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The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
Automatic diagnosis of electrocardiogram (ECG) signal is significant for timely and accurate diagnosis of heart diseases like arrhythmia. Several researchers have proposed different methods in last two decades. In this work we have employed a global ECG beat classification approach based on transformed features like discrete cosine transform (DCT) and discrete wavelet transform (DWT) rather than conventional...
Automatic defect detection on hot-rolled steel surface is challenging owing to its localization on a large surface, variation in appearance, and their rare occurrences. It is difficult to detect these defects either by physics-based models or by small-sample statistics using a single threshold. As a result, this problem is focused to derive a set of good-quality defect descriptors from the surface...
We propose a simple formulation for regularized kernel function approximation. The regression function is obtained by minimizing an unconstrained quadratic function. Further reduced kernel technique is also employed in its formulation which enables it to handle large database. The solution of this objective function is obtained by solving a system of linear equations and thus no need for any quadratic...
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