Syed Muhammad Mamduh Syed Zakaria
Thumbnail Image
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

Research Output

Filters

11
11
Reset filters

Settings
Now showing 1 - 10 of 11
  • 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.
  • Publication
    Reinforcement Learning for Mobile Robot's Environment Exploration
    ( 2023-01-01)
    Teoh S.W.H.
    ;
    Kamarulzaman Kamarudin
    ;
    Ali N.A.N.
    ;
    Zainal M.M.M.
    ;
    Mohd Rizal Manan
    ;
    Syed Muhammad Mamduh Syed Zakaria
    Mobile robots are being are being applied in various industries to perform repetitive or dangerous tasks for humans to carry out. Autonomous mobile robots are more capable than automated guided vehicles (AGV) due to their ability to be adaptable to their environment which is important for exploration of unknown environments. It is difficult to program autonomous mobile robots to adapt to various situations it may face, thus machine learning can be applied to allow a mobile robot to learn how to navigate through environments by itself. Reinforcement learning is applied in this project so that a differential drive mobile robot can learn how to navigate through its environment while avoiding collision with surrounding walls and obstacles. The reinforcement learning process is simulated by using the Robot Operating System (ROS) and its simulator Gazebo. Controlled simulation environments are created using Gazebo for the purposes of training and performance testing. Simultaneous Localization and Mapping (SLAM) will be applied to generate a map of the environment. At the end of this project, the Turtlebot3 is able to map smaller controlled environments ranging between 18m2 to 27m2 without colliding with the surrounding walls.
      1
  • 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.
      3
  • Publication
    Gas Source Localization via Mobile Robot with Gas Distribution Mapping and Deep Neural Network
    ( 2022-01-01)
    Ahmad Shakaff Ali Yeon
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Visvanathan R.
    ;
    Kamarulzaman Kamarudin
    ;
    Latifah Munirah Kamarudin
    With the growth of artificial intelligence compute technology, the gas source localization problem would be solved by mobile robots equipped with gas sensing system and artificial intelligence compute units. This work presented a feasibility study of deep learning approach towards gas source localization by mobile robots. A deep neural network strategy was developed and incorporated with the Kernel DM+V gas distribution mapping method. The gas source localization work in this paper was performed on a controlled indoor testbed. From this work, it is shown that by incorporating the developed deep neural network model, it may help improved the gas source location prediction accuracy. A comparison of accuracy between Kernel DM+V and the neural network model is also presented to better visualize the improvement.
      3
  • Publication
    Relative Localization Method of Wet Spot of Grain using Array of Passive RFID Tags
    ( 2021-12-01)
    Azmi N.
    ;
    Latifah Munirah Kamarudin
    ;
    Latifah Mohamed
    ;
    Ammar Zakaria
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Ndzi D.L.
    Radio Frequency Identification (RFID) enables a large number of object monitoring since semi/passive tags are independent of batteries. In our previous work, the possibility of using different wireless technologies such as Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) to determine the moisture content in rice was investigated. Finding from our previous work suggest that RFID can be used to determine the moisture content of rice. While numerous research have been conducted for moisture content of grain, however, to author's knowledge, there is only a few studies conducted on the localization of grain hostpot. Therefore, this study aims to investigate if the passive RFID array can be used to localize the location of the wet spot of grain. Prior, the experiment, a suitable setting for the RFID system were determined. In addition, a simple test was conducted to select a suitable operating frequency. From the investigation, the result indicates that only frequency channels 865, 866, 867, 868 and 869 MHz can detect all 30 tags. Meanwhile, frequency channel in the range 902 to 928 MHz detects 26 to 29 unique tags. Hence, 868 MHz was selected as the operating frequency throughout the experiment. The findings indicate that the RSSI value measured by the RFID reader decreased as the moisture of the sample increased when the tags were blocked by the sample placed at the designated location during the test.
      1
  • Publication
    Inhalation and Exhalation Detection for Sleep and Awake Activities Using Non-Contact Ultra-Wideband (UWB) Radar Signal
    ( 2021-03-01)
    Fatin Fatihah Shamsul Ariffin
    ;
    Latifah Munirah Kamarudin
    ;
    Najah Ghazali
    ;
    Nishizaki H.
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    Respiratory is one of the vital signs used to monitor the progression of the illness that are important for clinical and health care fields. From home rehabilitation to intensive care monitoring, the rate of respiration must be constantly monitored as it offers a proactive approach for early detection of patient deterioration that can be used to trigger therapeutic procedures alarms. The use of invasive procedures based on contact transducers is typically necessary to measure the quantity. Nevertheless, these procedures might be troublesome due to the inconvenience and sensitivity of physical contact. Therefore, non-contact human breathing monitoring as a non-invasive procedure is important in long term intensive-care and home healthcare applications. In this paper, respiratory signals from two type of resting activities had been acquired and proposed a Deep Neural Network (DNN) model that can classify the respiratory signal into inhalation and exhalation signal. Several pre-processing techniques has been done onto the signal before it is implemented into the proposed model. The average recognition rate of the respiratory signal using the proposed method was 84.1% when the subject was sleeping and 83.8% when awake.
      1
  • Publication
    Multi-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities
    ( 2021-03-01)
    Jadaa K.J.
    ;
    Latifah Munirah Kamarudin
    ;
    Hussein W.N.
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    Wireless Sensor Network (WSNs) provides promising solutions for monitoring in several domains including industrial monitoring and control, home automation and smart cities, etc. There are numerous restrictions on the current development of target detection and tracking algorithms which makes them unsuitable or effective for indoor use. Such constraints include changes in the direction and changing target speeds, missing a target, and target dynamics. These issues come with difficulty in detection and tracking multiple targets. Moreover, the majority of the target tracking algorithms were presented on the conditions that the target is typically smooth with no unexpected changes that are difficult absolutely. Moreover, sensing coverage considers the crucial issue in a wireless sensor network. This paper implies an algorithm for detection and tracking of moving targets (intruders) for an indoor environment based on the probabilistic model utilizing WSN for safety and security. A mathematical model is presented to determine the optimum number of sensor nodes needed. The findings of the simulation showed that the MTDT algorithm provides a low missing target rate of less than 0.7 % for worst-case and can be utilized for different kinds of environment scenarios.
      1
  • Publication
    Gas Source Localization via Mobile Robot with Gas Distribution Mapping and Deep Neural Network
    ( 2022-01-01)
    Ahmad Shakaff Ali Yeon
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Visvanathan R.
    ;
    Kamarulzaman Kamarudin
    ;
    Latifah Munirah Kamarudin
    With the growth of artificial intelligence compute technology, the gas source localization problem would be solved by mobile robots equipped with gas sensing system and artificial intelligence compute units. This work presented a feasibility study of deep learning approach towards gas source localization by mobile robots. A deep neural network strategy was developed and incorporated with the Kernel DM+V gas distribution mapping method. The gas source localization work in this paper was performed on a controlled indoor testbed. From this work, it is shown that by incorporating the developed deep neural network model, it may help improved the gas source location prediction accuracy. A comparison of accuracy between Kernel DM+V and the neural network model is also presented to better visualize the improvement.
      1
  • Publication
    Recent Advancements in Radio Frequency based Indoor Localization Techniques
    ( 2021-03-01)
    Kenny Fong Peng Wye
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Norhawati Ahmad
    Recent developments in location-based services have heightened the needs for decent accuracy localization system for different applications. The Global Positioning System (GPS) provides adequate accuracy in the outdoor environment but suffers from poor localization accuracy in the complex and dynamic indoor environment. Therefore, the number of indoor localization researches to apply in different applications with different technologies have increased in recent years. The purpose of this paper is to summarize recent advancements in Radio Frequency (RF) based indoor localization techniques and provide insights in the indoor localization field especially in the range based localization. The range based localization is then categorized into few common algorithms such as AOA, TOA, TDOA and RSS. The discussion between algorithms in term of their advantages, disadvantages and improvement strategies to improve their accuracy are also present in this paper.
      1
  • Publication
    Investigation on magnetometer as potential sensors for infusion pump utilisation status
    ( 2021-12-01)
    Engku Ahmad Rafiqi Engku Ariff
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Sakinah Zakaria
    ;
    Visvanathan R.
    The utilisation of medical device e.g. an infusion pump is commonly treated as nontrivial process in service and maintenance section albeit in actuality, it is a crucial process for determining its correct maintenance cycle. Therefore, a discussion on the capability of magnetometer sensor as one of potential sensors to be used to keep track the utilisation status of infusion pump. The variation of magnetic flux density and its frequency produced by the stepper motor inside the infusion pump is measured and analysed through magnetometer sensor, embedded inside a Bluetooth Low Energy (BLE) device. Obtained result shows that magnetometer sensor has the potential to track the utilisation status of the tested infusion pumps.
      1