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- 저자 : Talha Mubashir (검색결과 2 건)
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Faiza Khan,Talha Mubashir,Kainat Ahmed,Muhammad Shahoor,Abdul Mateen,이순일,Tauseef Ahmed 한국전기전자재료학회 2023 Transactions on Electrical and Electronic Material Vol.24 No.6
This paper investigates the electrical resistivity of piezoresistive CNT/PVAc-based nanocomposites. Different CNTs wt. % containing multi-walled carbon nanotubes (MWCNTs) dispersed in polyvinyl acetate and deposited on a flexible polymer substrate of ethylene glycol methacrylate (PVAc) matrix using conventional methods. The morphological changes were observed using SEM analysis. The resulting composites were subjected to compression by applying different values of pressure (9.3-2348.8 kPa). The results show that for lower wt. % of CNTs, the value of resistance decreased (~0.01526MΩ) with increasing applied pressure, which could be attributed to increasing the conducting paths with compression. However, increasing the concentration of CNTs to a higher value > 1.0 wt. %, results show the opposite behavior, an increase in resistance with an increase in pressure, which could be ascribed to the reorientation, bending, and entanglement of CNTs blocking the conducting paths. The percolation threshold for CNT/PVAc nanocomposite is 0.1 wt. %. This study provides valuable insights into the structural and sensing properties of CNT-based pressure Nanosensors and highlights their potential for use in various applications.
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A Robust sEMG base Hand Gesture Recognition System
Seemab Zakir,Talha Anwar,Muhammad Waqas,Vaneeza Iman,Mubashir Ali 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11
The use of surface electromyography has increase recently for hand gesture recognition because of the feasible usage of low cost, wearable, non-invasive devices. Hand gesture enhances human-machine interaction to great extent. This paper proposed a robust approach for hand gesture classification using various machine learning classifiers. Six different features such as; minimum, maximum, peak to peak, root mean square, zero crossing and waveform length are extracted from raw data and fed to machine learning classifiers. Data is comprised of 36 individuals and seven gestures are classified with an accuracy of 90% and F1 score of 87% using Support Vector Machine classifier. Our reproducible implementation is available at github.com/talhaanwarch/emg-gesture-classification
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