DESIGN OF EXACT REGENERATING HIERARCHICAL CODE FOR DISTRIBUTED STORAGE SYSTEM

Erasure code is widely used as the redundancy scheme in distributed storage system. When a storage node fails, the repair process often requires to transfer a large amount of data. Regenerating code and hierarchical code are two classes of codes proposed to reduce the repair bandwidth cost. Regenerating codes reduce the amount of data transferred by each helping node, while hierarchical codes reduce the number of nodes participating in the repair process. In this paper, we propose a "sub-code nesting framework" to combine them together. The resulting regenerating hierarchical code has low repair degree as hierarchical code and lower repair cost than hierarchical code. Our code can achieve exact regeneration of the failed node, and has the additional property of low updating complexity.

CADGen:Computer-aided design sequence construction with a guided codebook learning

Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constructs the CAD sequences containing the sketch-and-extrude modelling operations efficiently and with high quality.Starting from the sketch and extrusion operation sequences,we utilise the transformer encoder to encode them into different disentangled codebooks to represent their distribution properties while considering their correlations.Then,a combination of auto-regressive and non-autoregressive samplers is trained to sample the code for CAD sequence con-struction.Extensive experiments demonstrate that our model generates diverse and high-quality CAD models.We also show some cases of real digital twin applications and indicate that our generated model can be used as the data source for the digital twin platform,exhibiting designers'potential.

Formal Photograph Compression Algorithm Based on Object Segmentation

Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees （SPIHT） algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel （bpp）is equal to 0.078, peak signal-to-noise ratio （PSNR） of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.