99 results about "Functional dependency" patented technology

Systems and methods for automatically building a deadlock free inter-communication network in a multi-core system are described. The example implementations described herein involve a high level specification to capture the internal dependencies of various cores, and using it along with the user specified system traffic profile to automatically detect protocol level deadlocks in the system. When all detected deadlock are resolved or no such deadlocks are present, messages in the traffic profile between various cores of the system may be automatically mapped to the interconnect channels and detect network level deadlocks. Detected deadlocks then may be avoided by re-allocation of channel resources. An example implementation of the internal dependency specification and using it for deadlock avoidance scheme is presented on Network-on-chip interconnects for large scale multi-core system-on-chips.

In a method for determination of a position of a local coil on a bed in at least one spatial direction within a magnetic resonance scanner, a signal intensity value is initially, extracted from magnetic resonance measurements implemented with the appertaining local coil for acquisition of magnetic resonance images of an examination subject and / or for measurement of further system parameters at each of various positions of the recumbent bed relative to the tomograph. The functional dependency of the extracted signal intensity values on the position of the bed relative to the scanner is then determined. The position of the local coil on the bed is determined on the basis of the determined functional dependency. A corresponding control device for a magnetic resonance system and a computer program product implement the method.

In a method for determination of flip angle distributions for various antenna transmission configurations in a magnetic resonance system, magnetic resonance measurements are implemented with the various transmission configurations, with the reception configuration being identical for all implemented magnetic resonance measurements, and all magnetic resonance measurements for the various transmission configurations are implemented with a specific pulse sequence. This pulse sequence is selected such that the total function that describes the dependency of the image signal at a specific location on the flip angle achieved at this location with the radiated radio-frequency field, as well as on further MR-relevant parameters, can be factored into a first sub-function that describes the dependency of the image signal on the achieved flip angle and a second sub-function (Tb) that describes the dependency of the image signal on the further MR-relevant parameters, and such that the functional dependency of the image signal on the achieved flip angle is known. The absolute flip angle distribution is measured for a reference transmission configuration, and the flip angle distributions of the other transmission configurations are then respectively determined on the basis of the absolute flip angle distribution of the reference transmission configuration and on the basis of the ratio of the spatially-dependent image signals of the magnetic resonance measurements of the respective transmission configuration to the corresponding spatially-dependent image signals of the magnetic resonance measurement of the reference transmission configuration.

The specification describes data processes for analyzing large data steams for target anomalies. “Sequential dependencies” (SDs) are chosen for ordered data and present a framework for discovering which subsets of the data obey a given sequential dependency. Given an interval G, an SD on attributes X and Y, written as X→G Y, denotes that the distance between the Y-values of any two consecutive records, when sorted on X, are within G. SDs may be extended to Conditional Sequential Dependencies (CSDs), consisting of an underlying SD plus a representation of the subsets of the data that satisfy the SD. The conditional approximate sequential dependencies may be expressed as pattern tableaux, i.e., compact representations of the subsets of the data that satisfy the underlying dependency.

In a planning model, a decision variable optimization process (200) generates a planning function (122) describing the planning model, the planning function (122) depending upon a set of decision variables (125). The planning function (122) is separated into independent planning functions, SPi, each of which depend upon different decision variables (125). Each of the independent planning functions, SPi, is independently optimized to obtain decisions for the different decision variables (125), and an outcome is presented that indicates the decisions. The planning function (122) further includes an embedded constraint function that introduces an embedded constraint to weaken the coupling between decision variables (125) in the planning model, thereby reducing an N-dimensional optimization problem into a lower order optimization problem.