Lifting Biorthogonal B-spline Wavelets

Highly symmetric [formula omitted]-refinement Bi-frames for surface multiresolution processing

BackgroundMicrobial genomes at the National Center for Biotechnology Information (NCBI) represent a large collection of more than 35,000 assemblies. There are several complexities associated with the data: a great variation in sampling density since human pathogens are densely sampled while other bacteria are less represented; different protein families occur in annotations with different frequencies; and the quality of genome annotation varies greatly. In order to extract useful information from these sophisticated data, the analysis needs to be performed at multiple levels of phylogenomic resolution and protein similarity, with an adequate sampling strategy.ResultsProtein clustering is used to construct meaningful and stable groups of similar proteins to be used for analysis and functional annotation. Our approach is to create protein clusters at three levels. First, tight clusters in groups of closely-related genomes (species-level clades) are constructed using a combined approach that takes into account both sequence similarity and genome context. Second, clustroids of conservative in-clade clusters are organized into seed global clusters. Finally, global protein clusters are built around the the seed clusters. We propose filtering strategies that allow limiting the protein set included in global clustering.The in-clade clustering procedure, subsequent selection of clustroids and organization into seed global clusters provides a robust representation and high rate of compression. Seed protein clusters are further extended by adding related proteins. Extended seed clusters include a significant part of the data and represent all major known cell machinery. The remaining part, coming from either non-conservative (unique) or rapidly evolving proteins, from rare genomes, or resulting from low-quality annotation, does not group together well. Processing these proteins requires significant computational resources and results in a large number of questionable clusters.ConclusionThe developed filtering strategies allow to identify and exclude such peripheral proteins limiting the protein dataset in global clustering. Overall, the proposed methodology allows the relevant data at different levels of details to be obtained and data redundancy eliminated while keeping biologically interesting variations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1112-8) contains supplementary material, which is available to authorized users.

Recent work has demonstrated that composite finite-elements provide an effective means for physically based modeling of deformable bodies. In this paper we present a number of highly effective improvements of previous work to allow for a high-performance and high-quality simulation of boundary surfaces of deformable bodies with changing topology, for instance, due to cuts and incisions. Starting at a coarse resolution simulation grid, along a cut we perform an adaptive octree refinement of this grid down to a desired resolution and iteratively pull the fine level finite-element equations to the coarse level. In this way, the fine level dynamics can be approximated with a small number of degrees of freedom at the coarse level. By embedding the hierarchical adaptive composite finite-element scheme into a geometric multigrid solver, and by exploiting the fact that during cutting only a small number of cells are modified in each time step, high update rates can be achieved for high resolution surfaces at very good approximation quality. To construct a high quality surface that is accurately aligned with a cut, we employ the dual-contouring approach on the fine resolution level, and we instantly bind the constructed triangle mesh to the coarse grid via geometric constrains.

Adaptive typhoon cloud image enhancement using genetic algorithm and non-linear gain operation in undecimated wavelet domain

Wavelet decomposition of software entropy reveals symptoms of malicious code

A new progressive transmission scheme using spline biorthogonal wavelet bases is proposed in this paper. First, several wavelet bases are compared with the spline biorthogonal wavelet bases. By exploiting the properties of this set of wavelet bases, a fast algorithm involving only additions and subtractions is developed. Due to the multiresolutional nature of the wavelet transform, this scheme is compatible with hierarchical-structured rendering algorithms. The formula for reconstructing the functional values in a continuous volume space is given in a simple polynomial form. Lossless compression is possible, even when using floating-point numbers. When the algorithm is applied to data from a global ocean model, the lossless compression ratio is about 1.5:1. Even with a compression ratio of 50:1, the reconstructed data is still of good quality. Finally, the reconstructed data is rendered using various visualization algorithms and the results are demonstrated. >

An important issue in scientific visualization systems is the management of data sets. Most data sets in scientific visualization, whether created by measurement or simulation, are usually voluminous. The goal of data management is to reduce the storage space and the access time of these data sets to speed up the visualization process. A new progressive transmission scheme using spline biorthogonal wavelet bases is proposed in this paper. By exploiting the properties of this set of wavelet bases, a fast algorithm involving only additions and subtractions is developed. Due to the multiresolutional nature of the wavelet transform, this scheme is compatible with hierarchical-structured rendering algorithms. The formula for reconstructing the functional values in a continuous volume space is given in a simple polynomial form. Lossless compression is possible, even when using floating-point numbers. This algorithm has been applied to data from a global ocean model. The lossless compression ratio is about 1.5:1. With a compression ratio of 50:1, the reconstructed data is still of good quality. Several other wavelet bases are compared with the spline biorthogonal wavelet bases. Finally the reconstructed data is visualized using various algorithms and the results are demonstrated. >

Implicit neural representation (INR), particularly in combination with hash encoding, has recently emerged as a promising approach for computed tomography (CT) image reconstruction. However, directly applying INR techniques to 3D dental cone-beam computed tomography (CBCT) with a truncated field of view (FOV) is challenging. During the training process, if the FOV does not fully encompass the patient's head, a discrepancy arises between the measured projections and the forward projections computed within the truncated domain. This mismatch leads the network to estimate attenuation values inaccurately, producing severe artifacts in the reconstructedimages. This study aims to develop a computationally efficient INR-based reconstruction framework that leverages multi-resolution hash encoding for 3D dental CBCT with a truncatedFOV. To mitigate truncation artifacts, we train the network over an expanded reconstruction domain that fully encompasses the patient's head. For computational efficiency, we adopt an adaptive training strategy that uses a multi-resolution grid: finer resolution levels and denser sampling inside the truncated FOV, and coarser resolution levels with sparser sampling outside. To maintain consistent input dimensionality of the network across spatially varying resolutions, we introduce an adaptive hash encoder that selectively activates the lower-level features of the hash hierarchy for points outside the truncatedFOV. The proposed method with an extended FOV effectively mitigates truncation artifacts. Compared with a naive domain extension, using fixed resolution levels and a fixed sampling rate, the adaptive strategy reduces computational time by 60% for an image volume of , while preserving the peak signal-to-noise ratio (PSNR) within the truncatedFOV. We propose an INR-based reconstruction framework for 3D dental CBCT with a truncated FOV, effectively reducing truncation artifacts and trainingcost.

Single-cell RNA sequencing scRNA-seq provides critical insights into cellular diversity, essential for understanding complex biological dynamics. Traditional scRNA-seq analysis employs unsupervised clustering methods and supervised learning-based approaches to interpret cells or cell clusters. However, both of these approaches have limitations. Unsupervised learning-based methods struggle with selecting a single resolution, limiting their ability to reveal the multi-layered nature of cellular diversity. On the other hand, supervised learning-based methods lack the flexibility to adjust to the various levels of resolution needed to fully capture the complex spectrum of cell types and states. In response to these challenges, hierarchical clustering has emerged as a superior technique. It enables detailed exploration across various resolutions without predefined cluster counts, thus overcoming the limitations of both unsupervised and supervised methods. Nevertheless, the highdimensional nature of scRNA-seq poses significant analytical challenges. We introduce ALLSTATE, a novel pipeline that utilizes non-linear transition embedding for dimension reduction, facilitating hierarchical clustering in a computationally efficient manner. Our experiments demonstrate that ALLSTATE achieves satisfactory clustering performance and allows to explore the connections between cellular hierarchies and cell types at multiple levels of resolution. Additionally, ALLSTATE effectively captures complex cellular differentiation paths, offering a nuanced view of cellular heterogeneity with performance comparable to mainstream methods.

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.

The purpose of this paper is to evaluate spatial resolution effects on image classification. Classification maps were generated with a maximum likelihood (ML) classifier applied to three multi-spectral bands and variance texture images. A total of eight urban land use/cover classes were obtained at six spatial resolution levels based on a series of aggregated Colour Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets in urban and rural fringe areas of the San Diego metropolitan area. The classification results were compared using overall and individual classification accuracies.Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required.

<p>Article publication in IEEE Acess uploaded here to follow EU rules mandated for projects under Horizon 2020.</p>

At the time of fault, the system signal performances singularity, and the fault state can be diagnosed effectively through effective analysis and identification of singular signals. In this paper, for the problem that the singular signal modulus maximum point detection is inaccurate and the polarity is confused due to the improper selection of wavelet basis, the algorithm of local modulus maximum detection based on b-spline wavelet is proposed. B-spline scaling function is constructed by interpolation formula, the filter coefficients of b-spline wavelet function is calculated, in the process of signal decomposition of b-spline wavelet function and the effect of endpoint effect on signal singularity is removed, the signal local modulus maximum in the wavelet decomposition coefficient is extracted to accurately determine the time when the fault occurs. The result of simulation and experimental shows that the algorithm can not only detect the location of the maximum value of the singular point modulus accurately, but also distinguish the polarity of the modulus maximum and have good anti noise characteristics.

Wavelet transform is efficiently applied to multisensor image fusion because it's properties such as multiresolution analysis, accurate reconstruction and similar to people's visual understanding. But the selection of a particular wavelet filter and choosing different decomposition scheme affect the overall performance of multisensor image fusion. It also results in different computational complexity. Experiments were carried out using different wavelet filters(mainly using Daubechies wavelets and Biorthogonal wavelets) as well as the decimated and undecimated versions of the transform (Mallat algorithm and a trous algorithm). Optical and SAR images are used as test data. The performance of fusion results were compared and analyzed. It has been shown that choosing an inappropriate wavelet filter and decomposition scheme can significantly decrease the quality of the fusion results. Considering the conducted experiment and computational complexity, recommendations on choosing a particular wavelet transform to produce satisfactory fusion result in SAR and optical image fusion in different applications are given.

This paper reviews wavelet-based denoising techniques and presents an effective new algorithm using adaptive threshold method, processing of detailed and approximate coefficient, the optimal choice of level of decomposition for noise reduction in non-stationary signals. The method is based on computing of orthogonal wavelet transform and level-dependent estimation of the threshold. The proposed algorithm is tested with different wavelet bases: Haar, Daubechies; Cubic B-splines; Symplet; Coinflet; biorthogonal wavelets and a comparative analysis is performed. The proposed algorithm was applied to the real signals and has the task of assessing the application of different wavelet bases, threshold techniques, decomposition levels, and times to execute the procedures for accurate and fast-track procedures to reduce interference. The proposed algorithm can be used for single channel real non-stationary signals. For the purpose of comparative analysis of different methods a software program was created by the author, with graphical user interface, that implements the basic denoising techniques, enables the setting of the decomposition level, the wavelet basis, the size of the test signal, and calculates the evaluation characteristics of the denoising process. The software application enables the testing of new denoising algorithms, allows the addition of other types of noise and the investigation of their effects on the signals and is realized in the MATLAB development environment. The proposed results demonstrate that the presented algorithm is suitable for denoising of non-stationary real signals.