Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,059
datasets available to search
ShareScore release 0.9.0
Dataset results
6,059 results for “Journale”
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 9. A comparison between the results
<p>In figure 9 we present a comparison between the manual segmentation, Caselle, Chan&vese, Lankton and our method. </p> <p>A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue), Lankton (red curve), our method (purple curve), and the ground truth (Green Curve) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 8. The results of the segmentation of the hippocampus.
<p>The results show the hippocampus segmentation using both Caselle, Chan&Vese, Lankton and our method. </p> <p>The results of the segmentation of the hippocampus. The six lines present: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method, the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 shows a MCI (primary stage) and the third column corresponds to an Alzheimer's subject (advanced stage)</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 7. Training for classification
<p>AMED measures the average distance while HD measures the maximum distance between the two vectors. The aim of our method is to classify the test subject in three classes (N, MCI or AD), so for each vector element E we look for the four nearest neighbors. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 5. Improvement using variation
<p>In the figures below a constraint of the variation is used in order to show the limits. The contour may include more areas surrounding the hippocampus, which are not homogeneous with the desired area. Through the confidence interval of variation and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 6. Improvement using Surface
<p>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. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD
<p>The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 1. Three hippocampus: Normal, MCI, AD
<p>In this context is our work: performing a diagnostic computer-aided system for detecting Alzheimer's disease. Like any diagnostic system, our system contains three parts: preprocessing, segmentation and classification. Initially, we will present a new segmentation method to segment the Hippocampus and Corpus Callosum regardless of the patient's condition. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p> A. Segmentation It seeks to establish a model that describes the shape and typical fluctuations. This requires first the preparation of a learning base to reflect the possible variations in shape of the structure. The preparation of the training set Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 1. The whole-building switch concept for the power lines of standby devices
<p>68 million houses in North America and Europe will be smart by 2019 (Kurkinen, 2016) with a compound annual growth rate of 37 % and 61 %, respectively. The smart equipment is usually installed together with an upgrade (e.g. aluminum wires are replaced by copper ones) of the power grid. In this case, additional power lines for standby devices are cabled, and the WBS concept is applied using one power switch only (see figure 1). For instance, the Songle high-power relay T90 can control the whole building electricity with load up to 30 A using NodeMcu Lua ESP8266 WiFi and/or Arduino Uno / Mega boards.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 7. Execution plot, for instance Eil51 (left figure) and KroB100 (right figure)
<p>The evolution of the best solution found by the proposed algorithm is plotted in Figure 7 during a typical execution when solving instance Eil51 and KroB100. In this figure, the horizontal and vertical axes show the number of iterations and gained values of the proposed algorithm respectively. Besides, there is a fast convergence toward the BKS at the beginning of the execution while in the rest of the search the evolution of the BKS is not that fast. </p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C
<p>The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board
<p> for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 5. The process of MICALK for solving the TSP
<p>Moreover, in order to prevent the ICA from getting trapped in stagnation, we used a local searching algorithm when the algorithm attained a better solution compared to previous iterations. In fact, the probability of finding better solutions near a good solution is relatively high. There exist many algorithms for the local search and they have of course their pros and cons. Since LinKernighan algorithm is simple and it is one of the most successful methods for generating optimal or near optimal solutions for the TSP, we have used it in this study. The main steps of MICALK are summarized in the pseudo-code given in Figure 5. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 6. Some best routes found by the proposed algorithm
<p>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. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 3. Flowchart of the ICA
<p>At last, the most powerful empire will take the possession of other empires and will win the competition. In other words, imperialistic competition hopefully converges to a state in which there exists only one empire and its colonies are in the same position and have the same cost as the imperialist. Figure 3 shows the flowchart of the basic ICA.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis
<p>The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 1. The Initial Empires
<p>The ICA is a novel global search strategy which uses imperialism and imperialistic competition process as a source of inspiration. This algorithm is based on the fact that in a real world, countries try to extend their power over other countries in order to use their resources and bolster their own government. The first step in ICA is to generate an initial population like other evolutionary algorithms. The population set includes a number of feasible solutions called a ‘country’, which corresponds to the term ‘chromosome’ in the GA method. These countries are of two types: colonies and imperialists that altogether form some empires. As it is shown in Figure 1 (Atashpaz Gargari & Lucas, 2007), bigger and stronger empires have more colonies than smaller and weaker ones.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 2. Eliminate the weakest colony of the weakest empire
<p>After initial empires are formed, their colonies start moving toward their relevant imperialist country. This movement is a simple model of assimilation policy which was pursued by some of the imperialist states. If one of the colonies possesses more power than its relevant imperialist after this movement, they will exchange their positions. To begin the competition between empires, the total objective function of each empire should be calculated. It depends on the objective function of both an imperialist and its colonies. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take the possession of their colonies - Figure 2 (Atashpaz Gargari & Lucas, 2007). The empire, which has lost all its colonies, will collapse. </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 10. Graphic containing the results for 1000 bits
<p>The distribution obtained depending on the length of the input string shows that time depends on the input length, but for lengths even closer together, the times are also close (this can be seen in Figure 8 for 20 bits inputs). Time does not change so much depending on which of the 14 different 16th degree primitive polynomials has been used. </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 9. Graphic containing the results for 1000 bits
<p>The next two graphics show the obtained results from the execution of the main program for each of the 14 degrees, 16th primitive polynomials for three different situations depending on the lengths of the entrance data polynomial. The lengths of the input polynomials were 20. 30. 40, 50, 100 and 1000 bits. The maximum number of sequences is 216-1(Solomon, 1967). </p>
ScienceDex guides
Understand access before you commit
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.