This document discusses cooperative sensor network localization using Gaussian mixture modeling and expectation-conditional maximization (ECM) algorithms. It proposes using a Gaussian mixture to approximate the unknown non-Gaussian measurement error distribution. Centralized ECM algorithms are developed for parameter estimation with proofs of convergence properties. Distributed ECM algorithms are also created to improve scalability, using average consensus to update the Gaussian mixture model locally. Computer tests show the distributed algorithms can perform well even with model mismatch and unknown error statistics, outperforming alternatives.
Heterogeneous Information Network Embedding for Recommendation
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Email: jpinfotechprojects@gmail.com,
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Heterogeneous Information Network Embedding for Recommendation
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Email: jpinfotechprojects@gmail.com,
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In this project, we use leverage of centrality models for extracting the importance
of network graph in some determined topologies. The aim is to have scrutinizing
and analyzing the centralities in different network topologies. Three type of centrality
that are used in this project are Betweenness, Closeness and eigenvector
one. Moreover, we have show the results of this comparison in the experimental
results. Besides, we have extend the results of our experimental works for real
world problems. The Results of this part are grasped with visualization plots for
some centralities measurements clearly.
Creates heuristic guidelines for classifying types of networks empirically through a series of network metrics. Introduces metrics and theoretical background of what those network metrics measure with respect to the graph.
Probabilistic Programming for Dynamic Data Assimilation on an Agent-Based ModelNick Malleson
Usually in computer programming, variables are usually assigned to specific values (e.g. a virtual person in a computer simulation might have an 'age' variable which stores a number). Probabilistic programming, on the other hand, allows you to represent _random variables_. These are variables whose values we do not know precisely, so can be represented by a probability distribution rather than a single value.
This is potentially a very elegant way of capturing uncertainty in our models. However, using the probabilistic programming approach for agent-based modelling raises a number of questions. In the following slides, LIDA data science intern Luke Archer introduces his recent work that explores the use of probabilistic programming libraries for building agent-based models.
Heterogeneous Information Network Embedding for Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Heterogeneous Information Network Embedding for Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
In this project, we use leverage of centrality models for extracting the importance
of network graph in some determined topologies. The aim is to have scrutinizing
and analyzing the centralities in different network topologies. Three type of centrality
that are used in this project are Betweenness, Closeness and eigenvector
one. Moreover, we have show the results of this comparison in the experimental
results. Besides, we have extend the results of our experimental works for real
world problems. The Results of this part are grasped with visualization plots for
some centralities measurements clearly.
Creates heuristic guidelines for classifying types of networks empirically through a series of network metrics. Introduces metrics and theoretical background of what those network metrics measure with respect to the graph.
Probabilistic Programming for Dynamic Data Assimilation on an Agent-Based ModelNick Malleson
Usually in computer programming, variables are usually assigned to specific values (e.g. a virtual person in a computer simulation might have an 'age' variable which stores a number). Probabilistic programming, on the other hand, allows you to represent _random variables_. These are variables whose values we do not know precisely, so can be represented by a probability distribution rather than a single value.
This is potentially a very elegant way of capturing uncertainty in our models. However, using the probabilistic programming approach for agent-based modelling raises a number of questions. In the following slides, LIDA data science intern Luke Archer introduces his recent work that explores the use of probabilistic programming libraries for building agent-based models.
Extension of this method exists in recent paper here: https://arxiv.org/ftp/arxiv/papers/1708/1708.05712.pdf
Overview and tutorial of Morse-Smale regression prior to a new paper coming out exploring this idea further. It is a topologically-based piecewise regression method for supervised learning.
A short tutorial on Morse functions and their use in modern data analysis for beginners. Uses visual examples and analogies to introduce topological concepts and algorithms.
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATIONNexgen Technology
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NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Data Science Meetup: DGLARS and Homotopy LASSO for Regression ModelsColleen Farrelly
Short overview of two regression model extensions using differential geometry and homotopy continuation. Case study involves an open-source dataset that can be found on my ResearchGate page, along with the R code used in the analysis. Contains a short reference section for readers interested in learning more about the methods.
Iterative Closest Point Algorithm - analysis and implementationPankaj Gautam
Implemented ICP algorithm for 2D images using OpenCV.
ICP is used to align partially-overlapping point clouds, given an initial guess for relative transform.
Dissertation Abstract "MANAGEMENT OF MULTI-PURPOSE COOPERATIVES IN REGIONI.BA...Jo Balucanag - Bitonio
MANAGEMENTOFMULTI-PURPOSECOOPERATIVESINREGIONI.BASISFORANIMPROVEDINTERNALCONTROLSYSTEMOFCOOPERATIVES
St. Louis College, Lingsat, San Fernando City, La Union
Nexgen Technology Address:
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No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Extension of this method exists in recent paper here: https://arxiv.org/ftp/arxiv/papers/1708/1708.05712.pdf
Overview and tutorial of Morse-Smale regression prior to a new paper coming out exploring this idea further. It is a topologically-based piecewise regression method for supervised learning.
A short tutorial on Morse functions and their use in modern data analysis for beginners. Uses visual examples and analogies to introduce topological concepts and algorithms.
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATIONNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Data Science Meetup: DGLARS and Homotopy LASSO for Regression ModelsColleen Farrelly
Short overview of two regression model extensions using differential geometry and homotopy continuation. Case study involves an open-source dataset that can be found on my ResearchGate page, along with the R code used in the analysis. Contains a short reference section for readers interested in learning more about the methods.
Iterative Closest Point Algorithm - analysis and implementationPankaj Gautam
Implemented ICP algorithm for 2D images using OpenCV.
ICP is used to align partially-overlapping point clouds, given an initial guess for relative transform.
Dissertation Abstract "MANAGEMENT OF MULTI-PURPOSE COOPERATIVES IN REGIONI.BA...Jo Balucanag - Bitonio
MANAGEMENTOFMULTI-PURPOSECOOPERATIVESINREGIONI.BASISFORANIMPROVEDINTERNALCONTROLSYSTEMOFCOOPERATIVES
St. Louis College, Lingsat, San Fernando City, La Union
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE DATAMINING PROJECT Distributed web systems performance forecas...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming
very tedious process. The key disadvantage of high dimensional data which we can pen down is curse
of dimensionality. As the magnitude of datasets grows the data points become sparse and density of
area becomes less making it difficult to cluster that data which further reduces the performance of
traditional algorithms used for clustering. Semi-supervised clustering algorithms aim to improve
clustering results using limited supervision. The supervision is generally given as pair wise
constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are
designed for data represented as vectors [2]. In this paper, we unify vector-based and graph-based
approaches. We first show that a recently-proposed objective function for semi-supervised clustering
based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of
constraint penalty functions, can be expressed as a special case of the global kernel k-means objective
[3]. A recent theoretical connection between global kernel k-means and several graph clustering
objectives enables us to perform semi-supervised clustering of data. In particular, some methods have
been proposed for semi supervised clustering based on pair wise similarity or dissimilarity
information. In this paper, we propose a kernel approach for semi supervised clustering and present in
detail two special cases of this kernel approach.
Ieee transactions 2018 on wireless communications Title and Abstracttsysglobalsolutions
Final year BE, B.Tech, ME, M.Tech projects along with our professionals for developing Real Time Applications in Emerging Technologies.
We can support to your final year projects in all domains with latest technologies and simulation tool like NS2, NS3, Glomosim, Opnet, Matlab, IDL, Sumo, Gridsim, Bonita tool & Cloud deployments (Cloudsim, Google App Engine, Amazon Deployment, and Real time Cloud Deployment)also we are support for JOURNAL and CONFERENCE Preparation.
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SENSOR SELECTION SCHEME IN WIRELESS SENSOR NETWORKS: A NEW ROUTING APPROACHcsandit
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless
sensor networks, based on data mining formulation. The proposed adapting routing scheme for
sensors for achieving energy efficiency. The experimental validation of the proposed approach
using publicly available Intel Berkeley lab Wireless Sensor Network dataset shows that it is
possible to achieve energy efficient environment monitoring for wireless sensor networks, with a
trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
PAGE: A Partition Aware Engine for Parallel Graph Computation1crore projects
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DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
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4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
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2. Ns2 project
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An Adaptive Load Balancing Middleware for Distributed SimulationGabriele D'Angelo
The simulation is useful to support the design and performance evaluation of complex systems, possibly composed by a massive number of interacting entities. For this reason, the simulation of such systems may need aggregate computation and memory resources obtained by clusters of parallel and distributed execution units. Shared computer clusters composed of available Commercial-Off-the-Shelf hardware are preferable to dedicated systems, mainly for cost reasons. The performance of distributed simulations is influenced by the heterogeneity of execution units and by their respective CPU load in background. Adaptive load balancing mechanisms could improve the resources utilization and the simulation process execution, by dynamically tuning the simulation load with an eye to the synchronization and communication overheads reduction. In this work it will be presented the GAIA+ framework: a new load balancing mechanism for distributed simulation. The framework has been evaluated by performing testbed simulations of a wireless ad hoc network model. Results confirm the effectiveness of the proposed solutions.
15 9738 power paper id 0009 edit septianIAESIJEECS
State Estimation (SE) is the main function of power system where Energy Management System
(EMS) is obliged to estimate the available states. Power system is a quasi-static system and hence
changes slowly with time. Dynamic State Estimation (DSE) technique represents the time deviation nature
of the system, which allows the forecasting of state vector in advance. Various techniques for DSE are
available in the literature. This paper presents a review on different methodologies and developments in
DSE, based on comprehensive survey of the available literature. From the survey it can be concluded that
there are still areas in the developing DSE that can still be improved in terms of system computational
time, redundancy and robustness of the system.
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Evaluate the performance of K-Means and the fuzzy C-Means algorithms to forma...IJECEIAES
The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.
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Cooperative localization in ws ns using gaussian mixture modeling distributed ecm algorithms
1. Cooperative Localization in WSNs Using Gaussian Mixture Modeling:
Distributed ECM Algorithms
Abstract:
We study cooperative sensor network localization in a realistic scenario
where the underlying measurement errors more probably follow a non-
Gaussian distribution; the measurement error distribution is unknown
without conducting massive offline calibrations; and non-line-of-sight
identification is not performed due to the complexity constraint and/or
storage limitation. The underlying measurement error distribution is
approximated parametrically by a Gaussian mixture with finite number of
components, and the expectation-conditional maximization (ECM)
criterion is adopted to approximate the maximum-likelihood estimator of
the unknown sensor positions and an extra set of Gaussian mixture model
parameters. The resulting centralized ECM algorithms lead to easier
inference tasks and meanwhile retain several convergence properties with
a proof of the “space filling” condition. Tomeet the scalability requirement,
we further develop two distributed ECM algorithms where an average
consensus algorithm plays an important role for updating the Gaussian
mixture model parameters locally. The proposed algorithms are analyzed
systematically in terms of computational complexity and communication
overhead. Various computer based tests are also conducted with both
2. simulation and experimental data. The results pin down that the proposed
distributed algorithms can provide overall good performance for the
assumed scenario even under model mismatch, while the existing
competing algorithms either cannot work without the prior knowledge of
the measurement error statistics or merely provide degraded localization
performance when the measurement error is clearly non-Gaussian.