Neural network

Analysis of Attentiveness using Physiological and Environmental Factors Abstract—Wearable computing is picking up speed and devices like smart watches and fitness bands are increasingly equipped with heart rate sensors. Common applications for these devices include fitness and sleep tracking. Heart rate sensor data opens avenues for exploring newer applications. There is a close correlation between attentiveness and the variability in heart rate in adults. In this paper, we utilize this correlation

Are faces special? Critically evaluate the evidence that we have evolved a specialised neural network dedicated to processing faces. Brian Marron, 11461992, SF TSM. INTRODUCTION Processing faces is extremely important to humans as social beings. We are able to put and identity on thousands of faces (Gazzaniga, 2002) with ease, something we might take for granted. The value of this ability can be better understood when the world is viewed through the eyes of somebody with prosopagnosia, the inability

occurrence, but instead can be identified through a series of entries spanning several minutes [Abad03]. The amount of data logged per system can be in excess of several thousand events per minute. Additionally, these files are distributed across the network. In order to process and analyze the log data, it must be integrated. Integrating highly heterogeneous data from multiple sources requires a massive centralized data repository [Kott13]. This data repository meets the complexity requirements as defined

figure(1); plot(mel,Pf_AVG1); title( 'The PSD of the Letter "a" Sound '); xlabel( 'Frequency (Mel) '); ylabel( 'Power '); %----------------------------------------------------------------------- %Read data of letter "e" and "o" sounds also by neural networks f4=wavread( 'vc_e ',22050); F4=fft(f4,11025); Pf4 = F4.* conj(F4)/11025; Pf4 = transpose(Pf4); Pf4 = Pf4(1:2206); f5=wavread( 'vc_e2 ',22050); F5=fft(f5,11025); Pf5 = F5.* conj(F5)/11025; Pf5 = transpose(Pf5); Pf5 = Pf5(1:2206);

randomly among the training patterns and create only that many neurons. A clustering algorithm is a kind of an unsupervised learning algorithm and is used when the class of each training pattern is not known. But an RBFN is a supervised learning network. And we know at least the class of each training pattern. So we’d better take advantage of the information of these class memberships when we cluster the training patterns. Namely we cluster the training patterns class by class instead of the entire

artificial neural network, seed key generator and chaotic map functions, for interest of the reader. Later in section 2, we present literature review of various encryption techniques from two perspectives: applications to highlight scope of research in this domain; and approaches to provide overall view of multimedia encryption. The section three provides a new two-layer encryption technique for JPEG2000 compatible images. The first step provides a single layer of encryption using a neural network to generate

in a plenty of literatures. However, the comprehensive comparison of their applicability in particular short-term (i.e. hourly) water level prediction under heavy rainfall events was rarely discussed. Therefore, in this study, the artificial neural networks (ANN), Intelligent multi agent approach, Markov Chain Monte Carlo (MCMC) were selected for

the main problem. So the author proposed an Artificial Neural Network (ANN). This Artificial Neural Network is capable of dealing and gives accurate result for non- linear systems. This research paper aims to give the proper solution to control the process input parameters to make better spot weld joints with specified strength. In the second stage of the research work, Genetic Algorithm with fitness function based on Artificial Neural Network (ANN) model was used for determining a set of optimized

Right now, there are over thirty countries around the world developing drone technologies. Like all arms races, once a weapon is developed, there is no 'turning-back.' For instance, once the atomic bomb was created, other nations franticly sought to create their own earth-scorching nuclear weapons. Today, the same is true with regards to drones and, more recently, autonomous battlefield robots: the US may have been the first nation to deploy such weapons in combat, but it won't be the last. In addition

Artificial Neural Network (ANN) and Finite Element Method (FEM) was implemented to predict mechanical properties. It was observed that prediction results in this study are consistent with the real measurements for composites. keywords: nano SiC, finite element method, artificial neural network. NOMENCLATURE (Optional Section) Subscripts ANN Artificial Neural Network FEM Finite Element Method LMA Levenberg Marquardt Algorithm Vol% Volume Fraction Ep (w) The error in network of the