learning paradigms such as mobile learning are becoming increasingly popular. Mobile learning is said to integrate multiple contexts, learning types, mobilities and communications. As information and communications technology (ICT) plays a vital role in the delivery of mobile learning services, it is very essential to adopt sustainable IT resources to keep it viable. Cloud computing offers a range of affordable, scalable and on-demand solutions. This paper attempts to model important critical success factors (CSFs) in the area of cloud-based mobile learning using the interpretive structural modeling (ISM) technique. ISM helps in identifying the hierarchical inter-relationships between the variables of study with the help of experts in the field. Finally, Matrice d’Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) analysis is employed to classify the variables into dependent and independent variables. Management support has been identified as most rudimentary among sixteen CSFs identified through a literature review to establish a distinguished relative advantage. Further, the paper discusses the theoretical underpinning of all the constructs. This study will help organizations to implement mobile learning in sustainable ways.

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Correction to: Innovations in Electronics and Communication Engineering

Cited by 3 publications

References 0 publications

Fiber-Optic Hydrophone Based on Michelson’s Interferometer with Active Stabilization for Liquid Volume Measurement

Fiber-Optic Hydrophone Based on Michelson’s Interferometer with Active Stabilization for Liquid Volume Measurement

Evaluation of Free Space Optics Uplink Availability to LEO Satellite Using Climatic Data in Cairo

Developing a Cloud-Based Mobile Learning Adoption Model to Promote Sustainable Education

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