50 results about "Matrix entry" patented technology

An apparatus and method causes a response action in an electrical power distribution system having, inter alia, a plurality of intelligent electronic devices (IEDs) configured to provide protective monitoring and control of the electrical power system. The method includes detecting receipt of one of a number of matrix input signals that can be received from the plurality of IEDs, and comparing the matrix input signal to a configuration of a m×n cross-point switch matrix having a corresponding number of A[m,n] entries. Each of the n rows is associated with a matrix input signal and each of the m columns is associated with one of a number of matrix output signals that can be transmitted to the plurality of IEDs. The method also includes asserting a matrix output signal based on the comparison to cause the response action.

A semiconductor device analyzer has a substrate model reading module, a Y-matrix entry module, a discriminating module, a matrix reduction module, and an output format discriminating module. The substrate model reading module reads a substrate network model of three-dimensional meshes representing the substrate of a semiconductor device. The substrate network model is a network of resistive and capacitive elements and is used for the simulation and analysis of the semiconductor substrate. The Y-matrix entry module prepares a Y-matrix from the substrate network model, each element of the Y-matrix being expressed with a polynomial of differential operator “s”. The discriminating module discriminates internal nodes to be eliminated from external nodes to be left among the nodes of the substrate network model. The matrix reduction module eliminates the internal nodes, thereby reducing the Y-matrix. The output format determining module determines an output format for an operation result.

A variable code rate adaptive encoding / decoding method using LDDC code is disclosed, in which an input source data is encoded using the LDPC (low density parity check) code defined by a first parity check matrix configured with a plurality of submatrices. The present invention includes the steps of generating a second parity check matrix corresponding to a code rate by reducing a portion of a plurality of submatrices configuring a first parity check matrix according to the code rate to be applied to encoding an input source data and encoding the input source data using the second parity check matrix.

The present invention provides a method and apparatus for implementing a matrix algorithm for scheduling instructions. One embodiment of the method includes selecting a first subset of instructions so that each instruction in the first subset is the earliest in program order of instructions associated with a corresponding one of a plurality of sub-matrices of a matrix that has a plurality of matrix entries. Each matrix entry indicates the program order of one pair of instructions that are eligible for execution. This embodiment also includes selecting, from the first subset of instructions, the instruction that is earliest in program order based on matrix entries associated with the first subset of instructions.

Light distortion due to light noise or light scattering object in the path of a light beam from a projector to a camera's image sensor in projector-camera systems are mitigated, or eliminated, by a simple modification to a light transport matrix T. For each row in light transport matrix T, matrix entries along a common row are compared, and all but the highest valued entry in the row are zeroed out, i.e. nullified. It is preferred that an array and record notation be used in order to reduce the size of the light transport matrix T, and its modified version to two arrays.

The invention discloses a face recognition method based on dictionary learning models. The method comprises the following steps of: mapping trained and tested face images to a low-dimension space to acquire a training signal set matrix; establishing the dictionary learning models which comprise an irrelevant dictionary learning (IDL) model and an unconstrained irrelevant dictionary learning (U-IDL) model; inputting the training signal set matrix into the IDL and U-IDL models, and solving the models to acquire an irrelevant dictionary and a linear classifier; acquiring a corresponding sparse vector of each picture belonging to a test sample based on the dictionary acquired in the last step by using a sparse expression algorithm; and inputting the sparse vectors into the linear classifier to acquire category labels of test sample pictures, wherein the result expressed by the category labels is used as the face recognition result. The invention provides the new models and the new method for dictionary learning problems in sparse expression, and the models and the method can be applied to mode identification and image classification problem under common conditions; and particularly, aiming at face recognition application, the dictionary learning method can achieve relatively high face recognition accuracy.