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The IoT is evolving rapidly with cloud technology and smart hardware. It’s widely used in CAD: for building maintenance, construction management, intelligent manufacturing, smart city, etc. This session showcases one of the hottest businesses today: IoT workflow with a viewable real 3D model. Using the new UP Squared* Grove IoT Dev Kit UP2 and Autodesk Forge* cloud service, developers can get started easier than ever before using with an IoT + 3D model solution.
These two technologies broke the barriers to data connecting, increasing speed and accuracy across the enterprise through connected systems while reducing complexity due to isolated data islands. In this session we explore building applications using UP Squared* Grove IoT Dev Kit and Autodesk Forge services.
This paper introduces the development and industrial implementation of artificial intelligence in the security IOT industry, including products and solutions. How to take advantage of the Movidius chip on the production side in the smart front-end camera and how to provide more space for the extension of artificial intelligence.
Electricity is a major problem in Africa, where most people are not connected to their regional grid. This session presents a real-world use case for solar energy and a LoRa* network to provide and monitor electricity generated across a very long distance. Each system is equipped with a long range, low power LoRa* network to serve remote locations. Users can be monitored accordingly and given real-time information about their energy usage.
Artificial Intelligence, AI, is changing our lives from the past to the future. It enables machine intelligent by using a variety of training models to simulate and infer the status or appearance of an object. For example, the inference system with video analysis model can perform face and car plate analysis for safety and convenience purposes. IEI introducing TANK AIoT Dev. Kit features rich I/O and dual PCIe by 16 slots with by 8 signal for add-on card installation such as PoE (IPCEI-4POE) card & IEI AI acceleration card (Mustang-200, F100 & V100 series) to enhance function and performance for various AI applications.
Smart Fridge and Smart Shelf real-time monitoring for efficient inventory management can be used in different verticals such as convenience and grocery stores, laboratories, and healthcare facilities. This end-to-end IoT solution uses Bluetooth*, WiFi*, sensors, gateways and a cloud infrastructure to track, monitor, collect data, and alert end-users of inventory.
This session demonstrates both solutions and explains the technologies, tools and protocols used to create each:
• Smart Fridge uses temperature, humidity, weight, and other sensors to track refrigerator conditions, power usage, whether the doors are open or closed, and identify the number and types of products taken from the refrigerator.
• Smart Shelf uses load cells, proximity, temperature, humidity sensors and an e-paper display. This application can identify the weight on a shelf, temperature and humidity, and how many people have come within close proximity of the shelf. With the e-paper display, the color scheme, product, cost and other display attributes can be changed locally or remotely.
Securing your IoT devices is challenging. High-performance FPGAs such as the Cyclone* V and Arria* 10 can run computationally intensive security algorithms such as RSA and ECC, but the low-resource endpoints they connect to cannot. In addition to being too slow, these methods often do not fit in available RAM and ROM of constrained devices. As a result, many low-resource devices -- and therefore many systems -- are deployed with little or no protection. In addition, applications for automotive, industrial, medical, accounts payable, and others with multi-year lifecycles must address the coming threat from quantum computing that will break RSA and ECC.
In this session, experts from SecureRF and Intel will introduce security methods that are fast, flexible, and future-proof, running on low-resource devices. Topics will include:
• Current and future threats to connected low-resource devices
• How to use FPGAs for secure, flexible and upgradeable designs
• Strategies for delivering fast authentication and data protection for applications with resource constrained devices
• A real-world case study: The DE10-Nano development board and a low-resource device using SecureRF methods, including how-to tips for fast deployment.
IoT is at the center of exciting new capabilities in a variety of settings, including manufacturing, retail, health, transportation, and the home. It is also at the forefront of public, industry, and regulatory concerns about data, algorithms and automation. News reports of algorithmic bias, privacy, transparency, and the future of work abound, and a new crop of think tanks, research institutes, and standards development efforts have emerged in response.
Ethics issues are easy to spot in hindsight but much harder to catch and fix early on, before harm is done. What does it take to meaningfully examine social and ethical issues, given the realities of constant build/test/learn cycles? What questions need asking, and when? What creative solutions have others found to resolve these issues?
This session will help you move beyond the media hype cycles. We will clarify the areas where credible researchers believe there is cause for concern, and show you some approaches and frameworks to help you know the right questions to ask at the right time. We’ll also dive into real-world examples where things did go wrong, and pose promising interventions that could help.
This session looks at ways to resolve the pain points that affect express delivery terminal technology. We’ll evaluate the market size of intelligent self-express service machines in China, present a profile of Jiangsu Cloudbox Networks Co., Ltd., and demonstrate the value and future of intelligent self-express service machines.
In this session, learn how the Internet of Things is creating a lasting impact as you explore use cases both current and future. Throughout your waking day and into the night, the IoT is adding value at work, in the home, across your community and beyond. Products, services and processes are continuously being optimized through a world of connected technologies.
In recent months, Blockchain and other distributed ledger technologies have been greatly over-hyped. Yet, they show much potential beyond crypto values and finance.
In this session, learn about a real-world use case for provenance tracking using Blockchain and IoT technologies. See how a traditional production and handcraft industry can boost its value using a state-of-art solution linked with business process Improvements.
Today’s edge computing technologies are designed to operate in very low-power environments with little connectivity. However, if AI algorithms, which typically require very high compute resources, can be designed and optimized to run at the edge in a low-power environment, this creates numerous possibilities for AI-powered IoT applications at the edge.
In this session, the possibilities for AI applications/use cases ranging from smart homes, smart factories to smart vehicles/driver assist, etc. are discussed. In addition, we look at real-world cases of AI/ML enabled through an AI co-processor/accelerator and an NN/Deep Learning algorithms.
Problems with AI at the edge can involve object detection from video, speech/voice recognition, or analyzing input from vibration sensors in machinery, to name a few examples. While some use cases may need compute infrastructure on cloud, others can be suitable for AI at the edge. These can be in surveillance/compliance or a host of other possibilities in smart home/factory/city scenarios.
Real-world use cases involving object detection using CNN on an AI engine in an FPGA processor is examined. And, challenges to implementing solutions to operate within the power/efficiency, latency constraints and FPGA footprint, with no significant loss in accuracy are detailed.
Over the past 10 years we’ve watched cloud computing come of age as more and more companies send their data to the cloud for processing, storage, and management rather than keeping that data on a local server or edge gateway. The benefits of cloud computing are vast, but there’s another key development on the horizon as the Internet of Things matures: edge computing.
This session details the need for data from the intelligent edge, share real-world industrial use cases for making use of data at the edge, and communicate the benefits of a proper edge computing framework.
While only 10 percent of enterprise-generated data currently is processed at the edge, Gartner predicts that by 2022 that figure will reach 50 percent. Half of all processing power is expected to slowly shift from the cloud to edge devices, leading to IoT projects that utilize the power of both cloud computing and the intelligent edge to make smart business decisions.
Use cases for edge computing range from simple manufacturing projects to larger smart city or extended macro-level projects. The fundamental goal for all such projects is to collect data from a large body of industrial assets, and then put that data to use immediately. Some of the devices are ready for IoT, while others were never designed for IoT projects. The common thread is the challenge of gathering data from those disparate devices, then processing, analyzing and acting on the data right at the Edge of the network or device.
Connectivity is the core of the Internet of Things, yet numerous connectivity protocols exist for different IoT domains. MQTT stands out with its low-bandwidth, high latency, decoupled applications where systems don't require precise timings. It simplifies IoT application integration for all systems.
This session details the advantages of MQTT and guides you to available software libraries, security applications, real-world use cases, open source applications, and server creation using Docker*.
Intelligent machines are here, rapidly changing the way our society works, including the way we design, build, manage and inhabit the built environment. Huge advances in robotic equipment, construction materials and manufacturing techniques are completely changing the way construction work is carried out, putting the construction industry at the forefront of the Fourth Industrial Revolution.This session explores the technological advances in some core construction activities: concrete 3D printing, robotic bricklaying, robotic welding, and UAV site surveying. In addition, we’ll show how applications of machine learning, augmented and virtual reality and the integration of advanced sensors are disrupting the industry.
FPGAs play a critical role in heterogeneous compute platforms as flexible, reprogrammable, multi-function accelerators. They enable custom-hardware performance with the programmability of software. The industry trend toward software-defined hardware challenges not just the traditional architectures—compute, memory, network resources—but also the programming model of heterogeneous compute platforms. Traditionally, the FPGA programming model has been narrowly tailored and hardware-centric. As FPGAs become part of heterogeneous compute platforms and users expect the hardware to be “software-defined,” FPGAs must be accessible not just by hardware developers but also by software developers, which requires the programming model of FPGAs to evolve dramatically.
This session details a highly evolved, software-centric programming model that enables software developers to harness FPGAs through a comprehensive solutions stack. It encompasses FPGA-optimized libraries, compilers, tools, frameworks, SDK integration and an FPGA-enabled ecosystem. Your training will also include real-world examples using machine learning inference acceleration on FPGAs.
The Context Sensing SDK for IOT is a Node.js, Go, C#, Java and Python-based framework supporting the collection, storage, sharing, analysis and use of sensor information. Designed to simplify the work of developers, system integrators, and prototyping teams, the framework provides an expandable plugin system for physical and virtual sensors, local and fog sync mechanisms, and general-purpose analysis modules. The code base is designed to scale from Intel Atom® processor-class devices to Intel® Xeon® processor-class devices.
In this session, two practical use cases are discussed: IoTAR: Combining data from IoT devices with the visualization capabilities of Augmented Reality unleashes new possibilities for developers in the Retail, Automotive and Transportation, Industrial Automation and Energy sectors. The IoTAR project shows how devices connected through the SDK can be visualized and controlled through leading AR devices in today's market, giving rich user interfaces in an augmented world. Adaptive Learning: Traditional education systems apply one-size-fits-all learning strategies and cannot be easily modified to meet individual student needs. Technology can help provide personalized learning experiences. Adaptive Learning detects students’ emotional states (satisfied, bored and confused) and behavioral engagement (on-task, off-task) and fuses them to determine each student's overall engagement. Teachers can react and modify their lessons based on real-time engagement data provided via a dashboard.
Learn to integrate voice-enabled commands with Amazon Alexa* over a map using HERE location services built using AWS Serverless Architecture. This session gives you the ability to create your own unique microservices and then connect them with AWS IoT.