Wavelet Transform Based Techniques for Ultrasonic Signal Processing Sanand Prasad Iowa State University
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Learning and Estimation of Single Molecule Behavior
Learning and Estimation of Single Molecule Behavior Sivaraman Rajaganapathy1;a, James Melbourne1;b, Tanuj Aggarwal2;c, Rachit Shrivastava1;d, and Murti V. Salapaka1;e Abstract— Data analysis in single molecule studies often of tension. The unfolding of a domain leads to a step involves estimation of parameters and the detection of abrupt change in the length of the protein. How much time is changes in measured signals. For single molecule studies, tools needed for the domains to unfold, the relationship to the for automated analysis that are crucial for rapid progress, need to be effective under large noise magnitudes, and often must force applied, the changes in the structure under folding assume little or no prior knowledge of parameters being stud- and unfolding events are questions that are studied with ied. This article examines an iterated, dynamic programming AFM based force spectroscopy. Most of the studies involve based step detection algorithm (SDA). It is established that numerous iterations of experiments. Often, data is collected given a prior estimate, an iteration of the SDA necessarily from a large number of experiments, not just to ensure a improves the estimate. The analysis provides an explanation and a confirmation of the effectiveness of the learning and high confidence on statistical information, but also because estimation capabilities of the algorithm observed empirically. experiment success rates are sometimes intentionally kept Further, an alternative application of the SDA is demonstrated, low. For example, in single molecule force spectroscopy, wherein the parameters of a worm-like chain (WLC) model the protein solution is diluted enough to ensure that the are estimated, for the automated analysis of data from single the success rate of an attachment forming between the tip molecule protein pulling experiments. -
Comparison of Harmonic, Geometric and Arithmetic Means for Change Detection in SAR Time Series Guillaume Quin, Béatrice Pinel-Puysségur, Jean-Marie Nicolas
Comparison of Harmonic, Geometric and Arithmetic means for change detection in SAR time series Guillaume Quin, Béatrice Pinel-Puysségur, Jean-Marie Nicolas To cite this version: Guillaume Quin, Béatrice Pinel-Puysségur, Jean-Marie Nicolas. Comparison of Harmonic, Geometric and Arithmetic means for change detection in SAR time series. EUSAR. 9th European Conference on Synthetic Aperture Radar, 2012., Apr 2012, Germany. hal-00737524 HAL Id: hal-00737524 https://hal.archives-ouvertes.fr/hal-00737524 Submitted on 2 Oct 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. EUSAR 2012 Comparison of Harmonic, Geometric and Arithmetic Means for Change Detection in SAR Time Series Guillaume Quin CEA, DAM, DIF, F-91297 Arpajon, France Béatrice Pinel-Puysségur CEA, DAM, DIF, F-91297 Arpajon, France Jean-Marie Nicolas Telecom ParisTech, CNRS LTCI, 75634 Paris Cedex 13, France Abstract The amplitude distribution in a SAR image can present a heavy tail. Indeed, very high–valued outliers can be observed. In this paper, we propose the usage of the Harmonic, Geometric and Arithmetic temporal means for amplitude statistical studies along time. In general, the arithmetic mean is used to compute the mean amplitude of time series. -
University of Cincinnati
UNIVERSITY OF CINCINNATI Date:___________________ I, _________________________________________________________, hereby submit this work as part of the requirements for the degree of: in: It is entitled: This work and its defense approved by: Chair: _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ Gibbs Sampling and Expectation Maximization Methods for Estimation of Censored Values from Correlated Multivariate Distributions A dissertation submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial ful…llment of the requirements for the degree of DOCTORATE OF PHILOSOPHY (Ph.D.) in the Department of Mathematical Sciences of the McMicken College of Arts and Sciences May 2008 by Tina D. Hunter B.S. Industrial and Systems Engineering The Ohio State University, Columbus, Ohio, 1984 M.S. Aerospace Engineering University of Cincinnati, Cincinnati, Ohio, 1989 M.S. Statistics University of Cincinnati, Cincinnati, Ohio, 2003 Committee Chair: Dr. Siva Sivaganesan Abstract Statisticians are often called upon to analyze censored data. Environmental and toxicological data is often left-censored due to reporting practices for mea- surements that are below a statistically de…ned detection limit. Although there is an abundance of literature on univariate methods for analyzing this type of data, a great need still exists for multivariate methods that take into account possible correlation amongst variables. Two methods are developed here for that purpose. One is a Markov Chain Monte Carlo method that uses a Gibbs sampler to es- timate censored data values as well as distributional and regression parameters. The second is an expectation maximization (EM) algorithm that solves for the distributional parameters that maximize the complete likelihood function in the presence of censored data. -
Incorporating a Geometric Mean Formula Into The
Calculating the CPI Incorporating a geometric mean formula into the CPI Beginning in January 1999, a new geometric mean formula will replace the current Laspeyres formula in calculating most basic components of the Consumer Price Index; the new formula will better account for the economic substitution behavior of consumers 2 Kenneth V. Dalton, his article describes an important improve- bias” in the CPI. This upward bias was a techni- John S. Greenlees, ment in the calculation of the Consumer cal problem that tied the weight of a CPI sample and TPrice Index (CPI). The Bureau of Labor Sta- item to its expected price change. The flaw was Kenneth J. Stewart tistics plans to use a new geometric mean for- effectively eliminated by changes to the CPI mula for calculating most of the basic compo- sample rotation and substitution procedures and nents of the Consumer Price Index for all Urban to the functional form used to calculate changes Consumers (CPI-U) and the Consumer Price In- in the cost of shelter for homeowners. In 1997, a dex for Urban Wage Earners and Clerical Work- new approach to the measurement of consumer ers (CPI-W). This change will become effective prices for hospital services was introduced.3 Pric- with data for January 1999.1 ing procedures were revised, from pricing indi- The geometric mean formula will be used in vidual items (such as a unit of blood or a hospi- index categories that make up approximately 61 tal inpatient day) to pricing the combined sets of percent of total consumer spending represented goods and services provided on selected patient by the CPI-U. -
Wavelet Operators and Multiplicative Observation Models
Wavelet Operators and Multiplicative Observation Models - Application to Change-Enhanced Regularization of SAR Image Time Series Abdourrahmane Atto, Emmanuel Trouvé, Jean-Marie Nicolas, Thu Trang Le To cite this version: Abdourrahmane Atto, Emmanuel Trouvé, Jean-Marie Nicolas, Thu Trang Le. Wavelet Operators and Multiplicative Observation Models - Application to Change-Enhanced Regularization of SAR Image Time Series. 2016. hal-00950823v3 HAL Id: hal-00950823 https://hal.archives-ouvertes.fr/hal-00950823v3 Preprint submitted on 26 Jan 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Wavelet Operators and Multiplicative Observation Models - Application to Change-Enhanced Regularization of SAR Image Time Series Abdourrahmane M. Atto1;∗, Emmanuel Trouve1, Jean-Marie Nicolas2, Thu-Trang Le^1 Abstract|This paper first provides statistical prop- I. Introduction - Motivation erties of wavelet operators when the observation model IGHLY resolved data such as Synthetic Aperture can be seen as the product of a deterministic piece- Radar (SAR) image time series issued from new wise regular function (signal) and a stationary random H field (noise). This multiplicative observation model is generation sensors show minute details. Indeed, the evo- analyzed in two standard frameworks by considering lution of SAR imaging systems is such that in less than 2 either (1) a direct wavelet transform of the model decades: or (2) a log-transform of the model prior to wavelet • high resolution sensors can achieve metric resolution, decomposition. -
“Mean”? a Review of Interpreting and Calculating Different Types of Means and Standard Deviations
pharmaceutics Review What Does It “Mean”? A Review of Interpreting and Calculating Different Types of Means and Standard Deviations Marilyn N. Martinez 1,* and Mary J. Bartholomew 2 1 Office of New Animal Drug Evaluation, Center for Veterinary Medicine, US FDA, Rockville, MD 20855, USA 2 Office of Surveillance and Compliance, Center for Veterinary Medicine, US FDA, Rockville, MD 20855, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-240-3-402-0635 Academic Editors: Arlene McDowell and Neal Davies Received: 17 January 2017; Accepted: 5 April 2017; Published: 13 April 2017 Abstract: Typically, investigations are conducted with the goal of generating inferences about a population (humans or animal). Since it is not feasible to evaluate the entire population, the study is conducted using a randomly selected subset of that population. With the goal of using the results generated from that sample to provide inferences about the true population, it is important to consider the properties of the population distribution and how well they are represented by the sample (the subset of values). Consistent with that study objective, it is necessary to identify and use the most appropriate set of summary statistics to describe the study results. Inherent in that choice is the need to identify the specific question being asked and the assumptions associated with the data analysis. The estimate of a “mean” value is an example of a summary statistic that is sometimes reported without adequate consideration as to its implications or the underlying assumptions associated with the data being evaluated. When ignoring these critical considerations, the method of calculating the variance may be inconsistent with the type of mean being reported. -
Expectation and Functions of Random Variables
POL 571: Expectation and Functions of Random Variables Kosuke Imai Department of Politics, Princeton University March 10, 2006 1 Expectation and Independence To gain further insights about the behavior of random variables, we first consider their expectation, which is also called mean value or expected value. The definition of expectation follows our intuition. Definition 1 Let X be a random variable and g be any function. 1. If X is discrete, then the expectation of g(X) is defined as, then X E[g(X)] = g(x)f(x), x∈X where f is the probability mass function of X and X is the support of X. 2. If X is continuous, then the expectation of g(X) is defined as, Z ∞ E[g(X)] = g(x)f(x) dx, −∞ where f is the probability density function of X. If E(X) = −∞ or E(X) = ∞ (i.e., E(|X|) = ∞), then we say the expectation E(X) does not exist. One sometimes write EX to emphasize that the expectation is taken with respect to a particular random variable X. For a continuous random variable, the expectation is sometimes written as, Z x E[g(X)] = g(x) d F (x). −∞ where F (x) is the distribution function of X. The expectation operator has inherits its properties from those of summation and integral. In particular, the following theorem shows that expectation preserves the inequality and is a linear operator. Theorem 1 (Expectation) Let X and Y be random variables with finite expectations. 1. If g(x) ≥ h(x) for all x ∈ R, then E[g(X)] ≥ E[h(X)]. -
Notes on Calculating Computer Performance
Notes on Calculating Computer Performance Bruce Jacob and Trevor Mudge Advanced Computer Architecture Lab EECS Department, University of Michigan {blj,tnm}@umich.edu Abstract This report explains what it means to characterize the performance of a computer, and which methods are appro- priate and inappropriate for the task. The most widely used metric is the performance on the SPEC benchmark suite of programs; currently, the results of running the SPEC benchmark suite are compiled into a single number using the geometric mean. The primary reason for using the geometric mean is that it preserves values across normalization, but unfortunately, it does not preserve total run time, which is probably the figure of greatest interest when performances are being compared. Cycles per Instruction (CPI) is another widely used metric, but this method is invalid, even if comparing machines with identical clock speeds. Comparing CPI values to judge performance falls prey to the same prob- lems as averaging normalized values. In general, normalized values must not be averaged and instead of the geometric mean, either the harmonic or the arithmetic mean is the appropriate method for averaging a set running times. The arithmetic mean should be used to average times, and the harmonic mean should be used to average rates (1/time). A number of published SPECmarks are recomputed using these means to demonstrate the effect of choosing a favorable algorithm. 1.0 Performance and the Use of Means We want to summarize the performance of a computer; the easiest way uses a single number that can be compared against the numbers of other machines. -
Edge Detection with a Preprocessing Approach
Journal of Signal and Information Processing, 2014, 5, 123-134 Published Online November 2014 in SciRes. http://www.scirp.org/journal/jsip http://dx.doi.org/10.4236/jsip.2014.54015 Edge Detection with a Preprocessing Approach Mohamed Abo-Zahhad1, Reda Ragab Gharieb1, Sabah M. Ahmed1, Ahmed Abd El-Baset Donkol2 1Department of Electrical Engineering Communication and Electronics, Assuit University, Assiut, Egypt 2Department of Electrical Engineering and Computer Sciences, Nahda University, Beni-Suef, Egypt Email: [email protected], [email protected], [email protected], [email protected] Received 18 August 2014; revised 15 September 2014; accepted 7 October 2014 Copyright © 2014 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Edge detection is the process of determining where boundaries of objects fall within an image. So far, several standard operators-based methods have been widely used for edge detection. Howev- er, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard opera- tors-based edge detection methods. The proposed preprocessing approach involves computation of the histogram, finding out the total number of peaks and suppressing irrelevant peaks. From the intensity values corresponding to relevant peaks, threshold values are obtained. From these threshold values, optimal multilevel thresholds are calculated using the Otsu method, then multi- level image segmentation is carried out. -
E5df876f5e0f28178c70af790e47
Journal of Applied Pharmaceutical Science Vol. 8(07), pp 001-009, July, 2018 Available online at http://www.japsonline.com DOI: 10.7324/JAPS.2018.8701 ISSN 2231-3354 Application of Chemometrics for the simultaneous estimation of stigmasterol and β-sitosterol in Manasamitra Vatakam-an ayurvedic herbomineral formulation using HPLC-PDA method Srikalyani Vemuri1, Mohan Kumar Ramasamy1, Pandiyan Rajakanu1, Rajappan Chandra Satish Kumar2, Ilango Kalliappan1,3* 1Division of Analytical Chemistry, Interdisciplinary Institute of Indian System of Medicine (IIISM), SRM Institute of Science and Technology, Kattanku- lathur-603 203, Kancheepuram (Dt), Tamil Nadu, India. 2Clinical Trial and Research Unit (Metabolic Ward), Interdisciplinary Institute of Indian System of Medicine (IIISM), SRM Institute of Science and Tech- nology, Kattankulathur-603 203, Kancheepuram (Dt), Tamil Nadu, India. 3Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur-603 203, Kancheepuram (Dt), Tamil Nadu, India. ARTICLE INFO ABSTRACT Article history: A new research method has been developed to approach multiresponse optimization for simultaneously optimizing Received on: 08/05/2018 a large number of experimental factors. LC Chromatogram was optimized using Phenomenex RP C18 column (250 x Accepted on: 15/06/2018 4.6 mm; 5 µm); mobile phase was surged at isocratic mode with a flow of 1.0 mL/min using methanol and acetonitrile Available online: 30/07/2018 (95:5% v/v) at the detection max of 208 nm with the retention time of 16.3 and 18.1 min for Stigmasterol and β-Sitosterol respectively. Amount of Stigmasterol and β-Sitosterol was quantified and found to be 51.0 and 56.3 µg/ mg respectively. -
Modèles De Markov Cachés Récents Pour La Détection Et La Reconnaissance D’Activités À Partir D’Un Capteur IMU Placé Sur Une Jambe Haoyu Li
Modèles de Markov cachés récents pour la détection et la reconnaissance d’activités à partir d’un capteur IMU placé sur une jambe Haoyu Li To cite this version: Haoyu Li. Modèles de Markov cachés récents pour la détection et la reconnaissance d’activités à partir d’un capteur IMU placé sur une jambe. Autre. Université de Lyon, 2019. Français. NNT : 2019LYSEC041. tel-02537351 HAL Id: tel-02537351 https://tel.archives-ouvertes.fr/tel-02537351 Submitted on 8 Apr 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. No d’ordre NNT : 2019LYSEC41 THESE DOCTEUR DE L’ÉCOLE CENTRALE DE LYON Spécialité: Informatique Recent Hidden Markov Models for Lower Limb Locomotion Activity Detection and Recognition using IMU Sensors dans le cadre de l’École Doctorale InfoMaths présentée et soutenue publiquement par HAOYU LI December 2019 Directeur de thèse: Stéphane Derrode, PR à l’Ecole Centrale de Lyon Co-directeur de thèse: Wojciech Pieczynski, PR à Télécom SudParis JURY François Septier Université Bretagne Sud Rapporteur Faicel Chamroukhi Université de Caen Rapporteur Latifa Oukhellou IFSTTAR Présidente Lamia Benyoussef EPITA Examinatrice Stéphane Derrode É́cole Centrale de Lyon Directeur de thèse Wojciech Pieczynski Télécom SudParis Co-directeur de thèse Contents Abstract v Résumé vii 1 Introduction 1 2 Background 7 2.1 Available sensors for collecting data .................. -
Geometric Mean
Project AMP Dr. Antonio Quesada – Director, Project AMP Exploring Geometric Mean Lesson Summary: The students will explore the Geometric Mean through the use of Cabrii II software or TI – 92 Calculators and inquiry based activities. Keywords: Geometric Mean, Ratios NCTM Standards: 1. Analyze characteristics and properties of two-dimensional geometric shapes and develop mathematical arguments about geometric relationships (p.310). 2. Apply appropriate techniques, tools, and formulas to determine measurements (p.322). Learning Objectives: 1. Students will be able to construct a right triangle with an altitude drawn from the right angle. 2. Students will be able to use the geometric mean to find missing measures for parts of a right triangle. Materials Needed: 1. Cabri II Geometry Software 2. Pencil, Paper 3. Lab Handout Procedures/Review: 1. Attention Grabber: Begin by asking the students, “What are some ways we can find missing lengths of a right triangle?” (The students may already know how to apply the Pythagorean Theorem, or the formulas for special right triangles, or maybe even how to use trigonometric ratios). The answer you will be showing them is how to use the geometric mean. 2. Students can be grouped in teams of two or, if enough computers are available, they may work individually. 3. Assessment will be based on the students’ completion of the lab worksheet. 4. A review of ratios and proportions may be necessary prior to instruction. Project AMP Dr. Antonio Quesada – Director, Project AMP Exploring Geometric Mean Team Members: ____________________ ____________________ File Name: ____________________ Lab Goal: To analyze the relationship between different sides and segments of right triangles.