The technology we use, and even rely on, in our everyday lives –computers, radios, video, cell phones – is enabled by signal processing. Learn More »
1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
News and Resources for Members of the IEEE Signal Processing Society
For our August 2017 issue, we cover recent patents granted in the area of wavelet analysis
The presented application in patent no. 9,691,003 relates to a method of generating a keypoint descriptor for identifying an object in an image or a sequence of images, the keypoint descriptor being substantially invariant to a transformation of the object in the image. The present application further relates to a method of identifying an object in an image using a keypoint descriptor; and processing apparatus and computer program products for implementing a method of the present application.
Methods and systems for shear noise attenuation based on matching vertical particle velocity data and pressure data are described in patent no. 9,651,696. The shear noise attenuation is based on the fact that different stages of the analysis can be performed with different numbers of wavelet orientations. The analysis is performed for frequency sub-bands for all wave numbers and vice versa.
Patent no. 9,569,843 presents a method for denoising Magnetic Resonance Imaging (MRI) data, which includes receiving a noisy image acquired using an MRI imaging device and determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device. The noisy image is designated as the current best image. Then, an iterative denoising process is performed to remove noise from the noisy image. Each iteration of the iterative denoising process comprises (i) applying a bank of heterogeneous denoisers to the current best image to generate a plurality of filter outputs, (ii) creating an image matrix comprising the noisy image, the current best image, and the plurality of filter outputs, (iii) finding a linear combination of elements of the image matrix which minimizes a Stein Unbiased Risk Estimation (SURE) value for the linear combination and the noise model, (iv) designating the linear combination as the current best image, and (v) updating each respective denoiser in the bank of heterogeneous denoisers based on the SURE value. Following the iterative denoising process, the current best image is designated as a final denoised image.
In patent no. 9,549,677 methods for detecting a seizure, by use of a wavelet transform maximum modulus (WTMM) algorithm applied to body data. A non-transitive, computer-readable storage device for storing data that when executed by a processor, perform such a method.
As presented in patent no. 9,542,719 a device for decomposing images into at least three levels by wavelet transform comprises a first unit executing a first level of decomposition and a second unit executing the higher levels of decomposition by performing a sequence of processing tasks. The tasks are ordered in time by using a sequence of rows, a routing unit serving to configure the second unit when the level of decomposition associated with the processing task currently being executed changes relative to the level of decomposition associated with the processing task executed previously. The processing tasks are ordered so that any given row is associated with only one level of decomposition.
Apparatus and methods of spectral searching that employ wavelet coefficients as the basis for the searching is proposed in patent no. 9,523,635. The disclosed apparatus and methods employ a wavelet lifting scheme to transform spectroscopic data corresponding to an unknown pure material/mixture to a vector of wavelet coefficients, compare the wavelet coefficient vector for the unknown pure material/mixture with a library of wavelet coefficient vectors for known pure materials/mixtures, and identify the closest match to the unknown pure material/mixture based on the comparison of wavelet coefficient vectors. Because the wavelet lifting scheme can generate the wavelet coefficient vectors for the unknown pure material/mixture as well as the known pure materials/mixtures to conform to a desired compression level, the disclosed apparatus and methods can perform spectral searching with increased speed and reduced memory requirements, thereby making the disclosed apparatus and methods amenable for use in hand-held instruments for on-site material identification.
Patent no. 9,521,431 presents a method for compressing digital data. The method comprises creating a plurality of wavelet coefficients associated with an input signal by iteratively performing a plurality of wavelet transforms to a plurality of sub-bands of the input signal, adjusting a zerotree dataset according to each the wavelet transform, and encoding the plurality of wavelet coefficients according to the zerotree dataset.
In patent no. 9,518,839 systems and methods are provided for monitoring parameters within a system. A plurality of sensors each monitor at least one parameter associated with the system. A controller is configured to receive a signal representing the monitored at least one parameter from each of the plurality of sensors and adjust a function of the system based on the received signal. Respective discrete wavelet transform components are associated with each of the plurality of sensors. Each of the discrete wavelet transform components is configured to provide a set of discrete wavelet transform coefficients, representing a content of the signal for its associated sensor, to a discrete wavelet transform monitoring component.
In the embodiments described in patent no. 9,503,054, Linear Phase, Finite Impulse Response, filters incorporate a power complementarity property into a perfect reconstruction filter bank. Non-linear constraints for type A and type B filters are included in the Sequential Quadratic Programming design of the filters. An initial Quadrature Mirror Filter includes perfect reconstruction constraints, which might be optimized through iterative design techniques. Embodiments might be employed in noise reduction applications related to, for example, signal processing of images.
A plurality of data files is received. Thereafter, each file is represented as an entropy time series that reflects an amount of entropy across locations in code for such file. A wavelet transform is applied, for each file, to the corresponding entropy time series to generate an energy spectrum characterizing, for the file, an amount of entropic energy at multiple scales of code resolution. It can then be determined, for each file, whether or not the file is likely to be malicious based on the energy spectrum. Related apparatus, systems, techniques and articles are also described.
If you have an interesting patent to share when we next feature patents related to wavelet analysis, 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).
Title: Keypoint descriptor generation by complex wavelet analysis
Inventors: Bharath; Anil Anthony (London, GB), Ng Sing Kwong; Jeffrey (London, GB)
Issued: June 27, 2017
Assignee: Cortexica Vision Systems Limited (London, GB)
Title: Shear noise attenuation and data matching for ocean bottom node data using complex wavelet transforms
Inventors: Peng; Can (Houston, TX), Huang; Rongxin (Katy, TX), Asmerom; Biniam (Houston, TX)
Issued: May 16, 2017
Assignee: CGG SERVICES SAS (Massy, FR)
Title: Parameter-free denoising of complex MR images by iterative multi-wavelet thresholding
Inventors: Mailhe; Boris (Plainsboro, NJ), Nadar; Mariappan S. (Princeton, NJ), Kannengiesser; Stephan (Wuppertal, DE)
Issued: February 14, 2017
Assignee: Siemens Healthcare GmbH (Erlangen, DE)
Title: Seizure detection methods, apparatus, and systems using a wavelet transform maximum modulus algorithm
Inventors: Osorio; Ivan (Leawood, KS), Lyubushin; Alexey (Moscow, RU), Sornette; Didier (Zurich, CH)
Issued: January 24, 2017
Assignee: FLINT HILLS SCIENTIFIC, L.L.C. (Lawrence, KS)
Title: Device for image decomposition using a wavelet transform
Inventors: Courroux; Sebastien (Villebon-sur-Yvette, FR), Chevobbe; Stephane (Bourg-la-Reine, FR), Darouich; Mehdi (Vanves, FR), Paindavoine; Michel (Plombieres les Dijon, FR)
Issued: January 10, 2017
Assignee: Commissariat a L'Energie Atomique et aux Energies Alternatives (Paris, FR)
Title: Apparatus and methods of spectral searching using wavelet transform coefficients
Inventors: Tilden; Scott B. (Tucson, AZ)
Issued: December 20, 2016
Assignee: Rigaku Raman Technologies, Inc. (Tucson, AZ)
Title: Method and a system for wavelet based processing
Inventors:Bar-On; Ilan (Haifa, IL), Kostenko; Oleg (Haifa, IL)
Issued: December 13, 2016
Assignee: NUMERI LTD. (Haifa, IL)
Title: Wavelet based monitoring of system parameters
Inventors: Fansler; Aaron A. D. (Idaho Falls, ID)
Issued: December 13, 2016
Assignee: Northrop Grumman Systems Corporation (Falls Church, VA)
Title: Linear phase FIR biorthogonal wavelet filters with complementarity for image noise reduction
Inventors: DeGarrido; Diego Pinto (Philadelphia, PA)
Issued: November 22, 2016
Assignee: Avago Technolgies General IP (Singapore) Pte. Ltd. (Singapore, SG)
Title: Wavelet decomposition of software entropy to identify malware
Inventors: Wojnowicz; Michael (Irvine, CA), Chisholm; Glenn (Irvine, CA), Wolff; Matthew (Newport Beach, CA), Soeder; Derek A. (Irvine, CA), Zhao; Xuan (Irvine, CA)
Issued: October 11, 2016
Assignee: Cylance Inc. (Irvine, CA)
|Call for Nominations: Distinguished Industry Speakers and Distinguished Lecturers||31 May 2023|
|Call for Nominations: IEEE Medals & Recognitions||15 June 2023|
|Nominate a Colleague! Nominations Open for 2023 SPS Awards||1 September 2023|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2023|
© Copyright 2023 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.