Final Year Project (FYP) research methodology: preparing your FYP report

This publication was developed as a comprehensive guideline to assist students in preparing and completing their final year project, with a primary focus on engineering disciplines. However, the content and approach are broadly applicable to students in other fields as well. The material is drawn from my experience serving as the Final Year Project Coordinator from 2013 to 2020, as well as my ongoing role as a supervisor and examiner for both undergraduate and postgraduate students. Over the years, I have observed common challenges faced by students and recurring gaps in project planning, execution, and reporting. This guide aims to address these issues by offering practical advice, structured processes, and useful examples to support students throughout their final year project journey. It is my sincere hope that it will serve as a helpful companion for students and educators a like in navigating the final year project process with greater clarity and confidenc

Smart steering auto alert system

Drivers can easily be distracted by their handheld devices while they are driving and this ultimately contributed to the increase of road accidents. This work proposed a steering wheel cover that is designed using an array of touch sensors TTP223 and Raspberry Pi 3 microprocessor. A tilt sensor is also incorporated in order to mimic the movement of the system. Using Python as the main programming language and the Raspbian OS, for a sample size of 40 touch inputs, the system yielded an accuracy of 97.5 % and 75.0 % in its input detection during stationary and driving mode. The results have shown that as a proof of concept, the proposed system is capable of detecting touch inputs from the user’s hand and determining the position of the hands on the steering wheel

Electrical characterisation of highly doped triangular silicon nanowires

A top-down silicon nanowire fabrication using a combination of optical lithography and orientation dependent etching (ODE) has been developed using Silicon-on Insulator (SOI) as the starting substrate. Initially, the samples were doped with phosphorus using the diffusion process resulting in carrier concentration of 2 X 10 18 cm-3. After the silicon nanowires were fabricated, they were measured using a dual configuration method which is similar to the four-point probe measurement technique to deduce its resistivity. The data obtained had suggested that the doping distribution in the silicon nanowires were lower and this may have been affected by the surface depletion effect. In addition, with respect to carrier mobility, the effective mobility of electrons extracted using the four-point probe data had demonstrated that the mobility of carriers in the silicon nanowire is comparable with the bulk mobility. This is most probably due to the fact that in this research, the quantum confinement effect on these nanowires is not significant

Artificial neural network based fast edge detection algorithm for MRI medical images

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector

Artificial neural network based fast edge detection algorithm for MRI medical images

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector

Material characterization of a doped triangular silicon nanowire using raman spectroscopy

A top-down silicon nanowire fabrication using a combination of optical lithography and orientation dependent etching (ODE) has been developed using a doped Silicon-on Insulator (SOI) as the starting substrate. The use of ODE etchant such as potassium hydroxide (KOH) and Tetra-Methyl Ammonium Hydroxide (TMAH) is known to create geometrical structures due to its anisotropic mechanism of etching. The SOI is doped with an n-type dopant (phosphorus) and the doped silicon nanowire is then characterized using Raman Spectroscopy. Due to the changes in the silicon structure, the result shows that the highly doped silicon nanowire has a wider Full Width Half Maximum (FWHM) as compared to the undoped silicon substrate

Development of Photo Forensics Algorithm by Detecting Photoshop Manipulation using Error Level Analysis

Nowadays, image manipulation is common due to the availability of image processing software, such as Adobe Photoshop or GIMP. The original image captured by digital camera or smartphone normally is saved in the JPEG format due to its popularity. JPEG algorithm works on image grids, compressed independently, having size of 8x8 pixels. For unmodified image, all 8x8 grids should have a similar error level. For resaving operation, each block should degrade at approximately the same rate due to the introduction of similar amount of errors across the entire image. For modified image, the altered blocks should have higher error potential compred to the remaining part of the image. The objective of this paper is to develop a photo forensics algorithm which can detect any photo manipulation. The error level analysis (ELA) was further enhanced using vertical and horizontal histograms of ELA image to pinpoint the exact location of modification. Results showed that our proposed algorithm could identify successfully the modified image as well as showing the exact location of modifications

Multi-user mmWave MIMO channel estimation with hybrid Beamforming over frequency selective fading channels

In multi-user millimeter wave (mmWave) multiple input multiple output (MIMO) systems, obtaining accurate information/knowledge regarding the channel state is crucial to achieving multi-user interference cancellation and reliable beamforming (BF)-to compensate for severe path loss. This knowledge is nonetheless very challenging to acquire in practice since large antenna arrays experience a low signal-to-noise ratio (SNR) before BF. In this paper, a multi-user channel estimation (CE) scheme namely generalized-block compressed sampling matching pursuit (G-BCoSaMP), is proposed for multi-user mmWave MIMO systems over frequency selective fading channels. This scheme exploits the cluster-structured sparsity in the angular and delay domain of mmWave channels determined by the actual spatial frequencies of each path. As the corresponding spatial frequencies of multi-user mmWave MIMO systems with Hybrid BF often fall between the discrete Fourier transform (DFT) bins due to the continuous Angle of Arrival (AoA)/Angle of Departure (AoD), the proposed G-BCoSaMP algorithm can address the resulting power leakage problem. Simulation results show that the proposed algorithm is effective and offer a better CE performance in terms of MSE when compared to the generalized block orthogonal matching pursuit (G-BOMP) algorithm that does not possess a pruning step

Ripeness assessment and quality control of mango gold susu using an e-nose system

In this paper, the development and implementation of an electronic nose (e-nose) system utilizing the MQ sensor series from MOS-type gas sensors to classify mango gold susu ripeness is presented. The system's performance was enhanced through machine learning techniques, including Principal Component Analysis (PCA) for data dimensionality reduction and Support Vector Machine (SVM) for classification. The SVM classifier demonstrated high accuracy, particularly in identifying unripe and overripe mangoes, with accuracy scores of 1.00 and 0.99, respectively. A comprehensive database of volatile organic compound (VOC) profiles was established, leading to a precise prediction model for assessing the different stages of ripeness based on the mango’s VOC profile