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

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Enhanced experimental investigation of threshold determination for efficient channel detection in 2.4 GHz WLAN cognitive radio networks

2017-09-01 , Mohammad Nayeem Morshed , Sabira Khatun , Latifah Munirah Kamarudin , Syed Alwee Aljunid Syed Junid , R Badlishah Ahmad , Ammar Zakaria , Md. Moslemuddin Fakir

This paper presents an experimental investigation of threshold determination for efficient channel detection in wireless LAN (WLAN) based cognitive radio (CR) networks. The spectrum saturation problem is a critical issue in wireless communication systems worldwide due to on growing user demands day by day with many new applications to the limited frequency spectrum. Hence, present demand is an efficient and intelligent spectrum management and allocation system. In this paper, we proposed an adaptive threshold determination technique based on free space path loss (FSPL) model to detect the presence or absence of PUs. The model is designed especially for Android based smartphones and tablets. The smartphones act as secondary users (SUs) and existing 2.4 GHz WLAN channels as PUs. The network is prepared in a usual noisy lab/outdoor environment and tested for the robustness of the proposed model. Results show the desired range of usable threshold and the channel detection performance depends on the noise floor level of the surrounding environment.

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Adaptive threshold determination for efficient channel sensing in cognitive radio network using mobile sensors

2017-03-13 , Mohammad Nayeem Morshed , Sabira Khatun , Latifah Munirah Kamarudin , Syed Alwee Aljunid Syed Junid , R Badlishah Ahmad , Ammar Zakaria , Moslem Fakir

Spectrum saturation problem is a major issue in wireless communication systems all over the world. Huge number of users is joining each day to the existing fixed band frequency but the bandwidth is not increasing. These requirements demand for efficient and intelligent use of spectrum. To solve this issue, the Cognitive Radio (CR) is the best choice. Spectrum sensing of a wireless heterogeneous network is a fundamental issue to detect the presence of primary users' signals in CR networks. In order to protect primary users (PUs) from harmful interference, the spectrum sensing scheme is required to perform well even in low signal-to-noise ratio (SNR) environments. Meanwhile, the sensing period is usually required to be short enough so that secondary (unlicensed) users (SUs) can fully utilize the available spectrum. CR networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary user's licensed frequency bands. In this paper, we have proposed an adaptive threshold detection method to detect presence of PU signal using free space path loss (FSPL) model in 2.4 GHz WLAN network. The model is designed for mobile sensors embedded in smartphones. The mobile sensors acts as SU while the existing WLAN network (channels) works as PU. The theoretical results show that the desired threshold range detection of mobile sensors mainly depends on the noise floor level of the location in consideration.

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Online learning approach for predictive real-time energy trading in cloud-rans

2021-04-01 , Wan Nur Suryani Firuz Wan Ariffin , Zhang X. , Nakhai M.R. , Hasliza A Rahim @ Samsuddin , R Badlishah Ahmad

Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

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Mango ripeness classification system using hybrid technique

2019-05-01 , Mavi M.F. , Husin Z. , R Badlishah Ahmad , Yacob Y.M. , Farook R.S.M. , Tan W.K.

Nowadays there are many systems develop for agricultural purposes and most system implemented on the use of non-destructive technique not only to classify but also to determine the fruit ripeness. However, most of the studies concentrates using single technique to assess the fruit ripeness. Thi s paper presents the work on mango ripeness classification using hybrid technique. Hybrid stands for mix or combination between two different elements, thus this study combined two different technique that is image processing and odour sensing technique in a single system. Image processing technique are implemented using color image that is HSV image color method to determine the ripeness of fruit based on fruit peel skin through color changes upon ripening. Whereas, odour sensing technique are implemented using sensors array to determine the fruit ripeness through smell changes upon ripening. The “Harumanis” and “Sala” mango was used for sample collection based on two different harvesting condition that is unripe and ripe were evaluated using the image processing and followed by the odour sensor. Support Vector Machine (SVM) is applied as classifier for training and testing based on the data collected from both techniques. The finding shows around 94.69% correct classification using hybrid technique of image processing and odour sensing in a single system.

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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.

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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.

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A PSPT-MAC Mechanism for Congestion Avoidance in Wireless Body Area Network

2020-01-01 , Wan Abdullah W.A.N. , Yaakob N. , R Badlishah Ahmad , Elobaid M.E. , Azemi S.N. , Yah S.A.

A Remote Health Monitoring System (RHMS) is known as one of the promising applications that has been successfully developed with the help of Wireless Body Area Network (WBAN) technology nowadays. This RHMS offers a continuous monitoring of health’s status by sensing and collecting the physiological signals (medical data) from bio-sensors that are attached or implanted in the body. Then, these medical data are furthered transmitted to the clinicians to diagnose the diseases. If any abnormalities are detected, a quick medical actions would be carried out. However, these collections of medical data could lead to heavy traffic which increase the risk of data congestion in the network. Congestion could severely impact the overall’s network performances in terms of longer delay and packet loss. Thus, a Priority Selective Packet Timeslot (PSPT-MAC) mechanism is proposed to avoid congestion during transmitting these bulk of medical data in the network. This mechanism is initiated by classifying and prioritizing the data according to their importance through ECG Packet Classification and Prioritization (ECG-PCP) mechanism. Later, corrupted packets are earlier discarded by Prioritized Selective Packet Discarding (P-SPD) mechanism to save the limited network’s resources. Finally, the remain packets (after discarding packets from P-SPD mechanism) undergo fragmentation according to slot time via Fragmentation based Slot Time MAC (FST-MAC) mechanism in the MAC IEEE 802.15.4 protocol. From the findings, this mechanism has outperformed the standard IEEE 802.15.4 protocol and FCA-MAC mechanism by yielding low delay and packet loss as well as high throughput and packet delivery ratio (PDR) under different number of nodes in the network.

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