The following dataset consists of utterances, recorded using 24 volunteers raised in the Province of Manitoba, Canada. To provide a repeatable set of test words that would cover all of the phonemes, the Edinburg Machine Readable Phonetic Alphabet (MRPA) [KiGr08], consisting of 44 words is used. Each recording consists of one word uttered by the volunteer and recorded in one continuous session.

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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

Instructions: 

** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **

All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called README.md in that repository.

https://github.com/thomasmhall-newcastle/IEEE-TNSRE-2016-lfLFPs

You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here: http://ieeexplore.ieee.org/document/7742994/

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The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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The dataset consists a training and testing folder with received signal strength (RSS) data, obtained from a ray-tracing software (Wireless Insite). There are K=8 anchor nodes and N=12 regions.

- In the folder _training, it contains 8 * 12 = 96 separate .p2m files, each file corresponds to a RSS data collected from a grid number of user locations (coordinate is given in the .p2m file) with respect to a certain Anchor node

Instructions: 

In each .p2m file, the coordinate (X, Y, Z) of the target (transmitter in this case) is given, together with the distance with respect to the anchor node. The receiver signal power and phase is then calculated by ray-tracing, with the results printed at the end of each row.

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This  repository contains accompanying material and code needed for reproducing results in the paper:

R. Pilipović, V. Risojević, P. Bulić *"On the Design of an Energy Efficient Digital IIR A-weighting Filter using Approximate Multiplication"*

For Verilog to GDS synthesis flow, we employed OpenROAD Flow, representing a full RTL-to-GDS flow built entirely on open-source tools. Besides OpenROAD, we used the Vivado suite from Xilinx to deploy the proposed filter on the ZYBO Z7 board. The supplementary material is organized as follows:

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In this dataset, we performed a seven-day motor imagery (MI) based BCI experiment without feedback training on 20 healthy subjects. The MI tasks include left hand, right hand, feet and idle task.

Instructions: 

20 healthy subjects (11 males, mean age: 23.2±1.47 years, all right-handed) participated in this study. The recruited subjects were asked to participate seven sessions within two weeks. Each session lasted around 40 minutes and was organized into 6 runs. Subjects could have a short break between runs. During each run, subjects had to perform 40 trials (4 different MI-tasks, 10 trials per task, presented in random order), each trial lasting 9s. The direction of the arrow informed the subjects which task to perform, i.e., the left arrow corresponding to MI of the left hand, the right arrow corresponding to MI of the right hand, down corresponding to MI of both feet, up corresponding to the idle task.

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Electroretinography (ERG) has great potential in visual health detection in early diagnosis and intervention. To date, optical coherence tomography and other diagnostic tests are mainly used. Clinically used ERG is an important diagnostic assessment for various retinal diseases, such as hereditary diseases (retinitis pigmentosa, choroideremia, cone dystrophy, etc), diabetic retinopathies, glaucoma, macular degeneration, toxic retinopathies etc. A database of five types of adult and pediatric biomedical electroretinography signals is presented in this study.

Instructions: 

WHEN USING THIS RESOURCE, PLEASE CITE THE ORIGINAL PUBLICATION

1. A.E. Zhdanov, A.Yu. Dolganov, E. Lucian, X. Bao, V.I. Borisov, V.N. Kazajkin, V.O. Ponomarev, A.V. Lizunov, L.G. Dorosinskiy, “OculusGraphy: Overview of Retinal Toxicity Methods Based on Biomedical Signals Analysis” in 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, 2020.
2. A.E. Zhdanov, A.Yu. Dolganov, V.N. Kazajkin, V.O. Ponomarev, A.V. Lizunov, V.I. Borisov, E. Lucian, X. Bao, L.G. Dorosinskiy, “OculusGraphy: Literature Review on Electrophysiological Research Methods in Ophthalmology and Electroretinograms Processing Using Wavelet Transform” in 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, 2020.

DATA DESCRIPTION

The file "00 Description of Research Protocols.pdf" contains a description of the protocols used in this study. The file "01 Appendix 1.xlsx" contains the resulting analysis data of 5 signal types. The file contains filtered signals and the following information: diagnosis, age, wave amplitude, wave latency. The file "02 Appendix 2.xlsx" contains a series of signals. The file contains the following information: patient number, signal.
For further questions please contact Mr. Aleksei E. Zhdanov (correspondence e-mail: a.e.zhdanov@urfu.ru).

ACKNOWLEDGMENT

We express our most profound appreciation for cand. med. Oleg V. Shilovskikh CEO of IRTC Eye Microsurgery Ekaterinburg Center for the opportunity to publish the database and disseminate scientific knowledge. The ERG signals data decryption within the study was supported by RFBR, project number 20-07-00498, and 18-29-03088. The ERG signals data processing was supported by Act 211 Government of the Russian Federation, contract 02.A03.21.0006.

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This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms. 

Instructions: 

The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:

  • DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3 
  • Spektrum (4): DX5e, DX6e, DX6i, JR X9303
  • Futaba (1): T8FG
  • Graupner (1): MC32
  • HobbyKing (1): HK-T6A
  • FlySky (1): FS-T6
  • Turnigy (1): 9X
  • Jeti Duplex (1): DC-16.

In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms. 

The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.  

The drone RC RF dataset was used in the following papers:

  • M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
  • M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
  • E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.

Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.  

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Author Contributions:

  • Experiment design: O. Ozdemir and M. Ezuma
  • Data collection:  M. Ezuma
  • Scripts: F. Erden and C. K. Anjinappa
  • Documentation: F. Erden
  • Supervision, revision, and funding: I. Guvenc 

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Acknowledgment

This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A.

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MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U.

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