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Neal Lathia - Mining smartphone sensor data with python
 
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PyData London 2016 Data from smartphone sensors can be used to learn from and analyse our daily behaviours. In this talk, I'll discuss processing and learning from sensor data with Python. I'll focus on accelerometers - a triaxial sensor that measures motion - starting with an overview pre-processing the data and ending with supervised and unsupervised learning applications and visualisations. Our smartphones are increasingly being built with sensors, that can measure everything from where we are (GPS, Wi-Fi) to how we move (accelerometers) and other aspects of our environments (e.g., temperature, humidity). Many apps are now being designed to collect and leverage this data, in order to provide interesting context-aware services and quantify our daily routines. In this talk, I'll give an overview of collecting sensor data from an Android app and processing the data with Python. I'll focus on accelerometers - a triaxial sensor that measures the device's motion - which is now being used in apps that detect what you are doing (cycling, running, riding a train); if we have enough time I'll also briefly cover a similar example with Wi-Fi/location data. Using an open-sourced Android app and iPython notebook, I'll discuss the following questions: What does the raw data look like? There are a number of trade-offs when collecting sensor data: most notably, data collection needs to be balanced against battery consumption. Plotting the raw data gives a view of how the data was sampled and how it changes across activities. How can I pre-process and extract features from this data? Three kinds of features can be extracted from acceleromter data: statistical, time-series, and signal-based. Most of these are readily available in well-known Python libraries (scipy, numpy, statsmodels). How can these features be used to analyse behaviours? I'll show an example of using accelerometer data to cluster users into groups, based on how active they are. How can these features be used to detect behaviours? I'll show an example of training a supervised learning algorithm (using scikit-learn) to detect walking vs. running vs. standing. I'll close by discussing how these techniques are being applied in novel smartphone apps for health monitoring. GitHub Repo: https://github.com/nlathia/pydata_2016
Views: 3992 PyData
Temporal analysis: Generating time series from events based data
 
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Often data is captured in a different format than required for analysis. Have you ever needed to perform historical analysis on events-based data? For example, how do you calculate turnover based on employees' start and end dates? Or, if sensor data captures when a device switches between on, off, and idle, how do you calculate the percent of time that a device was active per period? Join this Jedi session to find out!
Views: 908 Tableau Software
Being there -- sensor data to sensory superpowers: Gershon Dublon at TEDxWarwick 2014
 
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Gershon Dublon is a PhD student in the Responsive Environments at the MIT Media Lab, where he develops new tools for exploring and understanding sensor data. In his research, he imagines networked sensors forming a collective electronic nervous system that becomes prosthetic through new interfaces to sensory perception—visual, auditory, and tactile. These interfaces can be located both on the body and in the surrounding environment. Gershon received a MS in Media Arts and Science from MIT and a BS in electrical engineering from Yale University. Before coming to MIT, he worked as a researcher at the Embedded Networks and Applications Lab at Yale, contributing to research in sensor fusion for tracking and identifying people. In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 3296 TEDx Talks
Paul Balzer - IPython and Sympy to Develop a Kalman Filter for Multisensor Data Fusion
 
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View slides for this presentation here: http://www.slideshare.net/PyData/paul-balzer PyData Berlin 2014 The best filter algorithm to fuse multiple sensor informations is the Kalman filter. To implement it for non-linear dynamic models (e.g. a car), analytic calculations for the matrices are necessary. In this talk, one can see, how the IPython Notebook and Sympy helps to develop an optimal filter to fuse sensor information from different sources (e.g. acceleration, speed and GPS position) to get an optimal estimate. more: http://balzer82.github.io/Kalman/
Views: 6102 PyData
Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data
 
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This talk proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyper-spectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively.
Views: 470 MIT Education
CADLM - Fusion data for improved vision
 
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How to exploit information from sensors? CADLM proposes the data fusion techniques to obtain a unified visualization.
Sensor data fusion in marine environment
 
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The video shows a data fusion system implemented with BASELABS Connect and Create. It fuses the data from several radar sensors. In the video, a second vessel is tracked continuously based on the radar data (marked in green).
Views: 622 BASELABS GmbH
Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion
 
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Authors: Tim Op De Beéck (KU Leuven) More on http://www.kdd.org/kdd2018/
Views: 127 KDD2018 video
[DEFCON 21] Open Public Sensors, Trend Monitoring and Data Fusion
 
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Open Public Sensors, Trend Monitoring and Data Fusion Speaker: Daniel Burroughs - Associate Director of Technology, Center for Law Enforcement Technology, Training and Research Our world is instrumented with countless sensors. While many are outside of our direct control, there is an incredible amount of publicly available information being generated and gathered all the time. While much of this data goes by unnoticed or ignored it contains fascinating insight into the behavior and trends that we see throughout society. The trick is being able to identify and isolate the useful patterns in this data and separate it from all the noise. Previously, we looked at using sites such as Craigslist to provide a wealth of wonderfully categorized information and then used that to answer questions such as "What job categories are trending upward?", "What cities show the most (or the least) promise for technology careers?", and "What relationship is there between the number of bikes for sale and the number of prostitution ads?" After achieving initial success looking at a single source of data, the challenge becomes to generate more meaningful results by combining separate data sources that each views the world in a different way. Now we look across multiple, disparate sources of such data and attempt to build models based on the trends and relationships found therein. The initial inspiration for this work was a fantastic talk at DC13, "Meme Mining for Fun and Profit". It also builds upon a similar talk I presented at DC18. And once again seeks to inspire others to explore the exploitation of such publicly available sensor systems. Daniel Burroughs first became interested in computer security shortly after getting a 300 baud modem to connect his C64 to the outside world. After getting kicked off his favorite BBS for "accidently" breaking into it, he decided that he needed to get smarter about such things. Since that time he has moved on to bigger and (somewhat) better things. These have included work in virtual reality systems at the Institute for Simulation and Training at the University of Central Florida, high speed hardware motion control software for laser engraving systems, parallel and distributed simulation research at Dartmouth College, distributed intrusion detection and analysis at the Institute for Security Technology Studies, and the development of a state-wide data sharing system for law enforcement agencies in Florida. Daniel was an associate professor of engineering at the University of Central Florida for 10 years prior to his current position as the Associate Technology Director for the Center for Law Enforcement Technology, Training, & Research. He also is a co-founder of Hoverfly Technologies, an aerial robotics company, and serves on the board of directors for Familab -- a hackerspace located in Orlando. He is also the proud owner of two DefCon leather jackets won at Hacker Jeopardy at DEF CON 8 & 9 (as well as few hangovers from trying to win more).
Views: 80 TalksDump
RS4.5 - Model-data fusion
 
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This video is part of the Australian National University course 'Advanced Remote Sensing and GIS' (ENVS3019 / ENVS6319).
Views: 1226 Albert VanDijk
Introduction to the  data fusion designer of BASELABS Create
 
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BASELABS Create delivers fast sensor fusion results with the intuitive data fusion designer. It is a prototyping software framework that is designed for the fast development of complex data fusion algorithms and environment models including 360 degree perception for level 1 to 5 autonomous vehicles.
Views: 981 BASELABS GmbH
Making sense of wearable accelerometer data by Vincent van Hees
 
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This eScience talk was presented at 4th National eScience Symposium Sports & eHealth Session 13 October 2016, Amsterdam ArenA https://www.esciencecenter.nl/event/program/4th-national-escience-symposium/ SPEAKER: Vincent van Hees - Netherlands eScience Center TALK: Making sense of wearable accelerometer data collected under uncontrolled real life conditions ABSTRACT: Measuring human physical activity and sleep is crucial in health research and increasingly performed with wrist-worn acceleration sensors. However, robust classification of detailed activity types and estimation of energy expenditure remains difficult under uncontrolled real life conditions. Our aim is to extract insight from the sensor data beyond the results from current heuristic algorithms, while trying to avoid over-interpretation given the uncertainties that are introduced by uncontrolled real life experimental conditions. We developed an unsupervised data-driven model for identifying clusters in the accelerometer time series data. To aid interpretation we fuse the model output with activity diary recordings as well as conventional heuristic algorithm output. ABOUT: Vincent holds a PhD in Epidemiology from the University of Cambridge and did a post-doc at Newcastle University. Central theme of Vincent’s work has been the development of algorithms to process data from wearable movement sensors as used for population research on human behaviour. At the Netherlands eScience Center, Vincent’s current focus is on novel approaches for time series and sensor data analysis. Vincent published several journal articles on algorithms for automatic interpretation of movement sensor data. His methods are available as generic open source software (R package GGIR), which is increasingly used by the research community.
Tree species mapping by combining hyperspectral with LiDAR data
 
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This study deals with data fusion of hyperspectral and LiDAR sensors for forest applications. In particular, the added value of different data sources on tree species mapping has been analyzed. A total of seven species have been mapped for a forested area in Belgium: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. Hyperspectral data is obtained from the APEX sensor in 286 spectral bands. LiDAR data has been acquired with a TopoSys sensor Harrier 56 at full waveform. Confirming previous research, it has been found that airborne LiDAR data, when combined with hyperspectral data, can improve classification results. The novelty of this study is in the quantification of the contribution of the individual data sources and their derived parameters.
Views: 1197 MIT Education
Opportunistic RF Localization: An Intelligent Data Mining Technique
 
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The title of this lecture, by Ted Morgan, CEO, Skyhook Wireless, is "An Intelligent Data Mining Technique for Emerging Location Based Applications".
Views: 322 WPI
Introduction to Clustering with R-Studio on IoT vibration accelerometer sensor data
 
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https://archive.ics.uci.edu/ml/datasets/Dataset+for+ADL+Recognition+with+Wrist-worn+Accelerometer
Views: 1187 Romeo Kienzler
Class labels in data mining
 
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Machine learning approaches
Views: 56 Sai Krishna
Making Sense of Sensor Data
 
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"Making Sense of Sensor Data" presented by Chris Howard to the Data Science Association Feb. 28, 2015 http://datascienceassn.org/content/machine-learning-contest-kick-and-data-science-presentations-february-28-2015
Views: 33 Michael Malak
Introduction to Bayesian data analysis - part 1: What is Bayes?
 
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Try my new interactive online course "Fundamentals of Bayesian Data Analysis in R" over at DataCamp: https://www.datacamp.com/courses/fundamentals-of-bayesian-data-analysis-in-r ---- This is part one of a three part introduction to Bayesian data analysis. This first part aims to explain *what* Bayesian data analysis is. See here for part 2: https://youtu.be/mAUwjSo5TJE Here are links to the exercises mentioned in the video: R - https://goo.gl/cxfnYK (if this link does not work for you try http://rpubs.com/rasmusab/257829) Python - https://goo.gl/ceShN5 More Bayesian stuff can be found on my blog: http://sumsar.net. :)
Views: 94826 rasmusab
What does Data Fusion mean?
 
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Mike Devrell of Rotork explains how data can be fused together to create usable informatin.
Sensor data from SQL Lego car analyzed by Metanautix Quest
 
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Metanautix Quest uses standard SQL to access and analyze sensor data generated by the SQL Lego Car and visualize results in Tableau. The video shows a Tableau Dashboard powered by Quest that plots the path taken by the SQL Lego Car and colors detected by sensors on the car.
Views: 320 Metanautix Inc.
Final Year Projects | Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/myprojectbazaar Mail Us: [email protected]
Views: 370 myproject bazaar
Usama Fayyad “Next Generation Cyber Security via Data Fusion, AI and Big Data“
 
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Usama Fayyad “Next Generation Cyber Security via Data Fusion, AI and Big Data“
Multimodal Sensor Interface
 
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This video demonstrates a multimodal sensor interface that consists of a microphone (voice control), an Electromyogramic channel (muscle control), a 9-axis Inertial Measurement Unit (inertial control), and a Joystick (manual control). The sensor modalities are being used for reliable and assistive robot control via sensor fusion and replacement when other sensors fail. The apparatus is connected to a nano Windows 7 PC, and robot control is induced via Bluetooth connection.
Views: 95 Theo Edu
Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data
 
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Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org Road traffic speed prediction is a challenging problem in intelligent transportation system (ITS) and has gained increasing attentions. Existing works are mainly based on raw speed sensing data obtained from infrastructure sensors or probe vehicles, which, however, are limited by expensive cost of sensor deployment and maintenance. With sparse speed observations, traditional methods based only on speed sensing data are insufficient, especially when emergencies like traffic accidents occur. To address the issue, this paper aims to improve the road traffic speed prediction by fusing traditional speed sensing data with new-type “sensing” data from cross domain sources, such as tweet sensors from social media and trajectory sensors from map and traffic service platforms. Jointly modeling information from different datasets brings many challenges, including location uncertainty of low-resolution data, language ambiguity of traffic description in texts, and heterogeneity of cross-domain data. In response to these challenges, we present a unified probabilistic framework, called Topic-Enhanced Gaussian Process Aggregation Model (TEGPAM), consisting of three components, i.e., location disaggregation model, traffic topic model, and traffic speed Gaussian Process model, which integrate new-type data with traditional data. Experiments on real world data from two large cities validate the effectiveness and efficiency of our model.
Views: 260 jpinfotechprojects
Introduction to data fusion with BASELABS Create
 
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BASELABS Create is an software development kit (SDK) that can be used in an assisted workflow or in a fully programmatic approach. BASELABS Create is designed for the fast development of complex data fusion algorithms for autonomous vehicles and driver assistance systems.
Views: 871 BASELABS GmbH
Data Fusion for Better Decisions | Webinar
 
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Watch this recorded webinar to learn about the applications and benefits of fusing LiDAR data with spectral information and how this can be achieved in an automated way. Learn how you can: -Quickly and accurately reproject point clouds to prepare the data for fusion with other modalities -Add RGB color and classification information to the point cloud to better understand the data -Incorporate elevation information to improve spectral processing results
09 Mars 2015, Analytics on Sensor Data by Christopher Ré
 
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Bio: Christopher (Chris) Re is an assistant professor in the Department of Computer Science at Stanford University and a Robert N. Noyce Family Faculty Scholar. His work's goal is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his PhD from the University of Washington in Seattle under the supervision of Dan Suciu. For his PhD work in probabilistic data management, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. He then spent four wonderful years on the faculty of the University of Wisconsin, Madison, before moving to Stanford in 2013. He helped discover the first join algorithm with worst-case optimal running time, which won the best paper at PODS 2012. He also helped develop a framework for feature engineering that won the best paper at SIGMOD 2014. In addition, work from his group has been incorporated into scientific efforts including the IceCube neutrino detector and PaleoDeepDive, and into Cloudera's Impala and products from Oracle, Pivotal, and Microsoft's Adam. He received an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, and a Moore Data Driven Investigator Award in 2014.
A Weighted Optimization Approach to Time of Flight Sensor Fusion
 
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A Weighted Optimization Approach to Time of Flight Sensor Fusion +91-9994232214,8144199666, [email protected], www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 19 PROJECTS2014
Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications
 
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Gagner Technologies offers M.E projects based on IEEE 2013 . Final Year Projects, M.E projects 2013-2014, mini projects 2013-2014, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT IEEE 2013 Projects in Data Mining, Distributed System, Mobile Computing, Networks, Networking. IEEE 2013 - 2014 projects. Final Year Projects at Chennai, IEEE Software Projects, Engineering Projects, MCA projects, BE projects, JAVA projects, J2EE projects, .NET projects, Students projects, Final Year Student Projects, IEEE Projects 2013-2014, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT, Contact: Gagner Technologies No.7 Police quarters Road, T.Nagar (Behind T.Nagar Bus Stand),Chennai-600017, call 8680939422,04424320908 www.gagner.in mail: [email protected]
Data Fusion
 
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Data Fusion
Views: 31 SIGRA Technologies
IOS 12, Swift 4, Tutorial : How to fetch Accelerometer Sensor Data   ( Core Motion )
 
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Welcome to Core Motion Accelerometer iOS Tutorial ! Level : intermediate In this tutorial we learn how to fetch core motion device Sensor Data ( Accelerometer). An accelerometer measures changes in velocity along one axis. All iOS devices have a three-axis accelerometer, which delivers acceleration values in each of the three axes shown. This tutorial is made with Xcode 9 and built for iOS 11. You will be using CoreMotion, CMMotionManager, accelerometerUpdateInterval , startAccelerometerUpdates, OperationQueue, current, acceleration, acceleration.x, acceleration.y, acceleration.z, Double and understand how to embedin items inside StackView by Xcodes. This Project Source Code : https://github.com/soonin/IOS-Swift-CoreMotionAccelerometer This Project Source Code : https://github.com/soonin/IOS-Swift-CoreMotionAccelerometer01 GitHub : https://github.com/soonin/ licensed under Creative Commons ::::: ATTN ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Also For better search in Youtube channel you can go to "SwiftVillage" Blog here : Blog : http://swiftvillage.blogspot.com/ Instagram : https://www.instagram.com/codingchallenge/ Twitter : https://twitter.com/swiftvillage1 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Wish the Best For you :) IOS, Swift, Tutorial, Tricks, programmatically , Xcode , IOS 11+, Swift 4+, Beginners, Tutorial , intermediate , senior , IOS 11, Swift 4, CoreMotion, CMMotionManager, accelerometerUpdateInterval , startAccelerometerUpdates, OperationQueue, current, acceleration, acceleration.x, acceleration.y, acceleration.z, Double References & Related links : https://developer.apple.com/ https://developer.apple.com/documentation/coremotion/getting_raw_accelerometer_events
Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking
 
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Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking Abstract: The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second, we propose a complete perception fusion architecture based on the evidential framework to solve the detection and tracking of moving objects problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project, which includes three main sensors: radar, lidar, and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car, and truck.
Views: 448 1 Crore Projects
DEFCON 17: Dangerous Minds: The Art of Guerrilla Data Mining
 
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Speaker: Mark Ryan Del Moral Talabis Senior Consultant, Secure-DNA Consulting It is not a secret that in today's world, information is as valuable or maybe even more valuable that any security tool that we have out there. Information is the key. That is why the US Information Awareness Office's (IAO) motto is "scientia est potential", which means "knowledge is power". The IAO just like the CIA, FBI and others make information their business. Aside from these there are multiple military related projects like TALON,ECHELON, ADVISE, and MATRIX that are concerned with information gathering and analysis. The goal of the Veritas Project is to model itself in the same general threat intelligence premise as the organization above but primarily based on community sharing approach and using tools, technologies, and techniques that are freely available. Often, concepts that are part of artificial intelligence, data mining, and text mining are thought to be highly complex and difficult. Don't mistake me, these concepts are indeed difficult, but there are tools out there that would facilitate the use of these techniques without having to learn all the concepts and math behind these topics. And as sir Isaac Newton once said, "If I have seen further it is by standing on the shoulders of giants". The combination of all the techniques presented in this site is what we call "Guerrilla Data Mining". It's supposed to be fast, easy, and accessible to anyone. The techniques provides more emphasis on practicality than theory. For example, these tools and techniques presented can be used to visualize trends (e.g. security trends over time), summarize large and diverse data sets (forums, blogs, irc), find commonalities (e.g. profiles of computer criminals) gather a high level understanding of a topic (e.g. the US economy, military activities), and automatically categorize different topics to assist research (e.g. malware taxonomy). Aside from the framework and techniques themselves, the Veritas Project hopes to present a number of current ongoing studies that uses "guerilla data mining". Ultimately, our goal is to provide as much information in how each study was done so other people can generate their own studies and share them through the project. The following studies are currently available and will be presented: For more information visit: http://bit.ly/defcon17_information To download the video visit: http://bit.ly/defcon17_videos
Views: 3929 Christiaan008
International Journal of Service Science, Management, Engineering, and Technology
 
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International Journal of Service Science, Management, Engineering, and Technology Ahmad Taher Azar (Benha University, Egypt) and Ghazy Assassa (Benha University, Egypt) http://www.bu.edu.eg/staff/ahmadazar14 Now Available Year Established: 2010 Publish Frequency: Quarterly ISSN: 1947-959X EISSN: 1947-9603 https://www.igi-global.com/journal/international-journal-service-science-management/1132 ___________ Description: The International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) is a multidisciplinary journal that publishes high-quality and significant research in all fields of computer science, information technology, software engineering, soft computing, computational intelligence, operations research, management science, marketing, applied mathematics, statistics, policy analysis, economics, natural sciences, medicine, and psychology, among others. This journal publishes original articles, reviews, technical reports, patent alerts, and case studies on the latest innovative findings of new methodologies and techniques. ___________ Topics Covered: • Agent technologies • Agricultural applications • Agricultural traceability and food safety • Artificial Intelligence • Autonomous systems • Big data technologies and management • Biochemistry • Biomedicine and bioinformatics • Biotechnology • Business Information Systems • Clinical decision support • Cloud Computing • Computational Intelligence • Computational techniques for service operations • Data mining and data security • Decision theory • Disease detection, management and monitoring • Distributed intelligence • Drug discovery • Ecological system modeling • Economic aspects of the service sector • Embedded sensor and mobile database • Evolutionary computing • Expert Systems • Financial innovation • Financial statements analysis • Fraud management • Geographic Information Systems • Heuristics • Image Processing • Information Technology • Intelligent systems and data mining • Life science and medical research • Machine Learning • Management accounting • Markov chains • Models of service systems, services as complex systems • Network management contingency issues • Neuroscience • Optimization Techniques • Pharmaceutical science • Policy, privacy, security, and legal issues regarding services • Reasoning and inferences • Security in software architecture and design • Security patterns • Sensor design, sensor-fusion and sensor-based control • Service design and modeling • Service innovation and marketing • Service oriented architecture and technologies • Service performance measurement and analysis • Service quality measurement, benchmarking, and management • Service risk management • Social Networking • Soft Computing • Software engineering • Stochastic models • Strategic Planning • Supply Chain Management • Systems engineering • Telecommunications and networking technologies • Teleoperation and telerobotics • Venture capital • Virtual Reality • Web informatics • Web intelligence and mining • Web services and technologies • Working capital management
Views: 90 IGI Global
What is SENSOR GRID? What does SENSOR GRID mean? SENSOR GRID meaning, definition & explanation
 
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What is SENSOR GRID? What does SENSOR GRID mean? SENSOR GRID meaning - SENSOR GRID definition - SENSOR GRID explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A sensor grid integrates wireless sensor networks with grid computing concepts to enable real-time sensor data collection and the sharing of computational and storage resources for sensor data processing and management. It is an enabling technology for building large-scale infrastructures, integrating heterogeneous sensor, data and computational resources deployed over a wide area, to undertake complicated surveillance tasks such as environmental monitoring. The concept of a sensor grid was first defined in the Discovery Net project where a distinction was made between “sensor networks” and “sensor grids”. Briefly whereas the design of a sensor network addresses the logical and physical connectivity of the sensors, the focus of constructing a sensor grid is on the issues relating to the data management, computation management, information management and knowledge discovery management associated with the sensors and the data they generate, and how they can be addressed within an open computing environment. In particular in a Sensor Grid is characterized by: Distributed Sensor Data Access and Integration: relating to both the heterogeneity and geographic distribution of the sensors within a sensor grid and how sensors can be located, accessed and integrated within a particular study. Large Data Set Storage and Management: relating to the sizes of data being collected and analyzed by multiple users at different locations for different purposes. Distributed Reference Data Access and Integration: relating to the need for integrating the analysis data collected from a Sensor Grid with other forms of data available of the Internet. Intensive and Open Data Analysis Computation: relating to the need for using a multitude of analysis components such as statistical, clustering, visualization and data classification tools that could be executing remotely on high performance computing servers on a computational Grid. The sensor grid enables the collection, processing, sharing, visualization, archiving and searching of large amounts of sensor data. There are several rationales for a sensor grid. First, the vast amount of data collected by the sensors can be processed, analyzed, and stored using the computational and data storage resources of the grid. Second, the sensors can be efficiently shared by different users and applications under flexible usage scenarios. Each user can access a subset of the sensors during a particular time period to run a specific application, and to collect the desired type of sensor data. Third, as sensor devices with embedded processors become more computationally powerful, it is more efficient to offload specialized tasks such as image and signal on the sensor devices. Finally, a sensor grid provides seamless access to a wide variety of resources in a pervasive manner. Advanced techniques in artificial intelligence, data fusion, data mining, and distributed database processing can be applied to make sense of the sensor data and generate new knowledge of the environment. The results can in turn be used to optimize the operation of the sensors, or influence the operation of actuators to change the environment. Thus, sensor grids are well suited for adaptive and pervasive computing applications. A sensor grid based architecture has many applications such as environmental and habitat monitoring, healthcare monitoring of patients, weather monitoring and forecasting, military and homeland security surveillance, tracking of goods and manufacturing processes, safety monitoring of physical structures and construction sites, smart homes and offices, and many other uses currently beyond our imagination. Various architectures that can be used for such applications as well as different kinds of data analysis and data mining that can be conducted. Examples of architectures that integrate a mobile sensor network and Grids are given in .
Views: 55 The Audiopedia
Prediction of effective rainfall and crop water needs using data mining - IEEE PROJECTS 2018
 
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Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project MOBILE COMPUTING 1. Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks 2. Service Provisioning in Mobile Environments through Opportunistic Computing 3. Traffic-Aware Efficient Mapping of Wireless Body Area Networks to Health Cloud Service Providers in Critical Emergency Situations 4. Distributed Faulty Node Detection in Delay Tolerant Networks Design and Analysis 5. Energy Efficient Multipath Routing Protocol for Mobile ad-hoc Network Using the Fitness Function 6. Research on Trust Sensing based Secure Routing Mechanism for Wireless Sensor Network 7. Lightweight Three-factor Authentication and Key Agreement Protocol for Internet-integrated Wireless Sensor Networks 8. MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic Segmentation 9. Recurring Contacts between Groups of Devices: Analysis and Application 10. Dynamic SON-Enabled Location Management in LTE Networks 11. Low Cost and High Accuracy Data Gathering in WSNs with Matrix Completion 12. CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets 13. Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization 14. Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks 15. Motivating Human-Enabled Mobile Participation for Data Offloading 16. Fast Cell Discovery in mm-Wave 5G Networks with Context Information 17. Wireless Charger Placement and Power Allocation for Maximizing Charging Quality 18. Dynamic Deployment and Cost-Sensitive Provisioning for Elastic MobileCloud Services 19. Multi-Objective Optimization Based Allocation of Heterogeneous Spatial Crowdsourcing Tasks 20. Two-Hop Distance-Bounding Protocols: Keep Your Friends Close 21. Energy-Aware Dual-Path Geographic Routing to Bypass Routing Holes in Wireless Sensor Networks 22. QLDS: A Novel Design Scheme for Trajectory Privacy Protection with Utility Guarantee in Participatory Sensing 23. An Energy-Efficient and Distributed Cooperation Mechanism for k -Coverage Hole Detection and Healing in WSNs 24. Centralized Cooperative Directional Spectrum Sensing for Cognitive Radio Networks 25. Robot Control Strategies for Task Allocation with Connectivity Constraints in Wireless Sensor and Robot Networks 26. Aggressive Voltage and Temperature Control for Power Saving in MobileApplication Processors 27. The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks 28. Distributed Coverage Control of Mobile Sensor Networks in Unknown Environment Using Game Theory: Algorithms and Experiments 29. HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices 30. Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems 31. High Resolution Beacon-Based Proximity Detection for Dense Deployment 32. Full-Duplex or Half-Duplex: A Bayesian Game for Wireless Networks with Heterogeneous Self-Interference Cancellation Capabilities 33. SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization 34. Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic 35. Measurement-Driven Modeling for Connection Density and Traffic Distribution in Large-Scale Urban Mobile Networks
Views: 42 Micans Infotech
Project 7-8. Sensor Data Analytics
 
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The aim of the project is to read the sensor data, transfer this to computer, and then plot the histogram of the sensor data in real-time. The system uses IR sensor to take the input from. It outputs the data onto computer screen via serial interface connection. MAX232 was used to establish serial between computer and microcontroller. The hardware part of the system exploits minimal set of elements. For software part, there are only three functions. Plus function increments all the values (+) when IR sensor detects the light. Minus function, on contrast, decrements the variable if sensor does not detect light (-). The Init function serves to establish serial communication. The output is the sum of plus and minus function. The PC screen displays sensor data in the PuTTY interface in the real time. These videos were created under the guidelines of Embedded Microcontrollers Class. There are 8 projects in total. Projects were carried out by 5 students. Professor: Dr. Alex James School: Nazarbayev University Department: Electrical & Electronic Engineering Place: Astana, Kazakhstan Team: Sanzhar Askaruly, Rassul Bairamkulov, Aidyn Myltykbayev, Yerlan Rizukov, Aibek Ryskaliyev. Link to a full project report is here: https://www.academia.edu/9763443/Project_7-8._Sensor_Data_Analytics
Views: 281 Sanzhar Askaruly
JPDA Tracker
 
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Example of a joint probabilistic data association (JPDA) tracker with constant velocity (CV) motion models. The example have been implemented using Matlab's Sensor Fusion and Tracking toolbox.
Views: 18 Gustaf Hendeby
QGRAD2 vs. Kalman Filter
 
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Our QGRAD2 Filter vs. a Kalman Filter 10x the performance over a traditional sensor fusion algorithm. https://yostlabs.com
Views: 1459 YOST Labs
Global Data Fusion System Demo
 
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Global Data Fusion System Demo with Ed Roy CEO
Views: 325 Ed Roy
Data LiDAR FOR MINING APPLICATIONS
 
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LIDAR DATA FUSION FOR MINNING. PT ASI PUDJIASTUTI GEOSURVEY Contact : Email : [email protected] Telp : +62-21-314 5182 Fax : +62-21-3193 5361 Website : www.geosurvey.co.id
IEEE 2013 DOTNETFULL DOCUMENT A Data Fusion Technique for Wireless Ranging Performance
 
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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: 34 ganesh pg
NextGIS Logger - Android app for collecting environmental and internal sensor data
 
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NextGIS Logger is for productive collection of data from surrounding sources of information and internal sensors. Besides logging in the background, it is also possible to create user markers linked to the log points by timestamps and view live data. You can provide an app with your own set of marker names and IDs https://play.google.com/store/apps/details?id=com.nextgis.logger
Views: 412 NextGIS
Mobile data gathering with bounded relay in wireless sensor networks- IEEE PROJECTS 2018
 
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Mobile data gathering with bounded relay in wireless sensor networks- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project MOBILE COMPUTING 1. Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks 2. Service Provisioning in Mobile Environments through Opportunistic Computing 3. Traffic-Aware Efficient Mapping of Wireless Body Area Networks to Health Cloud Service Providers in Critical Emergency Situations 4. Distributed Faulty Node Detection in Delay Tolerant Networks Design and Analysis 5. Energy Efficient Multipath Routing Protocol for Mobile ad-hoc Network Using the Fitness Function 6. Research on Trust Sensing based Secure Routing Mechanism for Wireless Sensor Network 7. Lightweight Three-factor Authentication and Key Agreement Protocol for Internet-integrated Wireless Sensor Networks 8. MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic Segmentation 9. Recurring Contacts between Groups of Devices: Analysis and Application 10. Dynamic SON-Enabled Location Management in LTE Networks 11. Low Cost and High Accuracy Data Gathering in WSNs with Matrix Completion 12. CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets 13. Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization 14. Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks 15. Motivating Human-Enabled Mobile Participation for Data Offloading 16. Fast Cell Discovery in mm-Wave 5G Networks with Context Information 17. Wireless Charger Placement and Power Allocation for Maximizing Charging Quality 18. Dynamic Deployment and Cost-Sensitive Provisioning for Elastic MobileCloud Services 19. Multi-Objective Optimization Based Allocation of Heterogeneous Spatial Crowdsourcing Tasks 20. Two-Hop Distance-Bounding Protocols: Keep Your Friends Close 21. Energy-Aware Dual-Path Geographic Routing to Bypass Routing Holes in Wireless Sensor Networks 22. QLDS: A Novel Design Scheme for Trajectory Privacy Protection with Utility Guarantee in Participatory Sensing 23. An Energy-Efficient and Distributed Cooperation Mechanism for k -Coverage Hole Detection and Healing in WSNs 24. Centralized Cooperative Directional Spectrum Sensing for Cognitive Radio Networks 25. Robot Control Strategies for Task Allocation with Connectivity Constraints in Wireless Sensor and Robot Networks 26. Aggressive Voltage and Temperature Control for Power Saving in MobileApplication Processors 27. The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks 28. Distributed Coverage Control of Mobile Sensor Networks in Unknown Environment Using Game Theory: Algorithms and Experiments 29. HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices 30. Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems 31. High Resolution Beacon-Based Proximity Detection for Dense Deployment 32. Full-Duplex or Half-Duplex: A Bayesian Game for Wireless Networks with Heterogeneous Self-Interference Cancellation Capabilities 33. SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization 34. Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic 35. Measurement-Driven Modeling for Connection Density and Traffic Distribution in Large-Scale Urban Mobile Networks
Views: 17 Micans Infotech