Hand gesture recognition is one of the system that can detect the gesture of hand in a real time video. The gesture of hand is classify within a certain area of interest. In this study, designing of the hand gesture recognition is one of the complicated job that involves two major problem. Firstly is the detection of hand. Another problem is to create the sign that is suitable to be used for one hand in a time. This project concentrates on how a system could detect, recognize and interpret the hand gesture recognition through computer vision with the challenging factors which variability in pose, orientation, location and scale. To perform well for developing this project, different types of gestures such as numbers and sign languages need to be created in this system. The image taken from the realtime video is analysed via Haar-cascaded Classifier to detect the gesture of hand before the image processing is done or in the other word to detect the appearance of hand in a frame. In this project, the detection of hand will be done using the theories of Region of Interest (ROI) via Python programming. The explanation of the results will be focused on the simulation part since the different for the hardware implementation is the source code to read the real-time input video. The developing of hand gesture recognition using Python and OpenCV can be implemented by applying the theories of hand segmentation and the hand detection system which use the Haar-cascade classifier.
<p>The widespread of coronavirus disease 2019 (COVID-19) pandemic led to a discovery that open distance learning (ODL) has turned out to be the only choice for teaching and learning by most institution (s) of higher learning (IHLs). In Malaysia, ODL is considered a new approach as physical laboratory practice has always been conducted for laboratory courses. This is a quantitative study which explores the perceptions of e-Lab among the students of bachelor’s in electrical and electronic engineering (EE) by focusing on the effectiveness and readiness in conducting the e-Lab. Simulation-based model is proposed for conducting the e-Lab using an interactive media and validated with the final score performance. With the future goals of improving the e-Lab in terms of delivering methods and engaging mediums between students and laboratory instructor, this study also discovered the levels of response from students’ perception to substitute the conventional laboratory by providing an equivalent and comparable learning experiences of the students.</p>
Electromagnetic elimination was used to eliminate unwanted radiation that may interface with machinery or affect human health. The use of microwave absorbers has become necessary for an environment with a safe electromagnetic wave level. The objectives of this project are to design a microwave absorber as a modern biomass wall tile and to investigate the absorbing performance of the modern wall tile absorbers with different materials. The absorber was designed with similar shapes and different biomass materials. The concept of modern wall tile had been applied to design the absorbers in terms of their shape and dimension by using biomass materials. Biomass materials such as kenaf and coconut coir were used in the study due to their lightweight and environmental-friendly material behavior. The simulation was done using CST Suite Studio software to predict the preliminary result of the absorbers. The proposed designs of the modern wall tile from the CST simulation are then fabricated while the mixtures of the materials are moulded into a microwave absorber. NRL Arch free space method was used to determine the absorption performance of the modern wall tile absorbers at a frequency range from 1GHz to 12GHz. Both results of the simulation and free space measurement are analyzed and discussed. This study showed that absorber KCA has the best performance among all the absorbers with the absorption of more than -10 dB. The mixture of biomass material with carbon has a great absorption performance compared to the mixture of biomass material without carbon.
Voltage sag and swell can cause serious problems like instability, short lifetime, and data errors in power quality. The objective of this paper is to present the detection and classification of voltage sag and swell. S-Transform is used as a base to detect the triggering point of disturbances using Root Mean Square (RMS) method. This paper also presents the type of sags and swells by applying the features into Extreme Learning Machine (ELM) neural network approach in MATLAB. In addition, ELM method is compared with Support Vector Machine (SVM) and Decision Tree method to observe the best classification between these three methods. The accuracy of the classifications was displayed in percentages. It was verified that the detection using RMS and classification using ELM are possible because the results are clearly showing the advantages of the RMS in detecting and ELM for classifying the power quality problems.
Nowadays, the resources of roofing materials are abundant, which are the essential materials to build houses. It is of great significance to develop the roofing material with absorbing function for shielding electromagnetic radiation. This study is conducted to design a corrugated bamboo roofing microwave absorber that can absorb electromagnetic wave for frequencies 1 to 12 GHz. Three types of roofing bamboo with different designs namely Model A, Model B and Model C has been developed. The size of all types of proposed roofing bamboo is 60 cm width x 60 cm in length. The design is simulated using Computer Simulation Technology (CST) Microwave Studio software. The arch method is used to analyse microwave absorber performance. It contains of a wooden structure in the shape of semi-circular for enabling the proper positioning towards transmitting and receiving the two horn antennas. Bamboo can be used as an absorbent material. The expected result for bamboo roofing microwave absorber is to get a high performance of microwave absorption which is above 20 dB. The third model or Model C has recorded the highest value of an absorption level.
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