8,778 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
Multiscale Adaptive Representation of Signals: I. The Basic Framework
We introduce a framework for designing multi-scale, adaptive, shift-invariant
frames and bi-frames for representing signals. The new framework, called
AdaFrame, improves over dictionary learning-based techniques in terms of
computational efficiency at inference time. It improves classical multi-scale
basis such as wavelet frames in terms of coding efficiency. It provides an
attractive alternative to dictionary learning-based techniques for low level
signal processing tasks, such as compression and denoising, as well as high
level tasks, such as feature extraction for object recognition. Connections
with deep convolutional networks are also discussed. In particular, the
proposed framework reveals a drawback in the commonly used approach for
visualizing the activations of the intermediate layers in convolutional
networks, and suggests a natural alternative
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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