Now showing 1 - 4 of 4
  • Publication
    Effect of roadways plantation on signal propagation analysis in connected autonomous vehicle communication
    ( 2019)
    J S C Turner
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    Zunaidi Ibrahim
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    M A Fadzilla
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    K A A Kassim
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    M S A Khalid
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    Z Jawi
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    M H M Isa
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    S A Z Murad
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    At present, the development of autonomous vehicle has altered the outlook of modern transportation worldwide. The state-of-the-art vehicular communication for transportation system is advancing, especially in vehicle to infrastructure (V2I) communication. An effective communication between vehicle and infrastructure has become a significant part of autonomous transportation criteria. The necessity for high quality of service communication inspire for good planning and preparation in communication process. Per se, this paper proposes vegetation attenuation models for advance planning of communication process between vehicle to infrastructure, defined mainly by plants, trees and vegetation along the roadways in Malaysia. The channel measurement performed in Universiti Malaysia Perlis test-bed having large tall trees and low shrubs along the routes resulted in several interesting results which would shape the planning of CAV communication. It is observed that communication close to low plantation or shrub requires high power consumption as the range is significantly reduced. It is also learned that certain types of plantations allows for different level of signal attenuation depending on the antenna heights. The research also found out that the attenuation profile follows strictly the log normal distribution and as such certain planning could be made to reshape the communication process to cater for this.
  • Publication
    Integration of asset tracking system through trilateration method as detection mechanism
    ( 2019)
    M A Fadzilla
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    Z. Ibrahim
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    J.S.C Turner
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    K.A.A Kassim
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    M.S.A Khalid
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    Z. Jawi
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    M.H.M Isa
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    Demands for localization system has been growing rapidly in the last several years both for an outdoor and indoor area. In conjunction with this, the capability and reliability of this system to precisely locate and track objects of interest for the indoor area has catered researchers and study on how to do so. One of the major ideas on making it more advance is by incorporating the use of wireless devices into the system. There are numbers of issues that could interrupt the efficiency and success of the system. One of the main problems is the signal loss mainly caused by the attenuation of the signal as they propagate through from the transmitter to the receiver. These attenuations are mostly due to the surface types the signal are traveling on and the objects that are in the Line of Sight in between the transmitter and receiver. In order to ensure the most reliable and efficient wireless connection between transmitter and receiver, a propagation study on the signal is needed for us to analyze and find the best way to trade off the signal attenuation based on the environment surrounding the system. By doing so, a thorough system that has models that can work efficiently even if we are to consider the attenuation factors. The system consists of nodes installed inside the research institute that acts as both transmitter and receivers. The transmitter and receiver will then process the signal that will then determine their location. The receiver is connected to the laptop in order to get a real-time reading so that we will be able to locate the transmitter. A networked of nodes are installed inside the research institute for experiment and the layout of the research is conferred for future references. Data from the experiment are then analyzed and a model for the signal propagation alongside the research institute is created. This model will be able to apprehend the signal attenuation despite the surrounding environment such as furniture and walls. A completed asset tracking system with models of signal attenuation will be built in the future for a more efficient signal transmission.
  • Publication
    Temperature control using fuzzy controller for variable speed vapor compression refrigerator system
    Keeping the cold chain vaccine is crucial to a stable immunisation programme; however, faulty processes may occur more frequently than are often thought in developing nations. This paper discusses the quick and accurate control process for designing fuzzy controllers for variable speed vapor compression refrigerator system. The suggested controller is based on the fuzzy logic intended to improve performance while keeping the cooler’s constant internal temperature and increasing the refrigerator efficiency. Despite the external changes such as the outside temperature change or the volume change in the refrigerator vaccine, the fuzzy logic controller is utilised to maintain the interior temperature. However, a variable speed compressor (VSC) must be used to control the thermophysical characteristics, which dramatically alter the temperature with a small pressure change. In this case, fuzzy rules of the sort developed by Mamdani are used to build up the system. The programming platforms utilised to implement the model include MATLAB, SIMULINK, and Fuzzy Logic Toolbox (FLT). The efficiency of fuzzy logic controller design membership will be compared to ensure that the refrigerator temperature is more accurate and until it achieves the best performance, maintains a temperature of 5°C, and adapts to its surroundings. From the research done, the membership 2 with load shows the near accurate temperature of 5°C with steady-state error ±1.97°C.
  • Publication
    Classification of Body Mass Index Based Facial Images using Empirical Mode Decomposition
    ( 2021-06-11) ;
    Yee, O.S.
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    Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding "facial information"from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) based on appearance based features of facial images using empirical mode decomposition (EMD) as feature extraction technique. The facial images that describe the body mass index was extracted using EMD to obtain a set of significant features. In this framework, the facial image was decomposed using EMD to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF features which exhibit the unique pattern were used to classify the BMI. The obtained features were then fed into machine learning classifier such as k-nearest neighbour and support vector machines (SVM) to classify the three BMI classes namely normal, overweight and obese. The obtained results show that the IMF2 feature using SVM classifier achieved recognition rate of 99.12% which show promising result.
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