Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication.pdf
•
0 likes•3 views
https://okokprojects.com/ IEEE PROJECTS 2023-2024 TITLE LIST WhatsApp : +91-8144199666 From Our Title List the Cost will be, Mail Us: okokprojects@gmail.com Website: : https://www.okokprojects.com : http://www.ieeeproject.net Support Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Video Tutorials * Supporting Softwares Support Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * Remote Connectivity * Document Customization * Live Chat Support
Report
Share
Report
Share
Download to read offline
Recommended
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
This document summarizes a research paper that proposes using a recurrent neural network trained with an extended Kalman filter to model the state space evolution of chaotic systems from time series output data. It applies this method to model the famous sunspot time series. The neural network is trained to minimize the error between the actual and predicted time series. Once trained, the network states generate the state space evolution. Analyzing this state space for the sunspot data, the researchers found it was similar to the Rossler chaotic system. They validated the model by calculating Lyapunov exponents from the state space evolution.
Artificial Neural Network Seminar Report
This document discusses the use of artificial neural networks (ANNs) for process control applications. It covers several key topics:
1) ANNs can model nonlinear systems through parallel processing and learning algorithms like backpropagation. Multi-layer neural networks are commonly used for pattern recognition and control.
2) Various ANN-based control configurations are described, including direct inverse control, direct adaptive control, and internal model control.
3) Learning algorithms like backpropagation and applications like system identification, modeling, fault detection, and temperature control are discussed.
4) The document concludes that multi-layer neural networks trained with backpropagation are well-suited for process identification and control, as they can handle nonlinearity using
A survey research summary on neural networks
This document summarizes research on neural networks. It discusses the basic structure and components of neural networks, including network topology (feed forward and recurrent), transfer functions, and learning algorithms (supervised, unsupervised, reinforcement). It also overview popular neural network models like multilayer perceptrons, radial basis function networks, Kohonen's self-organizing maps, and Hopfield networks. Finally, it outlines some applications of neural networks such as process control, pattern recognition, and more.
Echo state networks and locomotion patterns
1) The document discusses using echo state networks (ESNs) to model locomotion patterns of a legged robot. ESNs are a type of recurrent neural network that can model nonlinear dynamical systems in real-time.
2) The ESN is trained to take in ground contact sensor signals and output the average velocity profile of the robot. Locomotion patterns from a dynamic simulator are used as the training input and output data.
3) Results show that increasing the number of hidden neurons and time constant allows the ESN to better match the average speed of the teacher data, while a smaller spectral radius and feedback lead to better performance.
A FUZZY-BASED CONGESTION CONTROLLER FOR CONTROL AND BALANCE CONGESTION IN GRI...
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited power supply in a wide geographically area. These sensors collect information depending on application. The sensors transmit the data towards a base station called sink. Due to the relatively high node density and source-to-sink communication pattern, congestion is a critical issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the Monitor Node. Simulation results show that our approach has higher packet delivery ratio and lower packet loss than existing approaches.
A fuzzy based congestion controller for control and balance congestion in gri...
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited
power supply in a wide geographically area. These sensors collect information depending on
application. The sensors transmit the data towards a base station called sink. Due to the
relatively high node density and source-to-sink communication pattern, congestion is a critical
issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy
logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid
increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the
Monitor Node. Simulation results show that our approach has higher packet delivery ratio and
lower packet loss than existing approaches.
Attention correlated appearance and motion feature followed temporal learning...
Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.
Improve a Network Life Time by Least Troublesome Topology Repair Algorithm in...
In wireless sensor network, node reform and restoration is an active area for research. Each sensor node fails either when some critical event occurs at the node or when the battery of the node is completely drained. The Wireless sensor actor network (WSAN) is an enhances the wireless sensor networks by providing more power and recovering a energy consumption. In the WSAN, sensors sense the environment and sent back their data to an actor node. Actors have to coordinate their operations, keep up a firmly associated system topology at all times and achieve the predefined application mission. The Defect Node Reformation Algorithm (DNR) reform a defect node using genetic and the grade diffusion algorithms. A category of solutions for this problem is Least Troublesome Topology Repair Algorithm (LTTR). LTTR is a limited and circulated calculation that influences existing course revelation exercises in the system. LTTR algorithm is used to minimize the power consumption and reform a transmission path. Our results show that achieves performance in terms of packet delivery ratio, packet loss ratio and end to end latency suitable for real network deployments.
Recommended
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
This document summarizes a research paper that proposes using a recurrent neural network trained with an extended Kalman filter to model the state space evolution of chaotic systems from time series output data. It applies this method to model the famous sunspot time series. The neural network is trained to minimize the error between the actual and predicted time series. Once trained, the network states generate the state space evolution. Analyzing this state space for the sunspot data, the researchers found it was similar to the Rossler chaotic system. They validated the model by calculating Lyapunov exponents from the state space evolution.
Artificial Neural Network Seminar Report
This document discusses the use of artificial neural networks (ANNs) for process control applications. It covers several key topics:
1) ANNs can model nonlinear systems through parallel processing and learning algorithms like backpropagation. Multi-layer neural networks are commonly used for pattern recognition and control.
2) Various ANN-based control configurations are described, including direct inverse control, direct adaptive control, and internal model control.
3) Learning algorithms like backpropagation and applications like system identification, modeling, fault detection, and temperature control are discussed.
4) The document concludes that multi-layer neural networks trained with backpropagation are well-suited for process identification and control, as they can handle nonlinearity using
A survey research summary on neural networks
This document summarizes research on neural networks. It discusses the basic structure and components of neural networks, including network topology (feed forward and recurrent), transfer functions, and learning algorithms (supervised, unsupervised, reinforcement). It also overview popular neural network models like multilayer perceptrons, radial basis function networks, Kohonen's self-organizing maps, and Hopfield networks. Finally, it outlines some applications of neural networks such as process control, pattern recognition, and more.
Echo state networks and locomotion patterns
1) The document discusses using echo state networks (ESNs) to model locomotion patterns of a legged robot. ESNs are a type of recurrent neural network that can model nonlinear dynamical systems in real-time.
2) The ESN is trained to take in ground contact sensor signals and output the average velocity profile of the robot. Locomotion patterns from a dynamic simulator are used as the training input and output data.
3) Results show that increasing the number of hidden neurons and time constant allows the ESN to better match the average speed of the teacher data, while a smaller spectral radius and feedback lead to better performance.
A FUZZY-BASED CONGESTION CONTROLLER FOR CONTROL AND BALANCE CONGESTION IN GRI...
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited power supply in a wide geographically area. These sensors collect information depending on application. The sensors transmit the data towards a base station called sink. Due to the relatively high node density and source-to-sink communication pattern, congestion is a critical issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the Monitor Node. Simulation results show that our approach has higher packet delivery ratio and lower packet loss than existing approaches.
A fuzzy based congestion controller for control and balance congestion in gri...
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited
power supply in a wide geographically area. These sensors collect information depending on
application. The sensors transmit the data towards a base station called sink. Due to the
relatively high node density and source-to-sink communication pattern, congestion is a critical
issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy
logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid
increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the
Monitor Node. Simulation results show that our approach has higher packet delivery ratio and
lower packet loss than existing approaches.
Attention correlated appearance and motion feature followed temporal learning...
Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.
Improve a Network Life Time by Least Troublesome Topology Repair Algorithm in...
In wireless sensor network, node reform and restoration is an active area for research. Each sensor node fails either when some critical event occurs at the node or when the battery of the node is completely drained. The Wireless sensor actor network (WSAN) is an enhances the wireless sensor networks by providing more power and recovering a energy consumption. In the WSAN, sensors sense the environment and sent back their data to an actor node. Actors have to coordinate their operations, keep up a firmly associated system topology at all times and achieve the predefined application mission. The Defect Node Reformation Algorithm (DNR) reform a defect node using genetic and the grade diffusion algorithms. A category of solutions for this problem is Least Troublesome Topology Repair Algorithm (LTTR). LTTR is a limited and circulated calculation that influences existing course revelation exercises in the system. LTTR algorithm is used to minimize the power consumption and reform a transmission path. Our results show that achieves performance in terms of packet delivery ratio, packet loss ratio and end to end latency suitable for real network deployments.
Crude Oil Price Prediction Based on Soft Computing Model: Case Study of Iraq
This paper proposes using a multi-layer perceptron neural network (MLP-NN) soft computing model to accurately predict future crude oil prices in Iraq. The performance of the MLP-NN model is compared to other neural network approaches and found to perform better, especially with limited training data and high parameter variability. The paper describes the MLP-NN model and its training process using a dataset of Iraqi crude oil prices from 1990 to 2018. Features like mutual information analysis and data normalization are used as part of the model building process.
A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson’s Dis...
A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson’s Dis...Michael J. Montgomery
Abstract: Parkinson’s disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients’ responses to drug therapies and lead to better-targeted treatment regimens. In this paper we describe the algorithmic development of the system and an evaluation in patients for assessing the onset and duration of advanced PD motor symptoms.Cross layer design of distributed sensing-estimation with quality feedback— p...
Cross layer design of distributed sensing-estimation with quality feedback— p...ieeeprojectsbangalore
This paper presents low-complexity "myopic sensing-transmission policies" (MPs) for distributed sensing and estimation in a wireless sensor network. The MPs optimize a trade-off between performance and cost for each sensor node. For a coordinated MP scheme, the policy is computed in closed form, while an iterative algorithm is used for a decentralized scheme to reach a local optimum. The MPs dictate that only the best sensor nodes activate when estimation quality is poor, otherwise all nodes remain idle to preserve energy. Thresholds for activation are derived in closed form and do not depend on the number of channels used. A single channel is sufficient for energy-constrained networks. The MPs provide near-optimal performance to the optimal policy with much lowerH017376369
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large neural network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple simpler networks instead of one complex network for classification.
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple smaller networks over a single large network for classification.
Development of a virtual linearizer for correcting transducer static nonlinea...
This document describes the development of a virtual linearizer to correct for static nonlinearity in transducers. It involves developing two integrated software modules: 1) a data acquisition and management software using test-point software to acquire transducer input-output data and display results, and 2) a soft compensator using a multilayer feedforward backpropagation neural network trained with the Levenberg-Marquardt algorithm to model the inverse response of the transducer and provide compensation for nonlinearity. The virtual linearizer is able to acquire transducer data, train the neural network model, validate the model's performance, and provide a digital readout of the measured value compensated for nonlinearity.
A fuzzy congestion controller to detect and balance congestion in wsn
This document proposes a fuzzy logic-based approach to detect and control congestion in wireless sensor networks (WSNs). The approach divides the WSN into grids monitored by designated Monitor Nodes. These nodes use fuzzy logic and three input metrics (transmission delay, grid density, dropped packets) to determine the congestion level of their grid. Based on the congestion level, packets are either forwarded through the grid or through alternative relay nodes to avoid congestion. Simulation results show this approach achieves higher packet delivery and lower packet loss compared to an existing baseline approach.
Application of nn to power system
This document provides an overview of applying neural networks to power systems. It discusses how artificial neural networks (ANNs) can learn complex nonlinear relationships to help with problems like load forecasting, security assessment, and fault detection. The document reviews different types of ANNs like multi-layer perceptrons, Hopfield networks, and Kohonen maps that have been applied to issues in power systems. While ANNs show promise in addressing challenging power system problems, the document also notes ongoing work needed to overcome limitations like developing more efficient learning algorithms and handling large problem sizes.
Introduction to deep learning
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Final Poster
1. The document describes using a deep neural network to detect changes between two SAR images by preclassifying the images, training the neural network on selected samples, and analyzing the results.
2. A similarity matrix and variance matrix are calculated during preclassification to identify and jointly label similar pixels, while different pixels are labeled separately. Good samples are selected to train the neural network.
3. The neural network is tested on images with different types and levels of noise and performs well at change detection, with performance increasing as noise decreases. Future work could focus on accelerating the training process.
Control of Nonlinear Industrial Processes Using Fuzzy Wavelet Neural Network ...
Artificial intelligence control techniques, becomes one of the major control strategies and has received much attention as a powerful tool for the control of nonlinear systems. This paper presents a design of Fuzzy Wavelet Neural Network (FWNN) trained genetic algorithm (FWN-GA) for control of nonlinear industrial process. The FWNN is applied to approximate unknown dynamic of system and GA is used to train and optimize the FWNN parameters. In the proposed control scheme, neural control system synthesis is performed in the closed-loop control system to provide appropriate control input. For this, the error between desired system output and output of control object is directly utilized to tune the network parameters. The controller is applied to a highly nonlinear industrial process of continues stirred tank reactor (CSTR). Simulation results show that FWNN-GA controller has excellent dynamic response and adapt well to changes in reference trajectory and system parameters.
final_project_1_2k21cse07.pptx
The document describes a proposed approach for human action recognition using an attention based spatiotemporal graph convolutional network. The approach uses both temporal and spatial attention modules to select important frames and joints from skeletal data. The temporal attention module identifies informative frames, while the spatial attention module highlights significant joints within selected frames. Both attention modules help capture temporal and spatial relationships in skeletal data to improve action recognition accuracy. The proposed network incorporates temporal and spatial attention mechanisms into a graph convolutional network to efficiently recognize human actions from skeleton sequences.
Cross layer design of distributed sensing-estimation with quality feedback— p...
Cross layer design of distributed sensing-estimation with quality feedback— p...ieeeprojectsbangalore
This document summarizes a two-part paper that presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network. The framework considers cross-layer factors such as packet collisions and sensing-transmission costs. Each sensor node adapts its sensing-transmission action based on local observation quality and estimation quality feedback from the fusion center under cost constraints. The first part reduces optimization complexity by exploiting statistical symmetry and large network approximations. Structural properties of optimal coordinated and decentralized schemes are derived, proving that only the best sensing nodes need to activate despite fluctuations. The optimal policy dictates that the best nodes activate when estimation quality is poor, otherwise all nodes remain idle to preserve energy.A040101001006
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
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...
In Wireless Sensor Network (WSNs), gather the data by using mobile sinks has become popular. Reduce the number of messages which is used for sink location broadcasting, efficient energy data forwarding, become accustomed to unknown earthly changes are achieved by a protocol which is projected by a SinkTrail. The forecast of mobile sinks’ location are done by using logical coordinate system. When sensor nodes don’t have any data to send, at that time they switch to sleep mode to save the energy and to increase the network lifetime. And due to this reason there is a chance of the involvement of nodes that are in sleeping state between the path sources to the mobile sink which is selected by the SinkTrail protocol. Before become the fully functional and process the information, these sleeping nodes can drop the some information. Due to this reason, it is vital to wake-up the sleeping nodes on the path earlier than the sender can start transferring of sensed data. In this paper, on-demand wake-up scheduling algorithm is projected which is used to activates sleeping node on the path before data delivery. Here, in this work the multi-hop communication in WSN also considers. By incorporating wake-up scheduling algorithm to perk up the dependability and improve the performance of on-demand data forwarding extends the SinkTrail solution in our work. This projected algorithm improves the quality of service of the network by dishonesty of data or reducing the loss due to sleeping nodes. The efficiency and the effectiveness projected solution are proved by the evaluation results.
Ace an accurate and efficient multi entity device-free wlan localization system
The document describes ACE, a system that uses a probabilistic energy-minimization framework combining a conditional random field and Markov model to accurately track multiple entities in a device-free WLAN localization system. This overcomes limitations of previous single-entity tracking and exponentially increasing complexity with more entities. ACE introduces a novel cross-calibration technique that reduces calibration overhead from exponential to linear scaling with the number of locations tracked. Experimental results show ACE achieves sub-meter multi-entity tracking accuracy, an 11.8-33% enhancement over state-of-the-art systems. It also correctly estimates entity numbers 100% of the time.
A New Approach for Classification of Fault in Transmission Line with Combinat...
A New Approach for Classification of Fault in Transmission Line with Combinat...International Journal of Power Electronics and Drive Systems
An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.MAMMOGRAPHY LESION DETECTION USING FASTER R-CNN DETECTOR
Recently availability of large scale mammography databases enable researchers to evaluates advanced tumor detections applying deep convolution networks (DCN) to mammography images which is one of the common used imaging modalities for early breast cancer. With the recent advance of deep learning, the performance of tumor detection has been developed by a great extent, especially using R-CNNs or Region convolution neural networks. This study evaluates the performance of a simple faster R-CNN detector for mammography lesion detection using a MIAS databases.
A SELF-ORGANIZING RECURRENT NEURAL NETWORK
A recurrent neural network with a self-organizing structure based on the dynamic analysis of a task is presented in this paper. The stability of the recurrent neural network is guaranteed by design. A dynamic analysis method to sequence the subsystems of the recurrent neural network according to the fitness between the subsystems and the target system is developed. The network is trained with the network's structure self-organized by dynamically activating subsystems of the network according to tasks. The experiments showed the proposed network is capable of activating appropriate subsystems to approximate different nonlinear dynamic systems regardless of the inputs. When the network was applied to the problem of simultaneously soft measuring the chemical oxygen demand (COD) and NH3-N in wastewater treatment process, it showed its ability of avoiding the coupling influence of the two parameters and thus achieved a more desirable outcome.
Time delay neural network for continuous emotional dimension prediction from ...
I3E Technologies,
#23/A, 2nd Floor SKS Complex,
Opp. Bus Stand, Karur-639 001.
IEEE PROJECTS AVAILABLE,
NON-IEEE PROJECTS AVAILABLE,
APPLICATION BASED PROJECTS AVAILABLE,
MINI-PROJECTS AVAILABLE.
Mobile : 99436 99916, 99436 99926, 99436 99936
Mail ID: i3eprojectskarur@gmail.com
Web: www.i3eprojects.com
Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinea...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveil...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
More Related Content
Similar to Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication.pdf
Crude Oil Price Prediction Based on Soft Computing Model: Case Study of Iraq
This paper proposes using a multi-layer perceptron neural network (MLP-NN) soft computing model to accurately predict future crude oil prices in Iraq. The performance of the MLP-NN model is compared to other neural network approaches and found to perform better, especially with limited training data and high parameter variability. The paper describes the MLP-NN model and its training process using a dataset of Iraqi crude oil prices from 1990 to 2018. Features like mutual information analysis and data normalization are used as part of the model building process.
A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson’s Dis...
A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson’s Dis...Michael J. Montgomery
Abstract: Parkinson’s disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients’ responses to drug therapies and lead to better-targeted treatment regimens. In this paper we describe the algorithmic development of the system and an evaluation in patients for assessing the onset and duration of advanced PD motor symptoms.Cross layer design of distributed sensing-estimation with quality feedback— p...
Cross layer design of distributed sensing-estimation with quality feedback— p...ieeeprojectsbangalore
This paper presents low-complexity "myopic sensing-transmission policies" (MPs) for distributed sensing and estimation in a wireless sensor network. The MPs optimize a trade-off between performance and cost for each sensor node. For a coordinated MP scheme, the policy is computed in closed form, while an iterative algorithm is used for a decentralized scheme to reach a local optimum. The MPs dictate that only the best sensor nodes activate when estimation quality is poor, otherwise all nodes remain idle to preserve energy. Thresholds for activation are derived in closed form and do not depend on the number of channels used. A single channel is sufficient for energy-constrained networks. The MPs provide near-optimal performance to the optimal policy with much lowerH017376369
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large neural network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple simpler networks instead of one complex network for classification.
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple smaller networks over a single large network for classification.
Development of a virtual linearizer for correcting transducer static nonlinea...
This document describes the development of a virtual linearizer to correct for static nonlinearity in transducers. It involves developing two integrated software modules: 1) a data acquisition and management software using test-point software to acquire transducer input-output data and display results, and 2) a soft compensator using a multilayer feedforward backpropagation neural network trained with the Levenberg-Marquardt algorithm to model the inverse response of the transducer and provide compensation for nonlinearity. The virtual linearizer is able to acquire transducer data, train the neural network model, validate the model's performance, and provide a digital readout of the measured value compensated for nonlinearity.
A fuzzy congestion controller to detect and balance congestion in wsn
This document proposes a fuzzy logic-based approach to detect and control congestion in wireless sensor networks (WSNs). The approach divides the WSN into grids monitored by designated Monitor Nodes. These nodes use fuzzy logic and three input metrics (transmission delay, grid density, dropped packets) to determine the congestion level of their grid. Based on the congestion level, packets are either forwarded through the grid or through alternative relay nodes to avoid congestion. Simulation results show this approach achieves higher packet delivery and lower packet loss compared to an existing baseline approach.
Application of nn to power system
This document provides an overview of applying neural networks to power systems. It discusses how artificial neural networks (ANNs) can learn complex nonlinear relationships to help with problems like load forecasting, security assessment, and fault detection. The document reviews different types of ANNs like multi-layer perceptrons, Hopfield networks, and Kohonen maps that have been applied to issues in power systems. While ANNs show promise in addressing challenging power system problems, the document also notes ongoing work needed to overcome limitations like developing more efficient learning algorithms and handling large problem sizes.
Introduction to deep learning
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Final Poster
1. The document describes using a deep neural network to detect changes between two SAR images by preclassifying the images, training the neural network on selected samples, and analyzing the results.
2. A similarity matrix and variance matrix are calculated during preclassification to identify and jointly label similar pixels, while different pixels are labeled separately. Good samples are selected to train the neural network.
3. The neural network is tested on images with different types and levels of noise and performs well at change detection, with performance increasing as noise decreases. Future work could focus on accelerating the training process.
Control of Nonlinear Industrial Processes Using Fuzzy Wavelet Neural Network ...
Artificial intelligence control techniques, becomes one of the major control strategies and has received much attention as a powerful tool for the control of nonlinear systems. This paper presents a design of Fuzzy Wavelet Neural Network (FWNN) trained genetic algorithm (FWN-GA) for control of nonlinear industrial process. The FWNN is applied to approximate unknown dynamic of system and GA is used to train and optimize the FWNN parameters. In the proposed control scheme, neural control system synthesis is performed in the closed-loop control system to provide appropriate control input. For this, the error between desired system output and output of control object is directly utilized to tune the network parameters. The controller is applied to a highly nonlinear industrial process of continues stirred tank reactor (CSTR). Simulation results show that FWNN-GA controller has excellent dynamic response and adapt well to changes in reference trajectory and system parameters.
final_project_1_2k21cse07.pptx
The document describes a proposed approach for human action recognition using an attention based spatiotemporal graph convolutional network. The approach uses both temporal and spatial attention modules to select important frames and joints from skeletal data. The temporal attention module identifies informative frames, while the spatial attention module highlights significant joints within selected frames. Both attention modules help capture temporal and spatial relationships in skeletal data to improve action recognition accuracy. The proposed network incorporates temporal and spatial attention mechanisms into a graph convolutional network to efficiently recognize human actions from skeleton sequences.
Cross layer design of distributed sensing-estimation with quality feedback— p...
Cross layer design of distributed sensing-estimation with quality feedback— p...ieeeprojectsbangalore
This document summarizes a two-part paper that presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network. The framework considers cross-layer factors such as packet collisions and sensing-transmission costs. Each sensor node adapts its sensing-transmission action based on local observation quality and estimation quality feedback from the fusion center under cost constraints. The first part reduces optimization complexity by exploiting statistical symmetry and large network approximations. Structural properties of optimal coordinated and decentralized schemes are derived, proving that only the best sensing nodes need to activate despite fluctuations. The optimal policy dictates that the best nodes activate when estimation quality is poor, otherwise all nodes remain idle to preserve energy.A040101001006
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
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...
In Wireless Sensor Network (WSNs), gather the data by using mobile sinks has become popular. Reduce the number of messages which is used for sink location broadcasting, efficient energy data forwarding, become accustomed to unknown earthly changes are achieved by a protocol which is projected by a SinkTrail. The forecast of mobile sinks’ location are done by using logical coordinate system. When sensor nodes don’t have any data to send, at that time they switch to sleep mode to save the energy and to increase the network lifetime. And due to this reason there is a chance of the involvement of nodes that are in sleeping state between the path sources to the mobile sink which is selected by the SinkTrail protocol. Before become the fully functional and process the information, these sleeping nodes can drop the some information. Due to this reason, it is vital to wake-up the sleeping nodes on the path earlier than the sender can start transferring of sensed data. In this paper, on-demand wake-up scheduling algorithm is projected which is used to activates sleeping node on the path before data delivery. Here, in this work the multi-hop communication in WSN also considers. By incorporating wake-up scheduling algorithm to perk up the dependability and improve the performance of on-demand data forwarding extends the SinkTrail solution in our work. This projected algorithm improves the quality of service of the network by dishonesty of data or reducing the loss due to sleeping nodes. The efficiency and the effectiveness projected solution are proved by the evaluation results.
Ace an accurate and efficient multi entity device-free wlan localization system
The document describes ACE, a system that uses a probabilistic energy-minimization framework combining a conditional random field and Markov model to accurately track multiple entities in a device-free WLAN localization system. This overcomes limitations of previous single-entity tracking and exponentially increasing complexity with more entities. ACE introduces a novel cross-calibration technique that reduces calibration overhead from exponential to linear scaling with the number of locations tracked. Experimental results show ACE achieves sub-meter multi-entity tracking accuracy, an 11.8-33% enhancement over state-of-the-art systems. It also correctly estimates entity numbers 100% of the time.
A New Approach for Classification of Fault in Transmission Line with Combinat...
A New Approach for Classification of Fault in Transmission Line with Combinat...International Journal of Power Electronics and Drive Systems
An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.MAMMOGRAPHY LESION DETECTION USING FASTER R-CNN DETECTOR
Recently availability of large scale mammography databases enable researchers to evaluates advanced tumor detections applying deep convolution networks (DCN) to mammography images which is one of the common used imaging modalities for early breast cancer. With the recent advance of deep learning, the performance of tumor detection has been developed by a great extent, especially using R-CNNs or Region convolution neural networks. This study evaluates the performance of a simple faster R-CNN detector for mammography lesion detection using a MIAS databases.
A SELF-ORGANIZING RECURRENT NEURAL NETWORK
A recurrent neural network with a self-organizing structure based on the dynamic analysis of a task is presented in this paper. The stability of the recurrent neural network is guaranteed by design. A dynamic analysis method to sequence the subsystems of the recurrent neural network according to the fitness between the subsystems and the target system is developed. The network is trained with the network's structure self-organized by dynamically activating subsystems of the network according to tasks. The experiments showed the proposed network is capable of activating appropriate subsystems to approximate different nonlinear dynamic systems regardless of the inputs. When the network was applied to the problem of simultaneously soft measuring the chemical oxygen demand (COD) and NH3-N in wastewater treatment process, it showed its ability of avoiding the coupling influence of the two parameters and thus achieved a more desirable outcome.
Time delay neural network for continuous emotional dimension prediction from ...
I3E Technologies,
#23/A, 2nd Floor SKS Complex,
Opp. Bus Stand, Karur-639 001.
IEEE PROJECTS AVAILABLE,
NON-IEEE PROJECTS AVAILABLE,
APPLICATION BASED PROJECTS AVAILABLE,
MINI-PROJECTS AVAILABLE.
Mobile : 99436 99916, 99436 99926, 99436 99936
Mail ID: i3eprojectskarur@gmail.com
Web: www.i3eprojects.com
Similar to Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication.pdf (20)
Crude Oil Price Prediction Based on Soft Computing Model: Case Study of Iraq
Crude Oil Price Prediction Based on Soft Computing Model: Case Study of Iraq
A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson’s Dis...
A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson’s Dis...
Cross layer design of distributed sensing-estimation with quality feedback— p...
Cross layer design of distributed sensing-estimation with quality feedback— p...
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...
Development of a virtual linearizer for correcting transducer static nonlinea...
Development of a virtual linearizer for correcting transducer static nonlinea...
A fuzzy congestion controller to detect and balance congestion in wsn
A fuzzy congestion controller to detect and balance congestion in wsn
Control of Nonlinear Industrial Processes Using Fuzzy Wavelet Neural Network ...
Control of Nonlinear Industrial Processes Using Fuzzy Wavelet Neural Network ...
Cross layer design of distributed sensing-estimation with quality feedback— p...
Cross layer design of distributed sensing-estimation with quality feedback— p...
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...
Ace an accurate and efficient multi entity device-free wlan localization system
Ace an accurate and efficient multi entity device-free wlan localization system
A New Approach for Classification of Fault in Transmission Line with Combinat...
A New Approach for Classification of Fault in Transmission Line with Combinat...
MAMMOGRAPHY LESION DETECTION USING FASTER R-CNN DETECTOR
MAMMOGRAPHY LESION DETECTION USING FASTER R-CNN DETECTOR
Time delay neural network for continuous emotional dimension prediction from ...
Time delay neural network for continuous emotional dimension prediction from ...
More from OKOKPROJECTS
Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinea...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveil...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DLTIF Deep Learning-Driven Cyber Threat Intelligence Modeling and Identificat...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DGSSC A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspect...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Digital Restoration of Cultural Heritage With Data-Driven Computing A Survey.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Dependable Intrusion Detection System for IoT A Deep Transfer Learning Based ...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DendroMap Visual Exploration of Large-Scale Image Datasets for Machine Learni...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Dense Nested Attention Network for Infrared Small Target Detection.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Detection of Small Moving Targets in Cluttered Infrared Imagery.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Depression Screening in Humans With AI and Deep Learning Techniques.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DeepTx Deep Learning Beamforming With Channel Prediction.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DeHIN A Decentralized Framework for Embedding Large-Scale Heterogeneous Infor...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DefQ Defensive Quantization Against Inference Slow-Down Attack for Edge Compu...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Deep-Learning-Driven Proactive Maintenance Management of IoT-Empowered Smart ...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Deep-Distributed-Learning-Based POI Recommendation Under Mobile-Edge Networks...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DeepCog A Trustworthy Deep Learning-Based Human Cognitive Privacy Framework i...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
DeepCrowd A Deep Model for Large-Scale Citywide Crowd Density and Flow Predic...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
D2Net Deep Denoising Network in Frequency Domain for Hyperspectral Image.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Decentralized Federated Learning for Industrial IoT With Deep Echo State Netw...
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
Cyber Code Intelligence for Android Malware Detection.pdf
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
More from OKOKPROJECTS (20)
Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinea...
Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinea...
Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveil...
Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveil...
DLTIF Deep Learning-Driven Cyber Threat Intelligence Modeling and Identificat...
DLTIF Deep Learning-Driven Cyber Threat Intelligence Modeling and Identificat...
DGSSC A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspect...
DGSSC A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspect...
Digital Restoration of Cultural Heritage With Data-Driven Computing A Survey.pdf
Digital Restoration of Cultural Heritage With Data-Driven Computing A Survey.pdf
Dependable Intrusion Detection System for IoT A Deep Transfer Learning Based ...
Dependable Intrusion Detection System for IoT A Deep Transfer Learning Based ...
DendroMap Visual Exploration of Large-Scale Image Datasets for Machine Learni...
DendroMap Visual Exploration of Large-Scale Image Datasets for Machine Learni...
Dense Nested Attention Network for Infrared Small Target Detection.pdf
Dense Nested Attention Network for Infrared Small Target Detection.pdf
Detection of Small Moving Targets in Cluttered Infrared Imagery.pdf
Detection of Small Moving Targets in Cluttered Infrared Imagery.pdf
Depression Screening in Humans With AI and Deep Learning Techniques.pdf
Depression Screening in Humans With AI and Deep Learning Techniques.pdf
DeepTx Deep Learning Beamforming With Channel Prediction.pdf
DeepTx Deep Learning Beamforming With Channel Prediction.pdf
DeHIN A Decentralized Framework for Embedding Large-Scale Heterogeneous Infor...
DeHIN A Decentralized Framework for Embedding Large-Scale Heterogeneous Infor...
DefQ Defensive Quantization Against Inference Slow-Down Attack for Edge Compu...
DefQ Defensive Quantization Against Inference Slow-Down Attack for Edge Compu...
Deep-Learning-Driven Proactive Maintenance Management of IoT-Empowered Smart ...
Deep-Learning-Driven Proactive Maintenance Management of IoT-Empowered Smart ...
Deep-Distributed-Learning-Based POI Recommendation Under Mobile-Edge Networks...
Deep-Distributed-Learning-Based POI Recommendation Under Mobile-Edge Networks...
DeepCog A Trustworthy Deep Learning-Based Human Cognitive Privacy Framework i...
DeepCog A Trustworthy Deep Learning-Based Human Cognitive Privacy Framework i...
DeepCrowd A Deep Model for Large-Scale Citywide Crowd Density and Flow Predic...
DeepCrowd A Deep Model for Large-Scale Citywide Crowd Density and Flow Predic...
D2Net Deep Denoising Network in Frequency Domain for Hyperspectral Image.pdf
D2Net Deep Denoising Network in Frequency Domain for Hyperspectral Image.pdf
Decentralized Federated Learning for Industrial IoT With Deep Echo State Netw...
Decentralized Federated Learning for Industrial IoT With Deep Echo State Netw...
Cyber Code Intelligence for Android Malware Detection.pdf
Cyber Code Intelligence for Android Malware Detection.pdf
Recently uploaded
How to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
BBR 2024 Summer Sessions Interview Training
Qualitative research interview training by Professor Katrina Pritchard and Dr Helen Williams
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Community pharmacy- Social and preventive pharmacy UNIT 5
Covered community pharmacy topic of the subject Social and preventive pharmacy for Diploma and Bachelor of pharmacy
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdRecently uploaded (20)
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Community pharmacy- Social and preventive pharmacy UNIT 5
Community pharmacy- Social and preventive pharmacy UNIT 5
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication.pdf
- 1. Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication Abstract This work explores the distributed state estimation problem for an uncertain, nonlinear, and continuous-time system. Given a sensor network, each agent is assigned a deep neural network (DNN) that is used to approximate the system's dynamics. Each agent updates the weights of their DNN through a multiple timescale approach, i.e., the outer layer weights are updated online with a Lyapunov-based gradient descent update law, and the inner layer weights are updated concurrently using a supervised learning strategy. To promote the efficient use of network resources, the distributed observer uses event-triggered communication. A nonsmooth Lyapunov analysis demonstrates that the distributed event-triggered observer achieves uniformly ultimately bounded state reconstruction. A simulation example of a five-agent
- 2. sensor network estimating the state of a two-link robotic manipulator tracking a desired trajectory is provided to validate the result and showcase the performance improvements afforded by DNNs.