Set of proof of concept and use cases with internet of things technologies are presented with one sliders. In each case, the IoT challenge, result, benefits and use case example are given.
This document outlines an agenda for a meetup on Spark UDF performance. The meetup will include: an introduction of the speaker ([Guilherme Braccialli]); an overview of QuantumBlack, where the speaker works; a presentation on Spark UDF performance, including a live demo; conclusions about the performance of PySpark vs Scala UDFs; and time for questions. The speaker will share learnings from running Spark at scale and practical examples. He will conclude that PySpark Pandas UDFs can be faster than regular PySpark UDFs but not always, and that PySpark UDFs are slower than equivalent Scala UDFs. The speaker's approach is to use PySpark UDFs
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Big data in healthcare refers to large, diverse, and complex datasets that are difficult to analyze using traditional methods. The healthcare industry generates huge amounts of data from sources like electronic health records, medical imaging, and fitness trackers. Analyzing this big data can help improve patient outcomes, reduce costs, and advance personalized medicine. However, healthcare also faces challenges like data silos, privacy concerns, and resistance to change. Opportunities include disease prediction and prevention, reducing readmissions and fraud, and optimizing care through remote monitoring. Some organizations are starting to see benefits from big data initiatives focused on areas like evidence-based treatment and integrated health records.
This document provides an overview of artificial intelligence and its applications. It discusses how AI is inspired by biological neurons and how artificial neural networks were developed. It then covers several major applications of AI in healthcare, finance, and other industries. For healthcare, it describes how AI is being used for cancer detection and diagnosis. For finance, it discusses uses of AI for fraud detection, risk management, and algorithmic trading. The document concludes by listing several AI hardware developments and references for further reading.
From healthcare to homecare: The critical role of 5G in healthcare transforma...Ericsson Latin America
Today, consumers have the power to take control of their health through smartphone apps, wearables and other connected devices – and it has never been easier to lose weight, improve sleep, count calories and get fit. This kind of simple, immediate access is also changing consumer attitudes and expectations when it comes to healthcare. Here we explore the transformation across three healthcare situations: preventative, routine and post-operative care.
The document discusses disruptive digital technologies that are transforming healthcare, including telemedicine, the Internet of Medical Things, cloud computing, augmented/virtual/mixed reality, artificial intelligence, chatbots, data science, and blockchain. It outlines Thailand's national eHealth strategy and components like electronic medical records, health information exchange, and a quality framework for healthcare accreditation and information technology. Overall, the document emphasizes how healthcare organizations can gain competitive advantages by leveraging these disruptive digital technologies through digital optimization, business transformation, and defining their digital organization landscape.
This document proposes a theme on big data analytics research. It notes that the world's data storage capacity doubles every 40 months and discusses how big data can provide value across many areas like health, policymaking, education and more. The proposal recommends that Hong Kong develop a state-of-the-art big data platform to make a difference in areas like smart cities and support aging populations. It outlines objectives like large-scale machine learning from big data and discusses how Hong Kong is well-positioned for this research with experts across universities and potential collaborators in industry. The expected outcomes include new methodologies, applications impacting society and industry, and educational programs to cultivate big data leaders.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
What you'll get to learn :
Introduction: Healthcare IT and Liferay
Importance of Healthcare IT
Future of Healthcare
How Smart Healthcare is the game changer?
Challenges - Healthcare IT
How to overcome the challenges?
Liferay for digital transformation
Liferay for Healthcare IT
Why Liferay for healthcare IT solution?
Benefits of having unified omnichannel healthcare experience platform
Liferay case studies for Healthcare IT
How AIMDek can help ?
Q&A Session
CARTO for Retail: Driving Site Selection Decisions with Advanced Spatial Anal...CARTO
This document summarizes a presentation about using CARTO, a spatial analytics platform, for retail site selection and expansion decisions. It discusses CARTO's toolbox of analytics functions like commercial hotspot analysis, twin area analysis, and revenue prediction. It also overview CARTO's data sources and gives an example use case of using spatial analysis to find the best new locations for a Pizza Hut in Honolulu. The presentation demonstrates CARTO's ability to integrate internal and external data and perform advanced spatial analytics for tackling important retail decisions.
This document discusses digital health technologies and their future applications. It outlines how electronic health records, mHealth, telehealth, social media, gamification, and big data analytics can empower patients, improve access to care, and enhance prevention. While promising, digital health also faces challenges regarding privacy, data misuse, costs, and technical complexity that must be addressed for its full potential to be realized.
Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life. It can improve processes, enhance patient care, and save lives by using analytics to better predict patient needs and staff accordingly. Electronic health records store a patient's comprehensive medical history digitally, allowing doctors to track changes over time with no risk of lost data or duplication. Analyzing demographic health data allows for strategic planning to identify factors that discourage treatment uptake. Analytics also helps prevent security threats, fraud, and inaccurate insurance claims while streamlining the claims process. The patient experience, overall population health, and operational costs can all be improved through healthcare analytics.
Banking Circle: Money Laundering Beware: A Modern Approach to AML with Machin...Neo4j
The document discusses Banking Circle's use of graph technology and a data-driven approach to improve its anti-money laundering efforts. It represents payment data as a network to extract features for machine learning models that detect suspicious activity. This approach generates fewer false alarms than rules-based systems while identifying more high-risk payments and accounts. Network-based investigations also help analysts explore connections more efficiently. The new system screens over 1 million payments daily and has increased alerts leading to compliance actions by 1300% while reducing total alerts by 30%.
Computational approaches using AI are being used to speed up drug discovery and clinical trials in the following ways:
(1) AI is being applied to large datasets to help identify new biomarkers and repurpose existing drugs, with the global AI healthcare market expected to reach $36.1 billion by 2025.
(2) Major pharmaceutical companies are collaborating and sharing data using AI to accelerate target identification and automate molecule design.
(3) Startups are generating huge image datasets from high-throughput drug screening experiments to help identify new drug candidates in areas like oncology.
(4) AI can help improve clinical trials by identifying best patient populations, enabling dynamic trial design adjustments, and improving patient access and
We are living in the world of “Big Data”. “Big Data” is mainly expressed with three Vs – Volume, Velocity and Variety. The presentation will discuss how Big Data impacts Pharmaceutical Industry and how drug companies can lead this new Big Data environment.
The number of startups entering the healthcare AI space has increased in recent years, with over 50 companies raising their first equity rounds since January 2015. Deals to healthcare-focused AI startups went up from less than 20 in 2012 to nearly 70 in 2016.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
AI in Healthcare: Real-World Machine Learning Use CasesHealth Catalyst
Levi Thatcher, PhD, VP of Data Science at Health Catalyst will share practical AI use cases and distill the lessons into a framework you can use when evaluating AI healthcare projects. Specifically, Levi will answer these questions:
What are great healthcare business cases for AI/ML?
What kind of data do you need?
What tools / talent do you need?
How do you integrate AI/ML into the daily workflow?
The document discusses using Internet of Things (IoT) technology to address challenges facing modern cities. It notes that rapid urbanization, economic pressures, and environmental sustainability concerns are stressing city infrastructure and quality of life. The document then outlines how independent infrastructure investments by different city departments result in wasted resources and a lack of shared intelligence. It proposes that an integrated IoT platform allowing data sharing across departments could help optimize city management and operations.
This document discusses Internet of Things (IoT) use cases and how organizations can create business value from connecting devices and assets. It provides an overview of reports predicting massive growth in connected devices and trillions in economic value from the IoT. However, it notes that many organizations are still struggling to get started with IoT initiatives. It then outlines 26 specific IoT use cases organized by business function to help organizations identify opportunities to transform processes. Examples are provided of companies successfully applying IoT use cases in areas like operations, service, marketing and more. The document encourages organizations to identify which use cases are most relevant using a workshop and roadmap developed by PTC.
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
On December 9 & 10, Deloitte hosted over 20 business executives and thought leaders at the Internet of Things (IoT) Grand Challenge Workshop at the Tech Museum of Innovation in San Jose. The objective of the gathering was to work collectively to solve one of the more largely unexplored areas of IoT: revenue generating IoT use cases. The following report captures what was discussed during this extraordinary event where an open, collaborative dialogue focused on advancing the field of IoT.
Explore the key findings here or learn more at www2.deloitte.com/us/IoT-challenge.
Internet of Things and Big Data: Vision and Concrete Use CasesMongoDB
This document discusses Internet of Things (IoT) and big data. It provides an overview of key concepts in IoT such as the growing number of connected devices, drivers in the IoT ecosystem including enterprises and users, and examples of IoT applications from Bosch Group. It also discusses how big data and evolving data models are driving new requirements for databases including scalability, flexibility, support for analytics, and providing a unified view of data. The document promotes an upcoming webinar series on IoT and big data.
Big Data Analytics for the Industrial Internet of ThingsAnthony Chen
This document summarizes a presentation about big data analytics for the industrial internet of things. The presentation introduces the concepts of the industrial internet and how machine-generated data from sensors can be analyzed at large scale. Examples are given of how sensor data from aircraft engines, wind turbines, medical devices, and other systems can provide insights to improve efficiency, predict maintenance needs, and enhance operations. The presentation argues that big data analytics applied to industrial internet sensor data can help eliminate up to $150 billion in waste across industries through optimizations.
A proof of concept (POC) involves building a simple version of a product idea to test it with users before fully developing it. A POC should be completed in 1-4 weeks with a small team and focus on core functionality rather than polish. Usability testing the POC with real users provides critical feedback on whether the idea is worth pursuing further. For example, a POC for a stock trading app may include basic login, search, portfolio views, and simulated trading recommendations to get early feedback from potential users.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
If you have somehow missed the hype, the Internet of Things (IoT) is a fast-growing constellation of internet-connected sensors attached to a wide variety of 'things'. Sensors can take a multitude of possible measurements, Internet connections can be wired or wireless, while 'things' can literally be any object to which you can attach or embed a sensor. If you carry a smartphone, for example, you become a multi-sensor IoT 'thing', and many of your day-to-day activities can be tracked, analysed and acted upon.
Many of the conversations taking place around the IoT are incomplete without a mention of big data. Big data is characterised by “4 V’s”: volume, variety, velocity and veracity. That is, big data comes in large amounts (volume), is a mixture of structured and unstructured information (variety), arrives at (often real-time) speed (velocity) and can be different levels of uncertainty (veracity).
As organizations step into IoT, they must understand the symbiotic relationship between IoT and big data. Just like with any big-data play, merely collecting the data isn't enough. The data must be processed and analyzed to derive meaningful insights, and those insights must drive actionable steps that can improve the business.
What that means is that, without Big Data, the IoT can offer an enterprise little more than noise. But wait…! On the other hand, without IoT, the Big Data is little more than any other software lying idle. Actually you need two to Tango. That’s when you get the perfect marriage!
An example of a successful proof of conceptETLSolutions
In this presentation we explain how to create a successful proof of concept for software, using a real example from our work in the Oil & Gas industry.
RCG proposes a Big Data Proof of Concept (PoC) to demonstrate the business value of analyzing a client's data using Big Data technologies. The PoC involves:
1) Defining a business problem and objectives in a workshop with client.
2) The client collecting and anonymizing relevant data.
3) RCG loading the data into their Big Data lab and analyzing it using Big Data technologies.
4) RCG producing results, insights, and recommendations for applying Big Data and taking business actions.
The PoC requires no investment from the client and provides an opportunity to explore Big Data analytics without committing resources.
Understanding the Top Four Use Cases for IoTVoltDB
Dheeraj Remella, Director of Solutions Architecture at VoltDB explains how to use real-time data to make more informed decisions, decrease event to action latency and limit downtime. He’ll discuss four in-production customer case studies and highlight how implementing VoltDB has dramatically increased competitive advantage for avoiding unplanned downtime, asset tracking, utilities - smart meter management, and fleet management
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
The document discusses the Internet of Things (IoT). It defines IoT as the concept of connecting physical objects to the internet and being able to identify, sense and communicate with those objects. It describes how IoT allows both people and devices to communicate with each other and exchange data. Some key applications of IoT mentioned are smart homes, smart cities, industrial automation, logistics and supply chain management. The document also outlines several challenges to the large-scale implementation of IoT such as issues relating to privacy, security, standardization, and developing energy sources for billions of connected devices.
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
Virtual Reality Training for Upper Limb Prosthesis PatientsAnnette Mossel
Virtual reality training is proposed to help patients learn to use upper limb prosthetics. A system would allow training at home to improve control skills without risks. It aims to provide feedback during manufacturing to optimize fit. The system uses optical tracking of a head mounted display and arm target to control a virtual prosthetic hand in Unity. It demonstrates grasping objects. Future work includes testing with patients and developing games to enhance motivation.
IoT Application in Manufacturing & Advantage , Disadvantage.GHANASHYAM19
The document discusses how IoT can be applied in manufacturing. It describes how IoT enables digital/connected factories through remote monitoring and management of machinery. IoT sensors also allow for condition-based maintenance alerts and facility management. Production flow, inventory, plant safety and security, quality control, packaging, and logistics can all be optimized through IoT applications in manufacturing. The advantages include asset tracking, predictive maintenance, and process monitoring, while the disadvantages include security risks, high costs, and potential connectivity issues.
An Analysis of the Architecture of the Internet of Things.pdfCIOWomenMagazine
As we all know internet of things is a system of interrelated and inter-connected objects. These objects are able to collect and transfer data via a wireless network without any human intervention.
Industry 4.0 or the fourth industrial revolution, which has been introduced by German government in 2012 [1], which is depends on the integration of different categories of electrical and electronic devices, from personal computers, smartphones, smartwatches, machinery robotics and enterprise resource planning systems, which can be integrated together and communicated with others to analyse the optimal criteria of potential solutions for improving productivity via internet [2]. however, the requirements of the new technology will force the old technology to retired. which will will force the big companies to change the specification of the industrial components to keep up with the latest processors. Ultimately, the goal of Industry 4.0 is to produce smarter and resource-efficient factories which are more productive and competitive says Mika Lomax [3]. Which mean that the Devices are getting smarter. "Not only does the IIoT enable real-time monitoring on smartphones and via emails, but, in plants, everyone has LCDs (liquid-crystal displays), TV screens and marquees showing the production staff useful information," says Kumar. "The technology in the modern HMI, including drivers and connectivity, is moving to message displays and marquees. This will enable programming and monitoring in these smart displays. Technology is pushing PLC and HMI functionality to text displays and it will all be connected to the IIoT."[4] The characteristics of high-technology industries include steady order quantities, standardized product features and high product value [3].
Making io t a reality axeda _ may 8 2013 _mahbubul alamMahbubul Alam
The document discusses the importance of platforms and ecosystems for realizing the Internet of Things (IoT). It notes that IoT could be worth $14.4 trillion over the next decade across various industries like utilities, manufacturing, transportation and healthcare. Cisco takes a three-pronged approach to IoT involving understanding customer needs, developing vertical solutions based on a horizontal platform, and strategic partnerships. The platform needs to evolve from basic connectivity to one optimized for IoT and eventually enable the Internet of Everything. Standards, security, and educating partners will be important to define architectures and enable business innovation with IoT.
Making IoT a Reality_Axeda _ May 8 2013 _Mahbubul AlamMahbubul Alam
The document discusses the importance of platforms and ecosystems for realizing the Internet of Things (IoT). It notes that IoT could be worth $14.4 trillion over the next decade across various industries like utilities, manufacturing, transportation, and healthcare. Cisco takes a three-pronged approach to IoT involving understanding customer needs, developing vertical solutions based on a horizontal platform, and strategic partnerships. The platform needs to evolve from basic connectivity to being optimized for IoT and including elements like sensors, gateways, analytics, and security. Defining architectures, standards, and addressing policy issues will be important to enable business innovation with IoT.
Predictive Maintenance by analysing acoustic data in an industrial environmentCapgemini
This document discusses predictive maintenance through acoustic data analysis in an industrial setting. It describes using sensors and data collection from machines to detect potential issues through acoustic and vibration analysis. The customer case study involves implementing a predictive maintenance platform using acoustic data from an air compressor to identify mechanical, electrical, and other problems to decrease maintenance costs and increase machine availability. The solution involves collecting machine and sensor data, storing it in an IIoT platform, and performing analytics using predictive models and dashboards.
IoT and equipment connectivity are vital necessities for original equipment manufacturers, owners, and operators who want to maintain or increase market share.
Autonomous sensor nodes for Structural Health Monitoring of bridgesIRJET Journal
This document discusses using autonomous sensor nodes and wireless sensor networks for structural health monitoring of bridges. It aims to detect damage in structures early through continuous monitoring. Sensor nodes containing microcontrollers, temperature, vibration and pressure sensors would be attached to bridges and transmit data wirelessly. This would make inspections more efficient and improve safety by identifying issues early. The document reviews related work using similar wireless sensor network systems for structural monitoring. It discusses the need for such monitoring in India given the increasing construction of large buildings and infrastructure. The objectives are outlined as detecting, locating, identifying and quantifying any damage. Hardware and software components are listed including ESP32 microcontrollers and sensors to measure temperature, vibration and pressure.
The internet of things (IoT) is a steadily growing billion-dollar market largely driven by companies undergoing digitization for greater efficiency and transparency, as well as by 5G and emerging applications like smart cities. Satellite’s inherent capabilities — such as its ability to reach remote areas, its ability to scale, to extend coverage for other providers — make it an essential part of a hybrid network needed to support an interoperable IoT system.
Revue de presse IoT / Data du 26/03/2017Romain Bochet
Sommaire :
- From the Edge To the Enterprise
- The Internet of Energy: Smart Sockets
- Google's big data calculates US rooftop solar potential
- Energy management: Oracle Utilities launches smart grid and IoT device management solution in the cloud
- Are vehicles the mobile sensor beds of the future?
The EcoSteer software platform is designed to meet current business needs for monitoring smart devices and sensors while having a flexible architecture to accommodate future requirements. It is open, flexible, scalable and affordable. The core platform allows for multi-site, multi-user energy and environmental monitoring. Planned extensions include multi-tenancy and improved data analysis. Partners can help customers focus on managing energy and facilities while specialists provide solutions rather than a single proprietary vendor.
How Manufacturers Can Unlock Business Value via IoT AnalyticsCognizant
Internet of Things (IoT) analytics presents manufacturers with a vast opportunity to drive revenues and enhance operations, from real-time analytics at the edge of technologies (consumer products, instrumented devices, manufacturing machines, etc.) through systems to evaluate and act on the plethora of detailed insights IoT provides. We offer a roadmap for manufacturers to pursue this crucial monetizing analytics opportunity.
#Interactive Session by Pradipta Biswas and Sucheta Saurabh Chitale, "Navigat...Agile Testing Alliance
#Interactive Session by Pradipta Biswas and Sucheta Saurabh Chitale, "Navigating the IoT Performance Testing Landscape" at #ATAGTR2023.
#ATAGTR2023 was the 8th Edition of Global Testing Retreat.
To know more about #ATAGTR2023, please visit: https://gtr.agiletestingalliance.org/
The document is a training report submitted for an NPTEL course on Internet of Things (IoT). It discusses an IoT-based keychain finder project completed by three students - Pragya Jha, Rishik Sharma, and Shivam Pruthi. The project involves developing a circuit using an ESP8266 module, buzzer, and battery to allow finding lost keys by triggering the buzzer remotely via a web interface. The report details the components used, circuit diagram, code explaining how it works, and assembly of the printed circuit board.
Automatic Free Parking Slot Status Intimating SystemIRJET Journal
This document describes a proposed automatic free parking slot status intimating system using Internet of Things (IoT) technology. The system uses ultrasonic sensors to detect vehicles parked in indoor lots and magnetic sensors to detect vehicles in outdoor lots. It identifies vehicles using their Bluetooth signal and USIM ID from connected smartphones. The sensor data is sent to a server via a gateway. The system aims to help drivers find available parking slots quickly and reduce fuel consumption from unnecessary driving to full lots. It provides a low-cost alternative to existing RFID-based systems by utilizing smartphones and wireless sensor networks.
Engineer Sensors For Digital Transformation Webinar PPTSadatulla Zishan
Do you want to know the current Industrial sensor demands? Are you facing challenges in identifying the right #Industrial protocol? Want to know how to interface the #sensors with different industrial protocols?
Don't worry, we have answered these questions in a focused webinar on #sensorengineering titled “Engineer Sensors for Digital Transformation” on 9th June 2021 2021 at 12 PM EST (USA, Canada Time).
Our expert panelist Mr. Sarang P, Embedded Design Expert joined with Mr. Namdev Nayak, Embedded Design Specialist guided the attendees on the industrial sensors and trending communication protocols in field devices.
In the 60 minutes of the #webinar, they covered:
1. Overview of the industrial sensors and #communicationprotocols
2. How to choose the right communication protocol & sensor when designing
3. Architecture and implementation of #industrialsensor & its various components
4. How is AI implemented in sensor modules
You can view the webinar recording by clicking on the link https://youtu.be/zpknpt4_uhQ
Please feel free to share these links with your colleagues who may be interested.
If you have any queries or require more information regarding the topic or wish to know more about Utthunga you can mail us at contact@utthunga.com or visit our website https://utthunga.com/
This document describes a proposed LoRa-based data acquisition system for monitoring vehicles. Key points:
- The system would use LoRa technology and sensors to monitor various parameters in a vehicle and report the data to users via an IoT dashboard.
- LoRa allows long-range and low-power wireless connectivity for IoT applications. The system aims to leverage these capabilities of LoRa for vehicle monitoring.
- The goals of the data acquisition system are to monitor operations, provide effective communication to identify issues, collect and store diagnostic data, and analyze performance metrics in real-time to ensure reliable operation.
Performance Evaluation Of A Wimax TestbedAlison Reed
This article analyzes SDN traffic engineering focusing on four key areas: flow management, fault tolerance, topology updates, and traffic analysis. It discusses challenges in SDN traffic engineering solutions and research efforts to address these challenges. For flow management, it discusses load balancing schemes for the control and data planes. For fault tolerance, it covers failure recovery mechanisms in the data plane like restoration and protection. It also discusses topology update strategies in SDN.
A Survey on Mobile Sensing Technology and its PlatformEswar Publications
Now a days, mobile networks is increasingly becoming important part of everyday life due which there is a rapid evolution mobile phone. Mobile phone comes into a powerful sensing platform. There are many scientists which are engaged in the emerging field of mobile sensing from a variety of existing communities, such as, mobile systems, machine learning and human computer interaction. The research and development in this field is rapid resulting in indispensable carry-on of daily life. But with the increase in development, data integrity and security has also become an important factor to take into consideration. Importantly, today’s smart phones are programmable and come with a growing set of cheap powerful embedded sensors, which are enabling the emergence of personal, group, and community scale sensing applications. The mobile sensing platform provides many facilities like, it helps to communicate to Wireless sensor networks through a mobile sensor router Which attached to a users mobile phone. It helps in analysis of the sensed data which is derived from networks by cooperating with sensor middle- ware on a remote server to capture ones contexts. It also helps in providing context aware services for mobile users of cellular telephones. In this paper, we will discuss about
different mobile sensing platforms that provides context-aware services for mobile users by accessing the surrounding wireless sensor networks. Along with this, we will briefly discuss some of the emerging sensing paradigms.
Similar to Proof of concepts and use cases with IoT technologies (20)
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Are you interested in learning about creating an attractive website? Here it is! Take part in the challenge that will broaden your knowledge about creating cool websites! Don't miss this opportunity, only in "Redesign Challenge"!
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
What's Next Web Development Trends to Watch.pdfSeasiaInfotech2
Explore the latest advancements and upcoming innovations in web development with our guide to the trends shaping the future of digital experiences. Read our article today for more information.
Interaction Latency: Square's User-Centric Mobile Performance MetricScyllaDB
Mobile performance metrics often take inspiration from the backend world and measure resource usage (CPU usage, memory usage, etc) and workload durations (how long a piece of code takes to run).
However, mobile apps are used by humans and the app performance directly impacts their experience, so we should primarily track user-centric mobile performance metrics. Following the lead of tech giants, the mobile industry at large is now adopting the tracking of app launch time and smoothness (jank during motion).
At Square, our customers spend most of their time in the app long after it's launched, and they don't scroll much, so app launch time and smoothness aren't critical metrics. What should we track instead?
This talk will introduce you to Interaction Latency, a user-centric mobile performance metric inspired from the Web Vital metric Interaction to Next Paint"" (web.dev/inp). We'll go over why apps need to track this, how to properly implement its tracking (it's tricky!), how to aggregate this metric and what thresholds you should target.
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
AI_dev Europe 2024 - From OpenAI to Opensource AIRaphaël Semeteys
Navigating Between Commercial Ownership and Collaborative Openness
This presentation explores the evolution of generative AI, highlighting the trajectories of various models such as GPT-4, and examining the dynamics between commercial interests and the ethics of open collaboration. We offer an in-depth analysis of the levels of openness of different language models, assessing various components and aspects, and exploring how the (de)centralization of computing power and technology could shape the future of AI research and development. Additionally, we explore concrete examples like LLaMA and its descendants, as well as other open and collaborative projects, which illustrate the diversity and creativity in the field, while navigating the complex waters of intellectual property and licensing.
How Netflix Builds High Performance Applications at Global ScaleScyllaDB
We all want to build applications that are blazingly fast. We also want to scale them to users all over the world. Can the two happen together? Can users in the slowest of environments also get a fast experience? Learn how we do this at Netflix: how we understand every user's needs and preferences and build high performance applications that work for every user, every time.
this resume for sadika shaikh bca studentSadikaShaikh7
I am a dedicated BCA student with a strong foundation in web technologies, including PHP and MySQL. I have hands-on experience in Java and Python, and a solid understanding of data structures. My technical skills are complemented by my ability to learn quickly and adapt to new challenges in the ever-evolving field of computer science.
3. 3
IoT Challenge:
Dynamically and reliably measure and transmit info as close to teeth contacts as
possible to get a representative figure of affecting circumstances and load profiles
Potential customer segment is critical mechanical component e.g. gear, bearings
manufacturers and users e.g. in marine and wind power industry
Support model based lifetime estimation in industrial applications.
Result
1. DWTS to spray wear and high temperature tolerant stain gauges, wirings and
connectors on the functional surfaces of gear tooth profile. 2. Passive DWTS tooth
crack sensor successfully connected to NFC tag reader for wireless crack indication.
3. Ultrasound technology to measure and transmit deflections along the gear
contacts.
Technology Readiness Level: PoC at the laboratory level
Benefits:
Identification of operational gear teeth loadings and impending faults with
implemented sensors supporting for process management and model based lifetime
estimation. Robust sensors technologies will provide new possibilities for future gear
condition based maintenance solutions.
No existing low cost solutions on the market.
Contact person:
Jari Halme
Use case example:
Gear tooth loading and impact sensing
Utilization e.g. at
Wärtsilä, ATA Gears
etc. CBM cases for
extending overhaul
interval. Utilizatio
Further development/
utilization will be
carried out in ArTEco
project (Martec II)
4. 4
Flexible wireless sensor for structural and
condition monitoring
IoT Challenge
Limited number of wireless solutions to measure strain on curved
surfaces in structural and condition monitoring
Rigid sensors tend to get damaged or broken on curved surfaces
Result
Flexible wireless sensor demonstrator strain measurement of curved
steel structure
Mobile phone as a user interface
Well protected against environmental stresses by vacuum casting
encapsulation technique.
TRL 6 proof-of-concept
Customer segments: construction and building, machine shop industry,
healthcare
Benefits
Low-cost easily deployable measurement system
Energy efficient cable-free instrumentation
Customised measurement system
Protected against environmental stresses
High volume pilot manufacturing possibility at VTT PrintoCent
Contact person
Markus Tuomikoski
Use case: Distributed, wireless
monitoring of roof structures of large
buildings, such as supermarkets, music
and sport halls etc.
Mobile terminal
as display and
control unit
5. 5
Real-Time Sensor Life-Cycle Management and Sensor R&D
IoT Challenge:
Sensors & laboratory instruments are not managed efficiently:
– Most of the problems relate to SW.
– High risk involved in SW upgrades: versions, calibrations, etc.
Sensor network lifecycle management has severe problems:
– Lack of support & availability/usability issues.
– Manual calibrations and maintenance.
• Excessive needs for customization and modifications:
– Vendor and end-user systems are all different.
– Lack of interoperability, connectivity and interfacing.
Future Factories have interrogative sensors that are actively troubleshooting processes.
Result
We have demonstrated real-time sensor R&D and production versions that enable
simultaneous usage and development of sensor software.
Similarly, we can run multiple sensor calibrations and have smooth transition between them.
There is a proof-of-concept demonstrator, software packages, algorithm libraries, IPR
Maturity as Technology Readiness Level (TRL: 3-4), by the end of year 2014 (TRL: 5-6)
There should be 1-2 patents by the end of year. Already 7 invention disclosures.
In early October, we will have two real sensors for demonstration.
Benefits:
Sensor availability, robustness, connectivity, easy of maintenance and interoperability.
Real-time sensor R&D, comparisons, support, diagnostics.
Parallel SW versions (prod, R&D, trouble-shooting, disruptive R&D…).
Contact persons:
Pekka Teppola (Pekka.Teppola@vtt.fi, +358 40 834 0661)
Pasi Hyttinen (Pasi.Hyttinen@vtt.fi, +358 40 532 8724)
Use case example: With SpectraCloud
technology, sensor companies can
speed up their sensor R&D and
simultaneously validate sensor software
in real-time. This directly translates to
improved sensor performance and better
service quality for end-users. In addition,
end-users (and automation companies)
can validate safely and, in parallel,
different sensor calibrations before
implementing the closed-loop process
control.
CONFIDENTIAL
6. 6
Networked sensoring and measurements in mining
industry
IoT Challenge:
Mining industry needs more reliable on-line measurements for process
optimization to achieve energy and material savings as well as better
product quality
The most potential customer segment is mining industry (both mining
and instrument companies)
Data handling and calibration can be realized remotely
Result
Measurement needs in mining industry identified
VTT offering for those needs mapped (TRL 3-6)
A proof-of-concept study as well as development of algorithm for
particle size analysis of crushed stone for Metso Minerals (TRL 3)
Benefits:
Reliable, robust on-line measurement of particle size distribution
No existing robust, low cost solutions which can measure reliably the
whole particle size distribution of crushed stone on the market.
Contact person:
Matti Okkonen, Katariina Rahkamaa-Tolonen
Use case example:
Minimizing losses in batching
plants with the help of on-line
information about particle
size of chips.
7. 7
Sensor fusion for more accurate activity detection
(& navigation)
IoT challenge:
Accurate, low energy and connectivity-free navigation methods are urgently
needed in consumer devices and in special industry & security devices.
New emergency call standards also require the altitude/floor information of the
caller.
Barometric sensors together with inertial sensors and sensor fusion algorithms
could provide the needed functionality (activity & navigation).
Result:
Testing & utilisation of the VTT’s patented MEMS barometric sensor that has
excellent sensitivity (~0.5 Pa) and good absolute accuracy (~5 Pa), potentially
allowing detection of altitude differences as small as 5 cm.
Sensor fusion algorithm(s) that utilise the barometric sensor as well as
“regular/existing” integrated smartphone sensors to detect user activity and
support in navigation.
Benefits:
The accuracy of the sensor is better than the current market leader (Bosch
BMP180) and on-par with the upcoming successor (Bosch BMP280).
The sensor and the (sensor fusion) algorithm(s) together can provide competitive
edge for product manufacturers (wrist devices, smartphones) seeking to integrate
better activity detection and navigation functionality.
Contact persons:
Jussi Polet, Henry Rimminen, Dan Bendas, Antti Iivari
Use case example:
An end-user “exercise motivator”
mobile application, that uses the
sensor(s) and sensor fusion
algorithm(s) to display more detailed
user activity information in relation to
altitude information.
9. 9
An approach from data to informed decisions
IoT Challenge:
IoT solutions are typically used to optimise daily operation and
maintenance of a system but data collected and analysed by IoT
solutions can support strategic level decisions which determine
substantial part of the life cycle costs and profits
The approach is applicable to wide range of industrial branches
Result
The prototype tool demonstrates concretely the approach how raw
data collected by sensors and manually can be analysed and utilised
when prioritising development targets, defining investment portfolio
etc.
The approach for data utilisation is well established and the TRL level
of the tool is 4.
Benefits:
Customers are able to make informed and transparent decisions by
the structured approach.
Currently commercial tools are mainly focused on data analysis in
operational level but tools for strategic level decision support are rare
Contact person:
Susanna Kunttu
Use case example:
Investment portfolio definition
begin with selecting investment
targets by analysing system
condition, unavailability, production
bottlenecks, maintenance costs
etc. Each investment proposal is
then uniformly and transparently
evaluated to select most profitable
investment portfolio.
10. 10
Tools for Fast Data Discovery and Visualization
IoT Challenge:
Much of the business intelligence data is stored in legacy systems and databases
The data in legacy systems is often slow to integrate or export to applications other
than its main function
This challenge is likely to become more important as the number of data sources
increase (as is anticipated by the adoption of IoT)
Result:
FastData project result is a proof-of-concept demonstration of a data pipeline to
combine existing energy consumption data (from smart energy meters in Otaniemi
area) with hourly electricity price information from Nordpool (Nordic electricity stock)
This demonstrator utilizes semantic data integration framework S-APL
Online demo: http://wizard.erve.vtt.fi:10002/fastdata/
Benefits:
Real-time integration and data discovery from legacy systems and databases
without the need for costly and custom-made ETL tools
Real-time information is needed to drive timely decisions to improve resource
efficiency, improve customer retention and customer experience (just to name a few)
Information needs to be readily available (e.g. on online dashboards), not behind
legacy system interfaces and handcrafted Excel sheets
Contact person: Ville Antila
Example utilization process in
3 steps:
1. Connect legacy systems
and databases (real-time,
semantic integration)
2. Discover new relationships,
find correlations and hidden
variables/factors
3. Renew business processes
or business rules, find
potential new products
Hourly distribution electricity cost
of selected buildings in Otaniemi,
Espoo
11. 11
Online percentile estimation for statistical
compression of monitoring data
IoT Challenge:
The data growth due to increasing number of networked sensors or
measurement agents. Variable and possibly very high data rate. Recovering
observed history, trends or anomalies require saving of the past data.
Supporting situation awareness require accurate real-time state information.
IoT issue: Memory, CPU power and communication capacity are typically
limited resources at the places where data is measured/collected.
One potential customer segment is IoT based remote monitoring, e.g.
remote monitoring of wind turbines, mobile base stations or other hard-to-
reach systems. Another potential customer segment is network SLA
monitoring.
Result
The result is a generic and tested C program library. It is an online algorithm
for estimation/computation of the percentiles of the observed empirical
distribution of the data. It works with fixed memory and limited CPU power.
Market closeness is 5 or 6 on scale 1…7. The result works in real
applications but it is not intended to be sold as such. It can be a part of
many different IoT (and other) applications.
Benefits:
Significant reduction in the amount of data (bytes) needed to be stored.
Saved data will be automatically structured which makes it fast to recognize
trends or anomalies. Generic code can be tailored and applied to many
situations. It is a fast algorithm.
Contact person:
Jorma Kilpi
Use case example: Wind
turbines monitor themselves and
produce a lot of data. From
monitoring data the decision of
expensive repair actions are
made. Data connection from wind
turbines to internet is usually slow
and expensive. Processing at the
wind turbine is limited.
Information
High data rate.
Online statistical filtering of
data with fixed memory and
limited computing resouces.
Low
data
rate
.
12. 12
Individually adjusted thermal indoor environment
IoT Challenge:
Currently building automation does not take in to account individual
preferences on temperature.
Result
Individual’s preferences are used to adjust the temperature of the room
or space.
The person is identified with common RFID badge or similar. Preference
profile is fetched from database, and room temperature and other factors
are adjusted accordingly. No sensors carried by user.
In advanced case, person’s location in the room is used for local
temperature control.
Based on sophisticated Human Thermal Model, which is VTT’s IPR
Proof-of-concept, TRL 5 (September 2014)
Benefits:
Better occupant satisfaction and energy saving.
Enables new service solutions for customer segments such as hospitals,
hotels, offices, transportation.
Contact person:
Pekka Tuomaala
Use case example:
Building automation systems
detects that Mary has arrived
at her office. Utilizing this
information, as well as space
temperature level monitoring,
a the solution optimizes
temperature at Mary’s post
– maximising Mary’s thermal
satisfaction and avoiding
unnecessary energy
consumption.
13. 13
SolarSon: Automated infrared copter
system for photovoltaic solar plants
IoT Challenge:
Once photovoltaic solar panels are installed on a large solar plant,
maintenance becomes a big issue.
Solar energy operation & maintenance (O&M) companies, solar
cell manufacturers offering O&M services and large PV plant
owners need efficient tools for detecting faults in the plants.
IoT and industrial internet enable efficient solutions to the big plants.
Result
SolarSon IR UAV system detects faulty solar panels fast and a
fault map of the plant is created.
IPR of the copter and cloud based data analysis system is created.
Business roadmap is done.
Benefits:
Efficient and cost-effective way to ensure sustained profitability of
large photovoltaic systems.
System is faster than handheld and rectractable pole IR methods
and more cost effective than ordinary helicopter/airplane methods.
Contact person:
Pierre-Emmanuel Panouillot
Use case example: A 80 MW /
2,4 km2 photovoltaic solar plant
is inspected fast and
automatically and a map of
faulty panels is created.
Customer replaces faulty
panels with new ones, repairs
cable connections and the plant
maintains its electricity
production in full power.
15. 15
Big Data Architecture Survey
IoT Challenge:
VTT should be able to answer the architectural questions, make
sensible tool selections and focus our offering and research on
Big Data
Potentially all VTT customers interested in data analysis (NSN,
F-Secure,…)
Data volume, variety and velocity is increasing all the time –
new tools are being developed to handle it.
Result
Document giving a broad view on software components used
on different layers on Big Data analysis
Benefits:
VTT is able to recommend the right Big Data software
components for the customer and has the knowledge to use
applicable Big Data tools in projects
Contact person:
Arttu Lämsä, Ville Könönen
Use case example: A
customer has wants to collect
usage data from a mobile
application. Using the
knowledge on Big Data
architectures, VTT is able to
come up with a solution to
store, analyze and monitor the
data.
16. 16
Bluetooth roadmap for IoT
IoT Challenge:
IoT need low-power wireless hop from sensors to sensors and to
masternode or routing hub
Proprietary radio solutions do not give futureproof implementation
Interfacing wirelessly with smartphone gives benefits of smartphone
evolution, easy routing and futurepoof implementation platform
Result
New initiative for Bluetooth standard implements Mesh network over
BT4.1. Current proposal is proprietary but open source.
Initial evaluations of the proposed system are positive and provide
very good backwards compatibility
Tool for IoT projects to implement true internet of things over exsisting
smartphone base.
Benefits:
Low cost IoT implementation for IoT projects and products
Competition in smartphone space is scarce, since Bluetooth is
dominating shortrange radio solution in smartphones, and when
compared with wifi much more power efficient.
Contact person:
Mika S Sarén
Implementation example: Role
changing BT MESH nodes in
CSR poposal
18. 18
People Flow Monitoring with Radar Sensor Network
IoT challenge:
IoT needs digital representation of a real world
For example, people flow information is needed for dynamic operation
of the automation systems in urban infrastructure.
Current monitoring methods do not provide people tracking or do not
function adequately in outdoor conditions (e.g. Kinect: problems in direct
sunlight or rain/for/smoke, low range)
Result:
Radar is used for people tracking
24 GHz radar with TX power of 10 mW
Embedded µC/FPGA control
Radar prototype and detection algorithm in TRL4
Customer segments: lift vendors, building management system vendors
Benefits:
Reliable people and object tracking with low cost sensors (bill-of-
materials <100€)
Long range (up to 20m), ability to cover large areas with a single sensor
Works indoors and outdoors, also in direct sunlight and rain
Contact person:
Pekka Pursula
Use case examples:
At a subway station crowds are
being monitored using FlowRadar
and guided by the use of adaptive
lighting to use a correct number of
escalators (service level vs.
energy savings)
19. 19
People behaviour monitoring and classification
IoT Challenge:
Intelligent systems need to understand human needs
Customer segment: brick and mortar stores need to offer personalised
shopping experience to compete with online stores
Result
Implementation of people tracking by Kinect2
Implementation of people tracking by stereo camera
Implementation of algorithms for 1) classifying behaviour of shoppers and
2) predicting locations where shoppers will stop in near future
Offline tests of classification algorithms on real life data from Anttila;
accuracy 60-80%, depending on data and task at hand
Technology Readiness Level: 6
Benefits:
Existing solutions are mainly used in labs; our system was used in real stores
Our solution is less dependent on light and is less privacy-threatening than
video camera – based solutions
Allows more accurate and longer range tracking than previous version (> 8m.)
Now we can offer better people tracking solution based on modern sensors
Contact person:
Johannes Peltola
Use case example:
• Shop managers get accurate
information regarding
numbers of customers
stopping near certain stands
vs. numbers of passers-by
• Shop customers can get
timely assistance when they
stop near certain stands
20. 20
Wireless, energy-autonomous shelf-label (iLabel)
IoT Challenge:
Continuously changing prices cause a big amount of work in every retail-store.
Especially big super-markets need a proper solution for this (S-chain, K-chain in
Finland).
Recording customer’s routes in a super-market gives valuable information for its
operation.
Data from and to the end of IoT chain.
Result
Our project is still on-going, but it will offer the challengin part of a toolset for
solving above described challenges.
We will implement a prototype combining all the technologies for the actual label,
a few wireless technologies and understanding of configuring the potentially huge
(50 000 nodes) network.
TRL 6-7
Benefits:
Saving costs of big amount of manual work, data on customers behaviour for
layout design etc. Competitor (partner?) Marisense.
Energy autonomous, tracing customers in a super-market.
Competitors are battery powered, our utilises energy harvesting.
Competitors have only on part of the label reconfigurable (they use stickers to
complete the information given), we have all date configurable.
Our solution is planned for huge networks (50 000 nodes).
We include positioning of customers (trolleys, carts) in the concept.
Our data transmission solution is quite novel, UWB and/or modulated light.
Contact person:
Timo Lehikoinen
Use case example: Pricing of all goods
in Prismas.
21. 21
Designing Attraction Points for Productive Business
IoT Challenge:
How it is possible to measure reliably behaviour and actions of a consumer
in shopping environments and understand, what are their true attraction
points? This challenge is proposed by retail operators, manufacturers,
shop fittings manufacturers and e.g. some advertising agencies.
VTT’s People Tracker will (hopefully) developed further towards an easy-
to-use, low cost sensor network type solution. This will enhance
opportunities to utilize eye movement tracking technologies and build
novel, partially automated service concepts around it.
Result:
The basic result is a proof of concept (prototype) of a system, which
produces visualisation of consumers’ movements e.g. in a store and a heat
map of his attraction points. This data can be used by experts to analyse,
what should be done to the setup in order to meet the requirements of the
business. (Maturity of the technology: TRL7)
Benefits:
By getting information of customer’s attraction points the retailer can
optimize e.g. product placements or optimize locations of signage. VTT’s
solution helps to do decisions based on true behaviour of the customer
instead of guessing, what is interesting for the customer.
Contact person:
Jari Kaikkonen.
Use case example: Petteri wants to
improve sales by rearranging some parts
of his store. How he can reliably find out,
what should be changed and evaluate the
effect of changes to the behaviour of his
customers? Petteri’s problem can be
solved by using data gathered and
visualized with VTT’s system and
analysing the result by an expert.
(Practically, this is a new, fact-based way
to design customers’ attraction points in a
shopping environment.)
22. 22
Ultra Wide-Band WSN platform with positioning
IoT Challenge:
One low cost platform for high speed data transmission, low power operation, and accurate
positioning
Easy standardized start-up for WSN and positioning applications
In industrial environment tolerance for noise is essential. Easy adaptability for different
applications is required as well
The most potential customer segment is machine industry
Result:
Proof-of-concept demonstration for 3D positioning and user data transfer in mesh and star
topologies, containing VTT developed software and positioning algorithm & some VTT
designed HW
This has created interest and 1 customer project has started already
R&D is still needed: Completely VTT designed HW platform. improve features like reliability,
range and throughput by optimizing SW
Benefits:
Multipurpose platform for industrial WSNs and positioning
The UWB platform is noise tolerant and efficient data transmission. Yet advantages of
traditional WSNs are not sacrificed, nodes are small and low cost. There exists several WSN
platforms e.g. Zigbee based, also there are some UWB solutions but not combination like ours
There are no commercial UWB solutions on the market yet, according to our knowledge.
All plans we know usually concentrate on positioning only, we combine positioning and data
transmission.
I do not know any other institute or vendor having made MAC for IEEE 802.15.4 UWB.
I do not know any other institute or vendor having made mesh for IEEE 802.15.4 UWB.
MAC requires both start and mesh topologies; so our customers, too.
Contact person:
Timo Lehikoinen.
Use case example: A robotic crane
manufacturer can use the platform to
locate end of a moving boom while
rapidly transfering sensor data from the
boom to a server database
24. 24
MEMS Crypto
IoT Challenge:
Data security is crucial for IoT.
There is a risk that a successful attack which tampers with the
measurement or control data might paralyze parts of the operation.
Result
Two pieces of MEMS cryptography modules have been built and their
embedded software has been finished.
Fits directly to popular embedded platform (Beagle Bone Black)
Modules show good entropy of generated secret.
System level software development is ongoing
Design of a trust establishment protocol, which is secure and intuitive
for the person who installs the measurement/sensor node.
Potential customers: Industrial internet / IOT vendors. Industrial
measurement/automation providers, such as Metso.
Benefits:
Tamper proof secure sensor node with easy initialization phase
Competition is almost non-existent, since security of sensor networks
has been somewhat neglected
Contact person:
Henry Rimminen
Use case example: Successful
attack which tampers with the
measurement or control data might
paralyze parts of the grid. The
remote readable measurement
nodes are secured with MEMS
assisted cryptography.
25. 25
Combining network and physical system data
for added benefits to both
IoT Challenge:
ICT systems in industrial sites are being compromised by attackers
using cyber-, social- or physical weapons
Result
Definition of a shared context for all security services which can
be used to detect intrusions and deviations that are not
individually detectable, but as a group
Result type: IPR
Maturity of Technology: TRL-4 (Research to prove feasibility)
Customers: ICT and other areas using ICT
Benefits:
Better detection of security threats in high-security systems and sites
Competitive edge: VTT solution collection of data from separate
(competing) systems
The proposed solution improves cybersecurity through the use of big
data and IoT paradigms
Example: SAFE security monitoring system (by Combitech) has the
same kind of features, but it is based on strong customisation
Contact person:
Mirko Sailio
Use case example: Access from
a specific console to a high-
secure corporate information
system is temporarily restricted
from user A (e.g. until another
form of authentication is
performed), based on the fact that
according to the physical access
control system, she is currently
located in another part of the
facility.
27. 27
Enhanced Interactive Augmented Reality Experience
with Dynamic Print and Smart Phone (DynamicAR)
IoT Challenge:
Brand owners, consumer good and packaging manufacturers are looking for cost-effective,
scalable methods for 1) sales promoting to differentiate their products; 2) product
anticonterfeiting.
Result
A demo made for Android smart phone: an AR application using commercial temperature
responsive inks printed on product (picture).
First sales pitch with the demo coffee cups to a major brand owner (Nestle) and paper board
converter (MetsäBoard) on Aug 9.
A road map for developing ALVAR and ALVAR 3D Tracker created: Functionality for ALVAR
etc with free open-source OpenGL Game Engine integration to support rendering of third-
party 3D models for eg commercial purposes was achieved. In the future, the current AR SDK
utilized in this demo could be replaced with ALVAR.
TRL 5-7
Benefits:
The benefit to the customer (= brand owner): Advanced method to get more info with less
space in product: unlock many hidden stories/identification layers using just one marker.
Possibility to apply for antifounterfeiting.
Competitive edge: cost-effective, scalable opportunity by combining functional prints and
smart phones.
Contact person:
Eero Hurme
Use case example:
Functional thermochromic marker
printed on paper sleeve, mounted
on paper board coffee cup: AR
demo changing on smart phone
screen whether cold or hot
contents.
28. 28
Transparent Ultrasonic Transducers (TUT)
for advanced user interfacing
IoT Challenge:
New user interfacing and interaction technologies are strongly and
urgently needed to improve users’ experience towards immersive
environment, augmented reality, wearable devices and smart machines
etc.
Result
18.09.2014: fully released TUT deice demonstrated on Si wafer; devices
on glass wafers are coming soon; devices will be finallised by end of Sep.
Then devices can be tested by impedance analyser etc.
VTT unique ipr; TRL 3-4
Benefits:
This is new and disruptive technology for ultrasonic perception with lower
power, more efficient and more robust;
The transparent ultrasonic eye tracking is a unique solution without
blocking the sight and provides powerful touchless input method using
same type of transducers
Contact persons:
Bin Guo
Jani Mäntyjärvi
Use case example: Proposed
transparent ultrasonic
transducer can be freely placed
on top of the display element on
any display technology, and or
in the line of sight on eyewear.
They can be used in PCs, TVs,
mobile phones, google glasses,
wearable devices, smart
machines etc.