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.
This dataset contains one ideal fetal phonocardiography (fPCG) signal and twelve signals made by adding three types of interference having different amplitudes to the reference signal.
- Ambient noise - this is any noise from the surroundings. It occurs commonly when recording the fPCG. Its spec-trum includes frequencies from 10 Hz.
- White Gaussian noise - a random signal with the same power in any band of the same width.
- Maternal and fetal movement artifacts - a noise caused by muscle movement or breathing. They occur at low frequencies, usually up to 100 Hz.
The dataset is composed of digital signals obtained from a capacitive sensor electrodes that are immersed in water or in oil. Each signal, stored in one row, is composed of 10 consecutive intensity values and a label in the last column. The label is +1 for a water-immersed sensor electrode and -1 for an oil-immersed sensor electrode. This dataset should be used to train a classifier to infer the type of material in which an electrode is immersed in (water or oil), given a sample signal composed of 10 consecutive values.
The dataset is acquired from a capacitive sensor array composed of a set of sensor electrodes immersed in three different phases: air, oil, and water. It is composed of digital signals obtained from one electrode while it was immersed in the oil and water phases at different times.
## Experimental setup
The experimental setup is composed of a capacitive sensor array that holds a set of sensing cells (electrodes) distributed vertically along the sensor body (PCB). The electrodes are excited sequentially and the voltage (digital) of each electrode is measured and recorded. The voltages of each electrode are converted to intensity values by the following equation:
intensity = ( |Measured Voltage - Base Voltage| / Base Voltage ) x 100
Where the Base Voltage is the voltage of the electrode recorded while the electrode is immersed in air. The intensity values are stored in the dataset instead of the raw voltage values.
## Experimental procedure
The aim of the experiments is to get fixed-size intensity signals from one electrode (target electrode) when being immersed in water and oil; labeled as +1 (water) or -1 (oil). For this purpose, the following procedure was applied:
- The linear actuator was programmed to move the sensor up and down at a constant speed (20 mm / second).
- The actuator stops when reaching the upper and bottom positions for a fixed duration of time (60 seconds).
- At the upper position, the target electrode is immersed in oil; intensity signals are labeled -1 and sent to the PC.
- At the bottom position, the target electrode is immersed in water; intensity signals are labeled +1 and sent to the PC.
- The sampling rate is 100 msec; since each intensity signal contains 10 values, it takes 1 second to record one intensity signal
## Environmental conditions
The experiments were perfomed under indoors laboratory conditions with room temperature of around 23 degree Celsius.
## Dataset structure
The signals included in the dataset are composed of intensity signals each with 10 consecutive values and a label in the last column. The label is +1 for a water-immersed electrode and -1 for an oil-immersed electrode.
The dataset should be used to train a classifier to differentiate between electrodes immersed in water and oil phases given a sample signal.
Radar devices can be used to monitor vital signs, such as respiratory and cardiac rates. For this purpose, the phase of an echo signal received from the chest or back of a human is usually used; subsequently, respiratory rate detection (RRD) and cardiac rate detection (CRD) can be achieved by estimating two fundamental frequencies corresponding to the respiratory and cardiac rates, respectively.
The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
The experimental workbench consists of a three-phase induction motor coupled with a direct-current machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.
- Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.
- Load torque: is adjusted by varying the field winding voltage of direct current generator. A single-phase voltage variator with a filtered full-bridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.
- Broken rotor bar: to simulate the failure on the three-phase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.
All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.
- mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and non-drive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.
- electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.
Data Set Overview:
- Three-phase Voltage
- Three-phase Current
- Five Vibration Signals
The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.
This dataset contains several acquired Lamb Waves from a thermoset matrix composite plate at different temperatures (-40ºC:+50ºC).
The dataset corresponds to the article with the same name.
Use Matlab to load the files. Each file is named: Voltage[V]_Frequency[kHz]_Cycles_Temperature[K]_randomvalue.mat
Also included a .mat file with the coordinates of the transducers.
We introduce a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits.
The corpus consists of three sets: the core, evaluation, and complete set. The complete set contains all the data (i.e., complete set = core set + evaluation set) and allows the user to freely split the training/test set. Core/evaluation sets suggest a default training/test split. For each set, all *.wav files are in the /data directory and the meta information is in meta.csv file. The protocol is described in the readme.txt. A PyTorch data loader script is provided as an example of how to use the data. A python resample script is provided for resampling the dataset into the desired sample rate.
Spoken Indian Language Identifcation (9 languages, 8 different utterance lengths)
Wave file (16kHz, 16 bit)
Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
Copyright (C) 2018 Intel Corporation
Welcome to the Intel WiFi RTT (FTM) 40MHz dataset.
The paper and the dataset can be downloaded from:
To cite the dataset and code, or for further details, please use:
Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, and Yuval Amizur, "A Machine Learning Approach for Wi-Fi RTT Ranging," ION Technical Meeting ITM/PTTI 2019
For questions/comments contact:
The zip file contains the following files:
1) This README.txt file.
2) LICENSE.txt file.
3) RTT_data.csv - the dataset of FTM transactions
4) Helper Matlab files:
O mainFtmDatasetExample.m - main function to run in order to execute the Matlab example.
O PlotFTMchannel.m - plots the channels of a single FTM transaction.
O PlotFTMpositions.m - plots user and Access Point (AP) positions.
O ReadFtmMeasFile.m - reads the RTT_data.csv file to numeric Matlab matrix.
O SimpleFTMrangeEstimation.m - execute a simple range estimation on the entire dataset.
O Office1_40MHz_VenueFile.mat - contains a map of the office from which the dataset was gathered.
Running the Matlab example:
In order to run the Matlab simulation, extract the contents of the zip file and call the mainFtmDatasetExample() function from Matlab.
Contents of the dataset:
The RTT_data.csv file contains a header row, followed by 29581 rows of FTM transactions.
The first column of the header row includes an extra "%" in the begining, so that the entire csv file can be easily loaded to Matlab using the command: load('RTT_data.csv')
Indexing the csv columns from 1 (leftmost column) to 467 (rightmost column):
O column 1 - Timestamp of each measurement (sec)
O columns 2 to 4 - Ground truth (GT) position of the client at the time the measurement was taken (meters, in local frame)
O column 5 - Range, as estimated by the devices in real time (meters)
O columns 6 to 8 - Access Point (AP) position (meters, in local frame)
O column 9 - AP index/number, according the convention of the ION ITM 2019 paper
O column 10 - Ground truth range between the AP and client (meters)
O column 11 - Time of Departure (ToD) factor in meters, such that: TrueRange = (ToA_client + ToA_AP)*3e8/2 + ToD_factor (eq. 7 in the ION ITM paper, with "ToA" being tau_0 and the "ToD_factor" lumps up both nu initiator and nu responder)
O columns 12 to 467 - Complex channel estimates. Each channel contains 114 complex numbers denoting the frequency response of the channel at each WiFi tone:
O columns 12 to 125 - Complex channel estimates for first antenna from the client device
O columns 126 to 239 - Complex channel estimates for second antenna from the client device
O columns 240 to 353 - Complex channel estimates for first antenna from the AP device
O columns 354 to 467 - Complex channel estimates for second antenna from the AP device
The tone frequencies are given by: 312.5E3*[-58:-2, 2:58] Hz (e.g. column 12 of the csv contains the channel response at frequency fc-18.125MHz, where fc is the carrier wave frequency).
Note that the 3 tones around the baseband DC (i.e. around the frequency of the carrier wave), as well as the guard tones, are not included.