Search or ask a question

Are there research articles with fourier signal analysis connected to biocrust research?

Spectral density estimation

Least-squares spectral analysis

Discrete-time Fourier transform

Time–frequency analysis

Multidimensional signal processing

Best insight from top research papers

Fourier signal analysis has been applied to various fields, including biomedical signal analysis , shape quantification in detrital quartz grains , and the study of respiratory and circulatory systems . However, there is no mention of Fourier signal analysis specifically connected to biocrust research in the provided abstracts.

Answers from top 3 papers

PDF

Open Access

More filters

Papers (3)	Insight
Open access•Journal Article•DOI Use of Fourier Series for the Analysis of Biological Systems E.O. Attinger, A. Anné, D.A. McDonald - Show less +2 more 01 May 1966-Biophysical Journal 146 Citations	The provided paper does not mention anything about Fourier signal analysis connected to biocrust research.
Journal Article•DOI Spectral information from fourier analysis of digitized quartz grain profiles John D. Boon, David A. Evans, Harold F. Hennigar - Show less +2 more 01 Dec 1982-Mathematical Geosciences 18 Citations	The provided paper does not mention any research articles connecting Fourier signal analysis to biocrust research.
Journal Article•DOI Some mathematical tools for the analysis of biological signals M. Nichelatti, Paolo Pettazzoni, Giovanni Pallotti - Show less +2 more 01 Jun 2007-Journal of Mechanics in Medicine and Biology 1 Citations	The provided paper does not mention any research articles specifically connecting Fourier signal analysis to biocrust research.

My columns

Related Questions

Spectrum analysis in turbulence?5 answersSpectrum analysis in turbulence plays a crucial role in understanding various aspects of turbulent flows. Different spectral analysis techniques are utilized in different contexts. For instance, the formal and numerical spectral analysis developed for $\text{U}(1)$ symmetry-breaking quantum fluids enables the analysis of turbulent flows in quantum fluids like the Gross--Pitaevskii fluid. In the Earth's atmosphere, the spectral model technique is commonly used to derive kinetic energy dissipation rates of turbulent structures, with models like Heisenberg (1948) and Tatarskii (1971) being prominent choices. Global spectral analysis is employed to test the accuracy of numerical methods in simulating turbulent flows, aiding in calibrating simulations and capturing extreme events with high accuracy. Spectral numerical methods, based on Fourier expansions or orthogonal polynomials, are widely used for direct numerical simulation (DNS) and large eddy simulation (LES) of turbulence due to their rapid convergence and excellent resolution properties.

What can be said about biocrusts under climate change scenarios?5 answersBiocrusts, crucial in dryland ecosystems, face significant impacts under climate change. Studies show that biocrusts influence water fluxes, soil moisture, and plant-available water, with well-developed biocrusts enhancing water availability even in dry years. Climate change-induced warming and reduced rainfall affect soil organic carbon (SOC) pools differently based on initial biocrust cover, with biocrusts buffering SOC losses under climate manipulations. Biocrusts significantly alter soil pore structure, increasing porosity, pore volume, and connectivity, enhancing soil water holding capacity but reducing infiltrability. Elevated temperatures and drought lead to reduced CO2 fixation by biocrusts, impacting C and N cycling, with drought restricting microbial abundance and inducing a shift in microbial communities. Environmental factors like light intensity, humidity, temperature, CO2 concentration, and rainfall influence biocrust carbon balance along climatic gradients, emphasizing the importance of seasonal acclimation under climate change scenarios.

Are there any studies that use bio signal to predict symptoms?4 answersBio signals have been used in several studies to predict symptoms. Oinuma et al. developed a system that can detect aspiration symptoms in daily life by analyzing bio-signal features such as neck electrical impedance, swallowing sounds, and surface electromyogram of the suprahyoid muscles. Baird et al. created the BioSpeech Database, which uses acoustic features to monitor emotional states and predict blood volume pulse (BVP). Swapna et al. discussed the use of bio-signals and AI algorithms to predict health problems and monitor a patient's condition. Lastly, Shin et al. disclosed a psychiatric symptom evaluation system that utilizes bio-signal analysis to diagnose psychiatric symptoms and psychological states. These studies demonstrate the potential of bio signals in predicting symptoms and monitoring health conditions.

How is data mining in biosciences working?5 answersData mining techniques are being used in biosciences to extract valuable information from large biological datasets. These techniques allow for the automatic discovery of novel patterns and have shifted the focus of researchers towards data-intensive discovery. In the field of bioinformatics, data mining is used to store, analyze, and utilize information from biological data, such as genomics, proteomics, and medical data. One application of data mining in biosciences is the extraction of text fragments containing descriptions of bioassays from scientific publications. Machine learning analysis of abstracts can categorize papers into relevant and irrelevant, and text fragments extracted from full texts allow for further partitioning based on bioassay peculiarities. Data mining in genomics and proteomics combines computer science with biological aspects, providing a valuable tool for future biological research. Additionally, data mining techniques are used for biological data classification and analysis, allowing for the identification of patterns and the generation of optimal classification rules. Overall, data mining plays a crucial role in bioinformatics research, enabling the discovery of associations, identification of patterns, and analysis of biological data.

What are the articles on the application of typical correlation analysis in bioinformatics?5 answersCorrelation analysis in bioinformatics is a commonly used method for inferring taxon-taxon interactions. However, it has limitations in providing reliable information on direct biotic interactions in real-world ecosystems. Bioinformatics analysts follow a unique process when conducting a typical bioinformatics analysis, such as a functional analysis of a gene. Although each expert has their own process, there are significant similarities among them. A standard research protocol and procedural model have been developed to describe the process of conducting a functional analysis of a gene. Correlation network models obtained from biological assays, such as gene expression levels, have been effective in identifying elements of interest in bioinformatics applications. However, there is a need for further investigation into the robustness of these models and the filtering of the networks. Bioinformatics plays a role in various areas of biology, including experimental molecular biology, genetics and genomics, textual mining of biological literature, and the analysis of gene and protein expression and regulation. The correlation structure of fitness landscapes has been analyzed to characterize and classify landscapes, including dynamic, or coupled, fitness landscapes, which provide meaningful results for understanding coevolution.

How the fourier transform work in signal processing?5 answersThe Fourier transform is a method used in signal processing to analyze and convert signals. It allows us to analyze both digital and continuous signals, depending on the specific needs of the problem. The Fourier transform works by decomposing a signal into its frequency components using a continuum of continuous sinusoids over an infinite frequency range. This analysis can be used to compute the system response, modulate waves for communication, and perform other applications. In signal processing, the Fourier transform can be implemented using methods such as the discrete Fourier transform (DFT) and the fast Fourier transform (FFT). These methods involve converting the signal into a digital format, filtering it, and then applying the Fourier transform to obtain the frequency components of the signal. The Fourier transform is a powerful tool in signal processing that allows for the analysis and manipulation of signals in both the time and frequency domains.

See what other people are reading

What are the different techniques used in signal processing to extract relevant features from acoustic data for fault detection?

What are the most commonly used statistical methods for digital image encryption?

Statistical methods commonly used for digital image encryption include entropy, SSIM, NPCR, UACI, and histogram analysis. These metrics are crucial in evaluating image quality and ensuring secure encryption that can withstand various attacks. Additionally, the use of chaos maps and chaotic systems, such as the two-dimensional logistic map, has been proven effective for image encryption, providing confusion and diffusion properties for a secure cipher. Furthermore, the comparison of methods like the Discrete Fractional Fourier Transform (DFrFT) and Discrete Fractional Sine Transform (DFrST) with chaos functions showcases the importance of statistical analysis, including histogram comparisons and PSNR calculations, in assessing the validity and effectiveness of encryption techniques. The combination of symmetric and asymmetric key methods in encryption algorithms also enhances security by leveraging the strengths of both approaches while mitigating their individual weaknesses.How are transient evoked otoacoustic emissions recorded?

Transient evoked otoacoustic emissions (TEOAEs) are recorded by measuring the absorbed sound power and tympanometric peak pressure (TPP) in the ear canal across various frequencies, using both click and chirp stimuli. TEOAE responses are analyzed with reflectance/admittance data to assess cochlear outer hair cell function for detecting sensorineural hearing loss (SNHL). The linear-mode click stimulus has been found to produce reliable and stable TEOAE and medial olivocochlear reflex (MOCR) results in normal-hearing ears, with high detectability and excellent reliability. Additionally, a nonlinear-type time-frequency analysis technique called ConceFT has been proposed to model TEOAEs as a sum of intrinsic mode-type functions, providing accurate estimation of fluctuating instantaneous frequency (IF) even in noisy conditions.What are the concepts are mostly used in deriving fourier series of certain polynomials?

The concepts mostly used in deriving Fourier series of certain polynomials include the Cauchy residue theorem, analytic methods, and the specific calculation formula of Fourier coefficients based on the nature of the function. Classical Fourier methods like Fourier series and Fourier integral are utilized for continuous time signals, while more recent Fourier methods like the discrete time Fourier transform and discrete Fourier transform are applied to discrete time signals. Spectral methods, including Fourier series, were initially used for solving differential equations and later extended to resolve partial differential equations in fluid mechanics, emphasizing the expression of general functions as sums of fundamental functions like sines and cosines.More information digital than analog, when?

The emergence of more digital information than analog in certain biological contexts is highlighted in the study of RNA molecules, where the physical notion of information is considered in both analog and digital aspects. This is further supported by the argument that all information that can be copied is digital, emphasizing the importance of copying in understanding causality and other issues. Additionally, the analysis of the DNA in bacteria Escherichia coli reveals a comparison between analog information represented by thermodynamic stability and digital information measured by Shannon and Gibbs entropy, showcasing the interplay between the two types of information in the genetic coding structure. Therefore, the transition to a dominance of digital information over analog can be observed in various biological processes, shedding light on the intricate relationship between the two forms of information.How Structure Factor and the Electron Density Are Related by a Fourier Transform?

The relationship between Structure Factor and Electron Density is elucidated through Fourier Transform. Fourier synthesis techniques, such as the presented ME type approach, reconstruct Electron Density (ED) and its Laplacian distributions from structure-factor sets with varying resolutions. Initially applied in crystal structure determination in the 1920s, Fourier series, aided by phase information from the Patterson function, led to the visualization of electron density corresponding to atomic positions. Furthermore, in the context of dynamical X-ray diffraction, analytical formulae like Pendellosung are derived to expand electronic density via Fourier transformations, showcasing the intricate relationship between Structure Factor and Electron Density. The Fourier Transform serves as a crucial mathematical operation that interconverts these fundamental quantities, enabling the visualization and interpretation of molecular structures in crystals.How does computational control methods help in understanding I1-FFL genetic circuits better in these times?

Computational control methods play a crucial role in enhancing our understanding of I1-FFL genetic circuits. By utilizing mathematical models and sensitivity analysis, researchers can investigate how different levels of regulation at the transcription and translation stages impact circuit dynamics. Additionally, the design of feedback control circuits using nucleic acids allows for precise regulation of molecular-scale processes. These computational approaches aid in predicting circuit behavior, such as pulse generation, and guide the construction of synthetic biological circuits with desired characteristics. Moreover, digital signal processing and control techniques offer practical tools for modeling and controlling gene networks, providing a comprehensive framework for studying complex dynamic systems like gene circuits. Overall, computational methods enable researchers to gain insights into the behavior and regulation of genetic circuits, facilitating advancements in synthetic biology.How to analise EMG signals?

To analyze electromyography (EMG) signals, various methods are employed. One approach involves mathematical modeling to simulate muscle fiber trajectories and derive action potentials, aiding in signal interpretation and validation of processing techniques. Another method includes using surface or needle electrodes to register bioelectrical signals from muscles, which can then be analyzed for myopathic or neuropathic lesions through numerical parameters. Additionally, surface EMG signals can be processed by extracting features, applying dimensionality reduction techniques, and utilizing machine learning classifiers for movement classification. Furthermore, artificial intelligence algorithms based on big data can be utilized for bio-signal analysis, enabling simpler and more accurate interpretation of EMG signals, especially when dealing with noise-induced variations.How to analyse EMG signal?

To analyze electromyography (EMG) signals, a common approach involves signal processing, feature extraction, and machine learning classification. EMG signals are often contaminated by noise, requiring careful analysis. Surface EMG signals are acquired noninvasively and processed through feature extraction, followed by dimensionality reduction techniques and machine learning classifiers. Features extracted from EMG signals capture information from both time and frequency domains, with spectral analysis techniques like Short-Time Fourier Transform (STFT) used to produce time-frequency graphs for recognizing hand grip postures. It is crucial to ensure the quality of EMG signals by detecting and quantifying contaminants through various approaches, such as bottom-up and top-down strategies, to prevent misinterpretation and ensure accurate analysis.What are the most commonly used techniques for processing and analyzing EMG signals in clinical research?

Surface electromyography (EMG) signals in clinical research are commonly processed using feature extraction, dimensionality reduction techniques, and machine learning classifiers. Additionally, artificial neural networks (ANNs) are utilized for pattern identification in EMG signal recordings, enabling interaction with technological devices. Frequency analysis of EMG signals aids in movement classification, with methods like Hilbert envelope and Fast Fourier Transform (FFT) coefficients being employed for signal processing. These techniques allow for the extraction of valuable information from EMG signals, facilitating applications in clinical diagnostics, prosthetic device monitoring, biomedicine, and human-machine interactions. The integration of these methods showcases the versatility and effectiveness of EMG signal processing in various clinical research settings.What is the binomial expression for the inverse Z transform of a discrete-time signal?

The binomial expression for the inverse Z transform of a discrete-time signal can be obtained using the Universal Generating Function (UGF) technique, which allows for finding the entire performance distribution of a system based on the distributions of its elements through algebraic procedures. Additionally, the Z-transform analysis of discrete linear systems involves finding sums of products of the signal with complex exponentials, simplifying the stability analysis and solving difference equations characterizing systems. In cases where inversion of Z-transforms is challenging, numerical methods like the Discrete Fourier Transform (DFT) or solving linear algebraic equations after truncating the signal can be employed effectively. These methods are particularly useful when dealing with functions of the Z-transform variable that are complex or tedious to invert analytically.