35,024 research outputs found
Child labour: the case study in Bangladesh
Child labour involves of person that age below than 17 years old. Child labour often happen in poor countries such as Bangladesh. In Bangladesh, the issue of child labour might be the biggest issue. Bangladesh come up with Bangladesh Labour Act (BLA) that did not allow any person age below from fourteen years old to work (Nawshin et al, 2019). One of the aim or purpose of this act is to prevent teen workers in order to get the proper payment of any work. This is because when organization use child labour, they might be paid at lower rate because children usually do not have much responsible in their family compared to teen workers. This indirectly cause an economic matter in a family
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Parallel data compression
Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested
A study of data coding technology developments in the 1980-1985 time frame, volume 2
The source parameters of digitized analog data are discussed. Different data compression schemes are outlined and analysis of their implementation are presented. Finally, bandwidth compression techniques are given for video signals
Low-latency compression of mocap data using learned spatial decorrelation transform
Due to the growing needs of human motion capture (mocap) in movie, video
games, sports, etc., it is highly desired to compress mocap data for efficient
storage and transmission. This paper presents two efficient frameworks for
compressing human mocap data with low latency. The first framework processes
the data in a frame-by-frame manner so that it is ideal for mocap data
streaming and time critical applications. The second one is clip-based and
provides a flexible tradeoff between latency and compression performance. Since
mocap data exhibits some unique spatial characteristics, we propose a very
effective transform, namely learned orthogonal transform (LOT), for reducing
the spatial redundancy. The LOT problem is formulated as minimizing square
error regularized by orthogonality and sparsity and solved via alternating
iteration. We also adopt a predictive coding and temporal DCT for temporal
decorrelation in the frame- and clip-based frameworks, respectively.
Experimental results show that the proposed frameworks can produce higher
compression performance at lower computational cost and latency than the
state-of-the-art methods.Comment: 15 pages, 9 figure
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