A multi-mode terminal is required in order to offer users multiple communication services for ubiquitous communication and Software Defined Radio (SDR) technology has attracted considerable attention as a means of realizing a multi-mode terminal. In such a terminal, a multi-band receiver is required, since each communication service uses an individually specific frequency and bandwidth. It is a...

Recent work in k-t BLAST and undersampled projection angiography has emphasized the value of using training data sets obtained during the acquisition of a series of images. These techniques have used iterative algorithms guided by the training set information to reconstruct time frames sampled at well below the Nyquist limit. We present here a simple non-iterative unfiltered backprojection algo...

Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with majority of examples. Undersampling approaches reduce size class balance distributions. Evolutionary-based are prominent, treating undersampling as binary optimisation problem determines which examples removed. However, their ut...

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...

Abstract Training a machine learning algorithm on class-imbalanced dataset can be difficult task, process that could prove even more challenging under conditions of high dimensionality. Feature extraction and data sampling are among the most popular preprocessing techniques. is used to derive richer set reduced features, while mitigate class imbalance. In this paper, we investigate these two te...

Both the actual theft of a credit card and deletion private data are considered forms fraud. For detection, there numerous machine learning algorithms accessible. So, several that can be used to categorize transactions as fraudulent or lawful illustrated in this study. In experiment, fraud prediction dataset was utilized. The is extremely skewed, hence undersampling rather than oversampling. se...

A wide range of measuring applications rely on phase estimation on sinusoidal signals. These systems, where the estimation is mainly implemented in the digital domain, can generally benefit from the use of undersampling to reduce the digitizer and subsequent digital processing requirements. This may be crucial when the application characteristics necessarily imply a simple and inexpensive senso...