A computer-implemented technique is described herein for interpolating input data that includes image and/or audio content. The technique identifies plural sizes associated with different respective phenomena exhibited by the input data. The technique then interpolates the input data in a pipeline that includes plural passes. The plural passes are controlled using plural respective parameter values. The plural respective parameter values, in turn, are selected based on the plural respective sizes, arranged from largest to smallest. In other implementations, the technique chooses pass-specific algorithmic changes to be applied by the interpolation algorithms used by the different passes. In other implementations, the technique chooses its configurations without regard to sizes of phenomena that may be exhibited in the input data. The technique is advantageous because it reduces the presence of artifacts in output data produced by the computer-implemented technique.

A computer-implemented technique is described herein for interpolating input data that includes image and/or audio content. The technique identifies plural sizes associated with different respective phenomena exhibited by the input data. The technique then interpolates the input data in a pipeline that includes plural passes. The plural passes are controlled using plural respective parameter values. The plural respective parameter values, in turn, are selected based on the plural respective sizes, arranged from largest to smallest. In other implementations, the technique chooses pass-specific algorithmic changes to be applied by the interpolation algorithms used by the different passes. In other implementations, the technique chooses its configurations without regard to sizes of phenomena that may be exhibited in the input data. The technique is advantageous because it reduces the presence of artifacts in output data produced by the computer-implemented technique.

An apparatus for computing functions using polynomial-based approximation, comprising one or more processing circuitries configured for computing a polynomial-based approximant approximating a function by executing one or more iterations. Each iteration comprising computing the polynomial-based approximant using scaled fixed-point unit(s) according to a constructed set of coefficients, minimizing an approximation error of the computed polynomial-based approximant compared to the function while complying with one or more constraints selected from a group comprising at least: an accuracy, a compute graph size, a computation complexity, and a hardware utilization of the processing circuitry(s), adjusting one or more of the coefficients in case the approximation error is incompliant with the constraint(s) and initiating another iteration. The polynomial-based approximant and its adjusted set of coefficients for which the computed polynomial-based approximant complies with the constraint(s) may be output to one or more processing circuitries configured to approximate the function by computing the polynomial-based approximant.

An apparatus for computing functions using polynomial-based approximation, comprising one or more processing circuitries configured for computing a polynomial-based approximant approximating a function by executing one or more iterations. Each iteration comprising computing the polynomial-based approximant using scaled fixed-point unit(s) according to a constructed set of coefficients, minimizing an approximation error of the computed polynomial-based approximant compared to the function while complying with one or more constraints selected from a group comprising at least: an accuracy, a compute graph size, a computation complexity, and a hardware utilization of the processing circuitry(s), adjusting one or more of the coefficients in case the approximation error is incompliant with the constraint(s) and initiating another iteration. The polynomial-based approximant and its adjusted set of coefficients for which the computed polynomial-based approximant complies with the constraint(s) may be output to one or more processing circuitries configured to approximate the function by computing the polynomial-based approximant.

A method for generating and using multi-view masters involves selecting a master in a design environment. A widget is added to the master. A first view is selected for the master. A first widget characterization of the widget is received. The first widget characterization is associated with the first view of the master. A second view of the master is selected. A second widget characterization of the widget is received. The second widget characterization is associated with the second view of the master. An instance of the master is placed in a containing context. A first instance view selection is received, the first instance view selecting the first view of the master for the first instance of the master. The first instance of the master is displayed within the containing context, the widget being displayed in accordance with the first widget characterization associated with the first view of the master.

A method includes: generating a plurality of individuals of a current generation in accordance with a plurality of individuals of a previous generation to acquire values of an objective function for individuals each representing a variable by evolutionary computation; calculating, for each of partial individuals of the plurality of individuals of the current generation generated by the generating processing, a first value of the objective function by a predetermined method; approximately calculating, for each of the plurality of individuals of the current generation, a second value of the objective function with lower precision than the predetermined method; computing a fitness difference representing a difference between the plurality of individuals of the current generation in accordance with the first value or the second value; and controlling the precision of the approximate calculation based on the fitness difference and a precision difference between the first value and the second value.

A method includes: generating a plurality of individuals of a current generation in accordance with a plurality of individuals of a previous generation to acquire values of an objective function for individuals each representing a variable by evolutionary computation; calculating, for each of partial individuals of the plurality of individuals of the current generation generated by the generating processing, a first value of the objective function by a predetermined method; approximately calculating, for each of the plurality of individuals of the current generation, a second value of the objective function with lower precision than the predetermined method; computing a fitness difference representing a difference between the plurality of individuals of the current generation in accordance with the first value or the second value; and controlling the precision of the approximate calculation based on the fitness difference and a precision difference between the first value and the second value.

A malicious anchor node detection and target node localization method based on recovery of sparse terms, includes: S1: establishing an unknown disturbance term by using ranging value attack terms from an attacker to nodes in a wireless sensor network, and introducing a to-be-estimated location of a target node to the unknown disturbance term, to obtain an unknown sparse vector; S2: converting a problem of malicious anchor node detection and target node localization into a problem of recovery of the unknown sparse vector; S3: determining a location of an initial node according to a recursive weighted linear least square method, and recovering and reconstructing the unknown sparse vector with sparsity; and S4: determining a malicious anchor node determination range by approximating a threshold using a recovered value of the unknown sparse vector, to implement malicious anchor node detection, and recovering and determining location information of the target node.

A malicious anchor node detection and target node localization method based on recovery of sparse terms, includes: S1: establishing an unknown disturbance term by using ranging value attack terms from an attacker to nodes in a wireless sensor network, and introducing a to-be-estimated location of a target node to the unknown disturbance term, to obtain an unknown sparse vector; S2: converting a problem of malicious anchor node detection and target node localization into a problem of recovery of the unknown sparse vector; S3: determining a location of an initial node according to a recursive weighted linear least square method, and recovering and reconstructing the unknown sparse vector with sparsity; and S4: determining a malicious anchor node determination range by approximating a threshold using a recovered value of the unknown sparse vector, to implement malicious anchor node detection, and recovering and determining location information of the target node.

Techniques are described for using variables associated with vehicle wheels (e.g., linear velocity at a wheel and orientation of the wheel) to estimate velocity of a vehicle during a turn maneuver. In examples of the disclosure, in association with one or more wheels, a wheel orientation during the maneuver and a linear speed during the maneuver may be determined, and well as a yaw rate (e.g., from an inertial measurement unit, gyroscope, etc.) of the vehicle. Examples of the present disclosure include, based on the variables associated with the wheel(s) and the yaw rate associated with the turn maneuver, estimating a vehicle velocity, which may be used by various downstream components, such as to determine or update a pose of a vehicle as part of a localization operation.