Abstract: Some embodiments can concern an apparatus configured to detect an electrical state of one or more electrical devices. The one or more electrical devices are coupled to an electrical power infrastructure and generate one or more high-frequency electrical signals on the electrical power infrastructure. The apparatus can include: (a) a processing module configured to run on a processor of a computational unit; and (b) a sensing device configured to be coupled to an electrical outlet. The sensing device can have: (a) a data acquisition receiver configured to receive the one or more high-frequency electrical signals via the electrical outlet and convert the one or more high-frequency electrical signals into one or more first data signals when the sensing device is coupled to the electrical outlet. The electrical outlet can be electrically coupled to the electrical power infrastructure. The sensing device is in communication with the computational unit.

Abstract: 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.

Abstract: 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%.

Abstract: A radio device such as a wireless tag reader communicates with multiple types of wireless identification tags in a monitored region. The radio device includes a network interface to receive messages transmitted over a network. In response to receiving a message indicating to reconfigure the radio device to support an additional wireless tag protocol, the radio is reconfigured to support communications with a corresponding new type of wireless identification tag in a monitored region. Based on this technique of reconfiguring the radio device via network messages, the radio device optionally supports additional, new or latest versions of wireless tag protocols without having to physically reprogram or replace the radio device.

Abstract: Some embodiments include an improved electric power supply. Other embodiments of related systems and methods are disclosed.

Abstract: 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%.

Abstract: Some embodiments can concern a method of using a power consumption measurement device. The power consumption measurement device can be mechanically coupled to a surface of a circuit breaker box overlying at least part of one or more main electrical supply conductors for an electrical power infrastructure of a structure.

Abstract: Some embodiments can teach a system for monitoring usage of electrical power by a structure. The structure can have one or more main electrical power lines that supply the electrical power to a first load in the structure. A portion of the one or more main electrical power lines can run substantially parallel to a first axis. The structure can further have a panel that overlies the portion of the one or more main electrical power lines. The system can include: (a) a current sensor unit configured to be coupled to a portion of a surface of the panel, the current sensor unit having: (a) at least one magnetic field sensor having a length substantially parallel to a second axis, wherein the second axis is substantially perpendicular to the first axis, and the at least one magnetic field sensor is configured to detect a magnetic field generated by the one or more main electrical power lines; and (b) a processing unit configured to run on a processor.

Abstract: 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%.

Abstract: In some embodiments, an apparatus can be configured to detect gas usage. The apparatus can include: (a) a processing module configured to run on a computational unit; and (b) a sensing unit configured to be coupled to a gas regulator, the sensing unit having: (1) at least one acoustic sensor configured to detect two or more acoustic signals produced by the gas regulator and convert the two or more acoustic signals into one or more first data signals; and (2) a transmitter electrically coupled to the at least one acoustic sensor and configured to transmit the one or more first data signals to the computational unit. The processing module is configured to use the one or more first data signals to determine the gas usage. Other embodiments are disclosed.

Abstract: A radio device such as a wireless tag reader communicates with multiple types of wireless identification tags in a monitored region. The radio device includes a network interface to receive messages transmitted over a network. In response to receiving a message indicating to reconfigure the radio device to support an additional wireless tag protocol, the radio is reconfigured to support communications with a corresponding new type of wireless identification tag in a monitored region. Based on this technique of reconfiguring the radio device via network messages, the radio device optionally supports additional, new or latest versions of wireless tag protocols without having to physically reprogram or replace the radio device.

Abstract: A radio device such as a wireless tag reader communicates with multiple types of wireless identification tags in a monitored region. The radio device includes a network interface to receive messages transmitted over a network. In response to receiving a message indicating to reconfigure the radio device to support an additional wireless tag protocol, the radio is reconfigured to support communications with a corresponding new type of wireless identification tag in a monitored region. Based on this technique of reconfiguring the radio device via network messages, the radio device optionally supports additional, new or latest versions of wireless tag protocols without having to physically reprogram or replace the radio device.

Abstract: A radio device such as a wireless tag reader communicates with multiple types of wireless identification tags in a monitored region. The radio device includes a network interface to receive messages transmitted over a network. In response to receiving a message indicating to reconfigure the radio device to support an additional wireless tag protocol, the radio is reconfigured to support communications with a corresponding new type of wireless identification tag in a monitored region. Based on this technique of reconfiguring the radio device via network messages, the radio device optionally supports additional, new or latest versions of wireless tag protocols without having to physically reprogram or replace the radio device.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for placing RFID tags on objects are shown and described. The method includes scanning an object and analyzing one or more reflected signals from the to determine a suggested location for placement of an RFID tag. The scan can be accomplished using a scanning device having an antenna that operates in the near field of the object to measure at least one RF property of the reflected signal from the object.

Abstract: 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.

Abstract: Systems and methods for providing active noise cancellation in an RFID tag reader are described. In one aspect, the method features increasing the signal-to-noise-plus-interference ratio of a radio frequency tag response. The method includes receiving, with a first antenna element of a radio frequency tag reader, a radio frequency tag response having a noise-plus-interference portion and a response portion, receiving a noise-plus-interference signal with a second antenna element of the radio frequency tag reader, and processing the received radio frequency tag response and the noise-plus-interference signal to increase a signal-to-noise-plus-interference ratio of the received radio frequency tag response.

Abstract: Systems and methods for placing RFID tags on objects are shown and described. The method includes scanning an object and analyzing one or more reflected signals from the to determine a suggested location for placement of an RFID tag. The scan can be accomplished using a scanning device having an antenna that operates in the near field of the object to measure at least one RF property of the reflected signal from the object.

Abstract: 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%.