Browsing by Author "Rahman, SAM Matiur"

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Highly sensitive SPR based PCF for biological substance sensing: design and analysis
(SPIE., 2018-05-17) Asaduzzama, Sayed; Paul, Bikash Kumar; Ahmed, Kawsar; Bhuiyan, Touhid; Rahman, SAM Matiur
Proposed and numerically investigated by Finite Element Method (FEM). The proposed SPR-based In this paper, a surface Plasmon resonance (SPR) based photonic crystal fiber has been PCF shows higher average wavelength interrogation sensitivity than the previous structures. Different plasmonic materials have been used to show the difference in results. Liquid filled cores with metallic surface can be exited with leaky-Gaussian core guided mode. Numerical investigation of optical properties for the proposed PCF has been established by changing the designing parameters like pitch, diameters etc. The proposed PCF is simple in nature and can be easily fabricated by existing methods. Biological substances, biochemical, organic chemical analysis, bimolecules can be detected by our proposed SPR based PCF.
Proposed Soft Skills Development Model at the Tertiary Level Higher Education Institutes of Bangladesh
(Daffodil International University, 2017-12-01) Rahman, SAM Matiur; Bhuiyan, Touhid
Soft skill deficiency of tertiary level graduates in Bangladesh remains one of the major constraints to the continued growth of the employment sector. This paper proposes human capital development in university level education through implementing soft skills such as communication skills, critical thinking and problem solving skills, team work, lifelong learning and information management skills, entrepreneurial skills, ethics and professional skills and moral leadership skills. The Ministry of Education in collaboration with University Grants Commission may introduce the discussed soft skills to all tertiary level Higher Education Institutes (HEI) in Bangladesh. Suggestions on how these elements are to be integrated in the program of all disciplines are also put forward.
The Use of Wearable Sensors for the Classification of Electromyography Signal Patterns based on Changes in the Elbow Joint Angle
(Scopus, 2021) Rahman, SAM Matiur; Ali, Md. Asraf; Mamun, Md. Abdullah Al
Upper limb elbow movement in terms of flexion-relaxation is a complex physical phenomenon in human daily life, particularly during synchronized activities, such as exercising, reaching, pointing, and manipulating. Improper elbow movement might cause musculoskeletal injury, fatigue, pain, or disorders in the upper limb muscles. This study aimed to identify subject-specific electromyography (EMG) signal patterns based on changes in five elbow joint angles (at 0°, 30°, 60°, 90° and 120°) during maximum (100%) voluntary isometric (static) contraction. Surface electromyography (sEMG) signals were recorded from the upper arm biceps brachii muscle using a three-channel wearable sensor. A non-parametric machine learning algorithm called k-nearest neighbors (k-NN) was used to build a model that can determine the EMG characteristics and thus discriminate between elbow joint angles. Fifteen time domain features were extracted from the recorded EMG signal and those were used for classification purposes. Two cross validation (CV) methods, namely, leave-one-out (LOO) and k-fold, were used to examine and validate the model. The results showed that k-fold CV showed higher mean classification accuracies (89.68%) than the LOO method (82.49%). Our classification-based results from sEMG signals acquired with five elbow joint angles could aid the development of more advanced rehabilitation assistive devices and further improve the neuromuscular activities of the upper arms. Additionally, this result showed that wearable technology has potential application for remotely monitoring and controlling motor rehabilitation exercises.