scholarly journals Development of an Acoustic System for UAV Detection

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4870 ◽  
Author(s):  
Cătălin Dumitrescu ◽  
Marius Minea ◽  
Ilona Mădălina Costea ◽  
Ionut Cosmin Chiva ◽  
Augustin Semenescu
Keyword(s):  
Neural Networks ◽  
Adaptive Filters ◽  
Microphone Array ◽  
Acoustic System ◽  
Detection And Tracking ◽  
Acoustic Signal Analysis ◽  
Remotely Piloted Aircraft ◽  
Mems Microphones ◽  
Cohen Class ◽  
Functional Components

The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.

scholarly journals Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark

2021 ◽  
Author(s):  
Brian K. S. Isaac-Medina ◽  
Matt Poyser ◽  
Daniel Organisciak ◽  
Chris G. Willcocks ◽  
Toby P. Breckon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Deep Neural Networks ◽  
Visual Detection ◽  
Detection And Tracking ◽  
Aerial Vehicle ◽  
Performance Benchmark ◽  
A Performance

Acoustic system for aircraft detection and tracking based on passive microphones arrays

2004 ◽  
Vol 116 (4) ◽  
pp. 2633-2633 ◽  
Author(s):  
Gaetano Caronna ◽  
Ivan Roselli ◽  
Pierluigi Testa ◽  
Andrea Barbagelata
Keyword(s):  
Acoustic System ◽  
Detection And Tracking ◽  
Aircraft Detection

A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

2020 ◽  
Vol 16 (4) ◽  
pp. 278-287 ◽  
Author(s):  
Manish Kumar ◽  
Sudhansu Kumar Mishra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Filters ◽  
Medical Images ◽  
Human Beings ◽  
Comprehensive Review ◽  
Biomedical Image ◽  
Artificial Neural ◽  
Nature Inspired Algorithms

Background: Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of storing. The removal of noises from the digital medical images without losing any inherent features is always considered a challenging task because a successful diagnosis relies on them. Numerous techniques have been proposed to fulfill this objective, and each having their own benefits and limitations. Discussion: In this comprehensive review article, more than 65 research articles are investigated to illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image denoising. In particular, the zest of this article is to highlight the hybridized filtering model using nature-inspired algorithms and artificial neural networks for suppression of noise. Various other techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural network filter are also included. Conclusion: This article envisages how to train ANN using derivative free nature-inspired algorithms, and its performance in various medical images modalities and noise conditions.

The neural architecture of sleep regulation – insights from Drosophila

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Raquel Suárez-Grimalt ◽  
Davide Raccuglia
Keyword(s):  
Neural Networks ◽  
Slow Wave ◽  
Sensory Processing ◽  
Model Organism ◽  
Wave Activity ◽  
Slow Wave Activity ◽  
Sleep Regulation ◽  
Neural Architecture ◽  
Waking And Sleep ◽  
Functional Components

Abstract The neural mechanisms that balance waking and sleep to ensure adequate sleep quality in mammals are highly complex, often eluding functional insight. In the last two decades, researchers made impressive progress in studying the less complex brain of the invertebrate model organism Drosophila melanogaster, which has led to a deeper understanding of the neural principles of sleep regulation. Here, we will review these findings to illustrate that neural networks require sleep to undergo synaptic reorganization that allows for the incorporation of experiences made during the waking hours. Sleep need, therefore, can arise as a consequence of sensory processing, often signalized by neural networks as they synchronize their electrical patterns to generate slow-wave activity. The slow-wave activity provides the neurophysiological basis to establish a sensory gate that suppresses sensory processing to provide a resting phase which promotes synaptic rescaling and clearance of metabolites from the brain. Moreover, we demonstrate how neural networks for homeostatic and circadian sleep regulation interact to consolidate sleep into a specific daily period. We particularly highlight that the basic functions and physiological principles of sleep are highly conserved throughout the phylogenetic spectrum, allowing us to identify the functional components and neural interactions that construct the neural architecture of sleep regulation.

Sign in / Sign up

Export Citation Format

Share Document