Time-Frequency Domain Techniques For Identification Of Power System Transients

2011 ◽  
Vol 5 (2) ◽  
pp. 42-48
Author(s):  
Ashwani Kumar Chandel ◽  
◽  
P. Srikanth ◽  
Keyword(s):  
Power System ◽  
Frequency Domain ◽  
Time Frequency ◽  
Power System Transients ◽  
Frequency Domain Techniques

scholarly journals Cognitive Rehabilitation and EEG Analysis: A Review

2020 ◽  
Vol 9 (2) ◽  
pp. 1146-1152
Keyword(s):  
Frequency Domain ◽  
Cognitive Rehabilitation ◽  
Autism Spectrum ◽  
Speech Disorders ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Cognitive Levels ◽  
Processed Eeg ◽  
Frequency Domain Techniques ◽  
The Brain

Cognition is the capacity of the brain to register and decipher data dependent on information and experience. A portion of the subjective aptitudes are processing, memory and retention, logic and reasoning, attention and so forth. The subjective abilities begin to grow directly from the hour of birth of a person. There are situations where these advancements don't happen at the opportune time or in a proficient manner, which prompts scholarly disorders. The most commonly found intellectual disorders in children are attention deficit hyperactivity disorder (ADHD), epilepsy, encephalitis, autism spectrum disorder (ASD) and speech disorders. There are cognitive tasks and retraining intended for each sort of cognitive issue. These are planned so as to improve the cognitive degrees of the children who experience the ill effects of cognitive issues, for an improvement in their everyday lives. This paper gives an overview of some of the existing techniques for the improvement of cognitive levels along with the techniques of EEG analysis. The activities in the brain can be traced with the help of an electroencephalogram (EEG). Cognitive levels can also be studied with the help of EEG. The study that involves cognition requires careful pre-processing, feature extraction and appropriate analysis. The processed EEG information is analysed utilizing various techniques which can extensively be ordered into time domain, time frequency domain, frequency domain, non-linear methods and artificial neural network methods. Out of every one of these strategies, the frequency domain techniques and time-frequency strategies are most popularly used.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

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