Intelligent Radar Detectors

2011 ◽  
pp. 933-939
Author(s):  
Raúl Vicen Bueno ◽  
Manuel Rosa Zurera ◽  
María Pilar Jarabo Amores ◽  
Roberto Gil Pita ◽  
David de la Mata Moya
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Main Part ◽  
Point Of View ◽  
Communications Systems ◽  
Reflected Signal ◽  
Artificial Neural ◽  
The Brain

The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.

Intelligent Traffic Sign Classifiers

2011 ◽  
pp. 956-962
Author(s):  
Raúl Vicen Bueno ◽  
Elena Torijano Gordo ◽  
Antonio García González ◽  
Manuel Rosa Zurera ◽  
Roberto Gil Pita
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Main Part ◽  
Point Of View ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Neural ◽  
The Brain

The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.

Artificial Neural Networks for Prediction

2011 ◽  
pp. 54-58
Author(s):  
Rafael Marti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Human Capabilities ◽  
Design And Implementation ◽  
Starting Point ◽  
Input Signals ◽  
Artificial Neural ◽  
The Brain

The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

2021 ◽  
pp. 1-31
Author(s):  
Pankaj Dadheech ◽  
Ankit Kumar ◽  
Vijander Singh ◽  
Linesh Raja ◽  
Ramesh C. Poonia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neural Structure ◽  
Modern Agriculture ◽  
Different Types ◽  
Pest Detection ◽  
Real Time Applications ◽  
Artificial Neural ◽  
And Control ◽  
The Brain

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

Modularity in Artificial Neural Networks

2011 ◽  
pp. 1095-1101
Author(s):  
Ricardo Téllez ◽  
Cecilio Angulo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Systems ◽  
Intelligent Systems ◽  
Point Of View ◽  
Main Concern ◽  
Complex Structures ◽  
Computational Point ◽  
Points Of View

The concept of modularity is a main concern for the generation of artificially intelligent systems. Modularity is an ubiquitous organization principle found everywhere in natural and artificial complex systems (Callebaut, 2005). Evidences from biological and philosophical points of view (Caelli and Wen, 1999) (Fodor, 1983), indicate that modularity is a requisite for complex intelligent behaviour. Besides, from an engineering point of view, modularity seems to be the only way for the construction of complex structures. Hence, whether complex neural programs for complex agents are desired, modularity is required. This article introduces the concepts of modularity and module from a computational point of view, and how they apply to the generation of neural programs based on modules. Two levels, strategic and tactical, at which modularity can be implemented, are identified. How they work and how they can be combined for the generation of a completely modular controller for a neural network based agent is presented.

scholarly journals Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Francisco Cedron ◽  
Sara Alvarez-Gonzalez ◽  
Alejandro Pazos ◽  
Ana B. Porto-Pazos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Glial Cell ◽  
Process Information ◽  
Connectionist System ◽  
Artificial Neural ◽  
The Brain

The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.

scholarly journals Artificial Neural Networks Application in Modal Analysis of Tires

2013 ◽  
Vol 13 (5) ◽  
pp. 273-278 ◽  
Author(s):  
P. Koštial ◽  
Z. Jančíková ◽  
D. Bakošová ◽  
J. Valíček ◽  
M. Harničárová ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modal Analysis ◽  
Speckle Pattern ◽  
Point Of View ◽  
Electronic Speckle Pattern Interferometry ◽  
Data Sets ◽  
Artificial Neural

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

Using chaotic artificial neural networks to model memory in the brain

2017 ◽  
Vol 44 ◽  
pp. 449-459 ◽  
Author(s):  
Zainab Aram ◽  
Sajad Jafari ◽  
Jun Ma ◽  
Julien C. Sprott ◽  
Sareh Zendehrouh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
The Brain

scholarly journals Artificial Intelligence to Analyze the Cortical Thickness Through Age

2021 ◽  
Vol 4 ◽  
Author(s):  
Sergio Ledesma ◽  
Mario-Alberto Ibarra-Manzano ◽  
Dora-Luz Almanza-Ojeda ◽  
Pascal Fallavollita ◽  
Jason Steffener
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Left Hemisphere ◽  
Cortical Thickness ◽  
Right Hemisphere ◽  
Unseen Data ◽  
Artificial Neural ◽  
The Right ◽  
The Brain

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

scholarly journals Trends of Dust Transport Episodes in Cyprus Using a Classification of Synoptic Types Established with Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
S. Michaelides ◽  
F. Tymvios ◽  
S. Athanasatos ◽  
M. Papadakis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Point Of View ◽  
Synoptic Scale ◽  
Dust Transport ◽  
Synoptic Patterns ◽  
Artificial Neural ◽  
Dust Events ◽  
The Relationship

The relationship between dust episodes over Cyprus and specific synoptic patterns has long been considered but also further supported in recent studies by the authors. Having defined a dust episode as a day when the average PM10 measurement exceeds the threshold of 50 mg/(m3 day), the authors have utilized Artificial Neural Networks and synoptic charts, together with satellite and ground measurements, in order to establish a scheme which links specific synoptic patterns with the appearance of dust transport over Cyprus. In an effort to understand better these complicated synoptic-scale phenomena and their associations with dust transport episodes, the authors attempt in the present paper a followup of the previous tasks with the objective to further investigate dust episodes from the point of view of their time trends. The results have shown a tendency for the synoptic situations favoring dust events to increase in the last decades, whereas, the synoptic situations not favoring such events tend to decrease with time.

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