Abstract: Method and computing device using a neural network to determine whether or not to process images of an image flow. A predictive model of the neural network is generated and stored at a computing device. The computing device receives (b) an image of the image flow and executes (c) the neural network, using the predictive model for generating an indication of whether or not to process the image based on input(s) of the neural network, the input(s) comprising the image. The computing device determines (d) whether or not to process the image by an image processing module, based on the indication of whether or not to process the image. The image is processed by the image processing module if the determination is positive and not processed if the determination is negative. Steps (b), (c), (d) are repeated for consecutive images of the image flow.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: Remote control device and method for controlling interactions between the remote control device and a controlled appliance. The remote control device comprises a BLE interface and a battery for powering the BLE interface. Upon determination of a first condition being met, the remote control device sets the BLE interface in a standby mode where the power supplied by the battery to the BLE interface is limited to a minimal value. Upon determination of a second condition being met, the remote control device transmits one or more BLE advertising signal via the BLE interface. The remote control device receives a connection request from a controlled appliance via the BLE interface, establishes a connection between the remote control device and the controlled appliance through the BLE interface, and exchanges data with the controlled appliance via the BLE communication interface (e.g. transmission of a command for an actuator of the controlled appliance).

Abstract: Controlled appliance and method for controlling interactions between the controlled appliance and a remote control device. The controlled appliance comprises a BLE interface and an actuation module. The controlled appliance sets the BLE interface in a scanning mode where the BLE interface is capable of receiving BLE signals from other devices. The controlled appliance receives, via the BLE interface, a BLE advertising signal from the remote control device. The controlled appliance establishes a connection between the controlled appliance and the remote control device through the BLE interface. The controlled appliance exchanges data with the remote control device via the BLE interface. Upon reception from the remote control device via the BLE interface of a command for controlling operations of the actuation module, the controlled appliance applies the command to the actuation module.

Abstract: Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.

Abstract: Inference server and environment controller for inferring via a neural network one or more commands for controlling an appliance. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller transmits the at least one environmental characteristic, set point and room characteristic to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands for controlling the appliance. The inference is based on the received at least one environmental characteristic value, at least one set point and at least one room characteristic. The inference server transmits the one or more commands to the environment controller, which forwards the one or more commands to the controlled appliance.

Abstract: Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.

Abstract: Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

Abstract: Method and computing device for inferring an optimal wireless data transfer rate using a neural network. The method comprises storing a predictive model generated by a neural network training engine in a memory of a computing device. The method comprises determining, by a processing unit of the computing device, parameters of a data transfer through a wireless communication interface of the computing device. The method comprises executing, by the processing unit, a neural network inference engine using the predictive model for inferring an optimal data transfer rate based on the parameters of the data transfer through the wireless communication interface. The method comprises configuring the wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (ECD). The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

Abstract: A service is stored in a non-volatile memory of a computing device and comprises instructions executable by a processor of the computing device. The processor generates an operational instance of the service, which comprises a reference to the service. The processor stores the operational instance of the service in the non-volatile memory with a read-write access right. The processor launches an executable instance of the service associated to the operational instance of the service. The launching comprises copying the instructions of the service from the non-volatile memory to a volatile memory of the computing device. The launching further comprises executing the instructions of the service copied into the volatile memory. The processor adds data generated by the execution of the instructions of the service to the operational instance of the service for permanent storage in the non-volatile memory.

Abstract: Method providing resilient execution of a service on a computing device. The service is stored in a non-volatile memory of the computing device and comprises instructions executable by a processor of the computing device. The processor generates an operational instance of the service, which comprises a reference to the service. The processor stores the operational instance of the service in the non-volatile memory with a read-write access right. The processor launches an executable instance of the service associated to the operational instance of the service. The launching comprises copying the instructions of the service from the non-volatile memory to a volatile memory of the computing device. The launching further comprises executing the instructions of the service copied into the volatile memory. The processor adds data generated by the execution of the instructions of the service to the operational instance of the service for permanent storage in the non-volatile memory.

Abstract: Remote control device and method for controlling interactions between the remote control device and a controlled appliance. The remote control device comprises a BLE interface and a battery for powering the BLE interface. Upon determination of a first condition being met, the remote control device sets the BLE interface in a standby mode where the power supplied by the battery to the BLE interface is limited to a minimal value. Upon determination of a second condition being met, the remote control device transmits one or more BLE advertising signal via the BLE interface. The remote control device receives a connection request from a controlled appliance via the BLE interface, establishes a connection between the remote control device and the controlled appliance through the BLE interface, and exchanges data with the controlled appliance via the BLE communication interface (e.g. transmission of a command for an actuator of the controlled appliance).

Abstract: Controlled appliance and method for controlling interactions between the controlled appliance and a remote control device. The controlled appliance comprises a BLE interface and an actuation module. The controlled appliance sets the BLE interface in a scanning mode where the BLE interface is capable of receiving BLE signals from other devices. The controlled appliance receives, via the BLE interface, a BLE advertising signal from the remote control device. The controlled appliance establishes a connection between the controlled appliance and the remote control device through the BLE interface. The controlled appliance exchanges data with the remote control device via the BLE interface. Upon reception from the remote control device via the BLE interface of a command for controlling operations of the actuation module, the controlled appliance applies the command to the actuation module.

Abstract: Inference server and computing device for inferring an optimal wireless data transfer rate. The computing device determines parameters of a data transfer through a wireless communication interface of the computing device, and transmits the parameters of the data transfer to the inference server. The inference server receives the parameters of the data transfer, executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring an optimal data transfer rate based on the parameters of the data transfer, and transmits the optimal data transfer rate to the computing device. The computing device receives the optimal data transfer rate, and configures its wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (e.g. an environment controller, a sensor, a controlled appliance, and a relay).

Abstract: Method and environment controller for validating a predictive model of a neural network. The environment controller receives at least one environmental characteristic value and determines a plurality of input variables. At least one of the plurality of input variables is based on one among the environmental characteristic value(s). The environment controller executes an environment control software module for calculating at least one output variable based on the plurality of input variables. The environment controller transmits the plurality of input variables to a training server executing a neural network training engine using the predictive model; and receives at least one inferred output variable from the training server. Each inferred output variable corresponds to one of the at least one output variable calculated by the environment control software module.

Abstract: The present disclosure relates to an environment control system for controlling environmental conditions in a building. The environment control system comprises a plurality of sensors located in different areas of the building. Each sensor is used for determining a measured value for one of the environmental conditions in the area where the sensor is located. Further at least one of the plurality of sensors is configured for exchanging data with at least one mobile computing device for modifying a target value of one of the environmental conditions for the area where the sensor is located. The environment control system further comprises an environment controller for receiving the measured values and the modified target values from the plurality of sensors.

Abstract: Environment controller and method for controlling an environmental characteristic in an area of a building based on concurrent BLE requests. The environment controller receives a plurality of concurrent environmental characteristic target values originating from a plurality of BLE enabled user devices. The plurality of concurrent environmental characteristic target values is forwarded from the plurality of BLE enabled user devices to the environment controller via one or more BLE proxy device. The environment controller applies an algorithm to generate one or more command for controlling at least one controlled appliance based on the plurality of concurrent environmental characteristic target values. The environment controller further transmits the one or more command to the at least one controlled appliance. Examples of concurrent environmental characteristic target values include target temperatures, target humidity levels, target CO2 levels, target lightning levels, etc.

Abstract: Computing device and method for inferring via a neural network a two-dimensional temperature mapping of an area. A predictive model is stored by the computing device. The computing device receives a plurality of temperature measurements transmitted by a corresponding plurality of temperature sensors located at a corresponding plurality of locations on a periphery of the area. The computing device executes a neural network inference engine, using the predictive model for inferring outputs based on inputs. The inputs comprise the plurality of temperature measurements. The outputs consist of a plurality of temperature values at a corresponding plurality of zones, the plurality of zones being comprised in a two-dimensional grid mapped on a plane within the area. For instance, the area is a room of a building, the periphery is an interface of a ceiling and walls of the room, and the plane is a horizontal plane within the room.