Abstract: A charge controller for a vehicle passive energy system employs an interface to a plurality of charge sources including intermittent sources, such as vehicle mounted solar photovoltaic (PV) panels and the vehicle engine and alternator, and an interface to a plurality of charge loads, such as a comfort heating ventilation, refrigeration and air conditioning (HVAC) system and auxiliary vehicle loads. Charge logic computes which of the plurality of sources will supply the charge current and which of the plurality of loads will receive the charge current, and a switch responsive to the charge logic will direct the charge current from at least one of the plurality of sources to at least one of the loads, the plurality of charge sources mutually exclusive from simultaneously powering a common load.

Abstract: A charge controller for a vehicle passive energy system employs an interface to a plurality of charge sources including intermittent sources, such as vehicle mounted solar photovoltaic (PV) panels and the vehicle engine and alternator, and an interface to a plurality of charge loads, such as a comfort heating ventilation, refrigeration and air conditioning (HVAC) system and auxiliary vehicle loads. Charge logic computes which of the plurality of sources will supply the charge current and which of the plurality of loads will receive the charge current, and a switch responsive to the charge logic will direct the charge current from at least one of the plurality of sources to at least one of the loads, the plurality of charge sources mutually exclusive from simultaneously powering a common load.

Abstract: A front-end system collects diagnostically relevant information or data, including pulse waveform information, and optionally timing information, symptom information, and/or diagnostic information. One or more sensors or transducers detect pulse waveforms in a consistent manner, across various subjects and practitioners. A back-end system stores diagnostically relevant information and diagnostic information, typically for a large population of subjects and practitioners. The back-end system generates system generated diagnoses and/or suggested remedial actions based on submitted inquiries or requests. The back-end system can advantageously employ machine learning techniques to identify correlations between defining characteristics or features of pulse waveforms and diagnoses, over a large number of samples submitted by a substantial number of practitioners, for instance of traditional Chinese medicine. The back-end system can also employ primary and/or secondary symptoms in identifying correlations.

Abstract: A charge controller for a vehicle passive energy system employs an interface to a plurality of charge sources including intermittent sources, such as vehicle mounted solar photovoltaic (PV) panels and the vehicle engine and alternator, and an interface to a plurality of charge loads, such as a comfort heating ventilation, refrigeration and air conditioning (HVAC) system and auxiliary vehicle loads. Charge logic computes which of the plurality of sources will supply the charge current and which of the plurality of loads will receive the charge current, and a switch responsive to the charge logic will direct the charge current from at least one of the plurality of sources to at least one of the loads, the plurality of charge sources mutually exclusive from simultaneously powering a common load.

Abstract: A front-end system collects diagnostically relevant information or data, including pulse waveform information, and optionally timing information, symptom information, and/or diagnostic information. One or more sensors or transducers detect pulse waveforms in a consistent manner, across various subjects and practitioners. A back-end system stores diagnostically relevant information and diagnostic information, typically for a large population of subjects and practitioners. The back-end system generates system generated diagnoses and/or suggested remedial actions based on submitted inquiries or requests. The back-end system can advantageously employ machine learning techniques to identify correlations between defining characteristics or features of pulse waveforms and diagnoses, over a large number of samples submitted by a substantial number of practitioners, for instance of traditional Chinese medicine. The back-end system can also employ primary and/or secondary symptoms in identifying correlations.

Abstract: A process is described for diagnosing mammalian patients, including human patients, based on the spatial and temporal profile of the radial arterial pulse. Pulse patterns are measured, and the patterns and matching diagnoses added to an analytic module including a database system.

Abstract: A charge controller for a vehicle passive energy system employs an interface to a plurality of charge sources including intermittent sources, such as vehicle mounted solar photovoltaic (PV) panels and the vehicle engine and alternator, and an interface to a plurality of charge loads, such as a comfort heating ventilation, refrigeration and air conditioning (HVAC) system and auxiliary vehicle loads. Charge logic computes which of the plurality of sources will supply the charge current and which of the plurality of loads will receive the charge current, and a switch responsive to the charge logic will direct the charge current from at least one of the plurality of sources to at least one of the loads, the plurality of charge sources mutually exclusive from simultaneously powering a common load.

Abstract: A vehicle energy management system for gathering, storing the energy, and distributing the stored energy supports various subsystems on the vehicle such as driver comfort systems, payload refrigeration, liftgate mechanisms, and roof de-icing, for example. The system harvests energy from various sources available to the vehicle, such as solar panels, regenerative braking and shock absorbers. The harvested energy is gathered for battery storage. Management logic allocates the stored electricity to various vehicle loads defined by the subsystems, based on factors such as time of day, ambient temperature, and weather conditions, which are used to predict the electrical demand called for by the loads.

Abstract: A vehicle energy management system for gathering, storing the energy, and distributing the stored energy supports various subsystems on the vehicle such as driver comfort systems, payload refrigeration, liftgate mechanisms, and roof de-icing, for example. The system harvests energy from various sources available to the vehicle, such as solar panels, regenerative braking and shock absorbers. The harvested energy is gathered for battery storage. Management logic allocates the stored electricity to various vehicle loads defined by the subsystems, based on factors such as time of day, ambient temperature, and weather conditions, which are used to predict the electrical demand called for by the loads.