It’s not April so bram is probably taking to fellow scientist which will know what’s going on. Still a basic explanation if this irony would be apprecited
One-Number Encoding Based on Matrix Key Summation: A Novel Data Compression and Recovery Algorithm
In today’s world, where data volumes are growing exponentially, efficient methods for data storage and transmission are highly sought after. Existing algorithms are often complex to implement or limited to specific data types. This paper presents a novel algorithm that transforms any data into a single numerical representation, ensuring ease of storage and transmission, as well as the possibility of complete recovery of the original data.
Information, whether text, image, video, or audio, can be represented as a digital dataset, that is, a sequence of zeros and ones (bits), and then transformed into a Matrix. Data-to-matrix conversion is a useful method for organizing information for many data processing, machine learning, and analysis algorithms.
As is known, any matrix has a size and numbers in the range of 0 to 256, which define an infinite number of variations. Any integer from 0 to 256 must be encoded with a unique floating-point number in the range of 0 to 256! with a step from 0 to 1/256. Then, by replacing the integer values with a new range of numbers, we obtain a new matrix, which, using a spectral summation mechanism from left to right, we perform the summation of numbers in the new matrix, where the result will be some number of the form 149080.546535654632.93664, where 93664 is the number of elements in the matrix.
This number is a new standard for storing any data. We are already able to convert this number back into a matrix and, using the integer encoding key, return the matrix to its original form, and then convert it back into data.
What has been done so far:
Currently, we successfully encode any textual information into a single number, whether it is one or a group of files, and then successfully extract this data back, using the spectral addition algorithm and a set of unique keys.
How does it work?
1. Create unique keys for the spectral range.
2. Encode the information into a data matrix using unique keys.
3. Perform spectral addition within the matrix, obtaining a number with an indication of the number of elements at the end of the number after the decimal point (149080.546535654632.93664).
4. Perform reverse decoding of the number back into data.
Future Prospects:
With the improvement of the existing interaction algorithm, as well as with the use of greater computing power, applying the method of spectral addition and unique keys, we will be able to store any information in a single number that can be extracted back. This will lead to significant progress for humanity.
It’s kinda frustrating isn’t it. Do you think that P ?= NP will remain forever impossible to prove, or that we just lack the correct sort of tools to prove any reasonable impossibility, besides diagonalization?
It’s not April so bram is probably taking to fellow scientist which will know what’s going on. Still a basic explanation if this irony would be apprecited
One-Number Encoding Based on Matrix Key Summation: A Novel Data Compression and Recovery Algorithm
In today’s world, where data volumes are growing exponentially, efficient methods for data storage and transmission are highly sought after. Existing algorithms are often complex to implement or limited to specific data types. This paper presents a novel algorithm that transforms any data into a single numerical representation, ensuring ease of storage and transmission, as well as the possibility of complete recovery of the original data.
Information, whether text, image, video, or audio, can be represented as a digital dataset, that is, a sequence of zeros and ones (bits), and then transformed into a Matrix. Data-to-matrix conversion is a useful method for organizing information for many data processing, machine learning, and analysis algorithms.
As is known, any matrix has a size and numbers in the range of 0 to 256, which define an infinite number of variations. Any integer from 0 to 256 must be encoded with a unique floating-point number in the range of 0 to 256! with a step from 0 to 1/256. Then, by replacing the integer values with a new range of numbers, we obtain a new matrix, which, using a spectral summation mechanism from left to right, we perform the summation of numbers in the new matrix, where the result will be some number of the form 149080.546535654632.93664, where 93664 is the number of elements in the matrix.
This number is a new standard for storing any data. We are already able to convert this number back into a matrix and, using the integer encoding key, return the matrix to its original form, and then convert it back into data.
What has been done so far:
Currently, we successfully encode any textual information into a single number, whether it is one or a group of files, and then successfully extract this data back, using the spectral addition algorithm and a set of unique keys.
How does it work?
1. Create unique keys for the spectral range.
2. Encode the information into a data matrix using unique keys.
3. Perform spectral addition within the matrix, obtaining a number with an indication of the number of elements at the end of the number after the decimal point (149080.546535654632.93664).
4. Perform reverse decoding of the number back into data.
Future Prospects:
With the improvement of the existing interaction algorithm, as well as with the use of greater computing power, applying the method of spectral addition and unique keys, we will be able to store any information in a single number that can be extracted back. This will lead to significant progress for humanity.
It’s kinda frustrating isn’t it. Do you think that P ?= NP will remain forever impossible to prove, or that we just lack the correct sort of tools to prove any reasonable impossibility, besides diagonalization?
YOU LOST ME WITH MOUSE HOLE