Signal Processing

The synthetic data is generated loosely following the concepts developed by Skomedal and Deceglie (2020) 

"https://doi.org/10.1109/JPHOTOV.2020.3018219" , 

"https://github.com/asmunds/simulate_pv_time_series"

Individual physiotherapy is a significant part of the treatment for patients experiencing various forms of pain and health issues. Recent research shows rehabilitation as an important part of therapy for those with abdominal wall defects.  It also plays a crucial role in chest surgery, by helping optimize preoperative assessments and postoperative rehabilitation strategies essential for successful surgery outcomes. With recent technological advancements, new tools have become available to healthcare professionals.

To develop radio frequency-based drone recognition, we release an RF spectrogram dataset, named DroneRFb-Spectra. All signals of drones were collected by a Universal Software Radio Peripheral (USRP) device, recording three Industrial Scientific Medical (ISM) bands under urban scenarios. The classes cover 7 common brands, i.e., DJI, Vbar, FrSky, Futaba, Taranis, RadioLink, and Skydroid, with a total number of 14460. Each spectrogram has been downsampled to the size of 512x512 from the original IQ data with a length of 50ms by using the short-time Fourier transform.

A database was created in .XLSX and .CSV formats containing the processing of an EMG signal and the position and angle error during the execution of three dynamic tasks based on the three-dimensional movement of the upper limb. This data was recorded from the quantification of the hand position error.

The data was collected by outfitting one of the players with the experimental balloon, which incorporated the embedded circuit and sensors. The sensors positioned at the top-right to the player within the bubble balloon, where a player stand inside. The sensors' data  were collected at specific sampling frequencies (Accelerometer: 1000Hz, Gyroscope: 1000Hz, and Pressure: 40Hz). The experiment was conducted involving five different players.

One of the Dravidian language spoken majorly by 60 million people in and around Karnataka state of India is known as Kannada. It is one among 22 scheduled languages of India. Kannada langauge is written in Kannada scriptwhich has its traces back from kadamba script (325-550 AD). There are many languages which were used centuries back and aren’t being used currently whereas Kannada is one such language which is used even today for writing official documents and are being taught at schools which means it is going to be for many years.

Data over voice channel. The carrier is chirp. The dataset has been dealed. Except for the last column number, all other contents are the characteristic values of the signal.  you can use Deep learning model to classify the dataset. There are 8 labels in dataset, and this dataset is suit for data classification.

This paper presents a dataset of brain Electroencephalogram (EEG) signals created when Malayalam vowels and consonants are spoken. The dataset was created by capturing EEG signals utilizing the OpenBCI Cyton device while a volunteer spoke Malayalam vowels and consonants. It includes recordings obtained from both sub-vocal and vocal. The creation of this dataset aims to support individuals who speak Malayalam and suffer from neurodegenerative diseases.

This is the dataset of received signal strength indicator (RSSI) from Wi-Fi routers in different environments.

RSSI was collected by a person holding an Android tablet within one hour 

in S301, Graduate School of Informatic Science (Main Building), Kobe University, Japan.

We collected the RSSI between the terminal and Wi-Fi routers 

per second 30 times at 20 reference points with doors open. 

Then, we collected RSSI data again with doors closed. 

This study presents a method for detecting arc faults by combining load identification and MLP-SVM. The method addresses the issue of interfering loads on arc fault detection and the lack of significant arc fault features in some loads. Initially, the eigenvalues of the line currents for single and mixed loads are extracted in the time domain, both during arc fault and normal operation. Subsequently, load identification is performed using a complex matrix calculation method. After identification, an eigenmatrix and history matrix are created for each load.