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1.
公开(公告)号:US20220164995A1
公开(公告)日:2022-05-26
申请号:US17430987
申请日:2020-01-29
Applicant: Nokia Technologies Oy
Inventor: Caglar AYTEKIN , Francesco CRICRI , Mikko HONKALA
IPC: G06T9/00 , H04N19/15 , H04N19/132 , H04N19/196 , G06N3/08
Abstract: The embodiments relate to a method comprising compressing input data (I) by means of at least a neural network (E, 310); determining a compression rate for data compression; miming the neural network (E, 310) with the input data (I) to produce an output data (c); removing a number of elements from the output data (c) according to the compression rate to result in a reduced form of the output data (me); and providing the reduced form of the output data (me) and the compression rate to a decoder (D, 320). The embodiments also relate to a method comprising receiving input data (me) for decompression; decompressing the input data (me) by means of at least a neural network (D, 320); determining a decompression rate for decompressing the input data (me); miming the neural network (D, 320) with input data (me) to produce a decompressed output data (ï); padding a number of elements to the compressed input data (me) according to the decompression rate to produce an output data (ï); and providing the output data (ï).
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公开(公告)号:US20210161477A1
公开(公告)日:2021-06-03
申请号:US16770706
申请日:2018-12-10
Applicant: Nokia Technologies Oy
Inventor: Michael WOLDEGEBRIEL , Mikko HONKALA , Leo KARKKAINEN , Satu RAJALA , Harri LINDHOLM
Abstract: A method of detection of a recurrent feature of interest within a signal including: obtaining evidence, based on a signal, the evidence including a probability density function for each of a plurality of parameters for parameterizing the signal, including at least one probability density function for a parameter, of the plurality of parameters, that positions a feature of interest within signal data of the signal; parameterizing a portion of the signal data from the signal based upon a hypothesis that a point of interest in the signal data is a position of the feature of interest; determining a posterior probability of the hypothesis being true given the portion of the signal data by combining a prior probability of the hypothesis and a conditional probability of observing the portion of the signal data given the hypothesis.
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公开(公告)号:US20220353012A1
公开(公告)日:2022-11-03
申请号:US17624119
申请日:2019-07-04
Applicant: NOKIA TECHNOLOGIES OY
Inventor: Mikko HONKALA , Dani KORPI , Janne HUTTUNEN , Mikko UUSITALO , Leo KÄRKKÄINEN
Abstract: An apparatus, method and SW for HARQ control are disclosed. The method includes: inputting (504) a beginning part of a data packet received on a specific frequency band and in a scheduled slot between a user apparatus and a radio access network, and supplementary data related to the data packet, into a neural network with trained parameters to predict a success of decoding the data packet after received in full; and controlling (516) a hybrid automatic repeat request procedure associated with the data packet based on the predicted success using the specific frequency band and the scheduled slot for full-duplex inband signalling.
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公开(公告)号:US20220027709A1
公开(公告)日:2022-01-27
申请号:US17311895
申请日:2018-12-18
Applicant: Nokia Technologies Oy
Inventor: Mikko HONKALA
Abstract: Systems, apparatuses, and methods are described for configuring denoising models based on machine learning. A denoising model (301) may remove noise from data samples (451). A noise model (403) may include noise in the data samples. Data samples processed by the denoising model (453) and/or the noise model (455) and original data samples (457) may be input into a discriminator (405). The discriminator may make determinations to classify input data samples. The denoising model and/or the discriminator may be trained based on the determinations.
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公开(公告)号:US20190340754A1
公开(公告)日:2019-11-07
申请号:US16468964
申请日:2017-11-23
Applicant: Nokia Technologies Oy
Inventor: Mikko HONKALA , Akos VETEK , Tapio TAIPALUS , Harri LINDHOLM
Abstract: An apparatus configured to generate an output quality error estimate using a machine-learning error estimation model to in compare an output meeting a predetermined quality threshold with an output image reconstructed from a plurality of images, and provide the output quality error estimate for use in estimating if a second subsequent image is required, in addition to a first subsequent image to obtain a cumulative output having an output quality error meeting a predetermined error threshold. Also an apparatus configured, using a received output quality error estimate generated using a machine-learning error estimation model as above, to estimate if a second subsequent image is required, in addition to a first subsequent image, to obtain a cumulative output having an output quality error meeting a predetermined error threshold.
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6.
公开(公告)号:US20190200939A1
公开(公告)日:2019-07-04
申请号:US16332256
申请日:2016-09-20
Applicant: Nokia Technologies Oy
Inventor: Kim BLOMQVIST , Mikko HONKALA , Harri LINDHOLM
IPC: A61B5/00 , A61B5/0472 , A61B5/053
CPC classification number: A61B5/7285 , A61B5/0452 , A61B5/0472 , A61B5/0535 , A61B5/7264
Abstract: An approach is provided for synchronizing an impedance cardiography (“ICG”) measurement period with an electrocardiography (“ECG”) signal to reduce patient (105) auxiliary current. The approach involves measuring an ECG signal of a patient via an ECG device (103). The approach also involves processing the ECG signal to cause, at least in part, a detection of one or more ECG features of the signal. The approach further involves synchronizing a start, a stop, or a combination thereof of a measurement of an ICG signal of the patient via an ICG device (101) based, at least in part, on the detection of the one or more ECG features. The measurement of the ICG signal includes injecting an electrical current into the patient (105) for a duration of the measurement.
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公开(公告)号:US20190012581A1
公开(公告)日:2019-01-10
申请号:US16017742
申请日:2018-06-25
Applicant: Nokia Technologies Oy
Inventor: Mikko HONKALA , Francesco CRICRI , Xingyang NI
Abstract: The invention relates to a method comprising receiving a set of input samples, said set of input images comprising real images and generated images; extracting a set of feature maps from multiple layers of a pre-trained neural network for both the real images and the generated images; determining statistics for each feature map of the set of feature maps; comparing statistics of the feature maps for the real images to statistics of the feature maps for the generated images by using a distance function to obtain a vector of distances; and averaging the distances of the vector of distances to have a value indicating a diversity of the generated images. The invention also relates to technical equipment for implementing the method.
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