A Real-Time FPGA-Based Metaheuristic Processor to Efficiently Simulate a New Variant of the PSO Algorithm.

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.

A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion.

Nowadays, human action recognition has become an essential task in health care and other fields. During the last decade, several authors have developed algorithms for human activity detection and recognition by exploiting at the maximum the high-performance computing devices to improve the quality and efficiency of their results. However, in real-time and practical human action recognition applications, the simulation of these algorithms exceed the capacity of current computer systems by considering several factors, such as camera movement, complex scene and occlusion. One potential solution to decrease the computational complexity in the human action detection and recognition can be found in the nature of the human visual perception. Specifically, this process is called selective visual attention. Inspired by this neural phenomena, we propose for the first time a spiking neural P system for efficient feature extraction from human motion. Specifically, we propose this neural structure to carry out a pre-processing stage since many studies have revealed that an analysis of visual information of the human brain proceeds in a sequence of operations, in which each one is applied to a specific location or locations. In this way, this specialized processing have allowed to focus the recognition of the objects in a simpler manner. To create a compact and high speed spiking neural P system, we use their cutting-edge variants, such as rules on the synapses, communication on request and astrocyte-like control. Our results have demonstrated that the use of the proposed neural P system increases significantly the performance of low-computational complexity neural classifiers up to more 97% in the human action recognition.

Small universal spiking neural P systems with dendritic/axonal delays and dendritic trunk/feedback.

In spiking neural P (SN P) systems, neurons are interconnected by means of synapses, and they use spikes to communicate with each other. However, in biology, the complex structure of dendritic tree is also an important part in the communication scheme between neurons since these structures are linked to advanced neural process such as learning and memory formation. In this work, we present a new variant of the SN P systems inspired by diverse dendrite and axon phenomena such as dendritic feedback, dendritic trunk, dendritic delays and axonal delays, respectively. This new variant is referred to as a spiking neural P system with dendritic and axonal computation (DACSN P system). Specifically, we include experimentally proven biological features in the current SN P systems to reduce the computational complexity of the soma by providing it with stable firing patterns through dendritic delays, dendritic feedback and axonal delays. As a consequence, the proposed DACSN P systems use the minimum number of synapses and neurons with simple and homogeneous standard spiking rules. Here, we study the computational capabilities of a DACSN P system. In particular, we prove that DACSN P systems with dendritic and axonal behavior are universal as both number-accepting/generating devices. In addition, we constructed a small universal SN P system using 39 neurons with standard spiking rules to compute any Turing computable function.