This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock signal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows the generation of a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests (National Institute of Standards and Technology of U.S.). The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on a reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device. Therefore, it can be used for applications such as biological systems.

Two fixed-point (FP) square root designs based on a bit-by-bit non-restoring algorithm are unfolded, and compared to a FP pipelined design based on the same algorithm. One of them is unfolded from a bit-serial design, while the other design is unfolded from a smaller version of the pipelined design that has a digit size of 2, for this reason, it is cataloged as “unfolding based”. Hardware utilization is estimated with the theoretical use in the RTL designs and the synthesis report as an auxiliary tool. Results show that the “unfolding based” design is comparable and even surpasses the pipelined design when it comes to outputting a first result, being 19.47% faster and using 32.59% of memory of the pipelined design.

Solid-state fermentation (SSF) is a process used to produce enzymes and secondary metabolites; however, its low efficiency limits its application, as it does not cost-effectively meet market demand. This article proposes modeling the operation and determining the optimal parameters of SSF processes through the application of artificial intelligence systems. To this end, programming algorithms were designed in MATLAB software to implement an artificial neural network (ANN), a genetic algorithm (GA), particle swarm optimization (PSO), and the artificial bee colony (ABC) algorithm. To verify the proposed method, the production of proteases used in the cheese industry was modeled and optimized. The results show that the optimal process parameters were correctly identified, the modeling precision and accuracy were increased (R² > 0.90), and the process resources required can be reduced. These findings suggest that the use of artificial intelligence systems in SSF processes is an effective tool to maximize their production.

This work presents the design of a system of a highly flexible pseudorandom number generator system (PRNG) incorporating both conventional and neuro-generators. The system integrates four internal generators with different conditions to produce new output sequences with adequate bits distribution and complexity. Two generators function at a frequency of 100 MHz with adjustable frequency settings, while two neuro-generators employ impulse neurons with distinct behaviours at 4 kHz, also modifiable. The proposed system meets 12 statistical randomness standards based on NIST’s (National Institute of Standards and Technology of U. S.) test suite, including the Frequency test, Binary Matrix Rank test, Linear Complexity test, and Random Excursion test, among others. Each resulted in a P-value greater than 0.01, confirming the pseudo-randomness of the generated sequences. The system is implemented on a reconfigurable device FPGA (Field Programmable Gate Array), with a low occupancy percentage, demonstrating its feasibility for various applications.

The conversion of ocean wave energy into electrical energy occurs near beaches and is important for the design and implementation of wave energy conversion (WEC) systems. However, its generation depends on environmental conditions, which complicates the design and control of the devices. This work presents an approach to indirect adaptive control based on artificial neural networks to detect wave conditions for the proper functioning of WEC structures. The method involves generating a constant output voltage using a voltage boost converter and a direct current-alternating current (DC-AC) converter. Maintaining a constant output power despite variations in wave conditions to generate a voltage of 24 V with a current of 2 A is the primary proposal for the control design. The mechanical design integrates a rack and pinion system and a pulley transmission that connects a floating device to an electric generator. The implementation of control is carried out on an Arduino platform. The control system was implemented on an Arduino platform, occupying 48% of the available memory, with a convergence time of 4.29 ms, a mean squared error (MSE) of 0.13715, and a root mean squared error (RMSE) of 0.37034. These low values indicate that the proposed control system has greater accuracy. The experimental results validate the proposed control system, which reduces energy conversion errors and achieves greater efficiency.

This work presents the design of a system of a highly flexible pseudorandom number generator system (PRNG) incorporating both conventional and neuro-generators. The system integrates four internal generators with different conditions, to produce new output sequences with an adequate bits distribution and complexity. Two generators function at a frequency of 100 MHz with adjustable frequency settings, while two neuro-generators employ impulse neurons with distinct behaviours at 4 kHz, also modifiable. The proposed, system meets 12 statistical randomness based on NIST’s National Institute of Standards and Technology of U. S. test suite, including the Frequency test, Binary Matrix Rank test, Linear Complexity test, Random Excursion test, among others. Each resulted in a P-Value greater than 0.01, confirming the pseudo-randomness of the generated sequences. The system is implemented on an FPGA Field Programmable Gate Array Virtex 7xc7vx485t-2ffg1761, with a low occupancy percentage, demonstrating its feasibility for various applications.