Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: Disclosed in the present application are a method for improving performance of a low-intermediate frequency receiver, a storage medium, and a receiver. The method comprises: selecting a local oscillator signal from a preset local oscillator frequency set as an initial local oscillator signal to perform frequency mixing processing on an input signal, so as to obtain a low-intermediate frequency signal comprising a low-intermediate frequency useful signal and a low-intermediate frequency interference signal; determining whether an energy ratio of the low-intermediate frequency interference signal to the low-intermediate frequency useful signal is greater than a first preset ratio; and if the energy ratio is greater than the first preset ratio, selecting another local oscillator frequency from the preset local oscillator frequency set as the current local oscillator signal to process the input signal.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: An apparatus configured to control a user situation awareness modification of a user of a vehicle, which includes input module configured to repeatedly receive a first event detection signal comprising information related to a detected event outside a vehicle and to repeatedly receive a first user detection signal comprising information related to a reaction or a missing reaction of a user to the detected event outside the vehicle. The apparatus further includes a processing module configured to determine a user situation awareness level based on the information related to the detected event outside the vehicle and the information related to the reaction of the user to the detected event, and to generate a control signal configured to control a user situation awareness modification module based on the determined user situation awareness level.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: The invention relates to an apparatus that is configured to receive a first user detection signal comprising information usable for determining a user situation awareness level. The apparatus also includes a processing module configured to determine a currently necessary situation awareness level by selecting one predefined situation awareness level from a plurality of predefined situation awareness levels based on driving information. The processing module is configured to determine a current user situation awareness level based on the information usable for determining the user situation awareness level. The processing module is configured to generate a control signal to control a user situation awareness modification module based on the currently necessary situation awareness level and the current user situation awareness level.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: An apparatus configured to control a user situation awareness modification of a user of a vehicle, which includes input module configured to repeatedly receive a first event detection signal comprising information related to a detected event outside a vehicle and to repeatedly receive a first user detection signal comprising information related to a reaction or a missing reaction of a user to the detected event outside the vehicle. The apparatus further includes a processing module configured to determine a user situation awareness level based on the information related to the detected event outside the vehicle and the information related to the reaction of the user to the detected event, and to generate a control signal configured to control a user situation awareness modification module based on the determined user situation awareness level.

Abstract: The invention relates to an apparatus that is configured to receive a first user detection signal comprising information usable for determining a user situation awareness level. The apparatus also includes a processing module configured to determine a currently necessary situation awareness level by selecting one predefined situation awareness level from a plurality of predefined situation awareness levels based on driving information. The processing module is configured to determine a current user situation awareness level based on the information usable for determining the user situation awareness level. The processing module is configured to generate a control signal to control a user situation awareness modification module based on the currently necessary situation awareness level and the current user situation awareness level.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.

Abstract: A method of predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.