Find research datasets worth reusing

opencc-by-4.0Jan 2012View details →

Figure 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"

The next step consists of adding a Global Goal expressing a motivation required by the controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding to a constant output request (see figure 6). In the parameter field, its specified value is “1”.

opencc-by-4.0Aug 2015View details →

Figure 6. Outlinks degree distribution for all web sites-Study of a Random Navigation on the Web Using Software Simulation

Some of the most important aspects of the analysis is obtaining the parameters which can give the main information about a web. In the simulation implementation information as: page, number of inlinks, number of outlinks, value for Algorithmic Page Rank and Experimental Page Rank will be processed for obtaining the results of the analysis. For the first part it was necessary to use experimental values as: inlinks, outlinks and in and out frequencies.

opencc-by-4.0Aug 2015View details →

Figure 6. AGLO Presentation Sections-Generative Learning Objects Instantiated with Random Numbers Based Expressions

The seventh and eighth symbols named node1 and node2, both of type character, are initialized with the first two values from the keys array. These symbols will be used later in the feedback section. Their initialization is based on accessing the keys using the array indexing operator. Next, we discuss about the presentation sections of the AGLO depicted in figure 6.

opencc-by-4.0Aug 2015View details →

Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong emotional response to the distorted face to the right in figure 6.

opencc-by-4.0Nov 2015View details →

Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Particle swarm optimization algorithm first optimizes the neural networks weight and bias and provides the minimum mean square error with nearer by zero. The following figure 6 has shown that minimum mean square error when training the particular band features.

opencc-by-4.0Nov 2015View details →

Figure 6. Algorithm A6pseudocode-Efficient Filtering of Noisy Fingerprint Images

A6&the principle of quartiles applied to the entire image, using the quartiles q1 and q3 as thresholds, but the update refers only for the pixel values only if Q1 < q1 or Q3 > q3, where q1 and q3 are the quartiles calculated in the 10x10 pixels area (Figure 6.);

Figure 6. Three interfaces available on the mobile device

The device provides two important facilities: multimedia facilities (it allows recording, processing and playing audio samples) as well as graphic facilities (it provides a friendly and accessible interface). In Figure 6 is illustrated the main page of the application that is implemented on the mobile device (a), as well as two types of exercises; (b) the child is required to identify whether a sound is present in a word (which is indicated by an image); and (c), the child is required to choose a word from a group of paronyms.

Figure 6. An inside view of Mary and John Gray Library-Modeling, Designing, and Implementing an Avatar-based Interactive Map

Figure 6 exhibits the ambience of the study environment that allows students to have group discussions, and when to access Internet, and more. The photographs have been digitized in a very realistic way.

Figure 6: "Perception" as determination through a receiving universal-Brain Functors: A mathematical model of intentional perception and action

Before turning to right semiadjunctions, it might be useful to present a rather generic version of determination through a receiving universal as model of "recognition" or "perception" that captures many of the common features of the various examples. The determination through the receiving universal is the active internal process that supplies the "interpretation" or "intentionality" to the raw sense data. The red blotch is seen as a tomato; the sound "ya" is understood as indicating agreement, and so forth. In the passive/direct alternative, the raw sensory input supplies Lockean "perception" like writing on a blank slate or a stamp making an impression on wax.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 6. View cluster TAFC

For all these tables and views, table maintenance generators have been created and activated, in order to have the possibility to manage individual datasets in every table and view. The corresponding names of those function groups for the table maintenance generators are the same names as those for the views. The purpose of these maintenance views is only to take care of the input data more efficiently. These views will be used later in the view cluster, which ensures a hierarchical order of the data. Therefore, the maintenance views will also include the predecessor, in order to facilitate linking in the field dependency tab of the view cluster (Swapna, 2007). These three maintenance views are embedded in the following view cluster (figure 6)

Figure 6. Activities dealing with microlearning-Micro Learning: A Modernized Education System

There are many unique activities involved in microlearning, which provides a high rate of successful knowledge transformation. To the inquiry made in research on the activities dealing with microlearning that support knowledge transformation, figure 6 shows that mind mapping has the highest view of respondents, up to 72%, the nearest to it is story telling with 50%. The variation displayed in the figure is significant. 44% of the respondents opt for tagging followed by text with 38%.

BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 6. The general conception of our asynchronous system BCI (offline - online) for reinforcement of a joystick movement

Once the motor imagery is identified, a command may be associated to this mental task in order to control a machine (Prataksita et al., (2014)) (Guger et al., 1999). In this work, we constructed a new Simuhnk/MathWork model to translate on-line the EEG signals into low-level commands. Fig. 6 shows our experimental EEG-based BCI System

opencc-by-4.0Aug 2016View details →

BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".

From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 6. Improvement using Surface

In the figures below, a constraint of the surface is used to show the limits. The contour may include the hippocampus and more areas surrounding it, which are homogeneous with the desired area. Through the surface of the confidence interval and priori knowledge can overcome these limitations.

BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 6. Some best routes found by the proposed algorithm

Figure 6 shows some of the best solutions searched by the proposed method. In this figure, the horizontal axis represents the x-axis with increasing positive values to the right and the vertical axis represents the y-axis with increasing positive values upward.

RAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 6. Fibonacci Implementation for the Polynomial X^16+X^12+X^3+X+1

VlăduŃiu and Crişan (1989) show three types of schemes for a 4th degree polynomial. Similar to it, there were developed the three different implementations for the Primitive Polynomial X^16+X^12+X^3+X+1. It can be specified that these schemes are according to the well-known Galois Form, Fibonacci representation and some other Forms that are rarely used, called Ring Implementation. All of these Implementations have the same function, because they describe a linear feedback shift register. In the experimental work there has been analyzed the behavior of 14 Primitive Polynomial degrees, 16 randomly selected. For each of these polynomials there has been developed the simulation of the specific functioning with a program. Because the goal of this experimental work was to compare the different obtained results, a few rows of input data of a different length have been selected

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 6. Virtual Space on Panoramio+ with the experiments carried in Vădastra village

The fourth stage was the use of social media to build a social learning network based on content and experience sharing and creation. The following web 2.0 services were used as a distributed platform able to support our experimental learning system: a) Panoramio (http://www.panoramio.com/user/7606828) (Figure 6) as a geo-referenced photo-sharing service over Google Maps and Google Earth for sharing project’s essential results; b) Twitter (https://twitter.com/maps_of_time) (Figure 7), as a social network and micro-blogging service for short announcements and comments; c) Google+ (https://plus.google.com/114705936110835992130?hl=en#114705936110835992130/posts?hl=en) (Figure 8), as a platform for sharing and tagging multiple content (photo, video), blogging and video chatting service with the recent Google Hangout, for sharing educational content; d) Google Drive (Levin, 2013) for cloud storage and collaborative document editing. We also created a YouTube channel for public distribution of video content (https://www.youtube.com/TimemapsNet), (Rusu et al., 2013) and a Facebook page of Vădastra School (https://www.facebook.com/scoalaVădastra)

opencc-by-4.0Apr 2017View details →

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 6. Reducing size of Sketch

As previously described, there are a total of 1600 integers, which can be the input to the dataset for neural networks. But it is a huge number, so in order to minimize the size of inputs to the neural network the 40x40 matrix is reduced to 20x20 by skipping odd rows and columns of the original matrix. Figure 6 shows a matrix containing green and red rows and columns. If this was the 40x40 matrix, then the red part of this matrix would be skipped to convert it into a 20x20 sized matrix. Now there are only 20x20=400 values, which is a reasonable input size for the neural network.  The other part of the developed approach is to collect the data about the same two objects of the real pictures taken by the camera. These images are converted into black and white pictures and then treated the same way as the sketches, i.e. black and white pictures are also converted into integers based on the color of each pixel.

opencc-by-4.0Jul 2017View details →

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 6. Results from ANNSVN that used WL and HT

To identify which features of data influentially impacted data separability, we conducted experiments for ANNSVM with WL and HT (i.e., Figure 6). The WL contained only wavelet coefficients, whereas HT included only results of the Hough transformation. We found that, again, results obtained via the linear kernel were not significant; however, using the RBF kernel, accuracy for WL was higher than that of HT, indicating that wavelet coefficients provide influential features that make data separable.