Phak Len Al Eh Kan
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Phak Len Al Eh Kan
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Phak, Len Al Eh Kan
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Ehkan, Phaklen
Kan, P. L.Eh
Ehkan, Phak Len
Ehkan, P. L.
Eh Kan, P.
Eh Kan, P.
Al Eh Kan, Phak Len
Eh Kan, Phaklen
Kan, P. Eh
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Scopus Author ID
37005452000

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  • Publication
    Toward Adaptive and Scalable Topology in Distributed SDN Controller
    ( 2023-03-01)
    Kelian V.H.
    ;
    Mohd Nazri Mohd Warip
    ;
    R Badlishah Ahmad
    ;
    Phak Len Al Eh Kan
    ;
    Fazrul Faiz Zakaria
    ;
    Mohd Zaizu Ilyas
    The increasing need for automated networking platforms like the Internet of Things, as well as network services like cloud computing, big data applications, wireless networks, mobile Internet, and virtualization, has driven existing networks to their limitations. Software-defined network (SDN) is a new modern programmable network architectural technology that allows network administrators to control the entire network consistently and logically centralized in software-based controllers and network devices become just simple packet forwarding devices. The controller that is the network's brain, is mostly based on the OpenFlow protocol and has distinct characteristics that vary depending on the programming language. Its function is to control network traffic and increase network resource efficiency. Therefore, selecting the right controllers and monitoring their performance to increase resource usage and enhance network performance metrics is required. For network performance metrics analysis, the study proposes an implementation of SDN architecture utilizing an open-source OpenDaylight (ODL) distributed SDN controller. The proposed work evaluates the deployment of distributed SDN controller performance on three distinct customized network topologies based on SDN architecture for node-to-node performance metrics such as delay, throughput, packet loss, and bandwidth use. The experiments are conducted using the Mininet emulation tool. Wireshark is used to collect and analyse packets in real-time. The results obtained from the comparison of networks are presented to provide useful guidelines for SDN research and deployment initiatives.
      1
  • Publication
    Breast cancer classification using deep learning and FPGA inferencing
    ( 2023-02-21)
    Wong E.H.
    ;
    Fazrul Faiz Zakaria
    ;
    Mustapa M.
    ;
    Mohd Nazri Mohd Warip
    ;
    Phak Len Al Eh Kan
    Implementing deep learning technology with FPGA as an accelerator has become a popular application due to its efficiency and performance. However, given the tremendous data generated on medical diagnosis, normal inference speed is not sufficient. Hence, the FPGA technology is implemented for fast inference. In this context, the FPGA accelerates the deep learning inference process for fast breast cancer classification with minimal latency on real-time deployment. This paper summarizes the findings of model deployment across various computing devices in deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference.
      1
  • Publication
    A Proposal of Low Cost Home Automation System Using IoT and Voice Recognition
    ( 2020-03-20)
    Phak Len Al Eh Kan
    ;
    Keraf N.D.
    ;
    Kelian V.H.
    ;
    Fazrul Faiz Zakaria
    ;
    Bei, Sin Zhen
    Home Automation System is becoming more popular day by day due to its numerous benefits. This project proposes an idea in the design of low cost home automation system by using the Internet of Things (IoT) and voice recognition. The layout of the home divided into four areas and each area has own function and system. The Raspberry Pi 3 (RPi) Model B+ used as the main controller for the processing and transmitting the input data. IoT provided huge storage for data collection from sensors and home appliances. An Android application is developed to monitor the home environment and remotely control the home devices by using the button or voice. The speaker-independent recognition system by using Google Voice to Text on Android embedded in this project for physically challenged people to control the electrical appliances without moving. All the data will be stored in Firebase and can be retrieved at any time by the application and the RPi board. There is a side view of a prototype model with two floors and divided into four home areas. This Low-Cost Home Automation System using IoT and Voice Recognition is successfully achieved the project's objective.
      2  5
  • Publication
    Deep Learning with FPGA: Age and Gender Recognition for Smart Advertisement Board
    ( 2023-10-06)
    Yeoh W.S.
    ;
    Fazrul Faiz Zakaria
    ;
    Mustapa M.
    ;
    Mohd Nazri Mohd Warip
    ;
    Phak Len Al Eh Kan
    ;
    Mozi A.M.
    Age and gender recognition are helpful in various applications, especially in the field of advertising. To replace the traditional advertising method that can only display the same contents to all audiences, a smart advertisement board capable of detecting age and gender of audiences to display relevant contents is required to increase the effectiveness of advertising. This paper will use two image datasets to train and test the Convolutional Neural Network (CNN) based architecture models for age and gender recognition using deep learning. The dataset that produced the best performing model will be implemented on three different devices to observe the performance of the models on each device. A gender recognition model with accuracy of 91.53% and age recognition model with accuracy of 59.62% is produced. The results have also shown the use of Field Programmable Gated Array (FPGA) has greatly boosted the performance of the models in terms of throughput and latency.
      1  9