This paper presents an Adaptive Neuro-Fuzzy
Inference System (ANFIS) model for fractal image
compression. One of the image compression techniques in
the spatial domain is Fractal Image Compression (FIC)
but the main drawback of FIC using traditional
exhaustive search is that it involves more computational
time due to global search. In order to improve the
computational time and compression ratio, artificial
intelligence technique like ANFIS has been used. Feature
extraction reduces the dimensionality of the problem and
enables the ANFIS network to be trained on an image
separate from the test image thus reducing the
computational time. Lowering the dimensionality of the
problem reduces the computations required during the
search. The main advantage of ANFIS network is that it
can adapt itself from the training data and produce a
fuzzy inference system. The network adapts itself
according to the distribution of feature space observed
during training. Computer simulations reveal that the
network has been properly trained and the fuzzy system
thus evolved, classifies the domains correctly with
minimum deviation which helps in encoding the image
using FIC.
Image compression using embedded zero tree waveletsipij
Compressing an image is significantly different than compressing raw binary data. compressing images is
used by this different compression algorithm. Wavelet transforms used in Image compression methods to
provide high compression rates while maintaining good image quality. Discrete Wavelet Transform (DWT)
is one of the most common methods used in signal and image compression .It is very powerful compared to
other transform because its ability to represent any type of signals both in time and frequency domain
simultaneously. In this paper, we will moot the use of Wavelet Based Image compression algorithm-
Embedded Zerotree Wavelet (EZW). We will obtain a bit stream with increasing accuracy from ezw
algorithm because of basing on progressive encoding to compress an image into . All the numerical results
were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing
Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Lena Image .Experimental
results beam that the method is fast, robust and efficient enough to implement it in still and complex images
with significant image compression.
Image Splicing Detection involving Moment-based Feature Extraction and Classi...IDES Editor
In the modern age, the digital image has taken
the place of the original analog photograph, and the forgery
of digital images has become increasingly easy, and harder
to detect. Image splicing is the process of making a
composite picture by cutting and joining two or more
photographs. An approach to efficient image splicing
detection is proposed here. The spliced image often
introduces a number of sharp transitions such as lines,
edges and corners. Phase congruency is a sensitive measure
of these sharp transitions and is hence proposed as a
feature for splicing detection. Statistical moments of
characteristic functions of wavelet sub-bands have been
examined to detect the differences between the authentic
images and spliced images. Image splicing detection can be
treated as a two-class pattern recognition problem, which
builds the model using moment features and some other
parameters extracted from the given test image. Artificial
neural network (ANN) is chosen as a classifier to train and
test the given images.
The document discusses image recognition using convolutional neural networks (CNNs). It explains that CNNs consist of multiple layers of small neuron collections that look at small portions of an input image called receptive fields. The results are tiled to overlap and represent the original image better. CNNs learn filters through training rather than relying on hand-engineered features. Convolution involves calculating the overlap between functions as one is translated, and is used in CNNs to identify patterns across translated versions of inputs like images. Pointwise nonlinearities are applied between CNN layers to introduce nonlinearity.
Advanced applications of artificial intelligence and neural networksPraveen Kumar
The document summarizes a presentation given by Rajarajeshwari K Divate on advanced applications of artificial intelligence and neural networks. It provides an overview of artificial intelligence and neural networks, including definitions and key concepts. Specifically, it discusses biological neural networks, artificial neural networks, Hopfield networks, and an application of Hopfield networks for early lung cancer detection using sputum image segmentation. The presentation covers topics like network architecture, energy functions, features of Hopfield networks, and the Hopfield neural network segmentation algorithm.
The document discusses image captioning using deep neural networks. It begins by providing examples of how humans can easily describe images but generating image captions with a computer program was previously very difficult. Recent advances in deep learning, specifically using convolutional neural networks (CNNs) to recognize objects in images and recurrent neural networks (RNNs) to generate captions, have enabled automated image captioning. The document discusses CNN and RNN architectures for image captioning and provides examples of pre-trained models that can be used, such as VGG-16.
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
This document summarizes a technical seminar on using convolutional neural networks for P300 detection in brain-computer interfaces. The seminar covers an introduction to brain-computer interfaces and the P300 signal, describes existing P300 detection systems and the convolutional neural network approach, and presents the network architecture, learning process, evaluation results on two datasets showing improved detection rates over other methods, and conclusions. The seminar demonstrates that the convolutional neural network approach outperforms existing methods for P300 detection, especially with a limited number of electrodes or training epochs.
The document discusses character recognition using convolutional neural networks. It begins with an introduction to classifiers and gradient-based learning methods. It then describes how multiple perceptrons can be combined into a multilayer perceptron and trained using backpropagation. Next, it introduces convolutional neural networks, which offer improvements over multilayer perceptrons in performance, accuracy, and distortion invariance. It provides details on the topology and training of convolutional neural networks. Finally, it discusses the LeNet-5 convolutional neural network and its successful application to handwritten digit recognition.
Image compression using embedded zero tree waveletsipij
Compressing an image is significantly different than compressing raw binary data. compressing images is
used by this different compression algorithm. Wavelet transforms used in Image compression methods to
provide high compression rates while maintaining good image quality. Discrete Wavelet Transform (DWT)
is one of the most common methods used in signal and image compression .It is very powerful compared to
other transform because its ability to represent any type of signals both in time and frequency domain
simultaneously. In this paper, we will moot the use of Wavelet Based Image compression algorithm-
Embedded Zerotree Wavelet (EZW). We will obtain a bit stream with increasing accuracy from ezw
algorithm because of basing on progressive encoding to compress an image into . All the numerical results
were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing
Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Lena Image .Experimental
results beam that the method is fast, robust and efficient enough to implement it in still and complex images
with significant image compression.
Image Splicing Detection involving Moment-based Feature Extraction and Classi...IDES Editor
In the modern age, the digital image has taken
the place of the original analog photograph, and the forgery
of digital images has become increasingly easy, and harder
to detect. Image splicing is the process of making a
composite picture by cutting and joining two or more
photographs. An approach to efficient image splicing
detection is proposed here. The spliced image often
introduces a number of sharp transitions such as lines,
edges and corners. Phase congruency is a sensitive measure
of these sharp transitions and is hence proposed as a
feature for splicing detection. Statistical moments of
characteristic functions of wavelet sub-bands have been
examined to detect the differences between the authentic
images and spliced images. Image splicing detection can be
treated as a two-class pattern recognition problem, which
builds the model using moment features and some other
parameters extracted from the given test image. Artificial
neural network (ANN) is chosen as a classifier to train and
test the given images.
The document discusses image recognition using convolutional neural networks (CNNs). It explains that CNNs consist of multiple layers of small neuron collections that look at small portions of an input image called receptive fields. The results are tiled to overlap and represent the original image better. CNNs learn filters through training rather than relying on hand-engineered features. Convolution involves calculating the overlap between functions as one is translated, and is used in CNNs to identify patterns across translated versions of inputs like images. Pointwise nonlinearities are applied between CNN layers to introduce nonlinearity.
Advanced applications of artificial intelligence and neural networksPraveen Kumar
The document summarizes a presentation given by Rajarajeshwari K Divate on advanced applications of artificial intelligence and neural networks. It provides an overview of artificial intelligence and neural networks, including definitions and key concepts. Specifically, it discusses biological neural networks, artificial neural networks, Hopfield networks, and an application of Hopfield networks for early lung cancer detection using sputum image segmentation. The presentation covers topics like network architecture, energy functions, features of Hopfield networks, and the Hopfield neural network segmentation algorithm.
The document discusses image captioning using deep neural networks. It begins by providing examples of how humans can easily describe images but generating image captions with a computer program was previously very difficult. Recent advances in deep learning, specifically using convolutional neural networks (CNNs) to recognize objects in images and recurrent neural networks (RNNs) to generate captions, have enabled automated image captioning. The document discusses CNN and RNN architectures for image captioning and provides examples of pre-trained models that can be used, such as VGG-16.
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
This document summarizes a technical seminar on using convolutional neural networks for P300 detection in brain-computer interfaces. The seminar covers an introduction to brain-computer interfaces and the P300 signal, describes existing P300 detection systems and the convolutional neural network approach, and presents the network architecture, learning process, evaluation results on two datasets showing improved detection rates over other methods, and conclusions. The seminar demonstrates that the convolutional neural network approach outperforms existing methods for P300 detection, especially with a limited number of electrodes or training epochs.
The document discusses character recognition using convolutional neural networks. It begins with an introduction to classifiers and gradient-based learning methods. It then describes how multiple perceptrons can be combined into a multilayer perceptron and trained using backpropagation. Next, it introduces convolutional neural networks, which offer improvements over multilayer perceptrons in performance, accuracy, and distortion invariance. It provides details on the topology and training of convolutional neural networks. Finally, it discusses the LeNet-5 convolutional neural network and its successful application to handwritten digit recognition.
Here is my class on the multilayer perceptron where I look at the following:
1.- The entire backproagation algorithm based in the gradient descent
However, I am planning the tanning based in Kalman filters.
2.- The use of matrix computations to simplify the implementations.
I hope you enjoy it.
Image Compression using Combined Approach of EZW and LZWIJERA Editor
Image Processing is most popular and widely used technique. In this paper we had proposed a technique for image compression. Here the user can return the processed image to the original image without any loss. In this proposed technique the test images are processed through the image compression algorithm. Here we apply combined approach of EZW and LZW. This technique is implemented on different types of images like .bmp, .tiff,.dcm (medical images),binary images. Performance of the proposed technique can be evaluated compared to LBG techniques by the parameters like PSNR, Compression ratio and MSE
This document provides an overview of convolutional neural networks and summarizes four popular CNN architectures: AlexNet, VGG, GoogLeNet, and ResNet. It explains that CNNs are made up of convolutional and subsampling layers for feature extraction followed by dense layers for classification. It then briefly describes key aspects of each architecture like ReLU activation, inception modules, residual learning blocks, and their performance on image classification tasks.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
The document summarizes Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). It discusses how CNNs use kernels and pooling to extract features from images while reducing parameters. It provides examples of CNN architectures and visualizations of weights and activations. RNNs are described as allowing input/output sequences, with LSTMs addressing the vanishing gradient problem. Applications discussed include image captioning using CNN features with an RNN generator.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
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.
This document describes a neural network model for generating image captions to help visually impaired people understand images. A convolutional neural network extracts image features, which are fed into a recurrent neural network or long short-term memory network to generate natural language captions. The model achieves state-of-the-art performance on image captioning tasks and has the potential to greatly improve the lives of visually impaired individuals by allowing them to understand images through automatically generated captions.
P03 neural networks cvpr2012 deep learning methods for visionzukun
This document provides an overview of neural networks for computer vision tasks. It discusses using neural networks to build an object recognition system from raw pixels to labels in an end-to-end manner with no distinction between feature extraction and classification. The key ideas are to learn features from data, use differentiable functions to efficiently compute and train features, and use a "deep" architecture of simpler non-linear modules. Building complex functions from simple building blocks like logistic regression allows constructing highly non-linear systems for tasks like vision.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
This document provides an overview of convolutional neural networks (CNNs). It describes that CNNs are a type of deep learning model used in computer vision tasks. The key components of a CNN include convolutional layers that extract features, pooling layers that reduce spatial size, and fully-connected layers at the end for classification. Convolutional layers apply learnable filters in a local receptive field, while pooling layers perform downsampling. The document outlines common CNN architectures, such as types of layers, hyperparameters like stride and padding, and provides examples to illustrate how CNNs work.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
This document proposes a linear recurrent convolutional neural network model for segment-based multiple object tracking in video. The model takes images as input and uses a CNN to classify superpixels, then performs segmentation and uses nonlinear NNs and a linear recurrent tracker layer to match segments over time. The objectives are to improve the tracker layer efficiency by modifying the matrix inverse and determine parameters for the model. Evaluation will use a dataset with ground truth segmentation and optical flow to train and compare to state-of-the-art methods.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
AI&BigData Lab 2016. Александр Баев: Transfer learning - зачем, как и где.GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Поговорим об одной из базовых практических техник обучения нейронных сетей - предобучение, finetuning, transfer learning. В каких случаях применять, какие модели использовать, где их брать и как адаптировать.
This document provides an overview of neural networks. It discusses that artificial neural networks (ANNs) are computational models inspired by the human nervous system. ANNs are composed of interconnected processing units (neurons) that learn by example. There are typically three layers in a neural network: an input layer, hidden layers that process inputs, and an output layer. Neural networks can learn complex patterns and are used for applications like pattern recognition. The document also describes how biological neurons function and the key components of artificial neurons and neural network models. It explains different learning methods for neural networks including supervised, unsupervised, and reinforcement learning.
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
Enhanced MPPT Technique For DC-DC Luo Converter Using Model Predictive Contro...IJERD Editor
The present study explored an enhanced maximum power point tracking technique which ensures fast tracking in PV systems. This system represents a Model Predictive Control (MPC) MPPT technique. Extracting the maximum power from PV systems has been widely investigated. The main benefaction of this article is an improvement of the Perturb and Observe (P&O) method through a fixed step predictive control under measured fast solar radiation. The preferred predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the DC-DC Luo converter. Comparing the improved technique to the conventional P&O method indicates significant improvement in PV system performance. The proposed MPC-MPPT technique for a Luo converter is implemented using the MAT LAB SIMULINK
1. The document describes a hybrid wind and photovoltaic (PV) system connected to the main grid. It proposes using an adaptive neuro-fuzzy inference system (ANFIS) for maximum power point tracking (MPPT) of the PV array and a fuzzy logic controller for pitch angle control of the wind turbine.
2. Simulation results show that the ANFIS MPPT controller can track the maximum power point with less fluctuation and faster convergence compared to traditional MPPT methods. The fuzzy logic pitch controller provides faster response for high wind speeds, improving dynamic performance and preventing damage.
3. The complete hybrid system model is implemented in Matlab/Simulink, including the PV and wind systems, power converters
Here is my class on the multilayer perceptron where I look at the following:
1.- The entire backproagation algorithm based in the gradient descent
However, I am planning the tanning based in Kalman filters.
2.- The use of matrix computations to simplify the implementations.
I hope you enjoy it.
Image Compression using Combined Approach of EZW and LZWIJERA Editor
Image Processing is most popular and widely used technique. In this paper we had proposed a technique for image compression. Here the user can return the processed image to the original image without any loss. In this proposed technique the test images are processed through the image compression algorithm. Here we apply combined approach of EZW and LZW. This technique is implemented on different types of images like .bmp, .tiff,.dcm (medical images),binary images. Performance of the proposed technique can be evaluated compared to LBG techniques by the parameters like PSNR, Compression ratio and MSE
This document provides an overview of convolutional neural networks and summarizes four popular CNN architectures: AlexNet, VGG, GoogLeNet, and ResNet. It explains that CNNs are made up of convolutional and subsampling layers for feature extraction followed by dense layers for classification. It then briefly describes key aspects of each architecture like ReLU activation, inception modules, residual learning blocks, and their performance on image classification tasks.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
The document summarizes Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). It discusses how CNNs use kernels and pooling to extract features from images while reducing parameters. It provides examples of CNN architectures and visualizations of weights and activations. RNNs are described as allowing input/output sequences, with LSTMs addressing the vanishing gradient problem. Applications discussed include image captioning using CNN features with an RNN generator.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
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.
This document describes a neural network model for generating image captions to help visually impaired people understand images. A convolutional neural network extracts image features, which are fed into a recurrent neural network or long short-term memory network to generate natural language captions. The model achieves state-of-the-art performance on image captioning tasks and has the potential to greatly improve the lives of visually impaired individuals by allowing them to understand images through automatically generated captions.
P03 neural networks cvpr2012 deep learning methods for visionzukun
This document provides an overview of neural networks for computer vision tasks. It discusses using neural networks to build an object recognition system from raw pixels to labels in an end-to-end manner with no distinction between feature extraction and classification. The key ideas are to learn features from data, use differentiable functions to efficiently compute and train features, and use a "deep" architecture of simpler non-linear modules. Building complex functions from simple building blocks like logistic regression allows constructing highly non-linear systems for tasks like vision.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
This document provides an overview of convolutional neural networks (CNNs). It describes that CNNs are a type of deep learning model used in computer vision tasks. The key components of a CNN include convolutional layers that extract features, pooling layers that reduce spatial size, and fully-connected layers at the end for classification. Convolutional layers apply learnable filters in a local receptive field, while pooling layers perform downsampling. The document outlines common CNN architectures, such as types of layers, hyperparameters like stride and padding, and provides examples to illustrate how CNNs work.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
This document proposes a linear recurrent convolutional neural network model for segment-based multiple object tracking in video. The model takes images as input and uses a CNN to classify superpixels, then performs segmentation and uses nonlinear NNs and a linear recurrent tracker layer to match segments over time. The objectives are to improve the tracker layer efficiency by modifying the matrix inverse and determine parameters for the model. Evaluation will use a dataset with ground truth segmentation and optical flow to train and compare to state-of-the-art methods.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
AI&BigData Lab 2016. Александр Баев: Transfer learning - зачем, как и где.GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Поговорим об одной из базовых практических техник обучения нейронных сетей - предобучение, finetuning, transfer learning. В каких случаях применять, какие модели использовать, где их брать и как адаптировать.
This document provides an overview of neural networks. It discusses that artificial neural networks (ANNs) are computational models inspired by the human nervous system. ANNs are composed of interconnected processing units (neurons) that learn by example. There are typically three layers in a neural network: an input layer, hidden layers that process inputs, and an output layer. Neural networks can learn complex patterns and are used for applications like pattern recognition. The document also describes how biological neurons function and the key components of artificial neurons and neural network models. It explains different learning methods for neural networks including supervised, unsupervised, and reinforcement learning.
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
Enhanced MPPT Technique For DC-DC Luo Converter Using Model Predictive Contro...IJERD Editor
The present study explored an enhanced maximum power point tracking technique which ensures fast tracking in PV systems. This system represents a Model Predictive Control (MPC) MPPT technique. Extracting the maximum power from PV systems has been widely investigated. The main benefaction of this article is an improvement of the Perturb and Observe (P&O) method through a fixed step predictive control under measured fast solar radiation. The preferred predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the DC-DC Luo converter. Comparing the improved technique to the conventional P&O method indicates significant improvement in PV system performance. The proposed MPC-MPPT technique for a Luo converter is implemented using the MAT LAB SIMULINK
1. The document describes a hybrid wind and photovoltaic (PV) system connected to the main grid. It proposes using an adaptive neuro-fuzzy inference system (ANFIS) for maximum power point tracking (MPPT) of the PV array and a fuzzy logic controller for pitch angle control of the wind turbine.
2. Simulation results show that the ANFIS MPPT controller can track the maximum power point with less fluctuation and faster convergence compared to traditional MPPT methods. The fuzzy logic pitch controller provides faster response for high wind speeds, improving dynamic performance and preventing damage.
3. The complete hybrid system model is implemented in Matlab/Simulink, including the PV and wind systems, power converters
Performance Analysis of Positive Output Super-Lift Re-Lift Luo Converter With...IOSR Journals
This document presents a simulation study comparing PI and neural network (neuro) controllers for regulating the output voltage of a positive output super-lift re-lift Luo converter. The converter circuit and operating principles are described. Simulation results using MATLAB show the performance of each controller under startup conditions and in response to changes in input voltage and load. The neuro controller provided faster response times, while the PI controller produced less overshoot. Tables compare the performance of the PI controller under different operating conditions.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
This document discusses using neuro-fuzzy techniques to study the role of flexibility in improving the performance of flexible manufacturing systems (FMS). It outlines the objectives, motivation, and literature review on FMS, artificial intelligence, and neuro-fuzzy systems. The methodology involves collecting data, implementing an adaptive neuro-fuzzy inference system (ANFIS) for training, analyzing the results, and reporting findings to determine if neuro-fuzzy techniques can help design more cost-effective FMS for Indian industries. An implementation plan with timeline is also provided.
Partial Shading Detection and MPPT Controller for Total Cross Tied Photovolta...IDES Editor
This paper present Maximum Power Point Tracking
(MPPT) controller for solving partial shading problems in
photovoltaic (PV) systems. It is well-known that partial shading
is often encountered in PV system issue with many
consequences. In this research, PV array is connected using
TCT (total cross-tied) configuration including sensors to
measure voltage and currents. The sensors provide inputs for
MPPT controller in order to achieve optimum output power.
The Adaptive Neuro Fuzzy Inference System (ANFIS) is
utilized in this paper as the controller methods. Then, the
output of MPPT controller is the optimum power duty cycle
(α) to drive the performance DC-DC converter. The simulation
shows that the proposed MPPT controller can provide PV
voltage (VMPP) nearly to the maximum power point voltage.
The accuracy of our proposed method is measured by
performance index defined as Mean Absolute Percentage Error
(MAPE). In addition, the main purpose of this work is to
present a new method for detecting partial condition of
photovoltaic TCT configuration using only 3 sensors. Thus,
this method can streamline the time and reduce operating
costs.
This document proposes using an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by a Genetic Algorithm (GA) to track the maximum power point (MPP) of a photovoltaic (PV) system under varying weather conditions. Training data for the ANFIS is optimized by the GA. Simulation results in Matlab/Simulink show the proposed method achieves minimal error in tracking the optimal voltage (Vmpp) at the MPP with better performance than traditional tracking methods.
Anfis controller for solar powered cascade multilevel inverter 2IAEME Publication
This document discusses using an ANFIS (Adaptive Neuro Fuzzy Inference System) controller for a solar powered cascaded multilevel inverter. A cascaded multilevel inverter can improve power quality by producing an output voltage waveform that more closely resembles a sine wave. An ANFIS controller is proposed to regulate the switching of the inverter devices in order to reduce total harmonic distortion (THD) compared to a conventional controller. The performance of the ANFIS controller is evaluated through MATLAB/Simulink simulations. Photovoltaic cells are also modeled and connected to the cascaded multilevel inverter to power it from solar energy.
This document is a thesis that discusses the design and implementation of a solar charge controller IC using Cadence. It contains 6 chapters that cover an introduction, overview of solar photovoltaics, overview of solar charge controllers, challenges of implementing a solar charge controller in Cadence, Cadence implementation, and conclusions. The objective is to replace microcontrollers in commercial solar charge controllers with an analog design using transistors and MOSFETs implemented on a single IC in Cadence to reduce costs and space. The design is simulated in Proteus initially and then implemented in Cadence Virtuoso for schematic and layout design.
This article proposes using an adaptive neuro-fuzzy inference system (ANFIS) optimized by a genetic algorithm (GA) to track the maximum power point (MPP) of a grid-connected photovoltaic (PV) system under changing temperature and irradiance conditions. 360 data points of temperature and irradiance were optimized by GA and then used to train the ANFIS controller. Simulation results showed the ANFIS-GA controller could track the MPP with less fluctuation and faster convergence compared to conventional methods. A P/Q controller was also used to regulate both voltage and current for grid connection.
Design Of Charge Controller Using MPPT AlgorithmIJRES Journal
Recently non-conventional sources are in huge demand than the conventional sources of energy. Solar energy, though it is in great demand but it has low efficiency. So, to increase the efficiency of the system, we need to find the exact MPP. For this we employ a tracker called MPPT. The main aim will be to track the maximum power from the photovoltaic and feed the extracted power to the load via buck-boost converter. The purpose of this converter is to maintain the required voltage magnitude necessary for the load. In this paper, I have used P&O Algorithm to get the maximum power point and for efficiently designing the charge controller.
Design Document - Remote Solar Monitoring and Bateery Optimzation SystemMamoon Ismail Khalid
• Developed a Battery & Solar Photo Voltaic Monitoring System as part of Senior Year undergraduate project
• Ranked among Top 5 projects departmentally
• Through the use of real time data analysis (battery current,voltage, total load, battery capacity) via MATLAB /C++ script, we were able to increase the battery usage life of a standard 100 W battery by up to 100%, which would almost half the costs of any renewable energy system.
Este documento presenta un modelo de red ANFIS para simular el comportamiento dinámico de un bioreactor que produce etanol mediante la fermentación de Zymomonas mobilis. La red ANFIS se entrenó usando datos de simulación del bioproceso y logró predecir variables como la concentración de biomasa, sustrato y producto. Los resultados mostraron que genfis1 produjo predicciones más precisas que genfis2. El modelo ANFIS puede ser útil para comprender la dinámica compleja de este tipo de bioprocesos.
This Paper mainly deals with the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) in Pulse Width Modulation control of Single Ended Primary Inductor Converter (SEPIC). Generally PID, Fuzzy techniques are being used to control DC – DC converter. This paper presents a ANFIS controller based SEPIC converter for maximum power point tracking (MPPT) operation of a photovoltaic (PV) system. The ANFIS controller for the SEPIC MPPT scheme shows a high precision in current transition and keeps the voltage without any changes represented in small steady state error and small overshoot. The proposed scheme ensures optimal use of photovoltaic (PV) array, wind turbine and proves its efficacy in variable load conditions, unity and lagging power factor at the inverter output (load) side. The performance of the proposed ANFIS based MPPT operation of SEPIC converter is compared to those of the conventional PID and Fuzzy based SEPIC converter. The results show that the proposed ANFIS based MPPT scheme for SEPIC can transfer power to about 20 percent (approx) more than conventional system.
This document provides an overview of Adaptive Neural Fuzzy Inference Systems (ANFIS). It discusses how ANFIS aims to integrate the benefits of fuzzy systems and neural networks by using neural network learning methods to determine the parameters of fuzzy inference systems. The document outlines ANFIS architecture and computational complexity. It also describes how ANFIS uses a hybrid learning algorithm with a least squares estimate to identify linear parameters and backpropagation to adjust nonlinear parameters.
The document discusses the advantages and benefits of solar energy, including reduced dependency on fossil fuels, minimal operating costs, and environmental benefits such as reduced carbon emissions and water usage. It provides statistics on the carbon savings and pollution reduction from installing solar panels. The document also outlines the product range of a solar company including solar panels, charge controllers, inverters, and monitoring software.
A solar charge controller regulates voltage and current from solar panels to batteries to prevent overcharging. It uses op-amps, MOSFETs, diodes and other components. Solar panels produce more than 12 volts, so the controller ensures batteries charge to 14-14.5 volts. It also protects against overcharging, deep discharge, and undervoltage. Charge controllers are used in solar home systems, street lights, hybrid power systems, and water pumps to store solar energy in batteries.
A brief overview of PV market globally and regionally is presented and how it has disrupted the current network business model. Energy Storage has become a necessity as penetration of PV in the current network increases and created challenging ramping issues as the daily load curves have changed to what is now popularly called “Duck” curves. The value of energy storage in the network is presented, clearly demonstrating that maximum value is realised at end users, commercial and residential. Battery storage is one of the most practical option. Commercial battery technologies are presented, followed by selected case studies.
Neural networks and deep learning are machine learning techniques inspired by the human brain. Neural networks consist of interconnected nodes that process input data and pass signals to other nodes. The main types discussed are artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). ANNs can learn nonlinear relationships between inputs and outputs. CNNs are effective for image processing by learning relevant spatial features. RNNs capture sequential dependencies in data like text. Deep learning uses neural networks with many layers to learn complex patterns in large datasets.
Convolutional neural networks (CNNs) are a type of neural network that use local receptive fields, shared weights, and pooling to process input images. CNNs preserve the spatial structure of images using local receptive fields that are connected to small regions of the input image. Shared weights and biases are used across these local receptive fields to detect the same features in different locations. Pooling layers simplify the output of convolutional layers by downsampling feature maps. RNNs are useful for tasks involving sequential data like text by incorporating information about previous inputs/computations through a memory-like mechanism. Word embeddings represent words as dense vectors that are learned from surrounding context in text.
Deep learning is a type of machine learning that uses neural networks with multiple layers to progressively extract higher-level features from raw input. Lower layers may identify simple elements like edges in images while higher layers identify more complex concepts like digits or faces. Deep learning models learn representations of data by using backpropagation to indicate how a machine should change its internal parameters to best fit the training data. Convolutional neural networks are a type of deep learning model that use convolution operations to identify patterns in grid-like data like images or text.
Deferred Pixel Shading on the PLAYSTATION®3Slide_N
This document summarizes a deferred pixel shading algorithm implemented on the PlayStation 3 system. The algorithm runs pixel shaders on the Synergistic Processing Elements of the Cell processor concurrently with the GPU for rendering images. Experimental results found that running the pixel shading on 5 SPEs achieved a performance of up to 85Hz at 720p resolution, comparable to running on a high-end GPU. This indicates that the Cell processor can effectively enhance GPU performance by offloading pixel shading work.
This document is an internship report submitted by Raghunandan J to Eckovation about a project on classifying handwritten digits using a convolutional neural network. It provides an introduction to convolutional neural networks and explains each layer of a CNN including the input, convolutional layer, pooling layer, and fully connected layer. It also gives examples of real-world applications that use artificial neural networks like Google Maps, Google Images, and voice assistants.
This document provides an internship report on classifying handwritten digits using a convolutional neural network. It includes an abstract, introduction on CNNs, explanations of CNN layers including convolution, pooling and fully connected layers. It also discusses padding and applications of CNNs such as computer vision, image recognition and natural language processing.
This document discusses comparing the performance of different convolutional neural networks (CNNs) when trained on large image datasets using Apache Spark. It summarizes the datasets used - CIFAR-10 and ImageNet - and preprocessing done to standardize image sizes. It then provides an overview of CNN architecture, including convolutional layers, pooling layers, and fully connected layers. Finally, it introduces SparkNet, a framework that allows training deep networks using Spark by wrapping Caffe and providing tools for distributed deep learning on Spark. The goal is to see if SparkNet can provide faster training times compared to a single machine by distributing training across a cluster.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
Image Compression Using Wavelet Packet TreeIDES Editor
Methods of compressing data prior to storage and
transmission are of significant practical and commercial
interest. The necessity in image compression continuously
grows during the last decade. The image compression includes
transform of image, quantization and encoding. One of the
most powerful and perspective approaches in this area is
image compression using discrete wavelet transform. This
paper describes a new approach called as wavelet packet tree
for image compression. It constructs the best tree on the basis
of Shannon entropy. This new approach checks the entropy of
decomposed nodes (child nodes) with entropy of node, which
has been decomposed (parent node) and takes the decision of
decomposition of a node. In addition, authors have proposed
an adaptive thresholding for quantization, which is based on
type of wavelet used and nature of image. Performance of the
proposed algorithm is compared with existing wavelet
transform algorithm in terms of percentage of zeros and
percentage of energy retained and signals to noise ratio.
A broad ranging open access journal Fast and efficient online submission Expe...ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document describes a new method for compressing digital images using fractals. The key points are:
1) Traditional image compression methods can achieve ratios of 2-10x but a new method using fractals has achieved ratios over 10,000x.
2) The method works by breaking an image into segments that are then matched to fractal patterns in a library. Only the codes for the fractal patterns need to be stored, not the full image data.
3) An iterated function system (IFS) uses affine transformations to relate parts of an image. The IFS codes in the library can be used to reconstruct the original image segments. This achieves very high compression ratios.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
An efficient technique for color image classification based on lower feature ...Alexander Decker
This document discusses an efficient technique for color image classification using support vector machines with radial basis functions (SVM-RBF). It presents SVM-RBF as an improvement over other classification methods like SVM with ant colony optimization (SVM-ACO) and directed acyclic graph (SVM-DAG). The paper tests the different classifiers on 600 images across 3 classes, finding SVM-RBF achieved the highest precision and recall rates, with precision of 92.3-94% and recall of 84.8-91%. It concludes SVM-RBF more effectively reduces noise and the semantic gap to enhance image classification performance compared to the other methods.
Image classification is perhaps the most important part of digital image analysis. In this paper, we compare the most widely used model CNN Convolutional Neural Network , and MLP Multilayer Perceptron . We aim to show how both models differ and how both models approach towards the final goal, which is image classification. Souvik Banerjee | Dr. A Rengarajan "Hand-Written Digit Classification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42444.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42444/handwritten-digit-classification/souvik-banerjee
This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
IRJET-Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using a convolutional neural network (CNN) to detect breast cancer from medical images. CNNs are a type of deep learning model that can learn image features without manual feature engineering. The proposed system would take a sample medical image as input, preprocess it, and compare it to images in a database labeled as cancerous or non-cancerous. If cancer is detected, the system would determine the cancer stage and recommend appropriate treatment. The CNN model would be built and trained using libraries like Keras, TensorFlow, and Numpy to classify images and detect breast cancer at early stages for better treatment outcomes.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
IRJET- Image Captioning using Multimodal EmbeddingIRJET Journal
This document proposes a novel methodology to generate a single story or caption from multiple images that share similar context. It combines existing image captioning and natural language processing models. Specifically, it uses a Convolutional Neural Network to extract visual semantics from images and generate captions. It then represents the captions as vectors using Skip Thought vectors or TF-IDF values. These vectors are combined into a matrix and used to generate a new story/caption that shares the context of the input images. The results show that the Skip Thought vector approach achieves better performance based on RMSE and MAE error metrics. The model could potentially be applied to applications like medical diagnosis, crime investigations, and lecture note generation.
- Geoffrey Hinton gives a tutorial on deep belief nets and how to learn multi-layer generative models of unlabeled data by learning one layer of features at a time using restricted Boltzmann machines (RBMs).
- RBMs make it possible to efficiently learn deep generative models one layer at a time by approximating the intractable posterior distribution over hidden units given visible data.
- Layer-by-layer unsupervised pre-training of features followed by discriminative fine-tuning improves classification performance on benchmark datasets like MNIST compared to backpropagation alone.
Similar to Adaptive Neuro-Fuzzy Inference System based Fractal Image Compression (20)
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems