A detector device includes a light source disposed within a chamber, a sensor disposed within the chamber, a compensator circuit electrically coupled with the sensor, and a controller. The controller is operable to receive a sensor signal generated by the sensor, determine a compensation factor to adjust the sensor signal, and generate a compensation offset signal based on the compensation factor. The controller is further operable to output the compensation offset signal to the compensator circuit to produce a compensated sensor signal as an adjustment to the sensor signal, energize the light source, monitor the compensated sensor signal with respect to an alarm limit, and trigger an alarm event based on the compensated sensor signal exceeding the alarm limit.

A shock detector device (110) for premises security is described. The shock detector device (110) comprises a shock detector sensor (112) configured to sense physical motion and to output an electrical signal in response to the physical motion. Processing circuitry (114) is configured to process the electrical signal by: obtaining an indication that a shock event has occurred if a value for at least one parameter of the electrical signal is determined to exceed a threshold value; and processing instructions for adjusting at least one detection parameter of the shock detector device (110) in response to a determination that the shock event is a false alarm event, wherein the adjusting of the at least one detection parameter results in a decrease of a sensitivity of shock detection by the shock detector device (110).

Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

Electronic surveillance article systems reducing the number and likelihood of false alarms are provided. Such systems include two read zones, with a second read zone having an associated RFID reader configured to detect an RFID device at a trigger threshold. The trigger threshold may be set or modified in view of a value of a sensor of an RFID device (sensing a capacitance or dielectric permittivity or temperature or degree of movement, for example), the number of times the RFID device is detected in the first read zone, or whether the RFID device is detected in the first read zone under predetermined conditions. Such systems may also or alternatively initiate a response (e.g., modifying the trigger threshold or the amount of power transmitted by an RFID reader) when an RFID guard device associated with a piece of infrastructure in the first read zone is detected in the second read zone.

In-situ fluorimeters and methods and systems for collecting and analyzing sensor data to predict water source contamination are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water source. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a predicted contamination event for the water source may be identified based on the classified intervals. In another embodiment, an in-situ fluorimeter is provided. The in-situ fluorimeter comprises one or more UV LEDs centered around a pre-set excitation wavelength (e.g., a TLF excitation wavelength), a bandpass filter, a lens, a photodiode system, a machine learning platform; and an alarm triggered by contamination events, wherein the alarm is calibrated through the machine learning system.

In-situ fluorimeters and methods and systems for collecting and analyzing sensor data to predict water source contamination are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water source. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a predicted contamination event for the water source may be identified based on the classified intervals. In another embodiment, an in-situ fluorimeter is provided. The in-situ fluorimeter comprises one or more UV LEDs centered around a pre-set excitation wavelength (e.g., a TLF excitation wavelength), a bandpass filter, a lens, a photodiode system, a machine learning platform; and an alarm triggered by contamination events, wherein the alarm is calibrated through the machine learning system.

A method and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject's movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

There is provided a smoke detector including a light source, a reflective surface, a light sensor and a processor. The light sensor receives reflected light when the light source emits light toward the reflective surface, and generates a reference detection signal when there is no smoke. The processor receives the detection signal from the light sensor, and automatically selects a set of predetermined condition thresholds according to a profile of the detection signal to be compared with the detection signal thereby determining whether to generate an alarm according to the comparison result.

A method is provided for configuring a tracking system which estimates a location within a space by comparison of measurements of RF signals, made at the location, with a radio fingerprint map of the space. Estimated locations are correlated with synchronised detections of presence of a user (30) by sensors of a lighting system. RF signal measurements made by a user at a given time may thereby be associated with a user (30) detected at the same time in one or more respective sensing areas of lighting system sensors. The user (30) making RF signal measurements at a determined location may thereby be associated with a user (30) detected by the lighting system sensors at the same time. The association may be used to configure the radio fingerprint map. The location of the detected user (30) as determined by the tracking system may be provided to the lighting system.