This document discusses preprocessing, management, and analysis of mass spectrometry proteomics data. It introduces mass spectrometry and describes the major steps in an MS-based proteomics experiment: data generation, preprocessing, and analysis. Preprocessing aims to reduce noise and normalize data to allow comparison between samples. Techniques discussed include baseline subtraction, smoothing, binning, peak identification, and alignment. The document also presents MS-Analyzer, a software tool for managing, preprocessing, and analyzing MS proteomics data that implements described techniques. Performance evaluation on three cancer datasets shows preprocessing improves analysis time and quality.
Hybrid Algorithm for Dose Calculation in Cms Xio Treatment Planning SystemIOSR Journals
This study aimed at designing an improved hybrid algorithm by explicitly solving the linearized Boltzmann transport equation (LBTE) which is the governing equation that describes the macroscopic behaviour of radiation particles (neutrons, photons, electrons, etc). The algorithm accuracy will be evaluated using a newly designed in-house verification phantom and its results will be compared to those of the other XiO photon algorithms. The LBTE was solved numerically to compute photon transport in a medium. A programming code (algorithm) for the LBTE solution was developed and applied in the treatment planning system (TPS). The accuracy of the algorithm was evaluated by creating several plans for both the designed phantom and solid water phantom using the designed algorithm and other Xio photon algorithms. The plans were sent to a pre-calibrated Eleckta linear accelerator for measurement of absorbed dose.The results for all treatment plans using the hybrid algorithm compared to the 3 Xio photon algorithms were within 4 % limit. Calculation time for the hybrid algorithm was less in plans with larger number of beams compared to the other algorithms; however, it is higher for single beam plans. The hybrid algorithm provides comparable accuracy in treatment planning conditions to the other algorithms. This algorithm can therefore be employed in the calculation of dose in advance techniques such as IMRT and Rapid Arc by a radiotherapy centres with cmsxio treatment planning system as it is easy to implement.
In this article, 180 gastric images taken with Light Microscope help are used. Maximally Stable
Extremal Regions (MSER) features of the images for classification has been calculated. These MSER features
have been applied Discrete Fourier Transform (DFT) method. High-dimensional of these MSER-DFT feature
vectors is reduced to lower-dimensional with Local Tangent Space Alignment (LTSA) and Neighborhood
Preserving Embedding (NPE). When size reduction process was done, properties in 5, 10, 15, 20, 25, 30, 35, 40,
45, and 50 dimensions have been obtained. These low-dimensional data are classified by Random Forest (RF)
classification. Thus, MSER_DFT_LTSA-NPE_RF method for gastric histopathological images have been
developed. Classification results obtained with these methods have been compared. According to the other
methods, classification results for gastric histopathological images have been found to be higher.
Hybrid Algorithm for Dose Calculation in Cms Xio Treatment Planning SystemIOSR Journals
This study aimed at designing an improved hybrid algorithm by explicitly solving the linearized Boltzmann transport equation (LBTE) which is the governing equation that describes the macroscopic behaviour of radiation particles (neutrons, photons, electrons, etc). The algorithm accuracy will be evaluated using a newly designed in-house verification phantom and its results will be compared to those of the other XiO photon algorithms. The LBTE was solved numerically to compute photon transport in a medium. A programming code (algorithm) for the LBTE solution was developed and applied in the treatment planning system (TPS). The accuracy of the algorithm was evaluated by creating several plans for both the designed phantom and solid water phantom using the designed algorithm and other Xio photon algorithms. The plans were sent to a pre-calibrated Eleckta linear accelerator for measurement of absorbed dose.The results for all treatment plans using the hybrid algorithm compared to the 3 Xio photon algorithms were within 4 % limit. Calculation time for the hybrid algorithm was less in plans with larger number of beams compared to the other algorithms; however, it is higher for single beam plans. The hybrid algorithm provides comparable accuracy in treatment planning conditions to the other algorithms. This algorithm can therefore be employed in the calculation of dose in advance techniques such as IMRT and Rapid Arc by a radiotherapy centres with cmsxio treatment planning system as it is easy to implement.
In this article, 180 gastric images taken with Light Microscope help are used. Maximally Stable
Extremal Regions (MSER) features of the images for classification has been calculated. These MSER features
have been applied Discrete Fourier Transform (DFT) method. High-dimensional of these MSER-DFT feature
vectors is reduced to lower-dimensional with Local Tangent Space Alignment (LTSA) and Neighborhood
Preserving Embedding (NPE). When size reduction process was done, properties in 5, 10, 15, 20, 25, 30, 35, 40,
45, and 50 dimensions have been obtained. These low-dimensional data are classified by Random Forest (RF)
classification. Thus, MSER_DFT_LTSA-NPE_RF method for gastric histopathological images have been
developed. Classification results obtained with these methods have been compared. According to the other
methods, classification results for gastric histopathological images have been found to be higher.
Tropical Cyclone Determination using Infrared Satellite Imageijtsrd
Many sub continents in the world have the region that are affected by the cyclone in every year. To prevent the loss of life and their assets, cyclone prediction is a major role because of directly related to the lives and household of human being. Satellite images provide an excellent view of clouds which can be used in weather forecasting and especially Infrared Red IR satellite images play in many environmental applications. To find the tropical cyclone TC center, the basic stage is to extract the main cloud of the cyclone. In manual segmentation, selection of the storm region is complicated, time consuming task and it also need the human experts for every time processing. The semi and fully automatic storm detection is sophisticated and difficult process because of the overlapping between boundaries of the cloud. Fuzzy C means FCM clustering and morphological image processing is applied for segmentation each infrared satellite images. The effectiveness is tested for infrared cyclone image over Kalpana satellite which is obtained from the INSAT satellite of India. 45 tropical cyclones are occurred during the period 1989 2014 over Bay of Bengal. Cyclones Nargis that mainly affected to Myanmar in 2 May 2008 as case study. Experimental results show that the high location accuracy can be obtained. Thu Zar Hsan | Myint Myint Sein "Tropical Cyclone Determination using Infrared Satellite Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27934.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27934/tropical-cyclone-determination-using-infrared-satellite-image/thu-zar-hsan
A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSOrahulmonikasharma
The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise.
Mammographic Feature Enhancement using Singularities of Contourlet Transformidescitation
Early detection of breast cancer reduces the
mortality rate among women. Computer aided enhancement
techniques in mammograms assist radiologists in providing a
second opinion. In this work, selected modulus maxima of
Contourlet Transform and selected zero-crossings of the
Contourlet Transform were utilized for enhancing
microcalcification features in mammograms while reducing
noise. Relevant and strong edge information at various levels
was retained based on a parent-child relationship among
selected Contourlet coefficients. Experimental results of the
proposed techniques based on contourlet transform
demonstrate the superiority of the modulus maxima method
compared to the zero crossing method in mammographic
image enhancement. To validate the methods the mini – MIAS
database was employed. Various quality measures considered
for performance evaluation are Contrast improvement index,
Peak Signal to Noise Ratio, Target to Background Contrast
ratio and Tenengrad Criterion.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
MALDI...
This Presentation Contain following...
#Introduction
#Matrix and examples
#Considerations of Matrix Material
#MALDI Sample Preparation
#Mechanism of MALDI
#Mass Spectrometer
#Reproducibility and Performance
#Uses of MALDI
#Conclusion
#References
Thanks For Help and Guidance of Mr. D.V. Mahuli Sir and Mr. V.T. Pawar Sir
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
Comparison of Interpolation Methods in Prediction the Pattern of Basal Stem R...Waqas Tariq
Basal Stem Rot is a diseases that caused by Ganoderma Boinense that is the most serious disease for oil palm trees in Malaysia. The analysis of plant disease has been carried extensively with the advancement in computer technology. Particularly, in terms of spatial and temporal, it is very complicated to be processed. Furthermore, the application of GIS in plant disease analysis is becoming more popular, precise and advance. In previous studies, Kriging has been used to predict the pattern of BSR disease. In this study, two commonly used interpolation methods for GIS, Kriging and Inverse Distance Weighting (IDW), are used to interpolate and predict the pattern of Basal Stem Rot disease. Since the IDW method is an exact method and is more accurate one, it was expected to see more accurate results. However, the accuracy results of both methods are the same. Based on the characteristic of both methods and according to advantages and disadvantages, the Inverse Distance Weighted is recommended in this study but, for more informative data, Ordinary Kriging is suggested to be the preferable method to be used as an alternative method. .
ADAPTIVE SEGMENTATION OF CELLS AND PARTICLES IN FLUORESCENT MICROSCOPE IMAGEJournal For Research
Understanding the mechanisms of cell motility and their regulation is an important challenge in biomedical research. The ability of cells to exert forces on their environment and alter their shape as they move is essential to various biological processes such as the immune response, embryonic development, or tumor genesis .Recent technological advances in con-focal fluorescence microscopy have given researchers the opportunity to investigate these complex processes in vivo. However, they also lead to a tremendous increase in the amount of image data acquired during the studies. Therefore, the analysis of time-lapse experiments relies increasingly on automated image processing techniques. Namely, there is a high demand for fast and robust methods to help biologists to quantitatively analyze time-lapse image data. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively. The crucial tasks are, in particular, segmenting, tracking, and evaluating movement tracks and morphological changes of cells, sub-cellular components and other particles.
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Microwave imaging has been extensively studied in the past several years as a new technique for early stage breast cancer detection. The rationale of microwave imaging for breast tumour detection is significant contrast in the dielectric properties of normal tissue and malignant tumours. However, in practice noise present from the environments during screening/examination degrades the quality of the image. Inaccurate reconstructed image caused false/misleading interpretation of the image which leads to inappropriate diagnose or treatment to the patient. In the simulation works, noise is added to imitate the actual environment scenario. The two-dimensional (2D) object that identical to breast model is developed using numerical simulation to imitate the breast model. A filter is integrated with an iterative inversion technique for breast tumour detection to eliminate the noise. To assess the effectiveness of this approach, we consider the reconstruction of the electrical parameter profiles of 2D objects from measurements of the transient total electromagnetic field data contaminated with noise. Additive white Gaussian noise is utilized to mimic the effect of random processes that occur in the nature. This paper presents the filter settings and characteristics that affect the reconstruction of the image in order to obtain the most reliable and closer to the actual image. Selection of filter settings or design is important in order to achieve desired signal, most accurate image and provide reliable information of the object. Chebyshev low pass filter is applied in the Forward-Backward Time-Stepping (FBTS) algorithm to filter the noisy data and to improve the quality of reconstructed image. Keywords: Chebyshev low pass filter, microwave imaging and breast tumour detection
Lawline Presentation: Building a Law Practice Brick by Brick Cari Rincker
This presentation was used as a discussion tool for the Lawline presentation about starting a building a law practice. Live webcast (May 6, 2016 at 3:30pm) and recording available via Lawline. http://www.lawline.com/cle/course/building-a-law-practice-brick-by-brick
Tropical Cyclone Determination using Infrared Satellite Imageijtsrd
Many sub continents in the world have the region that are affected by the cyclone in every year. To prevent the loss of life and their assets, cyclone prediction is a major role because of directly related to the lives and household of human being. Satellite images provide an excellent view of clouds which can be used in weather forecasting and especially Infrared Red IR satellite images play in many environmental applications. To find the tropical cyclone TC center, the basic stage is to extract the main cloud of the cyclone. In manual segmentation, selection of the storm region is complicated, time consuming task and it also need the human experts for every time processing. The semi and fully automatic storm detection is sophisticated and difficult process because of the overlapping between boundaries of the cloud. Fuzzy C means FCM clustering and morphological image processing is applied for segmentation each infrared satellite images. The effectiveness is tested for infrared cyclone image over Kalpana satellite which is obtained from the INSAT satellite of India. 45 tropical cyclones are occurred during the period 1989 2014 over Bay of Bengal. Cyclones Nargis that mainly affected to Myanmar in 2 May 2008 as case study. Experimental results show that the high location accuracy can be obtained. Thu Zar Hsan | Myint Myint Sein "Tropical Cyclone Determination using Infrared Satellite Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27934.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27934/tropical-cyclone-determination-using-infrared-satellite-image/thu-zar-hsan
A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSOrahulmonikasharma
The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise.
Mammographic Feature Enhancement using Singularities of Contourlet Transformidescitation
Early detection of breast cancer reduces the
mortality rate among women. Computer aided enhancement
techniques in mammograms assist radiologists in providing a
second opinion. In this work, selected modulus maxima of
Contourlet Transform and selected zero-crossings of the
Contourlet Transform were utilized for enhancing
microcalcification features in mammograms while reducing
noise. Relevant and strong edge information at various levels
was retained based on a parent-child relationship among
selected Contourlet coefficients. Experimental results of the
proposed techniques based on contourlet transform
demonstrate the superiority of the modulus maxima method
compared to the zero crossing method in mammographic
image enhancement. To validate the methods the mini – MIAS
database was employed. Various quality measures considered
for performance evaluation are Contrast improvement index,
Peak Signal to Noise Ratio, Target to Background Contrast
ratio and Tenengrad Criterion.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
MALDI...
This Presentation Contain following...
#Introduction
#Matrix and examples
#Considerations of Matrix Material
#MALDI Sample Preparation
#Mechanism of MALDI
#Mass Spectrometer
#Reproducibility and Performance
#Uses of MALDI
#Conclusion
#References
Thanks For Help and Guidance of Mr. D.V. Mahuli Sir and Mr. V.T. Pawar Sir
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
Comparison of Interpolation Methods in Prediction the Pattern of Basal Stem R...Waqas Tariq
Basal Stem Rot is a diseases that caused by Ganoderma Boinense that is the most serious disease for oil palm trees in Malaysia. The analysis of plant disease has been carried extensively with the advancement in computer technology. Particularly, in terms of spatial and temporal, it is very complicated to be processed. Furthermore, the application of GIS in plant disease analysis is becoming more popular, precise and advance. In previous studies, Kriging has been used to predict the pattern of BSR disease. In this study, two commonly used interpolation methods for GIS, Kriging and Inverse Distance Weighting (IDW), are used to interpolate and predict the pattern of Basal Stem Rot disease. Since the IDW method is an exact method and is more accurate one, it was expected to see more accurate results. However, the accuracy results of both methods are the same. Based on the characteristic of both methods and according to advantages and disadvantages, the Inverse Distance Weighted is recommended in this study but, for more informative data, Ordinary Kriging is suggested to be the preferable method to be used as an alternative method. .
ADAPTIVE SEGMENTATION OF CELLS AND PARTICLES IN FLUORESCENT MICROSCOPE IMAGEJournal For Research
Understanding the mechanisms of cell motility and their regulation is an important challenge in biomedical research. The ability of cells to exert forces on their environment and alter their shape as they move is essential to various biological processes such as the immune response, embryonic development, or tumor genesis .Recent technological advances in con-focal fluorescence microscopy have given researchers the opportunity to investigate these complex processes in vivo. However, they also lead to a tremendous increase in the amount of image data acquired during the studies. Therefore, the analysis of time-lapse experiments relies increasingly on automated image processing techniques. Namely, there is a high demand for fast and robust methods to help biologists to quantitatively analyze time-lapse image data. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively. The crucial tasks are, in particular, segmenting, tracking, and evaluating movement tracks and morphological changes of cells, sub-cellular components and other particles.
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Microwave imaging has been extensively studied in the past several years as a new technique for early stage breast cancer detection. The rationale of microwave imaging for breast tumour detection is significant contrast in the dielectric properties of normal tissue and malignant tumours. However, in practice noise present from the environments during screening/examination degrades the quality of the image. Inaccurate reconstructed image caused false/misleading interpretation of the image which leads to inappropriate diagnose or treatment to the patient. In the simulation works, noise is added to imitate the actual environment scenario. The two-dimensional (2D) object that identical to breast model is developed using numerical simulation to imitate the breast model. A filter is integrated with an iterative inversion technique for breast tumour detection to eliminate the noise. To assess the effectiveness of this approach, we consider the reconstruction of the electrical parameter profiles of 2D objects from measurements of the transient total electromagnetic field data contaminated with noise. Additive white Gaussian noise is utilized to mimic the effect of random processes that occur in the nature. This paper presents the filter settings and characteristics that affect the reconstruction of the image in order to obtain the most reliable and closer to the actual image. Selection of filter settings or design is important in order to achieve desired signal, most accurate image and provide reliable information of the object. Chebyshev low pass filter is applied in the Forward-Backward Time-Stepping (FBTS) algorithm to filter the noisy data and to improve the quality of reconstructed image. Keywords: Chebyshev low pass filter, microwave imaging and breast tumour detection
Lawline Presentation: Building a Law Practice Brick by Brick Cari Rincker
This presentation was used as a discussion tool for the Lawline presentation about starting a building a law practice. Live webcast (May 6, 2016 at 3:30pm) and recording available via Lawline. http://www.lawline.com/cle/course/building-a-law-practice-brick-by-brick
Lawline: Overview of Common Agriculture ContractsCari Rincker
This presentation discusses a few of the major contractual issues that affect production agriculture including agriculture production contracts, purchase agreements (for land, livestock and farm equipment), leases (for land, livestock and farm equipment), special contracts (e.g., custom feeding arrangements, embryo transfer contracts, stocker cattle contracts, non-disclosure agreements) and partnership agreements. These materials were prepared for the Lawline presentation - more info on the webinar recording is available at https://www.lawline.com/course/overview-of-common-agriculture-contracts.
Bonjour Equipe de France,
je vous envoye le Pharma Caem pdf où j'ai ajouté compléments et nouveaux produits. Certaines lignes blanches sur les images ont été crées par Adobe pdf donc je ne puet mieux faire.
Ce pdf veut expliquer le produit, les codes, donner de donnés, donner une base pour parler avec les clients, avant du prix je veut repondre à la demande.. quoi vous faites ?
J'espère d'avoir tout mis et d'avoir fait les choses en façon comprensible et lisible utilisant des mots coérants.
Avec plaisir de lir vos commentaires.
Lawline: Counseling the Local Food Movement Part 1Cari Rincker
This presentation was given on October 24, 2013 to Lawline. It is Part 1 or a 2 Part series on "Counseling the Local Food Movement." It gives and overview and background of direct farm marketing, cottage food operations, liability and insurance. You can listen to the presentation and get a FREE CLE from Lawline here: http://bit.ly/15609Rj
Overview of the Veterinary Feed DirectiveCari Rincker
This presentation was prepared for the 2016 American Agriculture Law Association Annual Educational Symposium. This presentation will be given on October 6, 2016. It first lays the background of the Veterinary Feed Directive ("VFD") and then delves into the requirements of various stakeholders including (1) veterinarians, (2) livestock producers, (3) feed distributors, and (4) drug manufacturers.
Thread Safety is very important factor when implementing multithreaded applications. Code Synchronization helps in preventing multiple threads to access same code at a given time. This tutorial explains how Code synchronization works and i have explained how Object Locks work with multiple threads.
Download PPT and example : http://java9s.com/core-java/thread-sa...
facebook: http://facebook.com/java9s
twitter: http://twitter.com/java9s
GRC-MS: A GENETIC RULE-BASED CLASSIFIER MODEL FOR ANALYSIS OF MASS SPECTRA DATAcscpconf
Many studies uses different data mining techniques to analyze mass spectrometry data and
extract useful knowledge about biomarkers. These Biomarkers allow the medical experts to
determine whether an individual has a disease or not. Some of these studies have proposed
models that have obtained high accuracy. However, the black-box nature and complexity of the
proposed models have posed significant issues. Thus, to address this problem and build an
accurate model, we use a genetic algorithm for feature selection along with a rule-based
classifier, namely Genetic Rule-Based Classifier algorithm for Mass Spectra data (GRC-MS).
According to the literature, rule-based classifiers provide understandable rules, but not
accurate. In addition, genetic algorithms have achieved excellent results when used with
different classifiers for feature selection. Experiments are conducted on real dataset and the
proposed classifier GRC-MS achieves 99.7% accuracy. In addition, the generated rules are
more understandable than those of other classifier models
Comparison of signal smoothing techniques for use in embedded system for moni...Dalton Valadares
Paper about the comparison between some signal smoothing techniques for use in an embedded system responsible for monitoring the biofuels quality, specificaly the oxidative stability.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a data driven prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).
This paper introduces the Artifi cial Neural Networks (ANN) function to model probabilistic dependencies, in supervised classification tasks for discrimination between earthquakes and explosions problems. ANNs are regarded as the discriminating tools to classify the natural seismic events (earthquakes) from the artifi cial ones (Man-made explosions) based on the seismic signals recorded at regional distances. The bulk of our novel is to improve the obtained numerical results using this advance technique. The ANNs, by testing the different types of seismic features, showed the potential application of this method to discriminate the classes. During the above study, we found out that the Neural Networks have been used in a fully innovative manner in this work. Here the ARMA coefficients filters detects
the type of the source whenever a natural or artificial source changes the nature of the background noise of the seismograms. During the above study, we found out that this algorithm is sometimes capable to alarm the further natural seismological events just a little before the onset.
Acoustic Scene Classification by using Combination of MODWPT and Spectral Fea...ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. Mie Mie Oo | Lwin Lwin Oo "Acoustic Scene Classification by using Combination of MODWPT and Spectral Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27992.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/27992/acoustic-scene-classification-by-using-combination-of-modwpt-and-spectral-features/mie-mie-oo
A Review on Classification Based Approaches for STEGanalysis DetectionEditor IJCATR
This paper presents two scenarios of image steganalysis, in first scenario, an alternative feature set for steganalysis based on
rate-distortion characteristics of images. Here features are based on two key observations: i) Data embedding typically increases the
image entropy in order to encode the hidden messages; ii) Data embedding methods are limited to the set of small, imperceptible distortions.
The proposed feature set is used as the basis of a steganalysis algorithm and its performance is investigated using different
data hiding methods. In second scenario, a new blind approach of image Steganalysis based on contourlet transform and nonlinear
support vector machine. Properties of Contourlet transform are used to extract features of images, the important aspect of this paper is
that, it uses the minimum number of features in the transform domain and gives a better accuracy than many of the existing stegananlysis
methods. The efficiency of the proposed method is demonstrated through experimental results. Also its performance is compared
with the Contourlet based steganalyzer (WBS). Finally, the results show that the proposed method is very efficient in terms of its detection
accuracy and computational cost.
Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.
Simulation of the Linear Boltzmann Transport Equation in Modelling Of Photon ...IOSR Journals
A beam data modelling algorithm was developed by solving the linear Boltzmann Transport Equation (BTE). The Linear Boltzmann Transport Equation (LBTE) is a form of the Boltzmann transport equation that assumes that radiation particles only interact with the matter as they are passing through matter and not with each other. This condition is only valid when there is no external magnetic field. The numerical method proposed by Lewis et al., [9] was used to solve the LBTE. A programming code was computed for the LBTE and run on CMS XiO treatment planning system to generate beam data, the generated beam data were compared to experimentally determined data. The calculated percentage depth dose (PDD) completely overlap the measured PDDs for the small field sizes while there is a shift in the PDD tail for large field size. However the shift is negligible. For the wedge PDDs, the shift between the measured PDDs and the calculated occurs at the Dmax region and it increases with increase in field size. The calculated wedge profiles have a slight shift at the shoulder compared to the measured ones and this decreases with increase in field size, unlike the PDDs. There is also a slight shift between calculated in-plane profiles and measured ones. There is a good agreement between the measured beam data and the calculated ones using the algorithm. This algorithm can be implemented as an in-house algorithm for beam data modelling and also as an independent quality assurance tool for checking the accuracy of clinical TPS algorithms with regards to beam data modelling during quality assurance and TPS commissioning tests.
Particle Swarm Optimization for Nano-Particles Extraction from Supporting Mat...CSCJournals
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Empowering ACOs: Leveraging Quality Management Tools for MIPS and Beyond
Paper1
1. Preprocessing, Management, and Analysis of Mass Spectrometry
Proteomics Data
M. Cannataro, P. H. Guzzi, T. Mazza, and P. Veltri
Universit`a Magna Græcia di Catanzaro, Italy
1 Introduction
Mass Spectrometry (MS) based proteomics is becoming a powerful, widely used technique in order
to identify different molecular targets in different pathological conditions [1]. Proteomics experiments
involve different and heterogeneous technological platforms so a clear understanding of the function and
errors related to each one has to be taken into account. In particular, data produced by mass spectrometer
are affected by errors and noise due to sample preparation, sample insertion into the instrument (different
operators can lead to different results using the same sample) and instrument itself. Mass spectrometry-
based proteomics experiments usually comprise a data generation phase, a data preprocessing phase and
a data analysis phase (usually data mining, pattern extraction or peptide/protein identification). Mass
spectrometry produces a huge volume of data, said spectra, that are represented as a very large set of
measures (intensity, m/Z), representing the abundance (intensity) of biomolecules having certain mass
to charge ratio (m/Z) values.
In this paper, after introducing Mass Spectrometry, we survey different techniques for spectra pre-
processing and we present a first design of a software tool that allows to manage efficient storing and
preprocessing of mass spectrometry data. A first performance evaluation of MS-Analyzer is also pre-
sented.
2 Mass spectrometry proteomics data
Mass Spectrometry is a technique more and more used to identify macromolecules in a compound.
The mass spectrometer is an instrument designed to separate gas phase ions according to their m/Z (mass
to charge ratio) values. Matrix-Assisted Laser Desorption / Ionization - Time Of Flight Mass Spectrom-
etry (MALDI-TOF MS) is a relatively novel technique that is used for detection and characterization of
biomolecules, such as proteins, peptides, oligosaccharides and oligonucleotides, with molecular masses
between 400 and 350000 Da [2]. The Mass Spectrometry process [1] can be decomposed in three sub-
phases: (i) Sample Preparation (e.g. Cell Culture, Tissue, Serum); (ii) Proteins Extractions; and (iii)
Mass Spectrometry processing. Mass Spectrometry output is represented, at a first stage, as a (large)
sequence of value pairs, where each pair contains a measured intensity, which depends on the quantity
of the detected biomolecules and a mass to charge ratio (m/Z), which depends on the molecular mass of
detected biomolecules.
2. When obtaining a spectrum we have to consider some imperfection causes: (i) noise, (ii) peak broad-
ening, (iii) instrument distortion and saturation, (iv) isotopes, (v) miscalibration, (vi) contaminants of
various kinds. Data cleaning is performed in different phases by using: (i) best-practices sample prepa-
ration; (ii) mass spectrometer software; (iii) further data pre-processing algorithms. In the rest of the
paper we focus on pre-processing techniques conducted after data have been produced and eventually
cleaned by the spectrometer.
3 Preprocessing and analysis of mass spectrometry proteomics data
Each point of a spectrum is the result of two measurements, m/Z and intensity, that are corrupted
by noise. Preprocessing is the process that consists of spectrum noise and contaminants cleaning up.
Moreover, preprocessing can also be used to reduce dimensional complexity of the spectra, but it is
important to use efficient and biologically consistent algorithms. Currently this is an open problem. In
summary, preprocessing (see [5] for a survey) aims to correct intensity and m/Z values in order to: (i)
reduce noise, (ii) reduce amount of data, and (iii) make spectra comparable.
3.1 Noise reduction and normalization
Noise reduction and normalization are conducted in part by the spectrometer and in part by external
preprocessing tools. In the following we describe some approaches to noise reduction and normalization.
Base line subtraction and smoothing. Each of these techniques aims to reduce the noise. Base line
subtraction flattens the base profile of a spectrum while smoothing reduces the noise level in the whole
spectrum. Each mass spectrum exhibits a base intensity level (a baseline) which varies from fraction to
fraction and consequently needs to be identified and subtracted. This noise varies across the m/Z axis,
and it generally varies across different fractions, so that a one-value-fits-all strategy cannot be applied.
Base line subtraction uses an iterative algorithm to attempt to remove the baseline slope and offset
from a spectrum by iteratively calculating the best fit straight line through a set of estimated baseline
points. Smoothing is a process by which data points are averaged with their neighbors as in a time-series
of data. The main reason for smoothing is to increase signal to noise ratio.
Normalization of intensities. Normalization enables the comparison of different samples since the
absolutes peak values of different fraction of spectrum could be incomparable. The purpose of spectrum
normalization is to identify and remove sources of systematic variation between spectra due for instance
to varying amounts of sample or degradation over time in the sample or even variation in the instrument
detector sensitivity. We have analyzed and implemented four normalization methods not described here
due to space limits: the Canonical Normalization, the Inverse Normalization, cited in [3] and used in
[4], the Direct Normalization, and the Logarithmic Normalization, described by B. Wu [6] and Y. Yasui
[7].
3.2 Data Reduction
Binning. Binning is one of the most used preprocessing technique in MS data analysis. Its aim is to
preserve raw data information while performing a dimensional reduction for subsequent processing and
mining phases. Binning performs data dimensionality reduction by grouping measured data into bins.
This process involves grouping adjacent values and electing for each group a representative member.
2
3. 3.3 Identification and extraction of peaks
Algorithms that do not require human intervention are needed for rapid and repeatable quantitative
processing of spectra that often contain hundreds of discrete peaks. Peaks extraction consists of sepa-
rating real peaks (e.g. corresponding to peptides) from peaks representing noise. Although sometimes
such task can be performed by using the data-processing embedded in mass spectrometer, custom iden-
tification methods fitting both informatics and biological considerations are more effective.
3.4 Peaks alignment
A point in a spectrum represents a measurement of mass to charge ratio and electrical intensity. Each
of these measurements is affected by an error. Correction of error in m/Z measurement is also known
as data-calibration or alignment of correspondent peaks across samples. Without alignment, the same
peak (e.g. the same peptide) can have different values of m/Z across samples. To allow an easy and
effective comparison of different spectra, peaks alignment methods find a common set of peak locations
(i.e. m/Z values) in a set of spectra, in such a way that all spectra have common m/Z values for the
same biological entities. Each detected m/Z value is afflicted by noise causing the presence of a window
in which mass/charge ratio can be shifted. In [7] this window is defined as window of potential shift
indicating the range of potential mass/charge shifting for each m/Z point. Characteristics of shift are
strictly lied to the mass spectrometer used. Peaks alignment consists of shifting m/Z values in such
window such that peaks in all spectra will have the same m/Z.
4 MS-Analyzer
MS-Analyzer is a Grid-based Problem Solving Environment for proteomics applications, that uses
domain ontologies to model basic software tools and data sources and workflow techniques to design
complex in silico experiments. MS-Analyzer sits in the middle of proteomic facilities and data mining
software tools, so its main requirements are: interfacing with proteomics facility; storing and managing
MS proteomics data; interfacing with off-the-shelf data mining and visualization software tools (e.g.
WEKA, IBM Intelligent Miner, etc.). In particular, MS-Analyzer provides the following functions:
1. MS proteomic data acquisition loads MS raw spectra produced by different kind of Mass Spec-
trometers.
2. MS proteomic data pre-processing loads MS raw spectra and applies the pre-processing tech-
niques described before.
3. MS proteomic data preparation loads pre-processed spectra and prepare them to be given in
input to different kind of data mining tools.
4. Data Mining analysis allows to select and execute different data mining tasks (e.g. classification,
clustering, pattern analysis), and the corresponding data mining algorithms and tools (e.g. Q5, C5,
K-means, etc.), producing knowledge models.
5. Data Visualization and/or Visual Data Mining.
3
4. 5 Performance evaluation
The general scheme of a mass spectrometry-based data mining experiment comprises the following
activities: (i) loading of the raw spectra produced by mass spectrometer; (ii) preprocessing of the raw
spectra data, (iii) preparation of the data mining input file (e.g. the Weka ARFF file), and (iv) data
mining analysis (e.g. classification) of of mass spectra.
We considered three different mass spectra datasets publicly available on Internet: (i) Pancreatic Can-
cer dataset (it contains 142 spectra each one with 6,772 (m/Z, intensity) measurements); (ii) Prostate
Cancer dataset (it contains 322 spectra each one with 15,154 measurements); and (iii) Ovarian Cancer
dataset (it contains 49 spectra each one with 59,386 measurements). The Pancreatic and Prostate datasets
are available at 1
while the Ovarian datases is available at 2
.
For each dataset, we conducted a set of classification experiments and we measured: (i) execution
times of, respectively, loading and preparation, preprocessing, and classification activities ; (ii) memory
occupancy of, respectively, raw spectra, preprocessed spectra and ARFF files; (iii) quality indexes of
classification under different preprocessing conditions. 3
As an example of such measurements, figure 1 shows the sizes of different datasets when applying
none or different preprocessing techniques.
Figure 1. Datasets sizes and preprocessing
Figure 2 shows the execution time of a classification experiment when applying none or different
preprocessing techniques.
6 Conclusion
In this paper we presented a survey of preprocessing techniques for MS proteomics data and the de-
sign and a first prototype of MS-Analyzer, a tool for the management, preprocessing and analysis of
proteomics mass spectra. We currently implemented all the preprocessing tools and the data preparation
for the WEKA data mining platform. The performance evaluation of MS-Analyzer using three public
1
http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp
2
http://www-stat.stanford.edu/%7Etibs/PPC/Rdist/index.html
3
Execution times are expressed in milliseconds, and memory occupancy is expressed in KiloBytes.
4
5. Figure 2. Execution times and reprocessing
available mass spectra datasets showed how the selective use of preprocessing techniques improve ex-
ecution times, memory occupancy and quality of data mining (measures not reported here due to space
limitations).
References
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