Ruzelita Ngadiran
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Ruzelita Ngadiran
Official Name
Ruzelita, Ngadiran
Alternative Name
Ngadiran, Ruzelita
Ngadiran, R.
Binti Ngadiran, Ruzelita
Main Affiliation
Scopus Author ID
36548898800
Researcher ID
ABF-3045-2020

Research Output

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  • Publication
    IOT Smart Guidance Parking Search System for Open Space Parking Area
    ( 2021-07-26)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    Nazren A.R.A.
    ;
    Wafi N.M.
    ;
    Ramli N.
    ;
    Vijayasarveswari Veeraperumal
    ;
    Leow W.Z.
    Open parking facilities can be automated and parking spaces can be easily operated by the implementation of IoT technology (Internet of Things). In this article, we present the evolution and prototyping of the open space smart guidance-parking search system, an IoT-based smart parking search system. The Smart Guidance Parking Searches System consists of i) An IOT module to monitor the availability of a parking slot and to update the parking lot status; and (ii) A web-based software allows users to view parking spaces available for a specific open space area. This paper addresses the existing system, device description, its functional specifications, the methods, and technologies used, the development/deployment of prototypes, along with the findings from the demonstration. This device serves as a guide for the user/driver to search for the parking slot occupancy in open/outdoor environments.
  • Publication
    Tomato Diseases Classification Using Extreme Learning Machine
    ( 2023-10-06)
    Xian T.S.
    ;
    Ruzelita Ngadiran
    ;
    Iszaidy Ismail
    ;
    Amiza Amir
    ;
    Siti Zuraidah Ibrahim
    ;
    Taha T.B.
    Plant disease classification is essential to the agriculture industry. In this work, a tomato disease classification using Extreme Learning Machine (ELM) with image-based features. Extreme Learning Machine (ELM), a classification algorithm with a single layer feed-forward neural network where it can be combined with few activation functions. The image features as the input where the image is pre-processed via HSV colour space and extracted using Haralick textures, colour moments and HSV histogram. The features are then loaded in the ELM classifier to perform the ELM training and testing. The accuracy result of ELM classification is then analysed after the validation. The dataset used for disease detection is tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM demonstrate an accuracy of around 84.94% which is comparable to classifiers such as the Support Vector Machine and Decision Tree.
  • Publication
    Perceptual-based features for blind image quality assessment using extreme learning machine for biodiversity monitoring
    ( 2024-02-08)
    Verak N.
    ;
    Pakhlen Ehkan
    ;
    Ruzelita Ngadiran
    ;
    Jungjit S.
    ;
    Fazrul Faiz Zakaria
    ;
    Mohd Nazri Mohd Warip
    ;
    Ilyas M.Z.
    Blind Image Quality Assessment (BIQA) is crucial for various image processing applications, including image denoising, transmission evaluation, optimization, watermarking, and situations where a reference image is unavailable. However, existing state-of-the-art Image Quality Assessment (IQA) metrics are often specific to certain types of distortion and fail to align with human perception. To address this issue, our research study proposes a novel approach that incorporates perceptual-based features and utilizes a pooling algorithm based on Extreme Learning Machine (ELM). By considering human visual characteristics and the impact on image content, we aim to mimic human perception, which can detect noticeable differences at specific frequency ranges, akin to neurons. The work is divided into three phases. In the first phase, we derive perceptual features using lifting wavelet, focusing on texture, edge, and contrast components. Subsequently, in the second phase, these features are trained to generate output for pooling using Extreme Learning Machine (ELM). The pooling strategy of ELM is chosen due to its ability to overcome limitations found in previous pooling techniques like Neural Networks (NNs) and Support Vector Regression (SVR). This approach enables us to evaluate the quality score of images accurately, benefiting from ELM's superior learning accuracy and faster learning speed. The third phase involves performance evaluation, including statistical analysis for algorithm validation and a comparison with existing BIQA methods using MATLAB software. We verify our proposed approach on diverse image databases containing various distortion types, aiming to create a general-purpose BIQA solution. The outcomes of this work will have significant implications in several image processing applications, such as optimizing image enhancement for medical purposes like tumor or cancer detection, image watermarking for security applications, image coding and compression, and image forensic analysis. In biodiversity monitoring, image enhancement plays a crucial role in tracking and data collection.
  • Publication
    Digital image watermarking algorithm based on texture masking model
    ( 2019-01-01)
    Taha D.B.
    ;
    Taha T.B.
    ;
    Al Dabagh N.B.
    ;
    Ruzelita Ngadiran
    ;
    Ehkan P.
    The trade-off between invisibility and robustness in image watermarking algorithms is considered as one of the major issues in designing watermark-based copyright protection systems. Accordingly, different models had been proposed in the literature to obtain robust watermarked images while maintaining the perceptual quality. However, most of these studies are involved with complex algorithms as using multiple signal transformation tools within hybrid systems. In this paper, a low complexity texture-masking model based on Lifting Wavelet Transform (LWT) is utilized to find the blocks with the highest texture and choose them for watermark embedding. Choosing highly textured places helps to insert the watermark with a further intensity that leads to higher robustness and at the same time the Human Visual System (HVS) is less sensitive to changes in these areas. As a result, high quality watermarked images were produced in terms of objective and subjective evaluations, as the structural similarity value (SSIM) for tested images was larger than 0.99.
  • Publication
    IoT-based Door Access Using Three Security-Layers
    ( 2023-10-06)
    Aznan M.A.
    ;
    Siti Zuraidah Ibrahim
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Ruzelita Ngadiran
    ;
    Mohd Noh F.H.
    This paper demonstrates an Internet-of-Things, IoT-based Door Access using three security layers, which are biometric identification, authentication, and authorized reply. The IoT-based door access is developed with Closed Circuit Television (CCTV) monitoring to control door access by authorized users using facial recognition technique and the Telegram application along with a database to record user logs. In the first security layer, the user’s face will be captured by CCTV camera and then processed to match to the registered face. In the authentication layer, the system will use Telegram Bot to send a message to the user registered Telegram Chat Identification (ID) only for entering the password. In the third security layer, if the password is valid, the system will send a signal to the hardware to unlock the door. The results showed that the developed prototype of this system successfully operated as expected.
  • Publication
    An Analysis of Background Subtraction on Embedded Platform Based on Synthetic Dataset
    ( 2021-03-01)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Ramli N.
    ;
    Jais M.I.
    ;
    Vijayasarveswari Veeraperumal
    Background subtraction is a preliminary technique used for video surveillance and a widely used approach for indexing moving objects. Arange of algorithms have been introduced over the years, and it might be hard to implement the algorithms on the embedded platform because the embedded platform comes up with limited processing power. The goal of this study is to provide a comparative analysis of available background subtraction algorithms on the embedded platform:-Raspberry Pi. The algorithms are compared based on the segmentation quality (precision, recall, and f-measure) and hardware performance(CPU usage and time consumption) using a synthetic video from BMC Dataset with different challenges like normal weather, sunny, cloudy, foggy and windy weather.
  • Publication
    Threading implementation on different hardware for travel time estimation purpose
    ( 2017-03-06)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Mohd Ilman Jais
    ;
    Shuhaizar Daud
    The travel time estimation is one of traffic management system which provide time taken from one point to another point. Travel time estimation system consists of an embedded platform with image sensor for detecting and tracking the vehicle. Due to limited resources of embedded board, it makes challenging to measure the travel time especially for fast moving vehicle. Capturing system required a high capturing rate of the camera to capture most current frame for fast moving vehicle. Threading is implemented in this system to improve embedded board resource utilization and input-output latency between camera and embedded board. In this paper, the threading technology is applied to two types of Raspberry Pi model and the performance of the embedded board is recorded and analyzed.
  • Publication
    Video size comparison for embedded vehicle speed detection & travel time estimation system by using Raspberry Pi
    ( 2017-03-06)
    Iszaidy Ismail
    ;
    Alias A.
    ;
    Ruzelita Ngadiran
    ;
    R Badlishah Ahmad
    ;
    Mohd Ilman Jais
    ;
    Shuhaizar Daud
    As traffic continues to grow up, the issue regarding the road accident also growing quickly. The accident occurred due to the high speed of vehicles on the road. This paper proposed a vehicle speed detection and travel time estimation system using Raspberry Pi to estimate the speed of passing vehicles through this system. The system is designed to detect the moving vehicles and calculate its velocity. The system used OpenCV as an image processing software to detect and track the moving vehicles. Several types of capturing size of the video are used in this system to check and measure the performance of the embedded board.
  • Publication
    Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets
    ( 2022-01-01)
    Iszaidy Ismail
    ;
    Ruzelita Ngadiran
    ;
    Ramli N.
    ;
    Nazren A.R.A.
    ;
    Nasruddin M.W.
    ;
    Jais M.I.
    Background subtraction is an advanced method used for video monitoring and is commonly used for indexing of moveable objects. Over the years, several algorithms have been implemented and the implementation of algorithms on the embedded platform can be difficult because the embedded platform has minimal computing resources. The purpose of this study is to conduct a comparative review of background subtraction algorithms available on the embedded platform: Raspberry Pi. The algorithms are compared using a real video dataset based on segmentation accuracy (precision, recall, and f-measure) and hardware efficiency (CPU utilization and time consumption).
  • Publication
    FPGA Implementation of High-Capacity SD Card using VHDL Language
    ( 2024-01-01)
    Taha T.B.
    ;
    Ruzelita Ngadiran
    ;
    Phak Len Al Eh Kan
    Limitations in capacity of internal memory chips on embedded systems lead to the usage of external memories especially when processing large amount of data as images or videos. FPGAs as embedded system devices used in different signal processing applications require extra memory for data storage. Current attempts in programming Secure Digital (SD) cards as external memories attached to FPGAs came with utilization of aiding application or of shelf tools which consumes large amount of chip clock cycles and reduce the overall performance. In this paper, a hardware implementation of SD card programming is presented by using pure and standalone VHDL code with high programming flexibility. In addition, High-Capacity SD cards (SDHC) are implemented for maximum storage capacity to handle large amount of data.
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