scholarly journals Creation of a Hardware-Software Dynamic Signal Simulator of the Upgraded Inter-Satellite Radio Link of the GLONASS System

2021 ◽  
Vol 8 (3) ◽  
pp. 63-71
Author(s):  
A. A. Cherkasova ◽  
◽  
R. F. Salakhov ◽  
D. A. Astaсhov ◽  
◽  
...  
Keyword(s):  
Information Transfer ◽  
Doppler Frequency ◽  
Radio Link ◽  
Satellite Measurements ◽  
Process Measurement ◽  
Signal Simulator ◽  
Information Transfer Rate ◽  
The Difference ◽  
Measuring Signal ◽  
Orthogonal Phase

This work is aimed at creating a hardware-software signal simulator of the upgraded inter-satellite radio link (ISRL) of the GLONASS system. The simulator shapes ISRL signals with dynamically changing parameters of the Doppler frequency shift and delay, which correspond to the mutual dynamics of spacecraft (SC) motion of the GLONASS system. The upgraded inter-satellite radio link will provide (as compared to the current ISRL) an increase in the information transfer rate of up to four times, as well as boost the accuracy of measuring the distance between satellites by two times. Modernization consists in complementing the radio signal of the second orthogonal (phase-shifted carrier frequency by 90 degrees relative to the existing one) component. To modernize the ISRL, it is necessary to create and verify new equipment for receiving and transmitting signals of the upgraded ISRL of the GLONASS system. The simulator is designed to process measurement algorithms embedded in the on-board equipment for inter-satellite measurements and assess their consistency. Consistency evaluation consists in measuring and analyzing the difference between the Doppler parameters and delay introduced into the signal and the estimation of these parameters in the receiving equipment of the ISRL. This difference will be the measurement error. Dynamic simulation is performed for 24 system points, corresponding to GLONASS satellites, on the half-period of satellite revolution (20 280 seconds). The signal is generated at the input of the antenna-feeder device of one of the satellites in accordance with the information for generating the measuring signal, parameters of the transmitters of the signals of the upgraded ISRL and the almanac of the satellite constellation (because the signal at the input of the antenna-feeder device of the navigation receiver incomes from several SC) specified by the user.

scholarly journals Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhihua Huang ◽  
Minghong Li ◽  
Yuanye Ma
Keyword(s):  
Feature Extraction ◽  
Parallel Computing ◽  
Information Transfer ◽  
Small Scale ◽  
Online Experiments ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Desktop Computers ◽  
The Difference ◽  
Computing Scheme

This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.

scholarly journals Computer approximation of air gauge dynamic characteristics using the sine input test rig

2019 ◽  
Vol 252 ◽  
pp. 02002
Author(s):  
Michał Jakubowicz ◽  
Mirosław Rucki ◽  
Zbigniew Siemiątkowski
Keyword(s):  
Dynamic Error ◽  
Calibration Procedure ◽  
Back Pressure ◽  
Test Rig ◽  
Process Measurement ◽  
Dynamical Characteristics ◽  
Full Control ◽  
Circular Frequency ◽  
Measuring Signal ◽  
Input Test

The paper describes the test rig dedicated for air gauge dynamical characteristics assessment. The computerised system enables measurement of the amplitudes of back-pressure pk dependent on the input signal circular frequency ω. Dedicated software performs full control on the calibration procedure, which consists of setting a rotational speed and registration of measuring signal, and further data processing. Circular frequency ω was gradually changed with the appropriate step, in order to obtain a series of frequencies in the range from 0.1 to 20 Hz. The response of a measurement system was registered as a sinusoidal curve which after smoothening and interpolation procedures provided an amplitude-frequency graph with its main characteristics, such as time constant and the frequency f0.95 that generated dynamic error 5%. It was demonstrated that sine input dynamic calibration corresponds with real conditions of the in-process measurement with air gauges.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

scholarly journals A low-cost auditory multi-class brain-computer interface based on pitch, spatial and timbre cues

2013 ◽  
Author(s):  
Zacharias Vamvakousis ◽  
Rafael Ramirez
Keyword(s):  
Healthy Subjects ◽  
Transfer Rate ◽  
Information Transfer ◽  
Low Cost ◽  
Auditory Stimuli ◽  
Stimulus Interval ◽  
Inter Stimulus Interval ◽  
Auditory P300 ◽  
Information Transfer Rate ◽  
Cord Injury

P300-based brain-computer interfaces (BCIs) are especially useful for people with illnesses, which prevent them from communicating in a normal way (e.g. brain or spinal cord injury). However, most of the existing P300-based BCI systems use visual stimulation which may not be suitable for patients with sight deterioration (e.g. patients suffering from amyotrophic lateral sclerosis). Moreover, P300-based BCI systems rely on expensive equipment, which greatly limits their use outside the clinical environment. Therefore, we propose a multi-class BCI system based solely on auditory stimuli, which makes use of low-cost EEG technology. We explored different combinations of timbre, pitch and spatial auditory stimuli (TimPiSp: timbre-pitch-spatial, TimSp: timbre-spatial, and Timb: timbre-only) and three inter-stimulus intervals (150ms, 175ms and 300ms), and evaluated our system by conducting an oddball task on 7 healthy subjects. This is the first study in which these 3 auditory cues are compared. After averaging several repetitions in the 175ms inter-stimulus interval, we obtained average selection accuracies of 97.14%, 91.43%, and 88.57% for modalities TimPiSp, TimSp, and Timb, respectively. Best subject’s accuracy was 100% in all modalities and inter-stimulus intervals. Average information transfer rate for the 150ms inter-stimulus interval in the TimPiSp modality was 14.85 bits/min. Best subject’s information transfer rate was 39.96 bits/min for 175ms Timbre condition. Based on the TimPiSp modality, an auditory P300 speller was implemented and evaluated by asking users to type a 12-characters-long phrase. Six out of 7 users completed the task. The average spelling speed was 0.56 chars/min and best subject’s performance was 0.84 chars/min. The obtained results show that the proposed auditory BCI is successful with healthy subjects and may constitute the basis for future implementations of more practical and affordable auditory P300-based BCI systems.

scholarly journals Towards the non-zero field cesium magnetic sensor array for magnetoencephalography

2021 ◽  
Author(s):  
Mikhail Petrenko ◽  
Sergei Dmitriev ◽  
Anatoly Pazgalev ◽  
Alex Ossadtchi ◽  
Anton Vershovskii
Keyword(s):  
Information Transfer ◽  
Sensor Array ◽  
Response Speed ◽  
Magnetic Sensors ◽  
Zero Field ◽  
Optically Pumped ◽  
Noise Spectral Density ◽  
Information Transfer Rate ◽  
Field Sensor ◽  
Better Than

Magnetic sensors developed for application in magnetoencephalography must meet a number of requirements; the main ones are compactness, sensitivity and response speed. We present a quantum optically pumped atomic sensor with cell volume of 0.5cm<sup>3</sup> that meets these requirements and is operable in nonzero magnetic fields. The ultimate sensitivity of the sensor was estimated as (using the criteria of the ratio of the slope of the magnetic resonance signal to the shot noise spectral density) to be better than 5 fT/Hz<sup>1/2</sup>. The actual sensitivity, measured in a gradiometric scheme, reaches 13 fT/Hz<sup>1/2 </sup>per sensor. We also present a novel and fast algorithm for optimization of the geometric properties of non-zero field sensor array with respect to maximization of the information transfer rate for cortical sources.<br>

SSVEP-P300 HYBRID PARADIGM OPTIMIZATION FOR ENHANCED INFORMATION TRANSFER RATE

2020 ◽  
Vol 32 (01) ◽  
pp. 2050003
Author(s):  
Akshay Katyal ◽  
Rajesh Singla
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Brain Response ◽  
Information Transfer Rate ◽  
User Intentions ◽  
Activation Rate ◽  
Optimal Protocol ◽  
Brain Computer Interfacing ◽  
Average Information ◽  
Two Hybrid

Hybrid brain–computer interfacing (BCI), recently, has been the epicenter of research in the area of rehabilitation engineering. The concept is based on the principle that the paradigm used for the BCI elicits one BCI marker in combination with one or more BCI modalities or other physiological signals. These paradigms elicit human brain response to successfully determine user intentions. Steady-state visually evoked potential (SSVEP) has been the favourite amongst researchers to combine with other BCI modalities such as P300, Motor Imagery (MI), etc. to develop assistive devices (ADs) based on hybrid BCI. This research paper is a record of a comparative study conducted between two hybrid BCI’s, namely hybrid BCI-1, hybrid BCI-2 and traditional SSVEP BCI. Both hybrid paradigms are similar in schematics but differ in the operational protocol. The study aimed to find the optimal protocol which greatly enhances the average information transfer rate (ITR) of a BCI-based AD. Hybrid BCI-1 showed lower classification accuracy (90.36%) and higher false activation rate (FAR) (3.16%) as compared to Hybrid BCI-2 (92.35% and 2.78%, respectively) as well as traditional SSVEP (93.38% and 2.73%, respectively). However, the average ITR of Hybrid BCI-1 (80.76 bits/min) was much higher than that of Hybrid BCI-2 (41.21 bits/min) and traditional SSVEP paradigm (36.34 bits/min). This led to the conclusion, that Hybrid BCI-1 is the most viable option for developing an AD.

scholarly journals Enhancing the Usability of Brain-Computer Interface Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hyun Jae Baek ◽  
Min Hye Chang ◽  
Jeong Heo ◽  
Kwang Suk Park
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Signal Acquisition ◽  
Information Transfer Rate ◽  
Wide Range ◽  
First Results ◽  
And Performance ◽  
Improving Accuracy ◽  
User Friendly ◽  
Interface Designs

Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.

SSVEP Recognition by Using Higher Harmonics Based on Music

2016 ◽  
Vol 16 (4) ◽  
Author(s):  
Kun Chen ◽  
Fei Xu ◽  
Quan Liu ◽  
Haojie Liu ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Signal To Noise Ratio ◽  
Signal Classification ◽  
Recognition Time ◽  
Multiple Signal Classification ◽  
Computer Interfaces ◽  
Information Transfer Rate

Among different brain–computer interfaces (BCIs), the steady-state visual evoked potential (SSVEP)-based BCI has been widely used because of its higher signal to noise ratio (SNR) and greater information transfer rate (ITR). In this paper, a method based on multiple signal classification (MUSIC) was proposed for multidimensional SSVEP signal processing. Both fundamental and second harmonics of SSVEPs were employed for the final target recognition. The experimental results proved it has the advantage of reducing recognition time. Also, the relation between the duty-cycle of the stimulus signals and the amplitude of the second harmonics of SSVEPs was discussed via experiments. In order to verify the feasibility of proposed methods, a two-layer spelling system was designed. Different subjects including those who have never used BCIs before used the system fluently in an unshielded environment.

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