scholarly journals Ensemble Learning in BCI-SSVEP Systems for Short Window Lengths

2021 ◽  
Author(s):  
Henrique L. V. Giuliani ◽  
Patrick O. de Paula ◽  
Diogo C. Soriano ◽  
Ricardo Suyama ◽  
Denis G. Fantinato
Keyword(s):  
Steady State ◽  
Relevant Information ◽  
High Accuracy ◽  
Visually Evoked Potentials ◽  
Improve Performance ◽  
Activation Patterns ◽  
Promising Alternative ◽  
Adaboost Algorithm ◽  
Computer Interfaces ◽  
Simulation Results

Different approaches have been investigated to implement effective Brain-Computer Interfaces (BCI), translating brain activation patterns into commands to external devices. BCI exploring Steady-State Visually Evoked Potentials usually achieve relatively high accuracy, when considering 2-3 second sample windows, but the performance degrades for smaller windows. So, we investigate the use of an ensemble method, the Adaboost algorithm, combining two different structures, the Logistic Regressor and the Multilayer Perceptron, whose diversity shall bring relevant information for more accurate classification. Simulation results indicate that the proposed method can improve performance for smaller windows, being a promising alternative to reduce model variance.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

A New Methodology of Harmonic Detection Based on Windowed FFT and Wavelet Transform

2013 ◽  
Vol 732-733 ◽  
pp. 634-638 ◽  
Author(s):  
Xu Zheng ◽  
Shuo Wang ◽  
You Bing Zhang
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
High Accuracy ◽  
Voltage Sag ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Harmonic Detection ◽  
Harmonic Signals ◽  
System A ◽  
Simulation Results

Traditional FFT show good trait in steady-state harmonic testing, while it turn low accuracy when testing harmonic with voltage sag or transient, and can not illustrates the information on amplitude-time plane. The discrete wavelet transform is suitable for transient harmonic signals. However it is not intuitive and has more computing than FFT. Since both steady-state and transient components exist in power system, a new method of harmonic analysis algorithm based on Kaiser windowed FFT combing with Daubechies wavelet transform is proposed. The simulation results demonstrate this method possesses high accuracy and flexibility in harmonic detection.

scholarly journals Spatiotemporal Patterns Formed by a Discrete Nutrient-Phytoplankton Model with Time Delay

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Feifan Zhang ◽  
Wenjiao Zhou ◽  
Lei Yao ◽  
Xuanwen Wu ◽  
Huayong Zhang
Keyword(s):  
Steady State ◽  
Time Delay ◽  
Numerical Simulations ◽  
Functional Response ◽  
Bifurcation Analysis ◽  
Discrete Model ◽  
Continuous Model ◽  
Spatiotemporal Patterns ◽  
Homogeneous Steady State ◽  
Simulation Results

In this research, a continuous nutrient-phytoplankton model with time delay and Michaelis–Menten functional response is discretized to a spatiotemporal discrete model. Around the homogeneous steady state of the discrete model, Neimark–Sacker bifurcation and Turing bifurcation analysis are investigated. Based on the bifurcation analysis, numerical simulations are carried out on the formation of spatiotemporal patterns. Simulation results show that the diffusion of phytoplankton and nutrients can induce the formation of Turing-like patterns, while time delay can also induce the formation of cloud-like pattern by Neimark–Sacker bifurcation. Compared with the results generated by the continuous model, more types of patterns are obtained and are compared with real observed patterns.

scholarly journals Adaptive modulation based on steady-state mean square error for underwater acoustic communication

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhiyong Liu ◽  
Zhoumei Tan ◽  
Fan Bai
Keyword(s):  
Steady State ◽  
Mean Square Error ◽  
Adaptive Modulation ◽  
Blind Equalization ◽  
Training Sequence ◽  
Transmission Efficiency ◽  
Underwater Acoustic Communication ◽  
Mean Square ◽  
Modulation Mode ◽  
Simulation Results

AbstractTo improve the transmission efficiency and facilitate the realization of the scheme, an adaptive modulation (AM) scheme based on the steady-state mean square error (SMSE) of blind equalization is proposed. In this scheme, the blind equalization is adopted and no training sequence is required. The adaptive modulation is implemented based on the SMSE of blind equalization. The channel state information doesn’t need to be assumed to know. To better realize the adjustment of modulation mode, the polynomial fitting is used to revise the estimated SNR based on the SMSE. In addition, we also adopted the adjustable tap-length blind equalization detector to obtain the SMSE, which can adaptively adjust the tap-length according to the specific underwater channel profile, and thus achieve better SMSE performance. Simulation results validate the feasibility of the proposed approaches. Simulation results also show the advantages of the proposed scheme against existing counterparts.

Utilizing an Adaptive Controller (Azadi Controller) for Trajectory Planning of PUMA 560 Robot

2011 ◽  
Vol 403-408 ◽  
pp. 4880-4887
Author(s):  
Sassan Azadi
Keyword(s):  
Steady State ◽  
Trajectory Planning ◽  
Positive Feedback ◽  
Fuzzy Controller ◽  
Research Work ◽  
Adaptive Controller ◽  
The Novel ◽  
Transient Responses ◽  
Simulation Results ◽  
Good Substitute

This research work was devoted to present a novel adaptive controller which uses two negative stable feedbacks with a positive unstable positive feedback. The positive feedback causes the plant to do the break, therefore reaching the desired trajectory with tiny overshoots. However, the two other negative feedback gains controls the plant in two other sides of positive feedback, making the system to be stable, and controlling the steady-state, and transient responses. This controller was performed for PUMA-560 trajectory planning, and a comparison was made with a fuzzy controller. The fuzzy controller parameters were obtained according to the PSO technique. The simulation results shows that the novel adaptive controller, having just three parameters, can perform well, and can be a good substitute for many other controllers for complex systems such as robotic path planning.

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