摘要:
Disclosed is an indoor location system that uses an electrical power line, power line signal injection devices, and portable position receivers (tags) to generate location data relating to positions of the tags in a structure such as a residence or business. The indoor location system fingerprinting of multiple signals transmitted along the power line to achieve sub-room-level localization of the positioning receivers. Details regarding power line positioning are described along with how it compares favorably to other fingerprinting techniques.
摘要:
An indoor location system uses an electrical power line, power line signal injection devices, and portable position receivers (tags) to generate location data relating to positions of the tags in a structure such as a residence or business. The indoor location system fingerprinting of multiple signals transmitted along the power line to achieve sub-room-level localization of the positioning receivers. In one embodiment, the fingerprinting techniques utilizes wideband power line positioning (WPLP) that injects up to 44 different frequencies into the power line infrastructure of a structure. The WPLP technique improves upon overall positioning accuracy, improved temporal stability and may be implemented in commercial indoor spaces.
摘要:
An indoor location system uses an electrical power line, power line signal injection devices, and portable position receivers (tags) to generate location data relating to positions of the tags in a structure such as a residence or business. The indoor location system fingerprinting of multiple signals transmitted along the power line to achieve sub-room-level localization of the positioning receivers. In one embodiment, the fingerprinting techniques utilizes wideband power line positioning (WPLP) that injects up to 44 different frequencies into the power line infrastructure of a structure. The WPLP technique improves upon overall positioning accuracy, improved temporal stability and may be implemented in commercial indoor spaces.
摘要:
In some embodiments, a motion detecting device is configured to detect whether one or more movement events have occurred. The motion detecting device can include: (a) a processing module configured to run on a computational unit; and (b) a sensing device having: (1) one or more pressure sensors configured to provide two or more pressure measurements; and (2) a transmitter electrically coupled to the one or more pressure sensors and configured to transmit the two or more pressure measurements to the computational unit. The processing module is configured to use the two or more pressure measurements to determine whether the one or more movement events have occurred. The sensing device can be configured to be placed in at least one of ductwork of a heating, ventilation, and air conditioning system or an air handler of the heating, ventilation, and air conditioning system. Other embodiments are disclosed.
摘要:
In some embodiments, a motion detecting device is configured to detect whether one or more movement events have occurred. The motion detecting device can include: (a) a processing module configured to run on a computational unit; and (b) a sensing device having: (1) one or more pressure sensors configured to provide two or more pressure measurements; and (2) a transmitter electrically coupled to the one or more pressure sensors and configured to transmit the two or more pressure measurements to the computational unit. The processing module is configured to use the two or more pressure measurements to determine whether the one or more movement events have occurred. The sensing device can be configured to be placed in at least one of ductwork of a heating, ventilation, and air conditioning system or an air handler of the heating, ventilation, and air conditioning system. Other embodiments are disclosed.
摘要:
Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.
摘要:
Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.
摘要:
Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.
摘要:
Some embodiments teach an apparatus for determining a proximity of one or more first Bluetooth devices. The apparatus can include: (a) at least one Bluetooth base station with (1) a Bluetooth transmitter configured to transmit one or more service discovery requests to the one or more first Bluetooth devices; and (2) a Bluetooth receiver configured to receive one or more responses from the one or more first Bluetooth devices to the one or more service discovery requests; and (b) a computational module configured to run on one or more processors and further configured to determine one or more approximate distances between the at least one Bluetooth base station and the one or more first Bluetooth devices based on the one or more responses from the one or more first Bluetooth devices. Other embodiments are disclosed.
摘要:
Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.