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Ion mobility spectrometry (IMS) is a promising analytical technique for mass spectrometry (MS)-based compound identification by providing collision cross-section (CCS) value as an additional dimension with structural information. Here, GraphCCS was proposed to accurately predict the CCS value and expand the coverage of CCS libraries. A new adduct encoding method was proposed to encode SMILES strings and adduct types of compounds into adduct graphs. GraphCCS extended its predictive capability to ten different adduct types. A very deep graph convolutional network with up to 40 GC N layers was built to predict CCS values from adduct graphs. A curated dataset with 12,775 experimental CCS values was used to train, validate, and test the GraphCCS model. The resulting CCS predictions achieved a median relative error (MedRE) of 0.94 % and a coefficient of determination (R2) of 0.994 on the test set. Results on external test sets showed that GraphCCS outperformed AllCCS2, CCSbase, SigmaCCS, and DeepCCS. Based on the developed GraphCCS method, a large-scale in-silico database was built, including 2,394,468 CCS values. Those CCS values can be used to filter false positives complementary to retention times and tandem mass spectra. Finally, the effectiveness of GraphCCS in assisting compound identification was tested on a mouse adrenal gland lipid dataset with 1,960 lipids. The results demonstrated that the in-silico CCS values combined with MS spectra and retention times can efficiently filter the false positive candidates. • GraphCCS encodes different adduct ions by constructing adduct graphs as the inputs to the graph neural network. • A very deep graph convolutional network with up to 40 GCN layers was trained to predict CCS values. • GraphCCS can predict CCS values for different adduct types, different compound classes, and different IMS instruments. • The predicted CCS can be complemented with retention time and m / z , thus effectively filtering out false positive compounds. [ABSTRACT FROM AUTHOR]

In this paper the problem of parallel waveform enhancement via the multi-sensor fusion technology is carefully studied. Through representing the observed multiple noisy observations as a 3-D tensor, we propose two novel approaches in the time domain, i.e. the transforming and filtering (TAF) approach and the direct multidimensional filtering (DMF) approach, for parallel waveform recovery and interference suppression. The term “parallel” indicates the system can produce an estimate of the clean waveform in each sensor channel simultaneously. Specifically, the TAF approach transforms the observed tensor into a different domain where the noise can then be filtered by discarding the insignificant coefficients. The DMF approach directly reduces the noise level by applying multidimensional filtering on the observed tensor. Both DMF and TAF are “blind” because they do not require precise frequency responses between the desired source and distributed sensors. Simulations show that TAF is capable of yielding satisfactory performances for spatially white noise, while DMF can produce satisfactory results on spatially colored noise. Besides, both TAF and DMF can work well in complex real environments. [ABSTRACT FROM AUTHOR]

This paper proposes a new perspective on the problem of multidimensional spectral factorization, through helical mapping: d -dimensional ( d D) data arrays are vectorized, processed by 1D cepstral analysis and then remapped onto the original space. Partial differential equations (PDEs) are the basic framework to describe the evolution of physical phenomena. We observe that the minimum phase helical solution asymptotically converges to the d D semi-causal solution, and allows us to decouple the two solutions arising from PDEs describing physical systems. We prove this equivalence in the theoretical framework of cepstral analysis, and we also illustrate the validity of helical factorization through a 2D wave propagation example and a 3D application to helioseismology. [ABSTRACT FROM AUTHOR]

A 5-D depth-velocity filter is proposed for enhancing moving objects in noisy light field videos (LFVs) (also known as plenoptic videos). The proposed filter consists of an ultra-low complexity 5-D IIR depth filter and a 5-D FIR velocity filter. The 5-D IIR depth filter is employed to denoise a noisy LFV. The denoised LFV is then utilized to estimate the 3-D apparent velocity of the moving object of interest. The 5-D FIR velocity filter is designed based on the estimated 3-D apparent velocity and is used to enhance the moving object of interest while attenuating other interfering moving objects. Experimental results confirm the effectiveness of the proposed 5-D depth-velocity filter compared to previously reported 5-D depth-velocity filters. [ABSTRACT FROM AUTHOR]

We demonstrate the possibility of applying a data-driven nonlinear filtering scheme in processing ground penetrating radar (GPR) data. The algorithm is based on the recently developed multidimensional ensemble empirical mode decomposition (MDEEMD) method which provides a frame of developing a variety of approaches in data analysis. The GPR data processing is very challenging due to the large data volume, special format, and geometrical sensitive attributes which are very easily affected by various noises. Approaches which work in other fields of data processing may not be equally applicable to GPR data. Therefore, the MDEEMD has to be modified to fit the special needs in the GPR data processing. In this study, we first give a brief review of the MDEEMD, and then provide the detailed procedure of implementing a 2D GPR filter by exploiting the modified MDEEMD. A complete synthetic model study shows the details of algorithm implementation. To assess the performance of the proposed approach, models of various signal to noise ( S / N ) ratios are discussed, and the results of conventional filtering method are also provided for comparison. Two real GPR field examples and onsite excavations indicate that the proposed approach is feasible for practical use. [ABSTRACT FROM AUTHOR]

The paper proposes a new high-quality approach to fast Monte-Carlo path-tracing. The key feature of the approach is screen-space filtering with the help of additional information (depth, normal direction, etc.) of the illumination separated from material color. It allows to reach high-quality and edge-aware filtering. The proposed method yields 1-2 times speed-up without putting significant restrictions on the raytracing algorithm. [ABSTRACT FROM AUTHOR]

Whereas most previous works treating color or hyperspectral image restoration use hybrid filters or data splitting, some new approaches consider multidimensional or tensor signal processing techniques. Tensor processing methods are based on multilinear algebra and are more efficient than 2-D filtering. However, they rely on orthogonal tensor flattening. The aim of this letter is to show that this orthogonal flattening may not be optimal for multidimensional images. We propose a method to adapt the flattening depending on the data set. Our proposed method is based on the estimation of main directions in multidimensional data. For this purpose, we extend the straight line detection algorithm. Multidimensional filtering method HOSVD -- (K1,....,KN) is applied along the estimated directions. We also adapt a quadtree partitioning in order to split tensors into homogeneous sub-tensors to keep local characteristics. Considering some examples of color and hyperspectral images, we present some promising results. [ABSTRACT FROM AUTHOR]

A multidimensional filtering technique is proposed using fuzzy logic ideas and based on two locally estimated statistical parameters. This technique is applied to the 3D space of color images. Two fuzzy sets for each fuzzy variable are considered. These fuzzy variables assign the corresponding part of the image to a specific class. The parameters of the fuzzy sets are derived in respect to the corresponding “crisp” sets attributed to the ideal noiseless image. An additional fuzzy selection process is also introduced by applying a distance dependent weighted average as the filtering action of each rule. Both signal and noise characteristics are taken into account in the filtering process. Experimental results show that these fuzzy non-linear filters work very well. [ABSTRACT FROM AUTHOR]

Three different kinds of median type estimators for use in applications where the underlying probability distributions are multivariate are proposed and analyzed. The numerical complexity and the statistical characteristics of the estimators are studied and discussed. Numerical results give evidence that the estimator which is a simple extension of the scalar median has an overall performance that is the same or better than the other two proposed estimators. [ABSTRACT FROM AUTHOR]

The optimal synthesis of a single-input single-output (SISO) multidimensional (M-D) digital filter with fixed-point arithmetic is investigated. The necessary and sufficient conditions for optimal realizations for both optimal and equal wordlength registers are established. It is shown that these optimality conditions always can be satisfied for an arbitrary M-D filter. It is proven that optimal structures possess some favorable properties such as low coefficient sensitivity. It is found that the optimal realizations of a multidimensional filter demonstrate a remarkable property in needed number of multipliers per sample output is comparison to one dimensional (1-D) optimal structures. Two numerical examples are presented to illustrate the design procedure and usefulness of the proposed scheme. [ABSTRACT FROM AUTHOR]

An approach for massive parallel processing in multidimensional digital filtering, which has briefly been introduced for causal digital filters in previous publications, is generalized and examined in more detail. It is based on a suitably modified sampling procedure combined with diagonal processing and does not require any additional arithmetic operations in comparison with corresponding conventional digital filtering. The condition that has to be satisfied for making the approach suitable for full parallel processing is derived. Properties of diagonal hyperplanes as required for the present approach are discussed. [ABSTRACT FROM AUTHOR]