35 results about "Joint likelihood" patented technology

A method and apparatus for estimating the location of a wireless terminal are disclosed. The location of the wireless terminal is estimated based on one or more signal strength measurements in conjunction with: (i) one or more angle-of-arrival measurements, (ii) one or more time-of-arrival measurements, or (iii) a combination of (i) and (ii). At each measurement time, the illustrative embodiment (a) determines the candidate locations for the wireless terminal, (b) computes a measurement likelihood function for any signal strength measurements that are available at that time, (c) computes a measurement likelihood function for any geometry-of-arrival measurements that are available at that time, (d) computes a joint likelihood function for all measurements available at that time, and (e) updates the location probability distribution for the wireless terminal with the joint measurement likelihood function. Estimates of the location of the wireless terminal are obtained from the location probability distribution.

The claimed subject matter includes a system and method for recognizing mixed speech from a source. The method includes training a first neural network to recognize the speech signal spoken by the speaker with a higher level of a speech characteristic from a mixed speech sample. The method also includes training a second neural network to recognize the speech signal spoken by the speaker with a lower level of the speech characteristic from the mixed speech sample. Additionally, the method includes decoding the mixed speech sample with the first neural network and the second neural network by optimizing the joint likelihood of observing the two speech signals considering the probability that a specific frame is a switching point of the speech characteristic.

The present invention relates to a system and methodology to facilitate machine learning and predictive capabilities in a processing environment. In one aspect of the present invention, a Mutual Information Model is provided to facilitate predictive state determinations in accordance with signal or data analysis, and to mitigate classification error. The model parameters are computed by maximizing a convex combination of the mutual information between hidden states and the observations and the joint likelihood of states and observations in training data. Once the model parameters have been learned, new data can be accurately classified.

A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

A method for determining a semantic concept associated with an audio signal captured using an audio sensor. A data processor is used to automatically analyze the audio signal using a plurality of semantic concept detectors to determine corresponding preliminary semantic concept detection values, each semantic concept detector being adapted to detect a particular semantic concept. The preliminary semantic concept detection values are analyzed using a joint likelihood model based on predetermined pair-wise likelihoods that particular pairs of semantic concepts co-occur to determine updated semantic concept detection values. One or more semantic concepts are determined based on the updated semantic concept detection values. The semantic concept detectors and the joint likelihood model are trained together with a joint training process using training audio signals, at least some of which are known to be associated with a plurality of semantic concepts.

The claimed subject matter includes a system and method for recognizing mixed speech from a source. The method includes training a first neural network to recognize the speech signal spoken by the speaker with a higher level of a speech characteristic from a mixed speech sample. The method also includes training a second neural network to recognize the speech signal spoken by the speaker with a lower level of the speech characteristic from the mixed speech sample. Additionally, the method includes decoding the mixed speech sample with the first neural network and the second neural network by optimizing the joint likelihood of observing the two speech signals considering the probability that a specific frame is a switching point of the speech characteristic.

Non-line-of-sight (NLOS) identification and mitigation are carried out in a wireless positioning system based on channel statistics derived from multipath components of a received signal. The statistics may be based on the kurtosis, the mean excess delay spread, or the root mean square delay spread. The results are justified using IEEE 802.15.4a ultrawideband channel models. Amplitude and delay statistics based on the IEEE models are shown to be log-normal random variables. A joint likelihood ratio test is presented for the LOS and NLOS identification.

The present invention relates to a system and methodology to facilitate machine learning and predictive capabilities in a processing environment. In one aspect of the present invention, a Mutual Information Model is provided to facilitate predictive state determinations in accordance with signal or data analysis, and to mitigate classification error. The model parameters are computed by maximizing a convex combination of the mutual information between hidden states and the observations and the joint likelihood of states and observations in training data. Once the model parameters have been learned, new data can be accurately classified.

A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

Certain embodiments provide a computer apparatus operable to carry out a data processing method to position a set of anatomical landmarks in a three-dimensional image data set of a part or all of a patient, comprising: providing a trained supervised machine learning algorithm which has been trained to place each of the set of anatomical landmarks; applying the supervised machine learning algorithm to place the set of anatomical landmarks relative to the data set; providing a trained point distribution model, including a mean shape and a covariance matrix, wherein the mean shape represents locations of the set of landmarks in a variety of patients; and applying the point distribution model to the set of landmarks with the locations output from the supervised machine learning algorithm by: removing any landmarks whose locations have an uncertainty above a threshold with reference to the mean shape and covariance matrix; followed by: an optimisation of the locations of the remaining landmarks by maximising joint likelihood that a new shape, derived from linear combinations of eigenvectors of the covariance matrix, is plausible.

The present disclosure relates to an acoustic position determination system that includes a mobile communication device and at least one base transmitter unit. The mobile communication device is configured to identify a peak in the received signal, and to de-convolve the signal with all codes that are relevant to the area in which the signal is received. A joint likelihood that a potential code is correct is formed by determining a likelihood based on a signal parameter such as signal-to-noise ratio and a likelihood based on time-of arrival information.

The embodiment of the invention provides a prediction method and method based on multi-source heterogeneous data. The prediction method comprises the steps that at least two categories of historical data which can reflect an event result are obtained according to a set type of events; a joint likelihood model of attribute data of the set type of events and the historical data is built, and optimal estimation of the attribute data is determined according to the maximum posteriori principle; parameters of probability distribution related with the joint likelihood model in the attribute data are determined and used as a prediction result of a to-be-predicted event belonging to the set type of events. According to the prediction method and method based on the multi-source heterogeneous data, the hierarchical model is used, data from difference sources are introduced into different data layers, the heterogeneous data are unified in the same joint likelihood model to be analyzed, and therefore more accurate, real-time and stable prediction result can be obtained through effective fusion.

The invention belongs to the field of integrated circuits, relating to a method for establishing a space correlation model of technical errors in an integrated circuit chip. The method comprises the following steps of: extracting an unknown parameter of a space correlation function by utilizing the maximum likelihood estimation of a multi-test chip, and establishing the space correlation model ofthe errors in the chip; and multiplying a likelihood function of all test chips to obtain a joint likelihood function, solving through maximizing the joint likelihood function to obtain the space correlation function which is determined by a parameter value and can be directly used for circuit analysis design of the technical errors. The influence of pure random part of the error in the chip and the measurement error can be processed in the extraction process of the space correlation function, and the precision of an extracted result is remarkably improved. Determinant logarithms of symmetrical positive definite matrixes in the joint likelihood function are calculated by utilizing an LU (Logic Unit), and the problem of unstable number value occurring in direct calculation is solved.

A system and a method for a blind direction of arrival estimation are provided for a nonlinear 1-dimensional array of M receivers of J<M unknown signals transmitted through an unknown channel and subject to Gaussian noise. The method overcomes the conditions of that signals be disjoint in the time-frequency domain and / or statistically independent. A likelihood function evaluates a probability for the directions of arrivals. A weighting and regularization scheme selects relevant frequency components. A joint likelihood function of all frequencies is interpreted as a cost or objective function. The time domain is included into the estimation procedure by using a particle filter that operates on the constructed likelihood function and a simple transition kernel for the directions

The invention discloses a hyperspectral image super-resolution processing method based on a convolutional network, mainly solves the problem that a hyperspectral image generated in the prior art is low in resolution, and adopts the implementation scheme that firstly, a training sample and a test sample are formed by an acquired low-resolution hyperspectral image and a high-resolution multispectralimage; then constructing a convolutional network composed of an inference sub-network and a generation sub-network; training the convolutional network by using the training sample, and obtaining theconvolutional network with the highest similarity between the approximate distribution and the real distribution by maximizing a joint likelihood function of the low-resolution hyperspectral image andthe high-resolution multispectral image; and finally, inputting the test sample into the trained convolutional network, and carrying out optimization processing on the generated high-resolution hyperspectral image to obtain a final high-resolution hyperspectral image. According to the method, the resolution of generating the high-resolution hyperspectral image is improved by utilizing the deep convolutional neural network, and the method can be used for medical diagnosis, remote sensing, computer vision and monitoring.

The invention discloses a multistation radar asynchronous multiframe joint detection method, and belongs to the field of radar target detection. The method comprises the steps that the joint generalized log-likelihood ratio is calculated through multiframe asynchronous echo data provided by multiple radar stations in a radar network at each moment, and then a group of state sequences enabling the joint likelihood ratio to be maximum are found by solving an optimization problem to serve as estimation on target track. In addition, by combining a viterbi algorithm, the optimization problem can be quickly and efficiently solved, the detection real-time performance of the algorithm is guaranteed, and the dim target detection probability of a system and the detection performance of the radar system are improved.

A method for determining a semantic concept associated with an audio signal captured using an audio sensor. A data processor is used to automatically analyze the audio signal using a plurality of semantic concept detectors to determine corresponding preliminary semantic concept detection values, each semantic concept detector being adapted to detect a particular semantic concept. The preliminary semantic concept detection values are analyzed using a joint likelihood model based on predetermined pair-wise likelihoods that particular pairs of semantic concepts co-occur to determine updated semantic concept detection values. One or more semantic concepts are determined based on the updated semantic concept detection values. The semantic concept detectors and the joint likelihood model are trained together with a joint training process using training audio signals, at least some of which are known to be associated with a plurality of semantic concepts.

The invention provides a positron emission cerenkov-gamma bi-radiation imaging method. The positron emission cerenkov-gamma bi-radiation imaging method comprises the following steps: placing a visible light photon detector and a gamma photon detector, and acquiring a pulse data set; calculating a joint likelihood probability function of a multi-dimensional data sample during each time period; judging that whether the currently received data slot comes from a positron emission event or not; accumulating all the positron emission events; establishing a transfer function of the system for each voxel; and inverting the input of the transfer function. The invention further provides a positron emission cerenkov-gamma bi-radiation imaging device. The positron emission cerenkov-gamma bi-radiation imaging device comprises a proton-rich isotope injection module, a multi-radiation detector module, a multi-case time coincidence module, a system transfer function acquiring module and a nuclide distribution image reconstruction module. With the adoption of the method and the device provided by the invention, the spatial resolution, device sensitivity and imaging signal-to-noise ratio of the positron imaging device can be effectively improved, and the positron emission cerenkov-gamma bi-radiation imaging method is particularly suitable for the application of the positrons in nondestructive testing and biomedical imaging.

A method for controlling a device responsive to an audio signal captured using an audio sensor. A data processor is used to automatically analyze the audio signal using a plurality of semantic concept detectors to determine corresponding preliminary semantic concept detection values, each semantic concept detector being adapted to detect a particular semantic concept. The preliminary semantic concept detection values are analyzed using a joint likelihood model based on predetermined pair-wise likelihoods that particular pairs of semantic concepts co-occur to determine updated semantic concept detection values. One or more semantic concepts are determined based on the updated semantic concept detection values, and the device is controlled responsive to identified semantic concepts. The semantic concept detectors and the joint likelihood model are trained together with a joint training process using training audio signals, at least some of which are known to be associated with a plurality of semantic concepts.

A method for generating a probability map for a cell served by a network node is provided. The method includes generating a first probability map (P1) indicating a likelihood of primary beam directions (step 602). Generating the first probability map includes recording a first direction of a first user equipment (UE), the first direction indicating a direction of a first beam (e.g., a first set ofantenna weights (a.k.a., precoding vector)) associated with the first UE when the first UE appears in the cell served by the network node. The method further includes generating a second probabilitymap (P2) indicating a joint likelihood of primary and secondary beam directions (step 604). Generating the second probability map includes recording a second direction of a second beam associated withthe first UE when the first UE switches from the first beam to the second beam, the second direction being recorded in association with the first direction.

The invention discloses a graph learning model based on a reconstructed graph, and belongs to the field of image annotation, and the method comprises the following steps: searching a semantic nearestneighbor of a test image through an improved nearest neighbor algorithm, constructing a similar matrix, carrying out the clustering of the image through a random dot product graph, mining the internalconnection of the image, obtaining a weighted similar matrix, and obtaining a preliminary image annotation result through a graph learning algorithm. The relation between the labels is used for labeling, the co-occurrence imbalance between the labels is considered in the process, a nearest graph theory model is introduced, and the problem of label imbalance is effectively solved. The random dot product image is used for reconstructing a transfer matrix of labels, and the problem of image label coexistence asymmetry is solved. Further, a Naive Bayes nearest neighbor classifier is used to establish a joint likelihood function between the image and the tag. The image labeling model based on the reconstructed image model is provided according to the characteristic of unbalanced classificationof the image labels, and the recall rate of the labels can be effectively increased.

The invention discloses a Cherenkov event pulse digitization method and device, and belongs to the fields of event pulse detection systems, photoelectric signal processing and nuclear detection. The method comprises the following steps: S1, obtaining a data set of the of the Cherenkov event emission photons; S2, obtaining a data set of background light, S3, calculating a joint likelihood probability function or a posterior probability function of the data set of the Cherenkov event emission photons in each time period; S4, judging whether the data set of the background light in each time period is the data set of the background light or the data set of the Cherenkov event emission photons. The device comprises a Cherenkov event emission photon data set obtaining module, a background photondata set obtaining module, an independent photon correlator module and an event classifier module. The method and device are high in signal-to-noise ratio and spatial resolution.

The invention discloses a momentum wheel reliability evaluation method based on information fusion. By analyzing the magnitude of the performance parameter influenced by the external environment, choosing the remaining oil quantity of the lubricating system which is less affected by the external environment as the key performance index, through the analysis of degradation data, the joint likelihood function is constructed according to the properties of the degradation model, which successfully fuses the performance degradation data information with a small amount of sample lifetime information, estimates the parameters of the degradation model by using the maximum likelihood estimation method, and finally evaluates the reliability of the momentum wheel on the basis of the fused information. The invention makes full use of multi-source information in the reliability evaluation of momentum wheel, and improves the robustness of the evaluation result.

The invention discloses a downlink time frequency synchronization method jointly using a synchronization sequence and OFDM cyclic prefixes. The method achieves downlink time frequency synchronizationof a communication system by jointly using the synchronization sequence and multiple OFDM cyclic prefixes and can solve the problem that in a communication scene, due to factors such as large frequency offset and low signal to noise ratio, time frequency synchronization of an OFDM system is difficult. According to the method, one-dimensional time offset searching is conducted according to a time offset likelihood function, and after a time offset estimation value is obtained, a frequency offset estimation value is obtained by conducting one-dimensional frequency offset searching on a frequencyoffset likelihood function or directly using a low-complexity frequency offset estimation method. In the method, in a timing synchronization stage, time offset likelihood function values of multiplesynchronization cycles can be selected to be subjected to incoherent combination to improve the estimation accuracy degree. Compared with a synchronization method that two-dimensional time offset searching is directly conducted according to the time frequency joint likelihood function to obtain time offset and frequency offset, the method has the advantage that the searching complexity is greatlyreduced. The method is not only suitable for an initial synchronization stage of a communication system, but also suitable for a tracking synchronization stage of the communication system.

The invention discloses a multi-sensor joint detection method based on decision-level and signal-level data fusion. The multi-sensor joint detection method comprises the following steps: S1, obtaining observation values of target signals acquired by node sensors; S2, transmitting observation values acquired by the node sensors under different transmission conditions to a fusion center in different data transmission forms; S3, establishing a joint likelihood function according to the prior information; S4, performing maximum likelihood estimation on unknown parameters; S5, calculating a detection statistic based on a generalized likelihood ratio criterion; S6, setting a detection threshold by deducing approximate distribution of detection statistics; S7, comparing the detection statistic with the detection threshold, and outputting a detection result. According to the method, through combined utilization of multi-source and multi-level data, target information in the data can be reserved as much as possible, the problem that the information utilization rate is insufficient due to the fact that different levels of data are difficult to fuse is effectively solved, and meanwhile the detection performance of a multi-sensor system can be further improved by setting the optimal local judgment threshold value.

The invention discloses a multi-station radar asynchronous multi-frame joint detection method, which belongs to the field of radar target detection. At each moment, the present invention uses multi-frame asynchronous echo data provided by multiple radar sites in the radar network to calculate the joint generalized logarithmic likelihood ratio, and then finds a group of states that maximize the joint likelihood ratio by solving an optimization problem sequence as an estimate of the target track. In addition, the present invention combines the Viterbi algorithm to quickly and efficiently realize the optimal solution problem, while ensuring the real-time detection of the algorithm, it improves the detection probability of weak targets in the system and improves the detection performance of the radar system.

The invention belongs to the field of integrated circuits, relating to a method for establishing a space correlation model of technical errors in an integrated circuit chip. The method comprises the following steps of: extracting an unknown parameter of a space correlation function by utilizing the maximum likelihood estimation of a multi-test chip, and establishing the space correlation model of the errors in the chip; and multiplying a likelihood function of all test chips to obtain a joint likelihood function, solving through maximizing the joint likelihood function to obtain the space correlation function which is determined by a parameter value and can be directly used for circuit analysis design of the technical errors. The influence of pure random part of the error in the chip and the measurement error can be processed in the extraction process of the space correlation function, and the precision of an extracted result is remarkably improved. Determinant logarithms of symmetrical positive definite matrixes in the joint likelihood function are calculated by utilizing an LU (Logic Unit), and the problem of unstable number value occurring in direct calculation is solved.