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Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4828 MapR Technologies
Industrial Parameter Monitoring System Using CAN Bus
 
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1. Industrial Parameter Monitoring System Using CAN Bus, 2. CAN BASED INDUSTRIAL MONITORING SYSTEM KEYPAD, PASSWORD, CONVEYER BELT, CRUSHER MOTOR, 3. CAN Based Vehicle Speed control and Safety Prototype, 4. A review Control area network CAN based Intelligent vehicle system, 5. CAN protocol based vehicle security system with Finger print , 6. Monitoring and Control System for Industrial Parameters Using Can Bus, 7. Vehicle Parameter Monitoring Using CAN Protocol , 8. Vehicle control system implementation Using CAN protocol, 9. Real-Time Industrial Parameter Measurement Using CAN, Zigbee, 10. Controller Area Network for Monitoring and Controlling the Industrial, 11. Controller Area Network (CAN) Protocol, 12. can protocol based projects pdf, 13. can protocol implementation using arm7, 14. can protocol implementation source code, 15. can protocol implementation in c, 16. can protocol based embedded projects list, 17. can protocol based embedded projects pdf, 18. can protocol tutorial programming, 19. can protocol implementation using 8051, 20. CAN Protocol Based Industrial Parameter Monitoring System , 21. Implementation Of Can And Zigbee Networks Based Industrial, 22. Design and Development of a Vehicle Monitoring System Using CAN, 23. Implementation of CAN based Vehicle monitoring system using ARM 7, 24. Greenhouse Automation Using CAN Bus, 25. Vehicle Control Using CAN Protocol For Implementing, 26. Designing and Development of a CAN Bus Analyzer for Industrial, 27. CAN based System for Monitoring and Controlling Humidity , 28. Coal Monitoring System Using CAN, 29. can bus protocol based greenhouse monitoring and control system , 30. design and implementation of can communication system , 31. Vehicle Monitoring. System with Controller Area Network (CAN) bus, 32. Industrial automation and process control system using CAN bus, 33. Driver Alert System Using CAN Protocol, 34. A CAN Bus Based System for Monitoring and Fault, 35. wireless can bus for industrial, 36. ARM LPC2129 based Data Acquisition System using Controller Area, 37. Intelligent Accidental Control of Car System Using CAN, 38. Cloud Server Based Wind Turbine Parameters Monitoring, 39. Engineering Final Year Projects on Embedded (CAN/LIN Protocol), 40. CONTROL OF AIR BAGES USING CAN CONTROLLER, 41. AUTOMATED PLACE ANNOUNCEMENT SYSTEM FOR BTS BUS OR TRAIN WITH VOICE CHIP, 42. AUTOMOTIVE MONITORING SYSTEM, 43. Design and Implementation of Can Bus for Distributed Injection, 44. DATA LOGGER AND REMOTE MONITORING SYSTEM, 45. Introduction to the Controller Area Network (CAN), 46. A CAN bus based system for monitoring and fault diagnosis in wind turbine, 47. A Distributed CAN Bus Based Embedded Control System for 750 keV, 48. Industrial Process Parameter Monitor using Ethernet, 49. Design of Coal Mine Comprehensive Parameters Monitoring System, 50. Design and Application of Hybrid Network Control System Based on CAN, 51. Introduction of Industrial Automation using Zigbee Technology, 52. CAN bus interface in embedded systems- learn design, 53. Implementation of Can and Zigbee Networks Based Industrial, 54. Final Year Projects for Electronics Engineering Students, 55. Bus Monitoring System Based On ZigBee And GPRS, 56. Industry parameters monitoring and controlling system based on embedded web server, 57. Remote Monitoring Industrial Generators, 58. Implementation Of CAN Based Intelligent Driver Alert System, 59. GREENHOUSE INTELLIGENT CONTROL SYSTEM BASED ON CAN bus, 60. Wireless sensing of substation parameters for remote monitoring, 61. The Design of a Distributed Control System Based on CAN Bus, 62. CAN BASE VOLVO AUTOMATION, 63. GPS+GSM BASED REAL-TIME BUS/TRAIN LOCATION FINDER AND DISPLAY, 64. Electrical parameters in industries is monitoring through pc using wireless data, 65. Distributed remote temperature monitoring and Acquisition system based on CAN bus, 66. Can Base Patient Monitoring System, 67. Design & Implementation of Automotive Intelligent Node CAN bus, 68. Intelligent Monitoring of Patients in Hospitals Using CAN Protocols, 69. greenhouse management through can network and plc on a touch, 70. Design of Remote Monitoring and Control System, 71. Wireless Sensor Networks for Industrial Process Monitoring and Control, 72. Zigbee Based Remote Monitering Of Parameters of Trasformer Using CAN,
Views: 5634 svsembedded
Modeling Data Streams Using Sparse Distributed Representations
 
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In this screencast, Jeff Hawkins narrates the presentation he gave at a workshop called "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications." The workshop was held May 7-11, 2012 at the University of California, Berkeley. Slides: http://www.numenta.com/htm-overview/05-08-2012-Berkeley.pdf Abstract: Sparse distributed representations appear to be the means by which brains encode information. They have several advantageous properties including the ability to encode semantic meaning. We have created a distributed memory system for learning sequences of sparse distribute representations. In addition we have created a means of encoding structured and unstructured data into sparse distributed representations. The resulting memory system learns in an on-line fashion making it suitable for high velocity data streams. We are currently applying it to commercially valuable data streams for prediction, classification, and anomaly detection In this talk I will describe this distributed memory system and illustrate how it can be used to build models and make predictions from data streams. Live video recording of this presentation: http://www.youtube.com/watch?v=nfUT3UbYhjM General information can be found at https://www.numenta.com, and technical details can be found in the CLA white paper at https://www.numenta.com/faq.html#cla_paper.
Views: 20578 Numenta
Managing Uncertainty Using Probabilistic Databases
 
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Uncertainty is a fundamental problem underlying several modern database applications: exploratory queries in databases, data integration, querying information extracted from the Web, queries over sensor networks, scientific data management, reasoning about privacy breaches in data mining and many others. In this talk, I will describe probabilistic databases as a unifying framework to manage the various kinds of uncertainties that arise in these wide range of applications. In a probabilistic database, each data item has a probability of belonging to the database and queries return answers that are ranked by probabilities. The main challenge here is query evaluation. Unlike in traditional databases, some queries have a #P-complete complexity. I will present the results of our study of the complexity of queries and present algorithms and techniques for efficient query evaluation over probabilistic databases.
Views: 191 Microsoft Research
Building an Occupancy Model from Sensor Networks in Office Environments
 
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This video illustrate the room occupancy patterns of the dataset employed in the paper: F. Castanedo, D. López-de-Ipiña, H. Aghajan, R. Kleihorst." Building an Occupancy Model from Sensor Networks in Office Environments", 5th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC'11), Ghent, Belgium. August, 2011. The work presented here aims to answer this question: using just binary occupancy sensors is it possible to build a behaviour occupancy model over long-term logged data?. Sensor's measurements are grouped to form artificial words (activities) and documents (set of activities). The goal is to infer the latent topics which are assumed to be the common routines from the observed data. An unsupervised probabilistic model, namely the Latent Dirichlet Allocation (LDA) is applied to automatically discover the latent topics (routines) in the data. Experimental results using real logged data of 24 weeks from an office building, with different number of topics, are shown. The results shown the powerful of the LDA model in order to extract relevant patterns from sensor networks data. http://www.morelab.deusto.es/publications/2011/castanedo2011building.pdf
Views: 519 deustomorelab
Anomaly Detection Using Metrics and Exception Logs | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough, Ted Dunning, Chief Applications Architect at MapR, will talk about how you can use logs containing metrics and exceptions to detect anomalies in the behavior of a micro-service. For related material on this topic see: “A Better Way to Build A Fraud Detector” Whiteboard Walkthrough video by Ted Dunning https://www.mapr.com/blog/better-way-build-fraud-detector-streaming-data-and-microservices-architecture-whiteboard Free pdf of the book "Practical Machine Learning: A New Look at Anomaly Detection" https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection
Views: 4398 MapR Technologies
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 69928 WekaMOOC
How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 534893 Thales Sehn Körting
Cloud Computing Services Models - IaaS PaaS SaaS Explained
 
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https://ecoursereview.com/cloud-computing-pros-and-cons-for-business-it/ 3 Types of Cloud Computing Services: IaaS PaaS SaaS Explained You Might Wonder – What Is Cloud Computer: #Cloudcomputing is a set of forms that contain certain elements that allows for on-demand, network access, scalability, and shared resources. It's a platform for managing, storing, and processing data online through the internet. Some of the cloud computing features include the following: - On-Demand Services – Available when you need it - Network Access – When using the internet as your medium - Shared Resources – All resources are gathered together and used by multiple customers - Scalability - The ability of a computer system to adapt to increasing demands The Three Delivery Models: Cloud computing provides different services based on three delivery configurations. When they are arranged in a pyramid structure, they are in the order of SaaS, PaaS, and IaaS. The Three Services: #SaaS - Software as a Service This service provides on-demand pay per use of the application software for users and is independent of a platform. You do not have to install software on your computer, unlike a license paid program. Cloud runs a single occurrence of the software, making it available for multiple end-users allowing the service to be cheap. All the computing resources that are responsible for delivering SaaS are totally managed by the vendor. The service is accessible through a web browser or lightweight client applications. End customers use SaaS regularly. The most popular SaaS providers offer the following products and services: Google Ecosystem including Gmail, Google Docs, Google Drive, Microsoft Office 365, and SalesForce. #PaaS - Platform as a Service This service is mostly a development environment that is made up of a programming language execution environment, an operating system, web server, and database. It provides an environment where users can build, compile, and run their program without worrying about an hidden infrastructure. You manage the data and application resources. All the other resources are managed by the vendor. This is the realm for developers. PaaS providers offer the following products and services: Amazon Web services, Elastic Beanstalk, Google App Engine, Windows Azure, Heroku, and Force.com #IaaS - Infrastructure as a Service This service provides the architecture and infrastructure. It provides all computing resources but in a virtual environment so multiple users can have access. The resources include data storage, virtualization, servers, and networking. Most vendors are responsible for managing them. If you use this service, you are responsible for handling other resources including applications, data, runtime, and middleware. This is mostly for SysAdmins. IaaS providers offer the following products and services: Amazon EC2, Go Grid, and Rackspace.com. Here is a short list of some companies that use cloud computing: iCloud – Cloud from Apple is for Apple products. You can backup and store everything from multimedia to documents online. The content is then smoothly integrated onto your devices. Amazon's AWS - When you talk about companies using cloud computing, Amazon Web Services leads the pack. It offers IaaS and PaaS to all their customers. Google Cloud – This cloud platform is universal for Google's enormous ecosystem and for other products such as Microsoft Office. It provides storage of data and collaboration along with other services that are included in their cloud computing suite. Microsoft Azure – Offered by Microsoft, it provides SaaS, PaaS, and IaaS for its software and developer tools. If you have used Office 365, then you have used SaaS. IBM Smart Cloud - This offers private, public, and hybrid distribution platforms providing a full range of SaaS, PaaS, and IaaS cloud computing services for businesses. The pay as you go platform generates profits for IBM. New technology is popping up all over the internet and Cloud seems to be on the rise. This is only scratching the surface on what is already available and what will become available throughout 2017. --- 3 Types of Cloud Computing Services - IaaS PaaS SaaS Explained https://www.youtube.com/watch?v=36zducUX16w #CloudComputingServices #CloudComputing --- Follow us on https://www.facebook.com/EcourseReviews https://twitter.com/EcourseReviews https://plus.google.com/+Ecoursereview/posts https://ecoursereview.com/
Views: 417607 Ecourse Review
[PURDUE MLSS] Mining Heterogeneous Information Networks by Jiawei Han (Part 2/2)
 
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Lecture notes: http://learning.stat.purdue.edu/mlss/_media/mlss/han.pdf Mining Heterogeneous Information Networks Multiple typed objects in the real world are interconnected, forming complex heterogeneous information networks. Different from some studies on social network analysis where friendship networks or web page networks form homogeneous information networks, heterogeneous information network reflect complex and structured relationships among multiple typed objects. For example, in a university network, objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing rich information. We explore methodologies on mining such structured information networks and introduce several interesting new mining methodologies, including integrated ranking and clustering, classification, role discovery, data integration, data validation, and similarity search. We show that structured information networks are informative, and link analysis on such networks becomes powerful at uncovering critical knowledge hidden in large networks. The tutorial also presents a few promising research directions on mining heterogeneous information networks. See other lectures at Purdue MLSS Playlist: http://www.youtube.com/playlist?list=PL2A65507F7D725EFB&feature=view_all
Views: 388 Purdue University
How Random Forest algorithm works
 
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In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
Views: 313781 Thales Sehn Körting
More Data Mining with Weka (1.6: Working with big data)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Working with big data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10434 WekaMOOC
Venu Veeravilli: Data-Efficient Quickest Change Detection
 
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Presented January 11, 2013 at the University of Florida by the Laboratory for Cognition and Control in Complex Systems. Presentation slides: http://ccc.centers.ufl.edu/sites/default/files/files/CCC-Jan-13-Veeravalli.pdf More info: http://ccc.centers.ufl.edu/?q=CCSS_Venu_Veeravalli Visit us! http://ccc.centers.ufl.edu Abstract: In the classical quickest change detection problem, there is a sequence of observations whose distribution changes at an unknown time, and the goal is to detect the change as quickly as possible, subject to a false alarm constraint. In many engineering applications of quickest change detection, there may be a cost (e.g., energy) associated with acquiring observations. We therefore consider the quickest change detection problem with an additional constraint on the cost of the observations used in the detection process, i.e., we seek algorithms that are data-efficient or energy-efficient. The objective is to select an observation control policy along with the stopping time at which the change is declared, so as to minimize the average detection delay, subject to both a false alarm constraint as well as a constraint on the average number of observations used before the change point. We consider both Bayesian and minimax formulations of the problem, and extensions to the setting where the observations are available at a set of distributed sensors. In all of these cases, we develop algorithms that are first-order asymptotically optimal as the false alarm rate goes to zero. We also demonstrate through numerical studies that our algorithms can be considerably more efficient than algorithms based on fractional sampling, where the observations to be skipped are determined a priori in order to meet the observation constraint. (This is joint work with Taposh Banerjee.)
Views: 2138 cccUF
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
 
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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 31526 edureka!
How Chiller, AHU, RTU work - working principle Air handling unit, rooftop unit hvac system
 
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Views: 406573 The Engineering Mindset
Incremental Detection of Inconsistencies in Distributed Data
 
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Views: 150 DEEPTI SREE
ArcGIS Geostatistical Analysis: Spatial Interpolation (IDW)
 
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Spatial Analysis means to manipulate geographic data to extract new meaningful information. Interpolation is one of such geostatistical methods in which we use known values at sampled points to generate a continuous surface giving us prediction of values at unknown points. IDW is an interpolation technique in which values of cells are predicted by averaging known point values while processing each neighborhood cell. Points which are closer to the estimated cell have more weightage in the averages. IDW is preferred over Kriging in situations when sampled points are densely distributed over the surface. How to perform Spatial Interpolation in ArcGIS: 1. Open ArcGIS. 2. Add XY data in ArcMap. In this case, we have an Excel spreadsheet of Monthly Average Precipitation Data in .XLS format. 3. Convert XY data to Shapefile (.shp format). 4. Add boundary over data. 5. Select points which lie within the boundary. 6. Export selected points to new Shapefile. 7. Search for the IDW tool within the Interpolation toolset inside Spatial Analyst toolbox. 8. Choose the column of known point values as Z value field. 9. Mask the output of Raster Analysis to the given boundary in the Environments Settings. The Interpolated surface is obtained which can also be exported as a Raster Dataset for further analysis.
Views: 64518 Geospatial Geeks
Free Research Papers, Thesis, Seminar topics suggestions and reference papers
 
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Dear Students, You can free download the following research papers. So that these research will be very helpful for your research, seminar or thesis topic. Research Paper Titles are given below. You can download these research papers from the link http://ijritcc.org/vol_2_issue_1.html 01 Image Retrieval Using Gradient Operators Ajinkya P.Nilawar. 1 - 4 02 PAPR Reduction in the OFDM signal Using Partial Transmit Sequence Dipen B. Patel, Niraj Tevar, Anand Patel 5 - 10 03 Object Tracking Using Wireless Sensor Network Nandhini.S, Mehzabeen.S.M.M, Swantana.K.L 11 - 16 04 Design and Performance Evaluation of DL MAC Scheduling Model in LTE Siddharth Mahida, Aslam .S. Durvesh 17 - 19 05 AUSHDHI A Life Saver Vardhman Jain 20 - 24 06 Web Authoring Tool for Effective Content Management J.Shyam Jegadeesh, J.John Spencer, P.Libin Jacob, Anisha Pearl 25 - 32 07 A Survey Paper of Optical Fiber Sensor Parikh Meera J., Prof.Gharge Anuradha P. 33 - 39 08 Enterprise Resource Planning Driving Human Resource Management Vikas Bansal, Dr. Vani Narula 40 - 47 09 Maximizing Throughput of Decentralized Wireless Sensor Network Using Reinforcement Learning Mr. Hardik Kumar M. Patel, Mr. Krunal N. Patel 48 - 50 10 A Review to Improve the Productivity of the Hemispherical Type Solar Sill Ajayraj S Solanki, Palak Patel, Umang R Soni, Ashish R Patel 51 - 57 11 Localization Algorithm for Mobile Sensor Nodes Using 3D Space in Wireless Sensor Network Pratik S. Patel, Jignesh R. Patel 58 - 62 12 Optimization Handoff in Mobility Management for the Integrated Macrocell Femtocell LTE Network Ms.Hetal Surti, Mr.Ketan Goswami 63 - 67 13 Review on Software Testing Techniques Bindia Tarika 68 - 72 14 A Universal Similarity Model for Transactional Data Clustering Mr.G.Kamalraja, Mrs.K.Prathiba, Ms.C.M.Parameshwari 73 - 79 15 Cluster Based Intrusion Detection Technique for Wireless Networks Mr. P.Sundara Vadivel, Mr. J. Senthil Kumar, Mr. M.Sudharsanan 80 - 87 16 Cell Selection Techniques in Heterogeneous LTE Advanced System Dhaval M. Tandel, Tanvi Shah 88 - 91 17 PAPR Reduction in the OFDM Signal Using Selective Mapping Patel Jigar, Darshana Shah, Patel Anand 92 - 95 18 Research Paper on Basic of Artificial Neural Network Ms. Sonali. B. Maind, Ms. Priyanka Wankar 96 - 100 19 Testing of Analog and RF Circuits using Embedded Sensors Shrikant Bhoyar, Nilesh Chide, Ashwini Shende, Avinash Mahule 101 - 104 20 Efficient Measurement Procedure for Access Control to Maximizing Throughput in LTE Femtocell Networks Prof.Utkarsh V.Shah, Mr.Dhvanil J. Patel 105 -- 108 21 Evaluate the Performance of Video Transmission Using H.264 (SVC) Over Long Term Evolution (LTE) Mr. Jaimin Surati, Mr. Ketan Goswami 109 -- 113 22 Web Server Security and Survey on Web Application Security Shaikh Bushra Almin 114 -- 119 23 Revolution Technique for Internet Of Things "6LowPAN" Jaydev Vaidya, Punit Raninga, Dr.Jaymin Bhalani 120 -- 123 24 Smart Attendance System Pushpa S. Gagare., Priyanka A. Sathe, Vedant T. Pawaskar., Sagar S. Bhave 124 -- 127 25 Emotion Generation using LPC Synthesis Mrs.Sulakshana N. Bhatlawande, Prof. Dr. Shaila D. Apte 128 -- 134 26 Performance Evaluation of Face Recognition Algorithms S. Suganya, D. Menaka 135 -- 140 27 Parent Aid Mobile Application Tazeen Sayad, Jash Upadhyay, Chetan Naik, Priyanka Mane 141 -- 143 28 An Effective Sentence Ordering Approach For Multi-Document Summarization Using Text Entailment P Sukumar. K S Gayathri 144 -- 149 29 Analysis of Training Functions in a Biometric System Anusree K, Dr. Binu G S 150 -- 154 30 A Review on Different Image De-noising Methods Keyur Patel, Hardik N. Mewada 155 -- 159 31 Red Tacton Sayli M. Rane., Vishakha V. Dalvi., Bhushan Gaikwad., Sanchita D. Balsarf. 160 -- 164 32 An Efficient Distributed Intrusion Detection System in Grid Environment Using IDS Ms G.Supraja , Mr.C.Gopala Krishnan , Ms.S.Yasmin Junaithi Nisa 165 -- 169 33 Zigbee Technology Vinita A. Batini, Prachi P. Kamble., Snehal S. Mharse. 170 -- 172
Views: 4161 Rahul Sharma
Fridays of QGIS: How to model and predict land cover change
 
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In QGIS we can easily analyze and model land use/land cover change. Using the MOLUSCE plugin we can evaluate total change area at the land cover class level, model change probability, simulate change using cellular automata and perform model validation. One of the most interesting features of MOLUSCE is that it allows modeling transition potential using four different techniques: neural networks, logistic regression, multi-criteria evaluation or weights of evidence. Data for this tutorial were downloaded from: http://pie-lter.ecosystems.mbl.edu/content/spatial-gis-data-idrisi-files SUBSCRIBE for more QGIS video tutorials! To learn more about GIS and remote sensing visit my website: http://amsantac.co/
Views: 19536 Alí Santacruz
IOT: Display of Underground Cable Fault Locator To Find Exact Location over Internet
 
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1. IOT: Display of Underground Cable Fault Locator To Find Exact Location over Internet, 2. DISPLAY OF UNDERGROUND CABLE FAULT DISTANCE OVER, 3. IOT Underground Cable Fault Detector Project , 4. Display of Underground Cable Fault Distance over Internet – Edgefxkits, 5. Display of Underground Cable Fault Distance Over Internet using, 6. Underground Cable Fault Distance Locator Circuit and Working, 7. Iot Display of Underground Cable Fault Locator To Find Exact, 8. display of underground cable fault distance over internet, 9. underground cable fault distance locator – IJIERT, 10. Underground Power Cable - Fault Locator Manufacturer?, 11. underground cable fault distance locator project report pdf, 12. display of underground cable fault distance over internet ( iot ) of things using gsm pdf, 13. underground cable fault distance locator project ppt, 14. display of underground cable fault distance over internet ppt, 15. display of underground cable fault distance over internet pdf, 16. display of underground cable fault distance over internet project, 17. underground cable fault detection using Arduino, 18. iot based underground cable fault detection, 19. Detect Underground Utilities - New UtilityScan GPR system?, 20. Underground Power Cable - Fault Locator Manufacturer - tindia.in?, 21. Underground Cable Fault Distance Conveyed Over - IOSR Journals, 22. Precious Underground Cable Fault Locator To Find Exact Location, 23. Use an underground cable locator to find buried cable - Cabling Install, 24. Analysis of fault detection and its location using – IRJET, 25. Arduino Based Underground Cable Fault Detection | Circuits4you.com, 26. Display of Underground Cable Fault Distance over Internet, 27. Display of Underground Cable Fault Distance over #Internet – Pinterest, 28. Shop Underground Cable Fault Distance Locator - DIY(Do It Yourself, 29. Arduino Controller Based Ungrounded Cable Fault Detection Locator, 30. Underground Cable Fault Detection Over Internet Of Things (Iot) Google, 31. IOT: Display of Underground Cable Fault Locator To Find Exact Location, 32. IOT Vehicle Positioning System with Exact Location Address Based On , 33. Development of a Prototype Underground Cable Fault, - IRD India, 34. ECE final year projects for B.Tech & M.Tech students | IEEE 2017, 35. www.wineyard.in/ECE-Projects.php, 36. Smart street lights and fault location monitoring in cloud over IoT, 37. Underground cable fault distance identifier over internet of things (IoT), 38. The Wireless Transmission Design of a Novel Electronic Current Transformer, 39. Temperature data logger and display on pc through ZigBee using, 40. Iot display of underground cable fault locator to find exact location over, 41. Android Based College Guide System With RFID Technology, 42. Embedded webserver sensors monitoring on IOT Webpage, 43. Industrial Device Switching through RF & Arduino, 44. electronic touch home automation using android bluetooth, 45. Temperature ,Humidity Indicator Control using Arduino, 46. Automatic Intelligent Plant Watering System Using Arduino, 47. Automatic Door Opening and Room Illumination Control System Using IR and PIR Sensors, 48. Fire Detection and Water Sprinkler Robot using Arduino, 49. Bus Detection Device for the Blind Using RFID Application, 50. Temperature Based Fan Speed Control And Monitoring Using Arduino, 51. Development Of Electronic Shopping Trolley in SuperMarket, 52. human detection robot using pir sensor, 53. Automatic Public Tap Control System Using IR Sensor to Prevemt Wastage Of Water, 54. Embedded Password Based Door Lock System, 55. ARM7 LP2148 and Android BASED Smart Home Automation Using Wifi Esp8266, 56. IOT Based LPG GAS Booking & Sensor Alert System [ SMS / E - MAIL ], 57. SMS Based Multi Color LED Scrolling Display Using APP, 58. IOT Based Industrial Water Quality Monitoring System using Temperature, Ph and Turbidity Sensors, 59. IoT Based Health Monitoring System using Raspberry Pi, 60. DESIGN OF LOW COST SYSTEM REAL TIME MONITORING OF WATER QUALITY PARAMETERS IN IoT, 61. Digital Code Lock Using Arduino Uno, 62. IOT Based Electronic Door Opener, 63. IOT Based Garbage Monitoring, 64. Microcontroller Based Toxic Liquid Level Monitoring Control System, 65. Monitoring of Highway Hybrid Parameter & Controlling Highway Light Through IOT, 66. Aadhar Card Enabled Automatic Ration Distribution using RFID,GSM AND FINGERPRINT, 67. Iot Based Smart Agriculture Monitoring System, 68. Pendrive to pendrive data transfer without pc, 69. IOT Based Agriculture Crop - Field Monitoring System and Irrigation Automation, 70. An IoT Based Patient Monitoring System using Raspberry Pi,
Views: 7491 svsembedded
29 ELSEVIER  A simple, least time, and energy efficient routing protocol with one level data
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
From Data to Knowledge - 305 - Alexis Bondu
 
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Slides: http://lyra.berkeley.edu/CDIConf/pdfs/Bondu-BERKELEY-PDF.pdf Alexis Bondu: "A Supervised Approach for Change Detection in Data Streams". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012). Abstract Alexis Bondu (Electricite de France R&D) In recent years, the amount of data to process has increased in many application areas such as network monitoring, web click and sensor data analysis. Data stream mining answers to the challenge of massive data processing, this paradigm allows for treating pieces of data on the fly and overcomes exhaustive data storage. The detection of changes in a data stream distribution is an important issue which application area is wide. In this article, change detection problem is turned into a supervised learning task. We chose to exploit the supervised discretization method "MODL" given its interesting properties. Our approach is favorably compared with an alternative method on artificial data streams, and is applied on real data streams.
Views: 176 ckleinastro
SmartMesh ICO review| This coin having potential| watch complete video for Info|
 
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Site- https://smartmesh.io Whitepaper: https://smartmesh.io/SmartMeshWhitePaperCN.pdf telegram:http://bit.ly/smartmeshtelegram facebook:http://bit.ly/smartmeshfacebook twitter:http://bit.ly/smartmeshtwitter SmartMesh introduction :SmartMesh is the first blockchain-based Mesh project in the world. Relying on the underlying protocol of IoT(internet of things), SmartMesh enables smart mobile phones and other smart devices to connect each other without internet. SmartMesh has embedded blockchain light nodes inside to extend the secondary structure network protocol of blockchain such as Raiden Network and Lightning Network for the micropayment of tokens without internet. With the token incentivization, SmartMesh is able to establish a flexible, decentralized and self-healing Mesh Network. It provides a better near field velocity and bandwidth. Moreover, it is usually free. SmartMesh is assisting blockchain technology to break through the border of internet, the era of IoT(internet of things) and IoE(internet of everything) is approaching. MeshBox introduction : MeshBox is a mining capable hardware box based on SmartMesh protocol. It combines Mesh network routing with content storage and distribution functionality and is the next generation decentralized version of HUAWEI and CISCO technology. Interfacing directly with community users and enabling everyone to become operators. Following this paradigm, decentralized blockchain Mesh networks will gain worldwide prevalence. The MeshBox can be put anywhere a network is required and can connect to devices (smartphones, IoT hardware,etc.) and act as a gateway to internet connectivity for these devices. With MeshBox’s technology, locations that currently have limited or no access to the internet can harness a mesh network to facilitate the connection, as well as to develop an internal, closed network that allows the transfer of data between connected devices. Blockchain integration facilitates the use of tokens as a means of payment for access to the network, access to content shared across the network, internet access and more. For more information on SmartMesh and MeshBox please visit www.smartmesh.io and www.meshbox.network Articles: Quick Q&A with Henry Wang?—?Founder of Firefly Mesh: https://medium.com/@smartmesh/quick-q-a-with-henry-wang-founder-of-firefly-mesh-2bc1cf84cdfc Tech Talk: Raiden Network, Ethereum, and SmartMesh Ecosystem: https://medium.com/@smartmesh/tech-talk-raiden-network-ethereum-and-smartmesh-ecosystem-24f41e6a3b95 What’s Coming Up in November? Blockshow and SmartMesh’s ICO are On their Way!: https://medium.com/@smartmesh/whats-coming-up-in-november-blockshow-and-smartmesh-s-ico-are-on-their-way-9d6554485a10 Founder of SmartMesh.io and IBAF joins IBA advisory board: https://medium.com/@smartmesh/founder-of-smartmesh-io-and-ibaf-joins-iba-advisory-board-ae9535b5d44 CLOUT Attending 2017 Asia Blockchain Application Congress Aims to Be A New Home for Global Crypto Community: https://medium.com/@smartmesh/clout-attending-2017-asia-blockchain-application-congress-aims-to-be-a-new-home-for-global-crypto-36b4981cee98 SmartMesh at the Blockchain & Cryptocurrency Conference in Toyo: https://medium.com/@smartmesh/smartmesh-at-the-blockchain-cryptocurrency-conference-in-tokyo-12ebf91be802 SmartMesh Decentralized Mesh Networks: https://medium.com/@smartmesh/smartmesh-decentralized-mesh-networks-9d0b1937ded3 China’s Blockchain Billboard Announced That SmartMesh Won The “International Blockchain Innovation Award: https://medium.com/@smartmesh/chinas-blockchain-billboard-announced-that-smartmesh-won-the-international-blockchain-innovation-33ede184622f app: Android Download: http://smartmesh-1251029933.file.myqcloud.com/package/SmartMesh.apk iOS Download: itms-services://?action=download-manifest&url=https://smartmesh.io/ios/manifest.plist
Appearance-based landmark selection for efficient long-term visual localization
 
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In this video we present an online landmark selection method for efficient and accurate visual localization under changing appearance conditions. The wide range of conditions encountered during long-term visual localization by e.g., fleets of autonomous vehicles, offers the potential to exploit redundancy and reduce data usage by selecting only those visual cues which are relevant at the given time. Therefore co-observability statistics guide landmark ranking and selection, significantly reducing the amount of information used for localization while maintaining or even improving accuracy. The approach is demonstrated in two different environments: one with day-to-night appearance changes, and one with seasonal and weather changes spanning one year. Data is recorded with a car equipped with four cameras facing forwards, backwards, left, and right (representative images shown in the top-left during the video). For more information, please refer to the following publication M. Bürki, I. Gilitschenski, E. Stumm, R. Siegwart, and J. Nieto, "Appearance-based landmark selection for efficient long-term visual localization," (to appear) in IROS 2016. https://www.researchgate.net/profile/Mathias_Buerki/publication/305810404_Appearance-Based_Landmark_Selection_for_Efficient_Long-Term_Visual_Localization/links/57a2f3a008aeef8f312026c7.pdf Note: The poses of the vehicle are estimated from a vision-only non-linear least-squares optimization problem, that is, there is no temporal filtering or fusion with other sensor data (wheel-odometry, IMU, etc.). Hence the "shaking" behavior of the car in the visualization.
Views: 898 aslteam
A Generative Model of Urban Activities from Cellular Data
 
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Author: Mogeng Yin, UC Berkeley Abstract: Activity based travel models are the main tools used to evaluate traffic conditions in the context of rapidly changing travel demand. However, data collection for activity based models is performed through travel surveys that are infrequent, expensive, and reflect the changes in transportation with significant delays. Thanks to the ubiquitous cell phone data, we see an opportunity to substantially complement these surveys with data extracted from network carrier mobile phone usage logs, such as call detail records (CDRs). Activity based travel demand models describe travel itineraries of individual users, namely (1) what activities users are participating in; (2) when users perform these activities; and (3) how users travel to the activity locales. In this paper, we first present a method of extracting user stay locations while not over-filtering short-term travel. Second, we apply Input-Output Hidden Markov Models (IO-HMMs) to reveal the activity patterns in real CDR data (with a focus on the San Francisco Bay Area regular commuters) collected by a major network carrier. No personally identifiable information (PII) was gathered or used in conducting this study. The mobility data that was analyzed was anonymous and aggregated in strict compliance with the carrier’s privacy policy. Our approach delivers actionable information to the practitioners in a form of a modular activity-based travel demand model, and captures the heterogeneous activity transition probabilities conditioned on spatial-temporal context. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 383 KDD2016 video
Intrusion Detection in Homogeneous & Heterogeneous Wireless Sensor Networks pgembeddedsystems IEEE 2
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 1105 PG Embedded Systems
2013 IEEE  Harvesting Aware Energy Management for Time Critical Wireless Sensor Networks With Joint
 
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ECWAY TECHNOLOGIES @ IEEE EMBEDDED PROJECTS ECWAY TECHNOLOGIES @ IEEE MECHANICAL PROJECTS ECWAY TECHNOLOGIES @ IEEE VLSI PROJECTS ECWAY TECHNOLOGIES @ IEEE ROBTICS PROJECTS ECWAY TECHNOLOGIES @ IEEE POWER ELECTRONICS PROJECTS ECWAY TECHNOLOGIES @ IEEE JAVA PROJECTS ECWAY TECHNOLOGIES @ IEEE .NET PROJECTS ECWAY TECHNOLOGIES @ IEEE NS2 PROJECTS ECWAY TECHNOLOGIES @ IEEE MATLAB PROJECTS ECWAY TECHNOLOGIES @ IEEE ANDROID PROJECTS SUMMARY: Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training...... ---------------------------------------------------------------- JAVA & .NET PROJECTS: Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc., EMBEDDED SYSTEMS: Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc., ROBOTICS PROJECTS: Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc., MOBILE APPLICATION (ANDROID & J2ME): Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc., MECHANICAL PROJECTS: Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc., CONTACT US: ECWAY TECHNOLOGIES 15/1 Sathiyamoorthi Nagar, 2nd Cross, Thanthonimalai(Opp To Govt. Arts College) Karur-639 005. TamilNadu , India. Cell: +91 9894917187. Website: www.ecwayprojects.com | www.ecwaytechnologies.com Mail to: [email protected]
Views: 20 Ecway Erode
An Introduction to GPU Programming with CUDA
 
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If you can parallelize your code by harnessing the power of the GPU, I bow to you. GPU code is usually abstracted away by by the popular deep learning frameworks, but knowing how it works is really useful. CUDA is the most popular of the GPU frameworks so we're going to add two arrays together, then optimize that process using it. I love CUDA! Code for this video: https://github.com/llSourcell/An_Introduction_to_GPU_Programming Alberto's Winning Code: https://github.com/alberduris/SirajsCodingChallenges/tree/master/Stock%20Market%20Prediction Hutauf's runner-up code: https://github.com/hutauf/Stock_Market_Prediction Please Subscribe! And like. And comment. That's what keeps me going. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: http://supercomputingblog.com/cuda-tutorials/ http://www.nvidia.com/docs/IO/116711/sc11-cuda-c-basics.pdf https://devblogs.nvidia.com/parallelforall/even-easier-introduction-cuda/ https://developer.nvidia.com/cuda-education-training https://llpanorama.wordpress.com/cuda-tutorial/ https://www.udacity.com/course/intro-to-parallel-programming--cs344 http://lorenabarba.com/gpuatbu/Program_files/Cruz_gpuComputing09.pdf http://cuda-programming.blogspot.nl/p/tutorial.html https://www.cc.gatech.edu/~vetter/keeneland/tutorial-2011-04-14/02-cuda-overview.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ No, Nvidia did not pay me to make this video lol. I just love CUDA. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 200446 Siraj Raval
SD IEEE NS2 Design of a Probability Density Function Targeting Energy-Efficient Node Deply WSN
 
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Distributed adaptive network using blow fish
 
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Title: Distributed adaptive network using blow fish Domain: Networking Key Features: 1. The cognitive content of the paper delineates some key features like importing the data in Java using MySQL database, creating the text file and a pdf file for storing the output and importing the .csv (dot csv) file as raw data input. 2. The paper is coaxing in a way that it clearly hashes out the procedure to efficiently execute the Blowfish Algorithm for securely transferring the data within the system. In the initial encryption phase, the blowfish algorithm generates the encrypted output which is written in text file addressed to any drive. The algorithm can be effectively implemented in Eclipse software as it helps to debug the code. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 networking ieee projects 2. latest ieee projects in java 3. latest ieee projects in networking 4. 2017 – 2018 networking projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 networking ieee titles 8. 2017 – 2018 networking base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. networking mini projects using java 11. instruction detection projects in networking, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e networking projects 14. 2017 – 2018 m.e networking projects 15. 2017 – 2018 final year networking projects 16. networking final year projects 17. latest networking final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best networking projects in java 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 wireless networking projects 27. 2017 ieee projects on networking 28. 2017 final year networking projects 29. 2017 networking projects for b.e 30. 2017 networking projects for m.e 31. 2017 latest networking projects 32. latest networking projects 33. latest networking projects in java 34. networking projects demo 35. networking in intrusion detection system 36. intrusion detection system using networking 37. intrusion detection system using networking ppt 38. intrusion detection system using networking technique 39. networking approaches for intrusion detection 40. networking projects using omnet++ 41. networking projects using ns2 42. networking projects using ns3 43. networking projects titles from ieee 44. wireless networking projects titles from ieee 45. networking abstract 46. networking base paper 47. networking research papers 2017 - 2018 48. 2017 - 2018 networking research papers 49. 2017 networking research papers 50. networking IEEE Projects 52. networking and wireless ieee projects 53. 2017 wireless networking ieee projects 54. wireless networking ieee projects 55. ieee projects in wireless networking 56. 2017 wireless sensor networking projects 57. 2017 wireless sensor networking ieee projects 58. 2017 wireless sensor networking projects with source code 59. 2017 wireless sensor networking projects for final year students 60. 2017 wireless sensor networking projects in java 61. 2017 wireless sensor networking projects for students 62. 2017 mini projects on wireless sensor networking 63. latest mini projects on wireless sensor networking 64. wireless sensor networking projects for engineering students 65. cse projects on wireless sensor networking 66. wireless sensor networking related ieee projects 67. ieee projects in wireless sensor networking 68. wireless sensor networking ieee major projects 69. 2017 ieee projects on wireless sensor networking with abstract 70. 2017 wireless sensor networking with abstract 71. wireless sensor networking projects with source code 72. wireless sensor networking projects for students with demo 73. wireless sensor networking projects with source code in java 74. wireless sensor networking mini projects source code 75. list of mini projects in wireless sensor networking 76. 2017 wireless sensor networking mini projects topics 77. 2017 wireless sensor networking related projects 78. 2017 real time wireless sensor networking projects 79. 2017 wireless sensor networking projects titles from IEEE
The Third Industrial Revolution: A Radical New Sharing Economy
 
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The global economy is in crisis. The exponential exhaustion of natural resources, declining productivity, slow growth, rising unemployment, and steep inequality, forces us to rethink our economic models. Where do we go from here? In this feature-length documentary, social and economic theorist Jeremy Rifkin lays out a road map to usher in a new economic system. A Third Industrial Revolution is unfolding with the convergence of three pivotal technologies: an ultra-fast 5G communication internet, a renewable energy internet, and a driverless mobility internet, all connected to the Internet of Things embedded across society and the environment. This 21st century smart digital infrastructure is giving rise to a radical new sharing economy that is transforming the way we manage, power and move economic life. But with climate change now ravaging the planet, it needs to happen fast. Change of this magnitude requires political will and a profound ideological shift. To learn more visit: https://impact.vice.com/thethirdindustrialrevolution Click here to subscribe to VICE: http://bit.ly/Subscribe-to-VICE Check out our full video catalog: http://bit.ly/VICE-Videos Videos, daily editorial and more: http://vice.com More videos from the VICE network: https://www.fb.com/vicevideo Click here to get the best of VICE daily: http://bit.ly/1SquZ6v Like VICE on Facebook: http://fb.com/vice Follow VICE on Twitter: http://twitter.com/vice Follow us on Instagram: http://instagram.com/vice Download VICE on iOS: http://apple.co/28Vgmqz Download VICE on Android: http://bit.ly/28S8Et0
Views: 3367976 VICE
Можно ли бегать после силовой тренировки? Правда ли, что бег сжигает мышечную массу?
 
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Views: 104410 F1tPara
Flexible and Fine Grained Attribute Based Data Storage in Cloud Computing
 
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2016 IEEE Transaction on Cloud Computing For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com
Views: 1034 manju nath
Spitzer's Continuing Adventures (live public talk)
 
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Original air date: Aug. 9 at 7 p.m. PT (10 p.m. ET, 0200 UTC) The Spitzer Space Telescope is one of NASA’s Great Observatories, designed to observe the universe in infrared light. Launched in 2003 with an expected lifetime of five years, Spitzer has succeeded beyond our wildest expectations. This talk will cover engineering feats and technical challenges, as well as recent science highlights. These include science Spitzer was not designed to do, such as the discovery and characterization of seven rocky, potentially habitable planets in the nearby TRAPPIST-1 system. Speaker: Sean Carey, Manager of the Spitzer Science Center, Caltech/IPAC
DEF CON 21 - Teal Rogers and Alejandro Caceres - The dawn of Web 3.0
 
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The dawn of Web 3.0: website mapping and vulnerability scanning in 3D, just like you saw in the movies TEAL ROGERS TRINARY SOFTWARE, OWNER ALEJANDRO CACERES OWNER, HYPERION GRAY, LLC Remember that scene in Hackers where Jonny Lee Miller and Angelina Jolie get a bunch of hackers to attack Fisher Steven's network through vulnerabilities that they find while flying (literally) through Fisher's network? Even though it had no basis in reality at the time, it was still pretty awesome. This presentation will be like that, except real. This highly demo-focused presentation will unleash the next generation of web application visualization and security flaw detection. Created as part of DARPA's Cyber Fast Track, we have developed a completely awesome way of visualizing, in 3D, how massive numbers of web applications across the Internet are interconnected. This visualization engine provides a simple yet beautiful view of web applications and their vast, sprawling interconnections, all the while incorporating web application vulnerabilities into the visual metadata. Teal Rogers is a dedicated maker and software designer who has been advancing existing products through innovative new interfaces for years. Between being a brilliant imagineer, rogue inventor, warrior-poet, master of surprise, and student of the arcane he has managed to design and sell the highest quality laser gloves on the market. More recently, he has been inexorably drawn to the nascent power of the 3rd dimension. Alejandro Caceres is a computer network operations engineer focused on network offense software development and web application penetration testing and security. He is particularly interested in using distributed computing and offensive security principles to create cool/new/revolutionary open source and free applications with a global impact. Materials: https://www.defcon.org/images/defcon-21/dc-21-presentations/Rogers-Caceres/DEFCON-21-Rogers-Caceres-The-Dawn-of-Web-30.pdf
Views: 1095 DEFCONConference
Harnessing the Internet of Things with NoSQL by Michael Hausenblas
 
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NoSQL matters Conference 2013 - Barcelona,Spain - Harnessing the Internet of Things with NoSQL by Michael Hausenblas. http://2013.nosql-matters.org/bcn/ NThe emerging Internet of Things (IoT) comes in different shapes and one can easily claim that it has already started to penetrate our everyday life: from consumer devices such as smartphones and wearables over buildings to entire cities, where sensors are increasingly deployed; estimates range from 40 billion to 50 billion IoT devices by 2020. In order to manage the data torrent from IoT devices we need to re-think data processing options. Hence, in this talk we will discuss the requirements to capture, store and process data from IoT devices. We will also look into real-world use cases and deployments and make the case why NoSQL solutions are a great fit for harnessing the IoT. Slides are available: http://2013.nosql-matters.org/bcn/wp-content/uploads/2013/12/2013-11-30-Harnessing-the-Internet-of-Things-with-NoSQL-mhausenblas.pdf
Finding Optimal Skyline Product Combinations under Price Promotion
 
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Finding Optimal Skyline Product Combinations under Price Promotion Latest IEEE Projects 2018-2019 IEEE Dataminig dotnet Projects Topics Free download Thanks & Regards, 1Crore Projects dotnet data mining IEEE Projects , Head Office: No.68 & 70 ,Ground Floor, Raahat Plaza, Vadapalani,Chennai - 26. Contact: +91 7708150152 , +91 9751800789 Web :- http://1croreprojects.com/ E-Mail ID : [email protected]
Views: 180 1 Crore Projects
3-GIS Web
 
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3-GIS is mapping the future of the telecommunications and utility industries with products available for the web, for mobile and securely in the cloud. Ready to learn more about 3-GIS and its solutions for your industry? Or would you like a demo of our products? Visit our website at 3-GIS.com for more information. 3-GIS Web is the world's leading browser-based GIS. It has the power of a full GIS and the ease of use of a web browser -- any web browser. 3-GIS Web operates on Esri's ArcGIS Server platform.
Views: 2315 3-GIS
IEEE 2013 NS2 FULL DOCUMENT Selfish Attacks and Detection in Cognitive Radio Ad Hoc Networks existin
 
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Views: 183 ganesh pg
Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
 
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This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 171411 Rushdi Shams
MILITARY MANAGEMENT SYSTEM- IEEE PROJECTS 2018
 
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Defcon 19: Steve Ocepek - Blinkie Lights: Network Monitoring with Arduino
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net https://www.defcon.org/images/defcon-19/dc-19-presentations/Ocepek/DEFCON-19-Ocepek-Blinkie-Lights-Arduino.pdf https://www.defcon.org/images/defcon-19/dc-19-presentations/Ocepek/Extras.zip Remember the good old days, when you'd stare at Rx and Tx on your shiny new Supra 1200bps modem, and actually know what the heck was going on? Systems tend to talk a lot more nowadays, and somewhere along the line I completely lost track of who mine hangs out with. And I kind of miss my blinkie lights. But we live in a world of Arduino and cheap LEDs - maybe there's a way to play with electronics, talk about security, and show the kids a thing or two - all at the same time. Imagine if one of those USB toys on your desk could actually give you an indication of which countries you were trading packets with, or alert you to unusually long-running sessions. 'cerealbox' will demonstrate how an 8x8 multicolor LED matrix, Arduino, and a network monitoring program can be used to make an LED-based sniffer for around . And if that doesn't sound interesting, just wait until you see Port Scan Inferno. Steve Ocepek was one of the original team behind Wholepoint, a computer security consultancy that later merged with Trustwave. As Director of Security Research for Spiderlabs, he is in charge of all signature development for all products, maintaining and updating open source projects, researching new threats, providing intelligence to premier clients, pursuing security advisories, and supporting other SpiderLabs teams during technical engagements. Ocepek's accomplishments include discovering and patenting a new method of detecting wireless clients from the wired network, as well as creating the "thicknet" framework to analyze protocols for Man-in-the-Middle attack surface. He has a featured as a keynote speaker at industry conferences such as Blackhat in both the USA and Europe, and OWASP AppSec. Ocepek is a Certified Information Systems Security Professional (CISSP) and a member of Northeast Ohio Information Security Forum. Twitter: @nosteve
Views: 716 SecurityTubeCons
NDSS 2018 - Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
 
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Session 3A: Deep Learning and Adversarial ML - 03 Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection SUMMARY Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. However, a drawback of neural networks is the amount of resources needed to train them. Many network gateways and routers devices, which could potentially host an NIDS, simply do not have the memory or processing power to train and sometimes even execute such models. More importantly, the existing neural network solutions are trained in a supervised manner. Meaning that an expert must label the network traffic and update the model manually from time to time. In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner. Kitsune’s core algorithm (KitNET) uses an ensemble of neural networks called autoencoders to collectively differentiate between normal and abnormal traffic patterns. KitNET is supported by a feature extraction framework which efficiently tracks the patterns of every network channel. Our evaluations show that Kitsune can detect various attacks with a performance comparable to offline anomaly detectors, even on a Raspberry PI. This demonstrates that Kitsune can be a practical and economic NIDS. SLIDES http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/03/NDSS2018_03A-3_Mirsky_Slides.pdf PAPER https://www.ndss-symposium.org/wp-content/uploads/sites/25/2018/02/ndss2018_03A-3_Mirsky_paper.pdf AUTHORS Yisroel Mirsky (Ben-Gurion University of the Negev) Tomer Doitshman (Ben-Gurion University of the Negev) Yuval Elovici (Ben-Gurion University of the Negev) and Asaf Shabtai (Ben-Gurion University of the Negev) Network and Distributed System Security (NDSS) Symposium 2018, 18-21 February 2018, Catamaran Resort Hotel & Spa in San Diego, California. https://www.ndss-symposium.org/ndss2018/programme/ ABOUT NDSS The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies. https://www.ndss-symposium.org/ #NDSS #NDSS18 #NDSS2018 #InternetSecurity
Views: 844 NDSS Symposium
International Journal of Mobile Network Communications & Telematics ( IJMNCT)
 
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International journal of Mobile Network Communications & Telematics ( IJMNCT) is an open access peer-reviewed journal that addresses the impacts and challenges of mobile communications and telematics. The journal also aims to focus on various areas such as ecommerce, e-governance, telematics, telelearning nomadic computing, data management, related software and hardware technologies, and mobile user services. The journal documents practical and theoretical results which make a fundamental contribution for the development of mobile communication technologies.
Views: 15 AIRCC
Natural Language Processing (NLP) with Dr. Peter Molnár - Part 1
 
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Anova Analytics and the Data Science & Business Intelligence Society of Atlanta's June Meetup, Modern Tools in Sentiment Analysis & Natural Language Processing with Dr. Peter Molnar. Dr. Molnár covered high level NLP concepts. Dr. Péter Molnár is a data scientist at RentPath LLC, a leading digital marketing company for the residential rental industry. He is also a research scientist in the Institute for Insight at the J. Mack Robinson College of Business at Georgia State University. Dr. Molnar has been an associate professor of computer science at Clark Atlanta University where he taught artificial intelligence, high-performance computing, robotics, and development of web-applications. His research in social agent systems, collaborative systems, and distributed sensor-fusion has received funding from federal and state agencies. Addition information is available on Dr. Molnar's GitHub page - https://github.com/kingmolnar/natural_language Here are the links to the Toolkits • Natural Language Toolkit http://www.nltk.org/ • Stanford CoreNLP https://stanfordnlp.github.io/CoreNLP/ • OpenNLP http://opennlp.apache.org/ • Prolog Natuaral Language Tool (ProNTo) http://ai1.ai.uga.edu/mc/pronto/ • Facebook AI Research Sequence-to-Sequence Toolkit https://github.com/facebookresearch/fairseq • SyntaxNet: https://github.com/tensorflow/models/tree/master/syntaxnet Recommended Books Dan Jurafsky & James H. Martin, Speech and Language Processing (3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/ • Chris Manning & Hinrich Schütze, Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: May 1999. https://nlp.stanford.edu/fsnlp/ (PDF) • Michael A. Covington, Natural Language Processing for Prolog Programmers, Prentice-Hall, 1994. http://www.covingtoninnovations.com/books.html#nlp • Steven Bird, Ewan Klein, & Edward Loper, Natural Language Processing with Python, O'Reilly, 2009. http://www.nltk.org/book/
Views: 226 Anova Analytics
Thorium.
 
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http://ThoriumRemix.com/ Thorium is an abundant material which can be transformed into massive quantities of energy. To do so efficiently requires a very different nuclear reactor than the kind we use today- Not one that uses solid fuel rods, but a reactor in which the fuel is kept in a liquid state. Not one that uses pressurized water as a coolant, but a reactor that uses chemically stable molten salts. Such a reactor is called a "Molten Salt Reactor". Many different configurations are possible. Some of these configurations can harness Thorium very efficiently. This video explores the attributes of Molten Salt Reactors. Why are they compelling? And why do many people (including myself) see them as the only economical way of fully harnessing ALL our nuclear fuels... including Thorium. This video has been under development since 2012. I hope it conveys to you why I personally find Molten Salt Reactors so compelling, as do the many volunteers and supporters who helped create it. Much of the footage was shot by volunteers. All music was created by: http://kilowattsmusic.com To support this project, please visit: https://patreon.com/thorium Entities pursuing Molten Salt Reactors are... Flibe Energy - http://flibe-energy.com/ Terrestrial Energy - http://terrestrialenergy.com/ Moltex Energy - http://www.moltexenergy.com/ ThorCon Power - http://thorconpower.com/ Transatomic - http://www.transatomicpower.com/ Seaborg - http://seaborg.co/ Copenhagen Atomics - http://www.copenhagenatomics.com/ TerraPower - http://terrapower.com/ Bhabha Atomic Research Centre - http://www.barc.gov.in/ Chinese Academy of Sciences - http://english.cas.cn/ Regular Thorium conferences are organized by: http://thoriumenergyalliance.com/ http://thoriumenergyworld.com/ Table of Contents 0:00:00 Space 0:17:29 Constraints 0:28:22 Coolants 0:40:15 MSRE 0:48:54 Earth 0:59:46 Thorium 1:22:03 LFTR 1:36:13 Revolution 1:44:58 Forward 1:58:11 ROEI 2:05:41 Beginning 2:08:36 History 2:38:59 Dowtherm 2:47:57 Salt 2:51:44 Pebbles 3:06:07 India 3:18:44 Caldicott 3:35:55 Fission 3:56:22 Spectrum 4:04:25 Chemistry 4:12:51 Turbine 4:22:27 Waste 4:40:15 Decommission 4:54:39 Candlelight 5:13:06 Facts 5:26:08 Future 5:55:39 Pitches 5:56:17 Terrestrial 6:08:33 ThorCon 6:11:45 Flibe 6:20:51 End 6:25:53 Credits Some of this footage is remixed from non-MSR related sources, to help explain the importance of energy for both space exploration and everyday life here on Earth. Most prominently... Pandora's Promise - https://youtu.be/bDw3ET3zqxk Dr. Neil DeGrasse Tyson - https://youtu.be/Pun76NZMjCk Dr. Robert Zubrin - https://youtu.be/EKQSijn9FBs Mars Underground - https://youtu.be/tcTZvNLL0-w Andy Weir & Adam Savage - https://youtu.be/5SemyzKgaUU Periodic Table Videos - https://youtube.com/channel/UCtESv1e7ntJaLJYKIO1FoYw
Views: 143122 gordonmcdowell
What's new in Microsoft Excel: Machine learning-powered insights new data types new - BRK2122
 
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As the world’s more popular spreadsheet, Excel has continued to evolve and grow with its users. We have added new features, adapted them based on your feedback and continued to make the functionality in Excel better, faster & more efficient. Join us to learn more about the innovations coming in 2018 - Machine Learning-Powered Insights, New Data Types, New Visualizations, Custom Functions and More! From business analysts to line of business specialists, if you use Excel this is a session you don’t want to miss.
Views: 1761 Microsoft Ignite
Dual Link Failure Resiliency through Backup Link Mutual Exclusion PG EMBEDDED SYSTEMS
 
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CURRENT TRANSFORMER WORKING
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 955 PG Embedded Systems

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