This paper describes a new digital control method to enhance the dynamic performance of a dc-dc converter used in Plasma Display Panels (PDP). A simple digital PID compensator with duty ratio feed-forward control is proposed to minimize the output voltage variation for continuously changing load current. The duty ratio feed-forward is calculated using a noise-free load current information, which is predicted by the available video data of the PDP. No separate current sensing circuit is required. A small signal z-domain feed-forward model is derived for the performance analysis and controller design. The proposed control method is experimentally verified on an Asymmetrical Half Bridge (AHB) dc-dc converter, which supplies power to a 42 inch PDP.
Imagined speech is a neuro-paradigm that can provide an alternative communication channel for patients in a locked-in syndrome state. We have performed an experiment in which a 32 channel industry-standard electroencephalography (EEG) device was used to record 26 imagined English alphabets from 13 subjects. We denoised the imagined signals by discrete wavelet transform and extracted the spatial filters by common spatial pattern method, and time-domain features. Spatial features when classified with linear support vector machine, and time-domain features classified by random forest gave the best results. Alpha, beta, and theta bands could classify imagined alphabets better than other bands and had average classification accuracies of 88.59%, 87.39%, and 88.97%, respectively by using spatial features and 81.88%, 76.72%, and 79.25%, respectively, by time-domain features. The grand average accuracies of all the 26 alphabets in six EEG frequency bands was found to be 77.97% in a subject independent binary classification framework.
This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain.
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