aut aut      aut aut
   
MOHAMMAD RAHMATI
  Courses    Aims
 Digital Image Processing(FALL_2016)
* 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

 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
 Signals & Systems(FALL_2016)
* 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

 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
 Statistical Pattern Recognition(FALL_2016)
* IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

 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
 Digital Image Processing(SPRING_2016)
* IntroductionLight and Electromagnetic spectrumDifferent Image AcquisitionsImage enhancement in spatial domainpoint processingLinear transformationsHistogram approachesImage enhancement in Frequency domainFiltersin frequency domainHomomorphic filteringImage Restorations

 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
 Signals & Systems(SPRING_2016)
* 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

 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
 Statistical Pattern Recognition(SPRING_2016)
* 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

 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
 Statistical Pattern Recognition(SPRING_2016)
* IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

 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
 Digital Image Processing(FALL_2015)
* IntroductionLight and Electromagnetic spectrumDifferent Image AcquisitionsImage enhancement in spatial domainpoint processingLinear transformationsHistogram approachesImage enhancement in Frequency domainFiltersin frequency domainHomomorphic filteringImage Restorations

 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
 Signals & Systems(FALL_2015)
* 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

 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
 Statistical Pattern Recognition(FALL_2015)
* IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

 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
 Digital Image Processing(SPRING_2015)


 Nowadays, digital images are used in many applications as sources of information. Using proper tools to process these images is an important issue. In this class, the fundamental and basic concepts in image process are introduced. Different approaches to enhance images, image distortion modeling, image compression and image coding, and the mathematical tools such as Fourier and morphology transformations are introduced.
 Signals & Systems(SPRING_2015)
* 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

 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
 Statistical Pattern Recognition(SPRING_2015)
* IntroductionPattern RecognitionProbability and StatisticsStatistical Pattern RecognitionBayesian Decision TheoryQuadratic ClassifiersParameter and Density EstimationNearest NeighborsLinear DiscriminantsValidationDimensionality ReductionPrincipal Components AnalysisFisher’s Discriminants AnalysisFeature Subset SelectionClusteringMixture modelsHierarchical Clustering

 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
 Statistical Pattern Recognition(SPRING_2015)


 

 
 
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