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Clustering：k-means, expect-maximization and gaussian mixture model

This document discusses K-means clustering, Expectation Maximization (EM), and Gaussian mixture models (GMM). It begins with an overview of unsupervised learning and introduces K-means as a simple clustering algorithm. It then describes EM as a general algorithm for maximum likelihood estimation that can be applied to problems like GMM. GMM is presented as a density estimation technique that models data using a weighted sum of Gaussian distributions. EM is described as a method for estimating the parameters of a GMM from data.

Lecture 18: Gaussian Mixture Models and Expectation Maximization

This document discusses Gaussian mixture models (GMMs) and the expectation-maximization (EM) algorithm. GMMs model data as coming from a mixture of Gaussian distributions, with each data point assigned soft responsibilities to the different components. EM is used to estimate the parameters of GMMs and other latent variable models. It iterates between an E-step, where responsibilities are computed based on current parameters, and an M-step, where new parameters are estimated to maximize the expected complete-data log-likelihood given the responsibilities. EM converges to a local optimum for fitting GMMs to data.

GMM

This document discusses Gaussian mixture models (GMMs) and their use in applications like speaker recognition and language identification. GMMs represent a probability density function as a weighted sum of Gaussian distributions. GMM parameters are estimated from training data using Expectation-Maximization or Maximum A Posteriori estimation. GMMs are computationally inexpensive and well-suited for text-independent tasks without strong prior knowledge of content.

Lecture 1 graphical models

This document provides an overview of probabilistic graphical models. It discusses two types of probabilistic graphical models - Bayesian networks and Markov networks. Bayesian networks use directed graphs to represent conditional independence relationships between random variables. Markov networks use undirected graphs for the same purpose. The document outlines topics like representation, examples including naive Bayes classifiers and the Ising model, and inference and learning algorithms for probabilistic graphical models.

Expectation Maximization and Gaussian Mixture Models

Here are some other potential applications of EM:
- EM can be used for parameter estimation in hidden Markov models (HMMs). The hidden states are the latent variables estimated using EM.
- EM can be used for topic modeling using latent Dirichlet allocation (LDA). The topics are the latent variables estimated from documents.
- As mentioned in the document, EM can also be used for Gaussian mixture models (GMMs) for clustering and density estimation. The cluster assignments are latent.
- EM can be used for missing data problems, where the missing values are treated as latent variables estimated each iteration.
- Bayesian networks and directed graphical models more generally can also be estimated using EM by treating the conditional probabilities as latent

K-means and GMM

This document discusses clustering methods using the EM algorithm. It begins with an overview of machine learning and unsupervised learning. It then describes clustering, k-means clustering, and how k-means can be formulated as an optimization of a biconvex objective function solved via an iterative EM algorithm. The document goes on to describe mixture models and how the EM algorithm can be used to estimate the parameters of a Gaussian mixture model (GMM) via maximum likelihood.

Nonnegative Matrix Factorization

This document provides an introduction to blind source separation and non-negative matrix factorization. It describes blind source separation as a method to estimate original signals from observed mixed signals. Non-negative matrix factorization is introduced as a constraint-based approach to solving blind source separation using non-negativity. The alternating least squares algorithm is described for solving the non-negative matrix factorization problem. Experiments applying these methods to artificial and real image data are presented and discussed.

Feedforward neural network

This slide is prepared for the lectures-in-turn challenge within the study group of social informatics, kyoto university.

Clustering：k-means, expect-maximization and gaussian mixture model

This document discusses K-means clustering, Expectation Maximization (EM), and Gaussian mixture models (GMM). It begins with an overview of unsupervised learning and introduces K-means as a simple clustering algorithm. It then describes EM as a general algorithm for maximum likelihood estimation that can be applied to problems like GMM. GMM is presented as a density estimation technique that models data using a weighted sum of Gaussian distributions. EM is described as a method for estimating the parameters of a GMM from data.

Lecture 18: Gaussian Mixture Models and Expectation Maximization

This document discusses Gaussian mixture models (GMMs) and the expectation-maximization (EM) algorithm. GMMs model data as coming from a mixture of Gaussian distributions, with each data point assigned soft responsibilities to the different components. EM is used to estimate the parameters of GMMs and other latent variable models. It iterates between an E-step, where responsibilities are computed based on current parameters, and an M-step, where new parameters are estimated to maximize the expected complete-data log-likelihood given the responsibilities. EM converges to a local optimum for fitting GMMs to data.

GMM

This document discusses Gaussian mixture models (GMMs) and their use in applications like speaker recognition and language identification. GMMs represent a probability density function as a weighted sum of Gaussian distributions. GMM parameters are estimated from training data using Expectation-Maximization or Maximum A Posteriori estimation. GMMs are computationally inexpensive and well-suited for text-independent tasks without strong prior knowledge of content.

Lecture 1 graphical models

This document provides an overview of probabilistic graphical models. It discusses two types of probabilistic graphical models - Bayesian networks and Markov networks. Bayesian networks use directed graphs to represent conditional independence relationships between random variables. Markov networks use undirected graphs for the same purpose. The document outlines topics like representation, examples including naive Bayes classifiers and the Ising model, and inference and learning algorithms for probabilistic graphical models.

Expectation Maximization and Gaussian Mixture Models

Here are some other potential applications of EM:
- EM can be used for parameter estimation in hidden Markov models (HMMs). The hidden states are the latent variables estimated using EM.
- EM can be used for topic modeling using latent Dirichlet allocation (LDA). The topics are the latent variables estimated from documents.
- As mentioned in the document, EM can also be used for Gaussian mixture models (GMMs) for clustering and density estimation. The cluster assignments are latent.
- EM can be used for missing data problems, where the missing values are treated as latent variables estimated each iteration.
- Bayesian networks and directed graphical models more generally can also be estimated using EM by treating the conditional probabilities as latent

K-means and GMM

This document discusses clustering methods using the EM algorithm. It begins with an overview of machine learning and unsupervised learning. It then describes clustering, k-means clustering, and how k-means can be formulated as an optimization of a biconvex objective function solved via an iterative EM algorithm. The document goes on to describe mixture models and how the EM algorithm can be used to estimate the parameters of a Gaussian mixture model (GMM) via maximum likelihood.

Nonnegative Matrix Factorization

This document provides an introduction to blind source separation and non-negative matrix factorization. It describes blind source separation as a method to estimate original signals from observed mixed signals. Non-negative matrix factorization is introduced as a constraint-based approach to solving blind source separation using non-negativity. The alternating least squares algorithm is described for solving the non-negative matrix factorization problem. Experiments applying these methods to artificial and real image data are presented and discussed.

Feedforward neural network

This slide is prepared for the lectures-in-turn challenge within the study group of social informatics, kyoto university.

Image segmentation with deep learning

Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.

Face recognition using artificial neural network

This document provides an overview of a face recognition system that uses artificial neural networks. It describes the structure and processing of artificial neural networks, including convolutional networks. It discusses how the system works, including local image sampling, the self-organizing map, and the convolutional network. It then provides details about the implementation and applications of the system for face recognition, and concludes by discussing the benefits of the system.

Image Segmentation Using Deep Learning : A survey

1. The document discusses various deep learning models for image segmentation, including fully convolutional networks, encoder-decoder models, multi-scale pyramid networks, and dilated convolutional models.
2. It provides details on popular architectures like U-Net, SegNet, and models from the DeepLab family.
3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.

Fundamental, An Introduction to Neural Networks

This document provides an introduction to neural networks. It discusses how the first wave of interest emerged after McCullock and Pitts introduced simplified neuron models in 1943. However, perceptron models were shown to have deficiencies in 1969, leading to reduced funding and many researchers leaving the field. Interest re-emerged in the early 1980s after important theoretical results like backpropagation and new hardware increased processing capacities. The document then describes key components of artificial neural networks, including processing units that receive inputs and propagate outputs, different types of connections between units, and activation and output rules. It also covers different network topologies like feed-forward and recurrent networks.

Intro to Deep learning - Autoencoders

This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.

Autoencoders in Deep Learning

1. Autoencoders are unsupervised neural networks that are useful for dimensionality reduction and clustering. They compress the input into a latent-space representation then reconstruct the output from this representation.
2. Deep autoencoders stack multiple autoencoder layers to learn hierarchical representations of the data. Each layer is trained sequentially.
3. Variational autoencoders use probabilistic encoders and decoders to learn a Gaussian latent space. They can generate new samples from the learned data distribution.

Pattern recognition and Machine Learning.

Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.

U-Net (1).pptx

The document summarizes the U-Net convolutional network architecture for biomedical image segmentation. U-Net improves on Fully Convolutional Networks (FCNs) by introducing a U-shaped architecture with skip connections between contracting and expansive paths. This allows contextual information from the contracting path to be combined with localization information from the expansive path, improving segmentation of biomedical images which often have objects at multiple scales. The U-Net architecture has been shown to perform well even with limited training data due to its ability to make use of context.

Image segmentation

This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.

Explicit Density Models

This document summarizes recent advances in deep generative models with explicit density estimation. It discusses variational autoencoders (VAEs), including techniques to improve VAEs such as importance weighting, semi-amortized inference, and mitigating posterior collapse. It also covers energy-based models, autoregressive models, flow-based models, vector-quantized VAEs, hierarchical VAEs, and diffusion probabilistic models. The document provides an overview of these generative models with a focus on density estimation and generation quality.

Simultaneous Smoothing and Sharpening of Color Images

This document presents a new model for simultaneous sharpening and smoothing of color images based on graph theory. The model represents each pixel as a node in a weighted graph based on its color similarity to neighboring pixels. Smoothing is applied to pixels within the same connected component as the central pixel, while sharpening is applied to pixels in different components. Experimental results show the method can enhance details while removing noise. Future work includes optimizing parameters, measuring performance, and combining sharpening and smoothing parameters.

Transfer Learning: An overview

Transfer learning aims to improve learning in a target domain by leveraging knowledge from a related source domain. It is useful when the target domain has limited labeled data. There are several approaches, including instance-based approaches that reweight or resample source instances, and feature-based approaches that learn a transformation to align features across domains. Spectral feature alignment is one technique that builds a graph of correlations between pivot features shared across domains and domain-specific features, then applies spectral clustering to derive new shared features.

Vector quantization

The document discusses efficient codebook design for image compression using vector quantization. It introduces data compression techniques, including lossless compression methods like dictionary coders and entropy coding, as well as lossy compression methods like scalar and vector quantization. Vector quantization maps vectors to codewords in a codebook to compress data. The LBG algorithm is described for generating an optimal codebook by iteratively clustering vectors and updating codebook centroids.

Medical image processing

This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.

Gaussian Mixture Models

This document provides instructions for other teachers to use and modify slides from a lecture on clustering with Gaussian mixtures given by Andrew W. Moore. It notes that the PowerPoint originals are available and encourages comments and corrections. Users are asked to include attribution if using a significant portion of the slides.

Multilayer perceptron

In this study was to understand the first thing about machine learning and the multilayer perceptron.

Lossless predictive coding in Digital Image Processing

Lossless predictive coding eliminates inter-pixel redundancies in images by predicting pixel values based on surrounding pixels and encoding only the differences between actual and predicted values, rather than decomposing images into bit planes. The coding system consists of identical encoders and decoders that each contain a predictor. The predictor generates an anticipated pixel value based on past inputs, the difference between actual and predicted values is variable-length encoded, and the decoder uses the differences to reconstruct the original image losslessly.

Convolutional Neural Networks (CNN)

A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).

Image Filtering in the Frequency Domain

Image Filtering in the Frequency Domain
ILPF Filtering
Low Pass Filter
High Pass Filter
Band pass Filter
Blurring
Sharpening
Low pass Filter
Blurring - Ideal Low pass Filter

K - Nearest neighbor ( KNN )

Machine learning algorithm ( KNN ) for classification and regression .
Lazy learning , competitive and Instance based learning.

An Adaptive Masker for the Differential Evolution Algorithm

The document proposes an adaptive masker technique for the differential evolution algorithm to perform automatic fuzzy clustering. The adaptive masker aims to guide the search process towards the optimal clustering solution by dividing the mask matrix into three zones - a best masks zone, a global best influence zone where the number of clusters is a function of the best fitness, and a random zone. Experimental results on a remote sensing dataset show the proposed adaptive masker differential evolution algorithm performs better than other fuzzy clustering algorithms like iterative fuzzy c-means, improved differential evolution, and variable length genetic algorithm based fuzzy clustering in automatically detecting the optimal number of clusters.

Poisson distribution jen

The document discusses the Poisson distribution, which models the number of events occurring in a fixed interval of time or space if these events happen with a known average rate and independently of the time since the last event. The key points are:
1) The Poisson distribution gives the probability of a given number of events occurring, with the probability determined by the average rate of occurrences.
2) For a Poisson process, the mean and variance of the number of occurrences are both equal to the average rate of occurrences.
3) The Poisson distribution can model many real-world independent random processes, like machine failures, telephone calls, and traffic flows.

Image segmentation with deep learning

Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.

Face recognition using artificial neural network

This document provides an overview of a face recognition system that uses artificial neural networks. It describes the structure and processing of artificial neural networks, including convolutional networks. It discusses how the system works, including local image sampling, the self-organizing map, and the convolutional network. It then provides details about the implementation and applications of the system for face recognition, and concludes by discussing the benefits of the system.

Image Segmentation Using Deep Learning : A survey

1. The document discusses various deep learning models for image segmentation, including fully convolutional networks, encoder-decoder models, multi-scale pyramid networks, and dilated convolutional models.
2. It provides details on popular architectures like U-Net, SegNet, and models from the DeepLab family.
3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.

Fundamental, An Introduction to Neural Networks

This document provides an introduction to neural networks. It discusses how the first wave of interest emerged after McCullock and Pitts introduced simplified neuron models in 1943. However, perceptron models were shown to have deficiencies in 1969, leading to reduced funding and many researchers leaving the field. Interest re-emerged in the early 1980s after important theoretical results like backpropagation and new hardware increased processing capacities. The document then describes key components of artificial neural networks, including processing units that receive inputs and propagate outputs, different types of connections between units, and activation and output rules. It also covers different network topologies like feed-forward and recurrent networks.

Intro to Deep learning - Autoencoders

This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.

Autoencoders in Deep Learning

1. Autoencoders are unsupervised neural networks that are useful for dimensionality reduction and clustering. They compress the input into a latent-space representation then reconstruct the output from this representation.
2. Deep autoencoders stack multiple autoencoder layers to learn hierarchical representations of the data. Each layer is trained sequentially.
3. Variational autoencoders use probabilistic encoders and decoders to learn a Gaussian latent space. They can generate new samples from the learned data distribution.

Pattern recognition and Machine Learning.

Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.

U-Net (1).pptx

The document summarizes the U-Net convolutional network architecture for biomedical image segmentation. U-Net improves on Fully Convolutional Networks (FCNs) by introducing a U-shaped architecture with skip connections between contracting and expansive paths. This allows contextual information from the contracting path to be combined with localization information from the expansive path, improving segmentation of biomedical images which often have objects at multiple scales. The U-Net architecture has been shown to perform well even with limited training data due to its ability to make use of context.

Image segmentation

This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.

Explicit Density Models

This document summarizes recent advances in deep generative models with explicit density estimation. It discusses variational autoencoders (VAEs), including techniques to improve VAEs such as importance weighting, semi-amortized inference, and mitigating posterior collapse. It also covers energy-based models, autoregressive models, flow-based models, vector-quantized VAEs, hierarchical VAEs, and diffusion probabilistic models. The document provides an overview of these generative models with a focus on density estimation and generation quality.

Simultaneous Smoothing and Sharpening of Color Images

This document presents a new model for simultaneous sharpening and smoothing of color images based on graph theory. The model represents each pixel as a node in a weighted graph based on its color similarity to neighboring pixels. Smoothing is applied to pixels within the same connected component as the central pixel, while sharpening is applied to pixels in different components. Experimental results show the method can enhance details while removing noise. Future work includes optimizing parameters, measuring performance, and combining sharpening and smoothing parameters.

Transfer Learning: An overview

Transfer learning aims to improve learning in a target domain by leveraging knowledge from a related source domain. It is useful when the target domain has limited labeled data. There are several approaches, including instance-based approaches that reweight or resample source instances, and feature-based approaches that learn a transformation to align features across domains. Spectral feature alignment is one technique that builds a graph of correlations between pivot features shared across domains and domain-specific features, then applies spectral clustering to derive new shared features.

Vector quantization

The document discusses efficient codebook design for image compression using vector quantization. It introduces data compression techniques, including lossless compression methods like dictionary coders and entropy coding, as well as lossy compression methods like scalar and vector quantization. Vector quantization maps vectors to codewords in a codebook to compress data. The LBG algorithm is described for generating an optimal codebook by iteratively clustering vectors and updating codebook centroids.

Medical image processing

This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.

Gaussian Mixture Models

This document provides instructions for other teachers to use and modify slides from a lecture on clustering with Gaussian mixtures given by Andrew W. Moore. It notes that the PowerPoint originals are available and encourages comments and corrections. Users are asked to include attribution if using a significant portion of the slides.

Multilayer perceptron

In this study was to understand the first thing about machine learning and the multilayer perceptron.

Lossless predictive coding in Digital Image Processing

Lossless predictive coding eliminates inter-pixel redundancies in images by predicting pixel values based on surrounding pixels and encoding only the differences between actual and predicted values, rather than decomposing images into bit planes. The coding system consists of identical encoders and decoders that each contain a predictor. The predictor generates an anticipated pixel value based on past inputs, the difference between actual and predicted values is variable-length encoded, and the decoder uses the differences to reconstruct the original image losslessly.

Convolutional Neural Networks (CNN)

A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).

Image Filtering in the Frequency Domain

Image Filtering in the Frequency Domain
ILPF Filtering
Low Pass Filter
High Pass Filter
Band pass Filter
Blurring
Sharpening
Low pass Filter
Blurring - Ideal Low pass Filter

K - Nearest neighbor ( KNN )

Machine learning algorithm ( KNN ) for classification and regression .
Lazy learning , competitive and Instance based learning.

Image segmentation with deep learning

Image segmentation with deep learning

Face recognition using artificial neural network

Face recognition using artificial neural network

Image Segmentation Using Deep Learning : A survey

Image Segmentation Using Deep Learning : A survey

Fundamental, An Introduction to Neural Networks

Fundamental, An Introduction to Neural Networks

Intro to Deep learning - Autoencoders

Intro to Deep learning - Autoencoders

Autoencoders in Deep Learning

Autoencoders in Deep Learning

Pattern recognition and Machine Learning.

Pattern recognition and Machine Learning.

U-Net (1).pptx

U-Net (1).pptx

Image segmentation

Image segmentation

Explicit Density Models

Explicit Density Models

Simultaneous Smoothing and Sharpening of Color Images

Simultaneous Smoothing and Sharpening of Color Images

Transfer Learning: An overview

Transfer Learning: An overview

Vector quantization

Vector quantization

Medical image processing

Medical image processing

Gaussian Mixture Models

Gaussian Mixture Models

Multilayer perceptron

Multilayer perceptron

Lossless predictive coding in Digital Image Processing

Lossless predictive coding in Digital Image Processing

Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNN)

Image Filtering in the Frequency Domain

Image Filtering in the Frequency Domain

K - Nearest neighbor ( KNN )

K - Nearest neighbor ( KNN )

An Adaptive Masker for the Differential Evolution Algorithm

The document proposes an adaptive masker technique for the differential evolution algorithm to perform automatic fuzzy clustering. The adaptive masker aims to guide the search process towards the optimal clustering solution by dividing the mask matrix into three zones - a best masks zone, a global best influence zone where the number of clusters is a function of the best fitness, and a random zone. Experimental results on a remote sensing dataset show the proposed adaptive masker differential evolution algorithm performs better than other fuzzy clustering algorithms like iterative fuzzy c-means, improved differential evolution, and variable length genetic algorithm based fuzzy clustering in automatically detecting the optimal number of clusters.

Poisson distribution jen

The document discusses the Poisson distribution, which models the number of events occurring in a fixed interval of time or space if these events happen with a known average rate and independently of the time since the last event. The key points are:
1) The Poisson distribution gives the probability of a given number of events occurring, with the probability determined by the average rate of occurrences.
2) For a Poisson process, the mean and variance of the number of occurrences are both equal to the average rate of occurrences.
3) The Poisson distribution can model many real-world independent random processes, like machine failures, telephone calls, and traffic flows.

Vladimir Milov and Andrey Savchenko - Classification of Dangerous Situations...

Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems

Genetic algorithm

Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.

Report

This document discusses image denoising using total variation. It first introduces image formation models and types of noise such as Gaussian and Poisson noise. It then discusses conventional denoising methods like low pass filtering and their limitations. Total variation is introduced as a regularizer that can better preserve edges. The document formulates image denoising as an optimization problem with a data term and total variation regularization term. It describes implementing total variation denoising for Gaussian and Poisson noise using algorithms like MM, steepest descent, and conjugate gradient. Results show that total variation denoising achieves significant improvement in peak signal to noise ratio compared to conventional methods.

Adaptive Grouping Quantum Inspired Shuffled Frog Leaping Algorithm

The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.

Dong Zhang's project

- The document describes a study that uses a modified Kolmogorov-Smirnov (KS) test to test if the innovations of a GARCH model come from a mixture of normal distributions rather than a standard normal distribution.
- It establishes critical values for the KS test and modified KS (MKS) test through simulation under the null hypothesis. It then uses simulation to calculate the size and power of both tests when the innovations come from alternative distributions like the normal, Student's t, and generalized error distributions.
- The results show that the KS and MKS tests maintain the correct size when the innovations are actually from the mixture of normals. The power of both tests is greater than the nominal level when the innovations come

O hst-07 design-optimization_nit_agartala

This document summarizes a study using the Moving Least Squares Method (MLSM) to create approximation models for design optimization and stochastic analysis of an airbag inflation simulation. MLSM was used to build accurate surrogate models from simulation data to enable efficient Monte Carlo analysis. The technique was applied to automatically calibrate an airbag simulation model to experimental test data by minimizing differences between simulated and experimental acceleration curves. Once calibrated, MLSM approximations were also used to assess the robustness of the model to parameter variations.

Genetic Algorithm for the Traveling Salesman Problem using Sequential Constru...

This paper develops a new crossover operator, Sequential Constructive crossover (SCX), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The sequential constructive crossover operator constructs an offspring from a pair of parents using better edges on the basis of their values that may be present in the parents' structure maintaining the sequence of nodes in the parent chromosomes. The efficiency of the SCX is compared as against some existing crossover operators; namely, edge recombination crossover (ERX) and generalized N-point crossover (GNX) for some benchmark TSPLIB instances. Experimental results show that the new crossover operator is better than the ERX and GNX.

random variable and distribution

This document provides an overview of key concepts related to random variables and probability distributions. It discusses:
- Two types of random variables - discrete and continuous. Discrete variables can take countable values, continuous can be any value in an interval.
- Probability distributions for discrete random variables, which specify the probability of each possible outcome. Examples of common discrete distributions like binomial and Poisson are provided.
- Key properties and calculations for discrete distributions like expected value, variance, and the formulas for binomial and Poisson probabilities.
- Other discrete distributions like hypergeometric are introduced for situations where outcomes are not independent. Examples are provided to demonstrate calculating probabilities for each type of distribution.

E0212730

This document proposes a new technique called Fast Algorithm of Fractal Encoding based on Entropy Values (FAFEEVs) to reduce the encoding time of fractal image compression. FAFEEVs works by reducing the size of the domain pool used for matching range blocks during encoding. It does this by only comparing each range block to domain blocks whose entropy values are close to the range block's entropy, within some threshold ε. Experimental results on test images show that FAFEEVs achieves comparable reconstructed image quality to traditional fractal encoding, but with faster encoding times due to searching a smaller domain pool. The encoding time decreases as the number of partitioning blocks decreases.

Computational Motor Control: Optimal Estimation in Noisy World (JAIST summer ...

This is lecure 4 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=2-VRBIg5m0w

Lecture 7

This document discusses sharpening filters used in digital image processing. It describes two types of sharpening filters: high-pass filters that enhance high frequencies and frequency correcting filters that enhance high and medium frequencies. Unsharp masking is presented as a classical frequency correcting filter that subtracts a blurred version of an image from the original to highlight transitions. Practical implementations of linear and nonlinear unsharp masking filters are shown for local and global frequency correction. Parameters for controlling the level of sharpening and frequency enhancement are also discussed.

Roots of equations

This document discusses numerical methods for finding roots of equations. It begins by introducing graphical and closed methods, including bisection and false position. It then covers open methods such as fixed point iteration and Newton-Raphson. Multiple roots and polynomial roots are also addressed. Methods for polynomials include Muller's method and Bairstow's method.

Image compression based on

Many algorithms have been developed to find sparse representation over redundant dictionaries or
transform. This paper presents a novel method on compressive sensing (CS)-based image compression
using sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the three levels of
wavelet transform coefficients of the input image for compressive sampling. We have used three different
measurement matrix as Gaussian matrix, Bernoulli measurement matrix and random orthogonal matrix.
The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct each level of
wavelet transform separately. Experimental results demonstrate that the proposed method given better
quality of compressed image than existing methods in terms of proposed image quality evaluation indexes
and other objective (PSNR/UIQI/SSIM) measurements.

Unit3

This document discusses moments, skewness, kurtosis, and several statistical distributions including binomial, Poisson, hypergeometric, and chi-square distributions. It defines key terms such as moment ratios, central moments, theorems, skewness, kurtosis, and correlation. Properties and applications of the binomial, Poisson, and hypergeometric distributions are provided. Finally, the document discusses the chi-square test for goodness of fit and independence.

A CONVERGENCE ANALYSIS OF GRADIENT_version1

A CONVERGENCE ANALYSIS OF GRADIENT

ch03.ppt

This document provides an overview of probability and statistics concepts including:
- Random variables which can change from one experiment to another
- Probability distributions like the normal, binomial, and Poisson which describe probabilities of random variables
- Key concepts like mean, variance, and independence between random variables
- The central limit theorem which shows that sums of random variables will tend toward a normal distribution regardless of the original distributions

Genetic algorithm

Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.

A Condensation-Projection Method For The Generalized Eigenvalue Problem

This document describes a condensation-projection method for solving large generalized eigenvalue problems. The method works by selecting a small number of "master" variables to represent the full problem. The remaining "slave" variables are eliminated, resulting in a much smaller eigenvalue problem involving just the master variables. Good approximations of selected eigenvalues and eigenvectors of the original large problem can be obtained from the condensed problem if the master variables approximate the desired eigenvectors well. The method is well-suited for parallel computing.

An Adaptive Masker for the Differential Evolution Algorithm

An Adaptive Masker for the Differential Evolution Algorithm

Poisson distribution jen

Poisson distribution jen

Vladimir Milov and Andrey Savchenko - Classification of Dangerous Situations...

Vladimir Milov and Andrey Savchenko - Classification of Dangerous Situations...

Genetic algorithm

Genetic algorithm

Report

Report

Adaptive Grouping Quantum Inspired Shuffled Frog Leaping Algorithm

Adaptive Grouping Quantum Inspired Shuffled Frog Leaping Algorithm

Dong Zhang's project

Dong Zhang's project

O hst-07 design-optimization_nit_agartala

O hst-07 design-optimization_nit_agartala

Genetic Algorithm for the Traveling Salesman Problem using Sequential Constru...

Genetic Algorithm for the Traveling Salesman Problem using Sequential Constru...

random variable and distribution

random variable and distribution

E0212730

E0212730

Computational Motor Control: Optimal Estimation in Noisy World (JAIST summer ...

Computational Motor Control: Optimal Estimation in Noisy World (JAIST summer ...

Lecture 7

Lecture 7

Roots of equations

Roots of equations

Image compression based on

Image compression based on

Unit3

Unit3

A CONVERGENCE ANALYSIS OF GRADIENT_version1

A CONVERGENCE ANALYSIS OF GRADIENT_version1

ch03.ppt

ch03.ppt

Genetic algorithm

Genetic algorithm

A Condensation-Projection Method For The Generalized Eigenvalue Problem

A Condensation-Projection Method For The Generalized Eigenvalue Problem

Mechanical Engineering on AAI Summer Training Report-003.pdf

Mechanical Engineering PROJECT REPORT ON SUMMER VOCATIONAL TRAINING
AT MBB AIRPORT

Call Girls Chennai +91-8824825030 Vip Call Girls Chennai

Call Girls Chennai +91-8824825030 Vip Call Girls Chennai

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

SCALING OF MOS CIRCUITS m .pptx

this ppt explains about scaling parameters of the mosfet it is basically vlsi subject

一比一原版(USF毕业证)旧金山大学毕业证如何办理

原件一模一样【微信：95270640】【旧金山大学毕业证USF学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理旧金山大学毕业证毕业证学位证USF学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理旧金山大学毕业证USF学位证毕业证学位证【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理旧金山大学毕业证毕业证学位证USF学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理旧金山大学毕业证毕业证学位证USF学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

An Introduction to the Compiler Designss

compiler material

Blood finder application project report (1).pdf

Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.

FULL STACK PROGRAMMING - Both Front End and Back End

This ppt gives details about Full Stack Programming and its basics.

Digital Twins Computer Networking Paper Presentation.pptx

A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.

SENTIMENT ANALYSIS ON PPT AND Project template_.pptx

It is used for sentiment analysis project

Accident detection system project report.pdf

The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.

openshift technical overview - Flow of openshift containerisatoin

openshift overview

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group

Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.一比一原版(uoft毕业证书)加拿大多伦多大学毕业证如何办理

原版一模一样【微信：741003700 】【(uoft毕业证书)加拿大多伦多大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Levelised Cost of Hydrogen (LCOH) Calculator Manual

The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity

Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...

Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.

Applications of artificial Intelligence in Mechanical Engineering.pdf

Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.

SELENIUM CONF -PALLAVI SHARMA - 2024.pdf

Begin your journey to contribute to Selenium - A Talk at the Selenium Conference 2024

Butterfly Valves Manufacturer (LBF Series).pdf

We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.

Presentation on Food Delivery Systems

This presentation is about Food Delivery Systems and how they are developed using the Software Development Life Cycle (SDLC) and other methods. It explains the steps involved in creating a food delivery app, from planning and designing to testing and launching. The slide also covers different tools and technologies used to make these systems work efficiently.

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- 1. GMMGaussian mixture models 8/15/2014 1 Saurab Dulal IOE, pulchowk Campus
- 2. Introduction to GMM • Gaussian “Gaussian is a characteristic symmetric "bell curve" shape that quickly falls off towards 0 (practically)” • Mixture Model “mixture model is a probabilistic model which assumes the underlying data to belong to a mixture distribution” 2
- 3. Introduction to GMM • Mathematical Description of GMM p(x) = w1 p1 (x) + w2p2 (x) + w3 p3 (x) ……… +wn pn (x) where p(x) = mixture component w1, w2 ….. wn = mixture weight or mixture coefficient pi (x) = Density functions Fig :- Image showing Best fit Gaussian Curve 3
- 4. Introduction to GMM “The most common mixture distribution is the Gaussian (Normal) density function, in which each of the mixture components are Gaussian distributions, each with their own mean and variance parameters.” p(x) = w1N( x | µ1∑1 )+ w1N( x | µ2∑2 )… +w1N( x | µn∑n ) µi ‘s are means and ∑i ‘s are covariance-matrix of individual components(probability density function) 4 G1,w1 G2,w2 G3,w3 G4,w4 G5,w5
- 5. -5 0 5 10 0 0.1 0.2 0.3 0.4 0.5 Component 1 Component 2 p(x) -5 0 5 10 0 0.1 0.2 0.3 0.4 0.5 Mixture Model x p(x)
- 6. -5 0 5 10 0 0.1 0.2 0.3 0.4 0.5 Component 1 Component 2 p(x) -5 0 5 10 0 0.1 0.2 0.3 0.4 0.5 Mixture Model x p(x)
- 7. -5 0 5 10 0 0.5 1 1.5 2 Component Models p(x) -5 0 5 10 0 0.1 0.2 0.3 0.4 0.5 Mixture Model x p(x)
- 8. GMM for Speaker Recognition Motivation • Interpretation that Gaussian component represent some general speaker –dependent spectral shapes • Capabilities of Gaussian mixture to model arbitrary densities 8
- 9. Description of SR-using GMM • Speech Analysis • Model Description • Model Interpretations • Maximum Likelihood Parameters Estimation • Speaker Identification 9
- 10. Speech Analysis 10 • Linear predictive coding(LPC) •Mel-scale filter-bank(to reduce noise) Analysis is ended with the generation of Cepstrum coefficients x1 ’, x2 ’ x3’….xn ’ A cepstrum is the result of taking the Inverse Fourier transform (IFT) of the logarithm of the estimated spectrum of a signal. Cosine transform
- 11. 2000/05/03 11 Model Description Gaussian Mixture Density )()|( 1 xbpxp M i ii Where x D-dimensional random vector )()'( 2 1 exp )2( 1 )( 1 212 iii i Di xxxb iiip ,, Mi ,,1 Nodal, Grand,Global Nodal, diagonal (this) Covariance matrix Mean Component Density Speaker Model
- 12. Choice of Covariance Matrix 12 • Nodal Covariance One co-variance matrix per Gaussian component • Grand Covariance One co-variance matrix for all Gaussian component • Global Covariance single co-variance matrix shared by all speaker component
- 13. Model Interpretation • Intuitive notion Acoustic classes(vowels, nasals, fricatives) reflects some general speaker-dependent vocal tract configuration that are useful for characterizing speaker- identity • GMM have ability to form smooth approximation to arbitrary shaped density • It doesn’t only have smooth approx but also multimodal nature of densities 13
- 14. 2000/05/03 14 ML-Parameters Estimation Step: 1. Beginning with an initial model 2. Estimate a new model such that Mixture density 3. Repeated 2. until certain threshold is reached. …Maximum Likelihood )|()|( XpXp
- 15. 2000/05/03 15 (Mixture Weights) (Means) (Variances) T t ti xip T p 1 ),|( 1 T t t T t tt i xip xxip 1 1 ),|( ),|( 2 1 1 2 2 ),|( ),|( iT t t T t tt i xip xxip M k tkk tii t xbp xbp xip 1 )( )( ),|( Mixture Density Component Density and refers to arbitrary elements of vectors ii ,2 and tx ii ','2 'tx and
- 16. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 ANEMIA PATIENTS AND CONTROLS Red Blood Cell Volume RedBloodCellHemoglobinConcentration
- 17. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration EM ITERATION 1
- 18. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration EM ITERATION 3
- 19. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration EM ITERATION 5
- 20. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration EM ITERATION 10
- 21. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration EM ITERATION 15
- 22. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration EM ITERATION 25
- 23. 0 5 10 15 20 25 400 410 420 430 440 450 460 470 480 490 LOG-LIKELIHOOD AS A FUNCTION OF EM ITERATIONS EM Iteration Log-Likelihood
- 24. 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 Red Blood Cell Volume RedBloodCellHemoglobinConcentration ANEMIA DATA WITH LABELS Anemia Group Control Group
- 25. 2000/05/03 25 Speaker Identification A group of speakers S = {1,2,…,S} is represented by GMM’s λ1, λ2, …, λs, the obective is to find the speaker model which has the maximum a posteriori probability for a given observation sequence )( )Pr()|( maxarg)|Pr(maxargˆ 11 Xp Xp XS kk Sk k Sk )|(maxargˆ 1 k Sk XpS )|(logmaxargˆ 1 1 kt T t Sk xpS T t tiikt xbpxp 1 )()|( which logtake
- 26. References D. A. Reynolds and R. C. Rose, “Robust Text- Independent Speaker Identification Using Gaussian Mixture Speaker Models”, IEEE Trans. on Speech and Audio Processing, vol.3, No.1, pp.72-83,January 1995. • http://en.wikipedia.org/wiki/Probability_density_function • http://crsouza.blogspot.com/2010/10/gaussian-mixture- models-and-expectation.html • https://www.ll.mit.edu/mission/communications/ist/publications /0802_Reynolds_Biometrics-GMM.pdf • http://statweb.stanford.edu/~tibs/stat315a/LECTURES/em.pdf • http://eprints.pascal network.org/archive/00008291/01/SoftAssignReconstr_ICIP20 11.pdf • http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/km eans.html 26

- Linear predictive coding (LPC) is a tool used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive model. It is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate and provides extremely accurate estimates of speech parameters.