Masturah Tunnur Mohamad Talib
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Masturah Tunnur Mohamad Talib
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Masturah Tunnur, Mohamad Talib
Alternative Name
Mohamad Talib, Masturah Tunnur
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Scopus Author ID
57198358595

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  • Publication
    Human localisation in an outdoor environment using radio frequency tomography
    ( 2022)
    Masturah Tunnur Mohamad Talib
    Localisation of humans in an outdoor environment can offer great capabilities especially in security and perimeter surveillance applications. However, localizing humans in a nonlinear environment is a challenge. Although, GPS and CCTV successfully detect human location, these two techniques provide low localisation accuracy due to signal loss and camera view limitations. Device-free localisation (DFL) technique has been introduced to overcome these problems. This is because the DFL approach has the capability to detect the human body wirelessly in all environmental conditions and there is no losing signals problem as faced by GPS. Despite that, the accuracy of the DFL approach is still low due to the problem of variation in radio signal strength indicator. To overcome this problem, Radio Tomographic Imaging has been introduced. Basically, this approach characterized the differences in radio frequency (RF) response by exploiting the radio frequency attenuation. The differences in RF response occur due to the signal obstructed by the human body. Conventionally, RTI uses the Linear Back Projection algorithm (LBP) to reconstruct the tomographic image. However, it produces a low-quality image due to the ill-posed inverse problem caused by back-projection and the smearing effect due to the overlapping image. Several regularization approaches have been introduced by other researchers to improve the quality of tomographic images. These regularization techniques have been used to eliminate the smearing effect on the RTI image. However, the resulting image is still blurred because the target occupies only a small amount of space compared to the entire area monitored. Therefore, the new image reconstruction algorithms called as Hybrid Radio Tomographic Imaging Technique (HRTI) and Modified Hybrid Radio Tomographic Imaging Technique (HRTI-M) have been proposed to solve the RTI problem. Through these techniques, the area error analysis score for HRTI is lower than 3% and lower than 1% for HRTI-M. The benefit of using the proposed methods is the point location of the human can be identified from the reconstructed image and this will contribute to increasing the localisation accuracy. These RTI data are introduced to the Deep Neural Network (DNN) classification approach to improving localisation accuracy. This approach is known as the RTI-DNN approach. In this approach, the HRTI-M is used to eliminate the variation of sensor data and improve the quality image of RTI. While, the DNN is used to reduce the classification error due to variation in sensor data and increase the accuracy of localisation. This RTI DNN was then compared with RTI based on the Artificial Neural Network (RTI-ANN) approach to evaluate the performance of the proposed method. From the classification results, it is found that the performance of RTI-DNN is better than RTI-ANN which is 88% compared to RTI-ANN with only 64% accuracy.
  • Publication
    Small metal objects classification based on the deep learning approach
    ( 2024)
    Nur Safariah Inani M. Tahir
    ;
    Masturah Tunnur Mohamad Talib
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    N. S. Khalid
    ;
    Siti Marhainis Othman
    Classification of small metal objects plays a crucial role in various engineering fields, including manufacturing, robotics, and security. With advancements in deep learning techniques, the use of Convolutional Neural Networks (CNNs) has emerged as a powerful tool for image classification tasks. The methodology begins by collecting diverse datasets consisting of images of small metal objects. The datasets are labelled with corresponding object classes to facilitate supervised learning. Preprocessing techniques like re-sizing, normalization, and augmentation are used to improve the quality and diversity of the datasets. The use of CNNs in classification can be a better option compared to commonly used machine learning approaches. The CNN architecture is designed and trained to learn the distinguishing features of small metal objects. The main objective of this study is to assess the accuracy of this classification and explain how CNNs can enhance classification accuracy. The results of this study also show the effect of the optimizer on the classification process, which changes when different types of optimizers such as RMSprop, Adam, and SGD are used. While some optimizers yield slightly lower accuracy results, the Adam optimizer with the CNN ResNet-50 module proves suitable for use with this dataset, achieving a high classification accuracy of 86%.
  • Publication
    Hand-held shelf life decay detector for non-destructive fruits quality assessment
    ( 2024)
    Nurul Syahirah Khalid
    ;
    Nordiana Shariffudiin
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Humairah Mansor
    ;
    Masturah Tunnur Mohamad Talib
    ;
    Sukhairi Sudin
    ;
    Ismail I. Ibrahim
    ;
    Mohd Zamri Hasan
    ;
    Siti Marhainis Othman
    ;
    N.D.N Dalila
    ;
    M.Thaqif B.N Ashimi
    Perishable food such as fruits have a limited shelf life and can quickly degrade if not properly stored. One method for detecting decay in these foods is the use of ethylene gas. Ethylene is a naturally occurring hormone that is released by fruits as they ripen. By measuring the levels of ethylene in the storage area, it is possible to detect when fruits and vegetables are starting to degrade. This information can then be used to act, such as removing spoiled produce and adjusting storage conditions, to extend the shelf life of the remaining products. By utilizing ethylene gas for early detection of decay, it is possible to improve food safety and reduce food waste. The project aims to utilized ethylene gas from perishable food such as fruits before decay. This project proposed portable or hand-held detection ethylene gas by including temperature and humidity. The sensor will be measuring the level of ethylene gas, temperature and humidity. Next, machine learning method; K-Nearest Neighbour(KNN) were used to evaluate the accuracy of the proposed system. This project, a hand-held decay detector for perishable food products is believed can help to prevent food waste by detecting early signs of spoilage in fruits.