The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.
This dataset contain a PV Arrays Models Pack with some models of PV Solar Arrays carried out in Matlab and Simulink. The PV Models are grouped in three ZIP files which correspond to the papers listed above.
The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.
1. Open the "Banu_power_PVarray_grid_EPE2014_.slx" file with Matlab R2014a 64 bit version or a newer Matlab release. 2. To simulate various grid faults on PV System see the settings of the "Fault" variant subsystem block (Banu_power_PVarray_grid_EPE2014_/20kV Utility Grid/Fault) in Model Properties (File -> Model Properties -> Model Properties -> Callbacks -> PreLoadFcn* (Model pre-load function)): MPPT_IncCond=Simulink.Variant('MPPT_MODE==1') MPPT_PandO=Simulink.Variant('MPPT_MODE==2') MPPT_IncCond_IR=Simulink.Variant('MPPT_MODE==3') MPPT_MODE=1 Without_FAULT=Simulink.Variant('FAULT_MODE==1') Single_phases_FAULT=Simulink.Variant('FAULT_MODE==2') Double_phases_FAULT=Simulink.Variant('FAULT_MODE==3') Double_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==4') Three_phases_FAULT=Simulink.Variant('FAULT_MODE==5') Three_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==6') FAULT_MODE=1 3. For more details about the Variant Subsystems see the Matlab Documentation Center: https://www.mathworks.com/help/simulink/variant-systems.html or https://www.mathworks.com/help/simulink/examples/variant-subsystems.html
The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This dataset is the result of one of the first comprehensive studies of static Bluetooth AoA-based Indoor Localization in a real-world testbed using commercial off-the-shelf Bluetooth chipsets.
The positioning experiments were carried out on a 100 m² test area using four stationary Bluetooth sensor devices each equipped with eight antennas. With this setup, a median localization accuracy of up to 18 cm was achieved.
The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.Three 6-month cohort monitoring campaigns were carried out, each including of 65 COPD patients.
Automotive millimeter wave Frequency Modulated Continuous Wave radars are finding widespread use in various fields. At times, the hardware specifications of commercial-off-the-shelf products prohibit the use of these products for other diverse radar measurements. More often, velocity ambiguity results when an attempt is made to measure the velocity of a high-speed target with a radar that is not designed for that purpose in the first place.
The files are in "Matlab v5 mat-file (little endian) version 0x0100" thus the dataset is readable by Matlab as well as Octave.Detailed content and format description please find in the Word file.
Impulse response data used in article.
This is a dataset of 32 five-second-long vibration recordings. One human used a metal tool to perform one of two tool-mediated surface interactions (tapping or dragging) on the following four different surfaces: sandpaper (hard and rough), acrylic plastic (hard and smooth), rough paper (soft and rough), and rubber (soft and smooth). Each of the eight combinations of interaction and surface were recorded four times.
Vehicle-to-barrier (V2B) communications is an emerging communication technology between vehicles and roadside barriers to mitigate run-off-road crashes, which result in more than half of the traffic-related fatalities in the United States. To ensure V2B connectivity, establishing a reliable V2B channel is necessary before a potential crash, such that real-time information from barriers can help (semi-)autonomous vehicles make informed decisions. However, the characteristics of the V2B channel are not yet well understood.
In this repository, data for five different crash tests are uploaded. The details about each of these crash tests are discussed in the paper. The data for each crash test are categorized according to the USRP log files, Crash test photos & videos, Crash vehicle acceleration sensor data, and Crashed barrier design & dimensions.
This is a CSI dataset towards 5G NR high-precision positioning,
which is fine-grained, general-purpose and 3GPP R16 standards-complied.
5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).
Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.
The dataset_[SNR]_[date]_[time].mat contains:
1) a 4-D matrix, features, representing the feature data, and
2) a structure array, labels, labeling the ground truth of UE positions.
[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.
The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).
The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.
The value of Ng corresponds to the number of UEs in labels.
Colsed beta test is running.
In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.
In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.
Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.
To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.
1) Recruit participants for colsed beta test.
1)Expend our dataset with more CSI data with low SNR levels noise.
2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.
Closed beta test will start after suggestion collection.
1)Expend our dataset with more CSI data with different SNR levels noise.
2)Publish map files for Scenario 1 indoor office.
Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas.
Neural Audio Fingerprint Dataset
(c) 2021 by Sungkyun Chang
This dataset includes all music sources, background noises and impulse-reponses (IR) samples that have been used in the work "Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning" (https://arxiv.org/abs/2010.11910).
This data set was generated by processing several external data sets, such as the Free Music Archive (FMA), Audioset, Common voice, Aachen IR, OpenAIR, Vintage MIC and the internal data set from Cochelar.ai. See README.md for details.
Dataset-mini vs. Dataset-full: the only difference between these two datasets is the size of 'test-dummy-db'. So you can first train and test with `Dataset-mini`. `Dataset-full` is for testing in 100x larger scale.