Design of Broadband Dual-Frequency Microstrip Patch Antenna with Modified Sierpinski Fractal Geometry

Fractal antennas have the characteristic of radiating in multiple frequencies through the property of self similarity that fractal shapes possess. By connecting fractal shaped antennas, wideband coverage can be achieved. Microstrip patch antennas with Sierpinski fractal geometry can be tuned, by design, to work exactly at the bands of interest, through judicious choice of the fractal designs and iteration. Therefore, a broadband dual-frequency microstrip patch antenna with modified Sierpienski fractal geometry is designed by using Microwave Office 2002 simulation software. The broadband and multiple frequency characteristics of fractal antennas will be demonstrated. The performance of microstrip patch antenna with the classic and modified Sierpinski fractal geometries will be presente

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten.  Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Full Reference Image Quality Metrics and their Performance

This paper mainly aims to study the performance of objective assessment methods of image quality. It take into consideration the correlations between each objective assessment and the subjective assessment in order to determine objective test performance. Three objective assessment methods used in this study are the Structural Similarity (SSIM) index, the Peak Signal-to-Noise Ratio (PSNR) and the Mean Squared Error (MSE) calculating algorithm. The resulting data indicate what type of objective assessment was most suitable for which type of impairment imposed upon an image. This is clarified using the Pearson Correlation Coefficient as described in the paper. As an overall, SSIM index had the best correlation characteristics to the subjective assessment, followed by the MSE calculating algorithm. From this study, a better understanding of the requirements for developing an efficient image quality assessment method was gained

Electric and Magnetic Fields for the Proposed Microstrip Antenna with DGS for Breast Cancer Detection

This paper presents the design of microstrip antenna with defected ground structure (DGS) for the detection of breast tumor in microwave imaging system at operating frequency of 2.45GHz. Four types of microstrip patch antennas have been designed using microstrip feed inset with grounding patches at 2.45 GHz operating frequency using dielectric substrates, FR4 (ɛr = 4.4 F / m). The results are collected via the intensity of electric (E), magnetic fields (H) and current densities. The antenna is examined with a 3D breast model structure with specific dielectric value and conductivity. From the results, it shows that antenna with design structure, Design 4 produce a good intensities values of both E and H fields respectively with the presence of the tumour, and gives the value of 7083 V/m and 35.5 A/m while without the presence of tumour is 7186 V/m and 35.8 A/m compare to other proposed antennas

Deep learning applications for oil palm tree detection and counting

Oil palms are one of the essential crops in agricultural productivity for developing countries such as Malaysia and other tropical areas. For predicting the yield and production of palm oil, the counting process is often carried out. Manually counting oil palm trees is one of the solutions but it requires a massive labour force, and the result is often inaccurate. To overcome this problem, automated techniques for oil palm detection have been developed. However, the performance of existing automated techniques for oil palm detection deteriorates when the planting layout of the oil palm tree is not well organized. Deep learning applications for oil palm tree detection and counting offer a powerful solution to the challenges of precision agriculture, enabling plantations to increase productivity and sustainability while reducing costs and manual labour. Deep structured learning, more generally deep learning is one of the widely used computer vision technology, especially in agricultural engineering. Deep learning method is an essential tool when it comes to monitoring the plantation. Different deep learning networks are utilized for classification tasks towards oil palm trees. In order to promote the use of deep learning in the oil palm industry, this paper main contribution is to provide an understanding of the utilisation of deep learning and its application in oil palm tree counting. The gaps and opportunities for research in oil palm plantations based on deep learning techniques will also be discussed

Person Verification Based on Multimodal Biometric Recognition

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold t. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system

A study on Gamification toward Engineering Students’ Engagement in the University Level

Most engineering students may struggle with complicated theories, heavy assignments, lack of motivation and disengagement in the classroom. According to studies, student engagement in the classroom is critical in the learning process. It can increase students' attention and motivate them to practice critical thinking skill. It may also promote positive learning experiences. By this, the learning outcomes in technical understanding and application definitely can be improved. However, how to increase student engagement, particularly for engineering courses in the classroom? Educators are introducing several student-centred teaching methods to replace the traditional direct instruction teaching methods, such as inquiry-based learning, project-based learning, service-based learning and others. This study is to explore the gamification toward the engineering student to increase their engagement in class. A class with 109 students in the second year course of Electrical Engineering Technology (EET) are invited to participate in the study. A gamified learning model with 4 stage games is created in the online platform and participates by the EET students' willingness. This gamified learning model has implemented rank, interactive map and video guide. A survey related to gamification is collected from the EET students who completed the gamified learning model. This survey is mainly to obtain feedback on the gamification experience of the students. The results are generally positive and indicate that gamification can improve engineering student engagement and enjoyment toward the learning process

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten. Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems