This paper presents an evaluation conducted between human stress questionnaires with EEG Power Spectrum of Beta and Alpha band. Cohen's Perceived Stress Scale (PSS) was used as stress questionnaires to evaluate human stress. The EEG recording of 13 volunteers were carried out immediately after them answering the stress questionnaires. The scores from the stress questionnaires were calculated and used to figure out its relationship with the ratio of EEG Beta and Alpha band power. The results of the study showed the PSS was negatively correlated with the ratio of EEG Power Spectrum. Besides, the study suggested that it was feasible to use PSS and the ratio of EEG Power Spectrum to determine human stress.

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.

This paper presents the development of automatic vehicle plate detection system using image processing technique. The famous name for this system is Automatic Number Plate Recognition (ANPR). Automatic vehicle plate detection system is commonly used in field of safety and security systems especially in car parking area. Beside the safety aspect, this system is applied to monitor road traffic such as the speed of vehicle and identification of the vehicle's owner. This system is designed to assist the authorities in identifying the stolen vehicle not only for car but motorcycle as well. In this system, the Optical Character Recognition (OCR) technique was the prominent technique employed by researchers to analyse image of vehicle plate. The limitation of this technique was the incapability of the technique to convert text or data accurately. Besides, the characters, the background and the size of the vehicle plate are varied from one country to other country. Hence, this project proposes a combination of image processing technique and OCR to obtain the accurate vehicle plate recognition for vehicle in Malaysia. The outcome of this study is the system capable to detect characters and numbers of vehicle plate in different backgrounds (black and white) accurately. This study also involves the development of Graphical User Interface (GUI) to ease user in recognizing the characters and numbers in the vehicle or license plates.

The importance to recognize a learner's Learning Style (LS) is ever-essential as to substantiate success in a teaching and learning process. At the same time, the learner's IQ and personality traits such as Stress also being actively investigated in educational research as educationists consistently attempted to understand learners in a more adept way. Nevertheless, the effort was usually confined to psychoanalysis test. With the emergence of Electroencephalography (EEG) technology, learner's brain characteristics could be accessed directly and the outcome may well hand-in-hand supported the conventional test. In this study, the participants (n= 80) are grouped to the LS of Diverger, Assimilator, Converger or Accommodator using the Kolb's Learning Style Inventory (KLSI). Subsequently, their brain signals were then recorded using EEG at resting baseline state of Open Eyes and Closed Eyes. A statistical tool of SPSS 16 was used for data analysis purposes. Using the Two Step Cluster analysis, the participants’ EEG datasets were 100% classified to the corresponding LS. Then, EEG Alpha band was selected to link between LS, IQ and Stress. The study concluded that Diverger is the LS with highest IQ while Converger and Diverger are the LS that prone to Stress