The Wireless Sensor Network (WSN) has limitations in bandwidth and computational resources as they have limited communication and storage capabilities. WSN consists of cameras, which have some local image processing a...
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ISBN:
(纸本)9781479910243
The Wireless Sensor Network (WSN) has limitations in bandwidth and computational resources as they have limited communication and storage capabilities. WSN consists of cameras, which have some local image processing and one or more central computers, where image data from multiple cameras is further processed and fused. Because of these limitations, the encoding techniques used for transmitting the image data should be efficient in order to make use of the available resources properly. A new sampling method is also introduced in the Image/video encoder of the WSN called Compressed Sensing (CS), which is the process of acquiring and reconstructing a signal that is supposed to be sparse or compressible, thus reducing the computational complexity. The image is divided into dense and sparse components by applying 2 levels of wavelet transform. The dense component uses the standard encoding procedure such as JPEG and the sparse measurements obtained from the sparse components are encoded by the techniques such as exponential golomb coding followed by Run-length encoding and arithmetic coding and the performances in terms of compression ratio and bits per pixel are compared. The recovery algorithm may be anyone supporting the compressed sensing technique such as OMP, POCS etc. In this work, the measurements (used in CS) and the predicted sparse components as the initial values, the projection onto convex set (POCS) recovery algorithm is used to get back the original sparse components of two levels and hence the original image by applying the inverse of transform to the dense and recovered sparse components.
The advancement of the electric grid has led to tremendous growth in data generated from the installed sensors. Efficient storage and transmission of this data pose a challenge for the utilities. Thus, it is required ...
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ISBN:
(纸本)9781538645055
The advancement of the electric grid has led to tremendous growth in data generated from the installed sensors. Efficient storage and transmission of this data pose a challenge for the utilities. Thus, it is required to have a data compression technique to reduce the data size. There are state of the art compression algorithms that can be applied to reduce the amount of data for storage and transmission in the smart grid environment. Some of these algorithms exploit characteristics of the load profile data, where consecutive data samples have very small differences. However, performance of these algorithms deteriorate when there are frequent large differences. We propose a modification that improves compression performance when there are large value differences. The algorithm is evaluated on smart meter load profile data at different data resolution. We show that the proposed changes improve performance by 2 - 20% for different resolutions.
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