Analysis of variance

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In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. (Wikipedia.org)






Conferences related to Analysis of variance

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2013 6th International Symposium on Computational Intelligence and Design (ISCID)

Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.


2013 Fifth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

hardware, control structure and microprogramming, i/o and data communication, integrated circuits, computer system organization, communication/networking and information technology, computing methodologies, artificial intelligence, computer graphics, image processing and computer vision, simulation modeling and visualization, computer application, physical science and engineering, computer aided engineering, computers in other system, information technology and system, modal and principles, information te


2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Energy and Environment, Mechatronics, Power Electronics, Signal Processing, Network and Communication Technologies.



Periodicals related to Analysis of variance

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Advanced Packaging, IEEE Transactions on

The IEEE Transactions on Advanced Packaging has its focus on the modeling, design, and analysis of advanced electronic, photonic, sensors, and MEMS packaging.


Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Instrumentation and Measurement, IEEE Transactions on

Measurements and instrumentation utilizing electrical and electronic techniques.


Signal Processing, IEEE Transactions on

The technology of transmission, recording, reproduction, processing, and measurement of speech; other audio-frequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...



Most published Xplore authors for Analysis of variance

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Xplore Articles related to Analysis of variance

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Users' Satisfaction with a Time-Shared Computer System Is Measurable

IEEE Transactions on Systems, Man, and Cybernetics, 1972

None


Error Propagation in Decision Feedback Interference Rejection DS Spread Spectrum Systems

F. Takawira; L. B. Milstein MILCOM 1987 - IEEE Military Communications Conference - Crisis Communications: The Promise and Reality, 1987

In this paper the performance of a DS spread spectrum communication system operating in the presence of narrowband interference is studied when a decision feedback filter, subject to error propagation, is employed for suppression of the interference. It is shown that when the number of filter taps is less than the system processing gain, the system can be modelled as ...


Binary image classification using genetic programming based on local binary patterns

Harith Al-Sahaf; Mengjie Zhang; Mark Johnston 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), 2013

Image classification represents an important task in machine learning and computer vision. To capture features covering a diversity of different objects, it has been observed that a sufficient number of learning instances are required to efficiently estimate the models' parameter values. In this paper, we propose a genetic programming (GP) based method for the problem of binary image classification that ...


Performance of stochastic quantizers employing nonlinear processing

P. Carbone; C. Narduzzi; D. Petri IEEE Transactions on Instrumentation and Measurement, 1996

Dithered quantizers are often employed in numerical instrumentation to improve the resolution and accuracy of the analog-to-digital converters used. To this aim, linear post-processing is usually performed on the quantized samples. In the paper, a different processing scheme is described that exhibits a nonlinear behavior. The characteristics of both linear and nonlinear topologies are analyzed, and expressions are given for ...


Analysis and Modeling of Inertial Sensors Using Allan Variance

Naser El-Sheimy; Haiying Hou; Xiaoji Niu IEEE Transactions on Instrumentation and Measurement, 2008

It is well known that inertial navigation systems can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time. The requirements for an accurate estimation of navigation information necessitate the modeling of the sensors' error components. Several variance techniques have been devised for stochastic modeling of the error of inertial sensors. They ...


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Educational Resources on Analysis of variance

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eLearning

Users' Satisfaction with a Time-Shared Computer System Is Measurable

IEEE Transactions on Systems, Man, and Cybernetics, 1972

None


Error Propagation in Decision Feedback Interference Rejection DS Spread Spectrum Systems

F. Takawira; L. B. Milstein MILCOM 1987 - IEEE Military Communications Conference - Crisis Communications: The Promise and Reality, 1987

In this paper the performance of a DS spread spectrum communication system operating in the presence of narrowband interference is studied when a decision feedback filter, subject to error propagation, is employed for suppression of the interference. It is shown that when the number of filter taps is less than the system processing gain, the system can be modelled as ...


Binary image classification using genetic programming based on local binary patterns

Harith Al-Sahaf; Mengjie Zhang; Mark Johnston 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), 2013

Image classification represents an important task in machine learning and computer vision. To capture features covering a diversity of different objects, it has been observed that a sufficient number of learning instances are required to efficiently estimate the models' parameter values. In this paper, we propose a genetic programming (GP) based method for the problem of binary image classification that ...


Performance of stochastic quantizers employing nonlinear processing

P. Carbone; C. Narduzzi; D. Petri IEEE Transactions on Instrumentation and Measurement, 1996

Dithered quantizers are often employed in numerical instrumentation to improve the resolution and accuracy of the analog-to-digital converters used. To this aim, linear post-processing is usually performed on the quantized samples. In the paper, a different processing scheme is described that exhibits a nonlinear behavior. The characteristics of both linear and nonlinear topologies are analyzed, and expressions are given for ...


Analysis and Modeling of Inertial Sensors Using Allan Variance

Naser El-Sheimy; Haiying Hou; Xiaoji Niu IEEE Transactions on Instrumentation and Measurement, 2008

It is well known that inertial navigation systems can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time. The requirements for an accurate estimation of navigation information necessitate the modeling of the sensors' error components. Several variance techniques have been devised for stochastic modeling of the error of inertial sensors. They ...


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IEEE-USA E-Books

  • Immune Cooperation Mechanism-Based Learning Framework

    Inspired from the immune cooperation (IC) mechanism in biological immune systems (BIS), this chapter presents an IC mechanism-based learning (ICL) framework. In this framework, a sample is expressed as an antigen-specific feature vector and an antigen-nonspecific feature vector, simulating the antigenic determinant and danger features in the BIS. The ICL framework simulates the BIS in the view of immune signals and takes full advantage of the cooperation effect of the immune signals, which improves the performance of the ICL framework. The ICL-MD model involves two modules, feature extraction and classification. In the malware detection problem, malware are taken as antigens, while benign programs are non-antigens. In order to ensure that the experimental results are reliable and the proposed ICL-MD model outperforms the GC-MD and LC-MD approaches statistically, an analysis of variance (ANOVA) was done followed by two t hypothesis tests (t-test). Comprehensive experimental results demonstrate that the ICL framework is an effective learning framework.

  • Statistical Methods

    This chapter contains sections titled: Statistical Inference Assessing Differences in Data Sets Bayesian Inference Predictive Regression Analysis of Variance Logistic Regression Log-Linear Models Linear Discriminant Analysis Review Questions and Problems References for Further Study

  • Muscle Force and Myoelectric Manifestations of Muscle Fatigue in Voluntary and Electrically Elicited Contractions

    This chapter deals with the association between surface electromyography (sEMG) and force/fatigue and outlines this association, and the tools to study it. It considers myoelectric manifestations of muscle fatigue as investigation tools. The association between relative decrease of conduction velocity and relative decrease of spectral variables is based on the relations between conduction velocity and power spectrum and between fiber type and fiber size. Muscles are made of fibers which classified into three main types: slow oxidative fibers, fast oxidative glycolitic fibers, and fast glycolitic fibers. Analysis of variance (ANOVA), coefficient of variation (CoV), standard error of the mean (SEM), and intraclass correlation coefficient (ICC) are repeatability of sEMG measurements and fatigue indicators tools. The agreement between sEMG-based estimation of fatigue and perceived exertion, at least in isometric fatiguing contractions, is of considerable interest in occupational medicine.

  • Design and Analysis of Machine Learning Experiments

    This chapter contains sections titled: 19.1 Introduction, 19.2 Factors, Response, and Strategy of Experimentation, 19.3 Response Surface Design, 19.4 Randomization, Replication, and Blocking, 19.5 Guidelines for Machine Learning Experiments, 19.6 Cross-Validation and Resampling Methods, 19.7 Measuring Classifier Performance, 19.8 Interval Estimation, 19.9 Hypothesis Testing, 19.10 Assessing a Classification Algorithm's Performance, 19.11 Comparing Two Classification Algorithms, 19.12 Comparing Multiple Algorithms: Analysis of Variance, 19.13 Comparison over Multiple Datasets, 19.14 Notes, 19.15 Exercises, 19.16 References

  • Production AST with Computers Using the Taguchi Method - Reprinted from Environmental Stress Testing Experiment Using the Taguchi Method, IEEE Transactions on Components, Packaging, and Manufacturing Technology, Part A, Vol. 18, No.1, pp. 39, with permission from the author and the IEEE, 1995.

    Manufacturing process improvements which increase productivity, decrease test process time, and improve customer satisfaction are highly desirable in today's marketplace. The application of environmental stress screening (ESS), i.e, Production AST, is a method of achieving these improvements. ESS is the application of stresses applied beyond product specification limits in order to find latent product defects. Utilizing ESS achieves increased robustness and lowers infant mortality. An experiment was performed to identify the significance or relevancy of the selected stresses for application in the printed wiring assembly (PWA) production process by using a statistically significant controlled method. The design of experiments statistical approach (analysis of variance) is applied, combined with the Taguchi two-level, seven- factor design method. This experiment concentrated on three stresses?-?temperature cycling, random vibration, and power cyling?-?and two diagnostic levels?-?a prom-based (programmable memory chip), power-on self test (POST), and a functional diagnostic test suite, contained on disk storage. This was not an optimization experiment. Once the significance to the production process is identified, future optimizing of temperature cycling, power cycling, and vibration screens will be conducted. Also, voltage margining was not included to reduce the complexity of the experiment- treatment factors and interactions. Experimental results and conclusions on the effectiveness of different stress regimens are presented in this chapter. Introduction Objectives Stress Overview Stress Screen Designs Experiment Overview The Taguchi Method Response Variable Results and Conclusions of the Taguchi Experiment Intra-Experiment Summary Taguchi Method Conclusion Terms Acknowledgments References

  • Design and Analysis of Machine Learning Experiments

    This chapter contains sections titled: 19.1 Introduction, 19.2 Factors, Response, and Strategy of Experimentation, 19.3 Response Surface Design, 19.4 Randomization, Replication, and Blocking, 19.5 Guidelines for Machine Learning Experiments, 19.6 Cross-Validation and Resampling Methods, 19.7 Measuring Classifier Performance, 19.8 Interval Estimation, 19.9 Hypothesis Testing, 19.10 Assessing a Classification Algorithm's Performance, 19.11 Comparing Two Classification Algorithms, 19.12 Comparing Multiple Algorithms: Analysis of Variance, 19.13 Comparison over Multiple Datasets, 19.14 Multivariate Tests, 19.15 Notes, 19.16 Exercises, 19.17 References



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