Signal Processing
The prototype of the calibration is verified with a 12-bit SAR ADC manufactured in 28-nm standard CMOS process. It is based on non-binary weights differential SAR ADC with bottom-plate sampling. This data was captured using a logic analyzer. The data for fast Fourier transform (FFT) is an input 1 MHz sine wave at 50MS/s. The signal input amplitude is 15dbm. The sampling points are 131072. The MATLAB code includes both the original weight and the calibration weight.
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The synthetic data is generated loosely following the concepts developed by Skomedal and Deceglie (2020)
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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.
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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.
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1764 Views
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.
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681 Views
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. This approach allowed for the inclusion of diverse data samples, taking into account variations in player metrics, movements, and gameplay dynamics.
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219 Views
Developing mind-controlled prosthetics that seamlessly integrate with the human nervous system is a significant challenge in the field of bioengineering. This project investigates the use of labelled brainwave patterns to control a bionic arm equipped with a sense of touch. The core objective is to establish a communication channel between the brain and the artificial limb, enabling intuitive and natural control while incorporating sensory feedback.
The project involves:
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750 Views
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.
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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.
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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.
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2598 Views