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6,059 results for “Journale”
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 5. Results from CNN and ANNSVM that used 1Dimg and 2Dimg
<p>We compared the results of CNN_1Dimg, CNN_2Dimg, ANNSVM_1Dimg, and ANNSVM_2Dimg to confirm the validity of ANNSVM when applied to images. The 1Dimg represented the dataset of one-dimensional images, while 2Dimg represented the dataset of twodimensional images. Results are shown in Figure 5. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 1. Example displaying two scatter plots with different characteristics and patterns
<p>In addition to this introductory section, the remainder of this paper is organized as follows. In Section 2, we present previous work related to our present study. In Section 3, we describe details regarding the methodology used in this study. In Section 4, we describe our experiments and results, then discuss our findings. Finally, we summarize the key content of our study in Section 5. </p>
BRAIN Journal-Pros and Cons Gamification and Gaming in Classroom-Figure 3. Number of platform visits/week during first and second semester
<p>Figure 3 shows the number of course/activities accesses, per week, for the first and the second semester. Combining this with a lower rate of activities completion (not shown here) for the second semester, it is empirically proved that the introduction of the ranking block did not have the assumed motivational effect for students. A good explanation could be the fact that the top 4 students were at the end of semester far ahead from the others, which, most probably weakened the group motivation. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants
<p>The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli
<p>In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0<sup>o </sup>, 90<sup>o</sup> , 180<sup>o</sup> , 270<sup>o</sup> ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, C<sub>z</sub>, P1, P2, P<sub>z</sub>, PO3, PO4, PO7, PO8, PO<sub>z</sub>, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2<sup>nd</sup> group, the 180-degree stimuli the 3<sup>rd</sup> group and the 270-degree stimuli the 4<sup>th</sup> group. Thus, it is made possible to test the developed features in more datasets. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 2: Overlapped EEG segments
<p>In this study, the short time Fourier transformation (STFT) is used to examine the stability of the SSVEP. As the frequency resolution decreases with window size, overlapped EEG segments (Figure 2) that give sufficient frequency resolution are used. Coefficient of variation and variation speed detection features are proposed by using frequency spectrum of the segments. In Equation 1, x(t) sequence defines overlapped EEG segments in time domain, and in Equation 2 the x(f) sequence defines these segments in frequency domain and m is number of segments. </p> <p> </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 1: Time vs frequency analysis of 10 Hz SSVEP response
<p>The stability of the SSVEP signal was examined by using wavelet analysis (Wu and Yao 2008). Since there is a trade-off between time and frequency resolution in wavelet analysis, examining the stability of SSVEP with wavelet analysis is getting harder in systems where the visual stimulus frequencies are close to each other, as shown in Figure 1. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 2. Comparison of mean scores on the EPQ–R scales
<p>The data for the workshop participants were loaded from the data warehouse, while the summary data from the original EPQ–R study were loaded from a CSV file. The bar chart featured in Figure 2 shows mean scores on the EPQ–R scales for the selected workshop participants (denoted by blue bars) and the selected participants of the original EPQ–R study (denoted by yellow bars). The mean scores on the P scale agree between the two samples, but the overall scores for the other scales vary. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
BRAIN Journal-Pros and Cons Gamification and Gaming in Classroom-Figure 2. Course categories within UVAB University Moodle platform, (portal-eifr.ub.ro)
<p>A study was conducted aiming to assess the impact of introduction of ranking block plugin as a gamification element within Moodle learning management system (Ranking block Moodle, 2017). We mention that the Moodle platform, version 3.2 is dedicated to extramural and distance learning. It supports various gamification elements such as avatars, badges, leaderboard, levels, displaying quiz results or progress bars. The ranking block plugin was introduced and configured to be available for procedural programming course activities, at the beginning of the first semester of 2016, which starts in October. It displays a course leaderboard visible to all users as a way of obtaining recognition from other users. It is based on points instead of badges and it can monitor included activities based on accumulated points. The experiment involved first year bachelor students in computer science (32 students, extramural education) from UVAB University (www.ub.ro) who are using the Moodle platform in their tutorial based activities. The main page of UVAB Moodle platform is presented in Figure 2. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 5. SSVEP detection accuracies by using PSD, CV and VS features
<p>When the results in Figure 5 are analyzed, it is seen that CV and VS features provide detection results similar to PSD, which is a familiar feature. Considering that the chance level is 12.5% in these dataset, CV and VS can be used as discriminative features for SSVEP. Also on some subjects, like S3 on 1st and 4th datasets, and S11 on 1st, 2nd and 4th datasets, the proposed features have given clearly better detection results than PSD.</p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 3: (fi) sequences at different frequencies
<p>In Equation 4, f<sub>nbk</sub> defines neighboring frequency and L defines number of neighboring. ► sequences are shown graphically in Figure 3. </p> <p>The first developed parameter for the stability of SSVEP is the coefficient of variation (CV). The variation in <span class="math-tex">\( (fi)\)</span> sequence that is obtained at EEG component with SSVEP response is expected to be less than the other sequences. . Equation 5 shows the calculation of CV. <span class="math-tex">\( ( ) i f \)</span> standard deviation of (f<sub>i</sub>) sequence. ( ) i f is the averaged value of (f<sub>i</sub>) sequence. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 3. Radial visualization of scores across the EPQ–R scales for the male and female participants
<p>The radial visualization in Figure 3 depicts each participating student as a dot whose color indicates the gender of the student, blue for male students (M) and red for female students (F). The position of a dot in the visualization is determined by the scores of the associated student on the four EPQ–R scales. The radial overview may provide a much clearer outline of clustering within the analyzed group. Although there are only five female students, they are concentrated in a relatively narrow area within the radial coordinate system</p>
BRAIN Journal-Pros and Cons Gamification and Gaming in Classroom-Figure 1. Game-design elements and motives (Blohm, I. & Leimeister, J. M., 2013).
<p>Presenting gamification mechanics during classes by implementing them into grade system can be easily obtained by using eLearning environments hybridized with immersive interactive scenarios, like in Lifesaver- a learn by doing model to teach the basic steps in responding to a situation where a person suffers a heart attack or choking (Gamification in eLearning, 2017). The proper use of narrative layers can improve engagement of user and points can be gained using short assignments (missions). The students can choose the assignments as they like to obtain enough points to pass the classes. Obviously, for harder tasks they will get more points, but none of the tasks are obligatory. </p> <p>Other gamification elements include avatars, badges, levels, reputation level, tasks, etc. Details are presented in Figure 1. Making the rewards for accomplishing tasks visible to other players or providing leaderboards are ways of encouraging players to compete. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 25: The fifth obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 24: The fifth obstacle with 100 robots after passing some robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 23: The fifth obstacle with 100 robots before passing any robot
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 22: Fifth Obstacle (obstacle with two entries that each allow the passing of one robot)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 21: The fourth obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.