MQTT is a publish/subscribe protocol, one of the popular protocols being used for M2M (Machine to Machine) communications.
The two main components of MQTT are the MQTT clients and the MQTT broker.
The MQTT clients publish messages to a particular topic or, subscribe and listen, to a particular topic. The MQTT broker receives all published messages from MQTT publishers and forward the relevant messages to all MQTT subscribers. Subscribers and publishers do not have to be aware of each other, only the topics and messages are relevant. To properly communicate, publishers and subscribers have to agree to use a common topic name and message format.
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11 June 2015 - CNBC International
World's fist 5G mobile device
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BBC Click
Tech Talk 5G
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Ericsson 22 Nov 2013
More videos are there on YouTube on 5G
5G cellular systems Introduction
First 1G to Fifth Generation 5G (Status 2016)
First generation, 1G: These phones were analogue and were the first mobile or cellular phones to be used. They offered very low levels of spectrum efficiency and security.
Second generation, 2G: These were based around digital technology and offered much better spectrum efficiency, security and new features such as text messages. But data rate was low.
Third generation, 3G: The technology provided high speed data transfer. The original technology was enhanced to allow data up to 14 Mbps and more.
Fourth generation, 4G: This was an all-IP based technology capable of providing data rates up to 1 Gbps.
5th generation: 5G technology will provide a dramatic increase in connectivity and coverage. It will facilitate IoT networks in cost efficient manner. The term World Wide Wireless Web, or WWWW is being coined for this.
5G specifications
Standards bodies have not yet defined the parameters needed to meet a 5G performance level. Net work organisations have set their own aims and developing technology.
Typical parameters for a 5G standard may include:
SUGGESTED 5G WIRELESS PERFORMANCE
PARAMETERSUGGESTED PERFORMANCE
Network capacity ----10 000 times capacity of current network
Peak data rate ---10 Gbps
Cell edge data rate ---100 Mbps
Latency ---< 1 ms
There are several key areas that are being investigated by research organisations to improve 5G technology. These include:
Millimetre-Wave technologies: Using frequencies much higher in the frequency spectrum opens up more spectrum and also provides the possibility of having much wide channel bandwidth - possibly 1 - 2 GHz.
However this poses new challenges for handset development where maximum frequencies of around 2 GHz and bandwidths of 10 - 20 MHz are currently in use. For 5G, frequencies of above 50GHz are being considered. The circuit design, the technology, and also the way the system is used is to be developed as these frequencies do not travel as far and are absorbed almost completely by obstacles.
Future PHY / MAC: The new physical layer and MAC presents many new interesting possibilities in a number of areas:
Waveforms: One key area of interest is that of the new waveforms that may be seen. OFDM has been used very successfully in 4G LTE as well as a number of other high data rate systems. But, it does have some limitations. New formats being proposed include: GFDM, Generalised Frequency Division Multiplexing, as well as FBMC, Filter Bank Multi-Carrier, UFMC,
Multiple Access Schemes: A variety of new access schemes are being investigated for 5G technology. Techniques including OFDMA, SCMA, NOMA, PDMA, MUSA and IDMA are being investigated.
Modulation: Whilst PSK and QAM have provided excellent performance in terms of spectral efficiency, resilience and capacity, they have a drawback of a high peak to average power ratio. Modulation schemes like APSK could provide advantages and is under investigation
Duplex methods: Currently systems use either frequency division duplex, FDD or time division duplex, TDD. New possibilities are opening up for 5G including flexible duplex.
Massive MIMO: Although MIMO is being used in many applications from LTE to Wi-Fi, etc, the numbers of antennas is fairly limited -. Using microwave frequencies opens up the possibility of using many tens of antennas on a single equipment.
Dense networks Reducing the size of cells provides a much more overall effective use of the available spectrum. Techniques to ensure that small cells in the macro-network and deployed as femtocells can operate satisfactorily are required.
Emerging Research in Cloud Distributed Computing Systems
Bagchi, Susmit
IGI Global, Mar 31, 2015 - 447 pages
Traditional computing concepts are maturing into a new generation of cloud computing systems with wide-spread global applications. However, even as these systems continue to expand, they are accompanied by overall performance degradation and wasted resources.
Emerging Research in Cloud Distributed Computing Systems covers the latest innovations in resource management, control and monitoring applications, and security of cloud technology. Compiling and analyzing current trends, technological concepts, and future directions of computing systems, this publication is a timely resource for practicing engineers, technologists, researchers, and advanced students interested in the domain of cloud computing. https://books.google.co.in/books?id=U4EfCgAAQBAJ
Technology experts and stakeholders say they expect they will ‘live mostly
in the cloud’ in 2020 and not on the desktop, working mostly through
cyberspace-based applications accessed through networked devices.
“By 2020, most people won't do their work with software running on a general-purpose
PC. Instead, they will work in Internet-based applications such as Google Docs, and in
applications run from smartphones.
September 14, 2015
Source:
Carnegie Mellon University
However, a study published in the Proceedings of the Second (2015) ACM Conference on Learning @ Scale shows that interactive activities advocated by Carnegie Mellon University's Simon Initiative helps students learn about six times more than watching video lectures.
CMU's Simon Initiative approach uses CMU's Open Learning Initiative (OLI) courses, which are built to mimic intelligent tutors and they provide adaptive feedback and hints during learning by doing.
Learning by doing gives students deliberative practice opportunities to address a course's objectives students get immediate feedback. If they do not master a concept, they have to go back to re-watch or re-read and then demonstrate they have learned before they are able to move on.
A study compared two uses of an Introduction to Psychology as a Science class: 18,645 students took it as a MOOC only, while 9,075 enrolled in it as a combined MOOC and Simon OLI course. Eleven weekly quizzes and a final exam were given to all students.
First, the researchers compared how each group's students performed on the final exam. MOOC-only students had an average score of 57 percent, and the MOOC and OLI students averaged 66 percent. The difference is significant and the difference remains after adjusting for other contributors to student success including their prior educational background and their incoming psychology knowledge.
Then, the team investigated how different patterns of student use corresponded with different student learning outcomes. They found that while more watching, reading and doing all predict better learning outcomes, the amount of learning associated with each activity done was six times greater than for each video watched or page read.
Most of MOOCs' attention has been on scaling teaching -- making lectures available to more people, More attention needs to be now placed on learning by doing that is well-aligned with outcomes and assessments.
Carnegie Mellon University. "Learning is not a spectator sport: Students learn 6 times more with CMU's Simon Initiative approach than with MOOCs." ScienceDaily. ScienceDaily, 14 September 2015. <www.sciencedaily.com/releases/2015/09/150914220526.htm>.
How Much Frequency Can Be Reused in 5G Cellular Networks—A Matrix Graph Approach
Yaoqing Yang, Student Member, IEEE, Bo Bai, Member, IEEE, and Wei Chen, Senior Member, IEEE
Yaoqing Yang is with the Department of Electrical and Computer Engineering,
Carnegie Mellon University, Pittsburgh, PA, 15232 USA e-mail: yyaoqing@andrew.cmu.edu.
Bo Bai and Wei Chen are with the Department of Electronic
Engineering, Tsinghua University, Beijing, 100084 China e-mail: eebobai,wchen@tsinghua.edu.cn.
In this paper we are focusing on the ultimate limit of frequency allocation in a 5G network.
To study this problem, we proposed a matrix graph model and constructed an analytical framework combining matrix graph coloring (MGC) and maximum weighted independent set (MWIS),
based on properties of large-scale small-cell networks.
Utilizing this model, we obtain an approximation algorithm that achieves a bounded gap to the optimal performance with a complexity growing linearly with the network size, despite the NP-completeness of the MGC problem.
Therefore, if we could build a proper matrix graph, we can find the nearly-optimal way to allocate resources like frequencies and time slots. Moreover, the proposed scheduling algorithm has lower computational complexity if cells are smaller and inter-cell interference are more complicated. Thus, we conclude that frequency allocation in high-interference smallcell networks can be carried out efficiently and the small-cell networks are indeed practical for the future 5G network
construction.
Simulation results support our theories.
Fundamentals of 5G Mobile Networks
Jonathan Rodriguez
John Wiley & Sons, Jun 22, 2015 - 334 pages
Fundamentals of 5G Mobile Networks provides an overview of the key features of the 5th Generation (5G) mobile networks, discussing the motivation for 5G and the main challenges in developing this new technology. This book provides an insight into the key areas of research that will define this new system technology paving the path towards future research and development. The book is multi-disciplinary in nature, and aims to cover a whole host of intertwined subjects that will predominantly influence the 5G landscape, including Future Internet, cloud computing, small cells and self-organizing networks (SONs), cooperative communications, dynamic spectrum management and cognitive radio, Broadcast-Broadband convergence, 5G security challenge, and green RF. The book aims to be the first of its kind towards painting a holistic perspective on 5G Mobile, allowing 5G stakeholders to capture key technology trends on different layering domains and to identify potential inter-disciplinary design aspects that need to be solved in order to deliver a 5G Mobile system that operates seamlessly as a piece of the 5G networking jigsaw.
Key features:
• Addresses the fundamentals of 5G mobile networks serving as a useful study guide for mobile researchers and system engineers aiming to position their research in this fast evolving arena.
• Develops the Small cells story together with nextï¿1⁄2]generation SON (self-organizing networks) systems as solutions for addressing the unprecedented traffic demand and variations across cells.
• Elaborates Mobile Cloud technology and Services for future communication platforms, acting as a source of inspiration for corporations looking for new business models to harness the 5G wave.
• Discusses the open issues facing broadï¿1⁄2]scale commercial deployment of white space networks, including the potential for applications towards the future 5G standard.
• Provides a scientific assessment for broadcast and mobile broadband convergence coupled together with a ´win-win’ convergence solution to harmonize the broadcasting and mobile industry.
• Describes the key components, trends and challenges, as well as the system requirements for 5G transceivers to support multiï¿1⁄2]standard radio, a source of inspiration for RF engineers and vendors to tie down the requirements and potential solutions for next generation handsets.
D. Braha
Springer Science & Business Media, Mar 14, 2013 - 524 pages
Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making.
Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools. https://books.google.co.in/books?id=cfXiBwAAQBAJ
My library
My History
Books on Google Play
Evolutionary Design and Manufacture: Selected Papers from ACDM ’00
Front Cover
Ian Parmee
Springer Science & Business Media, Dec 6, 2012 - 372 pages
The fourth evolutionary/adaptive computing conference at the University of Plymouth again explores the utility of various evolutionary/adaptive search algorithms and complementary computational intelligence techniques within design and manufacturing. The content of the following chapters represents a selection of the diverse set of papers presented at the conference that relate to both engineering design and also to more general design areas. This expansion has been the result of a conscious effort to recognise generic problem areas and complementary research across a wide range of design and manufacture activity. There has been a major increase in both research into and utilisation of evolutionary and adaptive systems within the last two years. This is reflected in the establishment of major annual joint US genetic and evolutionary computing conferences and the introduction of a large number of events relating to the application of these technologies in specific fields. The Plymouth conference remains a long-standing. event both as ACDM and as the earlier ACEDC series. The conference maintains its policy of single stream presentation and associated poster and demonstrator sessions. The event retains the support of several UK Engineering Institutions and is now recognised by the International Society for Genetic and Evolutionary Computation as a mainstream event. It continues to attract an international audience of leading researchers and practitioners in the field. https://books.google.co.in/books?id=yh8MCAAAQBAJ
Predictive Analytics as an Engine Of R&D and New Product Launches
Predictive analytics is not only the way to discover the underlying patterns, but it can also help you with innovation. Here, we discuss the ways to innovate by combining it with business logic, marketing and bridging demand supply factors.
By Lana Klein, (Co-Founder, 4i) http://www.kdnuggets.com/2015/08/predictive-analytics-rnd-product-launches.html
Businesses Will Need One Million Data Scientists by 2018
International Data Corporation (IDC) predicts a need for 181,000 people with deep analytical skills in the US by 2018 and a requirement for five times that number of positions with data management and interpretation capabilities. http://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html
“Andy Frawley pours gasoline on the hot spots for marketers in Igniting Customer Connections. It’s about time we linked marketing success to customer satisfaction.
This smart, practical and approachable book clearly demonstrates why and how businesses can create meaningful connections with consumers.
“In Igniting Customer Connections, Andy Frawley provides an easy-to-follow roadmap for measuring and improving customer experience and engagement by leveraging relevance. Frawley draws upon his three decades of experience to give marketers sound strategies to improve ROE² (Return on Experience x Engagement).”
“Consumer interaction today is changing, and companies of all sizes need to take a new look at how they’re engaging with customers in ways that are truly relevant. Igniting Customer Connections is an important read for every marketer, offering a compelling guide to differentiating brands through a superior customer experience.”
http://www.ignitingcustomerconnections.com/book
Epsilon uses data analytics and marketing analytics to help our clients make better decisions and do smarter marketing in traditional and emerging channels, including social, mobile, web, and targeted display.
With an array of identification, segmentation, predictive modelling and measurement services, we transform massive amounts of data into useful insights. We turn chaos into clarity, transforming “what could be” into “what’s possible”. Because everything we do has measurable outcomes, we can continually monitor, re-evaluate and adjust your marketing mix to get the best results. Epsilon has over 125 statisticians, analysts and consultants who provide a comprehensive range of services to help you identify, reach, engage, convert and retain more customers.
Epsilon was founded in 1969, primarily as a database marketing company, and expanded into customer management and loyalty marketing programs--a rich source of customer data--in the 1970s and '80s. It was acquired by Alliance Data Systems, the Texas-based marketing, loyalty program and credit vendor, in 2004. In 2014, Alliance purchased digital marketing and personalization company Conversant and integrated it with Epsilon, creating fresh strengths in display, video, and especially mobile advertising.
Managing Social Media and Marketing Analytics for Competitive Advantage (Live Session)
Duke University - The Fuqua School of Business
Professor Christine Moorman.
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Duke University - The Fuqua School of Business
Google Analytics for Enhanced Marketing Measurement
Swapnil Sinha – Head of Conversions at Google India
He is a BE in Computer Science and an MBA from University of Utah.
digitalvidya
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Analytics for Marketers with Bitly CEO Mark Josephson
General Assembly upload
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Wearables, smart houses, and smart hotel desks - more and more smart consumer utilities are going to developed. Connected objects are set to become a part of consumers' lives.
The connected objects will do a better job finding you parking places, taking care of your health, and coordinating your deliveries from various vendors It could almost be like having a personal valet.
Some specific IOT consumer applications are given in MIT Sloan Management Review.
1. Famed design firm IDEO is trying to create a headband that lets people measure their brain activity and track their mental focus.
2. An Internet-connected washing machine model is quite smart. It may become the model for what we want in our appliances: connectivity without complexity.
3. The Internet of Things could mean smart light bulbs that wake you up by getting brighter without the sound alarm that disturbs people in other rooms also.
4. There’s already a big market for sensor-driven wearables, like Jawbone and FitBit wristbands. A smash hit will make its appearance shortly.
5. IoT connected smart locks and rooms will be provided by hotels.
6. IoT connected apparel would help sportsmen get better at sport. Also they will indicate injuries and warn players and coaches.
7. Of course, the consumer Internet will be subjected hacking risks and hence needs anti hacking software.
- Sparse Learning
- Why Sparse Learning?
- Sparse Learning: Joint dimension reduction and estimation
- The Lasso: The most popular sparse learning method
- The Group Lasso: Extends the lasso to accommodate grouped selection
- Various types of sparsity on matrices
- Various types of sparsity (matrix factorization)
- Application to GWAS - Genetic Basis of Complex Diseases
- Application to climate change attribution
- The approach: Sparse Learning with spatio-temporal data
- Application: Key influencers in online communities
- Application: IBM Lotus Bloggers
- Sparse Learning on Matrices: Image denoising
- Sparse Learning on matrices: network inference
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AnalyticsZone
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
IBM Business Analytics
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Honglak Lee, Assistant Professor - Computer Science and Engineering, University of Michigan
The 4th University of Michigan Data Mining Workshop
This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
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Michigan Engineering
Mod-04 Lec-28 Feature Selection : Problem statement and Uses
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nptelhrd
Mod-04 Lec-29 Feature Selection : Branch and Bound Algorithm
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Lec-30 Feature Selection : Sequential Forward and Backward Selection
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NPTELHRD
Mod-04 Lec-32 Feature Selection Criteria Function: Probabilistic Separability Based
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Mod-01 Lec-30 Principal Component Analysis (PCA)
Part of Multivariate Statistical Modeling
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Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis
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Clustering analysis is used in applications such as market research, pattern recognition, data analysis, and image processing.
Clustering can also help marketers discover distinct groups (Segmentation) based on the purchasing patterns in their customer base.
In the field of biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functionalities and gain insight into structures inherent to populations.
Clustering also helps in identification of areas of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city according to house type, value, and geographic location.
Clustering also helps in classifying documents on the web for information discovery.
Clustering is also used in outlier detection applications such as detection of credit card fraud.
As a data mining function, cluster analysis serves as a tool to gain insight into the distribution of data to observe characteristics of each cluster.
Mod-03 Lec-25 Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.
