S
Sumair Aziz
Researcher at University of Engineering and Technology
Publications - 88
Citations - 940
Sumair Aziz is an academic researcher from University of Engineering and Technology. The author has contributed to research in topics: Support vector machine & Feature extraction. The author has an hindex of 14, co-authored 73 publications receiving 525 citations. Previous affiliations of Sumair Aziz include College of Electrical and Mechanical Engineering & University of Engineering and Technology, Lahore.
Papers
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Journal ArticleDOI
Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features.
TL;DR: An automated computer-aided system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series is proposed and achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects.
Proceedings ArticleDOI
ECG-based Biometric Authentication using Empirical Mode Decomposition and Support Vector Machines
TL;DR: A novel methodology for ECG based biometric authentication system by first denoise single lead raw ECG signal through empirical mode decomposition (EMD), then feature extraction is performed by combination of five features from statistical, time and frequency domains.
Journal ArticleDOI
Fall detection through acoustic Local Ternary Patterns
TL;DR: A comparative analysis demonstrates that the proposed descriptor is more powerful and reliable in terms of fall detection than other methods, and it also performs well in a multi-class environment.
Journal ArticleDOI
Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics
Sumair Aziz,Muhammad Awais,Tallha Akram,Umar Shahbaz Khan,Musaed Alhussein,Khursheed Aurangzeb +5 more
TL;DR: A modified feature descriptor for sound is proposed which incorporates a combination of 1-D local ternary patterns (1D-LTP) and baseline method Mel-frequency cepstral coefficients (MFCC) and achieves accuracies of 97.38 % and 94.10 % .
Proceedings ArticleDOI
Classification of EMG Signals for Assessment of Neuromuscular Disorder using Empirical Mode Decomposition and Logistic Regression
TL;DR: A complete framework for accurate classification of EMG signals which includes denoising by empirical mode decomposition (EMD), feature extraction from both the time and frequency domains and classification by logistic regression (LR) and support vector machine (SVM).