Much of the work to date on data ow models for signal processing system design has focused decidable data ow models that are best suited for onedimensional signal processing. In this chapter, we review more general data ow modeling techniques that are targeted to applications that include multidimensional signal processing and dynamic data ow behavior. As data ow techniques are applied to signa...

A subarea of low-rank approximation that is not covered in this overview is tensors low-rank approximation [WVB10, LV00]. Tensor methods are used in higher order statistical signal processing problems, such as independent component analysis, and multidimensional signal processing, such as spatiotemporal modeling and video processing, to name a few. Other areas of research on low-rank approximat...

Many problems in digital signal processing can be converted to algebraic problems over polynomial and Laurent polynomial rings, and can be solved using the existing methods of algebraic and symbolic computation. This paper aims to establish this connection in a systematic manner, and demonstrate how it can be used to solve various problems arising from multidimensional signal processing. The me...

Tensors and tensor fields are commonly used in multidimensional signal processing to represent the local structure of the signal. This paper focuses on the case where the sampling on the original signal is anisotropic, e.g when the resolution of the multidimensional image varies depending on the direction which is common e.g. in medical imaging devices. To obtain a geometrically correct descrip...

bci is one of the most intriguing technologies among other hci systems, mostly because of its capability of recording brain activities. spelling bcis, which help paralyzed people to maintain communication, are one of the striking topics in the field of bci. in this scientific a spelling bci system with high transfer rate and accuracy that uses ssvep signals is proposed. in addition, we suggeste...