Abstract: Controller apparatus operative in conjunction with a climate control system including at least first and second climate control elements, the controller apparatus comprising a hardware processor providing hysteresis coupling between the at least first and second climate control elements, including providing at least one first hysteresis plan controlling the first climate control element, and at least one second hysteresis plan controlling the second climate control element. The first and second hysteresis plans each have at least one parameter, and at least one parameter of the second hysteresis plan is/are coupled to at least one parameter of the first hysteresis plan by computing the at least one parameter/s of the second hysteresis plan as a function of at least one parameter/s of the first hysteresis plan.

Abstract: There is provided a system for dynamic adaptation of a treatment applied to an agricultural field growing crops, comprising: a processor executing a code for: receiving a first and a second image from a first and a second imaging sensor, the first and second imaging sensors are located on an agricultural machine having a treatment application element(s) that applies the treatment to the agricultural field, the first and second image depict a portion of the agricultural field and overlap at an overlap region, analyzing the overlap region to compute a dynamic orientation parameter(s) of the agricultural machine, and generating instructions, according to the dynamic orientation parameter(s), for execution by a hardware component(s) associated with the agricultural machine for dynamic adaptation of the treatment applied by the treatment application element(s) to the portion of the agricultural field depicted in the first and second images to obtain a target treatment profile.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: A smart home system comprising a data repository defining categories or groupings of smart home appliances or devices, and storing, for each individual appliance, an indication of a controller which governs the individual appliance; a user interface which may present a menu of the categories and which may enable a smart home end-user to enter a selection of a command and an individual category from among the categories to which the command is to be applied; and a hardware processor which, responsive to the selection, retrieves from the data repository the indication of the controller which governs each appliance in the individual category, to define a set of controllers to which the command is to be communicated, and communicates the command to all controllers in the set.

Abstract: A central hub and database for a smart home environment enable the learning of states associated with items within the smart home and the training one or more machine-learned models associated with the items. After training the machine-learned models, the central hub can modify a state of an item based on the machine-learned model associated with the item. For instance, a window can be opened or shut, a light can be dimmed or turned off, and a door can be locked. Each state of the object can be associated with a set of conditions that, when satisfied, cause the central hub to change the state of the object using the corresponding machine-learned model, for instance without receiving an explicit input from a user.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: A central hub and database for a smart home environment enable the learning of states associated with items within the smart home and the training one or more machine-learned models associated with the items. After training the machine-learned models, the central hub can modify a state of an item based on the machine-learned model associated with the item. For instance, a window can be opened or shut, a light can be dimmed or turned off, and a door can be locked. Each state of the object can be associated with a set of conditions that, when satisfied, cause the central hub to change the state of the object using the corresponding machine-learned model, for instance without receiving an explicit input from a user.

Abstract: A technique allows delivering content in a videoconference among a plurality of conferees in multiple quality levels. Content distributed from a multipoint control unit that is connected to an endpoint that delivers the content to receiving endpoints may have two or more quality levels. The quality level can be adapted to the capabilities of the receiving endpoints and the current quality of the connection that is used to deliver the content toward the receiving endpoints. The quality level associated with an endpoint may change during a session dynamically, adapting the delivery mode to changes in the condition of the connection between the receiving endpoints and the multipoint control unit.

Abstract: A technique allows delivering content in a videoconference among a plurality of conferees in multiple quality levels. Content distributed from a multipoint control unit that is connected to an endpoint that delivers the content to receiving endpoints may have two or more quality levels. The quality level can be adapted to the capabilities of the receiving endpoints and the current quality of the connection that is used to deliver the content toward the receiving endpoints. The quality level associated with an endpoint may change during a session dynamically, adapting the delivery mode to changes in the condition of the connection between the receiving endpoints and the multipoint control unit.

Abstract: A technique allows delivering content in a videoconference among a plurality of conferees in multiple quality levels. Content distributed from a multipoint control unit that is connected to an endpoint that delivers the content to receiving endpoints may have two or more quality levels. The quality level can be adapted to the capabilities of the receiving endpoints and the current quality of the connection that is used to deliver the content toward the receiving endpoints. The quality level associated with an endpoint may change during a session dynamically, adapting the delivery mode to changes in the condition of the connection between the receiving endpoints and the multipoint control unit.

Abstract: Disclosed herein is a technique for providing cloud based scaleable videoconferencing infrastructure in a variety of ways. A combination of hardware based conferencing and software emulation of hardware conferencing equipment is disclosed. The disclosed systems and methods include load balancing algorithms and a software control architecture to allow for flexible and elastic scaling of each component individually or as a group. The disclosed systems and methods are also agnostic to the underlying hardware infrastructure utilized for any given conference.

Abstract: A technique allows delivering content in a videoconference among a plurality of conferees in multiple quality levels. Content distributed from a multipoint control unit that is connected to an endpoint that delivers the content to receiving endpoints may have two or more quality levels. The quality level can be adapted to the capabilities of the receiving endpoints and the current quality of the connection that is used to deliver the content toward the receiving endpoints. The quality level associated with an endpoint may change during a session dynamically, adapting the delivery mode to changes in the condition of the connection between the receiving endpoints and the multipoint control unit.