Rosyati Hamid

Speech processing for makhraj recognition

In our daily day, improvement of makhraj for Arabic alphabets is a topic that very useful in many applications and environments. The existing system cannot recognize the appropriate pronunciation of each alphabet with the existence of noise. As an example “ha”, with the disturbance from the noise, the system may recognize wrong alphabet like “kho”. This paper focus on noise removal in makhraj recognition using Least Mean Square (LMS) Algorithm based on Adaptive Filter to search for the optimal solution to adaptive filter, including system identification and noise cancellation. There are 30 Arabic alphabets from until. However, this project will only use 7 alphabets as samples that are from until. The speech processing will be used to obtain same waveform output from two different situations. The filtered data will be processed to match the standard pronunciations and it will be integrated with filter design process in MATLAB. As a result, the waveform of noise cancellation using LMS algorithm is quite similar with the waveform of reference signal. As a conclusion, it is proved that noise cancellation method remove noise from unknown system.

Unclean hand detection machine using vision sensor

This paper will discuss about the unclean hand detection using vision sensor for an automated hand wash screening system. Currently the hand wash screening audit is done manually by an expert to monitor the hand under ultraviolet light once its been washed. Hence, there is a need for more human experts to conduct the screening manually. This project is proposed to automate the hand wash screening audit by using a vision system. The vision system is designed to increase accuracy to detect the unclean area of washed hands. This system will not only detect the unclean area, but will also calculate the percentage of the unclean area which will be used as further analysis of the efficiency of the system. However, we need to build the hand wash prototype using ultraviolet light and a camera that is connected to the computer to process and display the results of hand wash screening.

Feature extraction of pus cells detection and counting in sputum slide images

This paper discusses on a feature extraction of pus cell in sputum slide image. This invention is developed to analyze and count the content of cells specifically pus cell within a biological sample, and more particularly sputum sample which is useful for sputum quality grading. This pus cell detection is addressed by mean intensity and area for single and overlapping pus cells. It is found that mean intensity and area for single pus cell are ranges from 130 - 163 and 35 - 81 respectively. Whereas, with considering other elements exist in sputum image such as epithelial cells and artifacts, the mean intensity for overlapping pus cells are ranges from 130 - 45 with area of 65 - 300. This system reliability is above 80% from the validation results.

Speech processing for makhraj recognition: The design of adaptive filter for noise canceller

In our daily life, improvement of makhraj for Arabic alphabets is a topic that very useful in many applications and environments. The existing systems cannot recognize the appropriate pronunciation of each alphabet with the existence of noise. As an example of “ha”, the system may recognize wrong alphabet like “kho”. This paper focuses on noise removal in makhraj recognition using Normalized Least Mean Square (NLMS) Algorithm based on Adaptive Filter to search for the optimal solution. There are 30 Arabic alphabets from **** until ****. However, this project will only use 7 alphabets as samples, they are **** to ****. The speech processing is used to obtain same waveform output from two different situations. The filtered data is processed to match the standard pronunciations and it is integrated with filter design process in MATLAB. From the result, the waveform of noise cancellation using NLMS algorithm is quite similar with the waveform of reference signal. It is proved that noise cancellation method remove noise from unknown system.

SKF-Based Image Template Matching for Distance Measurement by Using Stereo Vision

In this paper, a novel image template matching approach to tackle distance measurement problem has been proposed. There are many conventional algorithms to increase the accuracy of distance measurement as reported in the literature such as Semi-global algorithm to produce the disparity map. Meanwhile, in this paper, the reverse engineering technique had been implemented to get the correct depth value by applying the image template matching method as reference for the distance measurement. The traditional algorithm to solve image matching problem take a lot of memory and computational time. Therefore, image matching problem can be considered to optimization problem and can be solved precisely. The search of the image template has been performed exhaustively by using Simulated Kalman Filter (SKF) algorithm. The experiment is conducted with a set of images taken by using stereo vision system. Experimental results show the accuracy of the distance measurement by using stereo camera, after applying (1) the estimate error model, (2) SKF and (3) PSO algorithm are 89.95%, 96.09%, 95.29% and 58.51% respectively. The limitation of estimate error model that it can only be applied into the same setup of the experiment, environment, parameters of the camera and acquired images. Instead, the proposed algorithm which is SKF can be applied to original image and image under the vision problems like illumination and partially occluded. The SKF algorithm shows more robust, more efficient and more accurate to solve the distance measurement problem.

The detection and summation of squamous epithelial cells for sputum quality testing

Sputum with good quality is important to detect diseases. The quality of sputum is determined using Bartletts Criteria by considering the score of squamous epithelial cells (SEC), pus cell (neutrophils) and macroscopy. For squamous epithelial cells, the score is 0 if SEC is less than 10. Whereas if SEC is within 10 to 25, the score is -1 and the score is -2 if the number of SEC is greater than 25. Currently, the detection of SEC in sputum is manually done by technologists. However, the problem with manual detection is time consuming. So, an automated vision system using image processing technique to detect the sputum quality is desirable. Image processing such as image segmentation is used to detect and count the number of SEC, and then the score is determined. Lastly, the percentage of error for this project is calculated.

Automated detection and counting of pus cells on sputum images

This paper discusses on a feature extraction of pus cell in sputum slide image. This invention is developed to analyze and count the content of cells specifically pus cell within a biological sample, and more particularly sputum sample which is useful for sputum quality grading. This pus cell detection is addressed by mean intensity and area for single and overlapping pus cells. It is found that mean intensity and area for single pus cell are ranges from 130-163 and 35-81 respectively. Whereas, with considering other elements exist in sputum image such as epithelial cells and artifacts, the mean intensity for overlapping pus cells are ranges from 130-45 with area of 65-300. This system has the sensitivity of 92.11%, with specificity of 81.82%, accuracy of 87.32% and precision of 85.37.