Conference Agenda*
Tuesday, November 17, 2020
Jürgen joined Microsoft in 1990. In Windows Program Management he helped shape Windows releases over almost a quarter-century. Moving into an Architect role in Microsoft Services, he began connecting "things" to the cloud, planning and delivering industrial IoT Solutions. In the One Microsoft IoT & MR team he is now enabling partners to build innovative IoT solutions secured from the chip to the cloud.
Klaus-Dieter Walter is CEO of SSV Software Systems GmbH in Hanover and is well known through lectures at international events as well as articles in technical journals. He has published four professional books on the topic of embedded systems. In 2007 Mr. Walter co-founded the M2M Alliance e.V., and was on the board of directors for many years. He is also a board member of the industry forum VHPready to create a communication standard for virtual power plants. Mr. Walter has been a member of the Internet of Things expert group within the Intelligent Network focus group of the Digital Summit of the German Federal Government since 2012.
Lukasz Grzymkowski is working with Arrow as Technical Lead for Embedded Software. He started his career as a software engineer for Intel in the Data Center Group and joined Arrow in early 2018. In parallel, he is currently working on his Ph.D. degree focused on artificial intelligence and control theory.
As AI methods have matured in datacenters and on industrial PCs, we see people eager to apply them on embedded devices close to the application. The step from datacenters into constrained devices brings both challenges and opportunities.
In this talk we will consider the different mindsets needed for both environments and highlight challenges, tools and solutions to ease the transition.
Nicolas Lehment is a systems architect at NXP’s Industrial Competency Center, where he advises on strategic topics such as ML/AI, connectivity and safety for industrial automation. Before joining NXP, he designed cutting-edge computer vision and robotics systems for ABB and Smartray. He’s collaborated on research papers for topics ranging from ML-driven video classification over human pose tracking to collaborative robotics. This academic work earned him a doctoral degree at the Technical University of Munich.
Oliver Niggemann studied computer science at the University of Paderborn, where he received his doctorate in 2001 with the topic "Visual Data Mining of Graph-Based Data." He then worked as a software developer for Acterna in the telecommunications industry. Until 2008 he was a lead product manager at dSPACE. Niggemann was active in the AUTOSAR committee and until 2008 was chairman of the advisory board of the s-lab of the University of Paderborn. In 2008, Niggemann accepted the call to the newly established professorship for Computer Engineering at the Ostwestfalen-Lippe University of Applied Sciences. He headed the laboratory for "Artificial Intelligence in Automation." From 2008 to 2019, he was a member of the board of the Institute for Industrial Information Technologies (inIT). Until 2019, Niggemann was also deputy director of the Fraunhofer IOSB-INA Institute for Industrial Automation. On April 1, 2019, Niggemann took over the university professorship "Information Technology in Mechanical Engineering" at the Helmut Schmidt University of the Bundeswehr in Hamburg. There he is researching at the Institute for Automation Technology in Artificial Intelligence and Machine Learning for Cyber-Physical Systems.
Research and development in the area of artificial intelligence started in the 1950s. However, the actual integration and deployment for small- and medium-sized embedded systems companies is still a widely unestablished process. The main reasons for this are: The lack of an understanding of AI technology and AI-specific properties itself leads to the lack of knowledge about potential application areas and the uncertainty of whether a migration towards an AI-based solution will benefit.
The main problem in applying AI in the industry is a systematic approach to mapping the concerns of stakeholders to potential AI-enabled solutions, which, from the architectural point of view, also discusses consequences in terms of benefits and drawbacks.
Robin's work aims to support decision-makers who want to adopt AI's potential for their projects. He created an approach to enhance standard requirements engineering techniques such as the Adequacy Check with AI-specific property checks. This check allows us to grow awareness and confidence about specific processes and steps that need to be considered throughout an AI-based development. Furthermore, it assists in identifying shortcomings of the current status of your architecture and provide hints on how to engage it. The approach does not necessarily require an input but can straightforwardly be used as a support tool for the requirements elicitation process when it comes to AI-specific needs.
Working as Business Development Manager in the domain of embedded and industrial systems, Robin's daily challenge is to update customers with the latest upcoming technologies and trends - especially in reasonably new technology like artificial intelligence, which is going beyond traditional HW/SW approaches. As an NVIDIA employee and former Technical Feld Application Engineer at Arrow Electronics, Robin engages many different customers with a wide range of various projects and use cases. When it comes to AI, his primary task is to identify the current status of the customers' project and to provide support in case of fundamental questions, deep-dive technical assistance, and general shortcomings throughout the complete project lifetime.
Taking AI enabled Video Analysis to the Edge in order to support real-time data delivery and decision making. It requires extremely powerful microprocessor units (MPUs) combined with graphics processing units (GPUs) – which can accelerate compute-intensive applications by spreading computing workloads over multiple cores. Hear more about powerful Intel microprocessors which are dramatically boosted by the addition of NVIDIA GPUs; and deep learning platforms based on NVIDIA’s Jetson family provide a quick start for autonomous machine development.
Marco Krause is Global Account Director at Adlink. He is responsible for global TIER1,2 accounts and heading the CEE team. Marco has 15+ years of experience in the Embedded, IPC, Distribution & IT environment. His special interest is in AI/KI related topics and applications (i.e. robotics, AGV´s, autonomous cars).
• Connectivity – getting products to work seamlessly in a field of multiple wireless technologies
• Security - compliance with emerging privacy and security requirements
• Human-machine interface - aesthetically attractive industrial design
• The user experience - making technology plug-and-play, while delivering behind-the-scenes software updates
• Secure, scalable device management with easy on-boarding supporting major platforms or in-house servers
• Integration - making disparate technologies work together seamlessly
• Cloud support - secure, scalable device management with easy on-boarding
• Monetization - enhanced profitability through reduced support costs while providing for secure lifecycle management
• Low-power operation – minimizing heat dissipation while addressing environmental issues
Rob Conant has a long history in the Internet of Things and connected products. Rob was a pioneer in the Internet of Things as a co-founder of Dust Networks in 2002 where he ran sales and marketing for the company and set the product direction as the company enabled a new market for wireless connected products in the industrial markets. After Dust, Rob joined Trilliant to bring the benefit of connected products to the energy industry, where he delivered smart meters and energy management to millions of customers across the US, Europe, and Asia. Rob first ran the engineering team to ensure the company had market-leading products, then took the position of Chief Marketing Officer to drive the company's growth. Rob's deep technical background and business orientation (ran sales and marketing at multiple companies) gives him a unique perspective on the interactions between business models, technology, and consumers. Rob has a PhD in Electrical Engineering and a BS in Mechanical Engineering from UC Berkeley.
While communication speed for wireless technologies has been a priority for decades, the focus has started to shift to other parameters such as long-range and low-power connected devices. Networks with this focus are often referred to as low-power wide-area networks (LPWAN); Sensors in LPWAN networks communicate infrequently and can sleep from minutes to hours between every transmission. In such applications, high data throughput is not as important as being able to communicate over long distances, which is why the technology is optimized for long-range radio-frequency (RF) communication.
The Massive Internet of Things (MIOTY) protocol enables the division of data packets into smaller subpackets, causing them to spend less time in the air and therefore decreasing the risk of collisions and of data loss.The MIOTY solution offers a star network for low-power end nodes as well as a gateway solution for cloud connectivity with a complete long-range and low-power solution for worldwide Sub-1 GHz communication. The goal of the MIOTY Alliance, the governing body of MIOTY, is to enable the most accessible, robust and efficient connectivity solution on the market. The MIOTY protocol operates in license-free bands around the world, and there are no costs involved in using the radio spectrum, unlike narrowband IoT solutions.
The physical layer (PHY) and link layer of MIOTY technology are based on a publicly available document: the TS 103 357 public technical standard (TS) from the European Telecommunications Standards Institute (ETSI). Eliminating the risk of vendor lock-in, MIOTY has already been tested with three independent silicon providers, including Texas Instruments (TI) using the CC1310 microcontroller (MCU). As of today, MIOTY offers a private network but the expectation is that third parties will also offer a network solution as a service.
The strongest advantage with MIOTY is the TSMA method, splitting up the data into smaller equally sized subpackets that are distributed over the time and frequency domains. This method creates a technology that is less susceptible to interference while also being friendlier to other radio systems. Because the packets are sent in smaller radio bursts, they spend less time in air, resulting in lower power consumption and a longer battery life.
MIOTY can help overcome performance degradation in high-node-count networks and reaching remote sensors. Using MIOTY is suitable for a wide range of battery operated applications that requires high density of sensors and small data volumes.
Elin Wollert is an applications engineer at Texas Instruments in Norway. TI is a founding member of the MIOTY Alliance, and in Elin's role as project manager for MIOTY at TI, she is pushing the MIOTY technology forward.
Muhammad Shafique is Product Manager at Embedded Platform Solutions division in Mentor, a Siemens business. He has over 17 years of experience in the embedded software and his experience spans from deeply embedded software to all the way up to the higher layers of embedded software stack and device management in cloud (e.g. Internet of Things, edge stacks, industrial connectivity, etc.). In his current role, he is responsible for IoT/Cloud integration strategy for embedded Linux as well as real-time operating system products from Mentor Embedded Platform Solutions division. He holds a bachelor’s degree in electrical engineering.
Wednesday, November 18, 2020
TRACK 1 IN THE MORNING
9:00 AM
Veronika Zellner is an Architect for Data & AI at Microsoft and passionate about all data-related topics. Especially Machine Learning in the cloud and on the edge. She has several years of experience in the Business Intelligence and IT consulting. Currently, Veronika is working at Microsoft in Munich, Germany.
Quang Hai Nguyen is working as Technology Field Application Engineer at Arrow Central Europe GmbH. He joined Arrow in 2015 as a Junior Engineer in the Graduate program. After the Graduate program, Quang Hai worked as an Application Engineer focusing on microcontrollers. Since 2019, he has been working as a Technology Field Application Engineer for high-end processors and security in embedded systems. At the beginning of this year, he has taken up additional responsibility supporting customers with AI at the edge technology.
Dr.-Ing. Alexander Willner is head of the Industrial Internet of Things (IIoT) Center at the Fraunhofer Institute for Open Communication Systems (FOKUS) and head of the IIoT research group within the chair of Next Generation Networks (AV) at the Technical University Berlin (TUB). In collaboration with the Berlin Center of Digital Transformation (BCDT), he works with his groups in applying standard-based Internet of Things (IoT) technologies to industrial domains, such as Industry 4.0. With a focus on moving towards the realization of software-based industrial communication infrastructures, the most important research areas include industrial real-time networks (e.g. TSN/5G), middleware systems (e.g. OPC UA), distributed AI (e.g. via Digital Twins) and distributed Cloud Computing (e.g. Edge Computing) including management and orchestration.
Fiona Treacy is a strategic marketing manager for Process Control and Automation focused on Industrial Connectivity at Analog Devices. Before this role, Fiona led the Marketing effort for MeasureWare and other Precision Instrumentation initiatives. She also held positions in Application Engineering and Test Development. Fiona holds a BSc. in Applied Physics and an MBA from the University of Limerick.
TRACK 2 IN THE MORNING
9:00 AM
Throughout this paper, we will talk about the what and how that makes IoT solutions complex; it’s not as simple as taking a puzzle you already have completed and adding a few additional pieces to expand the puzzle’s picture. The ability to capitalize on the already-established edge pieces, and then customize and integrate, will make the difference.
Through IoT solutions, machines are getting smarter, gaining context about where they are and what is around them to react. Essentially, we are making machines more human - connecting them to their
human counterparts so that together, they can do more than ever before. When those intelligent machines are integrated into the enterprise, IoT allows you to remove tasks, decrease complexity and confusion for the
end-user, and drive accurate, data-driven decision making.
The collection and analysis of data is imperative to uncovering value with IoT - this may be the focus of your product or only an add-on that enhances the core functionality that your users expect. Either way, you will need to thoroughly think through each component that gives your device the “Internet of Things” label: data collection, hardware connectivity, communication protocol, and device-level security measures.
Ralf Pühler is President/CEO Europe of KUDA llc, and a business development as well as an operations professional. Understanding customer needs and priorities lead to continuous customer dialogue from the initial idea until market breakthrough incl. quantifying the value and impact of ideas by testing them on the market. Ralf has been connecting things to the Internet for years and has helped customers transform from traditional into connected processes, to optimize cost, eclipse the competition, and create new revenue. He believes in success through providing industry-relevant concepts for value-adding IoT solutions.
Tobias Gaukstern führt bei Weidmüller die Business Unit Industrial Analytics. In dieser Rolle baut er ein skalierendes SW-Geschäft für die Weidmüller Gruppe auf und führt Weidmüller von einem führenden Anbieter der elektrischen Verbindungstechnik zu einem Machine Learning Champion. Seine Vision: Die Anwendung von AI und ML zu vereinfachen und zu beschleunigen, so dass es gelingt mit diesen Technologien signifikante Wertschöpfungspotentiale in der Industrie in der Breite zu erschließen. Vor seiner aktuellen Tätigkeit war er als Industry Develo-pment Manager und als strategischer Produktmanager tätig.
Oliver Niedung grew up in Hannover, Germany, had various object-oriented software development roles before and after finishing his degree in Medical Informatics at the University of Hildesheim. He had development and sales roles at Berner & Mattner and Visio, which was acquired by Microsoft in 1999. At Microsoft, Oliver managed the Embedded Server activities in EMEA with leading global OEMs until 2015. Since then, Oliver works with the most strategic Microsoft OEMs and partners in Europe on digital transformation and highly innovative IoT solutions.
Markus Weishaar holds a degree in electrical engineering (B. Eng.) at Ulm University of Applied Sciences in 2012. He supplemented it in 2017 with a degree in industrial engineering (M. Sc.) while working. After 11 years in packaging machine construction in various software and management functions, Markus has been Product Manager for IIoT and software at Dunkermotoren since May 2019.
defined shopfloor solutions.
Johannes joined the BCG group four years ago. Since July 1, 2020, he is Associate Director, located in the Munich office. With BCG, and it’s subsidiary BCG Digital Ventures, he worked on multiple end-to-end software-driven, and Cloud/IoT related business builds and product definitions. Before BCG, Johannes held multiple senior software management and engineering positions. Johannes designed one of the first Edge-powered industrial robot setups. Therefore, BCG and AWS cooperated on a demo setup for a large manufacturer and demonstrated the results on the HMI in 2017. It was one of the first AWS Greengrass installations.
TRACK 1 IN THE AFTERNOON
1:45 PM
Dr. Pierre Gembaczka is a scientific assistant since 2014. He studied Microtechnology and Medical Technology and held a Master's degree from the University of Applied Sciences in Gelsenkirchen. Afterward, he completed his doctorate at the Fraunhofer IMS in cooperation with the University of Duisburg Essen and obtained the academic degree of a doctor of engineering. From 2014 to 2017, he worked as a research assistant in the department Micro- and Nanosystems - Pressure Sensors at Fraunhofer IMS. Since 2018 he works in the embedded systems group at Fraunhofer IMS and researches embedded AI solutions for various applications. He is the primary developer of the AI software framework AIfES (Artificial Intelligence for Embedded Systems)
Todays camera-enabled multimedia SoCs are getting more and more complicated. Embedded solution architects and system developers have to deal with various challenges on the hardware side and very extensive software frameworks from device drivers to networking stacks and full AI stacks on the other side. This speech will give an overview of how such a design might look like and tap in each of the needed building blocks, mainly from a software and system architectural point of view.
This speech aims to introduce the overall system architecture of a camera device, designed for usage at the edge and enable the audience to capture the most relevant today's technology topics. We will start by providing a hardware block diagram of such a system and look into the most critical modules to discuss their relevance for edge systems. Based on that block diagram, we will see how the hardware can be utilized and controlled from operation systems and how the most common interfaces look. Particular focus will be given to various operating system infrastructure to support certain features like camera, security-related functions, and networking.
We need a more in-depth look to get a sense of why security is so critical, especially in edge-based AI systems – what are the implications and what needs to be secured. Based on that foundation, we will start looking into userspace software frameworks and the corresponding APIs to make use of the hardware features to allow on-device data processing, minimizing data to be stored or exchanged and certain technologies to connect the device to the cloud.
We also need to talk about testing, why it is especially complicated to test AI-based systems, and what strategies we can use to perform those. How can we test connected devices and create environments which are "close to their real field of application" in terms of connection speed, connection reliability, power losses, and much more? We will close the lecture with an overview of typical use cases and their application-specific requirements.
Dieter Kiermaier works with Arrow as a Technical Solution Architect. He started his career as a developer for embedded Linux systems. For six years, he is active in the area of electronic component distribution, working as Technology Field Application Engineer for High-End processors and System-on-modules. At the beginning of this year, he moved into the role of a Technical Solution Architect for Embedded Systems and Cloud-based solutions at eInfochips (an Arrow Company).
Being aware of this requirement we took the decision to develop our own software tools and IP-Core architecture. To get a prove of concept of this new technology AVI decided to realize a concrete application, which is the truck turn assistant “CAREYE SAFETY ANGLE” that is already on the market and has the required type approvals from the Kraftfahrzeug-Bundesamt (KBA). This product includes a deep-learning based object detector running on a less power consuming and fan less controller box.
Johannes Traxler attended the HTBLuVA for telecommunications and computer science in St. Pölten. Thereupon he studied technical physics and astronomy at the TU in Vienna. During his studies, he concentrated on “Artificial Intelligence“-research and development with machine learning, based on early recognition of tumor cells. In between 1999 and 2002, he worked as director of research and development at “ArtiBrain Forschungs- und Entwicklungs GmbH” to implement machine learning-based tunnel safety and incident detection systems for the first time. In 2013, he founded the “AVI-Systems GmbH,” where he started with deep-learning-based applications for manufacturing inline inspection systems in 2014. Since this period, he acted as CEO, invented new approaches for real-time video transmission with his team, and collaborated with different research organizations to develop deep-learning applications. Mr. Traxler is head of the task force “Videodetektion in VBA, “which is a part of the German Road and Transportation Research association and makes significant contributions to the development of technical standards and guidelines for Germany.
The worldwide shift towards digitization has placed immense pressure on connectivity. Over 20 billion Internet of Things (IoT) connected devices have already been installed, and the number is estimated to grow to 75.44 billion by 2025. IoT devices benefit all industry sectors, promoting higher levels of convenience, productivity, and communication. To keep up with the vast amounts of data we need to utilize the processing power on the IoT devices and make the IoT edge intelligent.
Edge computing solves two problems by merging them together into one solution. On the one hand, the constraint on cloud data centers to handle increasing amounts of data is about to reach a breaking point. On the other hand, Artificial Intelligence (AI) systems consume information in such a speed that there doesn’t seem to be enough. Edge databases enables applications to bring Machine Learning (ML) models to the edge .
Powered by real-time databases and AI, the intelligent edge can provide real-time insights for the improvement of many industries. Last-mile delivery is made faster and more efficient with features such as smart tracking and real-time route navigation. Intelligent systems can provide stylistic insights for customers as they browse the shop, and facial recognition software can be used for fraud detection. Edge intelligence can provide predictive risk assessments for healthcare professionals and promote better health awareness. There’s no limit as to the benefits the intelligent edge can provide, not even the cloud.
David Nguyen is the Head of Engineering & QA at Raima. He started his career as a QA Software Engineer with work on the creation and maintenance of a fully automated testing QA framework and a complete daily build system. As the Director of Engineering he is leading the development in modernizing the database product line with a feature focus on AI, autonomous driving and edge IoT while ensuring the customer experience is as straightforward and easy to use as possible. David holds a B.S. in both Mathematics and Computer Science from the University of Washington. Photo: Attached.
Max ist Maschinenbauingenieur und fasziniert davon, Fertigungsprozesse mit Software zu verbessern. Er schloss sei Studien an der ETH Zürich, Caltech, Duke und EPFL Lausanne ab. Bevor er Actyx mitbegründete, forschte er auf dem Gebiet der physikalischen Chemie.
TRACK 2 IN THE AFTERNOON
1:45 PM
- Internet access technologies and radio technologies for condition monitoring solutions: When is WLAN, Lora, Sigfox or mobile radio best suited; considered according to the application
- Process monitoring and automation with sensor solutions
- Challenges of radio
- Application example: Motor monitoring at Murrelektronik
Thomas Schildknecht is the founder and CEO of Schildknecht AG, a specialist for industrial radio data transmission and the industrial Internet of Things. The company has developed a future-oriented radio system for industrial use to make secure and stable radio transmissions possible. Schildknecht is also a system provider in remote monitoring and remote maintenance, telemetry, and M2M.
However, without a fixed and reproducible data analysis workflow, even medium-sized data sets can easily be overwhelming, and one can get lost in its details, missing obvious patterns.
In his talk, Nikolai Hlubek will present an example workflow that starts from scratch by the planning of laboratory experiments and ends with a trained machine-learning model on a microcontroller. This workflow will use freely available tools. The explorative data analysis is done in python and jupyter notebooks. The model building is done with keras. The optimization of a keras model for the microcontroller is done with X-CUBE-AI from ST.
Nikolai Hlubek is a Research and Development Engineer at Bürkert Fluid Control Systems. He holds a Ph.D. in physics and has been doing data science for more than ten years. Nikolai is the winner of 2017 What The Data!? Hackathon.
It is more challenging to define techniques and measures to follow when AI is to be designed as a critical part of functional safety. Fundamental research is needed to give the required confidence in such solutions integrating AI in functionally safe systems.
The presentation is about ongoing activities in the areas of “process design” and “techniques and measures.” It should result in starting a discussion if these activities will result in more confidence in solutions with AI as a critical part of functional safety.
Frank Poignée is Chief Engineer and Safety Consultant at infoteam SET. He is a Project Manager for industrial automation, medical engineering, and life science. Frank is also the Product Owner of the infoteam Functional Safety Management Process iFSM. Frank is ASQF/ISTQB Certified Tester and TÜV Functional Safety Engineer for Hard- and Software.
AI / Edge computing requires large, compute-intensive tasks and complicated neural network models to be executed in a constrained environment.
These challenges are overcome by using a number of specialized processors, CPU, GPU, DSP and NPU, which evolve with each generation.
Using such a heterogeneous computer architecture for the execution of sophisticated neural networks allows low power operation within thermal limits, by selecting the right block for the right task. Together with the brand-new Qualcomm Hexagon™ Tensor Accelerator, this is pushing 15 trillion operations per second(TOPS) with maximum efficiency to run complex AI and deep learning workloads at the Edge. Thanks to the support for 5G connectivity speeds , this creates a framework for the development of a wide range of next-generation applications.
Dominik Bohn has been working for Atlantik Elektronik since his studies (B.Eng WI Electrical Engineering & M.Eng Information, Technology & Management). From 2013 on he was responsible for the embedded portfolio as Field Application Engineer. Since 2020 he is Product Manager for 5G & AI solutions based on Qualcomm & Thundercomm technologies.
Alexander Samuelsson is CTO of imagimob, which he founded together with Tony Hartley and Anders Hardebring in 2013. Alex has extensive experience in software development in areas such as mobile apps, mobile games and cloud systems. Previously he studied Computer Science at KTH Royal Institute of Technology. Imagimob offers artificial intelligence products for edge devices. Based in Stockholm, Sweden, the company has been serving customers within the automotive, manufacturing, healthcare and lifestyle industries since 2013.
