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.

Abstract: Method and environment controller using a neural network for bypassing a legacy environment control software module. 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 at least one environmental characteristic value. The environment controller transmits the plurality of input variables to an inference server executing a neural network inference engine. The environment controller receives at least one inferred output variable from the inference server. The environment controller uses the at least one inferred output variable received from the inference server in place of at least one output variable calculated by the legacy environment control software module based on the plurality of input variables.

Abstract: Computing device and method for inferring a predicted number of data chunks writable on a flash memory before the flash memory wears out. The computing device stores a predictive model generated by a neural network training engine. A processing unit of the computing device executes a neural network inference engine, using the predictive model for inferring the predicted number of data chunks writable on the flash memory before the flash memory wears out based on inputs. The inputs comprise a total number of physical blocks previously erased from the flash memory, a size of the data chunk, and optionally an operating temperature of the flash memory. In a particular aspect, the flash memory is comprised in the computing device, and an action may be taken for preserving a lifespan of the flash memory based at least on the predicted number of data chunks writable on the flash memory.

Abstract: Computing device and method for inferring a predicted number of physical blocks erased from a flash memory. The computing device stores a predictive model generated by a neural network training engine. A processing unit of the computing device executes a neural network inference engine, using the predictive model for inferring the predicted number of physical blocks erased from the flash memory based on inputs. The inputs comprise a total number of physical blocks previously erased from the flash memory, an amount of data to be written on the flash memory, and optionally an operating temperature of the flash memory. In a particular aspect, the flash memory is comprised in the computing device, and an action may be taken for preserving a lifespan of the flash memory based at least on the predicted number of physical blocks erased from the flash memory.

Abstract: The present method and electronic device are adapted for secured commissioning. A generic password is stored in memory of the electronic device, and a transmission power of the electronic device is set to a reduced transmission power. The electronic device receives a commissioning request including the generic password and a specific password. The generic password is replaced in the memory of the electronic device by the specific password, and the transmission power of the electronic device is increased to full transmission power.

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: Environment controller and method for proportionally adjusting a respective light intensity of a plurality of lighting devices. The environment controller determines a current light intensity for each one of the plurality of lighting devices; and determines a current average light intensity by calculating the average of the current light intensities. The environment controller determines a target average light intensity; and determines a new light intensity for each one of the plurality of lighting devices, by proportionally adjusting the light intensity of each one of the plurality of lighting devices from its respective current light intensity to its respective new light intensity, so that the average of the new light intensities is equal to the target average light intensity. The current and new light intensities may be expressed as a percentage; and the target average light intensity received from a computing device or via a user interaction with the environment controller.

Abstract: The present environmental condition controller and method provide for controlling an environmental condition in an area of a building. For doing so, a communication interface receives an environmental condition target value (xref), and an environmental condition measured value (x). A processing unit calculates an environmental condition adjustment value (yn) with a recursive function based on the environmental condition measured value (x), the environmental condition target value (xref) and an adaptive proportionality value (k). The processing unit also generates and transmits a command based on the environmental condition adjustment value (yn). The processing unit further stores in a memory the environmental condition adjustment value (yn) as a previously calculated environmental condition adjustment value (yn?1). Specific steps of the method are executed recursively. The present method may further be performed by a computer program product.

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: 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: The present relates to a system comprising a housing, a motor assembly, a printed circuit board (PCB) and a shaft. The motor assembly is located inside the housing. The motor assembly comprises a control for alternatively engaging and disengaging a functionality of the motor assembly. The printed circuit board (PCB) is located inside the housing and defines an aperture for receiving the shaft. The shaft extends through the aperture of the PCB. A first end of the shaft is adapted for actuating the control of the motor assembly. A second end of the shaft extends through an opening in the housing and is adapted for receiving an actuator.

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: Environmental condition controller and method for controlling an environmental condition in an area of a building. The controller stores a predictive model generated by a neural network training engine, and a previously calculated environmental condition adjustment value (yn?1). The controller receives an environmental condition target value (xref), and an environmental condition measured value (x). The controller recursively calculates an environmental condition adjustment value (yn) by executing a neural network inference engine using the predictive model for inferring the environmental condition adjustment value (yn) based on the previously calculated environmental condition adjustment value (yn?1), the environmental condition target value (xref), the environmental condition measured value (x), and an adaptive proportionality value (k). The controller also generates and transmits a command based on the environmental condition adjustment value (yn).

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: Environment controller and method for proportionally adjusting a respective light intensity of a plurality of lighting devices. The environment controller determines a current light intensity for each one of the plurality of lighting devices; and determines a current average light intensity by calculating the average of the current light intensities. The environment controller determines a target average light intensity; and determines a new light intensity for each one of the plurality of lighting devices, by proportionally adjusting the light intensity of each one of the plurality of lighting devices from its respective current light intensity to its respective new light intensity, so that the average of the new light intensities is equal to the target average light intensity. The current and new light intensities may be expressed as a percentage; and the target average light intensity received from a computing device or via a user interaction with the environment controller.

Abstract: The present relates to a system and a bidirectional differential pressure sensor. The system and bidirectional differential pressure sensor comprise a first adaptor comprising an end configured to receive a first pipe, and a second adaptor comprising an end configured to receive a second pipe. The system and bidirectional differential pressure sensor further comprise a pressure sensing element determining a pressure differential between fluid received via the first adaptor with respect to fluid received via the second adaptor. The system or bidirectional differential pressure sensor further comprise a processing unit executing an algorithm for generating an adjusted pressure differential based on the pressure differential determined by the pressure sensing element.

Abstract: A system comprising a housing, a printed circuit board and a differential pressure sensor located inside the housing, and first and second male adaptors. The first and second male adaptors respectively extend through first and second openings in the housing. The first male adaptor comprises a proximal end configured to receive a first pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the first pipe to the differential pressure sensor. The second male adaptor comprises a proximal end configured to receive a second pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the second pipe to the differential pressure sensor. The differential pressure sensor is configured to determine a pressure differential between fluid received via the first male connector and fluid received via the second male connector.