53 results about "Inverse discrete wavelet transform" patented technology

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The nth level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

A signal processing method and system combines multi-scale decomposition, such as wavelet, pre-processing together with a compression technique, such as an auto-associative artificial neural network, operating in the multi-scale decomposition domain for signal denoising and extraction. All compressions are performed in the decomposed domain. A reverse decomposition such as an inverse discrete wavelet transform is performed on the combined outputs from all the compression modules to recover a clean signal back in the time domain. A low-cost, non-drug, non-invasive, on-demand therapy braincap system and method are pharmaceutically nonintrusive to the body for the purpose of disease diagnosis, treatment therapy, and direct mind control of external devices and systems. It is based on recognizing abnormal brainwave signatures and intervenes at the earliest moment, using magnetic and / or electric stimulations to reset the brainwaves back to normality. The feedback system is self-regulatory and the treatment stops when the brainwaves return to normal. The braincap contains multiple sensing electrodes and microcoils; the microcoils are pairs of crossed microcoils or 3-axis triple crossed microcoils.

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2N frames, and the iteration is performed N−1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

An object of this invention is to efficiently attain noise removal. In order to achieve the object, the discrete wavelet transforms of an input image are computed to output wavelet coefficients of respective subbands (S301). Appropriate threshold values are respectively set for subbands HL, LH, and HH indicating high-frequency components (S302a, S302b, S302c). The wavelet coefficients of the subbands HL, LH, and HH then undergo threshold value processes using the set threshold values for the respective subbands (S303a, S303b, S303c). Pixels to be processed in a coefficient conversion process are determined based on the threshold value processing results of the respective subbands (S304). The wavelet coefficients of the respective subbands corresponding to the pixels to be processed determined in step S304 undergo coefficient conversion (S305), and the converted transformation coefficients undergo inverse discrete wavelet transformation, thus reconstructing and outputting a noise-removed image (S306).

A method and apparatus are provided for the removal of significant broad-band noise from waveforms acquired for time domain network analysis. The method may include the steps of providing the noisy waveform as an input waveform, determining a frequency domain noise shape associated with the input waveform, calculating a wavelet domain noise shape from the frequency domain noise shape, calculating a discrete wavelet transform of the input waveform to form a wavelet domain waveform, and estimating the noise statistics from the wavelet domain waveform. A threshold may be calculated from the estimated noise statistics and the wavelet domain noise shape, and the threshold may be applied to the wavelet domain waveform to form a denoised wavelet domain waveform. Finally, an inverse discrete wavelet transform of the denoised wavelet domain waveform may be calculated to form a denoised waveform.