Abstract: Method and computing device for inferring via a neural network environmental data of an area of a building based on visible and thermal images of the area. A predictive model generated by a neural network training engine is stored by the computing device. The computing device determines a visible image of an area based on data received from at least one visible imaging camera. The computing device determines a thermal image of the area based on data received from at least one thermal imaging device. The computing device executes a neural network inference engine, using the predictive model for inferring environmental data based on the visible image and the thermal image. The inferred environmental data comprise geometric characteristic(s) of the area, an occupancy of the area, a human activity in the area, temperature value(s) for the area, and luminosity value(s) for the area.

Abstract: A computing device stores a predictive model generated by a neural network training engine. The computing device receives first and second two-dimensional (2D) thermal images comprising temperature measurements from respective first and second infrared (IR) sensors. The first and second images have the same size. An image capturing visual field of the second IR sensor partially overlaps with an image capturing visual field of the first IR sensor. The computing device executes a neural network using a predictive model for generating outputs based on inputs. The inputs comprise the temperature measurements of the first and second images. The outputs comprise horizontal and vertical shifts defining a translation of the second image with respect to the first image. An overlapping area in the first image, having a rectangular shape and overlapping with the second image, is determined using the horizontal and vertical shifts.

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: 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: 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: 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 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: 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: Memory device comprising flash memory and method for controlling a write speed of a bus transmitting data for storage on the flash memory. The flash memory of the memory device comprises a plurality of physical blocks for writing data transmitted over the bus from a host device to the memory device. The writing of data in any one of the plurality of physical blocks is preceded by an erase of the physical block. The memory device also comprises a memory device controller controlling a write speed of the bus. The write speed of the bus defines a transmission rate at which data are transmitted over the bus from the host device to the memory device, for further writing on the flash memory. The control of the write speed of the bus comprises adapting the transmission rate based on a number of physical blocs erased.

Abstract: The present environment controller is adapted for being powered in low-voltage daisy-chained power configuration. The environment controller comprises a low-voltage daisy-chainable power supply comprising a Power Factor Conversion (PFC) flyback converter. The low-voltage daisy-chainable power supply receives a low-voltage power and outputs a high PFC low-voltage power for powering the environment controller.

Abstract: A master device, daisy-chained devices, and a method for configuring the daisy-chained devices are provided. The master device generates a signal having a pre-determined base frequency, and outputs the signal generated to a first device in the daisy chain communication configuration. Each daisy-chained device receives an input signal, having an input frequency, from a previous daisy-chained device. Each daisy-chained device generates an output signal having an output frequency different to and based on the input frequency of the received signal, and outputs the output signal to a following daisy-chained device. Each daisy-chained device further determines an address of a communication interface, for exchanging data with the master device, based on the input frequency of the received signal. For example, the output frequency of the output signal is half the input frequency of the received signal.

Abstract: A common zero volt reference AC/DC power supply with positive and negative rectification, and method of operation thereof. The power supply comprises an input for receiving an AC input voltage and an output for outputting a DC output voltage, the AC input and DC output having a common zero volt reference. The power supply comprises a first rectifier and a second rectifier, for respectively performing a half-wave rectification of a positive half cycle and negative half cycle of the AC input voltage. The power supply comprises control logic for detecting the negative half cycle of the AC input voltage and activating the second rectifier upon the detection. The power supply comprises a power converter for converting a DC rectified voltage received from at least one of the first and second rectifiers into the DC output voltage.