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Mohammad Rahmati
  Courses 

 Signals & Systems(FALL_2017)

Aims:

This course introduces students to the basic concepts of signals, system modeling, and system classification;. In this class students be able to understand time-domain and frequencydomain approaches to the analysis of continuous and discrete systems

Syllabus:

  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Z-Transform
  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Feedback Systems11. Z-Transform

Text Book:

  • Signals and SystemsAlan V. OppenheimAlan S. Willsky


 Statistical Pattern Recognition(FALL_2017)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

Text Book:

  • Pattern Recognition Sergios Theodoridis & Konstantinos KoutroumbasAcademic Press 2009


 Statistical Pattern Recognition(FALL_2017)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • Pattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering
  • Pattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

Text Book:

  • Pattern Recognition Sergios Theodoridis & Konstantinos KoutroumbasAcademic Press 2009


 Digital Image Processing(SPRING_2017)

Aims:

Nowadays the visual information and data have important roles in human societies. Images are acquired in many different forms and digital images are more familiar than the others. The Digital image processing techniques are used in many different applications. These applications include: medical image processing, surveillance applications, recognition and identification of people from their biometric images, remote sensing image processing, digital videos, etc. The objective of this course is

Syllabus:

  • IntroductionLight and Electromagnetic spectrumDifferent Image AcquisitionsImage enhancement in spatial domainpoint processingLinear transformationsHistogram approachesImage enhancement in Frequency domainFiltersin frequency domainHomomorphic filteringImage Restorations

Text Book:

  • Digital Image Processing, Rafael C. Gonzalez and Richard E. Woods, 3rd Edition, Prentice Hall


 Signals & Systems(SPRING_2017)

Aims:

This course introduces students to the basic concepts of signals, system modeling, and system classification;. In this class students be able to understand time-domain and frequencydomain approaches to the analysis of continuous and discrete systems

Syllabus:

  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Z-Transform
  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Feedback Systems11. Z-Transform

Text Book:

  • Signals and SystemsAlan V. OppenheimAlan S. Willsky


 Statistical Pattern Recognition(SPRING_2017)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

Text Book:

  • Pattern Recognition Sergios Theodoridis & Konstantinos KoutroumbasAcademic Press 2009


 Digital Image Processing(FALL_2016)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • Light and Electromagnetic spectrumDifferent Image AcquisitionsImage enhancement in spatial domainpoint processingLinear transformationsHistogram approachesImage enhancement in Frequency domainFilters in frequency domainHomomorphic filteringImage Restorations
  • Light and Electromagnetic spectrum Introduction Different Image AcquisitionsImage enhancement in spatial domainpoint processingLinear transformationsHistogram approachesImage enhancement in Frequency domainFilters in frequency domainHomomorphic filteringImage Restorations

Text Book:

  • Digital Image Processing, Rafael C. Gonzalez and Richard E. Woods, 3rd Edition, Prentice Hall


 Signals & Systems(FALL_2016)

Aims:

This course introduces students to the basic concepts of signals, system modeling, and system classification;. In this class students be able to understand time-domain and frequencydomain approaches to the analysis of continuous and discrete systems

Syllabus:

  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Z-Transform
  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Feedback Systems11. Z-Transform

Text Book:

  • Signals and SystemsAlan V. OppenheimAlan S. Willsky


 Statistical Pattern Recognition(FALL_2016)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

Text Book:

  • Pattern Recognition Sergios Theodoridis & Konstantinos KoutroumbasAcademic Press 2009


 Digital Image Processing(SPRING_2016)

Aims:

Nowadays the visual information and data have important roles in human societies. Images are acquired in many different forms and digital images are more familiar than the others. The Digital image processing techniques are used in many different applications. These applications include: medical image processing, surveillance applications, recognition and identification of people from their biometric images, remote sensing image processing, digital videos, etc. The objective of this course is

Syllabus:

  • IntroductionLight and Electromagnetic spectrumDifferent Image AcquisitionsImage enhancement in spatial domainpoint processingLinear transformationsHistogram approachesImage enhancement in Frequency domainFiltersin frequency domainHomomorphic filteringImage Restorations

Text Book:

  • Digital Image Processing, Rafael C. Gonzalez and Richard E. Woods, 3rd Edition, Prentice Hall


 Signals & Systems(SPRING_2016)

Aims:

This course introduces students to the basic concepts of signals, system modeling, and system classification;. In this class students be able to understand time-domain and frequencydomain approaches to the analysis of continuous and discrete systems

Syllabus:

  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Z-Transform
  • 1. Signals2. Systems3. Continuous-time Fourier Transform4. Communication Systems5. Discrete-Time Fourier Transform6. Sampling7. Applications of Discrete-Time Signal Processing8. Fourier Series Representation9. Laplace Transform10. Feedback Systems11. Z-Transform

Text Book:

  • Signals and SystemsAlan V. OppenheimAlan S. Willsky


 Statistical Pattern Recognition(SPRING_2016)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • Pattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering
  • Pattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

Text Book:

  • Pattern Recognition Sergios Theodoridis & Konstantinos KoutroumbasAcademic Press 2009


 Statistical Pattern Recognition(SPRING_2016)

Aims:

In this class, the fundamental subjects on statistical pattern recognition are introduced. The basic concepts such as dimensional reduction and extraction, design of classifiers, clustering and parameter estimation, and pdf estimation

Syllabus:

  • IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

Text Book:

  • Pattern Recognition Sergios Theodoridis & Konstantinos KoutroumbasAcademic Press 2009


 
 
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