Recent Patents in Signal Processing (October 2017) – Compressed sensing

For our October 2017 issue, we cover recent patents granted in the area of compressed sensing.

Patent no. 9,755,714 proposes methods and systems for performing compressed time domain joint channel estimation in a multi-user MIMO LTE wireless network include receiving training signals from a plurality of users, estimating a maximum delay spread for the received data according to a coherence bandwidth of the received data, limiting the received data in the time domain to the estimated maximum delay spread, selecting and estimating an active tap from the limited data set, and subtracting a contribution of the selected active tap from the reduced data set. These steps can be repeated until the residual signal falls below a specified minimum. The network can be a C-RAN network. The training data can be SRS or DMRS data. Limiting the received data ensures that only a few significant taps are analyzed, so that the system is not under determined and can be analyzed for accurate channel estimation using any of several existing algorithms.

The invention no. 9,733,328 relates to a method of MR imaging of at least a portion of a body  of a patient placed in an examination volume of a MR device, the method comprising the steps of: --subjecting the portion of the body to a first imaging sequence for acquiring a first signal data set; --subjecting the portion of the body to a second imaging sequence for acquiring a second signal data set, wherein the imaging parameters of the second imaging sequence differ from the imaging parameters of the first imaging sequence; --reconstructing a MR image from the second signal data set by means of regularization using the first signal data set as prior information. Moreover, the invention relates to a MR device and to a computer program for a MR device.

As described in patent no. 9,692,619 nonzero elements of a signal vector, which may be a sparse signal vector, may be determined based on an observation vector representing a set of underdetermined observations using a compressed sensing optimization and a non-underdetermined estimation method such as iterative linear minimum mean-square error ("LMMSE") estimation. Compressed sensing optimization may be used to obtain a subset of potentially nonzero elements of the signal vector, and LMMSE estimation may then be used to find the nonzero elements among the potentially nonzero elements. The identification of nonzero elements may then be used to recover the signal vector from the observation vector. This technique is useful for recovering compressed data such as a sparse frequency space representation of audio or video data from a measurement. The technique is also useful for identifying at a base station a relatively small number active devices in an overloaded communication network.

Implementations of the disclosure no. 9,672,639 relate to methods for reconstruction for bioluminescence tomography based on a method of multitask Bayesian compressed sensing in the field of medical image processing. The method includes the following operations. Firstly the high order approximation model is used to model the law of light propagation in biological tissues, then the inner-correlation among multispectral measurements is researched based on multitask learning method and incorporated into a reconstruction algorithm of bioluminescence tomography as prior information to reduce ill-posedness of BLT reconstruction, and then on this basis, three-dimensional reconstruction of bioluminescent source is realized. Compared with other reconstruction algorithms for BLT, the correlation among multispectral measurements is incorporated into the disclosure and the ill-posedness of BLT reconstruction is reduced. The bioluminescent source can be reconstructed and located accurately using the proposed algorithm, and computational efficiency can be greatly improved.

As introduced in patent no. 9,635,250, in an imaging device, a difference calculation unit calculates a differential signal between charge signals that have been accumulated and are held by first and charge holding units with different timings. A multiple sampling unit performs multiple sampling processing on the differential signal, and an analog digital conversion unit converts a signal that has undergone multiple sampling processing to a digital signal. That is, multiple sampling processing is performed on a differential signal with a higher sparsity than that of an image signal.

In patent no. 9,626,560, method and apparatus for compressed sensing yields acceptable quality reconstructions of an object from reduced numbers of measurements. A component x of a signal or image is represented as a vector having m entries. Measurements y, comprising a vector with n entries, where n is less than m, are made. An approximate reconstruction of the m-vector x is made from y. Special measurement matrices allow measurements y=Ax+z, where y is the measured m-vector, x the desired n-vector and z an m-vector representing noise. "A" is an n by m matrix, i.e. an array with fewer rows than columns. "A" enables delivery of an approximate reconstruction, x.sup.#, of x. An embodiment discloses approximate reconstruction of x from the reduced-dimensionality measurement y. Given y, and the matrix A, approximate reconstruction x.sup.# of x is possible. This embodiment is driven by the goal of promoting the approximate sparsity of x.sup.#.

In patent no. 9,619,904 a method is introduced for reconstructing high signal-to-noise ratio (SNR) magnetic resonance imaging (MRI) slices, including: receiving a thick MRI slice of bodily tissue acquired using a single MRI scan, wherein the thick slice has a high SNR; receiving two thin MRI slices of the bodily tissue acquired using a single MRI scan, wherein each of the two thin MRI slices has a low SNR; and reconstructing multiple high SNR thin slices of the bodily tissue using the thick slice and the two thin slices.

In patent no. 9,600,899 a measurement vector of compressive measurements is received. The measurement vector may be derived by applying a sensing matrix to a source signal. At least one first feature vector is generated from the measurement vector. The first feature vector is an estimate of a second feature vector. The second feature vector is a feature vector that corresponds to a translation of the source signal. An anomaly is detected to in the source signal based on the first feature vector.

If you have an interesting patent to share when we next feature patents related to compressed sensing, or if you are especially interested in a signal processing research field that you would want to be highlighted in this section, please send email to Csaba Benedek (benedek.csaba AT sztaki DOT mta DOT hu).

References

Number: 9,755,714
Title: Method and system for compressed sensing joint channel estimation in an LTE cellular communications network
Inventors: Bose; Sayak (Nashua, NH), Hombs; Brandon (Merrimack, NH), Dhakal; Sagar (Bedford, NH), Farkas; Joseph (Merrimack, NH)
Issued: September 5, 2017
Assignee: Collision Communications, Inc. Peterborough, NH, US

Number: 9,733,328
Title: Compressed sensing MR image reconstruction using constraint from prior acquisition
Inventors: Doneva; Mariya Ivanova (Hamburg, DE), Remmele; Stefanie (Hamburg, DE), Bornert; Peter (Hamburg, DE), Mazurkewitz; Peter (Hamburg, DE), Senegas; Julien (Hamburge, DE), Keupp; Jochen (Rosengarten, DE), Nehrke; Kay (Ammersbek, DE)
Issued: August 15, 2017
Assignee: KONINKLIJKE PHILIPS N.V. (Eindhoven, NL)

Number: 9,692,619
Title: Non-underdetermined estimation for compressed sensing
Inventors: Abdoli; Javad (Ottawa, CA), Jia; Ming (Ottawa, CA)
Issued: June 27, 2017
Assignee: Huawei Technologies Co., Ltd. (Shenzhen, CN)

Number: 9,672,639
Title: Bioluminescence tomography reconstruction based on multitasking bayesian compressed sensing
Inventors: Feng; Jinchao (Beijing, CN), Jia; Kebin (Beijing, CN), Wei; Huijun (Beijing, CN)
Issued: June 6, 2017
Assignee: Beijing University of Technology (Beijing, CN)

Number: 9,635,250
Title: Imaging device and imaging method using compressed sensing
Inventors: Sato; Satoshi (Kyoto, JP), Azuma; Takeo (Kyoto, JP), Motoyama; Hiroyuki (Osaka, JP), Ozawa; Jun (Nara, JP)
Issued: April 25, 2017
Assignee: Panasonic Intellectual Property Management Co., Ltd. (Osaka, JP)

Number: 9,626,560
Title: Method and apparatus for compressed sensing
Inventors: Donoho; David Leigh (Stanford, CA)
Issued: April 18, 2017
Assignee: The Board of Trustees of the Leland Stanford Junior University (Palo Alto, CA)

Number: 9,619,904
Title: Exploiting similarity in adjacent slices for compressed sensing MRI
Inventors: Weizman; Lior (Jerusalem, IL), Eldar; Yonina (Haifa, IL), Ben Bashat; Dafna (Tel-Aviv, IL)
Issued: April 11, 2017
Assignee: Technion Research & Development Foundation Limited (Haifa, IL), The Medical Research, Infrastructure and Health Services Fund of the Tel-Aviv Medical Center (Tel-Aviv, IL)

Number: 9,600,899
Title: Methods and apparatuses for detecting anomalies in the compressed sensing domain
Inventors: Haimi-Cohen; Raziel (Springfield, NJ), Jiang; Hong (Warren, NJ)
Issued: March 21, 2017
Assignee: Alcatel Lucent (Boulogne-Billancourt, FR)