R Badlishah Ahmad
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Preferred name
R Badlishah Ahmad
Official Name
R Badlishah, Ahmad
Alternative Name
Ahmad, R. Badlishah
Ahmad, B.
Badlishah Ahmad, R.
Ahmad, Rashidi
Ahmad, Badlisha
Ahmed, R. Badlishah
Ahmad, R. B.
Badlishah, R.
Ahmad, R. Badli
Ahmed, Rbadlishah
Main Affiliation
Scopus Author ID
57194844651
Researcher ID
U-3211-2019

Research Output

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  • Publication
    Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
    ( 2020-08-01)
    Vijayasarveswari V.
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    R Badlishah Ahmad
    ;
    Khatun S.
    ;
    Hasliza A Rahim @ Samsuddin
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multistage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
  • Publication
    Received signal strength indication (RSSI) code assessment for wireless sensors network (WSN) deployed Raspberry-Pi
    ( 2017-03-06)
    Mohd Ilman Jais
    ;
    Thennarasan Sabapathy
    ;
    Muzammil Jusoh
    ;
    Phak Len Al Eh Kan
    ;
    Murukesan L.
    ;
    Ismail I.
    ;
    R Badlishah Ahmad
    Wireless sensor network (WSN) is commonly used for localization applications. Through sniffing receive signal strength indicator (RSSI) in WSN system, localization and connection to access point highest RSSI can be done automatically. In this paper, we propose Raspberry-Pi (RasPi), based best access point selection method utilizing RSSI metric. The RasPi brings the advantages of a personal computer (PC) to the domain of sensor network, which makes it the perfect platform for interfacing with a wide variety of external peripherals. This work aims to investigate various source codes deployed on RasPi for localization purpose by sniffing the RSSI metric. Consequently, comparative analysis of its key elements and performances with some of the currently available wireless sensor nodes have shown that despite few disadvantages, RasPi remains an inexpensive single board computer (SBC) which has been used very successfully in sensor network domain and diverse range of research applications.
  • Publication
    Deployment of Resource Allocation and Power Control Schemes in Long Term Evolution Advanced (LTE-A) Hybrid Network
    ( 2017-09-01)
    Jing Huey Lim
    ;
    R Badlishah Ahmad
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    Long Term Evolution Advanced (LTE-A) Hybrid network integrates Femtocell and Macrocell networks to obtain better coverage and improved capacity. One of the limiting factors faced by Hybrid network is Inter-Cell Interference (ICI). One way to get rid of the ICI is through Inter-Cell Interference coordination technique. In the last few years, interference coordination such as Fractional Frequency Reuse (FFR) technique is considered as the utmost important research topic in LTE cellular technology. This paper revisited FFR method and deployed Dynamic Femtocell Resource Allocation (DFRA) scheme to ensure the resources assigned to Femtocells are mutually exclusive with adjacent Macrousers or adjacent Femtocells. Furthermore, in the scenario of high density Femtocells (when orthogonal resource exhausted), the power control schemes such as Power based Femtocell Base Station Power Control (PPC), SINR based Femtocell Base Station Power Control (SPC) and SINR based Neighboring Femtocell Power Control (SNPC) are integrated into the system. The deployment of the schemes has augmented the performance of the network and shows increment of 11.43% supported active users with an average data rate inclined by 22.19 Mbps.
  • Publication
    A Patternless Piezoelectric Energy Harvester for Ultra Low Frequency Applications
    ( 2020-01-01)
    Awal M.R.
    ;
    Jusoh M.
    ;
    Thennarasan Sabapathy
    ;
    R Badlishah Ahmad
    ;
    Kamarudin M.R.
    ;
    Osman M.N.
    ;
    Ahmad M.F.
    ;
    Rahman S.A.
    ;
    Dagang A.N.
    This paper presents a pattern less piezoelectric harvester for ultra low power energy applications. Usually patterned cantilevers are used as vibration energy harvester which results additional fabrication process. Hence, to reduce the process, a four layer cantilever configuration is used to design the harvester with Aluminum, Silicon and Zinc Oxide. The device dimension is settled to 12×10×≈0.5009 mm3 with ≈300 nm deposition thickness for each layer. The modeling and fabrication processes are demonstrated in detail. The induced voltage by the cantilever is obtained through the analytical and practical measurements. From the measurements, it is found that, the maximum induced voltage is 91.2 mV from practical measurement with voltage density of 1.517 mV/mm3. It is evident from the results that, this pattern less model can be useful for next generation vibration energy harvester with simpler technology.
  • Publication
    Gain Enhancement of Rectangular Dielectric Resonator Antenna Using Air Gap
    ( 2023-10-06)
    Shanmuka Rooban Gunasekaran
    ;
    Thennarasan Sabapathy
    ;
    Muzammil Jusoh
    ;
    R Badlishah Ahmad
    ;
    Mohamed Nasrun Osman
    ;
    Saidatul Norlyana Azemi
    ;
    Nur Farhan Kahar
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Saravanan Nathan Lurudusamy
    This paper presents a gain enhanced rectangular dielectric resonator antenna (DRA) using air gap. The air gap placed under the dielectric resonator antenna and above the ground plane. A gain of 6.605 dBi obtained from the DRA with air gap while gain of 5.956 dBi is achieved by DRA without air gap. The size of the antenna is approximately 50 mm by 40 mm, thus it can be considered a compact design. This antenna designed using Rogers RO4003 substrate and the DRA material is Eccostock HIK. All the design and simulation results are conducted using CST Studio Suite 2019 software. Based on the result, it shows that the antenna operates with reflection coefficient of less than -10 dB at the desired operating frequency range, centered at 3.5 GHz. The inclusion of air gap proved that it can enhance the gain value of the DRA.
      2  3
  • Publication
    Erratum: Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction (PLoS ONE (2020) 15:8 (e0229367) DOI: 10.1371/journal.pone.0229367)
    ( 2021-05-01)
    Vijayasarveswari Veeraperumal
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    R Badlishah Ahmad
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    Sabira Khatun
    ;
    Hasliza A Rahim @ Samsuddin
    The authors are listed out of order. Please view the correct author order, affiliations, and citation here: V. Vijayasarveswari1, A.M. Andrew1, M. Jusoh1, R.B. Ahmad1, T. Sabapathy1, R.A.A. Raof1, M.N.M. Yasin1, S. Khatun2, H.A. Rahim1 1 Advanced Communication Engineering (ACE) Centre of Excellence, Universiti Malaysia Perlis, Kangar, Perlis, West Malaysia, 2 Faculty of Electrical & Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang Vijayasarveswari V, Andrew AM, Jusoh M, Ahmad RB, Sabapathy T, Raof RAA, et al. (2020) Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction. PLoS ONE 15(8): e0229367. https://doi.org/10.1371/journal.pone.0229367 There are errors in the Funding statement. The correct Funding statement is as follows: The study was supported by Fundamental Research Grant Scheme (FRGS), Ministry of Education Malaysia under grant number: FRGS/1/2019/TK04/UNIMAP/02/3. No additional external funding was received for this study.
      1
  • Publication
    Frequency Reconfigurable DRA using RF Switch Loaded Feedline for 5G NR Band n48, n77 and n78
    ( 2022-01-01)
    Gunasekaran S.R.
    ;
    Thennarasan Sabapathy
    ;
    Muzammil Jusoh
    ;
    R Badlishah Ahmad
    ;
    Nur Farhan Kahar
    ;
    Muhamad W.Z.A.W.
    This paper presents a frequency reconfigurable DRA. The size of the antenna is approximately 50 mm by 40 mm, thus it can be considered a compact design. This antenna was designed using Rogers RO4003 substrate and the DRA material used was Eccostock HIK. To achieve frequency reconfiguration, the length of the antenna feedline was reconfigured by the means of RF switches. The proposed design consists of four configurations and all those conditions achieved reflection coefficient of less than -10 dB. The antenna also produced consistent gain for all configurations with average gain of 5 dBi. The switched frequencies from the proposed antenna can cover 5G New Radio Bands n48, n77 and n78.
      1  12
  • Publication
    Investigation on the Mutual Coupling Reduction in MIMO Antenna using Dual Split CSRR EBG
    ( 2021-07-26)
    Alsayaghi A.
    ;
    Thennarasan Sabapathy
    ;
    Muzammil Jusoh
    ;
    Kabir Hossain
    ;
    R Badlishah Ahmad
    ;
    Mohamed Nasrun Osman
    ;
    Jayaprakasam S.
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Raghava N.S.
    In many applications, it is now desirable to prevent a device from being affected by an external electromagnetic field or prevent the device's intrusion into the electromagnetic field. The elimination or reduction of coupling between neighbouring systems is desired, and this is achieved through the use of electromagnetic shields and absorbers. This project focuses on an investigation that analysed the band-gap region for unit cell EBG simulation and unit cell array simulation. It is revealed that unit cell simulation provides an accurate result where the band-gap region is from 2 GHz to 3.5 GHz. The performance of the EBG is validated using a microstrip MIMO antenna. Using EBG, the S21 is reduced from -18.63 dB to -28.80 dB. Meanwhile, the MIMO antenna gain with the proposed EBG is 2.78 dBi, greater than MIMO antenna without EBG (2.47 dBi).
      1
  • Publication
    A Frequency-Reconfigurable Microstrip Antenna with Constant Dipole-Like Radiation Patterns Using Single Bias, Triple Varactor Tuning with Reduced Complexity
    ( 2022-03-01)
    Hossain K.
    ;
    Thennarasan Sabapathy
    ;
    Muzammil Jusoh
    ;
    Soh Ping Jack
    ;
    R Badlishah Ahmad
    ;
    Jais M.I.
    ;
    Mohamed Nasrun Osman
    ;
    Mohd Najib Mohd Yasin
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Saluja N.
    ;
    Abbasi Q.H.
    This work proposes a novel frequency-reconfigurable circular patch antenna incorporated with a rectangular slot and a narrow slot capable of producing constant dipole-like radiation patterns. The antenna compactness is achieved with the integration of the rectangular slot defected ground structure (DGS) on the ground. The proposed antenna is able to perform continuous frequency tuning between 1.91 and 2.77 GHz with a frequency ratio of 1.5:1, in addition to stable dipole-like radiation patterns. The resonant frequency of the antenna is controlled by tuning a simple DC biasing network that consists of three RF varactor diodes located on the narrow slot DGS. Implementing the DC biasing network at the narrow slot DGS while maintaining the large slot DGS helps the antenna miniaturization and maintains the constant dipole-like radiation pattern over all frequency tuning range. The results are validated via simulations and experimental validations in terms of reflection coefficients and the radiation patterns. Measurements indicated that an impedance bandwidth of 85 MHz is featured for each tuned frequency band, with dipole-like patterns and an average gain of 1.57 dBi.
      1  14
  • Publication
    Mobile Green E-Waste Management Systems using IoT for Smart Campus
    ( 2021-07-26)
    Khairul Najmy Abdul Rani
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Ong B.T.
    ;
    Muzammil Jusoh
    ;
    Mohd Najib Mohd Yasin
    ;
    Thennarasan Sabapathy
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    R Badlishah Ahmad
    ;
    Hammood D.A.
    This paper presents the design and development of mobile "green"electronic waste (e-waste) management systems using Internet of Things (IoT) for smart campus. The system uses Raspberry Pi 3 Model B v1.2 microcontroller for monitoring e-waste object detection, e-waste count, and bin percentage level, respectively. TensorFlow Lite application programming interface (API) is used to run Single Shot Multibox Detector (SSD)Lite-MobileNet-v2 model trained on Microsoft Common Objects in Context (MSCOCO) dataset for e-waste object detection in image. All the monitoring data are stored and retrieved in ThingSpeak cloud platform using Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) protocol over the Internet and displayed via interactive Android-based mobile user interface (UI). Furthermore, automatic e-mail notification will be sent to waste collector for bin collection whenever e-waste bin percentage level is greater than predetermined threshold value of 80% full.
      1  18