Publications by authors named "Giovanny Sanchez"
Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments.
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- High-performance audio devices require enhanced audio quality, leading to the development of acoustic echo cancellers using particle swarm optimization (PSO).
- The traditional PSO faces issues with premature convergence, so a new variant using Markovian switching and dynamic population adjustment is proposed to improve performance and reduce computational costs.
- This approach is implemented in a parallel metaheuristic processor on a Stratix IV GX FPGA, allowing effective simulation of varying particle populations, which could significantly advance acoustic echo canceller systems.
Nowadays, image steganography has an important role in hiding information in advanced applications, such as medical image communication, confidential communication and secret data storing, protection of data alteration, access control system for digital content distribution and media database systems. In these applications, one of the most important aspects is to hide information in a cover image whithout suffering any alteration. Currently, all existing approaches used to hide a secret message in a cover image produce some level of distortion in this image.
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- Human action recognition is increasingly important in healthcare and other fields, with various algorithms developed over the last decade to improve detection and recognition efficiency using advanced computing.
- However, real-time applications face challenges such as camera movement and complex scenes, often overwhelming current computer systems.
- To address this, a new approach inspired by human visual perception—specifically selective visual attention—utilizes a spiking neural P system for efficient feature extraction, achieving over 97% performance improvement in low-computational complexity neural classifiers for action recognition.
Article Synopsis
- The text discusses spiking neural P (SNP) systems, which represent how neurons communicate through spikes and highlights the role of dendritic trees in learning and memory.
- It introduces a new variant called DACSN P systems, which incorporates biological features like dendritic feedback and delays to improve computational performance while using fewer resources.
- The study demonstrates that DACSN P systems can universally replicate any Turing computable function, establishing their significance in computational theory with a practical example of a small universal SNP system.
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation.
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