Mohamed Elshaikh Elobaid Said Ahmed
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Mohamed Elshaikh Elobaid Said Ahmed
Official Name
Mohamed Elshaikh Elobaid , Said Ahmed
Alternative Name
Ahmed, Mohamed Elshaikh Elobaid Said
Said Ahmed, Mohamed Elshaikh Elobaid
Elobaid, Mohamed Elshaikh
Main Affiliation
Scopus Author ID
57190012447
Researcher ID
R-7502-2019

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  • Publication
    Multipoint Relay Path for Efficient Topology Maintenance Algorithm in Optimized Link State Routing-Based for VANET
    ( 2024-01-01)
    Waleed Khalid Ahmed
    ;
    Mohd Nazri Mohd Warip
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Phak Len Al Eh Kan
    The Optimal Link State Routing (OLSR) protocol employs multipoint relay (MPR) nodes to disseminate topology control (TC) messages, enabling network topology discovery and maintenance. However, this approach increases control overhead and leads to wasted network bandwidth in stable topology scenarios due to fixed flooding periods. To address these challenges, this paper presents an Efficient Topology Maintenance Algorithm (ETM-OLSR) for Enhanced Link-State Routing Protocols. By reducing the number of MPR nodes, TC message generation and forwarding frequency are minimized. Furthermore, the algorithm selects a smaller subset of TC messages based on the changes in the MPR selection set from the previous cycle, adapting to stable and fluctuating network conditions. Additionally, the sending cycle of TC messages is dynamically adjusted in response to network topology changes. Simulation results demonstrate that the ETM-OLSR algorithm effectively reduces network control overhead, minimizes end-to-end delay, and improves network throughput compared to traditional OLSR and HTR-OLSR algorithms.
  • Publication
    Development of Cloud-based Electronic Nose for University Laboratories Air Monitoring
    ( 2020-12-18)
    Abu Hassan Abdullah
    ;
    Muhammad Aizat Abu Bakar
    ;
    Saad F.S.A.
    ;
    Sukhairi Sudin
    ;
    Zahari Awang Ahmad
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Ismail K.A.
    Indoor air in area such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles, radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiments. These activities and the environment will generate air pollutants which concentration depending on their parameters. Anyone in the environment that exposure to these pollutants may have safety and health issue. This paper propose a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of five dsPIC33-based electronic nose (e-nose) as node which measure main indoor air pollutants along with two thermal comfort variables, i.e. temperature and relative humidity. The nodes are placed at five different laboratories for acquiring air pollutants data in real time. The data will be sent to a web server and the cloud-based system will process, analyse and display by a website in real time. The system will monitor the laboratories main air pollutants and thermal comfort by forecast the contaminants concentration and dispersion in the area. In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the Air Pollution Index (API) of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollution in the university laboratories.
  • Publication
    Cloud-based System for University Laboratories Air Monitoring
    ( 2020-09-21)
    Abu Hassan Abdullah
    ;
    Sukhairi Sudin
    ;
    Mustafa M.H.
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Khairul Azwan Ismail
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Abdul Ghapar Ahmad
    ;
    Zahari Awang Ahmad
    ;
    Sara Yasina Yusuf
    Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.
  • Publication
    Smart irrigation system based IoT for indoor housing farming
    ( 2024-02-08)
    Hasimah Ali
    ;
    Nidzamuddin S.A.H.S.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Irrigation system is widely used in agriculture sector and has significant impacts to the growth of the plantation or crops. Traditional method of irrigation system always counter problems such as time consuming, human labour cost, inefficient of water usage and monitoring challenging throughout the process. Thus to address the issues, this paper proposed the development of smart irrigation system that embedded various types of sensor and Internet of Things (IoT) platform used for monitoring plant growth. In this work, there are three module have been developed which are hardware, software and integration module of the proposed system. In hardware module, Raspberry Pi is used to calculate and process the data based on the sensors parameters. Different types of sensors have been employed such as soil moisture, humidity, temperature, ultrasonic and vision sensors. In this framework, the reading of soil moisture sensor was obtained from the base station. The Raspberry Pi will receive the information and starts to pump the water from the tank until the condition of soil moisture content is normal (i.e. reach the threshold value). In addition, the DHT22 sensor will act as the monitoring system in terms of temperature and humidity data. While, the ultrasonic sensor will send the information to the microprocessor and calculate the water level. Furthermore, the webcam vision is used for monitoring the plant growth during the day and night. While, the dripping process runs in real-time application to the plant. The microcontroller ESP8266 used to control the state of ON or OFF light bulb depending on the value of LDR sensor. Based on the results and monitoring process, the proposed smart irrigation system able to works in promising environment with real time data in which it has been monitored through the IoT platform.
      1  6
  • 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.
      1
  • Publication
    GPSR Routing Performances Enhancement for VANET networks with Taguchi Optimization Mechanism
    ( 2021-07-26)
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Dafhalla A.K.Y.
    ;
    Naimah Yaakob
    ;
    Hasimah Ali
    Routing mechanism plays an important role in the performances of Vehicular Ad Hoc Networks (VANET). Hence, various routing mechanisms are proposed to enhance VANET performances, however few researches are dedicated to optimize these routing mechanisms. In this paper an optimization mechanism is proposed to improve the performances of Greedy Perimeter stateless Routing (GPSR) protocol. Design of Experiments is used along with Taguchi Optimization method to fine tune GPSR internal routing parameters against VANET network scenarios. The target of optimization in this work is set to network performances including network throughput, delay and packet delivery ration (PDR). These targets are mathematically combined to form a single optimization target. A simulation experiments are performed to evaluate VANET performances. Obtained results showed that the proposed optimization improves the VANET performances in terms of throughput, PDR and delay. Further real-time integration of Optimization and routing mechanism can improve network performances.
      1
  • Publication
    Feature extraction using Radon transform and Discrete Wavelet Transform for facial emotion recognition
    ( 2017-02-08)
    Hasimah Ali
    ;
    Vinothan Sritharan
    ;
    Muthusamy Hariharan
    ;
    Siti Khadijah Za'aba
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    This paper presents a new pattern framework of using Radon and wavelet transform for facial emotion recognition. The Radon transform is translation and rotation invariants, hence it preserves the variations in pixel intensities. In this work, Radon transform has been used to project the 2D image into Radon space before subjected to Discrete Wavelet Transform (DWT). In DWT framework, the approximate coefficients (cA2) at second level decomposition are extracted and used as informative features to recognize the facial emotion. Since there are a large number of coefficients, hence the principal component analysis (PCA) is applied on the extracted features. The k-nearest neighbor classifier is adopted as classifier to classify seven (anger, disgust, fear, happiness, neutral, sadness and surprise) facial emotions. To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Based on the results obtained, the proposed method demonstrates the recognition rate of 91.3%, thus it is promising.
      15  2
  • Publication
    A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images
    ( 2021-08-27)
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Tengku Amran T.S.
    ;
    Ahmad M.R.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
      1
  • Publication
    Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar
    ( 2021-06-11)
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Tengku Sarah Tengku Amran
    ;
    Mohamad Ridzuan Ahmad
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.
      1  11
  • Publication
    Design and analysis of small scale oyster mushroom cultivation
    ( 2024-02-08)
    Zahereel Ishwar Abdul Khalib
    ;
    Nur Hafizah Ghazali
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Tai W.J.
    ;
    Letchumanan N.
    ;
    Azman N.F.F.
    ;
    Rodhi M.N.Q.
    Cultivators that grow oyster mushrooms keep failing due to a of lack control in temperature, humidity, soil moisture, and concentration of carbon dioxide. In order to grow great oyster mushrooms and avoid crop failure, the temperature must be between 22°C to 28°C and the relative humidity must be between 85% to 90%. The concentration of carbon dioxide (CO2) needs to be controlled below 800 during its growth phase. Failure to meet those parameters will result in the mushroom drying out, slowing growth, or stopping. As a result, oyster mushroom cultivators will require a system that can control the surrounding temperature and humidity, soil moisture, and concentration of CO2 conditions in order to produce good-quality oyster mushrooms. An automated environmental control system is constructed to handle the problem. In this system, by means of a feedback control loop system that monitors and activates the actuators when necessary, promising results of the oyster mushroom cultivation system are produced and analyzed.
      1