Weighted Window Sliding Multivariate Empirical Mode Decomposition for Online Multichannel Filtering

Tao, Songbing; Li, Nan; Wei, Shanbi; Chai, Yi

doi:10.1109/access.2018.2859838

Cited by 4 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For multivariate signals, however, it is not straightforward to detect the local extrema and to estimate the mean envelope, since the fields of complex and hyper-complex numbers are not ordered. MEMD overcomes this difficulty by employing real-valued projections in an n-dimensional space, known as n-dimensional real-valued projections, where projection direction vectors start from the origin of n-dimensional coordinates and end at the points, which are uniformly distributed on the unit sphere, also known as the (n − 1)-sphere, in the n-dimensional space [16]- [20]. Two approaches have Algorithm 1 The Original MEMD Algorithm 1.…”

Section: Original Memdmentioning

confidence: 99%

“…Unlike Fourier or wavelet based methods, EMD does not impose a priori assumptions about the data while decomposing signals, and hence, it is particularly suitable for the timefrequency analysis of real-world nonlinear and nonstationary signals. Owing to the excellent characterization of intrinsic scales at local level by EMD, both its multivariate extensions and bidimensional ones [10]- [20] have been widely applied in heterogeneous image fusion [21]- [29], for which, a set of common frequency scales must be determined beforehand.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate extensions of EMD based data fusion schemes aim to address the problem of uniqueness within the original EMD by considering a multichannel signal as a whole and using multiple real-valued projections to find the local mean of the original signal, a key issue to find physically meaningful IMFs [16]- [20]. This is particularly important, given that univariate EMD processes multichannel signals componentwise, it cannot guarantee that decompositions of different data sources are matched, either in number or properties of local scales, making a multi-scale comparison often difficult.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion

et al. 2019

View full text Add to dashboard Cite

Empirical mode decomposition (EMD) is a fully data-driven technique designed for multiscale decomposition of signals into their natural scale components, called intrinsic mode functions (IMFs). When EMD is directly applied to perform fusion of multivariate data from multiple and heterogeneous sources, the problem of uniqueness, that is, different numbers of decomposition levels for different sources, is likely to occur, due to the empirical nature of EMD. Although the multivariate EMD (MEMD) has been proposed for temporal data, which employs real-valued projections along multiple directions on a unit hypersphere in the n-dimensional space to calculate the envelope and the local mean of multivariate signals, in order to guarantee the uniqueness of the scales, its direct usefulness in 2D multi-scale image fusion is still limited, due to its inability to maintain the spatial information. To address this issue, we propose a novel bidimensional MEMD (BMEMD) which directly projects a bidimensional multivariate signal, which is composed of multiple images, on the unit hypersphere in the n-dimensional space. This is achieved via real-valued surface projections and the mean surface is estimated by interpolating the multivariate scatter data so as to extract common spatio-temporal scales across multiple images. Case studies involving texture analysis and multi-focus image fusion are presented to demonstrate the effectiveness of the proposed method. INDEX TERMS Empirical mode decomposition (EMD), bidimensional multivariate EMD (BMEMD), realvalued surface projections, multi-scale image fusion.

show abstract

Section: Original Memdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Without establishing the stationarity of time series [330], it is not possible to infer meaningful analysis of time series dynamics. Sampling of time series requires that the sampled time window should at least show statistical weak sense stationarity which is akin to finding the optimal time window size [415]. Therefore, in this work, time graphical models are validated against weak sense stationarity locally (due to being heavy tailed) using variance fractal dimension algorithm as defined in sub-section 3.4.3.…”

Section: Adaptive and Sliding Time Windowmentioning

confidence: 99%

A Cognitive and Concurrent Cyber Kill Chain Model

Khan

Siddiqui

Ferens

2017

Computer and Network Security Essentials

View full text Add to dashboard Cite

The challenges of cyber security have outpaced the advantages of cyber tools and technologies. In 2018, World Economic Forum has already placed cyber security in the top five risks faced by the world. Cyber threats are evolving and can cripple economies and nations. The major tools of cyber threats are anonymity, deception and uncertainty. Current state of the art research is also evolving into addressing these challenges by applying new and proactive threat hunting approaches instead of doing reactive cyber defense, which is proving futile. Malware is an indispensable tool of cyber threat actors to accomplish malicious activities i.e. exfiltration, espionage and disruption. Using advanced obfuscation and mutation methods, malware adversaries are able to remain ahead of cyber defenders. Most malware detection technologies are based on finding a-priori known signatures of malware payload or known patterns of malware behavior. This dissertation addresses the challenge of hunting unknown behaviorally mutated malware inside a host computer by proposing a proof of concept framework named Malvidence for characterizing malware behavior within a host operating system process tree using cognitive machine intelligence. Using Malvidence framework, tools and techniques can be derived for variety of cyber security methods for threat detection. Cognitive Computing is a promising domain of machine intelligence which explores and develops new tools to incorporate human cognitive characteristics so that the performance of existing domain of artificial intelligence and machine learning can be improved. Therefore, cognitive complexity based fractal analysis is demonstrated and a methodology of extracting inherent but hidden patterns of malware dynamics using a temporal graph theoretical approach is proposed. Further, a set of graph theoretical features is analyzed and proposed for an effective characterization of malware behavior which can be subsequently used for malware hunting and detection. In addition, the proposed features are tested for their mathematical validity. Finally, using proposed cognitive complexity analysis, characterization performance of an unsupervised clustering algorithm is provided to demonstrate the validity of Malvidence framework.

show abstract

Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal by Combining Empirical Mode Decomposition and Time-Frequency Peak Filtering

2019

View full text Add to dashboard Cite

Magnetic resonance sounding (MRS) signals are always corrupted by random noise. Although time-frequency peak filtering (TFPF) has been proven to be an effective method to suppress the random noise, it shows shortcomings when processing the oscillating high-frequency MRS signal at about 2 kHz. In this study, a new method combining empirical mode decomposition (EMD) and TFPF is proposed to overcome the TFPF limitation when processing the MRS oscillating signal. With the help of EMD decomposition characteristics, the random-noise-corrupted MRS oscillating signal is first decomposed into several different components which contain frequencies ranging from the highest to the lowest ones. Then, the components which do not have signal frequency are discarded to bring down the level of random noise. The residual components are further processed by TFPF, respectively, based on the theory of instantaneous frequency estimation and the property of noise accumulation. Finally, the de-noised result is obtained by reconstructing the processed components. The numerical simulations on synthetic signals embedded in both artificial noise and real noise show the combined method can improve the signal-to-noise ratios and reduce the uncertainties of signal parameters. In addition, the combined method is applied following a standard processing scheme in field data, and better results are also obtained. INDEX TERMS Hydrogeophysics, magnetic resonance sounding, random noise, time-frequency filtering.

show abstract

Weighted Window Sliding Multivariate Empirical Mode Decomposition for Online Multichannel Filtering

Cited by 4 publications

References 28 publications

Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion

Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion

A Cognitive and Concurrent Cyber Kill Chain Model

Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal by Combining Empirical Mode Decomposition and Time-Frequency Peak Filtering

Contact Info

Product

Resources

About