Nowadays, the design of ultra-compact area advanced encryption standard (AES) architectures is highly demanded by the electronics industry since many of these architectures are embedded in portable devices, such as smart phones, tablets, etc., in which the area is critically limited. Until now, many approaches have been proposed to create high-processing and compact architectures. However, the area consumption is still a factor to be improved. In this paper, a highly compact encryption/decryption architecture, which is implemented in a low-cost FPGA, to efficiently simulate the AES algorithm, is proposed. Specifically, an optimized Galois Field Multiplier, which is the most demanding operation in terms of area consumption and processing speed, involved in Mix-Columns and Inverse Mix-Columns transformations, is presented. Therefore, the optimization of the proposed GF (28) multiplier by two has allowed to us create an ultra-compact Mix-Columns circuit since this circuit involves large number of multiplications. In addition, the design involves a routing circuit which allowed the proposed architecture to perform encryption or decryption by using common modules. The results demonstrate that the proposed digital circuit expends fewer LUTs and fewer registers when compared with the most compact encryption/decryption architectures reported to date.

Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.

Recently, the use of acoustic echo canceller (AEC) systems in portable devices has significantly increased. Therefore, the need for superior audio quality in resource-constrained devices opens new horizons in the creation of high-convergence speed adaptive algorithms and optimal digital designs. Nowadays, AEC systems mainly use the least mean square (LMS) algorithm, since its implementation in digital hardware architectures demands low area consumption. However, its performance in acoustic echo cancellation is limited. In addition, this algorithm presents local convergence optimization problems. Recently, new approaches, based on stochastic optimization algorithms, have emerged to increase the probability of encountering the global minimum. However, the simulation of these algorithms requires high-performance computational systems. As a consequence, these algorithms have only been conceived as theoretical approaches. Therefore, the creation of a low-complexity algorithm potentially allows the development of compact AEC hardware architectures. In this paper, we propose a new convex combination, based on grey wolf optimization and LMS algorithms, to save area and achieve high convergence speed by exploiting to the maximum the best features of each algorithm. In addition, the proposed convex combination algorithm shows superior tracking capabilities when compared with existing approaches. Furthermore, we present a new neuromorphic hardware architecture to simulate the proposed convex combination. Specifically, we present a customized time-multiplexing control scheme to dynamically vary the number of search agents. To demonstrate the high computational capabilities of this architecture, we performed exhaustive testing. In this way, we proved that it can be used in real-world acoustic echo cancellation scenarios.

Nowadays, high-performance audio communication devices demand superior audio quality. To improve the audio quality, several authors have developed acoustic echo cancellers based on particle swarm optimization algorithms (PSO). However, its performance is reduced significantly since the PSO algorithm suffers from premature convergence. To overcome this issue, we propose a new variant of the PSO algorithm based on the Markovian switching technique. Furthermore, the proposed algorithm has a mechanism to dynamically adjust the population size over the filtering process. In this way, the proposed algorithm exhibits great performance by reducing its computational cost significantly. To adequately implement the proposed algorithm in a Stratix IV GX EP4SGX530 FPGA, we present for the first time, the development of a parallel metaheuristic processor, in which each processing core simulates the different number of particles by using the time-multiplexing technique. In this way, the variation of the size of the population can be effective. Therefore, the properties of the proposed algorithm along with the proposed parallel hardware architecture potentially allow the development of high-performance acoustic echo canceller (AEC) systems.

In recent years, active noise control (ANC) systems have been widely used in advanced electronic appliances. Nowadays, several authors use gradient-optimization algorithms since they can be easily implemented in these devices. However, these algorithms need to estimate the secondary path in advance. As consequence, this factor can limit its use in real-ANC applications since the secondary path can undergo significant variations over time. To solve this problem, we propose an ANC system with switching filter selection based on particle swarm optimization (PSO) algorithms. Specifically, we use two sets of populations of particles with different acceleration coefficients and inertia weights to create an advanced structure in which the first PSO algorithm guarantees a high convergence speed while the use of the second PSO algorithm allows to achieve a high-level noise reduction. The results demonstrate that the proposed algorithm exhibits better convergence properties compared with previously reported solutions.