Abstract: A system for noninvasive extraction, identification, and marking of the heart valve signals to evaluate and monitor elevated left ventricular end-diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) using at rest assessment of hemodynamic performance, based on quantitative measurements of heart and lung related parameters and cardiac events for diagnostic and therapeutic purposes includes one or more signals from one or more noninvasive sensors or transducers that measure one or more physiological effects that are correlated with cardiopulmonary functions, transmission of the data to a computing device and analysis software where a trained algorithm processes the data to determine the state or condition of elevated LVEDP or PCWP and provides an output indicative of the state or condition of the analysis.

Abstract: A semiconductor wafer cooling system and method and a pressure regulating apparatus for mitigating pressure increases in a semiconductor wafer conditioning circuit are disclosed. The pressure regulating apparatus comprises: a buffer vessel, the buffer vessel comprising an inlet and outlet channel; wherein the inlet channel is configured in operation to be in fluid communication with a higher pressure location of the semiconductor wafer conditioning circuit, and the outlet channel is configured in operation to be in fluid communication with a lower pressure location. the inlet channel comprises a pressure controlled valve configured to close the inlet channel during normal operation such that the buffer vessel is isolated from the higher pressure location of the conditioning circuit and to open the inlet channel in response to the pressure within the semiconductor conditioning circuit rising above a predetermined level.

Abstract: A health sensor system and method can include a wearable non-invasive biosensor for capturing cardiac waveform signals such as electrocardiogram (ECG) signals and composite vibration objects over one or more channels, one or more processors operatively coupled to the wearable non-invasive biosensor, and memory having computer instructions which causes the system to perform certain operations. In some embodiments, the operations can include powering the health sensor system in response to receiving a radio frequency signal using a near field communication protocol, monitoring pulmonary artery pressures based on cardiac time intervals during a period when the health sensor system is powered by the radio frequency signal, performing a heart and lung function assessment based on the monitoring of the pulmonary artery pressures, and presenting the heart and lung function assessment. In some embodiments, the biosensor can be a single NFC patch biosensor.

Abstract: A temperature control apparatus for supplying fluid to control a temperature of at least one semiconductor wafer within a semiconductor processing chamber, the temperature control apparatus comprising: a mixed refrigerant refrigeration system; the temperature control apparatus being configured to supply the mixed refrigerant to at least one conditioning circuit within the semiconductor processing chamber and to receive the mixed refrigerant from the at least one conditioning circuit. The temperature control apparatus comprises a temperature control circuitry for controlling a temperature of the at least one conditioning circuit to one of a plurality of predetermined temperatures, at least one of the temperatures being below -100° C., the temperature control circuitry being configured to control the temperature of the at least one conditioning circuit by controlling at least one of a mass flow rate, composition or temperature of the mixed refrigerant supplied to the at least one conditioning circuit.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats using at least one or more of a PCA algorithm or deep learning.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats using at least one or more of a PCA algorithm or deep learning.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.

Abstract: A sensor device and a method using the sensor device includes a portable device (110) configured to capture composite vibration objects from at least one sensor (102b) and further configured to communicate data to a wireless node (105) in some embodiments. The sensor device further includes at least one or more processors (103, 105, or 106) operatively coupled to the portable device and configured to separate and identify separated vibration sources and further configured to identify a plurality of individual heart vibration events (302, 303, 304, 305) from the composite vibration objects where the one or more processors is further configured to mark individual heart events from the plurality of individual heart vibration events. In some embodiments, the one or more processors marks and presents individual heart events from the plurality of individual heart vibration events.

Abstract: A system (100) for monitoring and diagnosing heart conditions includes a sensor array (102) with accelerometers, an electrocardiogram sensor, and a system to synchronously capture and process (103, 105, or 106) composite heart signals. The system performs separation, identification and marking (201-205 or 501-509) of the signals, to extract information in cardiac vibrations. Machine learning, auditory scene analysis, or spare coding approaches can be used to resolve source separation problems. Analysis of the composite signals separates different vibration signals (302, 303, 304, 305), identifies streams for particular valve signal, and marks them with event time information with respect to a synchronous electrocardiogram signal (306). The psychoacoustic analysis mimics human auditory processing to enhance hearing of heart valve sounds by separating valve vibrations from the composite signal, and providing extraction, identification, marking and display of individual valve signals.

Abstract: A method and system of assessing and monitoring of cardiopulmonary diseases can include the steps of receiving a composite signal representative of individual events from a plurality of sources associated with a patients cardio-pulmonary system and separating an individual signal as a separate component of the composite signal where the individual signal i s representative of an individual event from one of a plurality of cardio-pulmonary events. The method and system can further include and deriving clinical findings responsive from the separating and presenting the clinical findings.

Abstract: A system for monitoring and diagnosing heart conditions includes a sensor array with accelerometers, an electrocardiogram sensor, and a system to capture and process the composite heart signals. The system performs source separation to extract information contained in cardio pulmonic vibrations. The system can use machine learning, auditory scene analysis, spare coding, determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, K-SVD Matching Pursuit, Non-negative matrix factorization and Deep Belief Networks (Restricted Boltzmann Machine) approaches to the source separation problem. Other embodiments are disclosed.

Abstract: A system for identifying heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory, where the one or more processors are configured to perform the steps of separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks and tagging the plurality of individual heart vibration events using at least one among principal component analysis, Gabor filtering, generalized cross correlation, phase transform, smoothed coherent transformation, Roth correlation or band filtering.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.

Abstract: A system and method for monitoring and diagnosing heart conditions may include capturing and processing a composite heart signal and isolating individual components of such a composite signal. The disclosed techniques of measuring hemodynamic parameters may extract information contained in cardio-pulmonic vibrations. In operation, a system and method may separate different vibration signals along with event time information with respect to a synchronous electrocardiogram signal, and may provide for measurement of hemodynamic parameters from these separated signals.

Abstract: A system for monitoring and diagnosing heart conditions includes a sensor array with accelerometers, an electrocardiogram sensor, and a system to capture and process the composite heart signals. The system performs source separation to extract information contained in cardio pulmonic vibrations. The system can use machine learning, auditory scene analysis, spare coding, determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, K-SVD Matching Pursuit, Non-negative matrix factorization and Deep Belief Networks (Restricted Boltzmann Machine) approaches to the source separation problem. Other embodiments are disclosed.

Abstract: A system (100) for monitoring and diagnosing heart conditions includes a sensor array (102) with accelerometers, an electrocardiogram sensor, and a system to synchronously capture and process (103, 105, or 106) composite heart signals. The system performs separation, identification and marking (201-205 or 501-509) of the signals, to extract information in cardiac vibrations. Machine learning, auditory scene analysis, or spare coding approaches can be used to resolve source separation problems. Analysis of the composite signals separates different vibration signals (302, 303, 304, 305), identifies streams for particular valve signal, and marks them with event time information with respect to a synchronous electrocardiogram signal (306). The psychoacoustic analysis mimics human auditory processing to enhance hearing of heart valve sounds by separating valve vibrations from the composite signal, and providing extraction, identification, marking and display of individual valve signals.

Abstract: A noise cancellation device includes a plurality of first computation modules, a formant detection module, a direction of arrival module and a beamformer. The plurality of first computation modules receives raw audio data and generates a respective transformed signal as a function of formants. A first transformed signal relates to speech data and a second transformed signal relates to noise data. The formant detection module receives the first transformed signal and generates a frequency range data signal. The direction of arrival module receives the first and second transformed signals, determines a cross-correlation between the first and second transformed signals, and generates a spatial orientation data signal. The beamformer receives the first and second transformed signals, the frequency range data signal, and the spatial orientation data signal and generates modification data at selected formant ranges to eliminate a maximum amount of the noise data.

Abstract: IP unicast communication is provided between mobile devices in a wide area network and a wireless local area network coupled by a gateway component. Data communication is established between a mobile device and the wireless local area network, thereby receiving a first unicast IP address associated with the mobile device. A multicast group is joined within the wireless local area network and registering with that multicast group using a predetermined multicast IP address as a destination IP address and the first unicast IP address as a source address. The device disconnects from data communication within the wireless local area network, thereby releasing the first unicast IP address. Data communication is established between the mobile device and the wide area network, thereby receiving a second unicast IP address associated with the mobile device.

Abstract: A method for dynamically providing location-based services, including receiving at least one user input indicating a request for a suggested point of interest; and generating a query based on the user input and a time reference. In addition, the generated query can be processed to identify a plurality of points of interest. The points of interest can be presented to a user.