Ahmad Fadzli Nizam Abdul Rahman

Development of a Fingerprint Gender Classification Algorithm Using Fingerprint Global Features

In forensic world, the process of identifying and calculating the fingerprint features is complex and take time when it is done manually using fingerprint laboratories magnifying glass. This study is meant to enhance the forensic manual method by proposing a new algorithm for fingerprint global feature extraction for gender classification. The result shows that the new algorithm gives higher acceptable readings which is above 70% of classification rate when it is compared to the manual method. This algorithm is highly recommended in extracting a fingerprint global feature for gender classification process.

Analysis of movement for unmanned underwater vehicle using a low cost integrated sensor

This paper presents the development of low cost integrated Smart Sensor for Unmanned Underwater Vehicle (UUV) namely as underwater Remotely Operated Vehicle (ROV). In the underwater industries, the most crucial issues are the sensors that are needed for the underwater task. The sensors that are utilized in this area are quite expensive and sensitive. Every sensors used in the underwater vehicle are not in the form of integrated sensors and most of them based on case to case basis. However, nowadays, a lot of industries are involved in the development of the integrated sensor in order to reduce the production cost as well as to increase accuracies, efficiencies and productivities. Therefore, this research proposes an integrated sensor to be applied in the underwater operations. The integrated sensor is designed based on three goal performances which are; the accuracies; the sensitivities and the cost efficiencies. This integrated sensor is the combination of pressure sensor, inertial measurement unit (IMU), digital compass and temperature sensor that are placed in a waterproof casing. This integrated sensor is targeted to be used to control the movement of ROV to maintain its position called station keeping. The purpose of the station keeping is to ensure the ROV to remain stationary at the desired depth by utilizing the pressure sensor. The experimental studies have been carried out in order to see the responses of each sensor.

A New HMCR Parameter of Harmony Search for Better Exploration

As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. Several studies have pointed that Harmony Search (HS) is an efficient and flexible tool to resolve optimization problems in diversed areas of construction, engineering, robotics, telecommunication, health and energy. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing the local exploitation and the global exploration. These parameters influence the overall performance of HS algorithm, and therefore it is very crucial to fine turn them. However, when performing a local search, the harmony search algorithm can be easily trapped in the local optima. Therefore, there is a need to improve the fine tuning of the parameters. This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. The result of the study showed a better global optimum in comparison to the standard HS.

Modified Parameters of Harmony Search Algorithm for Better Searching

The scheduling and rostering problems are deliberated as integrated due to they depend on each other whereby the input of rostering problems is a scheduling problems. In this research, the integrated scheduling and rostering bus driver problems are defined as maximising the balance of the assignment of tasks in term of distribution of shifts and routes. It is essential to achieve is fairer among driver because this can bring to increase in driver levels of satisfaction. The latest approaches still unable to address the fairness problem that has emerged, thus this research proposes a strategy to adopt an amendment of a harmony search algorithm in order to address the fairness issue and thus the level of fairness will be escalate. The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration. These parameters influence the overall performance of the HS algorithm, and therefore it is crucial to fine-tune them. The contributions to this research are the HMCR parameter using step function while the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter. The model of constant step function is introduced in the alteration of HMCR parameter. The experimental results revealed that our proposed approach is superior than parameter adaptive harmony search algorithm. In conclusion, this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers, which contributed to the lowering of illness, incidents, absenteeism and accidents.

Fingerprint Gender Classification using Univariate Decision Tree (J48)

Data mining is the process of analyzing data from a different category. This data provide information and data mining will extracts a new knowledge from it and a new useful information is created. Decision tree learning is a method commonly used in data mining. The decision tree is a model of decision that looklike as a tree-like graph with nodes, branches and leaves. Each internal node denotes a test on an attribute and each branch represents the outcome of the test. The leaf node which is the last node will holds a class label. Decision tree classifies the instance and helps in making a prediction of the data used. This study focused on a J48 algorithm for classifying a gender by using fingerprint features. There are four types of features in the fingerprint that is used in this study, which is Ridge Count (RC), Ridge Density (RD), Ridge Thickness to Valley Thickness Ratio (RTVTR) and White Lines Count (WLC). Different cases have been determined to be executed with the J48 algorithm and a comparison of the knowledge gain from each test is shown. All the result of this experiment is running using Weka and the result achieve 96.28% for the classification rate.

System identification of a prototype small scale ROV for depth control

This paper present the design and development of a small scale underwater Remotely Operated Vehicle (ROV) and modelling the depth response of this ROV using System Identification Toolbox. The design of a small scale ROV has been done to minimize the hydrodynamic force and increase energy efficiency compared to the previous model that was developed by Underwater Technology Research Group (UTeRG). The performance of the designed ROV will be tested in UTeRG laboratory (lab tank test). The output signal from the pressure sensor (MPX4250GP) and the Inertial Measurement Unit (IMU) sensor are interpreted via an NI-card which was used for the data transfer. The prototype ROV was compared with the previous version in terms of depth control performance. System identification toolbox in MATLAB was used to infer a model from open-loop experiments. Then the obtained model was used to design a controller for the ROV. The focus of the controller design will be to ensure that the ROV is stable and can maintain position at a certain depth in a real underwater environment. After all the experiment has been conducted, the ROV managed to operate in a certain depth underwater using the controller designed successfully.

Observer based output feedback tuning for underwater remotely operated vehicle based on linear quadratic performance

This paper describes the effectiveness of observer-based output feedback for Unmanned Underwater Vehicle (UUV) with Linear Quadratic Regulation (LQR) performance. Tuning of observer parameters is crucial for tracking purpose. Prior to tuning facility, the ranges of observer and LQR parameters are obtained via system output cum error. The validation of this technique using unmanned underwater vehicles called Remotely Operated Vehicle (ROV) modelling helps to improve steady state performance of system response. The ROV modeling is focused for depth control using ROV 1 developed by the Underwater Technology Research Group (UTeRG). The results are showing that this technique improves steady state performances in term of overshoot and settling time of the system response.