Phak Len Al Eh Kan
Thumbnail Image
Preferred name
Phak Len Al Eh Kan
Official Name
Phak, Len Al Eh Kan
Alternative Name
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
Main Affiliation
Scopus Author ID
37005452000

Research Output

Filters

20
Reset filters

Settings
Now showing 1 - 10 of 20
No Thumbnail Available
Publication

Enhancement and segmentation of Ziehl Neelson sputum slide images using contrast enhancement and Otsu threshold technique

2023 , Ainul Kamilah Mohd Yusoff , Rafikha Aliana A Raof , Norfadila Mahrom , Siti Suraya Md Noor , Fazrul Faiz Zakaria , Phak Len Al Eh Kan

Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.

View
Thumbnail Image
Publication

Topology Design of Extended Torus and Ring for Low Latency Network-on-Chip Architecture

2017-06-01 , Ng Yen Phing , Mohd Nazri Mohd Warip , Phak Len Al Eh Kan , Farah W. Zulkefli , R Badlishah Ahmad

In essence, Network-on-Chip (NoC) also known as on-chip interconnection network has been proposed as a design solution to System-on-Chip (SoC). The routing algorithm, topology and switching technique are significant because of the most influential effect on the overall performance of Network-on-Chip (NoC). Designing of large scale topology alongside the support of deadlock free, low latency, high throughput and low power consumption is notably challenging in particular with expanding network size. This paper proposed an 8x8 XX-Torus and 64 nodes XX-Ring topology schemes for Network-on-Chip to minimize the latency by decrease the node diameter from the source node to destination node. Correspondingly, we compare in differences on the performance of mesh, full-mesh, torus and ring topologies with XX-Torus and XX-Ring topologies in term of latency. Results show that XX-Ring outperforms the conventional topologies in term of latency. XX-Ring decreases the average latency by 106.28%, 14.80%, 6.7 1%, 1.73%, 442.24% over the mesh, fully-mesh, torus, XX-torus, and Ring topologies.

View
Thumbnail Image
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.

View
Thumbnail Image
Publication

Improving Energy Detection Performance Using Segment of Primary User Signal Energy in Wide-band Cognitive Radio Systems

2023-10-06 , BaAli K. , Abo-Zahhad M. , Farrag M. , Ahmed S. , Phak Len Al Eh Kan

Energy detection based cognitive radio has gained remarkable attention. This is because of its simple implementation and it does not involve any prior knowledge of the detecting signal. Although, it is prone to noise uncertainty. Recently, the use of compressed measurements improved the performance of energy detection. In this paper, we investigate the effect of using a segment of primary user signal energy ƞE on detection performance regarding compression ratios. The simulation results show the detector performance becomes better as ƞE increased. However, increasing ƞE should be bounded, so it does not involve sacrificing the use of the compressed measurement system as a suggestion to improve energy detection performance.

View
Thumbnail Image
Publication

Imporved MPR selection algorithm-based WS-OLSR routing protocol

2024-05-01 , Ahmed W.K. , Mohd Nazri Mohd Warip , Mohamed Elshaikh Elobaid Said Ahmed , Phak Len Al Eh Kan

Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR’s performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.

View
Thumbnail Image
Publication

Automatic vehicle identification system using number plate recognition in POLIMAS

2020-03-20 , Keraf N.D. , Phak Len Al Eh Kan , Kelian V.H. , Noor N.M. , Darus H. , Loke E.

The security and management of transportation system becomes an important key in controlled place such as campus area. With an increased number of vehicles, there is a need for vehicle identification mechanism that is effective, affordable and efficient. This paper presents the development of automatic vehicle identification system using NP recognition in POLIMAS. Number Plate (NP) Recognition is an image processing technology in computer vision which captures the image of the vehicle and recognizes its NP. The system is installed at the main entrance to ensure that only the authorized vehicles can enter the campus area. Once the vehicle is detected by the input sensors, a system will capture the image of vehicle plate number. An image is then extracted and investigated character segmentation by using optical character recognition. The method used for detection of a plate number is by pre-processing of the image and using a combination of Sobel Edge Detection and Laplacian Edge Detection Techniques. Bounding Box technique is used to find the NP and character recognition. The accuracy of NP recognition has an average of 87%. The system is sustainable as the camera will only be switched on when a vehicle is present.

View
Thumbnail Image
Publication

Large Sized Slug on Solid State Lighting Stress and Temperature Analysis

2013 , Zaliman Sauli , Vithyacharan Retnasamy , Rajendaran Vairavan , Phak Len Al Eh Kan

View
Thumbnail Image
Publication

Design and Implementation of Lifting Wavelet Transform Using Field Programmable Gate Arrays

2020-03-20 , Taha T.B. , Ruzelita Ngadiran , Phak Len Al Eh Kan

Lifting Wavelet transform (LWT) has an extensive usage in different image processing applications as image compression and information hiding. LWT is considered a good solution for hardware designs as it relies only on integer calculations while applying the wavelet transform. In this paper, an FPGA design and implementation of LWT is presented, the implementation is achieved using VHDL coding without importing off-shelf components which make the proposed design applicable to a wide range of devices. The design is based on parallel execution to perform LWT implementation with real time response. The design utilized 421 logic registers of DE2 Cyclone II (EP2C35F672C6) FPGA device with 151.91MHz frequency.

View
Thumbnail Image
Publication

Implementation and analysis of GMM-based speaker identification on FPGA

2012 , Phak Len Al Eh Kan

The use of highly accurate identification systems is required in today’s society. Existing systems such as pin numbers and passwords can be forgotten or forged easily and they are no longer considered to offer a high level of security. The use of biological features (biometrics) is becoming widely accepted as the next level for security systems. One of the biometric is the human voice and it leads to the task of speaker identification. Speaker identification is the process of determining whether a speaker exists in a group of known speakers and identifying the speaker within the group. Speaker specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting Mel-frequency Cepstral Coefficients (MFCCs) from the speech signal. A statistical modelling process known as Gaussian Mixture Model (GMM) is used to model the distribution of each speaker’s MFCCs in a multi-dimensional acoustic space. GMM involves with two phases called training and classification. The training phase is complex and is better suited for implementation in software. The classification phase is well suited for implementation in hardware and this allows for real time processing of multiple voice streams on large population sizes. Several innovative techniques are demonstrated which enable hardware system to obtain two orders of magnitude speed up over software while maintaining comparable levels of accuracy. A speedup factor of eighty six is achieved on hardware-based FPGA compared to a software implementation on a standard PC for this approach.

View
Thumbnail Image
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.

View