A key challenge of oversampling in imbalanced classification is that the generation new minority samples often neglects usage majority classes, resulting most sampling spreading whole space. In view this, we present a framework based on counterfactual theory. Our introduces objective by leveraging rich inherent information classes and explicitly perturbing to generate territory It can be analyt...

An N-tone Σ∆ converter is obtained by introducing N zeros in the noise transfer function (NTF) while maintaining a flat signal transfer function (STF). N-tone Σ∆ converters are particularly well suited for multi-carrier systems. While oversampling is a widely used technique to improve the resolution of the converter, different methods will have to be explored for systems with low over-sampling....

This paper studies three techniques for outliers detection in the context of Wireless Sensor Networks, including a machine learning technique, a Principal Component Analysis-based methodology and an univariate statistics-based approach. The first methodology is based on a Least Squares-Support Vector Machine technique, together with a sliding window learning. A modification to this approach is ...

This paper presents a scheme and circuitry for demultiplexing and synchronizing high-speed serial data using the matched delay sampling technique. By simultaneously propagating data and clock signals through two diierent delay taps, the sampler achieves a very ne sampling resolution which is determined by the diierence between the data and clock delays. Thus, the sampler is capable of oversampl...

Ray-tracing is one of the most popular techniques for rendering 3D images. Effects such as shadows, reflection, refraction and so on can be produced with this technique. However, raytracing is a point-sampling technique with well-known aliasing problems. In particular, small objects and small shadows can be hidden between rays and not be detected. No ray-tracing method, even using oversampling,...

Data skewness continues to be one of the leading factors which adversely impacts machine learning algorithms performance. An approach reduce this negative effect data variance is pre-process former dataset with level resampling strategies. Resampling strategies have been seen in two forms, oversampling and undersampling. strategy proposed article for tackling multiclass imbalanced datasets. Thi...

We have previously described a design for a 4Gb/s signaling system that uses transmitter equalization to overcome the frequency-dependent attenuation due to skin effect in transmission lines. We present here experimental results from an implementation of this idea in 0.5μ CMOS, showing the effectiveness of a simple transition-filter equalization technique. Our experimental chips use a tracking ...

The aim of this work is to solve a question raised for average sampling in shift-invariant spaces by using the well-known matrix pencil theory. In many common situations in sampling theory, the available data are samples of some convolution operator acting on the function itself: this leads to the problem of average sampling, also known as generalized sampling. In this paper we deal with the ex...

Traffic measurement and monitoring are an important component of network management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement and monitoring are not only desirable, but also increasingly becoming a necessity. Since the utility of sampling depends on the accuracy and economy of measurement, it is i...