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52 results for “IEEE”
IEEE-30 energy system data of multi-period market with intertemporal constraints
<p>This is the dataset that is used for the original article: "Locational marginal pricing in multi-period AC OPF environment"</p> <p>The dataset consists of the following files</p> <ul> <li>Case1.zip</li> <li>Case2.zip</li> <li>Case3.zip</li> <li>case_modifications.py</li> <li>data_spec.py</li> <li>OPF_formulation.pdf</li> </ul> <p>Multiperiod AC OPF is given in OPF_formulation.pdf. Modifications of traditional IEEE 30-node case are given in case_modifications.py</p> <p>The case files incorporate input and output multiperiod AC OPF and LMP decomposition data in csv and pickle formats. Data structure of case files is given in data_spec.py.</p> <p>For python users pickle files are given. Nevertheless, python environment is not required. Specification can be read as a text file. All necessary data are repeated in csv format.</p> <p>Step 1 are to define LMPs of limited energy resources or storage resources that are formed by actual marginal resources from all time periods (first LMP definition in fig. 6 in the paper).</p> <p>Step 2 are to define all other LMPs at price-taking nodes (second LMP definition in fig. 6 in the paper).</p> <p>The following interrelation between Lagrange multipliers, LMP components, and price-bonding factors holds true:</p> <pre>assert np.max(np.abs(output_ramp.sensitivities.dot(output_ramp.offer_gen_data.price).tolist() - output_ramp.ramping_gen_data.price)) < 1e-2 if output_pt_step1.components.shape[0]: step1_pf_filter = (~output_pf.is_limited_energy) & (~output_pf.is_storage) assert np.max(np.abs(output_pt_step1.components.node_price - (output_pt_step1.components.f + output_pt_step1.components.tc_sum + output_pt_step1.components.vc_sum))) < 1e-2 assert (output_pt_step1.components.f - output_pt_step1.w_f.dot(output_pf.node_price[step1_pf_filter])).abs().max() < 1e-2 assert (output_pt_step1.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price[step1_pf_filter]) for w in output_pt_step1.w_tc_list), axis=1 ).sum(axis=1)).abs().max() < 1e-2 assert np.max(np.abs(output_pt_step2.components.node_price - (output_pt_step2.components.f + output_pt_step2.components.tc_sum + output_pt_step2.components.vc_sum))) < 1e-2 assert (output_pt_step2.components.f - output_pt_step2.w_f.dot(output_pf.node_price)).abs().max() < 1e-2 assert (output_pt_step2.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price) for w in output_pt_step2.w_tc_list), axis=1 ).sum(axis=1) ).abs().max() < 1e-2</pre> <p> </p>
Dataset of the journal article "Spatial Reuse in IEEE 802.11ax WLANs"
<p>This dataset contains both the inputs and the outputs from the conference article "Spatial Reuse in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina, Cristina Cano, Ioannis Selinis and Boris Bellalta. The article has been sent to IEEE Surveys & Tutorials.</p> <p>Regarding the Komondor's input, we provide the files used in the Komondor simulator, as well as the execution scripts, for generating the results presented in the paper. We find "toy" and "random" scenarios, which cover different parts of the paper. In the random case, we have 39,800 different scenarios, which correspond to 4 network densities, 3 strategies on applying SR, 3 traffic loads, 50 deployments, and 21 different OBSS/PD values. More details are provided in the article. </p> <p>Apart from the Komondor's input, we also provide other Matlab files used in the context of the SFCTMN analytical model (refer to <a href="https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR">https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR</a>). </p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p>
Modified IEEE 123-bus MG system
<p>Modified IEEE 123-bus MG system for paper "Piece-wise Linear Function Based Continuous ADP Algorithm for Multiple Battery Energy Storage Lifespan Benefit Ensurance in Real-Time Schedule"</p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection Phase 2
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p> <p> </p> <p>These files constitute the second phase of the VIP CUP 2023 Challenge at ICIP 2023. This test set has a more general patient base than the first one and as such is a better indicator of the performance of models. This test set was created by taking a subset of the data from a publicly available OCT dataset and then asking our medical partners to provide fine-grained biomarker labels for the competition. We provide the citation for the source of these images below: </p> <p>Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.</p> <p> </p> <p>This zenodo repository contains the images and submission template file needed for the second phase of the competition.</p>
Supplementary figures for paper "Should robots have accents?" published at IEEE RO-MAN 2020
<p>These plots show the preference towards a robot's accent (displayed on the x-axis), broken down by participants' region of origin in the UK. For example, "plot_Wales" shows that around 25% of the 23 participants from Wales indicated that they would like a robot to have an SSBE accent, around 15% a Welsh accent, etc.</p>
[ITU-T AI Challenge] Input/Output of project "Improving the capacity of IEEE 802.11 WLANs through Machine Learning"
<p>This data set will be used by participants of the ITU-T AI Challenge. </p> <p>The data set contains:</p> <ul> <li>Input files: contain information such as nodes labels, nodes position, or channels used. These files have been used to simulate the behavior of random WLAN deployments under different channel bonding conditions. </li> <li>Output files: contain the output of the simulations - throughput per STA, RSSI that each STA receives from its AP, interference map from APs' point of view, average SINR experienced by each device during packet receptions.</li> </ul> <p>More details can be found on the official website of the challenge: <a href="https://www.upf.edu/web/wnrg/ai_challenge">https://www.upf.edu/web/wnrg/ai_challenge</a></p> <p><strong>[Update - 28 July 2020] </strong>A script (<a href="https://zenodo.org/api/files/88053224-d3a9-417e-b034-f08c763069ac/script_process_dataset.sh?versionId=2817eabd-ab9e-496a-a7d6-f67d227c51bf">script_process_dataset.sh</a>) has been added to process the output files. In particular, the results of each deployment are separated into different files. Besides, different files are created according to the type of label/feature (throughput, airtime, RSSI map, and interference list).</p> <p><strong>[Update - 22 September 2020] </strong>A new feature has been added to all the files in the data set. In particular, we have added the average Signal-to-Interference-plus-Noise Ration (SINR) experienced by each STA during packet receptions (including data and control packets). The SINR values in APs are marked as Inf because we focus on downlink transmissions only.</p> <p><strong>[Update - 30 September 2020] </strong>The test data set has been released, which corresponds to the simulations of a set of deployments with different characteristics. Input node files are contained in <a href="https://zenodo.org/api/files/88053224-d3a9-417e-b034-f08c763069ac/input_node_files_test.zip">input_node_files_test.zip</a>, while <a href="https://zenodo.org/api/files/88053224-d3a9-417e-b034-f08c763069ac/output_simulator_test.zip">output_simulator_test.zip</a> includes the output generated by the simulator. The label (i.e., the throughput) of the test data set will not be included in this repository until the next update (estimated date: 15 October 2020).</p> <p><strong>[Update - 19 October 2020] </strong>After participants have submitted their solutions, we provide the entire test data set, including the actual throughput obtained by each AP and STA in the test deployments.</p>
Supplementary material for the IEEE Software paper-Engagement of practitioners and SE researchers
<p>Supplementary material for the IEEE Software paper-Engagement of practitioners and SE researchers</p>
Artifact for paper "Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?" published at IEEE SANER 2022
<p>The zip file contains all the source code and data for the experiments.</p>
Data on IEEE and Synthetic Test Power Systems for Oriol Cartiel's PhD
<p>The data used in my PhD thesis comes from internet repositories (see within files). It is based on detailed information from both IEEE n-bus test power systems and synthetic power grid test cases developed by the scientific community. The dataset, stored in spreadsheet format (.xlsx), includes comprehensive technical details such as line impedances, equivalent internal impedances, locations of generators, load demands and their locations, and shunt element specifications. All values are normalized to per-unit (pu), assuming a base power of 100 MVA. Additionally, the reference to the internet repository is available in the same file for potential further consultation.</p>
Datasets of Kruskopf et al., IEEE Transactions on Electron Devices 68, 3672 (2021), "Graphene QHE devices for ac and dc electrical metrology"
<p>Compilation of the datasets used to generate the figures in the following journal publication:</p> <p>"Graphene quantum Hall effect devices for ac and dc electrical metrology"</p> <p>by Mattias Kruskopf, Stephan Bauer, Yaowaret Pimsut, Atasi Chatterjee, Dinesh K. Patel,<br> Albert F. Rigosi , Randolph E. Elmquist, Klaus Pierz, Eckart Pesel, Martin Götz, and Jürgen Schurr,</p> <p>IEEE Trans. on Electron Devices 68, 3672 (2021).</p> <p>DOI: 10.1109/TED.2021.3082809</p>
Data Corpus for the IEEE-AASP Challenge on Acoustic Source Localization and Tracking (LOCATA)
<p>This repository contains the final release of the development and evaluation datasets for the LOCATA Challenge.</p> <p>The challenge of sound source localization in realistic environments has attracted widespread attention in the Audio and Acoustic Signal Processing (AASP) community in recent years. Source localization approaches in the literature address the estimation of positional information about acoustic sources using a pair of microphones, microphone arrays, or networks with distributed acoustic sensors. The IEEE AASP Challenge on <strong>acoustic source LOCalization And TrAcking (LOCATA)</strong> aimed at providing researchers in source localization and tracking with a framework to objectively benchmark results against competing algorithms using a common, publicly released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment.</p> <p>Four different microphone arrays were used for the recordings, namely:</p> <ul> <li>Planar array with 15 channels (DICIT array) containing uniform linear sub-arrays</li> <li>Spherical array with 32 channels (Eigenmike)</li> <li>Pseudo-spherical array with 12-channels (robot head)</li> <li>Hearing aid dummies on a dummy head (2-channel per hearing aid).</li> </ul> <p>An optical tracking system (OptiTrack) was used to record the positions and orientations of talker, loudspeakers and microphone arrays. Moreover, the emitted source signals were recorded to determine voice activity periods in the recorded signals for each source separately. The ground truth values are compared to the estimated values submitted by the participants using several criteria to evaluate the accuracy of the estimated directions of arrival and track-to-source association. </p> <p>The datasets encompass the following six, increasingly challenging, scenarios:</p> <ul> <li><strong>Task 1:</strong> Localization of a single, static loudspeaker using static microphones arrays</li> <li><strong>Task 2:</strong> Multi-source localization of static loudspeakers using static microphone arrays</li> <li><strong>Task 3:</strong> Localization of a single, moving talker using static microphone arrays</li> <li><strong>Task 4:</strong> Localization of multiple, moving talkers using static microphone arrays</li> <li><strong>Task 5:</strong> Localization of a single, moving talker using moving microphone arrays</li> <li><strong>Task 6:</strong> Multi-source localization of moving talkers using moving microphone arrays.</li> </ul> <p>The development and evaluation datasets in this repository contain the following data:</p> <ul> <li>Close-talking speech signals for human talkers, recorded use DPA microphones</li> <li>Distant-talking recordings using four microphone arrays: <ul> <li>Spherical Eigenmike (32 channels)</li> <li>Pseudo-spherical prototype NAO robot (12 channels)</li> <li>Planar DICIT array (15 channels)</li> <li>Hearing aids installed in a head-torso simulator (4 channels)</li> </ul> </li> <li>Ground-truth annotations of all source and microphone positions, obtained using an OptiTrack system of infrared cameras. The ground-truth positions are provided at the frame rate of the optical tracking system</li> </ul> <p>The following software is provided with the data:</p> <ul> <li>Matlab code to read the datasets: <a href="https://github.com/cevers/sap_locata_io">github.com/cevers/sap_locata_io</a></li> <li>Matlab code for performance evaluation of localization and tracking algorithms: <a href="https://github.com/cevers/sap_locata_eval">github.com/cevers/sap_locata_eval</a></li> </ul> <p>For further information, see:</p> <ul> <li>C. Evers, H. W. Löllmann, H. Mellmann, A. Schmidt, H. Barfuss, P. A. Naylor, W. Kellermann<br> <em>"</em>The LOCATA Challenge: Acoustic Source Localization and Tracking<em>," </em>in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>, vol. 28, pp. 1620-1643, 2020, doi: <a href="https://doi.org/10.1109/TASLP.2020.2990485">10.1109/TASLP.2020.2990485</a></li> <li>Documentation: <a href="https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf">https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf</a></li> </ul>
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.