Syed Muhammad Mamduh Syed Zakaria
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Preferred name
Syed Muhammad Mamduh Syed Zakaria
Official Name
Syed Muhammad Mamduh, Syed Zakaria
Alternative Name
Syed Zakaria, Syed Muhammad Mamduh
Syed Zakaria, S. M.M.
Zakaria, S. M.M.S.
Zakaria, Syed Muhammad Mamduh Bin Syed
Zakaria, Syed Muhammad Mamduh Syed
Main Affiliation
Scopus Author ID
55193486100
Researcher ID
AAY-1214-2020

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Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

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Publication

Novel Approach Using Passive UHF RFID for Grain Moisture Detection

2022-01-01 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

This paper proposes a novel method to detect moisture hotspots and irregularities in rice grain storage using low-cost passive UHF RFID technology. Experiments were designed with a UHF RFID handheld reader to test rice moisture levels of 12%, 16%, 20% and 24%. Two containers were used in the study where Container A was filled with grains with a 12% moisture level and container B is inserted into Container A and contained 2 kg rice samples of varying moisture contents. The RFID reader was positioned outside the container to measure the received signal strength indicator (RSSI) from the RFID tags placed within the containers. The attenuation of the signal is analyzed to obtain a correlation between moisture content (MC) and RSSI values. Results show a positive correlation between the RSSI and MC of rice which can be used to identify inconsistencies in moisture distribution in stored grain. An empirical model has been proposed which can be used to estimate RSSI values given the moisture content or vice versa for RFID operating at 915 MHz.

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Publication

Novel approach using passive UHF RFID for grain moisture detection

2022-01-01 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

This paper proposes a novel method to detect moisture hotspots and irregularities in rice grain storage using low-cost passive UHF RFID technology. Experiments were designed with a UHF RFID handheld reader to test rice moisture levels of 12%, 16%, 20% and 24%. Two containers were used in the study where Container A was filled with grains with a 12% moisture level and container B is inserted into Container A and contained 2 kg rice samples of varying moisture contents. The RFID reader was positioned outside the container to measure the received signal strength indicator (RSSI) from the RFID tags placed within the containers. The attenuation of the signal is analyzed to obtain a correlation between moisture content (MC) and RSSI values. Results show a positive correlation between the RSSI and MC of rice which can be used to identify inconsistencies in moisture distribution in stored grain. An empirical model has been proposed which can be used to estimate RSSI values given the moisture content or vice versa for RFID operating at 915 MHz.

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Publication

Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map

2022-01-01 , Zaffry Hadi Mohd Juffry , Kamarulzaman Kamarudin , Abdul Hamid Adom , Mao X. , Latifah Munirah Kamarudin , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria , Abdulnasser Nabil Abdullah

The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.

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Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map

2022-01-01 , Zaffry Hadi Mohd Juffry , Kamarulzaman Kamarudin , Abdul Hamid Adom , Mao X. , Latifah Munirah Kamarudin , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria , Abdullah A.N.

The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.

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Publication

Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Radzi A.S.M. , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

View
Thumbnail Image
Publication

Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

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