Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Noor Ain Kamsani
Universiti Putra Malaysia

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A 1 V -21 dBm threshold voltage compensated rectifier for radio frequency energy harvesting Seyed Arash Zareianjahromi; Noor Ain Kamsani; Fakhrul Zaman Bin Rokhani; Roslina Bt Mohd Sidek; Shaiful Jahari Bin Hashim
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp28-36

Abstract

Due to the limitations of battery life and capacity, power supply has been the bottleneck for scaling of a wireless sensor network in thousands or millions of nodes. RF energy harvester (RFEH) is a promising solution to power up sensors and wireless devices due to increasing accessibility of RF energy sources, better silicon integration of the harvester circuit and compatibility with wireless networks. One of the significant limitations of RF energy harvester is low power efficiency rectifier where the main function of the micropower rectifier is to convert RF energy into DC energy. To achieve higher power conversion energy (PCE), this paper presents a five-stage charge pump rectifier, with implementation of diode-connected MOS transistors and an auxiliary circuit to produce compensation voltage to the charge pump to achieve higher efficiency over a wide input range. This work is designed and implemented using 130nm CMOS technology and achieved a wide input power range of 15 dBm with efficiency higher than 20%; and at -21 dBm sensitivity for 1V output is achieved while driving 1 MΩ load at 920MHz.
Development of fall detection and activity recognition using threshold based method and neural network Sai Siong Jun; Hafiz Rashidi Ramli; Azura Che Soh; Noor Ain Kamsani; Raja Kamil Raja Ahmad; Siti Anom Ahmad; Asnor Juraiza Ishak
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1338-1347

Abstract

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
Co-Authors Asnor Juraiza Ishak Azura Che Soh Fakhrul Zaman Bin Rokhani Hafiz Rashidi Ramli Raja Kamil Raja Ahmad Roslina Bt Mohd Sidek Sai Siong Jun Seyed Arash Zareianjahromi Shaiful Jahari Bin Hashim Siti Anom Ahmad