MIKROPEMPROSES 68000: prinsip dan aplikasi

Buku ini memperkenalkan mikropemproses keluaran Motorola, 68000 sebagai langkah pertama mendedahkan pembaca kepada mikropemproses. Setelah menguasai langkah-langkah sebuah sistem asas yang menggunakan mikropemproses 68000, pembaca boleh mengembangkan pengetahuan bagi mereka bentuk sistem menggunakan mikropemproses terkini. Bab 1 memperkenalkan aplikasi mikropemproses dalam pelbagai bidang kehidupan, Bab 2 pula memfokuskan kepada bagaimana mereka bentuk sistem pengesan banjir. Bab 3 memuatkan aliran kerja sistem pengesan banjir manakala Bab 4 memperkenalkan set suruhan mikropemproses 68000. Bab 5 menerangkan tentang pendawaian skematik peranti sistem pengesan banjir dan bab 6 merupakan kesimpulan kepada keseluruhan bab di mana cadangan bagi menambah kecekapan sistem pengesan banjir disertakan. Buku ini sesuai dijadikan teks atau bahan bacaan tambahan bagi pelajar yang mengikuti kursus mikropemproses sama ada di universiti, kolej, dan sekolah vokasional

Correlation of Student’s Precursor Emotion towards Learning Science Interest using EEG

Mathematics and science are two important subjects for students to do well in school. Unfortunately majority of the students are having difficulties in coping with these subjects. Malaysia is ranked third lowest in the Program for International Student Assessment (PISA) for mathematics and science. An emotionally disturbed student seems to have problem coping with the learning of mathematics and science thus it is important to identify the demotivating factors affecting the performance of such students. In this paper, it analyze the correlation of precursor emotion towards student interest in learning mathematics and science using electroencephalogram (EEG) device. This correlation and their respective emotion can be analyzed based on the 2-D Affective Space Model (ASM) using four basic emotions of happiness, calmness, fear and sadness as reference stimuli. EEG device was used to extract brain waves signal while answering the mathematics and science questions. The EEG signals were captured on the scalp of the student and features extracted using Mel Frequency Cepstral Coefficient (MFCC). Neural network classifier of Multilayer Perceptron (MLP) was used to classify the valence and arousal axes for the ASM. Preliminary results show the relationship of precursor emotion and the dynamic emotions of the student while taking the mathematics and science test. We hope that these results can help us further relate the behavior and interest of students towards the learning of mathematics and science

Gray-level co-occurrence matrix bone fracture detection

Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and nonfractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically

Wireless data gloves Malay sign language recognition system

This paper describes the structure and algorithm of the whole Wireless Bluetooth Data Gloves Sign Language Recognition System, which is defined as a Human-Computer Interaction (HCI) system. This project is based on the need of developing an electronic device that can translate sign language into speech (sound) in order to make the communication take place between the mute & deaf community with the general public possible. Hence, the main objective of this project is to develop a system that can convert sign language into speech so that deaf people are able to communicate efficiently with normal people. This Human-Computer Interaction system is able to recognize 25 common words signing in Bahasa Isyarat Malaysia (BIM) by using Hidden Markov Models (HMM) methods. Both hands are involved in performing the BIM with all the sensor connecting wirelessly to PC with Bluetooth module. In the future, the system can be shrunk to become a stand alone system without any interaction with PC

Design of educational software for automatic speech recognition (asr) techniques

Speech recognition has been an important subject for research, and it has come to a stage where it has been actively applied in a lot of industrial and consumer applications, overseas. However, speech recognition research is still in its infancy stage in Malaysia. The main reason is that speech recognition systems are highly complex and teaching students in this subject matter with the underlying technologies is a challenging task. Currently, some instructors use slide show presentations and white board in giving such courses. At the end of the course, students are not able to figure out the real output of the algorithms given. In this case, students are not exposed to the real technical systems and would easily get bored. This research is mainly on the improvement over the limitations and problems of the traditional teaching method in speech recognition by developing a set of interactive and practical education software to guide and assist students in studying, and performing experiments for speech recognition

Different techniques and algorithms for biomedical signal processing

This paper is intended to give a broad overview of the complex area of biomedical and their use in signal processing. It contains sufficient theoretical materials to provide some understanding of the techniques involved for the researcher in the field. This paper consists of two parts: feature extraction and pattern recognition. The first part provides a basic understanding as to how the time domain signal of patient are converted to the frequency domain for analysis. The second part provides basic for understanding the theoretical and practical approaches to the development of neural network models and their implementation in modeling biological system

Application of texture analysis in echocardiography images for myocardial infarction tissue

Texture analysis is an important characteristic for surface and object identification from medical images and many other types of images. This research has developed an algorithm for texture analysis using medical images do trained from echocardiography in identifying heart with suspected myocardial infarction problem. A set of combination of wavelet extension transform with gray level co-occurrence matrix is proposed. In this work, wavelet extension transform is used to form an image approximation with higher resolution. The gray level co-occurrence matrices computed for each subband are used to extract four feature vectors: entropy, contrast, energy (angular second moment) and homogeneity (inverse difference moment). The classifier used in this work is the Mahalanobis distance classifier. The method is tested with clinical data from echocardiography images of 17 patients. For each patient, tissue samples are taken from suspected infarcted area as well as from non-infarcted (normal) area. For each patient, 8 frames separated by some time interval are used and for each frame, 5 normal regions and 5 suspected myocardial infarction regions of 16×16 pixel size are analyzed. The classification performance achieved 91.32% accuracy

Development of real-time embedded system with speech recognition for smart house

The hidden Markov model is used for the acoustic modeling of the speech recognition system. The Continuous Density HMM (CDHMM) which models acoustic observation directly using estimated continuous probability density function (pdf) without VQ, has shown to have higher recognition accuracy than DHMM. In this paper, CDHMM with Multivariate Gaussian Density is used for speaker dependent (SD) and speaker independent (SI) Malay isolated digit recognition and comparison is made with DHMM. The CDHMM was trained by different algorithms- Baum-Welch (BW), Viterbi (VB) (with segmental k-mean estimation) algorithm and combination of BW and VB then comparison is discussed. The training database consisted of 26 speakers with 5 utterances for each Malay digit. In SD task, another 5 utterances each digit from the same speakers are used for testing. Recognition accuracy of CDHMM with BW, VB training and combination of BW and VB is 99.00%, 98.85% and 98.69% respectively while 96.62% for DHMM. The accuracy for BW and Segmental K-Mean training is comparable, but the latter consumed less computational time. In SI task, 40 speakers, different from the training speakers, with each recorded 2 tokens are used for testing. The CDHMM achieves 85.63% accuracy and outperform DHMM with 8.17% improvement

Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks

The paper investigates the use of Singular and Modular Neural Networks in classifying the Malay syllable sounds in a speaker-independent manner. The syllable sounds are initialized with plosives and followed by vowels. The speech tokens are sampled at 16 kHz with 16-bit resolution. Linear Predictive Coding (LPC) is used to extract the speech features. The Neural Networks utilize standard three-layer Multi-Layer Perceptron (MLP) as the speech sound classifier. The MLPs are trained with stochastic Back-Propagation (BP). The weights of the networks are updated after presentation of each training token and the sequence of the epoch is randomized after every epoch. The speech training and test tokens are obtained from 25 (17 females and 8 males) and 4 (all females) Malay adult speakers respectively. The total training and test token number are 1600 and 320 respectively. The result shows that modular neural networks outperform singular neural network with a recognition rate of about 92%