Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Signal processing for slug flow analysis: MATLAB algorithm.

ABSTRACT: This paper presents an easy-to-use MATLAB© program to characterize slug flow, one of the most observed gas-liquid flow patterns in pipes. "BSignalProcessing2020.m" based on the study reported in [1] is a flexible, expandable, and adaptable statistical algorithm used to calculate the film and slug cut threshold values, the disregard cut value to group slug pulses, and the disregard cut value to remove slug pulses, which are required to determine the slug characteristics. The code is provided for unlimited and unrestricted use. • The statistical algorithm does not depend on any subjective criteria. • The methodology is illustrated using voltage time-series, but it is applicable without changes to instantaneous liquid holdup time-series.

SUBMITTER: Soto-Cortes G

PROVIDER: S-EPMC8563815 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Signal processing for slug flow analysis: MATLAB algorithm.

Soto-Cortes Gabriel G Pereyra Eduardo E Sarica Cem C Torres Carlos C Soedarmo Auzan A

MethodsX 20211011

This paper presents an easy-to-use MATLAB© program to characterize slug flow, one of the most observed gas-liquid flow patterns in pipes. "BSignalProcessing2020.m" based on the study reported in [1] is a flexible, expandable, and adaptable statistical algorithm used to calculate the film and slug cut threshold values, the disregard cut value to group slug pulses, and the disregard cut value to remove slug pulses, which are required to determine the slug characteristics. The code is provided for ...[more]

PMID: 34754814

Similar Datasets

An analysis of signal processing algorithm performance for cortical intrinsic optical signal imaging and strategies for algorithm selection.

Project description:Intrinsic Optical Signal (IOS) imaging has been used extensively to examine activity-related changes within the cerebral cortex. A significant technical challenge with IOS imaging is the presence of large noise, artefact components and periodic interference. Signal processing is therefore important in obtaining quality IOS imaging results. Several signal processing techniques have been deployed, however, the performance of these approaches for IOS imaging has never been directly compared. The current study aims to compare signal processing techniques that can be used when quantifying stimuli-response IOS imaging data. Data were gathered from the somatosensory cortex of mice following piezoelectric stimulation of the hindlimb. The effectiveness of each technique to remove noise and extract the IOS signal was compared for both spatial and temporal responses. Careful analysis of the advantages and disadvantages of each method were carried out to inform the choice of signal processing for IOS imaging. We conclude that spatial Gaussian filtering is the most effective choices for improving the spatial IOS response, whilst temporal low pass and bandpass filtering produce the best results for producing temporal responses when periodic stimuli are an option. Global signal regression and truncated difference also work well and do not require periodic stimuli.

| S-EPMC5543096 | biostudies-other

Windowed persistent homology: A topological signal processing algorithm applied to clinical obesity data.

Project description:Overweight and obesity are highly prevalent in the population of the United States, affecting roughly 2/3 of Americans. These diseases, along with their associated conditions, are a major burden on the healthcare industry in terms of both dollars spent and effort expended. Volitional weight loss is attempted by many, but weight regain is common. The ability to predict which patients will lose weight and successfully maintain the loss versus those prone to regain weight would help ease this burden by allowing clinicians the ability to skip treatments likely to be ineffective. In this paper we introduce a new windowed approach to the persistent homology signal processing algorithm that, when paired with a modified, semimetric version of the Hausdorff distance, can differentiate the two groups where other commonly used methods fail. The novel approach is tested on accelerometer data gathered from an ongoing study at the University of Michigan. While most standard approaches to signal processing show no difference between the two groups, windowed persistent homology and the modified Hausdorff semimetric show a clear separation. This has significant implications for clinical decision making and patient care.

| S-EPMC5428980 | biostudies-literature

SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging.

Project description:Structural magnetic resonance imaging (MRI) has elicited increasing attention in morphological surface studies due to its stability and sensitivity to neurodegenerative processes, particularly in exploring brain aging and psychiatric disease. However, a user-friendly toolbox for the surface-based analysis of structural MRI is still lacking. On the basis of certain software functions in FreeSurfer, CAT and ANTs, a MATLAB toolbox called "surface-based processing and analysis of MRI" (SPAMRI) has been developed, which can be performed in Windows, Linux and Mac-OS. SPAMRI contains several features as follows: (1) open-source MATLAB-based package with a graphical user interface (GUI); (2) a set of images that can be generated for quality checking, such as Talairach transform, skull strip, and surface reconstruction; (3) user-friendly GUI with capabilities on statistical analysis, multiple comparison corrections, reporting of results, and surface measurement extraction; and (4) provision of a conversion tool between surface files (e.g., mesh files) and volume files (e.g., NIFTI files). SPAMRI is applied to a publicly released structural MRI dataset of 44 healthy young adults and 39 old adults. Findings showed that old people have decreased cortical thickness, especially in prefrontal cortex, relative to those of young adults, thereby suggesting a cognitive decline in the former. SPAMRI is anticipated to substantially simplify surface-based image processing and MRI dataset analyses and subsequently open new opportunities to investigate structural morphologies.

| S-EPMC9301123 | biostudies-literature

Improved scheduling algorithm for signal processing in asynchronous distributed ultrasonic total-focusing-method system.

Project description:Compared to the conventional ultrasonic phased-array system, a large-element phased-array system employing the total focusing method (TFM) can yield improved image resolution and accuracy, providing more flexible scanning methods and image merging functionality. In order to meet various forms of ultrasonic multi-group scanning, an architecture for multi-group scan integration called the "asynchronous distributed ultrasonic TFM system" is proposed, and a novel scheduling algorithm called "the sum of start time and processing time adjacent (SSPA) algorithm" is presented. The architecture adds a focus and group scheduler (FGS) and signal processing scheduler (SPS) to the traditional ultrasonic phased array system and constructs a signal processing arbitration (SPA) with several signal processing modules (SPMs). The FGS provides the focus parameters, pixel memory range, and number of pixels in each group. The SPS controls the SPA for the ultrasonic scanning data obtained from the elements, with SPM-sharing output data; hence, the optimal priority order and SPM assignment are realized, enabling switching of reading operations among the first-in-first-out memories for signal processing and minimal time-slot waiting. The SSPA algorithm is used to solve the job-shop scheduling problem with start time, which considers the processing time and start time, in order to reduce the time slot after each scheduling using adjacent operations. Therefore, the architecture enhances the flexibility of the multi-group scan, and this algorithm decreases the makespan, achieving higher efficiency compared to conventional scheduling algorithms. The reliability and validity of the algorithm are substantiated after its implementation using FPGA technology. The SPM utilization rate and the real-time performance of the ultrasonic TFM are improved. Thus, the proposed algorithm and architecture have considerable potential application in multi-sensor systems.

| S-EPMC6375623 | biostudies-literature

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information.

Project description:BackgroundAutomatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences.ResultsWe developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted.ConclusionsWe have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.

| S-EPMC6882060 | biostudies-literature

Direct mass spectrometry analysis of biofluid samples using slug-flow microextraction nano-electrospray ionization.

Project description:Direct mass spectrometry (MS) analysis of biofluids with simple procedures represents a key step in the translation of MS techniques to clinical and point-of-care applications. The current study reports the development of a single-step method using slug-flow microextraction and nano-electrospray ionization for MS analysis of organic compounds in blood and urine. High sensitivity and quantitation precision have been achieved in the analysis of therapeutic and illicit drugs in 5 μL samples. Real-time chemical derivatization has been incorporated for analyzing anabolic steroids. The monitoring of enzymatic functions has also been demonstrated with cholinesterase in wet blood. The reported study encourages the future development of disposable cartridges, which function with simple operation to replace the traditional complex laboratory procedures for MS analysis of biological samples.

| S-EPMC4469778 | biostudies-literature

Uncovering the fast, directional signal flow through the human temporal pole during semantic processing.

Project description:The temporal pole (TP) plays a central role in semantic memory, yet its neural machinery is unknown. Intracerebral recordings in patients discriminating visually the gender or actions of an actor, yielded gender discrimination responses in the ventrolateral (VL) and tip (T) regions of right TP. Granger causality revealed task-specific signals travelling first forward from VL to T, under control of orbitofrontal cortex (OFC) and neighboring prefrontal cortex, and then, strongly, backwards from T to VL. Many other cortical regions provided inputs to or received outputs from both TP regions, often with longer delays, with ventral temporal afferents to VL signaling the actor's physical appearance. The TP response timing reflected more that of the connections to VL, controlled by OFC, than that of the input leads themselves. Thus, visual evidence for gender categories, collected by VL, activates category labels in T, and consequently, category features in VL, indicating a two-stage representation of semantic categories in TP.

| S-EPMC10133264 | biostudies-literature

A post-processing algorithm for miRNA microarray data

Project description:One of the main disadvantages of using DNA microarrays for miRNA expression profiling is inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e. false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA represented by MIMAT accession number and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data.

2019-10-04 | GSE138411 | GEO

Genetic Algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code.

Project description:This data article presents the structure of a Genetic Algorithm model written in a MATLAB code for finding the 1D JONSWAP spectra parameters when measured raw spectra is not available. The JONSWAP spectra is widely used in Coastal, Offshore, and Ocean Engineering for determining wave parameters for structure designing and numerical modeling. However, finding proper spectra parameters may be difficult because of the limitations of parameterized equations to do so and the high non-linear relation between the alpha and gamma coefficients. This GA model can find the alpha and gamma parameters for specific locations considering sea-state's evolution and water-depth transitions. The application of the GA model of this data article is shown in Rueda-Bayona et al. [1].

| S-EPMC7452513 | biostudies-literature

BUHO: a MATLAB script for the study of stress granules and processing bodies by high-throughput image analysis.

Project description:The spontaneous and reversible formation of foci and filaments that contain proteins involved in different metabolic processes is common in both the nucleus and the cytoplasm. Stress granules (SGs) and processing bodies (PBs) belong to a novel family of cellular structures collectively known as mRNA silencing foci that harbour repressed mRNAs and their associated proteins. SGs and PBs are highly dynamic and they form upon stress and dissolve thus releasing the repressed mRNAs according to changes in cell physiology. In addition, aggregates containing abnormal proteins are frequent in neurodegenerative disorders. In spite of the growing relevance of these supramolecular aggregates to diverse cellular functions a reliable automated tool for their systematic analysis is lacking. Here we report a MATLAB Script termed BUHO for the high-throughput image analysis of cellular foci. We used BUHO to assess the number, size and distribution of distinct objects with minimal deviation from manually obtained parameters. BUHO successfully addressed the induction of both SGs and PBs in mammalian and insect cells exposed to different stress stimuli. We also used BUHO to assess the dynamics of specific mRNA-silencing foci termed Smaug 1 foci (S-foci) in primary neurons upon synaptic stimulation. Finally, we used BUHO to analyze the role of candidate genes on SG formation in an RNAi-based experiment. We found that FAK56D, GCN2 and PP1 govern SG formation. The role of PP1 is conserved in mammalian cells as judged by the effect of the PP1 inhibitor salubrinal, and involves dephosphorylation of the translation factor eIF2?. All these experiments were analyzed manually and by BUHO and the results differed in less than 5% of the average value. The automated analysis by this user-friendly method will allow high-throughput image processing in short times by providing a robust, flexible and reliable alternative to the laborious and sometimes unfeasible visual scrutiny.

| S-EPMC3527446 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data