Signify Holding B.v.

Abhishek Murthy

Olaitan Philip Olaleye

Rohit Kumar

Sirisha Rangavajhala

Alexandru Darie

Hassan Mohanna

Jasleen Kaur

Talmai Brandao De Oliveira

Jin S Yu

Talmai Brandão De Oliveira

Harry Broers

Peter Deixler

Dong Han

Jan Ekkel

Yuting Zhang

Jia Hu

Eric Bertrand Shen

Daksha Yadav

Kalpathy Sitaraman Sivaraman

Xiaoke Shen

Arhum Savera

Shahin Mahdizadehaghdam

Chingiz Kabytayev

Mathan Gopalsamy

Hanuma Teja Maddali

Tamir Hegazy

The brain control interface system comprises: a brain control interface configured to detect brain signals indicative of brain activity of a user in an environment, an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment, a memory configured to store processing methods associated with different light scenes, one or more processor configured to: select, from the processing methods stored in the memory, a processing method in accordance with the current light scene, apply the selected processing method to obtain and/or process the brain signals, derive a control command and/or a mental state of the user from the brain signals, and control the controllable device based on the derived control command and/or the derived mental state.

Brain control interface system for controlling a controllable device

A method for optimizing a neural network is provided, including: (1) capturing, via a first sensor group having a first field of view, a first sample set having a first sensor domain corresponding to the first field of view; (2) capturing, via a second sensor group having a second field of view, a second sample set having a second sensor domain corresponding to the second field of view; (3) generating regions of interest of the second sample set; (4) translating the regions of interest to the first sensor domain; (5) identifying nodes of the neural network which correspond to the translated regions; and (6) optimizing the neural network by at least one of (a) increasing the weight value of the nodes corresponding to the one or more translated regions and (b) decreasing the weight value of the nodes not corresponding to the one or more translated regions.

System and methods to optimize neural networks using sensor fusion

A system for determining occupancy in an environment is provided. The system includes plurality of sensor bundles, with each bundle including a presence sensor and a motion sensor. The system further includes a controller in communication with each sensor bundle. The controller is configured to designate one of the sensor bundles as presence triggered if persons are present within a field of view of the presence sensor. The controller is further configured to designate one of the sensor bundles as motion triggered if persons are moving within a field of view of the motion sensor. The controller is further configured to determine a triggered bundle count of the sensor bundles which are both presence triggered and motion triggered. The controller is further configured to determine an occupancy count for the environment based upon the triggered bundle count.

Systems and methods for fusing data from single pixel thermopiles and passive infrared sensors for counting occupants in open offices

The present invention is related to an application of a Single Pixel Thermopile (SPT). The invention provides a sensor device comprising a Single Pixel Thermopile and a controller, wherein the Single Pixel Thermopile is configured to monitor a detection region and to measure over time a temperature signal of said detection region; wherein the detection region is bounded by a surface and the Single Pixel Thermopile is oriented at an angle of at least 20 degrees normal to the surface; wherein a projection of the detection region onto the surface renders an elongated detection area with a length axis and a width axis; wherein the controller is configured to: obtain the temperature signal of the detection region; determine a movement characteristic of a person moving across said surface by detecting a pattern in the temperature signal of the detection region; output an output signal configured to control an electrical device upon determining the movement characteristic.

Sensor device

A system and method for providing object recognition using artificial neural networks. The method includes capturing a plurality of reference images with a camera associated with an edge node on a communication network. The reference images are received by a centralized server on the communication network. The reference images are analyzed with a parent neural network of the centralized server to determine a subset of objects identified by the parent neural network in the reference images. One or more filters that are responsive to the subset of objects are selected from the parent neural network. A pruned neural network is created from only the one or more filters. The pruned neural network is deployed to the edge node. Real-time images are captured with the camera of the edge node and objects in the real-time images are identified with the pruned neural network.

System and method for object recognition using neural networks

The present disclosure is directed to inventive systems, methods, and devices for use in an outdoor lighting network for drone detection. The drone detection system includes one or more lighting fixtures, one or more radar sensors to detect a moving object, and one or more controllers. The controllers receive data from the sensors, determine the velocity and velocity change rate of the object over a period of time, and analyze flight data pertaining to the moving object to determine if the object is a drone. The system can determine the starting location of the moving object, and send a signal indicating its starting location. The system can also track the position of the moving object in the outside environment.

Systems, methods, and devices for drone detection using an outdoor lighting network

A system and method for determining a number of occupants at a location using multiple modalities. The method includes gathering a first data set with a motion sensor of the lighting system. A second data set is gathered with a transceiver of an RF subsystem. First and second estimates are calculated from the second data set using first and second algorithms. The first estimate and the second estimate are fused to create a fused occupant estimate. The first algorithm, the second algorithm, or both the first algorithm and the second algorithm are trained by inputting the second occupant estimate and/or the second set of data to recalibrate parameters of the first algorithm and/or inputting the first occupant estimate and/or the first set of data to recalibrate parameters during training of the second algorithm. A building control system can operated in response to the fused occupant estimate.

Systems and methods using multiple modalities for occupancy sensing

The present disclosure is directed to inventive systems and methods for automatically controlling illumination of a workplace. The lighting system includes one or more tunable luminaires and a calendar module executing on a computer-implemented device configured to receive a calendar entry. The calendar entry includes a starting time of a future event, the identity of one or more invitees to the future event, and a lighting scene selection for the workplace. A lighting controller receives the lighting scene selection from the calendar module and generates one or more control signals to automatically adjust at least one characteristic of light emitted by the tunable luminaires in the workplace for the future event based on the lighting scene selection in the calendar entry.

Systems and methods for automatically controlling illumination of a workplace

A system and method for compressive sensing using edge nodes of a distributed computing network. The method includes collecting a raw data signal continuously by a sensor of the edge node. A signal energy indicator is dynamically updated that quantifies an energy distortion in the raw data signal. One or more compression characteristics are determined as a function of the signal energy indicator as the signal energy indicator is updated. The raw data signal is subsampled in accordance with current values of the one or more compression characteristics to create a compressed data signal. An output is transmitted that includes the compressed data signal to a centralized node.

Compressive sensing systems and methods using edge nodes of distributed computing networks

A system and method for compressing sensor data at an edge node of a distributed computing network. The method includes training the edge node to with a plurality of known signal templates. Each known signal template corresponding to a corresponding one of a plurality of events observable by the sensor. A raw data signal is collected by a sensor of the edge node. The raw data signal is classified to one of the known signal templates based on a degree of similarity between the raw data signal and the known signal template. A compression scheme is selected based on the classification of the raw data signal. The raw data signal is compressed in accordance with the compression scheme.

Systems and methods for compressing sensor data using clustering and shape matching in edge nodes of distributed computing networks

The described embodiments relate to systems, methods, and apparatus for employing a network () of smart luminaires () to perform tasks typically reserved for remote servers. The network of smart luminaires can include lighting elements for illuminating an area, as well as a computer system for processing and transmitting data. Various computational tasks can be parsed and delegated to certain smart luminaires in the network () in order to optimize the use of each smart luminaire in the network. Computational tasks can originate at the smart luminaires or be delegated to the smart luminaires by another device. Additionally, data that is processed by the smart luminaires can be transmitted to other devices, thereby allowing other devices to leverage the computing power of a nearby network of smart luminaires.

Systems, methods, and apparatuses for distributing computational resources over a network of luminaires

The described implementations provided herein relate to systems, methods, and apparatuses for using a network of luminaires to perform distributed computation of sensor data to identify hardware and network anomalies. In some implementations, a method is set forth as including operations such as receiving, from a first luminaire (), first sensor data () in response to a stimulus () affecting a network of luminaires (), and receiving, from a second luminaire () in the network of luminaires, second sensor data. The method can also include determining a correlation between the first sensor data and the second sensor data, and modifying a luminaire spatial-temporal model () based at least partially on the correlation. The method can also include receiving subsequent sensor data from the first luminaire or the second luminaire, and providing a signal () to the first luminaire or the second luminaire based on a comparison of the subsequent sensor data to the luminaire spatial-temporal model.

System, methods, and apparatuses for distributed detection of luminaire anomalies

The described embodiments relate to system, methods, and apparatuses for compensating sensor data from a luminaire based on an ambient temperature estimate that is generated from operating characteristics of the luminaire. The sensor data can be provided from a sensor, such as a passive infrared sensor, that is connected to the luminaire, and by compensating the sensor data, more accurate metrics can be generated from the sensor data. For instance, the compensated sensor data can be used to generate occupancy metrics that can be used as a basis for controlling a network of luminaires or other devices that can be influenced by occupants of an area. The compensated sensor data can also be used to calibrate the sensor.

Systems, methods and apparatus for compensating analog signal data from a luminaire using ambient temperature estimates

The described embodiments relate to systems, methods, and apparatuses for controlling a network of lights () according to whether people () who are observing the lights are visitors from a different location (). Visitors can be identified by using network connection data () that includes entries corresponding to personal devices of visitors to a city. The network connection data can be filtered to identify how many personal devices are associated with people visiting from a different city. If the number of visitors exceeds some threshold relative to non-visitors, a lighting arrangement in the city can be controlled to display features that would appeal to the visitors. The features can correspond to aspects of the city where the visitors are from and can therefore make the city more attractive.

Visitor identification for lighting control

The described embodiments relate to systems, methods, and apparatuses for controlling energy resources available to micro-grids of a city based on mobility patterns of people moving within the micro-grids. The mobility patterns can be identified using a network of sensors within each micro-grid for collecting data related to the movement of people within the micro-grids. The mobility patterns can be used to estimate energy demand for each micro-grid and prioritize the energy demands to determine the energy resources that would be suitable for supplying power to each micro-grid. This allows for micro-grids to dynamically and efficiently change their power sources according to predictions about the movement of people within the micro-grids.

Growing community of inventors

Arlington, MA, United States of America

Abhishek Murthy