# Next-Generation Blockchain Technology:
## 5 Generating the Entropic Barcode of a Digital File  
Using the dataset windows segmentation procedure described above, together with the \(m\)-block procedure, the entropic barcode of a set is obtained by computing the information entropy IE value of each window and plotting the IE values as a function of the window index location within the new set. The IE of each window is computed identically using (1) for single-character sets, or (3) for sets of \(m\)-block characters, with each window containing WS characters, \(X=\{x_{1},x_{2},\ldots,x_{\mathrm{WS}}\}\), and a number of \(U\) distinct characters. The entropic barcoding technology is the subject of a recent patent application (GB2404348.1) and allows for the conversion of the information contained within any dataset into a compressed numerical set that can be used for further data processing on its own or as a graphical optically readable barcode [8; 9]. The generated entropic barcode is a representation of the dataset, which is irreversibly encrypted, and it has a massively compressed digital footprint size. In addition to the one-way encryption and data compression, the entropic barcode method offers a representation of the original dataset that could be used for data integrity checking, fraud detection, data labeling, and fast identification via laser barcode scanning. This technology is applicable to any dataset, including genomes, but one of the main applications of the entropic barcoding technology is to digital files. Any digital file is composed of 0s and 1s in machine code/binary language. This can be seen as a set of \(N\) characters containing two distinct characters, 0 and 1, so \(U=2\), \(a_{l}=\{0,1\}\), and fractions distributions are \(p=\{p_{0},p_{1}\}\). Using (1), the IE of the set can be easily calculated. To demonstrate this process, let us use a random set of \(N=16\) bits:  
\[\{0110101101011100\}.\]  
If the bits within this set would occur with equal fractions (\(p_{j}=1/2\)), then the set would have \(\mathrm{IE}=1\) and a total entropy of \(N\cdot\mathrm{IE}=16\) bits of information. However, counting the single bits, the above set has the following fractions:  
\[p=\bigg{\{}p_{0}=\frac{7}{16},p_{1}=\frac{9}{16}\bigg{\}}, \tag{12}\]  
resulting in  
\[\mathrm{IE}=-\bigg{(}\frac{7}{16}\log_{2}\bigg{(}\frac{7}{16}\bigg{)}+\frac{9 }{16}\log_{2}\bigg{(}\frac{9}{16}\bigg{)}\bigg{)}=0.989. \tag{13}\]  
Hence, the IE of this binary set is 0.989 bits instead of 1 bit, and the total IE of the set is 15.84 bits instead of 16 bits. This is the basis of calculating the IE of each window within a set to generate an entropic barcode of the set, including a set of bits. The proposed entropic barcoding technology can only be implemented using fully automated computer software, and we created a proof-of-concept software called ENtropic BARCoding (ENBARC, version 1.a, [PERSON]), which is freely available and can be obtained by contacting the authors.  
For any given digital file, one can compute the entropic barcode representation of the original digital file by implementing the following steps:  
1. The file targeted for barcoding is decomposed from its own format into a string of bytes.
2. The bytes are then converted into a text file containing a long string of bits, 0 s and 1 s. All other characters are removed.
3. The string of 1 s and 0 s is split into windows.
4. The IE per each window is calculated.
5. The entropic barcode of the file is generated, which is a text file containing the IE values per window versus window location index.
6. The graphical representation of the entropic barcode of the file is obtained by plotting the IE values per window versus window location index.  
Figure 2 diagrammatically shows the proposed methodology with which to generate the entropic barcode of a digital file, including an example of a graphical entropic barcode representation.