30 results about "Entropy estimation" patented technology

A method for assessing intelligibility of speech represented by a speech signal includes providing a speech signal and performing a feature extraction on at least one frame of the speech signal so as to obtain a feature vector for each of the at least one frame of the speech signal. The feature vector is input to a statistical machine learning model so as to obtain an estimated posterior probability of phonemes in the at least one frame as an output including a vector of phoneme posterior probabilities of different phonemes for each of the at least one frame of the speech signal. An entropy estimation is performed on the vector of phoneme posterior probabilities of the at least one frame of the speech signal so as to evaluate intelligibility of the at least one frame of the speech signal. An intelligibility measure is output for the at least one frame of the speech signal.

This invention relates to methods and devices for channel identification. The invention is particularly concerned with techniques for non-line of sight channel identification. In embodiments of the invention the methods and devices are used for channel identification in wireless geolocation systems. Embodiments of the invention make use of an entropy estimation of the channel to distinguish channel conditions and in particular to identify line-of-sight and non-line-of-sight channels and which can be used to solve the NLOS problem of determining relative distances between transmitter and receiver. In particular embodiments an entropy estimation of the channel impulse response (CIR) is used to construct a robust entropy-based channel identification technique. As a result, more accurate localization in indoor and other multipath environments may be possible.

A seed value making method and device for a PRNG (Pseudo Random Number Generator) are provided. The seed value making method includes the steps of: accumulating in memory units of a First Data Pool data from various external sources, analyzing the data to determine a source type, computing entropy estimations for each of the external sources basing on the source type as determined, and generating a seed value by using the entropy estimations and the data accumulated in the memory units of the First Data Pool. Accordingly, in generating a seed value, dynamic estimation of random sources rate, and classification of sources on slow and fast ones, and reliable and unreliable ones, can be provided, and also, seed values can be made with taking in account rate and reliability of the sources.

The invention is directed to a Quantum Random Number Generator comprising an emitting device (110) triggered by a signal representing an input bit x and adapted to generate and send a physical system(130) characterized by one of two possible quantum states determined by said input bit x, a measurement device (120) adapted to detect said physical system, to identify the quantum state of said physical system through an unambiguous state discrimination measurement and to generate an output b first representing whether the quantum state has been identified or not and, if it has been identified, which quantum state among the two possible quantum states was detected by the unambiguous state discrimination measurement to a processing device (140),the processing device (140) being adapted to estimate the entropy of the output b given the probabilities p(b|x) representing the probability of observing output b for a state preparation x, and a randomness extraction device (150) adapted to extract final random bit stream given the entropy estimate provided by the processing device (140).

The present invention provides an atrial fibrillation detection device and a storage medium. The atrial fibrillation detection device includes: an extraction module for extracting P wave waveform information and QRS wave waveform information in ECG signals; a first determination module connected to the extraction module , for determining the change characteristics of the PR interval according to the P wave waveform information; the second determination module is connected with the extraction module, and is used for determining the change characteristics of the RR interval according to the QRS wave waveform information; the calculation module is connected with the second determination module, using The entropy estimation method is used to calculate the sample entropy of the change characteristics of the RR interval; the third determination module is connected with the first determination module and the calculation module, and is used to determine the ECG Whether the signal is atrial fibrillation. The invention can improve the robustness of atrial fibrillation detection.

The invention discloses a human body meridian point positioning method based on a genetic cluster. The method comprises the following steps of: selecting any one meridian, which has an acupuncture point needing to be determined, in human body meridians, wherein a signal generator is respectively connected with a stimulation point and a ground point on the meridian through silver electrodes, and sending a stimulation signal; selecting an area with a diameter being 3cm on skin where the acupuncture point needs to be positioned, dividing the area by adopting a network structure, taking meshes in the network structure as test points, and adopting a signal collector to monitor response signals of the test points; carrying out three-level decomposition on each response signal of each test point by adopting a dbl wavelet packet, reconstructing each decomposed signal at single scale, carrying out entropy estimation on each reconstructed single signal, and adopting an entropy estimation value as an energy characteristic value of each test point of the acupuncture point; and taking an obtained entropy energy characteristic vector of each response signal of each test point as a sample, carrying out clustering division on energy through the genetic cluster, and accurately positioning the acupuncture point of the meridian.

The invention discloses an entropy theory based assembly contact stress distribution assessment method which includes the steps of firstly, building a measurement coordinate system and a three-dimensional solid model to a to-be-tested part, subjecting the three-dimensional solid model to finite element cell meshing, and calculating strain energy density of each cell; secondly, calculating the overall entropy estimate value, the overall maximum entropy, the overall normalized entropy value Hs according to the strain energy density of each cell; thirdly, judging whether Hs is greater than or equal to the set threshold value, if so, then taking Hs as an assessment index to assess assembly stress distribution uniformity of surfaces of the to-be-tested part, and if not, then going to the next step; fourthly, building multiple sections perpendicular to the Z-axis of the measurement coordinate system; fifthly, searching a section with convex hulls of which quantity and area accounting for the maximum ratio to the surface area of the to-be-tested part, and calculating the convex hull height entropy estimate value, the convex hull height maximum entropy, and the convex hull normalized entropy value Hcs; sixthly, building the assessment index Ec that is equal to AHS + BHCS; and finally assessing the assembly stress distribution uniformity of the surfaces of the to-be-tested part according to the assessment index Ec.

The invention provides a median-based network flow entropy evaluation method and apparatus. The method includes the following steps: obtaining a data packet of network data flow and sending the data packet to slave nodes in a Storm cluster; receiving estimations value of an intermediate item S returned by the slave nodes, wherein the estimation values of the S are obtained through a preset entropy estimation algorithm by the slave nodes in the Storm cluster according to the received data packet; ordering the received estimation values of the S according to the magnitude; obtaining a median of the ordered estimation values of the S, taking the median as a final estimation value of the S; and obtaining an estimation value of an entropy of the network data flow according to the final estimation value of the S. In the method, the Storm cluster includes at least one salve node. The method increases the accuracy of entropy estimation while the storage space and the calculating complexity are not increased.

The present invention provides an atrial fibrillation detection device and a storage medium. The atrial fibrillation detection device includes: an extraction module for extracting P wave waveform information and QRS wave waveform information in ECG signals; a first determination module connected to the extraction module , used to determine the P wave change characteristics according to the P wave waveform information; the second determination module is connected to the extraction module and used to determine the PR interval change characteristics according to the QRS wave waveform information; the third determination module is connected to the extraction module for Determine the RR interval change characteristics according to the QRS wave waveform information; the calculation module is connected with the third determination module, and is used to calculate the information entropy of the RR interval change characteristics by using the entropy estimation method; the fourth determination module is connected with the first determination module and the second determination module. 2. The determination module is connected with the calculation module, and is used to determine whether the electrocardiographic signal is atrial fibrillation according to the change characteristics of the P wave, the change characteristics of the PR interval, information entropy and a preset classification model. The invention can improve the robustness of atrial fibrillation detection.