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Md. Rafiqul Islam

Researcher at Khulna University of Engineering & Technology

Publications -  408
Citations -  2515

Md. Rafiqul Islam is an academic researcher from Khulna University of Engineering & Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 20, co-authored 334 publications receiving 1915 citations. Previous affiliations of Md. Rafiqul Islam include National University of Singapore & International Islamic University, Islamabad.

Papers
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Proceedings Article

Strain and crystal orientation-dependent optical properties of mid-infrared Gasb-based quantum well laser

TL;DR: In this article, the optical properties of GaSb-based mid-infrared quantum well lasers are numerically studied by solving one-dimensional Schrodinger equation and the simulation results demonstrate that there is a strong correlation of peak emission wavelength and optical gain with crystal orientation and strain.
Proceedings ArticleDOI

Comparative Study on the Performance of 3D DMTG and SMTG III-VON MOSFETs

TL;DR: In this article, a comparative study of 3D dual-material triple-gate (DMTG) and SMTG III-V semiconductors-on-nothing (III-VON) MOSFETs is presented.
Posted ContentDOI

Genetic Gains in IRRI’s Rice Salinity Breeding and Elite Panel Development as a Future Breeding Resource

TL;DR: In this paper , a two-stage mixed-model approach accounting for experimental design factors and pedigrees was adopted to obtain the breeding values for yield and estimate genetic trends under the salinity conditions.
Proceedings ArticleDOI

Impact of chipshape on the performance of DS-OCDMA in dispersive fiber medium

TL;DR: In this article, the chip shape-dependent bit error rate (BER) performance of direct sequence optical code division multiple access (CDMA) was analyzed in fiber optic communication with cascaded in-line optical amplifiers in presence of group velocity dispersion.
Journal ArticleDOI

Wearable Smart Phone Sensor Fall Detection System

TL;DR: In this paper , a wearable smart phone sensor fall event detection system is introduced, which utilizes real-time raw sensory accelerometer, gyroscope and gravity data collected from the AndroSensor App and is then processed and applied to a machine learning classifier.