Bulletin of Electrical Engineering and Informatics, 2024
QoS in computer networking is the capability to provide better service to network traffic over va... more QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.
Epilepsy is a form of neurological brain disorder. It is identified by the frequent occurrence of... more Epilepsy is a form of neurological brain disorder. It is identified by the frequent occurrence of symptoms called epileptic seizure due to abnormal activities. Using an electroencephalogram (EEG), a diagnosis of epilepsy can be done. For detection and classification purpose, there are many techniques applied in detecting epilepsy seizure such as machine learning, and nowadays deep learning algorithms are most famous to biomedical research. However, most of the deep learning methods are only analyze the epilepsy classification performance based on accuracy percentages. In term of elapsed time or learning rate analysis, it is become a rare study. Therefore, this paper proposes an epilepsy seizure detection and classification using several Residual Neural Network (ResNet) architectures and identify which ResNet architecture gives the best performance. For comparison purpose, the EEG performance analysis will be analyzed using other convolution neural network (CNN) architecture, namely GoogLeNet. Based on the results obtained, ResNet architecture give the best performance analysis for seizure detection and classification with superb performance of 100% accuracy and shortest elapsed time which only recorded 1 minute and 25 seconds
Power quality is main concern for the electrical energy consumptions and electrical equipment. He... more Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing to the accuracy of the classification analysis. In this paper, an approach to classify the power quality disturbances is presented using the deep neural network algorithm. A raw data containing various types of the power quality disturbances, like swell, interruption, harmonics, and normal signal is evaluated. This several types of power quality disturbance will be extracted using the Sparse Autoencoder (SAE). The various values of weight decay parameter, $\lambda$ and sparsity parameter, $\rho$ are applied to determine which features give optimal values. Optimal features learned from the SAE are then used to train a neural network classifier for identifying power quality disturbances.
2017 International Conference on Electrical, Electronics and System Engineering (ICEESE)
Weed classification a necessity in identifying species of weeds to control management practice in... more Weed classification a necessity in identifying species of weeds to control management practice in agricultural systems, which are essential for maintaining crop productivity and quality. Many classification techniques were used to identify weeds based on images, and most of the techniques using a binary Support Vector Machine (SVM) for measuring the percentage of accuracy. No visualization of decision boundary is illustrated to prove the best performances. To analyzing weed pattern images using One Class Support Vector Machine (SVM), feature vectors of weed images extracted using Gabor Wavelet and Fast Fourier Transform (FFT) were applied. The decision boundaries of the combination extracted feature vectors are visualized and optimal feature vectors are identified. The proposed method also improve the accuracy rate in weed classification task.
International Journal of Emerging Technology and Advanced Engineering
Water quality is critical in fish farming activities, where criteria must be measured to ensure w... more Water quality is critical in fish farming activities, where criteria must be measured to ensure water quality. Unwanted amounts of water quality factors will affect aquatic life. It has been discovered that some breeders fail to maintain their ponds, causing water quality to worsen and affecting fish hibernation and mortality. Manual pond water quality testing was ineffective and time-consuming, causing the water quality to suffer. This study created a fishpond IoT system to monitor a pond's water quality, temperature, pH level, and ammonia toxicity. A real-time data analytics platform was created to collect data from the water temperature, pH level, and toxicity of ammonia sensors embedded into the IoT system. The NodeMCU ESP32 controller was used to process the data collected from all sensors, and real-time data may be viewed via mobile devices using the Blynk application. Three sensors are embedded to the system which are an ammonia gas sensor, an analog pH sensor, and a temp...
Protective face mask identification is essential today to users as it is a prominent protective w... more Protective face mask identification is essential today to users as it is a prominent protective wearable to shield from being infected by Covid-19 viruses. Protective face masks consist of layers of fibers that can capture large respiratory droplets and microscopic particles such as viruses or dust. Thus, mask filtration efficiency results depend on the materials used for each layer. Detail about mask description and efficiency are still anonymous to users, which is vital in this COVID-19. Therefore, this paper reviews designing 3D augmented reality for the protective mask with its detail parameter and mask sizing recommendation on android mobile. About 73 articles on the protective face mask, 3D augmented reality modeling, masks inward leakage testing, breathing resistance, and measuring faces have been reviewed. The result examines the existing protective face mask, inward leakage testing parameter, breathing resistance parameters, 3D modeling techniques, mobile applications, and ...
2019 IEEE 9th International Conference on System Engineering and Technology (ICSET)
The cross-entropy stopping criterion (CE) with binary phase-shift keying can perform well at high... more The cross-entropy stopping criterion (CE) with binary phase-shift keying can perform well at high sound-to-noise ratio regions in additive white Gaussian noise (AWGN). However, because of the high request of high-speed networks nowadays, the CE must be tested with high order modulation to determine its performance. In this paper, a CE with a threshold equal to 0.0001 was tested in quadrature amplitude modulation (QAM) in the AWGN channel. In addition, the research also tested the CE with various frame sizes, M-ary QAM, and code generators. From the results obtained, the CE is poor in average iteration number (AIN) at a high noisy channel while it can terminate early at a low noisy channel. The CE is also capable of maintaining the bit error rate (BER) performance for various noisy channel stages. Therefore, the CE is suitable to be used with QAM for the low noisy channel to save the AIN and maintain the BER performance of turbo codes.
2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)
In this modern society, the majority of e-commerce platform have a recommender system. Recommende... more In this modern society, the majority of e-commerce platform have a recommender system. Recommender system is a popular and powerful way to introduce users with suggestions that they are most probably going to buy or use. The research conducted mainly focuses on implementation of genre-based and topic modeling model in a recommender system to predict rating of games for a user using a public Steam dataset. Both models will also be combined to implement a hybrid recommender system. Our models use KNN algorithm to predict rating of a targeted user. The system is fully implemented in Python programming language. Multiple Python libraries were utilized for data cleaning process. All predicted ratings generated were evaluated and compared to each other. Based on results evaluated, genre-based model outperforms both topic modeling and hybrid models. However, the performance of genre-based model doesn’t outperform the model performance from previous research. Therefore, it can be concluded that genre isn’t a suitable parameter for recommending games.
2022 IEEE International Conference in Power Engineering Application (ICPEA)
Opinion classifications from Twitter are still in demand among research works on related opinions... more Opinion classifications from Twitter are still in demand among research works on related opinions or feelings expressed on various issues. One of the concerns expressed in Twitter is on water-related issues such as the lack of clean water supply. It has been found that the issue highlighted in Twitter is the frequent disruption of clean water supply in Malaysia. The discussions concerning this issue contain positive and negative emotions like anger, joy, worry, and frustration. The focal point of this article is to evaluate hybrid sentiment analysis using a machine learning classifier to analyze the polarity of opinions employing real data from Twitter. A series of experiments were performed on a hybrid of deep learning, support vector machine, Naïve Bayes and random forest with a lexicon-based model. In addition, the Malay sentiment lexicon score is proposed. The Malay sentiment lexicon scores have improved the accuracy and F1-score of all hybrid methods. The analysis uncovers that negative and positive polarity opinions can be beneficial to the relevant authorities to overcome the water supply disruption issue.
2021 IEEE 11th International Conference on System Engineering and Technology (ICSET), 2021
Mechanical Ventilation plays a major role for life support of critically-ill patients in the Inte... more Mechanical Ventilation plays a major role for life support of critically-ill patients in the Intensive Care Unit. Medical practitioners assess patient's oxygenation status by observing the blood gases from arterial blood samples. However, this procedure to sample arterial blood is invasive and must be done cautiously. This paper proposes new fuzzy logic-based models for estimating non-invasively the relative ratio of dead space to the tidal volume, known as relative dead-space and the production of carbon-dioxide of ventilated patients. These parameters are needed for a non-invasive and automatic blood gas estimation system called the SOPAVent system. The fuzzy models are designed using fuzzy c-means clustering and new-structure particle swarm optimization technique which looks at the coefficient of determination and the mean squared error as performance indices. The prediction results are validated with actual ICU patients. The simulation results showed high accuracy in prediction of the relative dead-space parameter and the production of carbon-dioxide parameter.
2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC), 2016
This paper presents a development of a Dynamic Home Automation Security (DyHAS) Alert System with... more This paper presents a development of a Dynamic Home Automation Security (DyHAS) Alert System with Laser that interfaces on Webpages and Windows 10 Mobile Application. Home security has been identified as one of an important issue in today's life. Cases of home burglary continue to arise daily. Existing home security systems faces drawback to home users where certain flaws occurred in the system such as not web-supported, lack of dynamic features and difficult to handle. Thus, enhancing to dynamic home automation security alert is the objective of this research. DyHAS is developed which comprised of lasers, lights, alarm and python programmed that interfaces with webpage and Windows 10 mobile devices. Dynamics alert are triggered according to identified parameters and it is adaptively set with lights, alarm and alert messages to home owner's mobile devices and webpage. Triggers messages are updated and data are logged. Adaptive bypass security can be configured if needed. Windows 10 mobile application and Raspberry Pi 2 are used in the designed system. Wireless connectivity is used as the communication medium between the hardware and software components of the system. Successful system is tested on small circuit board with alerts data sent to home users on Windows 10 mobile application and webpages platform. Selected outdoor are tested at the security levels that includes installation of laser beam module. This research is significant and benefits for recent technology on home security system which suitable for small or large covered areas.
2015 IEEE 7th International Conference on Engineering Education (ICEED), 2015
This paper presents a study on self-learning website development through online internet knowledg... more This paper presents a study on self-learning website development through online internet knowledge among engineering students. The objective of this study is to analyze how capable and experience students' skills on self-learning with online internet knowledge to develop their own website. Students are exposed to technology and online communication in today's learning environment which has motivated this research to identify engineering students' skills and independence level. Two engineering students group from Faculty of Electrical Engineering are chosen which are the Electronic students and Communications Engineering students which consist of 32 and 26 students. They are addressed to develop a website by self-learning from online internet knowledge. Students are to follow set of derived rules and criteria tracked by some rubrics for their score marks. Website development involved planning and designed in the methodology. Analysis present most students can develop their self-learning website but some are unable to follow the derived rules and rubrics. This means that they are not capable in presenting a good website designed. Results present Electronic engineering students are far better than communications engineering students. Best result are presented by the electronics group where 84.4% score with grade A and above compared to Communications group that presented only 19% scored with the same grade although studied has targeted that communication's students are more capable to develop their own website.
this paper presents the development of a new scheme called Adaptive Throughput Policing and Shapi... more this paper presents the development of a new scheme called Adaptive Throughput Policing and Shaping (ATPS) algorithm to control internet inbound throughput and burst flow in a Campus IP-based network. Real live throughput collected from a Campus Network with Committed Access Rate (CAR) of 16 Mbps bandwidth speed are simulated and analyzed. New mathematical model with identified parameters on ATPS is derived. Adaptive throughput policies with burst shaping are simulated using Token Bucket theory control mechanism with 16 Mbps threshold policy. Three main adaptive policy conditions called P1, P2 and P3 which is controlled on 110%, 100% and 50% threshold rate are defined as filtered condition. Burst throughputs are shaped into next free bucket capacity for the next flow time. The throughputs are continuously shaped if the next bucket is full until free buckets are available. This new ATPS algorithm is numerically evaluated and analyzed on traffic performance which controlled the bandwidth and burst throughput. Performance results present reduced bucket capacity, reduced bandwidth rate in throughput transfer, no burst throughput and no byte loss in conforming traffic transferred in a network compared to previous implemented ATP algorithm, which held burst throughput and byte loss in the system.
Bulletin of Electrical Engineering and Informatics, 2024
QoS in computer networking is the capability to provide better service to network traffic over va... more QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.
Epilepsy is a form of neurological brain disorder. It is identified by the frequent occurrence of... more Epilepsy is a form of neurological brain disorder. It is identified by the frequent occurrence of symptoms called epileptic seizure due to abnormal activities. Using an electroencephalogram (EEG), a diagnosis of epilepsy can be done. For detection and classification purpose, there are many techniques applied in detecting epilepsy seizure such as machine learning, and nowadays deep learning algorithms are most famous to biomedical research. However, most of the deep learning methods are only analyze the epilepsy classification performance based on accuracy percentages. In term of elapsed time or learning rate analysis, it is become a rare study. Therefore, this paper proposes an epilepsy seizure detection and classification using several Residual Neural Network (ResNet) architectures and identify which ResNet architecture gives the best performance. For comparison purpose, the EEG performance analysis will be analyzed using other convolution neural network (CNN) architecture, namely GoogLeNet. Based on the results obtained, ResNet architecture give the best performance analysis for seizure detection and classification with superb performance of 100% accuracy and shortest elapsed time which only recorded 1 minute and 25 seconds
Power quality is main concern for the electrical energy consumptions and electrical equipment. He... more Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing to the accuracy of the classification analysis. In this paper, an approach to classify the power quality disturbances is presented using the deep neural network algorithm. A raw data containing various types of the power quality disturbances, like swell, interruption, harmonics, and normal signal is evaluated. This several types of power quality disturbance will be extracted using the Sparse Autoencoder (SAE). The various values of weight decay parameter, $\lambda$ and sparsity parameter, $\rho$ are applied to determine which features give optimal values. Optimal features learned from the SAE are then used to train a neural network classifier for identifying power quality disturbances.
2017 International Conference on Electrical, Electronics and System Engineering (ICEESE)
Weed classification a necessity in identifying species of weeds to control management practice in... more Weed classification a necessity in identifying species of weeds to control management practice in agricultural systems, which are essential for maintaining crop productivity and quality. Many classification techniques were used to identify weeds based on images, and most of the techniques using a binary Support Vector Machine (SVM) for measuring the percentage of accuracy. No visualization of decision boundary is illustrated to prove the best performances. To analyzing weed pattern images using One Class Support Vector Machine (SVM), feature vectors of weed images extracted using Gabor Wavelet and Fast Fourier Transform (FFT) were applied. The decision boundaries of the combination extracted feature vectors are visualized and optimal feature vectors are identified. The proposed method also improve the accuracy rate in weed classification task.
International Journal of Emerging Technology and Advanced Engineering
Water quality is critical in fish farming activities, where criteria must be measured to ensure w... more Water quality is critical in fish farming activities, where criteria must be measured to ensure water quality. Unwanted amounts of water quality factors will affect aquatic life. It has been discovered that some breeders fail to maintain their ponds, causing water quality to worsen and affecting fish hibernation and mortality. Manual pond water quality testing was ineffective and time-consuming, causing the water quality to suffer. This study created a fishpond IoT system to monitor a pond's water quality, temperature, pH level, and ammonia toxicity. A real-time data analytics platform was created to collect data from the water temperature, pH level, and toxicity of ammonia sensors embedded into the IoT system. The NodeMCU ESP32 controller was used to process the data collected from all sensors, and real-time data may be viewed via mobile devices using the Blynk application. Three sensors are embedded to the system which are an ammonia gas sensor, an analog pH sensor, and a temp...
Protective face mask identification is essential today to users as it is a prominent protective w... more Protective face mask identification is essential today to users as it is a prominent protective wearable to shield from being infected by Covid-19 viruses. Protective face masks consist of layers of fibers that can capture large respiratory droplets and microscopic particles such as viruses or dust. Thus, mask filtration efficiency results depend on the materials used for each layer. Detail about mask description and efficiency are still anonymous to users, which is vital in this COVID-19. Therefore, this paper reviews designing 3D augmented reality for the protective mask with its detail parameter and mask sizing recommendation on android mobile. About 73 articles on the protective face mask, 3D augmented reality modeling, masks inward leakage testing, breathing resistance, and measuring faces have been reviewed. The result examines the existing protective face mask, inward leakage testing parameter, breathing resistance parameters, 3D modeling techniques, mobile applications, and ...
2019 IEEE 9th International Conference on System Engineering and Technology (ICSET)
The cross-entropy stopping criterion (CE) with binary phase-shift keying can perform well at high... more The cross-entropy stopping criterion (CE) with binary phase-shift keying can perform well at high sound-to-noise ratio regions in additive white Gaussian noise (AWGN). However, because of the high request of high-speed networks nowadays, the CE must be tested with high order modulation to determine its performance. In this paper, a CE with a threshold equal to 0.0001 was tested in quadrature amplitude modulation (QAM) in the AWGN channel. In addition, the research also tested the CE with various frame sizes, M-ary QAM, and code generators. From the results obtained, the CE is poor in average iteration number (AIN) at a high noisy channel while it can terminate early at a low noisy channel. The CE is also capable of maintaining the bit error rate (BER) performance for various noisy channel stages. Therefore, the CE is suitable to be used with QAM for the low noisy channel to save the AIN and maintain the BER performance of turbo codes.
2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)
In this modern society, the majority of e-commerce platform have a recommender system. Recommende... more In this modern society, the majority of e-commerce platform have a recommender system. Recommender system is a popular and powerful way to introduce users with suggestions that they are most probably going to buy or use. The research conducted mainly focuses on implementation of genre-based and topic modeling model in a recommender system to predict rating of games for a user using a public Steam dataset. Both models will also be combined to implement a hybrid recommender system. Our models use KNN algorithm to predict rating of a targeted user. The system is fully implemented in Python programming language. Multiple Python libraries were utilized for data cleaning process. All predicted ratings generated were evaluated and compared to each other. Based on results evaluated, genre-based model outperforms both topic modeling and hybrid models. However, the performance of genre-based model doesn’t outperform the model performance from previous research. Therefore, it can be concluded that genre isn’t a suitable parameter for recommending games.
2022 IEEE International Conference in Power Engineering Application (ICPEA)
Opinion classifications from Twitter are still in demand among research works on related opinions... more Opinion classifications from Twitter are still in demand among research works on related opinions or feelings expressed on various issues. One of the concerns expressed in Twitter is on water-related issues such as the lack of clean water supply. It has been found that the issue highlighted in Twitter is the frequent disruption of clean water supply in Malaysia. The discussions concerning this issue contain positive and negative emotions like anger, joy, worry, and frustration. The focal point of this article is to evaluate hybrid sentiment analysis using a machine learning classifier to analyze the polarity of opinions employing real data from Twitter. A series of experiments were performed on a hybrid of deep learning, support vector machine, Naïve Bayes and random forest with a lexicon-based model. In addition, the Malay sentiment lexicon score is proposed. The Malay sentiment lexicon scores have improved the accuracy and F1-score of all hybrid methods. The analysis uncovers that negative and positive polarity opinions can be beneficial to the relevant authorities to overcome the water supply disruption issue.
2021 IEEE 11th International Conference on System Engineering and Technology (ICSET), 2021
Mechanical Ventilation plays a major role for life support of critically-ill patients in the Inte... more Mechanical Ventilation plays a major role for life support of critically-ill patients in the Intensive Care Unit. Medical practitioners assess patient's oxygenation status by observing the blood gases from arterial blood samples. However, this procedure to sample arterial blood is invasive and must be done cautiously. This paper proposes new fuzzy logic-based models for estimating non-invasively the relative ratio of dead space to the tidal volume, known as relative dead-space and the production of carbon-dioxide of ventilated patients. These parameters are needed for a non-invasive and automatic blood gas estimation system called the SOPAVent system. The fuzzy models are designed using fuzzy c-means clustering and new-structure particle swarm optimization technique which looks at the coefficient of determination and the mean squared error as performance indices. The prediction results are validated with actual ICU patients. The simulation results showed high accuracy in prediction of the relative dead-space parameter and the production of carbon-dioxide parameter.
2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC), 2016
This paper presents a development of a Dynamic Home Automation Security (DyHAS) Alert System with... more This paper presents a development of a Dynamic Home Automation Security (DyHAS) Alert System with Laser that interfaces on Webpages and Windows 10 Mobile Application. Home security has been identified as one of an important issue in today's life. Cases of home burglary continue to arise daily. Existing home security systems faces drawback to home users where certain flaws occurred in the system such as not web-supported, lack of dynamic features and difficult to handle. Thus, enhancing to dynamic home automation security alert is the objective of this research. DyHAS is developed which comprised of lasers, lights, alarm and python programmed that interfaces with webpage and Windows 10 mobile devices. Dynamics alert are triggered according to identified parameters and it is adaptively set with lights, alarm and alert messages to home owner's mobile devices and webpage. Triggers messages are updated and data are logged. Adaptive bypass security can be configured if needed. Windows 10 mobile application and Raspberry Pi 2 are used in the designed system. Wireless connectivity is used as the communication medium between the hardware and software components of the system. Successful system is tested on small circuit board with alerts data sent to home users on Windows 10 mobile application and webpages platform. Selected outdoor are tested at the security levels that includes installation of laser beam module. This research is significant and benefits for recent technology on home security system which suitable for small or large covered areas.
2015 IEEE 7th International Conference on Engineering Education (ICEED), 2015
This paper presents a study on self-learning website development through online internet knowledg... more This paper presents a study on self-learning website development through online internet knowledge among engineering students. The objective of this study is to analyze how capable and experience students' skills on self-learning with online internet knowledge to develop their own website. Students are exposed to technology and online communication in today's learning environment which has motivated this research to identify engineering students' skills and independence level. Two engineering students group from Faculty of Electrical Engineering are chosen which are the Electronic students and Communications Engineering students which consist of 32 and 26 students. They are addressed to develop a website by self-learning from online internet knowledge. Students are to follow set of derived rules and criteria tracked by some rubrics for their score marks. Website development involved planning and designed in the methodology. Analysis present most students can develop their self-learning website but some are unable to follow the derived rules and rubrics. This means that they are not capable in presenting a good website designed. Results present Electronic engineering students are far better than communications engineering students. Best result are presented by the electronics group where 84.4% score with grade A and above compared to Communications group that presented only 19% scored with the same grade although studied has targeted that communication's students are more capable to develop their own website.
this paper presents the development of a new scheme called Adaptive Throughput Policing and Shapi... more this paper presents the development of a new scheme called Adaptive Throughput Policing and Shaping (ATPS) algorithm to control internet inbound throughput and burst flow in a Campus IP-based network. Real live throughput collected from a Campus Network with Committed Access Rate (CAR) of 16 Mbps bandwidth speed are simulated and analyzed. New mathematical model with identified parameters on ATPS is derived. Adaptive throughput policies with burst shaping are simulated using Token Bucket theory control mechanism with 16 Mbps threshold policy. Three main adaptive policy conditions called P1, P2 and P3 which is controlled on 110%, 100% and 50% threshold rate are defined as filtered condition. Burst throughputs are shaped into next free bucket capacity for the next flow time. The throughputs are continuously shaped if the next bucket is full until free buckets are available. This new ATPS algorithm is numerically evaluated and analyzed on traffic performance which controlled the bandwidth and burst throughput. Performance results present reduced bucket capacity, reduced bandwidth rate in throughput transfer, no burst throughput and no byte loss in conforming traffic transferred in a network compared to previous implemented ATP algorithm, which held burst throughput and byte loss in the system.
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Papers by PROFESOR MADYA TS DR MURIZAH KASSIM