Sazali Yaacob
Thumbnail Image
Preferred name
Sazali Yaacob
Official Name
Sazali, Yaacob
Alternative Name
Yaacob, S.
Bin Yaacob, Sazali
Yaacob, S. B.
Yaacob, Sazali B.
Main Affiliation
Scopus Author ID
6602262501

Research Output

Filters

2 1
11
Reset filters

Settings
Now showing 1 - 10 of 11
No Thumbnail Available
Publication

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

2020 , SatheesKumar Nataraj , MP Paulraj , Sazali Yaacob , Abdul Hamid Adom

Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought‑controlled wheelchair navigation system with communication assistance. Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd‑ball paradigm. The proposed system records EEG signals from 10 individuals at eight‑channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross‑correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross‑correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross‑correlated signals. Finally, the extracted feature sets were validated through online sequential‑extreme learning machine algorithm. Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross‑correlation signals had the best performance with a recognition rate of 91.93%.

View
Thumbnail Image
Publication

Failure envelope modelling of glass/epoxy composite pipes using system identification method

2017-11-07 , Ang Jia Yi , Mohd Shukry Abdul Majid , Azuwir Mohd Nor , Sazali Yaacob , Mohd Ridzuan Mohd Jamir

The paper aims to model the performance of the Glass Fibre Reinforced Epoxy (GRE) composite pipe under multiaxial loading via system identification approach. System identification modelling depends on the input and output data of the experimental result. In this study, the experimental data used are obtained from a pressurised test rig. The model is based on pure hydrostatic (2H: 1A) loading using GRE pipes with three different winding angles (±45°, ±55°, ±63°). Several models based on different model structures are derived for comparison to obtain the best modelling accuracy. The result shows that the transfer function method could model and has the highest efficiency compared with the experimental result. The ±45°pipe model have achieved 92.41% and 85.13% for both its hoop and axial model. The ±55°pipe model has achieved 96.64% and 86.1%. Follow by the ±63°which the best fit is 92.41% and 94.26%. At the last part of this research, the ±55°pipe model and experimental data has been use to identified when the damage occur and found that the axial strain of 78 bar can damage the experimental pipe in this research.

View
No Thumbnail Available
Publication

Motorbike engine faults diagnosing system using entropy and functional link neural network in wavelet domain

2009-10-11 , Paulraj M P , Sazali Yaacob , Mohd Zubir Md Zin

The sound of working vehicle provides an important clue for engine faults diagnosis. Endless efforts have been put into the research of fault diagnosis based on sound. It offers concrete economic benefits, which can lead to high system reliability and save maintenance cost. A number of diagnostic systems for vehicle repair have been developing in recent years. Artificial Neural Network is a very demanding application and popularly implemented in many industries including condition monitoring via fault diagnosis. This paper presents a feature extraction algorithm using total entropy of 5 level decomposition of wavelet transform. The engine noise signal is decomposed into 5 levels (A5, D5, A4, D4, A3, D3, A2, D2, A1, D1) using Daubechies “db4” wavelet family. From the decomposed signals, the entropy is applied for each levels and the feature are extracted and used to develop a functional link neural network.

View
No Thumbnail Available
Publication

Simultaneous localization and map building – a guided tour

2009-10-11 , William Low , R. Nagarajan , Sazali Yaacob

Simultaneous Localization and Mapping (SLAM) has been one of the active research areas in robotic community for the past decade of years. SLAM addresses the problem of a robot navigating and building a map of an unknown environment, without an initial map or an absolute localization means. This paper attempts to provide a comprehensive overview of the SLAM problem. Successful SLAM implementations using laser, sonar and radar can be found in the literature. However, recent extensions to the general SLAM problem has looked into the possibility of using 3-dimensional features and the use of vision sensors. We will focus on these two approaches to the SLAM problem using vision: one with single or monocular camera and another with stereovision. Current applications and future challenges will also be discussed.

View
Thumbnail Image
Publication

Analysis of accent-sensitive words in multi-resolution mel-frequency cepstral coefficients for classification of accents in Malaysian English

2013-06 , M.A. Yusnita , M.P. Paulraj , Sazali Yaacob , R. Yusuf , Shahriman Abu Bakar

This paper investigates the most accent-sensitive words for Malaysian English (MalE) speakers in multi-resolution 13 Mel-frequency cepstral coefficients. A text-independent accent system was implemented using different numbers of Mel-filters to determine the optimal settings for this database. Then, text-dependent accent systems were developed to rank the most accent-sensitive words for MalE speakers according to the classification rates. Prior work has also been conducted to test the significance of the wordlist for both gender and accent factors, and to investigate any interaction between these two factors. Experimental results show that male speakers have a higher intensity of accent effects compared with female speakers by 3.91% on text-independent and 3.47% on text-dependent tasks. Another finding has proven that by selecting appropriate words that carry severe accent markers could improve the task of speaker accent classification. An improvement of at most 8.45% and 8.91% was achieved on the male and female datasets, respectively, following vocabulary selection.

View
Thumbnail Image
Publication

Failure prediction of ±55° glass/epoxy composite pipes using system identification modelling

2017-10-29 , Yi A. , Mohd Shukry Abdul Majid , Nor A. , Mohd Ridzuan Mohd Jamir , Sazali Yaacob

Black-box modelling using system identification method to predict the performance of glass fibre reinforced epoxy (GRE) composite pipe under multiaxial loading stress ratio is presented. In this study, both linear and nonlinear models were derived namely; linear time-invariant parametric model and artificial neural network model. The models derived are to approximate the pure hydrostatic loading performance using GRE pipes with winding angles of ±55°. Three different linear model structures were derived, and the best fit model achieved at 96.64% of best fit. On the other hand, the Artificial Neural Network (ANN) modelling showed better accuracy with the best fit of 99.82%. Finally, the point of failure at which first damage takes place predicted by the models derived was validated using experimental data.

View
No Thumbnail Available
Publication

Development of attitude control system on RCM3400 microcontroller for Nano-satellite applications

2009-10-11 , Muhyi Yaakop , Sazali Yaacob , Abdul Rahman Mohd Saad , M Harihran , R. Nagarajan

This paper describes the development of a nano-satellite attitude control system (ACS) which employ a Kalman filter based controller and a simple adaptive predictive fuzzy logic controller (APFLC) for a 1, 2 and 3 axis orientation using RCM3400 microcontroller. This paper presents the performance comparison of the APFLC and Kalman filter based controller implemented in the hardware. The physical interface module, configuration with several key features, communication protocol and data handling for the micro-controller are also described.

View
Thumbnail Image
Publication

Home-based ankle rehabilitation system: Literature review and evaluation

2017-09-01 , Lim Chee Chin , Shafriza Nisha Basah , Marwan Affandi , Muhammad Nazrin Shah Shahrol Aman , Sazali Yaacob , Yeap Ewe Juan , Mohamad Yazid Din

Ankle sprain Injury is one of the most common ankle injuries due to domestic or sporting accidents. There is a need for greater demand for quick and effective ankle rehabilitation system (ARS). Nowadays, research on ARS has gained a great attention than manual clinical method in medical areas such as orthopedic injuries, pediatrics sport medicine and industrial services. It can improve the treatment conditions by reducing the dependency of doctors’ supervision, help patient with less movable to have home-based rehab exercise and help to speeds up recovery. There are currently available ARS that can provide effective ankle rehabilitation treatment such as Visual, Non-Visual and Robot-aided. In this paper, the critical review of ARS is conducted to evaluate the effectiveness of ARS in terms of provided setting criteria. The strengths, weaknesses, opportunities and threats of each ARS is discussed and compared to identify the most suitable home application of ARS for ankle sprain patient. From the comparison, the most suitable home application ARS is the visual marker-less based ARS system which give user-friendly, efficiency, validity in performance and cheaper cost.

View
No Thumbnail Available
Publication

Modified energy based time-frequency features for classifying human emotions using EEG

2009-10-11 , M. Murugappan , R.Nagarajan , Sazali Yaacob

In this paper we summarize the emotion recognition from the electroencephalogram (EEG) signals. The combination of surface Laplacian filtering, time-frequency analysis (Wavelet Transform) and linear classifiers are used to detect the discrete emotions (happy, surprise, fear, disgust, and neutral) of human through EEG signals. EEG signals are collected from 20 subjects through 62 active electrodes, which are placed over the entire scalp based on International 10-10 system. All the signals are collected without much discomfort to the subjects, and can reflect the influence of emotion on the autonomic nervous system. An audio-visual (video clips) induction based protocol has been designed for evoking the discrete emotions. The raw EEG signals are preprocessed through Surface Laplacian filtering method and decomposed into five different EEG frequency bands using Wavelet Transform (WT). In our work, we used “db4” wavelet function for extracting the statistical features for classifying the emotions. A new statistical features based on frequency band energy and it’s modified from are discussed for achieving the maximum classification rate. The validation of statistical features is performed using 5 fold cross validation. In this work, KNN outperforms LDA by offering a maximum average classification rate of 78.4783 % on 62 channels and 73.6087% on 24 channels respectively. Finally we present the average classification accuracy and individual classification accuracy of two different classifiers for justifying the performance of our emotion recognition system.

View
No Thumbnail Available
Publication

Two axis adaptive predictive fuzzy logic controller for a pico-satellite

2009-10-11 , Nagarajan, R. , Paularaj, M.P. , Sazali Yaacob , Zaridah Mat Zain , Soh Kay Hoh, Warren , Ahmad Sabirin Arshad

In this paper, extensive simulations of Adaptive Predictive Fuzzy Logic Controller (APFLC) has been made to study the performances of the attitude control system for a Pico-satellite. A fuzzy controller is developed based on optimal control theory. The controller depends on the feedback signal to calculate the torque signal for the actuator of the Pico-satellite. A predictive control is needed to estimate required control torque at the next sampling time since the satellite system is having time delays. The adaptive portion is using a model reference and the response of the satellite is compared to minimize the output error by adapting the adaptive gain to the controller torque. The design schemes of modeling APFLC are described as follow: Basic FLC, Predictive FLC [PFLC) aud APFLC. The second axis system is expressed with coupling effect between orthogonal axes due to magnetic field coupling. The satellite is forced to follow the reference signal and performed two axis slew maneuver to reach the desired attitude in space. The simulation results show that the attitude motion can be controlled with APFLC even in the presence of noise, disturbance and nonlinearity.

View