Hazry Desa
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Hazry Desa
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Desa, Hazry
Alternative Name
Desa, H.
Hazry, Desa
Desa, Hazy
Hazry, D.
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Scopus Author ID
16642497100

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  • Publication
    Study of eddy current density distribution in a contactless breast cancer detection mechanism using magnetic induction spectroscopy
    ( 2017-01-01)
    Gowry Balasena
    ;
    Shahriman Abu Bakar
    ;
    Zulkarnay Zakaria
    ;
    Ryojun Ikeura
    ;
    Zuradzman Mohamad Razlan
    ;
    Hazry Desa
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Cheng Ee Meng
    ;
    Mohd Afendi Rojan
    Breast cancer is a throbbing disease that no longer needs an introduction. This is especially true among women due to their unique breast structure that naturally has more breast tissues compared to that of man’s. It is been forecasted that in 2015, a minimum of 60290 new cases of breast cancer will be reported. The goal of this study is to analytically evaluate the changes in the induced Eddy current densities as a function of di-electrical properties of the breast tissue with respect to tumor positioning as well as its size. This is achieved by running numerical simulations on the proposed mechanism of magnetic induction to detect tumors among healthy breast tissue via a 2D breast model configuration. The analytical results presented in this article, proved that the multi frequency magnetic induction principle is viable in detecting the breast lesions as small as 0.2 cm non-invasively through the distributions of the induced Eddy current density. While important pattern of the induced current were reflected when the tumors are located at the far ends of the breast diameter. The minimum results computational time with the proposed system is 10 s.
  • Publication
    Application of coal bottom ash as raw material for concrete brick in housing construction
    ( 2020-03-25)
    Muhammad Azizi Azizan
    ;
    Nik Noriman Zulkepli
    ;
    Nurfadzillah Ishak
    ;
    Hazry Desa
    ;
    Omar S. Dahham
    ;
    Umar M.U.
    ;
    Jaya H.
    Coal and coal-fired power plants are crucial for supplying global energy demand. However, coal-fired power plants produce significant quantities of bottom ash as by-products. Through these power plants, it produces approximately 18 tons of bottom ash each day. Malaysia has the challenging task of dealing with an increasing amount of waste generated by power plants each year. The high cost of construction and operation of incineration plants coupled with a lack of landfills have given rise to the need for waste minimization and recycling. The use of coal ash in concrete is a new dimension in concrete mix design and if applied on large scale can revolutionize the construction industry, by economizing the construction cost and decreasing the ash content. The used of the coal bottom ash in the concrete brick can reduce the wastage of the coal bottom ash in Malaysia, beside that the cost of the housing can be reduced. In addition, the sustainability in the housing construction in Malaysia can be achieved using the wastage material in the construction.
  • Publication
    Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application
    ( 2023-01-01)
    Jack Lee L.
    ;
    Hazry Desa
    ;
    Muhammad Azizi A.
    ;
    Abadal-Salam T.H.
    ;
    Hassan T.M.
    This article introduces a lightweight and efficient model for measuring applications, aimed at enhancing the current UAV monitoring system. The primary objective of this project is to develop a measuring application capable of determining and displaying the distance between the camera on the UAV and the facial model. The YOLOV8 framework is employed as a detection model to identify and interpret objects within the region of interest. Additionally, the algorithm incorporates the concept of focal length in lenses to calculate the distance between the facial expressions of a human face and the camera. To assess the algorithm's accuracy, facial models were placed at various distances from the camera during testing. The predicted distance values obtained through the algorithm were then compared to the actual measured distances using a measuring tape. The results demonstrated a maximum tolerance of ±0.9 cm, indicating the algorithm's reliable performance in predicting distance measurements.
  • Publication
    Development of IoT-Enabled Smart Water Metering System
    ( 2024-01-01)
    Wen S.D.
    ;
    Hazry Desa
    ;
    Muhammad Azizi Azizan
    ;
    Hussain A.S.T.
    ;
    Tanveer M.H.
    ;
    Patan R.
    This paper introduces a smart water meter that utilizes the capabilities of the Internet of Things (IoT) to automate the collection of meter readings. The primary goal of this project is to create an IoT-based device for reading water meters, while simultaneously developing a compatible mobile application. Instead of relying on manual meter reading, which requires human effort, this project proposes the use of an IoT-enabled water meter to collect the data automatically. The device employs a camera and Convolutional Neural Network (CNN) for image processing, making it easy to detect the meter reading accurately. The IoT system architecture involves the use of an ESP32 CAM for data collection, a laptop as a gateway, and the Message Queuing Telemetry Transport (MQTT) protocol for data transfer. The collected data is stored in Firebase's real-time database, and the mobile application is designed to monitor and analyze the data. A functional prototype of the device is constructed and tested in a housing area. The collected data is then monitored through the developed mobile application. Lastly, the data is analyzed to assess the suitability of the proposed method, and recommendations for future improvements are provided.
  • Publication
    Implementation of deep neural networks learning on unmanned aerial vehicle based remote-sensing
    ( 2024-03-01)
    Ahmed S.A.
    ;
    Hazry Desa
    ;
    Hussain A.S.T.
    ;
    Taha T.A.
    Due to efficient and adaptable data collecting, unmanned aerial vehicle (UAV) has been a popular topic in computer vision (CV) and remote sensing (RS) in recent years. Inspiring by the recent success of deep learning (DL), several enhanced object identification and tracking methods have been broadly applied to a variety of UAV-related applications, including environmental monitoring, precision agriculture, and traffic management. In this research, we present efficient neural network (ENet), a unique deep neural network architecture designed exclusively for jobs demanding low latency operation. ENet is up to quicker, takes fewer floating-point operations per second (FLOPs), has fewer parameters, and offers accuracy comparable to or superior to that of previous models. We have tested it on the street and cityscapes reports on comparisons with current state-of-the-art approaches and the tradeoffs between a network's processing speed and accuracy. We give measurements of the proposed architecture's performance on embedded devices and offer software enhancements that might make ENet even quicker.
  • Publication
    Experimenting with Variable Arm Quadrotors: Realizing Dynamic Configurations for Enhanced Flight Performance
    ( 2024-01-01)
    Hazry Desa
    ;
    Azizi M.
    This paper introduces two innovative concepts for variable arms designed for a quadrotor, enabling precise control of its movement through manipulation of the bending moment via varying arm lengths. The primary objective of this research is to develop and identify the most suitable variable arm configuration that facilitates smooth and stable quadrotor movement. The study delves into two concept designs that are well-suited for the quadrotor application. By employing a suitable variable arm, the quadrotor’s maneuverability can be effectively regulated based on the bending moment adjustments made possible by the variable arm. Ultimately, the paper presents the design and performance testing of two types of variable arms. The obtained results confirm that the variable arm of the electric actuator with linear guide-Type 2 exhibits smooth and stable movement.
  • Publication
    Child tracking system using smartphone
    ( 2023-01-01)
    Taha T.A.
    ;
    Hussain A.S.T.
    ;
    Hazry Desa
    ;
    Taha F.H.
    ;
    Bektaş Y.
    The number of missing children and kidnapping is on the rise in recent years. Every parent wills definitely going through an agonizing experience to have their children missing. Therefore, there are many safety measurements to prevent this incident from happening. The help of modern technologies is one of the ways to reduce children missing and kidnapping. A child can be tracked by using the global positioning system (GPS) and global system for mobile communication (GSM) technology. Advanced child monitoring systems are expensive. Not all families have the same living standards. For this purpose, a low-cost child tracking system is proposed in this study. The implementation of the proposed approach is reported in real-time.
  • Publication
    Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application
    ( 2023-06-01)
    Ahmed S.A.
    ;
    Hazry Desa
    ;
    Hussain A.S.T.
    The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.
  • Publication
    Automated RFID-Based Attendance and Access Control System for Efficient Workforce Management
    ( 2023-01-01)
    Hussain A.S.T.
    ;
    Taha T.A.
    ;
    Ahmed S.R.
    ;
    Ahmed S.A.
    ;
    Ahmed O.K.
    ;
    Hazry Desa
    This paper focuses on designing an automated attendance and access control system using Radio Frequency Identification (RFID) technology. The current methods used by companies to track employee attendance are often inefficient and prone to errors. The proposed system aims to improve the process by automatically recording the working hours of employees using RFID tags in the form of ID cards. The RFID system consists of three components: an antenna, a transceiver, and transponders (tags). The antenna transmits a signal to activate the tag, which then transmits data back to the antenna. Unlike barcodes, RFID tags can be read from a distance and through various materials. The ID number on the RFID tag corresponds to the user's information stored in a database. The work's objective is to efficiently manage and analyze attendance data according to workplace regulations. The implementation includes designing the system using a PIC16F876A microcontroller, simulating the system with Proteus software, and analyzing the performance of the hardware and software components. The paper's scope encompasses its applicability in both educational institutions and industries, ensuring effective modeling and evaluation of the RFID-based attendance system.
  • Publication
    Object Detection and Instance Segmentation with YOLOV8: Progress and Limitations
    ( 2024-01-01)
    Lee L.J.
    ;
    Hazry Desa
    ;
    Muhammad Azizi Azizan
    ;
    Hussain A.S.T.
    ;
    Tanveer M.H.
    This research employs object detection and instance segmentation algorithms to distinguish between objects and backgrounds and to interpret the detected objects. The YOLOV8 (You Only Look Once) framework and COCO dataset are utilized for detecting and interpreting the objects. Additionally, the accuracy of detection, segmentation, and interpretation is tested by placing objects at various distances from the camera. The algorithm's performance was evaluated, and the results were documented. In the experiments, a sample of 11 objects was tested, and 8 of them were successfully detected at distances of 45cm, 75cm, 105cm, and 135cm. For instance, segmentation, segmentation maps appeared clean when detecting a single object but faced challenges when multiple objects overlapped.