This code demonstrate the example use of FOPDT (First-Order-Plus-Dead-Time) model identification. The Algorithm used in "FOPDT_fun" is available in the reference:

S. Sharma and P. K. Padhy, "A Novel Iterative System Identification and Modeling Scheme with Simultaneous Time-Delay and Rational Parameter Estimation," in IEEE Access, 

vol. 8, pp. 64918-64931, 2020, doi: 10.1109/ACCESS.2020.2985132.

Instructions: 

The FOPDT_fun uses the input, output and sample time for identification.

Categories:
70 Views

This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.

Instructions: 

fGn series used for simulations in the article "Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series".  Available at:

https://arxiv.org/abs/2103.02091.

https://www.techrxiv.org/articles/preprint/Preliminaries_on_the_Accurate....

https://easychair.org/publications/preprint/RQsp.

https://osf.io/3sk7a/.

They should be used in Selfis01b.

Categories:
121 Views

Data are collected on a 5m×10msized test bed, which is set up at Kadir Has University,Istanbul. Wireless access points are located around the corners of the testbed and markers are placed at every 45 cm. RSSI measurements done on the grid shown in Figure are stored via NetSurveyor program running on a Lenovo IdeapadFLEX 4 laptop, which has an Intel Dual Band Wireless-AC8260 Wi-Fi adaptor.At each measurement point, RSSI data are collected for1 min with a sampling interval of 250 ms.

Instructions: 

Data  are  collected  on  a  5m×10msized  test  bed,  which  is  set  up  at  Kadir  Has  University,Istanbul. Wireless access points are located around the cornersof  the  test  bed  and  markers  are  placed  at  every  45  cm.RSSI  measurements  done  on  the  grid  shown  in  Figure  2  arestored via NetSurveyor program running on a Lenovo IdeapadFLEX  4  laptop,  

which  has  an  Intel  Dual  Band  Wireless-AC8260 Wi-Fi adaptor.At  each  measurement  point,  RSSI  data  are  collected  for1  min  with  a  sampling  interval  of  250  ms.  XML file is read with MATLAB for data of full area and applied trajectory.

Categories:
294 Views

This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples.

Instructions: 

This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples. That is, 6×16×4096 = 393216 pieces in total.

Categories:
626 Views

The provided dataset computes the exact analytical bit error rate (BER) of the NOMA system in the SISO broadcast channels with the assumption of i.i.d Rayleigh fading channels. The reader has to decide on the following input: 1) Number of users. 2) Modulation orders. 3) Power assignment. 4) Pathloss. 5) Transmit signal-to-noise ratio (SNR). The output is stored in a matrix where different rows are for different users while different columns are for different transmit SNRs.

Categories:
258 Views

 

Instructions: 

This dataset is used for i) analyzing the influence of process information on monitoring signals through signal processing methods; ii) training and testing models of tool monitoring and tool wear prediction especially for cutting conditions with large variations including cutting parameters, material and geometry of cutting tools, and workpiece materials, and also cutting conditions with continuous changes. This data set includes monitoring signals collected from machining process of sidewalls and closed pockets. The sidewall machining belongs to the cutting process with fixed cutting conditions; the closed pocket machining belongs to the cutting process of continuously varying cutting conditions for the reason that the tool path of closed pocket includes line, arc, full cutting and non-full cutting. Although cutting parameters are given fixed in the arc tool path area, the actual cutting parameters (such as feed, cutting width) are constantly changing due to the change of cutting geometry.

Categories:
855 Views

This dataset contains synthetic data for training the two KNN algorithms in the paper A. Coluccia, A. Fascista, and G. Ricci, "A KNN-based Radar Detector for Coherent Targets in non-Gaussian Noise", IEEE Signal Processing Letters, 2021.

 

 

Categories:
122 Views

To address the possible lack or total absence of pulses from particle detectors during the development of its associate electronics, we propose a model that can generate them without losing the features of the real ones. This model is based on artificial neural networks, namely Generative Adversarial Networks (GAN). This dataset contains the pulses of Na-22 and Cs-137 and the Python code to generate new synthetic pulses.

Categories:
96 Views

The UBFC-Phys dataset is a public multimodal dataset dedicated to psychophysiological studies. 56 participants followed a three-step experience where they lived social stress through a rest task T1, a speech task T2 and an arithmetic task T3. During the experience, the participants were filmed and were wearing a wristband that measured their Blood Volume Pulse (BVP) and ElectroDermal Activity (EDA) signals. Before the experience started and once it finished, the participants filled a form allowing to compute their self-reported anxiety scores.

Instructions: 

Please find more details about the UBFC-Phys dataset's organization in the READ_ME file.

If you use this dataset, please cite the following paper:

 

R. Meziati Sabour, Y. Benezeth, P. De Oliveira, J. Chappé, F. Yang. "UBFC-Phys: A Multimodal Database For Psychophysiological Studies Of Social Stress", IEEE Transactions on Affective Computing, 2021.

Categories:
3985 Views

Pages