Module-IV 094.pdf
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Huffman coding is a popular data compression technique that assigns variable-length binary codes to symbols based on their frequency of occurrence, with more common symbols getting shorter codes. It works by building a binary tree from the frequency of each symbol and assigning codes based on paths from the root to each leaf node. This results in more efficient transmission of data compared to fixed-length codes like ASCII by using fewer total bits. While it does not achieve optimal compression like entropy encoding since it uses an integral number of bits per code, Huffman coding is simple to implement and provides good compression for many types of data.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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Module-IV 094.pdf
- 3. Huffman Coding • Huffman coding is a popular compression technique that assigns variable length codes (VLC) to symbols, so that the most frequently occurring symbols have the shortest codes. • On decompression the symbols are reassigned their original fixed length codes. 3
- 4. • The idea is to use short bit strings to represent the most frequently used characters • and to use longer bit strings to represent less frequently used characters. 4
- 5. • That is, the most common characters, usually space, e, and t are assigned the shortest codes. • In this way the total number of bits required to transmit the data can be considerably less than the number required if the fixed length ASCII representation is used. • A Huffman code is a binary tree with branches assigned the value 0 or 1. 5
- 6. 6
- 7. Huffman Algorithm • To each character, associate a binary tree consisting of just one node. • To each tree, assign the character’s frequency, which is called the tree’s weight. • Look for the two lightest-weight trees. If there are more than two, choose among them randomly. 7
- 8. • Merge the two into a single tree with a new root node whose left and right sub trees are the two we chose. • Assign the sum of weights of the merged trees as the weight of the new tree. • Repeat the previous step until just one tree is left. 8
- 9. Huffman Coding Example • Character frequencies – A: 20% (.20) – B: 9% (.09) – C: 15% – D: 11% – E: 40% – F: 5% • No other characters in the document 9
- 11. Huffman Code • Codes – A: 010 – B: 0000 – C: 011 – D: 001 – E: 1 – F: 0001 • Note – None are prefixes of another 11 ABCDEF 1.0 E .4 C .15 A .20 D .11 F .05 BF .14 AC .35 BFD .25 ABCDF .6 B .09 0 0 0 0 0 1 1 1 1 1
- 12. Huffman Coding • TENNESSEE 9 0/ 1 5 e(4) 0/ 1 s(2) 3 0/ 1 t(1) n(2) • ENCODING • E : 1 • S : 00 • T : 010 • N : 011 Average code length = (1*4 + 2*2 + 3*2 + 3*1) / 9 = 1.89 12
- 13. Average Code Length Average code length = i=0,n (length*frequency)/ i=0,n frequency = { 1(4) + 2(2) + 3(2) + 3(1) } /(4+2+2+1) = 17 / 9 = 1.89 13
- 14. ENTROPY Entropy = - i=1,n (pi log2 pi) ( p - probability of the symbol) = - ( 0.44 * log20.44 + 0.22 * log20.22 + 0.22 * log20.22 + 0.11 * log20.11 ) = - (0.44 * log0.44 + 2(0.22 * log0.22 + 0.11 * log0.11) / log2 = 1.8367 14 Entropy is a measure of information content: the more probable the message, the lower its information content, the lower its entropy
- 15. Advantages & Disadvantages • The problem with Huffman coding is that it uses an integral number of bits in each code. • If the entropy of a given character is 2.5 bits,the Huffman code for that character must be either 2 or 3 bits , not 2.5. 15
- 16. • Though Huffman coding is inefficient due to using an integral number of bits per code , it is relatively easy to implement and very efficient for coding and decoding. • It provides the best approximation for coding symbols when using fixed width codes. 16