Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by S.A. Samad
Total Records ( 8 ) for S.A. Samad
  S.A.R. Al-Haddad , S.A. Samad , A. Hussain , K.A. Ishak and A.O.A. Noor
  The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). Speech signals are often corrupted with background noise and the changes in signal characteristics could be fast. These issues are especially important for robust speech recognition. Robustness is a key issue in speech recognition. The algorithm is tested on speech samples that are a part of a Malay corpus. It is shown that the fusion technique can be used to fuse the pattern recognition outputs of DTW and HMM. Furthermore refinement normalization was introduced by using weight mean vector to obtain better performance. Accuracy of 94% on pattern recognition was obtainable using fusion HMM and DTW compared to 80.5% using DTW and 90.7% using HMM separately. The accuracy of the proposed algorithm is increased further to 98% by utilization the RLS adaptive noise cancellation.
  S.A.R. Al-Haddad , S.A. Samad , A. Hussain and K.A. Ishak
  This paper is presents a pattern recognition fusion method for isolated Malay digit recognition using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). The aim of the project is to increase the accuracy percentage of Malay speech recognition. This study proposes an algorithm for pattern recognition fusion of the recognition models. The endpoint detection, framing, normalization, Mel Frequency Cepstral Coefficient (MFCC) and vector quantization techniques are used to process speech samples to accomplish the recognition. Pattern recognition fusion method is then used to combine the results of DTW and HMM which uses weight mean vectors. The algorithm is tested on speech samples that are a part of a Malay corpus. This paper has shown that the fusion technique can be used to fuse the pattern recognition outputs of DTW and HMM. Furthermore it also introduced refinement normalization by using weight mean vector to get better performance with accuracy of 94% on pattern recognition fusion HMM and DTW. Unlikely accuracy for DTW and HMM, which is 80.5% and 90.7% respectively.
  S. Sulaiman , A. Hussain , N. Tahir , S.A. Samad and M.M. Mustafa
  The main objective of this study is to develop an algorithm that is capable of detecting the presence of human based on motion and background subtraction technique along with post-morphology processes to extract the foreground pixels from its background thus separating the human silhouette. The extracted silhouette information can later be used for traffic monitoring and analysis, human tracking and monitoring, video surveillance since silhouette-based technique tend to offer speed and simplicity. Results obtained indicate that the developed algorithm achieves its objective and successfully extract human silhouette from the analyzed video scenes.
  M.Z. Ilyas , S.A. Samad , A. Hussain and K.A. Ishak
  This study describes two approaches of improving speaker verification in noisy environments. The first approach is implementation of a speaker verification classification technique base on hybrid Vector Quantization (VQ) and Hidden Markov Models (HMMs) in clean and noisy environments. The second approach is implementation of Adaptive Noise Cancelation (ANC) as pre-processing for noise removal. The motivation to implement hybrid classification technique is to improve the HMMs performance. It is shown that, by using the hybrid technique, an Equal Error Rate (EER) of 11.72% is achieved compared to HMM alone, which achieved 16.66% in clean environments. However, both techniques show degradation in noisy environments. In order to address these problems, an Adaptive Noise Cancellation (ANC) technique using adaptive filtering is implemented in the pre-processing stage due to its ability to separate overlapping speech frequency bands. Investigations using Least-Mean-Square (LMS), Normalized Least-Mean-Square (NLMS) and Recursive Least-Squares (RLS) adaptive filtering are conducted to find the best solution for the speaker verification system.
  M.S. Javadi , M.A. Hannan , S.A. Samad and A. Hussain
  The intelligence of the vehicle is identified by the surrounding environment. Lane detection is one of the vision-based features that used for assisting and controlling tasks for the intelligent vehicles. In this study, an overview of lane detection approaches is presented and then a model, based on inverse perspective mapping, edge detection and fitting lines algorithm is introduced. The system was tested on the urban road image data base in different light conditions. The performance of the system in term of lane marking detection was 97.2%. The results were accurate and robust with respect to the shadows and worn lane markings and also appropriate for real time procedure.
  S. Habib , M.A. Hannan , M.S. Javadi , S.A. Samad , A.M. Muad and A. Hussain
  Wireless communication technologies have emerged vehicular networks in the forms of Intra-Vehicle (InV), Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) communications. These technologies enable a variety of applications for driver and passenger needs, such as safety, convenience and entertainment facilities incorporated into modern automobile designs. The researchers exploit the different services that will enable to exchange useful information with-inside and with-outside vehicle via vehicular networks. Vehicles exchange information about their state, view of current road, navigation information and other general information about weather report and digital map update. A key for exchanging information in timely manner is an opportunity to access the medium for longer life with low power consumption in various ranges. They provide high reliability without experiencing long and uncertain delay. Thus, widespread adoption of vehicular networks is fast becoming a reality, where additional functions will be provide by the car electronics and the passengers will be able to access the Internet and other core network resources. This study presents an overview of the potential wireless technologies for data exchange in a various ranges, its current research activities, issues and challenges that exist in each wireless technology.
  M.A. Hannan , A. Hussain , A. Mohamed and S.A. Samad
  The main objective of the study is to analyze Tire Pressure Monitoring System (TPMS) data that contributes significantly towards the enhancement of the intelligent vehicle performance evaluation. TPMS pressure and temperature data were collected from the prototype model of the MEMS Tire Pressure Module (TPM) that was fitted on to an intelligent tire rim through its receiver. In this study, we are focusing only analytical data analysis of TPMS. In the analytical study, a novel method for data classification, goodness of fit and hypothesis testing was proposed. A classification scheme was employed to classify the temperature and pressure data based on ID at the quadrant basis operating zone of the Front Right (FR), Front Left (FL), Rear Left (RL) and Rear Right (RR) tires. Principle Component Analysis (PCA) with polynomial fitting for exploring goodness of fit of tire data was also applied. Finally, hypothesis testing using Satterthwaite statistic was carried out. Results obtained are in agreement with the null hypothesis and as such validate the usefulness of the TPMS system in maintaining and enhancing vehicle performance.
  M.M. Sani , K.A. Ishak and S.A. Samad
  This study presents an efficient face recognition system based on Support Vector Machine. A lighting correction method, i.e., Adaptive multiscale retinex is introduced to reduce various lighting conditions before performing the classification task. The performance of this method is evaluated using the Yale and ORL databases. The recognition rate of the proposed method achieved up to 92% compared to the principal component analysis method with 73.7%.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility