Pusat Pengajian Kejuruteraaan Elektrik dan Elektronik - Monograf
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- Publication1.5-Bit stage analogue-to-digital converter (adc)(2023-08-01)Ch’ng, Ooi KeatThis report presents the design of 1.5-bit stage Analogue-to-Digital Converter (ADC). In this project, with some assumption considered, the minimum open-loop DC gain, AOL required for this project is calculated as 40dB. Meanwhile, the minimum unity gain frequency, fu of the OTA must be at least 16.0MHz as the ADC designed in this project will be clocked at 8MHz. Average power consumption of the main OTA is expected to be less than 40mW. Silterra 180nm process technology is used in this work. The architecture of 1.5-bit stage ADC mainly consists of an operational transconductance amplifier (OTA) as the core element. Generally, the 1.5-bit stage is made up of a 2-bit comparator, a digital logic circuit and a residue amplifier. The main function of the comparators is to emonstrate that an input signal is either larger or less than a predetermined reference voltage. The digital logic circuit serves as switches to allow the residue amplifier to function by receiving a 2-bit digital input from the comparator. Based on the results obtained, the main OTA in this project has specifications such that 45.0dB for the open-loop DC gain, 470.11MHz for unity-gain frequency, 79.52° for phase margin, and 27.117mW for average power consumption for the main OTA. In short, all the specifications for this main OTA have all achieved the minimum requirement of the main OTA design. The three blocks for the overall design of 1.5-bit stage ADC, which are the 2-bit comparator, digital logic circuit, and lastly the residue amplifier, are all carried out their specific. functions successfully as all of them are verified throughout the testbench simulations with the desired outcomes.
- PublicationDevelopment of a pigeohole letter detector system with higher number of users(2012-06-01)Ng, Kok MengTime is ticking every second as we are walking or doing daily chores and it passes unnoticeably. The time that has been spent cannot be recovered and has become the past in the histories of our lives. Thus, every second is precious and time planning is important to avoid doing activities that are going to waste our time. So, this system is developed in conjunction to this statement as it helps users to save time even if for a mere few minutes. Hence, they could use this spare time to complete other more important work. A user does not need to go and check for letters every day. For some people, they may only receive letters twice a week. So they could save time and energy especially those who are living in high rise buildings. With this system, they will be acknowledged by a notification telling them that there is a letter in the pigeon hole. This system required a root user to operate in PC, it is a very user friendly system and the instruction is also easy to understand. Database will store information of all pigeonhole users. Each pigeon hole consists of a sensor to sense the presence of letter and a microcontroller is used to monitor its’ changes. Then it will continue to communicate with PC via transmission of signal using serial port communication. From PC, a program will run automatically. This program will then send a notification in the form of Email or SMS to that particular pigeonhole user. Basically the system runs continuously until the root user disables it. In overall, the system performs accurately and easy to manage. It is as convenient as a calculator where the user just key in the required information and the result will come out in no time. The sense of worrying whether there is letter inside the letter box is no longer an issue. Lastly, users will definitely gain benefits for using the pigeon hole letter detector system.
- PublicationDevelopement of algorithim to reduce the shadow in digital image(2012-06-01)Ong, Kok TongA shadow is usually appears in arbitrary shape. The shadow shape may or may not be the same as the shape of object casting it. The shadow shape depends on the incident angle of light source and the object casting it. Hence, it is difficult to recognize the shadow shape based on the object geometry. Other than that, the removal of shadow need to consider the original pixel intensity in the region that is previously covered by shadow. The original shadowed region may become too bright if the removal process of shadow is too aggressive or may be still dark if the removal process of shadow is not effective. Therefore, a new method of reducing shadow in digital image has been proposed in this project. Throughout the development of the new algorithm, a total of four shadow reduction algorithms have been implemented. The results from these four algorithms are compared with the same set of input images to make fair comparison. From hypothesis testing of Randomized Complete Block Design conclusion, it is shown that all implemented methods can change the pixel intensity of the image. The performance of algorithms is further confirmed with the result of confident interval and main factor plot. It is found that the best algorithm is the one that calculates the scaling factor based on the mean and surrounding mean for shadow reduction. The proposed algorithm can detect the shadow implicitly and there is no need for user input to identify the shadow region explicitly. There are limitations that the proposed algorithm cannot detect shadow edge if the shadow penumbra region is narrow and the shadowed region cannot be on the boundary of image. However, the result shows that it can detect shadow with arbitrary shape correctly and reduce the shadow by scaling the pixel intensity back to background pixel intensity.
- PublicationImage segmentation using ant and bee algorithms(2012-06-01)Khor, Wai PengWith breast cancer being one of the main cause of cancer death, identifying tumour cells in breast Magnetic Resonance Imaging (MRI) images is very helpful for patients to undergo treatment at an early stage of cancer and thus reduce the rate of fatalities due to last minute treatment. In this respect, it is very important to identify accurately the tumour cells in the MRI images of breasts. This includes differentiating the skin tissues from breast tissues as well as segmenting the tumour cells from the breast tissues. Creatures such as ants and bees have been able to identify best food sources to survive. Their methods have been used as subjects of studies and evolved into animal inspired algorithms. Applying the Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) Optimization algorithms is helpful in identifying tumour cells from among the breast tissues. By treating the tumour cells as the best food source, both the ant and bee algorithms are applied to find the tumour cells with the help of some preprocessing operations. Preprocessing operations such as using median filter to reduce noise, thresholding to reduce area of search for region of interest, morphological operations to close holes and remove dots and also labelling of regions of interest prepares the area of search for the application of the ant and bee algorithms. Both algorithms are able to identify the tumour cells from the breast MRI images, with a difference in speed whereby the bee algorithm takes lesser time.
- PublicationImage segmentation algorithm based on integration of k-means clustering, watershed and binary partition tree(2012-06-01)Wong, Kok ChoyNowadays, computer vision technology had grown at a supreme speed. Along with the advancement of computer vision technology, many computer scientists had focused in the development of image processing algorithm. With the aim to construct automated feature detection and information extraction image processing algorithm, the image segmentation is being explored by many researchers. Although many types of image segmentation technique had been proposed, their accuracy and efficiency are still far away from detection done by human, especially when the image experiences some illumination variations (Uneven lighting, light reflection, etc.). Thus, the main objective of this project is to improve the robustness of an image segmentation algorithm against various illumination conditions. The improved image segmentation algorithm presented here is the use of K-Means Clustering algorithm as presegmentation, and its output will undergo Watershed Transform before Binary Partitioning Tree (BPT) merging process takes place. K-Means Clustering is implemented to reduce illumination changes, and its output image undergoes Watershed Transform. The resultant regions obtained from Watershed Transform are then used as the leaves node in BPT. Based on merging criteria, these regions will be merged two by two until it reaches the root of the tree (the entire image). To evaluate the performance of the proposed algorithm, images and ground truth result in Sharon Alpert’s segmentation database are used to evaluate the illumination compensation effect. From the result obtained we can say that by adding K-Means Clustering algorithm, the segmentation process is now more robust against illumination variation.