In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function RandBetween by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio. A PRNG based on the Absolute Value Function and due to its characteristic can generate a sequence of aperiodic numbers with good statistical properties.

1. ITAMARACÁ
A N O V E L S I M P L E W A Y T O G E N E R A T E
P S E U D O R A N D O M N U M B E R S
F R N S = A B S [ N - ( P N * X R N ) ]
D H P E R E I R A

2. U N D E R S TA N D I N G
I TA M A R AC Á
• Itamaracá or simply "Ita" is a novel, simple, fast and
'non-periodic' mathematical basis for PRNG that
generates an "infinite" sequence of numbers within
an interval [0,1] considering a uniform distribution.
• Its name is derived by the Tupi-Guarani
language in which means "Stone Shaker" or
"Singing Stone", in this sense, a clear reference to
something random or unexpected.

3. H O W I TA W O R K S
Like all PRNGs algorithms, Ita have some distinctive features. Below we
have some initial conditions:
• Firstly, choose N, that is, a maximum value within a range between 0
and N selected by a user criterion, where N ∈ ℕ.
• In this model, there are 3 seeds S0, S1 and S2. For each of these seeds
choose any number ∈ ℕ belonging to the interval between 0 and N
(already choose previously).

4. H O W I TA W O R K S
After selected all the 3 seeds, S0, S1 and S2, the calculation process is
divided in two main and very simple steps:
• Pn (n Process)
• Final Calculation

5. H O W I TA W O R K S
Pn (n Process) or Intermediate State
In this stage we need taking into account the absolute values considering the
differences between the 2 seeds that must be moving in the sequence.
Pn = ABS (S2 – S0)

6. H O W I TA W O R K S
Final Calculation or General Formula
In this step, we must multiply the “x” result obtained in the first step (in Pn) by the
Xrn, that is, any value in which its founded value is desirable to be close to 2 (i.e.
1.97, 1.98, 1.99789...).
FRNS = ABS [N – (Pn * Xrn)]

7. A P P L I C AT I O N E X A M P L E
Let's assume we want to generate numbers from 0 to 10,000:
N 10,000
Seed 0 8,777
Seed 1 11
Seed 2 8

8. A P P L I C AT I O N E X A M P L E
We can generate the first number using the intermediate state (Pn) and then using
the main formula, as we can see below:
P1 = ABS (8 – 8,777) = 8,769
FRNS1 = ABS [10,000 - (8,769*1.97) = 7,275

9. A P P L I C AT I O N E X A M P L E
2nd number:
P2 = ABS (7,275 – 11) = 7,264
FRNS2 = ABS [10,000 - (7,264*1.97) = 4,310
3rd number:
P3 = ABS (4,310 – 8) = 4,302
FRNS3= ABS [10,000 - (4,302*1.97) = 1,525

10. A P P L I C AT I O N E X A M P L E
So, we have the first three numbers generated:
7,275 - 4,310 and 1,525...
The next sequences from now on follow the same calculation logic.

11. R E S U L T S O F S O M E T O O L S A N D
S TAT I S T I C A L T E S T S
Tests Ita Random Org
Chi-Square 11.26 3.65
Repeated Numbers / N 3,618 3,763
Average / Standard Deviation 4,941 / 2,884 4,925 / 2,905
Run Test (Even/Odd) -0.914634 0.004101
Run Test (Median) 0.759184 0.603023
Autocorrelation (Average of the
first 10 k-lags different from 0)
0.000103 0.000980
Shannon Entropy 3.45327 3.45284
Comparing the results between Ita and TRNG by Random Org considering 10,000 numbers generated
Note:Methodology used for evaluating the results are exactly the same as those contained in the published version.

12. R E S U L T S O F S O M E T O O L S A N D
S TAT I S T I C A L T E S T S
Histogram for Ita model

13. R E S U L T S O F S O M E T O O L S A N D
S TAT I S T I C A L T E S T S
Run Sequence for Ita model
0
2000
4000
6000
8000
10000
12000
1
19
37
55
73
91
109
127
145
163
181
199
217
235
253
271
289
307
325
343
361
379
397
415
433
451
469
487
505
523
541
559
577
595
613
631
649
667
685
703
721
739
757
775
793
811
829
847
865
883
901
919
937
955
973
991
Line Graph for 1,000 numbers generated by Itamaracá

14. R E S U L T S O F S O M E T O O L S A N D
S TAT I S T I C A L T E S T S
Scatter Plot for Ita model
0
2000
4000
6000
8000
10000
12000
0 200 400 600 800 1000 1200
Scatter Plotfor 1,000 numbers generated by Ita
Série1

15. S O M E C O N S I D E R AT I O N S
• Ita model has proven to be a good random number generator, especially in the
criteria that evaluate independence and uniformity. Despite being a recent study,
there are good perspectives regarding the computational cost and its applicability
for the cryptography area and becoming a CSPRNG.
• Another point to be highlighted is that it was not observed any rule of choice
regarding the value of the seeds, it is enough they are chosen arbitrarily with
values are within the range from 0 to N where N ∈ ℕ, their maximum value.

16. S O M E C O N S I D E R AT I O N S
• Regardless of the initial seed values used, there is a strong tendency for the
algorithm to pass basic statistical tests for uniformity and independence. However,
although approved, some chosen values can cause the results of certain tests to be
"better" or "worse" than when using other seeds.

17. S O M E C O N S I D E R AT I O N S
• Ita model as every PRNG also has some identified limitations. As an example,at some point
probably after a large amount of generated numbers, the repetition of the same sequence of
generated numbers only tendsto repeat if and only if the values of the 3 initial seeds (S0, S1 and
S2) appearin the middle of the generated sequence.
• Despite this limitation, we can see it is very difficult for this sequence of numbers to be repeated
itself completely, as we increase the value of N and considering an uniform distribution [0,1].
Well, we can infer it is a generator that generates"infinite" and 'non-periodic'psedourandom
numbers.

18. C O N C L U S I O N
The generation of random numbers is too important
for several fields of study and practical applications
for the development of mankind.
The present study, presented a new and simple
proposal of a Pseudo Random Number Generator
(PRNG) called "Itamaracá" (Ita in a abbreviated
form). Ita model, like all PRNG algorithms, has some
limitations, but in general, it showed good results in
the statistical tests considered, and thus, as one more
model in the portfolio, it is fully available for use and
above all, for new studies, especially those applied
to a specific objective and real problems.