Giovanny Sanchez

Finite control set model predictive control (FCSMPC) offers several benefits for power electronic applications, including fast response, precise tracking, and high performance. However, reliance on an accurate model and its parameters has hindered the advancement of FCSMPC methods in power electronics inverter systems due to the uncertain model and parameter variations of the load and filter side. Recently, model-free FCSMPC (MF-FCSMPC) methods have been presented in the literature to determine the model parameters of the employed auto-regressive with exogenous input (ARX) model of the output resistance-inductor (R-L) load side. The majority of existing MF-FCSMPC methods employ the recursive least squares (RLSs) algorithm to update parameters in real-time. However, the conventional RLS method critically suffers from numerical instability and high computational cost, which limits its application in practical power inverter systems. Additionally, the performance of the RLS-based MF-FCSMPC methods relies on the initial model of the system. To overcome this issue, this article presents an improved MF-FCSMPC method based on the affine projection (AP) algorithm, which guarantees numerical stability and low computational cost. The proposed AP-based MF-FCSMPC is less sensitive to initial model parameters. The experimental results of the proposed AP-based MF-FCSMPC method demonstrate its ability to perform fast control using FCSMPC without prior knowledge of the physical plant, compared to classical model-based FCSMPC.

Acoustic echo is a common challenge in modern communication systems, particularly in applications such as hands-free and conferencing systems. Several adaptive algorithms have been proposed to mitigate this issue. Among them, the recursive successive over-relaxation (RSOR) algorithm offers convergence properties comparable to the recursive least squares (RLS) algorithm. However, its computational cost becomes prohibitive for high-order filters, as required in acoustic echo cancellation (AEC) systems. Recently, new adaptive algorithms based on the nearest Kronecker product (NKP) decomposition have emerged to reduce computational complexity, potentially allowing the development of efficient AEC systems integrated into portable devices. In this work, we propose for the first time the RSOR algorithm based on the NKP strategy. Simulation results show that the proposed algorithm achieves convergence rates similar to RSOR and RLS algorithms while reducing computational cost by 89% compared to conventional RSOR algorithm, making it suitable for efficient AEC implementations in embedded and portable devices.

This work introduces Hybrid Membrane–Neural P systems (HMN P systems), a computational model that integrates principles from membrane computing and spiking neural P systems. The resulting framework offers a versatile foundation for the development of bio-inspired arithmetic architectures. Within this setting, we propose a compact family of arithmetic kernels capable of executing signed addition, subtraction, multiplication, and division in both modular and non-modular arithmetic domains. By leveraging intrinsic spike aggregation, spike–anti-spike annihilation, and exhaustive rule application, the proposed designs achieve efficient and reliable arithmetic computation in a constant number of simulation steps under exhaustive semantics and assuming synchronized input, independent of operand values. Addition and subtraction are executed intrinsically upon spike arrival, requiring no internal computation steps, while multiplication and division are completed in a single simulation step by one neuron. Furthermore, we introduce a modular-reduction kernel that operates in two simulation steps with a single neuron, and leverage its modular structure to construct modular multiplication and division through composition with non-modular arithmetic modules. Comparative evaluations against representative SNP and SNQ arithmetic designs demonstrate that HMN kernels achieve operand-independent execution time while requiring fewer neurons. Distinct from most existing approaches, the HMN framework natively supports signed operands through a dual-spike representation, thereby eliminating the need for auxiliary sign-handling mechanisms. Asynchronous spike arrivals can be managed by an optional synchronization membrane; since this mechanism is decoupled from the arithmetic kernels, its overhead is excluded from kernel performance and reported separately. Collectively, these results establish HMN systems as an efficient and modular platform for constant-time arithmetic computation, offering reusable arithmetic kernels that serve as a foundation for higher-level constructions, including those arising in elliptic-curve and modular arithmetic.

Recent studies indicate that sound levels in neonatal intensive care units (NICUs) frequently exceed 70 dB, surpassing the limits recommended by the American Academy of Pediatrics (AAP). Extended exposure to high noise levels may negatively impact infant health, leading to elevated respiratory and heart rates and disrupted sleep. To address this issue, active noise control (ANC) systems are increasingly recognized as an effective solution for reducing noise in infant incubators, demonstrating average noise reductions between 10 and 17 dB. However, despite these improvements, ambient noise levels often remain above recommended thresholds. Additionally, many existing ANC implementations rely on low-cost adaptive algorithms that suffer from slow convergence rates, limiting their effectiveness in real-world applications. This paper introduces the implementation of an ANC system tailored for infant incubators, employing a novel variant of the recursive inverse (RI) algorithm. The proposed method features an alternating structure that integrates both the RI and least mean squares (LMS) algorithms. Experimental results reveal that the system effectively reduces noise levels within the incubator. Moreover, the new algorithm offers faster convergence, improved noise attenuation, and reduced computational complexity compared to traditional RI-based approaches.

Currently, Kronecker Matrix–Matrix Multiplication play a crucial role in many advanced applications across science and engineering, such as Quantum Computing (Tensor Representation of Quantum States, Quantum Gate Construction), Machine Learning and Data Science (Kernel Methods, Tensor Decompositions), and Signal and Image Processing (Multi-dimensional Filtering, Compression Algorithms). However, the implementation of the Kronecker Matrix–Matrix Multiplication increasingly relies on systems with enhanced computational capabilities. Specifically, current implementations expend large amounts of external memory and requires a large number of processing units to perform this operation. As is commonly acknowledged, cutting-edge high-performance computing schemes still faces limitations in terms of energy and performance due to the bottleneck in data transfer between processing units and memory. To mitigate this limitation, memory processing units (MPUs) enable direct computation on in-memory data, reducing latency and eliminating the need for data transfer. On the other hand, spiking neural P systems, with their inherent parallelism and distributed processing capabilities, are therefore well-suited as foundational components for implementing such memory architectures efficiently. From the mathematical point of view, we present for the first time a neural, synaptic, and dendritic model to support the Kronecker Matrix–Matrix multiplication. To this end, the proposed spiking neural P system with their cutting-edge variants, such as anti-spikes, communication on request, synaptic weights, and dendritic–axonal delays, facilitates the creation of neural memory cells and spike-based routers. Hence, these elements potentially allow the design of novel processing memory architectures that markedly enhance data transfer efficiency between computational units and memory.

The use of virtual reality (VR) for therapeutic purposes has expanded rapidly; however, few studies have specifically addressed amaxophobia, or driving-related fear, using immersive exposure techniques. This pilot study evaluated the feasibility and preliminary effectiveness of an immersive VR-based exposure intervention for pre-drivers with elevated driving fear. Five participants completed a five-session exposure program within a technically stable VR environment designed to support perceptual continuity and reduce cybersickness risk. Driving fear was assessed before and after the intervention using the Cuestionario para la Evaluación del Miedo a Conducir en Preconductores (CEMICP). Results showed a statistically significant reduction in fear, supported by both a one-tailed paired-samples t-test and the exact Wilcoxon signed-rank test, each revealing a large effect size. Reliable and clinically significant change analyses indicated that all participants experienced meaningful improvement, with 60% achieving clinically significant reduction in driving-related fear. These findings suggest that immersive VR exposure may offer a promising and acceptable approach to mitigate amaxophobia in young adults. Given the small single-group sample, further research is required to establish long-term effects, explore behavioral performance outcomes, and determine the mediating role of presence and emotional engagement. Future controlled trials with larger and more diverse samples are warranted to validate the clinical utility of this VR intervention.

During the last five years, spiking neural P (SN P) systems have attracted a lot of attention in the field of cryptography since these systems can more efficiently support advanced and complex cryptographic algorithms due to their high computational capabilities. Specifically, these systems can be seen as a potential solution to efficiently performing asymmetric algorithms, which are more demanding than symmetric systems. This factor becomes critical, especially in resource-constrained single-board computer systems, since many of these systems are currently used to ensure the security of IoT applications in portable systems. In this work, we present for the first time the implementation of an asymmetric encryption algorithm called ElGamal based on spiking neural P systems and their cutting-edge variants. The proposed design involves the encryption and decryption processes. Specifically, we propose the design of a neural network to efficiently perform the extended Euclidean algorithm used in the decryption task. Here, we exert major efforts to create a compact and high-performance circuit to perform the extended Euclidean algorithm since the calculation of this algorithm is the most demanding when the decryption process is required. Finally, we perform several tests to show the computational capabilities of our proposal in comparison to conventional implementations on single-board computer systems. Our results show that the proposed encryption/decryption scheme potentially allows its use to ensure confidentiality, data integrity, and secure authentication, among other applications for resource-constrained embedded systems.

Recently, various active noise control (ANC) systems have been developed to attenuate unwanted signals within airplane cabins. Most of these works use the filtered-x least mean squares (FxLMS) algorithm due to its low computational cost, however its convergence speed is slow. To overcome this, several variations of the LMS algorithm have been developed to offer better convergence properties, such as affine projection algorithms and recent cutting-edge methods based on the q-gradient concept. Here, we present for the first time, a new affine projection algorithm based on the q-gradient, and the design of an ANC system applied to headrests of airplane seats. To evaluate the effectiveness of the proposal, we perform tests inside a Boeing 727-200 airplane cabin. The results demonstrate that the proposed algorithm achieves faster convergence speed than the conventional version, and its practical implementation shows good performance in attenuating noise signals within an airplane.