Nahrul Khair Alang Md Rashid

Performance Comparison between RGB and HSV Color Segmentations for Road Signs Detection

This paper compares the performance of RGB and HSV color segmentations method in road signs detection. The road signs images are taken under various illumination changes, partial occlusion and rotational changes. The proposed algorithms using both RGB and HSV color space are able to detect the 3 standard types of colored images namely Red, Yellow and Blue. The experiment shows that the HSV color algorithm achieved better detection accuracy compared to RGB color space.

Autonomous Navigation of Mobile Robot Using Kinect Sensor

The problem of achieving real time process in depth camera application, in particular when used for indoor mobile robot localization and navigation is far from being solved. Thus, this paper presents autonomous navigation of the mobile robot by using Kinect sensor. By using Microsoft Kinect XBOX 360 as the main sensor, the robot is expected to navigate and avoid obstacles safely. By using depth data, 3D point clouds, filtering and clustering process, the Kinect sensor is expected to be able to differentiate the obstacles and the path in order to navigate safely. Therefore, this research requirement to propose a creation of low-cost autonomous mobile robot that can be navigated safely.

Performance comparison between ANN and PCA techniques for road signs recognition

This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class were implemented which are based on Feed Forward Neural Network and Principal Component Analysis (PCA). The performance of the trained classifier using Scaled Conjugate Gradient (SCG) back propagation function in Neural Network and PCA technique were evaluated on our test datasets. The experiments show that the system using PCA has a higher accuracy as compared to Neural Network with a minimum of 94% classification rate of road signs.

Performance analysis of robust road sign identification

This study describes performance analysis of a robust system for road sign identification that incorporated two stages of different algorithms. The proposed algorithms consist of HSV color filtering and PCA techniques respectively in detection and recognition stages. The proposed algorithms are able to detect the three standard types of colored images namely Red, Yellow and Blue. The hypothesis of the study is that road sign images can be used to detect and identify signs that are involved with the existence of occlusions and rotational changes. PCA is known as feature extraction technique that reduces dimensional size. The sign image can be easily recognized and identified by the PCA method as is has been used in many application areas. Based on the experimental result, it shows that the HSV is robust in road sign detection with minimum of 88% and 77% successful rate for non-partial and partial occlusions images. For successful recognition rates using PCA can be achieved in the range of 94–98%. The occurrences of all classes are recognized successfully is between 5% and 10% level of occlusions.

ANFIS -Based Navigation for HVAC Service Robot with Image Processing

In this paper, we present an ongoing work on the autonomous navigation of a mobile service robot for Heat, Ventilation and Air Condition (HVAC) ducting. CCD camera mounted on the front-end of our robot is used to analyze the ducts openings (blob analysis) in order to differentiate them from other landmarks (blower fan, air outlets and etc). Distance between the robot and duct openings is measured using ultrasonic sensor. Controller chosen is ANFIS where its architecture accepts three inputs; recognition of duct openings, robot positions and distance while the outputs is maneuver direction (left or right).45 membership functions are created from which produces 46 training epochs. In order to demonstrate the functionality of the system, a working prototype is developed and tested inside HVAC ducting in ROBOCON Lab, IIUM.

Vision Aided Path Planning for Mobile Robot

Path planning is very important for autonomous mobile robots to navigate from the beginning to the ending position. Vision aided path planning for mobile robot system is discussed in this paper. The paper reveals the accounts from a historical overview and provides a study on how to develop a single vision system for a mobile robot, which implements an obstacle avoidance algorithm, detecting the objects by the colour. Also, we aim at highlighting and analyzing the use of single vision cameras such as webcam in providing data and useful information required for navigation purposes. The system is able to detect obstacles and provide position information from the image of indoor environment. The result is accurate enough to detect the static obstacles and avoid any possible contact with that obstacle. Thus, it is best suggested that the proposed colour approach would be significant as a navigational aid for the autonomous mobile robot.

A comparative study of the difference between MFCC and PLP in the recognition of sound

Sound is one of the most important tools for classification, recognition and identification of objects in the environment. The raw sound signal is complex and is not suitable to be feed as input to the sound identification system; hence the need for a good front-end arises. The identification rate using the RNN classifier and MFCC is 72.7%, 73.7%, 78.9% 57.1% and 58.3% for aircraft, car, rain, thunder and train respectively as compared to what was obtained by using MLP. 31.6%, 19.4%, 18.5%, 38.0% and 26.4% decline is achieved for aircraft, car, rain, thunder and train respectively when comparing between MLP and RNN for MFCC. As far as sound recognition using the input used in this experiment is concerned, MFCC outperforms PLP and MFCC and PLP using MLP as classifier.

Human Body Parts Detection Using YCbCr Color Space

Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

Electrical properties of neutron-irradiated silicon and GaAs commercial diodes

Neutron radiation testing was performed on silicon and GaAs diodes to investigate changes in the device parameters after neutron exposure. Californium-252 source was used to irradiate these diodes up to total dose of 1117.87mSv. The effects of nuclear radiation on the forward and reverse current-voltage (I-V) characteristics of Silicon and GaAs diodes were studied at room temperature. It was found that the magnitudes of forward bias electrical characteristics were in most instances unaffected by irradiation in both materials. The increments in TSKS5400S GaAs infrared emitting diode reverse currents were large after irradiation. These changes were interpreted as effects of displacement damage generating generation-recombination currents due to defects created. However, reverse bias (RB) characteristics of 1N4148 silicon diodes showed decrement in dark current. This is attributed to the type of diodes used.

Graphical user interface for Humanoid Head Amir-II

This paper presents the development of graphical user interface (GUI) for Humanoid Head Robot Amir-II. The GUI serves as a monitoring tool for the robot operation which includes the visual input from a webcam as its eye and the positioning of the Dynamixel AX-12 robot actuators. Matlab Graphical User Interface Development Environment (Matlab GUIDE) is used in developing the GUI which enables Matlab codes on face tracking, facial expression recognition and servo callbacks to be integrated within the GUI. The humanoid head is programmed to detect a human face within its view and automatically follow the tracked face through the movement of its neck joints. There is also a dedicated control panel within the GUI to capture the facial expression screenshots and save them into the database with their related expression labels.