Abstract: A system and method for scheduling data transmissions in a data transmission cycle by devices in a communications network. A schedule may be determined by determining an initial transmission order of the devices in the data transmission cycle, providing a procedure which uses a transmission order as input and which outputs a feasible schedule and a total duration of the data transmission cycle for the feasible schedule, wherein the procedure generates the feasible schedule to satisfy one or more data transmission constraints imposed by the communications network and/or the devices, performing the procedure using the initial transmission order as input, and iteratively adjusting the initial transmission order and performing the procedure to obtain an adjusted schedule with an adjusted transmission order which minimizes the total duration of the data transmission cycle.

Abstract: A computer-implemented method of configuring a communication network. Devices of the network are configured to communicate according to scheduling parameters that indicate a timing of transmitting and/or receiving data over the communication network. To configure the network, the method uses a configuration procedure that is customizable by a set of customization parameters. For a current scheduling instance, representing multiple data streams to be scheduled, the configuration procedure is applied to determine current scheduling parameters, which are provided to the respective devices. Concurrently with the current customization parameters being used, the customization parameters are optimized with respect to a performance indicator of scheduling the current scheduling instance according to the customization parameters, with the updated customization being used to determine scheduling parameters for future scheduling instances with improved performance with respect to the performance indicator.

Abstract: Sensor data is encoded for transmission to a receiver by applying an object detection technique to the sensor data to detect an object in the sensor data and, to determine one or more attributes of the object, encoding the sensor data to obtain encoded sensor data. The encoding includes applying a data compression technique to the sensor data. The data compression technique is configurable by one or more compression parameters. The applying of the data compression technique to the sensor data includes selecting at least one of the compression parameters based on the attributes of the object, generating object data indicative of the attributes of the object, and sending the encoded sensor data and the object data to the receiver to enable the receiver to select one or more decompression parameters of a data decompression technique based on the one or more attributes of the object.

Abstract: A computer-implemented method of configuring a communication network. Devices of the network are configured to communicate according to scheduling parameters that indicate a timing of transmitting and/or receiving data over the communication network. To configure the network, the method uses a configuration procedure that is customizable by a set of customization parameters. For a current scheduling instance, representing multiple data streams to be scheduled, the configuration procedure is applied to determine current scheduling parameters, which are provided to the respective devices. Concurrently with the current customization parameters being used, the customization parameters are optimized with respect to a performance indicator of scheduling the current scheduling instance according to the customization parameters, with the updated customization being used to determine scheduling parameters for future scheduling instances with improved performance with respect to the performance indicator.

Abstract: A method for training a polyhedral classifier is described including obtaining training data in a data space, the training data including first data points associated with a first label and second data points associated with a second label, determining a pair of hyperplanes by determining an orientation of the pair of hyperplanes based on a minimization of a distance between the pair of hyperplanes such that the first data points lie between the hyperplanes in relation to a distance between the pair of hyperplanes such that both the first data points and the second data points lie between the hyperplanes and determining the position of the pair of hyperplanes such that the first data points lie between the pair of hyperplanes and the second data points are at least partially separated from the first data points by the pair of hyperplanes.

Abstract: A computer-implemented method of scheduling jobs of a manufacturing or logistics process using a priority function. The priority function is evaluated on multiple to be scheduled jobs to obtain multiple respective priority values. The priority function is defined to invoke a kernel function. The kernel function is defined to compare representations of two respective jobs. Evaluating the priority function on a selected job comprises evaluating the kernel function on representations of the selected job and one or more reference jobs. The schedule for the multiple jobs is determined based on the priority values and is then output to enable the multiple jobs to be carried out according to the schedule.

Abstract: A method for training a polyhedral classifier is described including obtaining training data in a data space, the training data including first data points associated with a first label and second data points associated with a second label, determining a pair of hyperplanes by determining an orientation of the pair of hyperplanes based on a minimization of a distance between the pair of hyperplanes such that the first data points lie between the hyperplanes in relation to a distance between the pair of hyperplanes such that both the first data points and the second data points lie between the hyperplanes and determining the position of the pair of hyperplanes such that the first data points lie between the pair of hyperplanes and the second data points are at least partially separated from the first data points by the pair of hyperplanes.