There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.

Variational Mode Decomposition

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

[1] Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and nonstationary processes in nature, the analysis method would have to be adaptive. Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research. In this review, we will briefly introduce the method, list some recent developments, demonstrate the usefulness of the method, summarize some applications in various geophysical research areas, and finally, discuss the outstanding open problems. We hope this review will serve as an introduction of the method for those new to the concepts, as well as a summary of the present frontiers of its applications for experienced research scientists.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2007RG000228

A review on Hilbert‐Huang transform: Method and its applications to geophysical studies

Classifications, applications, and design challenges of drones: A review

Maximal functions which measure the smoothness of a function are intro" duced and studied from the point of view of their relationship to classical smoothness and their use in proving embedding theorems, extension theorems and various results on differentiation. New spaces of functions which generalize Sobolev spaces are introduced. AMS(MOS) subject classifications (1980). Primary 26B35, 46E35, 26Al5 Secondary 42B25

Maximal Functions Measuring Smoothness

https://people.math.sc.edu/sharpley/Papers_PDF/13.Ann_Math_1981.pdf

Weak-$L^\infty$ and BMO

The Empirical Mode Decomposition is a process for signals which produces Intrinsic Mode Functions from which instantaneous frequencies may be extracted by simple application of the Hilbert transform. The beauty of this method to generate redundant representations is in its simplicity and its effectiveness. Our study has two objectives: first, to provide an alternate characterization of the Intrinsic Mode components into which the signal is decomposed and, second, to better understand the resulting polar representations, specifically the ones which are produced by the Hilbert transform of these intrinsic modes.

/pdf/analysis-of-the-intrinsic-mode-functions-2xkgoifpk1.pdf

Analysis of the Intrinsic Mode Functions

We study Besov spaces 5°(LP(I2)), 0<p,q,a<oo,on do- mains I2 in Rd  We show that there is an extension operator W which is a bounded mapping from B°(LP(U)) onto B%(Lp(Rd)) This is then used to derive various properties of the Besov spaces such as interpolation theorems for a pair of B%(Lp(il)), atomic decompositions for the elements of 5°(Lp(f2)), and a description of the Besov spaces by means of spline approximation

/pdf/besov-spaces-on-domains-in-rd-39bhjfagn6.pdf

BESOV SPACES ON DOMAINS IN Rd

Fractional integration and convolution results are given for Orlicz spaces using an inequality earlier developed for Aa(X~) spaces which generalize Lorentz L(p,q) spaces. The extension problem for convolution operators encountered previously by other authors is almost entirely avoided.

/pdf/fractional-integration-in-orlicz-spaces-1csbgc2gmm.pdf

Robert Sharpley

Papers

Maximal Functions Measuring Smoothness

Weak-$L^\infty$ and BMO

Analysis of the Intrinsic Mode Functions

BESOV SPACES ON DOMAINS IN Rd

Fractional integration in Orlicz spaces