An oversampling 2D sigma-delta converter by cellular neural networks

Abstract

The sigma-delta cellular neural network (SD-CNN) is a novel framework of spatial domain sigma-delta modulation utilizing neuro dynamics. Also, it has signal reconstruction and noise shaping characteristics that are important sigma-delta properties. Although the noise shaping effect with the oversampling technique plays very important role for drastic quantization noise reduction in binary digital sequences, the conventional SD-CNN could not use it effectively since it can be thought that the time-domain and spatial-domain oversampling are effective for the SD-CNN. In this paper, a novel SD-CNN with the oversampling technique for an analogue DC input is proposed. Experimental results of various standard test images in several oversampling ratios suggest that the proposed oversampling SD-CNN has an excellent AD and DA performance.

Dense Delta-Sigma Phased Arrays

“…The results are summarized in Table I, along with the corresponding density ratios required to obtain the same SQNR values with the two-bit array. These results match quite closely to the relationship (18) which provides a sense of how the appropriate density ratio should be selected to match the performance associated with specified number of phase shifter bits in a conventional array.…”

“…Spatial domain versions of have received attention in applications other than antenna array design; examples include image processing, wave computing, and pattern recognition [18], [19]. For instance, in the context of image processing, an approach known as error diffusion uses quantization to reproduce images from low-resolution but oversampled data.…”