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54 results for “algorithmic system”
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
Dataset for Algorithms and Complexity for Counting Configurations in Steiner Triple Systems
<p>This dataset contains the classification of full n-line configurations (for all n <= 13, filename: "full_line_config_<n>.txt.gz") and w_3 configurations (for all w <= 16, filename: "w_3_config_<w>.txt.gz") together with the sizes of minimum generating sets. Each file lists "m<s>" so that s is the size of the minimum generating set of the subsequent configuration, which is denoted by writing the points of each of its lines row-wise. For example</p> <p>m3<br> 0 1 4<br> 0 2 6<br> 0 3 5<br> 1 2 5<br> 1 3 6<br> 2 3 4<br> 4 5 6</p> <p>is the Fano plane and its minimum generating set has size 3.</p> <p>Additionally, the file "fulllineconjecture.txt.gz" contains the 623 Steiner triple systems of order 25 (i.e., all rows which contain curly brackets) used in Theorem 7 in the paper below, followed by a row starting with 1 and then describing the number of occurrences of all 179 full n-line configurations for n <= 8, i.e., first the number of occurrences of Pasch configurations, then mitre configurations, then the 5 full 6-line configurations, the 19 full 7-line configurations, and finally the 153 full 8-line configurations contained in the STS(25) in the preceding row. The ordering follows the ordering within the files "full_line_config_<n>.txt.gz". This file is built so that omitting all lines with curly brackets is a valid gap code and results in a prove of said theorem (i.e., zgrep -v "{" fulllineconjecture.txt.gz | gap yields 180).</p> <p><br> Further details can be found in the corresponding publication</p> <p>"Algorithms and Complexity for Counting Configurations in Steiner Triple Systems"</p> <p>by Daniel Heinlein and Patric R. J. Östergård.</p> <p>All files are compressed with gzip.</p>
Dataset for "Estimating truncation effects of quantum bosonic systems using sampling algorithms"
<p>Markov Chain Monte Carlo simulation data for the preprint.</p> <p>T010ad***S10000M*_1.txt: simulation history for a_{dig} = 0.3, 0.5, 0.7, m^2 = 1, -1, B_max = 5000, used for Table 1 and Figure 1.</p> <p>T010R100L401S10000M1_1.txt: simulation history for a_{dig} = 0.5, m^2 = 1, B_max = 1, used for Figure 2.</p> <p>Table2.zip: contains simulation history for Table 2 and Figure 3. File name "T1a0.2S1250M1L4s2101.txt" indicates that the temperature is 1, a_{dig} = 0.2, Delta = 1250, m^2 = 1, lattice size is 4 * 4, and the random seed is 2101. Lines contain the expectation values of the potential energy and the two correlation functions obtained for successive steps. The largest estimated auto-correlation length d_q, which is used for the analysis, is as follows:</p> <table align="center"> <tbody> <tr> <td><em>a</em><sub>dig</sub></td> <td><em>d</em><sub>(0,0)</sub></td> <td><em>d</em><sub>(π,π)</sub></td> </tr> <tr> <td>0.2</td> <td>37</td> <td>4</td> </tr> <tr> <td>0.25</td> <td>38</td> <td>4</td> </tr> <tr> <td>0.3</td> <td>37</td> <td>4</td> </tr> <tr> <td>0.4</td> <td>41</td> <td>4</td> </tr> <tr> <td>0.5</td> <td>59</td> <td>5</td> </tr> <tr> <td>0.6</td> <td>130</td> <td>7</td> </tr> <tr> <td>0.7</td> <td>369</td> <td>15</td> </tr> <tr> <td>0.8</td> <td>968</td> <td>55</td> </tr> <tr> <td>0.9</td> <td>2174</td> <td>148</td> </tr> <tr> <td>1.0</td> <td>4491</td> <td>319</td> </tr> </tbody> </table> <p>The initial 10 d_q steps are discarded as a burn-in period, regardless of whether we conducted a warm-up run prior to the steps contained in this dataset.</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
BRAIN Journal - Lamport's algorithm - Figure 2 from paper "Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time"
<p>Figure 2. Lamport’s algorithm</p> <p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took place. This relation can be directly observed in two situations (figure 2): 1. If two events happen during the same process, the order of the happening is indicated by the common clock; 2. When two processes communicate through a message, the event that corresponds to sending the precise message always happens before the event of receiving it (i.e. the message). If two events, 1 a and 2 a , are produced in different processes that do not exchange messages (neither directly nor indirectly), then it is not certain if 1 2 a → a or 2 1 a → a . In this case it is said that these events are competitive, which means that it is not known which one happened first (and it is not a must-know thing either).</p>
Figure 2. Lamport's algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened<br> before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it<br> means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took<br> place. This relation can be directly observed in two situations (figure 2):<br> 1. If two events happen during the same process, the order of the happening is indicated by<br> the common clock;<br> 2. When two processes communicate through a message, the event that corresponds to<br> sending the precise message always happens before the event of receiving it (i.e. the<br> message).</p>
Figure 1. Cristian's Algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>Cristian’s Algorithm (figure 1) is a method for clock synchronization which can be used in<br> many fields of distributive computer science. It suffers, though, in implementations using a single<br> server, making it unsuitable for many distributive applications where redundancy may be crucial.</p>
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
(c) simulation on Repast: after queen adaptive development-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On figures (b) and (c), simulations on RePast [11, 16, 18] are<br> provided at successive times. The last figure shows the adaptive mechanism<br> of the queen which grows with time according to the material density around<br> it, like in natural observations.</p>
Figure 7: Cultural equipment dynamics modeling-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The multi-template modelling can be used to model cultural equipment<br> dynamics as described in figure 7. On this figure, we associate a queen to each<br> cultural center (cinema, theatre, ...). Each queen will emit many pheromon<br> templates, each template is associated to a specific criterium (according to age,<br> sex, ...). Initially, we put the material in the residential place. Each material<br> has some characteristics, corresponding to the people living in this residential<br> area. The simulation shows the self-organization processus as the result of the<br> set of the attractive effect of all the centers and all the templates.</p>
Figure 4: Complexity of geographical space with respect of emergent organizations-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The applications we focus on in the models that we will propose in the<br> following, concerns specifically the multi-center (or multi-organizational) phenomona<br> inside urban development. As an artificial ecosystem, the city development<br> has to deal with many challenges, specifically for sustainable development,<br> mixing economical, social and environmental aspects. The decentralized<br> methodology proposed in the following allows to deal with multi-criteria problems,<br> leading to propose a decision making assistance, based on simulation<br> analysis.</p>
Figure 1: Complex spatial organizational model-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On Figure 1, we describe a two-level model of spatial self-organizations with<br> interactions in both directions between these two levels: the emergence of organizations<br> from entities interactions but also the feed-back process describing<br> how organizations are regulating their own entities.</p>
Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>We have presented the system for a fully autonomous navigation of an UAV based on Omni<br> directional vision system and image processing. we explain vision system configuration ,image<br> processing and feature extraction methods and finaly suggest an algorithm based on potential field<br> for navigation of an UAV.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart and results of ICP algorithm
<p>The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)</p>
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality - Collected Data
<p>The deposited data files were used to perform the analysis introduced in the paper: Tamas Galli, Francisco Chiclana and Francois Siewe, "Genetic Algorithm Based Fuzzy Inference System for Describing Execution Tracing Quality", Mathematics, MDPI, 2021.</p> <p>The data were collected through an online questionnaire. The questionnaire has been exported in pdf format and uploaded as file: form_data_collection.pdf. The paper above introduces the steps of analysing, processing the data, constructing, pre-validating the model. The final validation was done over the online questionnaire exported and uploaded in pdf format as form_model_validation.pdf.</p> <p>Questionnaire Part 1, data file: all_usecases_wide.csv</p> <p>The CSV file contains the responses for each use case of part 1 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. The following variables are linked to each use case: Accuracy, Legibility, DesignAndImplementation, and Security. These form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. Each fifth column is followed by a column UseCase to designate the use case which is described by the previous five columns. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 2, data file: all_real_projects_scores.csv</p> <p>The CSV file contains the responses for real projects in part 2 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. Six variables are linked to each response: Accuracy, Legibility, DesignAndImplementation, and Security, which form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. In addition, the variable Type indicates the type of the project, such as server application, desktop application, web UI, mobile application, or embedded application. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 3, data file: all_extrem_values_wide.csv</p> <p>The CSV file contains the assigned execution tracing quality value to the provided combination of extreme input values in part 3 of the online questionnaire enclosed. The column IDs represent the question IDs in the survey. The definitions of the variables can be found in the questionnaire.<br> </p>
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
An approach for modelling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm.
<p>This is a data set associated with the paper <em>An approach for modeling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. </em>by Felipe A. Perdomo, George Jackson, Amparo Galindo, Claire S. Adjiman. The manuscript is presented as a proceeding of the 33<sup>rd</sup> European Symposium on Computer-Aided Process Engineering (ESCAPE33), June 18-21, 2023, in Athens, Greece.</p>
Data set for A Novel VNS-based Algorithm for SVC Allocation in the Brazilian Interconnected Power System
<p>This release includes the 107-bus version of the Brazilian Interconnected Power System (available <a href="https://www.sistemas-teste.com.br/">here</a>). The system consists of 107 buses, 104 lines, and 67 transformers distributed across three areas: South, Southeast, and Mato Grosso. This test system provides extensive applications for problems related to steady-state analysis.</p>
Testing the Acoustic Localisation Positioning System-algorithm: Data sets
<p>The data is from 3 separate experimental setups: Tracking a device of constant speed using the Audio1 (single processor) software (B.1); and tracking a faster device using the al-Qt (multi processor) software (B.2) The experiments were also recorded on video. (see Video folder). For the experiments in B.1 a visualisation of the data has been made with the help of a MATLAB script which also included. The dimensions of the room and loudspeaker spacings were identical for both set-ups, namely 3 x 2 meters, with the loudspeakers at the corners of the rectangle. the loudspeakers were situated on the floor, approximately level with the tracked devices.</p> <p>Setup C collects debug log examples of experiments with very short capture cycles with 2 mics and 2 loudspeakers running ALPS al-Qt.</p> <p>The source code for Audio is available from: <a href="https://github.com/spatmus/alps/tree/master/Audio1">https://github.com/spatmus/alps/tree/master/Audio1</a><br> The source code for al-Qt is available from: <a href="https://github.com/spatmus/alps/tree/master/al-Qt">https://github.com/spatmus/alps/tree/master/al-Qt</a><br> A binary of al-Qt for macOS 11, can be found in the assets folder of the release <a href="https://github.com/spatmus/alps/releases">https://github.com/spatmus/alps/releases</a></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.