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The document proposes a reversible data hiding scheme that achieves good payload-distortion performance. It finds the optimal rule for modifying host data values under a payload-distortion criterion using an iterative algorithm. This derives an optimal value transfer matrix. The scheme then hides secret data and auxiliary information by modifying the estimation errors of host pixels according to the matrix. It divides the host image into subsets and embeds the auxiliary data of one subset into the next's estimation errors. This allows extraction of the secret data and recovery of the original image subsets in inverse order. The scheme achieves higher payloads than existing methods that modify histograms or can carry only one bit per block.
Presentation: Wind Speed Prediction using Radial Basis Function Neural NetworkArzam Muzaffar Kotriwala
This document describes a project using a radial basis function neural network to predict wind speed. It discusses motivations for wind speed prediction and for using neural networks. It outlines objectives to design and test an RBF network model using historical wind data. The methodology describes collecting wind data, selecting input variables, training the RBF network with different configurations, and comparing its performance to other techniques. Results show the RBF network outperformed persistence and backpropagation models with some configurations achieving more accurate 1-hour ahead predictions. The conclusion discusses findings and recommendations for further improving wind speed prediction.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
WIND SPEED & POWER FORECASTING USING ARTIFICIAL NEURAL NETWORK (NARX) FOR NEW...Journal For Research
Continuous Depleting conventional fuel reserves and its impact as increasing global warming concerns have diverted world attention towards non-conventional energy sources. Out of different non-conventional energy sources wind energy can be consider as one of the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Today Wind energy can play a vital role to meet our energy demands; however, it faces various issues such as intermittent nature and frequency instability. To reduce such issues the knowledge of futuristic weather conditions and wind speed trend are required. This work mainly describes the implementation of NARX Artificial neural network for wind speed & power forecasting with the help of historical data available from wind farms.
This document evaluates different deep learning algorithms and data preprocessing techniques for demand power prediction. It finds that a recurrent neural network model achieves the best prediction performance. All algorithms show improved accuracy when trained on preprocessed data that balances the dimension of power load and weather feature data, rather than raw data of varying dimensions. Further research into prediction using extreme learning machine algorithms is suggested.
This document evaluates the performance of deep learning algorithms and data preprocessing for demand power prediction. Experiments were conducted using raw and preprocessed daily electricity load, temperature, and weather data from Australia. Recurrent neural networks and convolutional neural networks generally had better prediction accuracy when trained on preprocessed rather than raw data. Preprocessing scaled the temperature data and increased the data points, leading to more stable and accurate results across all tested algorithms. The best performance was achieved using a recurrent neural network on preprocessed data. Further analysis of extreme learning machine algorithms was recommended.
Predicting Drug Target Interaction Using Deep Belief NetworkRashim Dhaubanjar
With the advancement in AI field, machine learning methods are being used to train the classifier for separating intractable drug-target pair as it is difficult to classify dockable and non-dockable ligands due to non-linear nature of big-biological data. As deep learning has been shown to produce state-of-the-art results on various tasks, we propose a new approach to predict the interaction between drug and targets efficiently. The DBN is used to extract the high level features from 2D chemical substructure represented in fingerprint format. DBN is trained in a greedy layer-wise unsupervised fashion and the result from this pre-training phase is used to initialize the parameters prior to BP used for fine tuning. Similarly, logistic regression layer is staked as output layer. Then it is fine-tuned using BP of error derivative to build classification model that directly predict whether a drug interacts with a target of interest or not. In addition to this we too propose an approach to reduce the time complexity of training the learning method with the use of GPU which is highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart.
This document summarizes a research paper that implemented Levenberg-Marquardt artificial neural network training using graphics processing unit (GPU) hardware acceleration. The key points are:
1) This appears to be the first description of implementing artificial neural networks using the Levenberg-Marquardt training method on a GPU.
2) The paper describes their approach for implementing the Levenberg-Marquardt algorithm on a GPU, which involves solving the matrix inversion operation that is typically computationally expensive.
3) Results show that training networks using the GPU implementation can be up to 10 times faster than using a CPU-only implementation on the same hardware.
The document proposes a reversible data hiding scheme that achieves good payload-distortion performance. It finds the optimal rule for modifying host data values under a payload-distortion criterion using an iterative algorithm. This derives an optimal value transfer matrix. The scheme then hides secret data and auxiliary information by modifying the estimation errors of host pixels according to the matrix. It divides the host image into subsets and embeds the auxiliary data of one subset into the next's estimation errors. This allows extraction of the secret data and recovery of the original image subsets in inverse order. The scheme achieves higher payloads than existing methods that modify histograms or can carry only one bit per block.
Presentation: Wind Speed Prediction using Radial Basis Function Neural NetworkArzam Muzaffar Kotriwala
This document describes a project using a radial basis function neural network to predict wind speed. It discusses motivations for wind speed prediction and for using neural networks. It outlines objectives to design and test an RBF network model using historical wind data. The methodology describes collecting wind data, selecting input variables, training the RBF network with different configurations, and comparing its performance to other techniques. Results show the RBF network outperformed persistence and backpropagation models with some configurations achieving more accurate 1-hour ahead predictions. The conclusion discusses findings and recommendations for further improving wind speed prediction.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
WIND SPEED & POWER FORECASTING USING ARTIFICIAL NEURAL NETWORK (NARX) FOR NEW...Journal For Research
Continuous Depleting conventional fuel reserves and its impact as increasing global warming concerns have diverted world attention towards non-conventional energy sources. Out of different non-conventional energy sources wind energy can be consider as one of the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Today Wind energy can play a vital role to meet our energy demands; however, it faces various issues such as intermittent nature and frequency instability. To reduce such issues the knowledge of futuristic weather conditions and wind speed trend are required. This work mainly describes the implementation of NARX Artificial neural network for wind speed & power forecasting with the help of historical data available from wind farms.
This document evaluates different deep learning algorithms and data preprocessing techniques for demand power prediction. It finds that a recurrent neural network model achieves the best prediction performance. All algorithms show improved accuracy when trained on preprocessed data that balances the dimension of power load and weather feature data, rather than raw data of varying dimensions. Further research into prediction using extreme learning machine algorithms is suggested.
This document evaluates the performance of deep learning algorithms and data preprocessing for demand power prediction. Experiments were conducted using raw and preprocessed daily electricity load, temperature, and weather data from Australia. Recurrent neural networks and convolutional neural networks generally had better prediction accuracy when trained on preprocessed rather than raw data. Preprocessing scaled the temperature data and increased the data points, leading to more stable and accurate results across all tested algorithms. The best performance was achieved using a recurrent neural network on preprocessed data. Further analysis of extreme learning machine algorithms was recommended.
Predicting Drug Target Interaction Using Deep Belief NetworkRashim Dhaubanjar
With the advancement in AI field, machine learning methods are being used to train the classifier for separating intractable drug-target pair as it is difficult to classify dockable and non-dockable ligands due to non-linear nature of big-biological data. As deep learning has been shown to produce state-of-the-art results on various tasks, we propose a new approach to predict the interaction between drug and targets efficiently. The DBN is used to extract the high level features from 2D chemical substructure represented in fingerprint format. DBN is trained in a greedy layer-wise unsupervised fashion and the result from this pre-training phase is used to initialize the parameters prior to BP used for fine tuning. Similarly, logistic regression layer is staked as output layer. Then it is fine-tuned using BP of error derivative to build classification model that directly predict whether a drug interacts with a target of interest or not. In addition to this we too propose an approach to reduce the time complexity of training the learning method with the use of GPU which is highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart.
This document summarizes a research paper that implemented Levenberg-Marquardt artificial neural network training using graphics processing unit (GPU) hardware acceleration. The key points are:
1) This appears to be the first description of implementing artificial neural networks using the Levenberg-Marquardt training method on a GPU.
2) The paper describes their approach for implementing the Levenberg-Marquardt algorithm on a GPU, which involves solving the matrix inversion operation that is typically computationally expensive.
3) Results show that training networks using the GPU implementation can be up to 10 times faster than using a CPU-only implementation on the same hardware.
The document discusses implementing the Smith-Waterman algorithm for sequence alignment on a GPU. It aims to optimize existing algorithms to reduce computational cost and overhead. The algorithm computes a memory matrix to determine optimal local alignment between sequences using dynamic programming. Future enhancements proposed include shifting memory matrices to shared memory and checking blocks of characters instead of single characters for sequence similarity.
Investigation and Evaluation of Microgrid Optimization TechniquesNevin Sawyer
The document summarizes an investigation into optimizing a microgrid consisting of distributed generators, solar panels, batteries, HVAC units, and loads using mixed integer linear programming and stochastic modeling. The objective was to minimize total costs by determining optimal power levels from each source at each time step under uncertainty. Constraints modeled technical limitations of the sources. Shadow price analysis evaluated the sensitivity of optimized solutions to demand changes and provided insights into the microgrid's economic competitiveness. Stochastic modeling increased accuracy but also complexity compared to a deterministic approach. The techniques were evaluated for practicality and effectiveness in utilizing microgrids.
Target Response Electrical usage Profile Clustering using Big DataIRJET Journal
This document discusses using clustering algorithms to analyze large datasets from smart meters to identify patterns in electricity usage. It proposes a new method for clustering micro-clusters that uses a density graph to explicitly represent the density of data points between micro-clusters. This allows the micro-clusters to be re-clustered into a smaller number of final clusters. The algorithm involves constructing a minimum spanning tree from the density graph, partitioning it into trees representing clusters, and selecting representative features from each micro-cluster. This clustering-based feature subset selection aims to improve the efficiency and accuracy of load profiling and short-term load forecasting using big data from smart meters.
A general weighted_fuzzy_clustering_algorithmTA Minh Thuy
This document proposes a framework for adapting iterative clustering algorithms to handle streaming data. The key ideas are:
1) As data arrives in chunks, cluster each chunk and represent the clustering results as a set of weighted centroids, with the weights indicating the number of data points assigned to each cluster.
2) Add the weighted centroids from previous chunks to the current chunk as it is clustered. This allows the algorithm to incorporate historical information from all previously seen data.
3) The weighted centroids produced by clustering the entire stream can then be used to assign labels or groups to new data points.
Experimental results on a large dataset treated as a stream show the streaming algorithm produces clusters almost identical to clustering all data at once
This document provides a review of evolutionary algorithms that have been used to optimize wireless sensor networks (WSNs). It begins with background on WSNs and discusses common issues like energy efficiency. It then reviews heuristic and metaheuristic approaches that have been used for clustering and routing in WSNs. The main part of the document focuses on four commonly used evolutionary algorithms - genetic algorithms, particle swarm optimization, harmony search algorithm, and flower pollination algorithm. For each algorithm, it provides an overview and details on how the algorithm works and pseudo-code. It concludes that these nature-inspired metaheuristic techniques can help optimize challenges in WSNs like cluster formation and energy consumption better than classical algorithms.
Joint Approach of Routing , Rate Adaptation and Power Control in Wireless Mes...Manjunath Badiger
This document proposes a joint approach for routing, rate adaptation, and power control in wireless mesh networks. It aims to provide an energy-aware rate adaptation technique based on a new routing metric. The objectives are to provide the best routing metric and rate adaptation, achieve minimum delay and shortest routes. It calculates SNR at the MAC layer based on received power and noise. Distance is calculated using node positions. The energy optimization flowchart is presented. Energy is calculated based on distance, transmission power, time, and number of packets using the given formula. This joint approach aims to reduce energy required for transmission in wireless networks.
Joint Approach of Routing , Rate Adaptation and Power Control in Wireless Mes...Manjunath Badiger
This document proposes a joint approach of routing, rate adaptation, and power control in wireless mesh networks. It aims to provide an energy-aware rate adaptation technique based on a new routing metric. The objectives are to provide the best routing metric and rate adaptation, achieve minimum delay and shortest routes. It calculates SNR at the MAC layer based on received power and noise. Distance is calculated using nodes' x,y positions. The energy optimization flowchart is presented. Energy is calculated based on distance, transmission power, time, and number of packets using the given formula.
This document summarizes a project report on optimizing fracking simulations for GPU acceleration. The simulations model hydraulic fracturing and consist of three phases. The focus was on the second phase, which calculates interaction factors and stresses between grid cells and takes 80% of the CPU execution time. This phase was implemented on a GPU using techniques like finding parallelism at the cell and grid level, optimizing data transfers, memory access, and using streams to execute cells concurrently. These optimizations led to speedups of up to 56x compared to the CPU implementation.
Deep Learning Fast MRI Using Channel Attention in Magnitude DomainJoonhyung Lee
My presentation on how we participated in the fastMRI Challanege in 2019.
Aside from theoretical considerations, it also explains key implementation issues that arise in all deep learning for MRI such as disk I/O and CPU/GPU load balancing.
Used for presentation at ISBI 2020 Oral session.
Accidentally wrote the title as "Deep Learning Sum-of-Squares Images in Accelerated Parallel MRI". Sorry for the mistake!
The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).
Advanced Techniques for Mobile RoboticsPrasanth Jaya
This document discusses Gaussian mixture models (GMMs) as an advanced clustering technique for mobile robotics. GMMs assume data points are generated from a mixture of Gaussian distributions rather than fixed clusters. The expectation-maximization (EM) algorithm is used to estimate the parameters of each Gaussian component to maximize the likelihood of the data. EM iterates between assigning data points to components (E-step) and re-estimating the component parameters (M-step) until convergence. GMMs can represent any continuous distribution and are more flexible than k-means clustering, but are also more computationally expensive.
This document discusses using a fuzzy-neural network to forecast electricity demand. It proposes combining a neural network with fuzzy logic to overcome some limitations of only using artificial neural networks (ANNs). Specifically, it implements a fuzzy logic front-end processor to handle both numeric and fuzzy inputs before feeding them to a three-layer backpropagation neural network. This allows the neural network to capture unknown relationships between input variables like temperature, rain forecast, season and day type with the target output of electricity load. The strengths of this hybrid technique are its ability to incorporate both quantitative and qualitative knowledge and to produce more accurate forecasts.
This document discusses using an artificial neural network to forecast electricity demand. It describes preprocessing data, creating a feed-forward neural network model with input, hidden and output layers, and training the model using backpropagation and incremental training. The model is trained on 80% of the data and tested on the remaining 20%. Mean square error is used to evaluate accuracy on both the training and test sets, with a lower error on the test set indicating better generalization of the model to new data. The goal is to accurately forecast future electricity demand based on input variables like population, GDP, price indexes, and past consumption data.
This document presents a GPU-accelerated algorithm for solving the Group Steiner Problem (GSP) which arises in routing phases of VLSI circuit design. The algorithm uses a depth-bounded heuristic to construct approximate minimum-cost Steiner trees in parallel using CUDA-aware MPI. Evaluation on a supercomputer shows the parallel implementation achieves up to 302x speedup over serial algorithms and scales well with problem size and processor count.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
Matlab reversible data hiding with optimal value transferEcway Technologies
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
REVERSIBLE DATA HIDING WITH GOOD PAYLOAD DISTORTIONPpt 3 Parthipan Parthi
This document proposes a reversible data hiding scheme that can embed secret data in images for military and medical applications. It uses an iterative procedure to calculate an optimal value transfer matrix, which allows secret data and original pixel values to be recovered with good payload-distortion performance. The system embeds data by modifying pixel value estimates based on the matrix. It then extracts the secret data and recovers the original image values inversely at the receiver.
The document discusses implementing the Smith-Waterman algorithm for sequence alignment on a GPU. It aims to optimize existing algorithms to reduce computational cost and overhead. The algorithm computes a memory matrix to determine optimal local alignment between sequences using dynamic programming. Future enhancements proposed include shifting memory matrices to shared memory and checking blocks of characters instead of single characters for sequence similarity.
Investigation and Evaluation of Microgrid Optimization TechniquesNevin Sawyer
The document summarizes an investigation into optimizing a microgrid consisting of distributed generators, solar panels, batteries, HVAC units, and loads using mixed integer linear programming and stochastic modeling. The objective was to minimize total costs by determining optimal power levels from each source at each time step under uncertainty. Constraints modeled technical limitations of the sources. Shadow price analysis evaluated the sensitivity of optimized solutions to demand changes and provided insights into the microgrid's economic competitiveness. Stochastic modeling increased accuracy but also complexity compared to a deterministic approach. The techniques were evaluated for practicality and effectiveness in utilizing microgrids.
Target Response Electrical usage Profile Clustering using Big DataIRJET Journal
This document discusses using clustering algorithms to analyze large datasets from smart meters to identify patterns in electricity usage. It proposes a new method for clustering micro-clusters that uses a density graph to explicitly represent the density of data points between micro-clusters. This allows the micro-clusters to be re-clustered into a smaller number of final clusters. The algorithm involves constructing a minimum spanning tree from the density graph, partitioning it into trees representing clusters, and selecting representative features from each micro-cluster. This clustering-based feature subset selection aims to improve the efficiency and accuracy of load profiling and short-term load forecasting using big data from smart meters.
A general weighted_fuzzy_clustering_algorithmTA Minh Thuy
This document proposes a framework for adapting iterative clustering algorithms to handle streaming data. The key ideas are:
1) As data arrives in chunks, cluster each chunk and represent the clustering results as a set of weighted centroids, with the weights indicating the number of data points assigned to each cluster.
2) Add the weighted centroids from previous chunks to the current chunk as it is clustered. This allows the algorithm to incorporate historical information from all previously seen data.
3) The weighted centroids produced by clustering the entire stream can then be used to assign labels or groups to new data points.
Experimental results on a large dataset treated as a stream show the streaming algorithm produces clusters almost identical to clustering all data at once
This document provides a review of evolutionary algorithms that have been used to optimize wireless sensor networks (WSNs). It begins with background on WSNs and discusses common issues like energy efficiency. It then reviews heuristic and metaheuristic approaches that have been used for clustering and routing in WSNs. The main part of the document focuses on four commonly used evolutionary algorithms - genetic algorithms, particle swarm optimization, harmony search algorithm, and flower pollination algorithm. For each algorithm, it provides an overview and details on how the algorithm works and pseudo-code. It concludes that these nature-inspired metaheuristic techniques can help optimize challenges in WSNs like cluster formation and energy consumption better than classical algorithms.
Joint Approach of Routing , Rate Adaptation and Power Control in Wireless Mes...Manjunath Badiger
This document proposes a joint approach for routing, rate adaptation, and power control in wireless mesh networks. It aims to provide an energy-aware rate adaptation technique based on a new routing metric. The objectives are to provide the best routing metric and rate adaptation, achieve minimum delay and shortest routes. It calculates SNR at the MAC layer based on received power and noise. Distance is calculated using node positions. The energy optimization flowchart is presented. Energy is calculated based on distance, transmission power, time, and number of packets using the given formula. This joint approach aims to reduce energy required for transmission in wireless networks.
Joint Approach of Routing , Rate Adaptation and Power Control in Wireless Mes...Manjunath Badiger
This document proposes a joint approach of routing, rate adaptation, and power control in wireless mesh networks. It aims to provide an energy-aware rate adaptation technique based on a new routing metric. The objectives are to provide the best routing metric and rate adaptation, achieve minimum delay and shortest routes. It calculates SNR at the MAC layer based on received power and noise. Distance is calculated using nodes' x,y positions. The energy optimization flowchart is presented. Energy is calculated based on distance, transmission power, time, and number of packets using the given formula.
This document summarizes a project report on optimizing fracking simulations for GPU acceleration. The simulations model hydraulic fracturing and consist of three phases. The focus was on the second phase, which calculates interaction factors and stresses between grid cells and takes 80% of the CPU execution time. This phase was implemented on a GPU using techniques like finding parallelism at the cell and grid level, optimizing data transfers, memory access, and using streams to execute cells concurrently. These optimizations led to speedups of up to 56x compared to the CPU implementation.
Deep Learning Fast MRI Using Channel Attention in Magnitude DomainJoonhyung Lee
My presentation on how we participated in the fastMRI Challanege in 2019.
Aside from theoretical considerations, it also explains key implementation issues that arise in all deep learning for MRI such as disk I/O and CPU/GPU load balancing.
Used for presentation at ISBI 2020 Oral session.
Accidentally wrote the title as "Deep Learning Sum-of-Squares Images in Accelerated Parallel MRI". Sorry for the mistake!
The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).
Advanced Techniques for Mobile RoboticsPrasanth Jaya
This document discusses Gaussian mixture models (GMMs) as an advanced clustering technique for mobile robotics. GMMs assume data points are generated from a mixture of Gaussian distributions rather than fixed clusters. The expectation-maximization (EM) algorithm is used to estimate the parameters of each Gaussian component to maximize the likelihood of the data. EM iterates between assigning data points to components (E-step) and re-estimating the component parameters (M-step) until convergence. GMMs can represent any continuous distribution and are more flexible than k-means clustering, but are also more computationally expensive.
This document discusses using a fuzzy-neural network to forecast electricity demand. It proposes combining a neural network with fuzzy logic to overcome some limitations of only using artificial neural networks (ANNs). Specifically, it implements a fuzzy logic front-end processor to handle both numeric and fuzzy inputs before feeding them to a three-layer backpropagation neural network. This allows the neural network to capture unknown relationships between input variables like temperature, rain forecast, season and day type with the target output of electricity load. The strengths of this hybrid technique are its ability to incorporate both quantitative and qualitative knowledge and to produce more accurate forecasts.
This document discusses using an artificial neural network to forecast electricity demand. It describes preprocessing data, creating a feed-forward neural network model with input, hidden and output layers, and training the model using backpropagation and incremental training. The model is trained on 80% of the data and tested on the remaining 20%. Mean square error is used to evaluate accuracy on both the training and test sets, with a lower error on the test set indicating better generalization of the model to new data. The goal is to accurately forecast future electricity demand based on input variables like population, GDP, price indexes, and past consumption data.
This document presents a GPU-accelerated algorithm for solving the Group Steiner Problem (GSP) which arises in routing phases of VLSI circuit design. The algorithm uses a depth-bounded heuristic to construct approximate minimum-cost Steiner trees in parallel using CUDA-aware MPI. Evaluation on a supercomputer shows the parallel implementation achieves up to 302x speedup over serial algorithms and scales well with problem size and processor count.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
Matlab reversible data hiding with optimal value transferEcway Technologies
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
REVERSIBLE DATA HIDING WITH GOOD PAYLOAD DISTORTIONPpt 3 Parthipan Parthi
This document proposes a reversible data hiding scheme that can embed secret data in images for military and medical applications. It uses an iterative procedure to calculate an optimal value transfer matrix, which allows secret data and original pixel values to be recovered with good payload-distortion performance. The system embeds data by modifying pixel value estimates based on the matrix. It then extracts the secret data and recovers the original image values inversely at the receiver.
Reversible Image Data Hiding with Contrast EnhancementIRJET Journal
This document proposes a reversible image data hiding technique with contrast enhancement. It aims to embed data into a cover image in a reversible manner while also enhancing the contrast of the cover image. The technique first calculates prediction errors of pixel values in the cover image. It then generates a histogram of the prediction errors and selects carriers for data embedding from peaks in the histogram. Binary secret data is embedded into the carriers by dynamically shifting the prediction error histogram. This allows data to be embedded while increasing cover image quality compared to other reversible data hiding methods. The original cover image can be recovered by extracting the embedded data and reversing the histogram shifts. The technique is meant to achieve a higher peak signal-to-noise ratio than the original cover image after data
An enhanced difference pair mapping steganography method to improve embedding...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Encryption and Authentication of Image by using Data HidingIRJET Journal
The document discusses a proposed system for encrypting and authenticating images using data hiding. At the sender side, a palette image is encrypted using an encryption key. Data is then hidden in the encrypted image using a data hiding key. At the receiver side, the image is decrypted and the original image can be reconstructed after extracting the hidden data. The system aims to securely transfer images from sender to receiver while protecting the owner's privacy and allowing accurate recovery of the original image content. Several existing reversible data hiding and encrypted image techniques are reviewed and the proposed system is described as providing satisfactory data hiding capacity while maintaining high image quality after decryption.
Segmentation of Images by using Fuzzy k-means clustering with ACOIJTET Journal
Abstract— Super pixels are becoming increasingly popular for use in computer vision applications. Image segmentation is the process of partitioning a digital image into multiple segments (known as super pixels). In this paper, we developed fuzzy k-means clustering with Ant Colony Optimization (ACO). In this propose algorithm the initial assumptions are made in the calculation of the mean value, which are depends on the colors of neighbored pixel in the image. Fuzzy mean is calculated for the whole image, this process having set of rules that rules are applied iteratively which is used to cluster the whole image. Once choosing a neighbor around that the fitness function is calculated in the optimization process. Based on the optimized clusters the image is segmented. By using fuzzy k-means clustering with ACO technique the image segmentation obtain high accuracy and the segmentation time is reduced compared to previous technique that is Lazy random walk (LRW) methodology. This LRW is optimized from Random walk technique.
This document proposes a novel reversible data hiding method called "Reserving Room Before Encryption" (RRBE) for color images. The key steps are: (1) Reserve space in the cover image by embedding pixel LSBs before encryption using an LSB plane method. (2) Encrypt the cover image. (3) Data can then be hidden in the reserved spaces of the encrypted image. (4) Data extraction and image recovery are possible without error since space was reserved before encryption, separating these processes from decryption. Using color images provides more data hiding capacity across the three channels. The method allows for reversible data hiding in encrypted images without the errors introduced by previous techniques that vacate space after encryption.
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Reversible Data Hiding in Encrypted color images by Reserving Room before Enc...ijceronline
International 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.
The document proposes a new method for enhancing security in data hiding using radiographic images. The method applies Burrows-Wheeler Transform (BWT) to distort the original data before encoding and hiding it in a cover image. BWT groups similar data patterns, distorting the original data. The decoding process decodes from the safe format and applies Inverse BWT to retrieve the original data from the stego image, realizing a two-level security scheme. Analysis shows the stego image is less deviated from the original cover image, with satisfactory quality metrics, while cryptanalysis of the hidden data is more difficult due to the original data distortion.
The document proposes a new hybrid steganographic method that embeds data in grayscale images while preserving the histogram characteristics. It uses pixel value differencing (PVD) and least significant bit (LSB) substitution. The method divides the pixel range into lower and higher levels and embeds more bits in higher levels. Each 3x3 block has a base pixel where 3 bits are embedded using LSB replacement. Remaining pixels are embedded using PVD. Experiments show it provides higher embedding capacity and image quality than existing methods, while introducing fewer changes to the histogram.
A new hybrid steganographic method for histogram preservation IJEEE
The document proposes a new hybrid steganographic method that embeds data in grayscale images while preserving the histogram characteristics. It uses pixel value differencing (PVD) and least significant bit (LSB) substitution. The method divides the pixel range into lower and higher levels and embeds more bits in higher levels. Each 3x3 block has a base pixel where 3 bits are embedded using LSB replacement. Remaining pixels are embedded using PVD. Experiments show it provides higher embedding capacity and image quality than existing methods while introducing fewer changes to the histogram.
Data Hiding Using Reversibly Designed Difference-Pair MethodIJERA Editor
This document presents a reversible data hiding technique called the difference-pair method. The technique embeds data into digital images by modifying pixel values in a way that allows perfect recovery of the original image. It aims to increase the embedding capacity compared to previous related work. The proposed method allows modification of either the first or second pixel in a pixel-pair, providing four possible modification directions rather than just two as in prior work. This increased flexibility in pixel modifications can boost data hiding capacity while maintaining reversibility and image quality. The technique is evaluated by comparing results to existing reversible data hiding schemes.
A New Chaos Based Image Encryption and Decryption using a Hash FunctionIRJET Journal
This document proposes a new chaos-based image encryption and decryption scheme using Arnold's cat map for pixel permutation and the Lorenz system for diffusion. A hash function, specifically MurmurHash3, is used to generate the permutation and diffusion keys. This helps accelerate the diffusion process and reduces the number of cipher cycles needed compared to previous schemes. The encryption process involves first permuting the pixel positions using the cat map, with control parameters determined by the hash value of the original image. Then diffusion is performed using the Lorenz system to generate the keystream. Decryption follows the reverse process using the same keys. Security analysis demonstrates the scheme has a large key space and the encrypted images pass various statistical tests, indicating the
Efficient Reversible Data Hiding Algorithms Based on Dual Predictionsipij
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of
prediction error histograms is proposed. The algorithm extends the efficient modification of prediction
errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data
embedding. The motivation behind using two predictors is driven by the fact that predictors have different
prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The
key feature of the proposed algorithm lies in using two predictors without the need to communicate
additional overhead with the stego image. Basically, the identification of the predictor that is used during
embedding is done through a set of rules. The proposed algorithm is further extended to use two and three
bins in the prediction errors histogram in order to increase the embedding capacity. Performance
evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in
boosting the embedding capacity while providing competitive quality for the stego image.
This document summarizes and compares various algorithms used to implement video surveillance systems, including pixel matching, image matching, and clustering algorithms. It first provides background on video surveillance systems and their need for automatic abnormal motion detection. It then reviews several specific algorithms: pixel matching, agglomerative clustering, reciprocal nearest neighbor pairing, sub-pixel mapping, patch matching, tone mapping, and k-means clustering. For each algorithm, it provides a brief overview of the approach and complexity. The document also discusses image matching algorithms like classic image checking, pixel-based identity checking, and pixel-based similarity checking. Overall, the document analyzes algorithms that can be used to detect and classify motion in video surveillance systems.
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JAVA 2013 IEEE IMAGEPROCESSING PROJECT Reversible data hiding with optimal value transfer
1. Reversible Data Hiding With Optimal Value Transfer
ABSTRACT:
In reversible data hiding techniques, the values of host data are modified according to some
particular rules and the original host content can be perfectly restored after extraction of the
hidden data on receiver side. In this paper, the optimal rule of value modification under a
payload-distortion criterion is found by using an iterative procedure, and a practical reversible
data hiding scheme is proposed. The secret data, as well as the auxiliary information used for
content recovery, are carried by the differences between the original pixel-values and the
corresponding values estimated from the neighbors. Here, the estimation errors are modified
according to the optimal value transfer rule. Also, the host image is divided into a number of
pixel subsets and the auxiliary information of a subset is always embedded into the estimation
errors in the next subset. A receiver can successfully extract the embedded secret data and
recover the original content in the subsets with an inverse order. This way, a good reversible
data hiding performance is achieved.
EXISTING SYSTEM:
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2. A data-hider can also employ histogram modification mechanism to realize reversible data
hiding. In the host image is divided into blocks sized 4 ×4, 8 ×8, or 16 ×16, and gray values are
mapped to a circle. After pseudo-randomly segmenting each block into two sub-regions,
rotation of the histograms of the two sub-regions on this circle is used to embed one bit in each
block. On the receiving side, the original block can be recovered from a marked image in an
inverse process. Payload of this method is low since each block can only carry one bit. Based
on this method, a robust lossless data hiding scheme is proposed, which can be used for semi-
fragile image authentication. A typical HM method presented for utilizes the zero and peak
points of the histogram of an image and slightly modifies the pixel grayscale values to embed
data into the image. In binary tree structure is used to eliminate the requirement to communicate
pairs of peak and zero points to the recipient, and a histogram shifting technique is adopted to
prevent overflow and underflow. The histogram modification mechanism can also be
implemented in the difference between sub-sampled images and the prediction error of host
pixels and several good prediction approaches have been introduced to improve the
performance of reversible data hiding.
DISADVANTAGES OF EXISTING SYSTEM:
In these reversible data hiding methods, a spare place can always be made available to
accommodate secret data as long as the chosen item is compressible, but the capacities
are not very high.
Payload of this method is low since each block can only carry one bit.
PROPOSED SYSTEM:
In this paper, we will find the optimal rule of value modification under a payload-distortion
criterion. By maximizing a target function using iterative algorithm, an optimal value transfer
matrix can be obtained. Furthermore, we design a practical reversible data hiding scheme, in
which the estimation errors of host pixels are used to accommodate the secret data and their
3. values are modified according to the optimal value transfer matrix. This way, a good payload-
distortion performance can be achieved
ADVANTAGES OF PROPOSED SYSTEM:
A smarter prediction method is exploited to make the estimation errors closer to zero, a
better performance can be achieved, but the computation complexity due to the prediction
will be higher.
The payload-distortion performance of the proposed scheme is excellent.
The host image is divided into a number of subsets and the auxiliary information of a
subset is always embedded into the estimation errors in the next subset. This way, one
can successfully extract the embedded secret data and recover the original content in the
subsets with an inverse order.
SYSTEM CONFIGURATION:-
HARDWARE REQUIREMENTS:-
Processor - Pentium –IV
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
4. SOFTWARE REQUIREMENTS:
• Operating system : - Windows XP.
• Coding Language : C#.Net
REFERENCE:
Xinpeng Zhang, Member, IEEE “Reversible Data Hiding With Optimal Value Transfer” IEEE
TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 2, FEBRUARY 2013.