119 results about "Recursive computation" patented technology

The method of predicting tag detection probability for RFID framed slotted ALOHA anti-collision protocols is uses recursive calculations to accurately estimate the probability of discovering RFID tags in a multiple rounds discovery system. First, the method estimates the probability of detecting a given number of tags in a single round. Then, using a probability map, the method estimates the probability of detecting the given number of tags in multiple rounds. The probabilities are used to adjust the number of slots in a frame and the number of interrogation rounds used by the RFID tag reader to minimize collisions and optimize tag reading time.

The invention relates to the communication field and provides a method and a device for requesting a computing path in multiple-domain networks. The method for requesting a computing path in multiple-domain networks, which is provided by the embodiment of the invention, comprises the following steps: receiving a path computing request message, wherein the path computing request message carries PCE information and domain information of a path computing unit, and the domain information corresponds to the PCE information; acquiring the information of a boundary route device according to the domain information; and implementing PCE-based backward recursive computation by using the information of the boundary route device. By using the method and the device provided by the embodiment of the invention, the path computing request message received by PCE carries the PCE information and the domain information, so the information of the boundary route device, which is needed for BRPC computation, can be acquired, and BRPC can be used for realizing the computation of cross-domain paths.

The invention discloses an EKF-based rotating speed control method of a sensorless ultra-high speed permanent magnet synchronous motor. The method comprises the following steps of building a mathematical model of the motor and converting the mathematical model into a state equation; carrying out linearization and discretization, substituting the discrete linear state equation into an extended Kalman filter (EKF) for recursive computation, updating a variance matrix P and a gain K in the EKF in real time to update a state variable; and finally estimating the rotating speed of the motor in the dynamic operation process in real time. The rotating speed estimation method is applied to an SVPWM-based ultra-high speed motor direct torque control system; the adaptivity of the system is improved by a fuzzy PID controller; and fast, stable and reliable rotating speed control on the ultra-high speed permanent magnet synchronous motor is effectively achieved.

A processor executes a mask update instruction to perform updates to a first mask register and a second mask register. A register file within the processor includes the first mask register and the second mask register. The processor includes execution circuitry to execute the mask update instruction. In response to the mask update instruction, the execution circuitry is to invert a given number of mask bits in the first mask register, and also to invert the given number of mask bits in the second mask register.

Compile-time recognition of graph structure where graph has arbitrary connectivity and is constructed using recursive computations is provided. In one aspect, the graph structure recognized at compile time may be duplicated at runtime and can then operate on runtime values not known at compile time.

The invention discloses a method for mining enterprise data in supply chain finance. The method comprises the following steps of: acquiring tax involved data of enterprises in a tax bureau system; respectively determining an upstream and downstream enterprise list of each enterprise in N enterprises, wherein the N enterprises belong to a same industry; respectively carrying out recursive computation on transaction data between each enterprise in the N enterprises and tax payers in the respective upstream and downstream enterprise list, so as to obtain transaction information corresponding to the N enterprises, wherein the transaction information of each enterprise comprises upstream transaction data and downstream transaction data of the enterprise; and determining all the core enterprises by utilizing the transaction information corresponding to the N enterprises. According to the method, the core enterprise data obtaining efficiency is improved, and the finally obtained core enterprise data is more correct and objective. Moreover, the invention correspondingly discloses an enterprise data mining system.

In one aspect, the present invention provides a dynamic super-resolution technique that is computationally efficient. A recursive computation takes as input a previously computed super-resolved image derived from a sequence of low-resolution input frames. Combining this super-resolved image with a later low-resolution input frame in the sequence, the technique produces a new super-resolved image. By recursive application, a sequence of super-resolved images is produced. In a preferred embodiment, the technique uses a computationally simple and effective method based on adaptive filtering for computing a high resolution image and updating this high resolution image over time to produce an enhanced sequence of images. The method may be implemented as a general super-resolution software tool capable of handing a wide variety of input image data.

The invention discloses a speech signal endpoint detection method based on dynamic cumulant estimation, including sliding window-based high-order cumulant recursive computation and sliding window kurtosis-based endpoint detection. The sliding window-based high-order cumulant recursive computation includes: adding a rectangular window to original sample data, subjecting intra-window data to cumulant estimation, updating the intra-window data each time when one sample point is slid, and thus dynamically estimating the cumulant. The sliding window kurtosis-based endpoint detection includes: computing sliding window kurtosis and energy features by the use with the sliding window-based high-order cumulant recursive computation, thus subjecting speech signals to endpoint detection. Compared with the prior art, the method has the advantages that endpoint detection is based on sliding window kurtosis and energy double-threshold, the sliding window kurtosis is highly sensitive to the start point of a speech segment, resistance to noise interference is better, and the method is of better robustness in the noise environment.

The invention discloses a time varying network link packet loss probability estimation method based on a Kalman filter. The time varying network link packet loss probability estimation method based on the Kalman filter mainly comprises a training phase and an estimation phase. In the training phase, a source node sends back-to-back detection packets to multiple destination nodes so as to obtain path data, then, prior information of time varying link packet loss probability is estimated according to the path data, and a state transition equation of the Kalman filter is established. In the estimation phase, under the condition that the detection packets do not need to be sent, recursive computation and estimation of the time varying link packet loss probability are completed through a feedback control method according to the state transition equation and the path data obtained from network background flow. Due to the fact that a Kalman filter module is introduced to estimate the link packet loss probability of a time varying network, an obtained link packet loss probability estimated result has a minimum mean square error as well as high estimated accuracy, and the time-variation characteristic of the time varying network link packet loss probability can be reflected in real time.

The invention discloses a method for analyzing the velocity vector of a flow field of a heart based on a gray scale ultrasound image. The method is characterized by comprising the following steps of: dividing the gray scale ultrasound image into a grid unit; selecting a pixel point of which the pixel is closest to the average scale value in each cell of the grid unit, wherein the pixel point is used as a control point, and updating the gray value of the gray scale ultrasound image by an interpolation algorithm to fulfill the aim of removing speckle noise; and calculating by an optical flow method to obtain the heart flow field movement vector of the denoised gray ultrasound image. According to the method, multiple times of recursive computation is not needed in the interpolation algorithm process, and speckles can be removed and preprocessed in real time; and a calculation result is closest to the actual condition, and detailed information of flow such as eddy can be accurately acquired.

The invention discloses a near space circular synthetic aperture radar rapid back-direction projection imaging method. In view of the problem of near space circular synthetic aperture radar rapid imaging, the method replaces a phase error in coherent imaging method with a pixel amplitude error as the evaluation criterion of imaging precision, approximately expands through low order of Green function which is based on chebyshev nodes and lagrange interpolation according to the principle of minimum mean square error, switches the problem of calculation of sub aperture projection to the problem of calculation of limited number of approximate expansion coefficients, and reduces as much as possible the number of expansion coefficients under the condition of meeting the focusing accuracy so that algorithm computational complexity is reduced, and then accomplishes the imaging from coarse focusing to fine focusing by applying recursive computation. Compared with the existing circular SAR back-direction projection imaging method, the near space circular synthetic aperture radar rapid back-direction projection imaging method has the advantages that computing speed is greatly improved, big scene imaging is achieved and imaging accuracy is much higher.

A dual evaluation mode method for use with computer software that includes the acts of determining, for certain functions and expressions within input computer code, whether each function and expression may have any recursive dependencies, generating eager evaluation mode executable code for one or more elements of the input computer code based on the act of determining and providing both eager evaluation mode executable code and non-eager evaluation mode executable code to runtime software that supports both eager and non-eager evaluation modes.

A radiographic apparatus obtains lag-free radiation detection signals with lag-behind parts removed from radiation detection signals taken from a flat panel X-ray detector as X rays are emitted from an X-ray tube. The lag-behind parts are removed by a recursive computation on an assumption that the lag-behind part included in each X-ray detection signal is due to an impulse response formed of exponential functions, N in number, with different attenuation time constants. X-ray images are created from the lag-free radiation detection signals.

The invention discloses a multi-head attention memory network for short text sentiment classification. The network comprises a multi-hop memory sub-network, the multi-hop memory sub-network comprises a plurality of independent calculation modules which are connected in sequence, and each independent calculation module comprises a first multi-head attention coding layer, a first linear layer and an output layer which are connected in sequence. The input of each multi-head attention coding layer in the multi-hop memory sub-network comprises original memory and historical information memory, and the multi-head attention memory network learns more complex and abstract nonlinear features contained in a text through stacking conversion of independent calculation modules with enough hop counts; the emotion semantic structure in the text is effectively coded. Furthermore, the original memory of the input multi-hop memory sub-network is fully interacted by the recursive calculation process of the multi-head attention coding layer, so that the remote dependency relationship between the text features is modeled with more components, and the context emotion semantic relationship with higher level is mined, thereby improving the classification performance of the model.