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About 10,000 papers for “computational time analysis”

Time-lapse imaging computational analysis.

Abby R. Kroken (12055316), Naren Gajenthra Kumar (5917370), Timothy L. Yahr (12055319) et al. · Figshare · 2022

<p>For each time-lapse field for PAO1 <b>(A)</b> versus PAO1Δe<i>xoSTY</i> <b>(F)</b>, individual nuclei were segmented into ultimate points, tracked by mTrack2 plugin, and assigned an ID number <b>(B)</b> versus <b>(G)</b> respectively. Nuclei were excluded if tracks were not present in all frames. A distance map was drawn from each nucleus to designate its cytoplasmic area <b>(C)</b> versus <b>(H)</b>. A distance limit of 50 was determined empirically based on average corneal epithelial cell size <b>(D)</b> versus <b>(I)</b>. Some bacteria were excluded from analysis when located further from the nucleus but prevented from being assigned to neighboring cells using this limit. Measurements were recorded as a series of TIF images with one row per nucleus, and one column per timepoint <b>(E)</b> versus <b>(J)</b>. The ratio of PI to Hoechst signal was measured for each nucleus. GFP area, max intensity, and median intensity were measured within each cell’s defined area. A value to indicate cell death was determined empirically based on value changes when PI became visible in corresponding images, and background values for GFP area and GFP max were determined using uninfected controls and fields where bacteria were not present.</p>

Cited by 0Open access

Computational Analysis of Run-time Monitoring

Moonjoo Kim, Sampath Kannan, Insup Lee et al. · Electronic Notes in Theoretical Computer Science · 2002

A run-time monitor shares computational resources, such as memory and CPU time, with the target program. Furthermore, heavy computation performed by a monitor for checking target program's execution with respect to requirement properties can be a bottleneck to the target program's execution. Therefore, computational characteristics of run-time monitoring cause a significant impact on the target program's execution. We investigate computational issues on run-time monitoring. The first issue is the power of run-time monitoring. In other words, we study the class of properties run-time monitoring can evaluate. The second issue is computational complexity of evaluating properties written in process algebraic language. Third, we discuss sound abstraction of the target program's execution, which does not change the result of property evaluation. This abstraction can be used as a technique to reduce monitoring overhead. Theoretical understanding obtained from these issues affects the implementation of Java-MaC, a toolset for the run-time monitoring and checking of Java programs. Finally, we demonstrate the abstraction-based overhead reduction technique implemented in Java-MaC through a case study.

Cited by 39Open access

Computational Methods for Time Series Analysis

Genshiro Kitagawa, Tomoyuki Higuchi, Seisho Sato · COMPSTAT · 2002

By the progress of fast computing facilities and various computing technologies, it becomes realistic to apply computer intensive methods to statistical analysis. In time series analysis, sequential Monte Carlo methods was developed for general state space models which enables to consider very complex nonlinear non-Gaussian models. In this paper, we show algorithms, implementations and parameter estimation for Monte Carlo filter and smoother. Various ways of the use of parallel computer are also discussed. The usefulness of the general state space modeling is illustrated with several examples.

Cited by 0

Computational topology for time series analysis

Vanderlei Luiz Daneluz Miranda · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019

Mudanças de padrão são variações nos dados da série temporal. Tais mudanças podem representar transições que ocorrem entre estados. A análise de dados topológicos (TDA) permite uma caracterização de dados de séries temporais obtidos a partir de sistemas dinâmicos complexos. Neste trabalho, apresentamos uma técnica de detecção de mudança de padrão baseada em TDA. Especificamente, a partir de uma determinada série temporal, dividimos o sinal em janelas deslizantes sem sobreposição e para cada janela calculamos a homologia persistente, ou seja, o barcode associado. A partir desse barcode, o intervalo médio e a entropia persistente são calculados e plotados em relação à duração do sinal. Resultados experimentais em conjuntos de dados reais e artificiais mostram bons resultados do método proposto: 1) Detecta mudança de padrões identificando a mudança no intervalo médio e calculando a entropia persistente para os barcodes gerados pelo conjunto de dados de entrada. 2) Mostra qualitativamente quão sensível é a escolha do método de filtragem para evidenciar características topológicas do espaço original sob exame. Isto é conseguido usando duas filtragens: uma filtragem métrica e uma do tipo lower-star. 3) Variando o tamanho da janela, o método pode caracterizar a presença de estruturas locais do conjunto de dados, como o período de convulsão nos sinais EEG. 4) O método proposto é capaz de caracterizar a complexidade pela medida de entropia persistente dos barcodes, uma medida de entropia baseada na definição de entropia de Shannon. Além disso, neste trabalho, mostramos a evidência de mudanças de complexidade associadas a um período de convulsão de um sinal de EEG

Cited by 0

Seminar on Computational Engineering: Time Series Analysis

Teemu Leppänen · 2001

This report reviews some basic ideas of time series analysis. The handling of the subjects is practical, and examples are used to simplify things to the reader. First we introduce a very important linear time series model ARMA. In chapter 3 we take a quick look at state-space reconstruction. Chapter 4 handles the essential art of characterization. Finally, we present some simple ideas about forecasting

Cited by 0

Computational Time Analysis for the Excalibur Method.

Simon Boutry (10956118), Raphaël Helaers (796995), Tom Lenaerts (21921) et al. · Figshare · 2023

<p>Boxplot illustrating the computational time (in seconds) required by the Excalibur method across varying cohort sizes. The x-axis represents the cohort size, while the y-axis denotes the computational time in seconds. The distribution of computational time is visualized using boxplot, providing insights into the method’s efficiency as cohort size changes.</p> <p>(PNG)</p>

Cited by 0Open access

Computational Time Analysis of Signal Processing Algorithm-An Analysis

Iosr Journals, T. Karthikeyan · Figshare · 2014

Current manufacturing systems, including dedicated transfer lines and flexible manufacturing systems, do not provide adequate and most economical solutions for products with variable market demands. One of the crucial problems in manufacturing is the machine tool failure due to loss of surface material in cutting operations like drilling and milling. Proceeding with a damaged tool may result in unfit fabricated work piece. On the other hand, it is unnecessary to change the cutting tool if it is still able to continue cutting operation. Hence, an effective diagnosis mechanism is necessary for the automation of machining processes so that production loss and downtime can be avoided. This study concerns with the development of a tool wear condition-monitoring technique. Tool Wearing Monitoring technique can be established based on fuzzy logic. Fuzzy logic scheme explains Tool wearing Monitoring based on Signal processing by obtaining statistical parameters derived from thrust force, machine sound and vibration signals were used as inputs to fuzzy process and their output values serves as the input parameters to the second stage. The second stage with a remarkable threshold function, then assess the condition of the tool. Index Terms- FFT, Singular Spectrum Analysis, Tool Wearing Monitoring.

Cited by 2Open access

Novel computational structure for real-time wavelet analysis

V.P. Devassia, M. G. Mini, Tessamma Thomas · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003

The advent of high-speed signal processors combined with real-time complex algorithms has resulted in truly elegant industrial systems and controllers. Wavelet Transform (WT) is one among the smart signal processing tools, which has already excelled over the conventional ones. In this paper we present an efficient implementation structure for real-time computation of the Discrete Wavelet Transform (DWT) and its Inverse (IDWT). The wavelet and scaling function coefficients at each level are computed by successive convolutions and the computation of transform coefficients at all levels is performed in parallel. Adopting the principle of polyphase splitting, the input sequence and filter coefficients at each level ’j’ are divided into 2<sup>j</sup> subsequences, incorporating additional parallelism within levels. Expressions for the computational complexity are derived and a comparison of complexity against the popular filter-bank tree structure is made, for variations of signal length, order of wavelet and the number of levels. The Parallel Multiple Subsequence (PMS) structure suggested here, involves much less computation than state-of-the-art algorithms up to 6 levels of processing for Haar wavelet and 3 levels for others, for any data length. For higher decomposition levels and for real-time applications, the proposed algorithm is made superior by optimal selection of processing frame size.

Cited by 0

Modern EMC analysis I: time-domain computational schemes

Kantartzis, Nikolaos V, Tsiboukis, Theodoros D · CERN Document Server (European Organization for Nuclear Research) · 2008

The objective of this two-volume book is the systematic and comprehensive description of the most competitive time-domain computational methods for the efficient modeling and accurate solution of contemporary real-world EMC problems. Intended to be self-contained, it performs a detailed presentation of all well-known algorithms, elucidating on their merits or weaknesses, and accompanies the theoretical content with a variety of applications. Outlining the present volume, the analysis covers the theory of the finite-difference time-domain, the transmission-line matrix/modeling, and the finite i

Cited by 0

COMPUTATIONAL METHODS OF TIME SERIES FORECASTING IN DATA ANALYSIS

Prity Kumari, PD Vekariya, Y. A. Garde · 2024

The chapter delves into the crucial role of computational techniques in predicting future trends and patterns from time series data. Time series forecasting has become an indispensable tool across disciplines such as economics, weather prediction, and stock market analysis, where understanding and predicting temporal dynamics are paramount. It begins by elucidating the essence of time series data, which captures observations sequentially over time. To extract valuable insights from such data, advanced computational methods have been devised to model and predict future observations based on historical patterns. The chapter delves into prominent computational methods, including autoregressive integrated moving average (ARIMA), exponential smoothing, and more contemporary approaches like machine learning and deep learning techniques. These methods capitalize on historical data patterns, seasonality, and external factors to generate accurate forecasts. Furthermore, the study recognizes the challenges inherent in time series forecasting, such as non-stationarity, irregular intervals, and noisy data. It emphasizes how computational advancements have addressed these challenges by enabling the development of more robust and adaptable forecasting models. In conclusion, illuminates the critical role of computational techniques in distilling meaningful insights from time series data. By leveraging a spectrum of methodologies, researchers and practitioners can decipher complex temporal patterns, thereby empowering accurate predictions and informed decision-making in a diverse array of fields.

Cited by 0

Space–Time Computational Analysis of Ventricle-Valve-Aorta Flow

Takuya Terahara, Kensuke Shiozaki, Ryotaro Kobayashi et al. · Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu · 2019

In this paper, we present fluid mechanics computation of left ventricle, aorta and aortic valves. We use an interface-tracking method, which is based on the space–time slip-interface, topology change and isogeometric discretization methods. The method allows us to obtain the accurate flow solution also near the boundaries when the leaflets come into contact. In addition, the flow computation with the aorta and the left ventricle make the solution more realistic.

Cited by 0Open access