Time interleaved sigma-delta converter is a potential candidate for multi-mode wideband analog to digital (A/D) converters dedicated for multistandard receivers. However, the interpolation by zeros recquired to compress the useful signal bandwidth at the input of the sigma-delta modulator imposes constraints on the implementation of the analog part leading to a very large die area due to the hi...

Most classifiers work well when the class distribution in the response variable of the dataset is well balanced. Problems arise when the dataset is imbalanced. This paper applied four methods: Oversampling, Undersampling, Bagging and Boosting in handling imbalanced datasets. The cardiac surgery dataset has a binary response variable (1=Died, 0=Alive). The sample size is 4976 cases with 4.2% (Di...

Orthogonal frequency and code division multiplexing (OFCDM) has received large attention as a modulation scheme to realise high data rate transmission. The OFCDM system with fractional sampling (FS) is investigated. FS is a diversity scheme with a single antenna, which achieves path diversity through oversampling and parallel signal reception. However, the correlation among noise components may...

The problem of oversampling parameter estimation for noisy sinusoidal signals is addressed. We first extend the weighted least squares (WLS) approach to the complex sinusoids. Then the oversampling weighted least squares (OSWLS) estimator is proposed based on data decimation. Estimation performance of the OSWLS method is analyzed via theoretical and simulation studies. Results are also compared...

The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to different approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with the kernel classifiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean sp...

Most medical datasets are not balanced in their class labels. Indeed in some cases it has been no ticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without cons...

The classification of real-world problems alwaysconsists imbalanced and multiclass datasets. A dataset having unbalanced andmultiple classes will have an impact on the pattern modeland accuracy, which be decreased. Hence,oversampling method keeps class balanced avoids theoverfitting problem. purposes study were to handle multiclassimbalanced datasets improve effectivenessof model. This proposed...

In this paper, sampling rate selection diversity (SRSD) scheme for Direct-Sequence/Spread-Spectrum (DS/SS) is proposed. In DS/SS communication systems, oversampling may be employed to increase the signal-to-noise ratio (SNR). However, oversampling enlarges the power consumption because signal processing of the receiver has to be carried out at a higher clock rate. Higher sampling rate does not ...

We demonstrate that the soft X-ray diffraction pattern from a micron-size noncrystalline specimen can be recorded and inverted to form a high-resolution image. The phase problem is overcome by oversampling the diffraction pattern. The image is obtained using an iterative algorithm. The technique provides a method for X-ray microscopy requiring no highresolution X-ray optical elements or detecto...