Pusat Pengajian Kejuruteraaan Elektrik dan Elektronik - Tesis

Browse

Now showing 1 - 5 of 470

A Simplified Consensus Protocol Simulator With Applications On Proof Of Contribution-X
(2023-03)
Al Ogaili, Riyadh Rahef Nuiaa
Blockchain consensus protocols are the major architectural components of blockchain networks. Numerous enhancements of popular blockchain consensus protocols, such as Proof of Work (PoW) and Proof of Stake (PoS), have led to the emergence of alternative consensus protocols, some of which cater to specific areas such as medicine or transportation. A considerable amount of research has been done on these alternative protocols, one class of which is known as Consensus Protocols Based on Past Behaviour (CPPB). However, these protocols remain relatively unknown and lack performance analysis, which hinders their possible deployment in real-world blockchain networks because the strengths and weaknesses of these consensus protocols cannot be determined. This problem stems from the lack of simulation tools for other consensus protocols that are not mainstream. This gap is bridged by proposing a simple simulation framework called SIM-P, which can accurately simulate the behaviour of these consensus protocols with ease. SIM-P is an agent-based stochastic simulator that relies on the sequential Monte Carlo method to model how block publishers are selected. Simulation models are developed for PoW as a base model for benchmarking purposes, as well as for two selected CPPBs:
Implementation of a pairwise test data generation with constraint using matlab
(2012-06-01)
Edham, Azamudin
The combinatorial strategy so-called Pairwise GeneratorM which consists of Exhaustive Algorithm, Binary Selection algorithm, and Pairwise algorithm have been studied and migrated from Java to Matlab. The new migrated strategy, Pairwise GeneratorM, has been improved and modified to include constraint algorithm. Constraint in Pairwise GeneratorM has functioned as a filter that excludes the unwanted test from the generator. The existing Pairwise GeneratorM can be seen to have larger space utilization and not optimize to handle mathematical algorithm. Four experiments have been carried-out to demonstrate the correctness of the implementation.
Prediction of standing height for hospitalized elderly using artificial neural network
(2012-06-01)
Kelvin, Ng Kai Ming
Measurement of the height of patients is required for determination of basic energy requirements, standardization of measures of physical capacity and for adjusting drug dosage. Prediction of elderly patient’s height by measuring their arm span length is not a new thing in medical field. Through the concept of Artificial Neural Network (ANN), training a network with known input of data can generate desired output data. Here, the input data consists of arm span length and age of patients while height is given as output data. Both gender of male and female is considered, resulting in 2 sets of experiment. Standing height and arm span lengths of 205 male patients and 126 female patients between the ages of 60 and 80 were measured. MATLAB Neural Network Toolbox is used as the application programming interface (API) for height prediction in these experiments. The theory of Artificial Neural Network is studied before the initial weight bias and parameters of network are conducted. After that, given data of arm span length and ages are input to the network to undergo supervised learning. Network is trained until maximum epoch is reached or validation stops, generating result data. The generated result data, also known as actual output is compared with the desired output in terms of Accuracy, Error Rate and Regression. Experiments of both gender is done by increasing the number of hidden neurons in network, resulting in 8 set of network respectively. From that, analysis of performance for network is done. The comparison result shows that there will be an optimum number of hidden neurons to generate highest accuracy result. Gender is not an important factor in height prediction using arm span length.
8 Bit Cmos Hybrid Digital-To-Analog Converter For Bluetooth Low Energy Application
(2019-03)
Rosli, Alia
In the bluetooth low energy implementation, the digital-to-analog converters act as the bridge gap between digital signal processing chips, and power amplifiers that transmit analog signals. This thesis presents the design of a hybrid Digital-lo-Analog Converter( DAC) intended for RF transmitter in 2.45 GHz Bluetooth Low Energy (BLE) application by using CMOS 180 nm technology. The hybrid DAC design strategy is based on iterative scheme whose variables are adjusted in a simple way, minimizing the power consumption as well as area and also meeting the design specifications.
Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch
(2015-01)
Fadilah, Norasyikin
Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested at the optimum stage for maximum oil production. Current harvesting methods based on observing the number of loose fruits on ground and the color of the fruits using human vision lead to subjective evaluation, laborious work, and low quality oil. Therefore, this research focuses on the development of an automated system with the ability to process the image of oil palm FFB and determine its ripeness category. The system consists of an image acquisition system, image processing component and oil palm FFB classification system. Images of oil palm FFBs of type DxP Yangambi are acquired using an IP camera which is attached to the end of a pole and connected to a computer via the RJ45 cable. The images are collected and analyzed using digital image processing techniques. k-means clustering algorithm is used to segment the image into two separate regions which are fruit and spike regions. Then, the color features of the fruit region are extracted from the images and used as inputs to an Artificial Neural Network (ANN) model learning algorithm.

Browse

Recent Submissions