Abstract: A method and system is provided for generating and distributing heat at a target area of a patient's body for treating lesions, tumours, cancers, body pain and nerve pain. The generated heat and the tissue temperature are monitored in real time. The system comprises a radio frequency (RF) antenna for receiving the RF waves generated from the RF generator. A RF absorber comprising several closed loop circuits and a miniaturized thermometer are implanted inside the body close to the target tissue. A controller/optimizer regulates a frequency and a transmission timing of the RF waves based on the measured target tissue temperature. The thermometer, the RF absorber and the wireless transmitter are placed in a screw. The RF absorber is made of metal with RF absorption rate higher than that of biological tissues. A ultrasound energy is also used to treat the target area.

Abstract: Real-time, short-term analysis of ECG, by using multiple signal processing and machine learning techniques, is used to determine counter shock success in defibrillation. Combinations of measures when used with machine learning algorithms readily predict successful resuscitation, guide therapy and predict complications. In terms of guiding resuscitation, they may serve as indicators and when to provide counter shocks and at what energy levels they should be provided as well as to serve as indicators of when certain drugs should be provided (in addition to their doses). For cardiac arrest, the system is meant to run in real time during all current resuscitation procedures including post-resuscitation care to detect deterioration for guiding care such as therapeutic hypothermia.

Abstract: Provided is a new hierarchical methodology having a series of computational steps such as adaptive window creation, 2-D SWT application, masking, and boundary tracing is proposed. The techniques and systems are able to detect and quantify fracture as well as to generate recommendations for decision-making and treatment planning in traumatic pelvic injuries.

Abstract: Automated quantitative analysis of microcirculation, such as density of blood vessels and red blood cell velocity, is implemented using image processing and machine learning techniques. Detection and quantification of the microvasculature is determined from images obtained through intravital microscopy. The results of quantitatively monitoring and assessing the changes that occur in microcirculation during resuscitation period assist physicians in making diagnostically and therapeutically important decisions such as determination of the degree of illness as well as the effectiveness of the resuscitation process. Advanced digital image processing methods are applied to provide quantitative assessment of video signals for detection and characterization of the microvasculature (capillaries, venules, and arterioles).

Abstract: A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.

Abstract: Accurate pelvic fracture detection is accomplished with automated X-ray and Computed Tomography (CT) images for diagnosis and recommended therapy. The system combines computational methods to process images from two different modalities, using Active Shape Model (ASM), spline interpolation, active contours, and wavelet transform. By processing both X-ray and CT images, features which may be visible under one modality and not under the other are extracted and validates and confirms information visible in both. The X-ray component uses hierarchical approach based on directed Hough Transform to detect pelvic structures, removing the need for manual initialization. The X-ray component uses cubic spline interpolation to regulate ASM deformation during X-ray image segmentation. Key regions of the pelvis are first segmented and identified, allowing detection methods to be specialized to each structure using anatomical knowledge.

Abstract: Automated quantitative analysis of microcirculation, such as density of blood vessels and red blood cell velocity, is implemented using image processing and machine learning techniques. Detection and quantification of the microvasculature is determined from images obtained through intravital microscopy. The results of quantitatively monitoring and assessing the changes that occur in microcirculation during resuscitation period assist physicians in making diagnostically and therapeutically important decisions such as determination of the degree of illness as well as the effectiveness of the resuscitation process. Advanced digital image processing methods are applied to provide quantitative assessment of video signals for detection and characterization of the microvasculature (capillaries, venules, and arterioles).

Abstract: A decision-support system and computer implemented method automatically measures tee midline shift in a patient's brain using Computed Tomography (CT) images. The decision-support system and computer implemented method applies machine learning methods to features extracted from multiple sources, including midline shift, blood amount, texture pattern and other injury data, to provide a physician an estimate of intracranial pressure (ICP) levels. A hierarchical segmentation method, based on Gaussian Mixture Mode! (GMM), is used. In this approach, first an Magnetic Resonance Image (MRI) ventricle template, as prior knowledge, is used to estimate the region for each ventricle. Then, by matching the ventricle shape it) CT images to fee MRI ventricle template set, the corresponding MRI slice is selected. From the shape matching result, the feature points for midline estimation in CT slices, such as the center edge points of the lateral ventricles, are detected.

Abstract: Accurate pelvic fracture detection is accomplished with automated X-ray and Computed Tomography (CT) images for diagnosis and recommended therapy. The system combines computational methods to process images from two different modalities, using Active Shape Model (ASM), spline interpolation, active contours, and wavelet transform. By processing both X-ray and CT images, features which may be visible under one modality and not under the other are extracted and validates and confirms information visible in both. The X-ray component uses hierarchical approach based on directed Hough Transform to detect pelvic structures, removing the need for manual initialization. The X-ray component uses cubic spline interpolation to regulate ASM deformation during X-ray image segmentation. Key regions of the pelvis are first segmented and identified, allowing detection methods to be specialized to each structure using anatomical knowledge.

Abstract: A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.

Abstract: The present invention relates to advanced signal processing methods including digital wavelet transformation to analyze heart-related electronic signals and extract features that can accurately identify various states of the cardiovascular system. The invention may be utilized to estimate the extent of blood volume loss, distinguish blood volume loss from physiological activities associated with exercise, and predict the presence and extent of cardiovascular disease in general.

Abstract: The present disclosure provides an automated method for the detection and identification of money service business transactions, including: performing a preprocessing operation, wherein the preprocessing operation includes filtering a dataset; performing a feature extraction operation, wherein the feature extraction operation includes extracting predetermined features from a transaction signal; performing a statistical analysis operation for the testing of significance of extracted features and dimension reduction; and performing one or more of a nonlinear classification operation and a linear classification operation, wherein the nonlinear or linear classification operation includes classifying data that appears to be related to a money service business transaction.

Abstract: Disclosed is a hierarchical computational method to predict the expression value of genes in time-series microarray data. The method may include the step of first applying a nonlinear independent component analysis (NICA) algorithm that extracts the major components covering all considered genes. An autoregressive exogenous (ARX) model may subsequently be used to quantitatively express the dynamic interactions of all components with each other. Then, using the predicted values for the components, and the nonlinear independent component analysis in the inverse form, the data may be used to predict the expression parameters for individual genes. The method may be used for the analysis of a eukaryotic gene expression throughout the cell cycle.

Abstract: Disclosed are methods and systems for applying independent component analysis (ICA) and other advanced signal processing techniques to automatically identify an optimal number of independent gene clusters and to efficiently separate gene expression data into biologically relevant groups. Embodiments of the methods and systems of the present invention provide an interface that allows the user to review the results at various stages during the analysis, thereby optimizing the type of analysis performed for a specific experiment. Also disclosed are methods and systems to mathematically define the relationship for gene expression within a group of interrelated genes.

Abstract: The present disclosure provides an automated method for the detection and identification of money service business transactions, including: performing a preprocessing operation, wherein the preprocessing operation includes filtering a dataset; performing a feature extraction operation, wherein the feature extraction operation includes extracting predetermined features from a transaction signal; performing a statistical analysis operation for the testing of significance of extracted features and dimension reduction; and performing one or more of a nonlinear classification operation and a linear classification operation, wherein the nonlinear or linear classification operation includes classifying data that appears to be related to a money service business transaction.

Abstract: Embodiments of the present invention provide digital watermarking methods that embed a digital watermark in both the low and high frequencies of an image or other production, providing a digital watermark that is resistant to a variety of attacks. The digital watermarking methods of the present invention optimize the strength of the embedded digital watermark such that it is as powerful as possible without being perceptible to the human eye. The digital watermarking methods of the present invention do this relatively quickly, in real-time, and in an automated fashion using an intelligent system, such as a neural network. The digital watermarking methods of the present invention may also be used in a variety of new applications, such as the digital watermarking of sensitive aircraft parts and military equipment.

Abstract: Embodiments of the present invention provide digital watermarking methods that embed a digital watermark in both the low and high frequencies of an image or other production, providing a digital watermark that is resistant to a variety of attacks. The digital watermarking methods of the present invention optimize the strength of the embedded digital watermark such that it is as powerful as possible without being perceptible to the human eye. The digital watermarking methods of the present invention do this relatively quickly, in real-time, and in an automated fashion using an intelligent system, such as a neural network. The digital watermarking methods of the present invention may also be used in a variety of new applications, such as the digital watermarking of sensitive aircraft parts and military equipment.

Abstract: Disclosed are methods and systems for applying independent component analysis (ICA) and other advanced signal processing techniques to automatically identify an optimal number of independent gene clusters and to efficiently separate gene expression data into biologically relevant groups. Embodiments of the methods and systems of the present invention provide an interface that allows the user to review the results at various stages during the analysis, thereby optimizing the type of analysis performed for a specific experiment. Also disclosed are methods and systems to mathematically define the relationship for gene expression within a group of interrelated genes.

Abstract: Disclosed are methods and systems for applying independent component analysis (ICA) and other advanced signal processing techniques to automatically identify an optimal number of independent gene clusters and to efficiently separate gene expression data into biologically relevant groups. Embodiments of the methods and systems of the present invention provide an interface that allows the user to review the results at various stages during the analysis, thereby optimizing the type of analysis performed for a specific experiment. Also disclosed are methods and systems to mathematically define the relationship for gene expression within a group of interrelated genes.

Abstract: Disclosed are methods and systems for applying independent component analysis (ICA) and other advanced signal processing techniques to automatically identify an optimal number of independent gene clusters and to efficiently separate gene expression data into biologically relevant groups. Embodiments of the methods and systems of the present invention provide an interface that allows the user to review the results at various stages during the analysis, thereby optimizing the type of analysis performed for a specific experiment. Also disclosed are methods and systems to mathematically define the relationship for gene expression within a group of interrelated genes.