Report
Share
Recommended
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHM
A quantum computation problem is discussed in this paper. Many new features that make
quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform
algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is
presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is
analysed. The probability distribution of the measuring result of phase value is presented and
the computational efficiency is discussed.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTION
Radar images can reveal information about the shape of the surface terrain as well as its
physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has
applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge
detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed
over time. Some of the well-known edge detection operators based on the first derivative of the
image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image
with masks. Also Gaussian distribution has been used to build masks for the first and second
derivative. However, this distribution has limit to only symmetric shape. This paper will use to
construct the masks, the Weibull distribution which was more general than Gaussian because it
has symmetric and asymmetric shape. The constructed masks are applied to images and we
obtained good results.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUES
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Recommended
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHM
A quantum computation problem is discussed in this paper. Many new features that make
quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform
algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is
presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is
analysed. The probability distribution of the measuring result of phase value is presented and
the computational efficiency is discussed.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTION
Radar images can reveal information about the shape of the surface terrain as well as its
physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has
applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge
detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed
over time. Some of the well-known edge detection operators based on the first derivative of the
image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image
with masks. Also Gaussian distribution has been used to build masks for the first and second
derivative. However, this distribution has limit to only symmetric shape. This paper will use to
construct the masks, the Weibull distribution which was more general than Gaussian because it
has symmetric and asymmetric shape. The constructed masks are applied to images and we
obtained good results.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUES
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
PLANNING BY CASE-BASED REASONING BASED ON FUZZY LOGIC
The treatment of complex systems often requires the manipulation of vague, imprecise and
uncertain information. Indeed, the human being is competent in handling of such systems in a
natural way. Instead of thinking in mathematical terms, humans describes the behavior of the
system by language proposals. In order to represent this type of information, Zadeh proposed to
model the mechanism of human thought by approximate reasoning based on linguistic
variables. He introduced the theory of fuzzy sets in 1965, which provides an interface between
language and digital worlds. In this paper, we propose a Boolean modeling of the fuzzy
reasoning that we baptized Fuzzy-BML and uses the characteristics of induction graph
classification. Fuzzy-BML is the process by which the retrieval phase of a CBR is modelled not
in the conventional form of mathematical equations, but in the form of a database with
membership functions of fuzzy rules.
McGowan - World Congress of Medical Meetings - Is learning the goal of your m...
McGowan - World Congress of Medical Meetings - Is learning the goal of your m...Brian S. McGowan, PhD, FACEhp
The document discusses how to plan medical meetings with a focus on learning. It addresses the challenges of cognitive load and how this limits learning. It introduces the concept of natural learning actions - note taking, reminders, searching, and social learning - and how education should support these. The document advocates for an agile education design approach where the learning experience and environment are continuously refined based on data to optimize cognitive load and ensure learning is occurring.MRI IMAGES THRESHOLDING FOR ALZHEIMER DETECTION
This document summarizes a research paper that proposes a new method for thresholding MRI images to detect Alzheimer's disease. The method improves on Otsu's thresholding method by using a mixture of gamma distributions to model the histogram of MRI images, which allows it to handle asymmetric distributions. It introduces a "valley-emphasis" approach that selects threshold values located in valleys of the histogram to better detect small objects. Experimental results on MRI images demonstrate the method can effectively segment images and may help with early Alzheimer's detection.
Walt And Wilf
The document discusses best practices for presenting lesson objectives to students in a way that encourages student-directed learning. It recommends that objectives should be seen as challenging yet achievable, important, and focused on specific skills and understanding. Objectives work best when they look ahead to what students will be learning and doing, help students think about challenges, and are presented in terms of learning rather than just doing. Using a WALT (We Are Learning To) and WILF (What I'm Looking For) framework can help clearly communicate the aim, skill, and focus of assessment for each lesson.
Learning+goals+and+success+criteria
The document discusses learning goals and success criteria. It defines a learning goal as a curriculum expectation phrased in student-friendly language. Success criteria are "I can" statements that outline what students need to do to achieve the learning goal. Using learning goals and success criteria can improve student understanding, empower students, encourage independent learning, enable accurate feedback, and help teachers and students work toward common goals.
More Related Content
Viewers also liked
PLANNING BY CASE-BASED REASONING BASED ON FUZZY LOGIC
The treatment of complex systems often requires the manipulation of vague, imprecise and
uncertain information. Indeed, the human being is competent in handling of such systems in a
natural way. Instead of thinking in mathematical terms, humans describes the behavior of the
system by language proposals. In order to represent this type of information, Zadeh proposed to
model the mechanism of human thought by approximate reasoning based on linguistic
variables. He introduced the theory of fuzzy sets in 1965, which provides an interface between
language and digital worlds. In this paper, we propose a Boolean modeling of the fuzzy
reasoning that we baptized Fuzzy-BML and uses the characteristics of induction graph
classification. Fuzzy-BML is the process by which the retrieval phase of a CBR is modelled not
in the conventional form of mathematical equations, but in the form of a database with
membership functions of fuzzy rules.
McGowan - World Congress of Medical Meetings - Is learning the goal of your m...
McGowan - World Congress of Medical Meetings - Is learning the goal of your m...Brian S. McGowan, PhD, FACEhp
The document discusses how to plan medical meetings with a focus on learning. It addresses the challenges of cognitive load and how this limits learning. It introduces the concept of natural learning actions - note taking, reminders, searching, and social learning - and how education should support these. The document advocates for an agile education design approach where the learning experience and environment are continuously refined based on data to optimize cognitive load and ensure learning is occurring.MRI IMAGES THRESHOLDING FOR ALZHEIMER DETECTION
This document summarizes a research paper that proposes a new method for thresholding MRI images to detect Alzheimer's disease. The method improves on Otsu's thresholding method by using a mixture of gamma distributions to model the histogram of MRI images, which allows it to handle asymmetric distributions. It introduces a "valley-emphasis" approach that selects threshold values located in valleys of the histogram to better detect small objects. Experimental results on MRI images demonstrate the method can effectively segment images and may help with early Alzheimer's detection.
Walt And Wilf
The document discusses best practices for presenting lesson objectives to students in a way that encourages student-directed learning. It recommends that objectives should be seen as challenging yet achievable, important, and focused on specific skills and understanding. Objectives work best when they look ahead to what students will be learning and doing, help students think about challenges, and are presented in terms of learning rather than just doing. Using a WALT (We Are Learning To) and WILF (What I'm Looking For) framework can help clearly communicate the aim, skill, and focus of assessment for each lesson.
Learning+goals+and+success+criteria
The document discusses learning goals and success criteria. It defines a learning goal as a curriculum expectation phrased in student-friendly language. Success criteria are "I can" statements that outline what students need to do to achieve the learning goal. Using learning goals and success criteria can improve student understanding, empower students, encourage independent learning, enable accurate feedback, and help teachers and students work toward common goals.
Viewers also liked (6)
PLANNING BY CASE-BASED REASONING BASED ON FUZZY LOGIC
PLANNING BY CASE-BASED REASONING BASED ON FUZZY LOGIC
McGowan - World Congress of Medical Meetings - Is learning the goal of your m...
McGowan - World Congress of Medical Meetings - Is learning the goal of your m...
السوق عرض
- 6. :