Digital signal processing

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.



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.


This dataset is utilized for the research of blind identification of CPM signal modulation order. The signal parameters in the dataset range as follows: modulation index from 0.125 to 1, modulation order of 2, 4, 8, pulse types including REC, RC, SRC, TFM, and GMSK, and correlation lengths of 1 to 8. The signals are oversampled by a factor of 10, transmitted through an additive white Gaussian noise (AWGN) channel, and the signal-to-noise ratio (SNR) ranges from 0 to 30dB. 


Data set used in an IEEE TIM paper where the objectives of this paper are to test an open-source phase noise analyzer, the direct digital phase noise measurement bench developed by A. Holme (called AH analyzer in this paper), and compare it to a commercial phase noise analyzer, the 53100A.


Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.


Study on Sleep Positions using a Wearable Device

This device constantly collects data about acceleration in three directions (tri-axial) 50 times a second (50 Hz). It has several components: a microcontroller for gathering and sending data (ESP8266), a battery (lithium-ion), a sensor for measuring acceleration (ADLX345 accelerometer), and a protective case made of plastic. The device can also store data temporarily on a microSD card in case the wireless connection is lost.


This radar raw datasets are collected with the FMCW radar system --- PARSAX in Delft University of Technology, the Netherlands. Two datasets are included, in which one is the echo scattered from a stationary industrial chimney and the other one is the echo of rain droplets.  The radar echo of chimney was acquired in one FMCW signal sweep. And the echo of the rain droplets was measured over 512 sweeps, which can be used for both range profile and range-Doppler processing. In both radar datasets, the echoes of targets were contaminated by interferences.


The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).

All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.


The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).

The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).

Each file consists of a signals from one sigle ride.


An IEEE 802.15.4 backscatter communication dataset for Radio Frequency (RF) fingerprinting purposes.

It includes I/Q samples of transmitted frames from six carrier emitters, including two USRP B210 devices (labeled as c#) and four CC2538 chips (labeled as cc#), alongside ten backscatter tags (identified as tag#). The carrier emitters generate an unmodulated carrier signal, while the backscatter tags employ QPSK modulation within the 2.4 GHz frequency band, adhering to the IEEE 802.15.4 protocol standards.