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Articles by Aini Hussain
Total Records ( 16 ) for Aini Hussain
  Mohd Ridzuwary Mohd Zainal , Salina Abdul Samad , Aini Hussain and Che Husna Azhari
  This research describes the pitch and timbre determination of the angklung, a musical instrument made entirely out of bamboo. An angklung has two main parts: the frame and the rattle tubes. The pitch of the rattle tubes can be determined using a formula that takes into consideration the length and diameter of the air resonator. This is compared with the results obtained using sound analysis with the fast Fourier transform as well as with measured results. The coupling effects of having two rattles on the pitch and timbre are investigated. It is found that the pitch of the angklung is closely related to the fundamental frequency of air resonance in the bamboo tubes of the angklung rattles. Therefore, the pitch of an angklung can be estimated by calculating that fundamental frequency using information from the length and diameter of the closed cylinder air column of each rattle. The timbre of the angklung is also determined to be a mix of the sound output from each of its individual rattles. The timbre has an identifying characteristic of having two prominent peaks with each one corresponding to the pitch of each rattle.
  Ahmed M.A. Haidar , Azah Mohamed and Aini Hussain
  Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits.
  Suzaimah Ramli , Mohd Marzuki Mustafa , Aini Hussain and Dzuraidah Abdul Wahab
  Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process.As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80%.
  Siti Raihanah Abdani , W. Mimi Diyana W. Zaki , Aini Hussain and Mohamad Hanif Md. Saad
  Pterygium tends to affect those who are exposed to prolonged uv radiation in which Asian people are more susceptible to develop such condition. Thus, a computerized method that can automatically detect pterygium is greatly needed and is being proposed using image processing approach. The main focuses of this researchers is corneal segmentation of eye region in the Anterior Segment Photographed Images (ASPI). A heuristic approach based on Particle Swarm Optimization (PSO) thresholding is explored to segment the corneal to further improve the segmentation rate of non-ideal images that contain pterygium condition. To obtain the corneal after applying PSO, the three steps thresholding method is used based on frame differencing between multi color channels (HSV). Brazil Pterygium (BP) database is used to represent the pterygium condition while UBIRIS.V1, UBIRIS.V2 and Miles databases are used to represent the normal eye condition. Both pterygium and normal ASPIs have been tested rigorously to validate the robustness of the proposed algorithm. The results showed that BP have the highest accuracy (94%) while UBIRIS.V1 have the highest accuracy (93.2%) for the non pterygium database. In conclusion, it shows that the proposed method is mostly suitable for application in Asian countries in which there is increasing trend of pterygium cases.
  Salina Abdul Samad , Dzati Athiar Ramli and Aini Hussain
  In this study, we investigate the implementation of correlation filter for lower face verification with different expression of images for each speaker. The motivation to implementing lower face images instead of face images is because the mouth is subject to fundamental changes during speech. Furthermore, the smaller size of a lower face image compared that of a face can reduce storage capacity and increase the speed of computation. The performance of lower face verification using Minimum Average Correlation Energy (MACE) filter is evaluated. The results are promising and offer good potential for lower face verification compare with face verification performance.
  Khairul Anuar Ishak , Salina Abdul Samad and Aini Hussain
  Face recognition is one of the key components for future intelligent vehicle applications such as determining whether a person is authorized to operate the vehicle. This study describes the development and implementation of an automatic face recognition system in the car environment. The challenge is to build a fast and accurate system that is able to detect, recognize and verify a driver`s identity with the constraint introduced in the car environment in daylight lighting conditions. A further constraint is to use a low-cost web camera to capture the frontal images. The system consists of two parts. The first is face detection, which is based on combining fast and classical Neural Networks (NN) methods. The second is face recognition and verification, which is based on combining Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. Lighting correction techniques are applied to improve the overall performance. The proposed system has been tested in the car environment and has a recognition rate of 91.4% with 0.75% false acceptance rate. Face detection is achieved in 3.7 sec while face recognition for a car with two persons authorized to operate the vehicle is 1.4 sec.
  Mohammad Syuhaimi Ab-Rahman , Norhana Arsad , Aini Hussain , Hafizah Husain and Sahbudin Shaari
  According to accreditation manual set by the Engineering Acreditation Council (EAC) has listed compulsory subjects which have been identified as core subject based on curriculum requirement in Academic Program (AP). This is also to comply requirement and recognition from. Thus, the department has implemented proactive steps by improving not only curriculum in AP but also at the lower stage which is at the subject itself. This step has been taken to match with the department’s activities in order to achieve excellence by fully accredited for all offered AP. In the process of improving at the subject level, important elements such as subject delivery methods, syllabus contents, measurement and assessment methods, Generic Competency (GC) absorption and so on have been mainly focused on by considering input and comments obtained from stakeholders which consists of Industrial Advisory Panels, industrial representatives, alumni and students. This has been embedded into program curriculum. This study presents and discusses the improvement being applied on Photonics Technology (KC4013) subject which is part of the department’s core subjects under Microelectronics program for undergraduates’ students of Department of Electric, Electronic and System Engineering (EESE). It is also reporting effective teaching methods used in activities in increasing the understanding, students skills and competitive evaluation and CQI to guarantee the quality of the graduates students.
  Aini Hussain , M.A. Hannan , Hilmi Sanusi , Azah Mohamed and Burhanuddin Yeop Majlis
  Assessment to the investigated emerging sensor technologies indicates that the capacitive Microelectro-mechanical System (MEMS) pressure sensor has the highest potential used for the development of tire Pressure Monitoring System (TPMS). The potential owes to its robustness, small size and low power consumption, besides fitted well in total electronic system integration. This study describes the basic configuration, operating principle and experimental results of MEMS sensor for the development of TPMS. In addition, the application performance and experimental set up of the sensor are also discussed in this paper. Input and output pins configurations of the MEMS sensors at both standby and measured modes are studied, in order to relate to their unique construction. The threshold check is used by the tire pressure monitoring system through sensed signal extracted the data acquisition environment of the MEMS sensor. This study provides preliminary insight of using MEMS pressure sensor in a TPMS.
  Nooritawati Md. Tahir , Aini Hussain , Salina Abdul Samad , Hafizah Husain and Andrew Teoh Beng Jin
  This study affords the method of using advance correlation filters in human posture recognition task. Two types of correlation filters were implemented and their efficacy evaluated. The correlation filters under consideration are Minimum Average Correlation Energy (MACE) and Unconstrained Minimum Average Correlation Energy (UMACE). Initial results prove that correlation filters offer significant potential used in posture recognition task with UMACE outperforming the MACE filter. In this research, both filters were subjected to a challenging task to recognize human posture without any restriction on the gender, clothing and posture variations. The UMACE filter performs remarkably well with an average accuracy of 89% compared to MACE filter which attained 42%.
  Dzati Athiar Ramli , Salina Abdul Samad and Aini Hussain
  Visual speech information, for example the appearance and the movement of lip during speech utterance can characterize a person`s identity and therefore it can be used in personal authentication systems. In this study, we propose a novel approach by using lipreading data i.e., the sequence of entire region of mouth area produced during speech and the implementation of the Unconstrained Minimum Average Correlation Energy (UMACE) filter as a classifier for biometric authentication. The system performance is also enhanced by the implementation of multi sample fusion scheme using average operator. The results obtained from using a Digit Database shows that the use of lipreading information and UMACE filter has good potentials and is highly effective in reducing false acceptance and false rejection rates for speaker verification system performance.
  Noor Izzri Abdul Wahab , Azah Mohamed and Aini Hussain
  This study presents transient stability assessment of electrical power system using two artificial neural network techniques which are Probabilistic Neural Network (PNN) and Least Squares Support Vector Machine (LS-SVM). Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as a classifier to determine whether the power system is stable or unstable. To verify the effectiveness of the proposed PNN and LS-SVM methods, they are compared with the Multi Layer Perceptron Neural Network (MLPNN). Results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM network and MLPNN in terms of classification results.
  Ahmed El-Shafie , Abdallah Osman , Aboelmagd Noureldin and Aini Hussain
  Problem statement: An accurate knowledge of geographic positions of sonobuoys is critical for the conduct of antisubmarine warfare operations and detected target localization. Deployed from an airborne platform or a surface vessel, arrays of sonobuoys could be used to efficiently track and localize submarines. Lastly, some sonobuoys were being equipped with GPS for improving system accuracy and potentially allowing networked Sonobuoy positioning. However, the computation of the range using the propagation loss profile and the data of one sonobuoy usually leads to inaccurate target localization due to several effects and uncertainties. It was, alternatively, reported that if the target is within the detection rage of two or more sonobuoys, greatly improved target localization can be achieved. Approach: Aim of this research was to investigate the feasibility of fusing data from a distributed field of GPS sonobuoys to create an Artificial Intelligence (AI) based model for the error of the range computation in case of the target being detected by only one sonobuoy. Proposed module was designed utilizing Adaptive Neuron-Fuzzy Inference Systems (ANFIS) to estimate the range error associated with the computation using the propagation loss profile when the target is within the detection range of only one sonobuoy. The architecture of the proposed ANFIS system had two unique features. First was the real-time cross-validation applied during the update (training) procedure of the ANFIS-based module while the target was detected by two sonobuoys and the range was computed. Second feature was the use of non-overlapping and moving window for the real-time implementation of the ANFIS-based data fusion module. Results: Performance of the proposed system was examined with simulation data considering different scenarios for both the array of GPS sonobuoys and the target. Results showed that the corrected positioning by one sonobuoy is completely following the positioning by two sonobuoys over the entire experiment with the error in between evaluated to have RMSE value of 0.004 Nm and 0.008 for both scenarios. Conclusion: These results revealed that with aided from the proposed ANFIS model; significant enhancements to the underwater target tracking accuracy in cases of single sonobuoy detection could be achieved and thus maintaining consistent levels of accuracy over the whole tracking mission.
  Aini Hussain , Rosniwati Ghafar , Salina Abdul Samad and Nooritawati Md Tahir
  Problem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR) of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI) was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.
  Edgar Scavino , Dzuraidah Abdul Wahab , Hassan Basri , Mohd Marzuki Mustafa and Aini Hussain
  Problem statement: Segmentation is the first and fundamental step in the process of computer vision and object classification. However, complicate or similar colour pattern add complexity to the segmentation of touching objects. The objective of this study was to develop a robust technique for the automatic segmentation and classification of touching plastic bottles, whose features were previously stored in a database. Approach: Our technique was based on the possibility to separate the two objects by means of a segment of straight line, whose position was determined by a genetic approach. The initial population of the genetic algorithm was heuristically determined among a large set of cutting lines, while further generations were selected based on the likelihood of the two objects with the images stored in the database. Results: Extensive testing, which was performed on random couples out of a population of 50 bottles, showed that the correct segmentation could be achieved in success rates above 90% with only a limited number of both chromosomes and iterations, thus reducing the computing time. Conclusion: These findings proved the effectiveness of our method as far as touching plastic bottles are concerned. This technique, being absolutely general, can be extended to any situation in which the properties of single objects were previously stored in a database.
  Nur Syazwani Samanu , Mohd Asyraf Zulkifley Nor Afiqah Ramdun and Aini Hussain
  This study provides a brief review on human behaviour analytics system used in video surveillance applications. The topic of aggressive human behaviour detection system is chosen because of its rising popularity in the field of computer vision, specifically for video surveillance applications. Most of the existing systems were designed for closed room application such as detention room or prison to prevent any anxious situations that may lead to violence activities. Furthermore, the advancement of video surveillance system has allowed safety features to be embedded so that human activities both in public and private places can be monitored and analysed. Hence, many researchers have developed an intelligent system to study human behaviours to differentiate between normal and abnormal behaviours with various computer vision methodologies. Therefore, the main objective of this study is to explore the related researches that have been done in automatic aggressive behaviour detection system to observe a small crowd behaviours for real-time video surveillance application. The review conludes that there are many commonly and favourable methods for study about human behaviours by using video applications and describes the purpose of highlighted methods of each researches.
  Mohammad Syuhaimi Ab-Rahman , Siti Salasiah Mokri , Hafizah Hussin and Aini Hussain
  In purpose of engineering program accreditation, the Engineering Accreditation Council (EAC) requires a list of compulsory subjects that need to be incorporated according to respective academic programs. The inclusion of these subjects precisely known as core subjects contained in their accreditation manual is also in agreement with Washington Accord requirement. As for this reason, the Department of Electrical, Electronics and System, Faculty of Engineering and Environmental Built of Universiti Kebangsaan Malaysia has aggressively implementing steps to improve the program, especially at the course level aimed to obtain full accreditation and achieving excellence. Course improvements will consider important elements such as delivery method, course syllabus, methods of assessment and measurement, generic competency and others taking into accounts suggestions from stakeholders such as Industrial Advisor Panel (IAP), industrial representatives, alumni and students. This study presents and discusses the improvement measures carried out for KKKL3173 Communication Theory course which is a core subject for Bachelor Degree in Microelectronics program. Researchers highlight the activities that have been carried out to enhance the effectiveness of teaching and learning by means of quiz-students pre assessment, projects-ethics, professionalism and collected technical, presentation-improving through open critic, technical visit, technical seminar, seminar on ethics and professionalism, industrial, embedded laboratory to course, questionnaire/survey.
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