Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: At least one embodiment of the disclosure is directed to a method for processing brainwave signals for treating a patient having neurological disorder or mental disorder or a combination of neurological and mental disorder. The method comprises: measuring a brainwave signal from the patient, the measured brainwave signal containing noise; denoising the brainwave signal to obtain a clean brainwave signal; matching the clean brainwave signal to a database of brainwave signals for neurological or mental conditions or a combination of neurological and mental conditions to identify the patient's neurological or mental status or a combination of neurological and mental conditions; and applying a therapeutic treatment to the patient based on the identified neurological or mental status or a combination of neurological and mental conditions.

Abstract: 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 non-intrusive 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.

Abstract: 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 non-intrusive 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.

Abstract: 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 non-intrusive 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.

Abstract: 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 non-intrusive 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.

Abstract: An array of sensor elements is formed by the incorporation of sensing materials into porous structures, creating sensing systems with extremely large surface areas with sensing molecules attached to mimic the large number of cilia of an olfactory system. In each sensor element, the sensing material or molecules are attached to spacer molecules or groups, which are attached to linker molecules or groups, which are attached to the porous substrate material. More specifically, a porphyrin doped aerogel material is used. The porphyrin sensing material is attached to the aerogel throughout its high surface area pore space. The porphyrin is covalently bonded to the silica network of the aerogel with a triethoxysilyl group linker that covalently attaches to the aerogel, and an alkyl group spacer.

Abstract: 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.

Abstract: A set of volatile markers are determined which are characteristic of a particular condition or disease, and which will be found in the exhaled breath of a person or odor from other parts of a body or from an entity. These markers are detected in the breath odor or gaseous emanations from the body or entity noninvasively using a volatile substance detector of sufficient sensitivity, such as an artificial olfactory system. The detected marker data is processed in an artificial neural network/fuzzy filter system with an algorithm that intelligently adapts to the individual body or entity and also optionally (if necessary) with a correction algorithm to eliminate environmental and other erroneous contributions to the markers. Any number of markers may be used, depending on how well they correlate with the condition and how accurate a result is desired, i.e. general screening or accurate diagnosis and monitoring.

Abstract: An array of sensor elements is formed by the incorporation of sensing materials into porous structures, creating sensing systems with extremely large surface areas with sensing molecules attached to mimic the large number of cilia of an olfactory system. In each sensor element, the sensing material or molecules are attached to spacer molecules or groups, which are attached to linker molecules or groups, which are attached to the porous substrate material. More specifically, a porphyrin doped aerogel material is used. The porphyrin sensing material is attached to the aerogel throughout its high surface area pore space. The porphyrin is covalently bonded to the silica network of the aerogel with a triethoxysilyl group linker that covalently attaches to the aerogel, and an alkyl group spacer.

Abstract: The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.

Abstract: The artificial olfactory system is an ultra-sensitive and selective odor sensing system for the detection of odorant molecules down to the part per trillion level. The system includes multiple ultra sensitive frequency sensors, such as sensors based on piezoelectric substrates or micro-machined resonators, capable of detecting frequency changes resulting from the interaction of odorant molecules with the sensor. A coating applied to the sensor greatly increases the surface of interaction between the odorant molecules or biological agents and the sensor. An array of these sensors, each responding to the interaction of an odorant molecule species but in a different manner, results in different frequency shifts. An ultra sensitive frequency measurement device measures as small as part per billion shift in frequency.

Abstract: The artificial olfactory system is an ultra-sensitive and selective odor sensing system for the detection of odorant molecules down to the part per trillion level. The system includes multiple ultra sensitive frequency sensors, such as sensors based on piezoelectric substrates or micro-machined resonators, capable of detecting frequency changes resulting from the interaction of odorant molecules with the sensor. A coating applied to the sensor greatly increases the surface of interaction between the odorant molecules or biological agents and the sensor. An array of these sensors, each responding to the interaction of an odorant molecule species but in a different manner, results in different frequency shifts. An ultra sensitive frequency measurement device measures as small as part per billion shift in frequency.

Abstract: A set of volatile markers are determined which are characteristic of a particular condition or disease, and which will be found in the exhaled breath of a person or odor from other parts of a body or from an entity. These markers are detected in the breath odor or gaseous emanations from the body or entity noninvasively using a volatile substance detector of sufficient sensitivity, such as an artificial olfactory system. The detected marker data is processed in an artificial neural network/fuzzy filter system with an algorithm that intelligently adapts to the individual body or entity and also optionally (if necessary) with a correction algorithm to eliminate environmental and other erroneous contributions to the markers. The marker need only be as volatile as the sensitivity of the detector requires. Thus an ultrasensitive detector can detect a marker of very low volatility or concentration.

Abstract: The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.

Abstract: Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased.

Abstract: A method of storing information from filled-in form-documents comprises extracting the unique user information in the foreground from the document form information in the background. The contrast of the pixels is enhanced by a gamma correction on an image array, and then the color value of each of pixel is enhanced. The color pixels lying on edges of an image are converted to black and an adjacent pixel is converted to white. The distance between black pixels and other pixels in the array is determined, and a filled-edge array of pixels is created. User information is then converted to a two-color format by creating a first two-color image of the scanned image by converting all pixels darker than a threshold color value to black. All the pixels that are lighter than the threshold color value to white. Then a second two-color image of the filled-edge file is generated by converting all pixels darker than a second threshold value to black and all pixels lighter than the second threshold color value to white.

Abstract: An image represented in a first image array of pixels is first decimated in two dimensions before being compressed by a predefined compression algorithm such as JPEG. Another possible predefined compression algorithm can involve a wavelet technique. The compressed, reduced image is then transmitted over the limited bandwidth transmission medium, and the transmitted image is decompressed using an algorithm which is an inverse of the predefined compression algorithm (such as reverse JPEG). The decompressed, reduced image is then interpolated back to its original array size. Edges (contours) in the image are then sharpened to enhance the perceptual quality of the reconstructed image. Specific sharpening techniques are described.