Embedded World 2020

•••8••• Innovationen 1NCE GmbH www.1nce.com Halle: 3 • Stand: 425 Christ-Elektronik GmbH www.christ-es.com Halle: 1 • Stand: 252 demmel products gmbh www.ilcd.info Halle: 1 • Stand: 371 Embedded Office GmbH & Co. KG www.embedded-office.com Halle: 4 • Stand: 547 Embedded Systems Automation UG (haftungsbeschränkt) www.embedded-systems.de Halle: 3A • Stand: 629b Fortec AG www.fortecag.de Halle: 1 • Stand: 261 nymea GmbH www.nymea.io Halle: 3 • Stand: 501 OPAL-RT Germany GmbH www.opal-rt.com Halle: 4 • Stand: 108 Swissbit AG www.swissbit.com Halle: 1 • Stand: 534 Nichtaussteller: PNY Technologies Quadro GmbH www.pny.eu/de Messetelegramm Hybrid device for solar energy Researchers design device to use solar energy for IoT Researchers at the University of Houston have designed a device that efficiently cap- tures solar energy and stores it for use by ap- plications for the Internet of Things (IoT) and industrial IoT. Unlike solar panels and solar cells, which use photovoltaic technology for direct electricity generation, the hybrid de- vice leverages the physics of molecular en- ergy and the accumulation of latent heat to make the collection and storage of energy a 24/7 process, addressing a primary short- coming of current solar products. The re- searchers synthesised the device using nor- bornadiene-quadricyclane (NBD–QC), an organic compound with high specific energy and extended storage times, as the molecu- lar storage material (MSM), separated from a localised phase-change material (L-PCM) by a silica aerogel to maintain the necessary dif- ference in working temperature. The com- mon approach for storing solar energy is the use of batteries coupled with photovoltaic systems for both small- and large-scale in- stallations. It is not only electricity that needs to be stored: An equally useful aspect of energy transition is the ability to capture and store solar thermal energy. The new de- vice is based on a hybrid paradigm that uses daytime heat localisation to provide 73 per cent collection efficiency on a small scale and 90 per cent on a large scale. In particu- lar, at night, the energy stored by the hybrid system is recovered with 80 per cent effi- ciency and at a higher temperature than dur- ing the day, setting it apart from other state- of-the-art systems, according to a paper published by the researchers in the Decem- ber issue of Joule. Anzeige Developing embedded systems faster New development platform for image processing components A t first glance drones, driver assistance systems and mo- bile medical diagnostic equip- ment don’t appear to have much in common. But in reality they do: they all make increasing use of im- age processing components, for example for detecting obstacles and pedestrians. Image process- ing can also be used with mobile X-ray equipment to ensure ad- equate image quality at reduced radiation levels, thus considerably reducing radioactive exposure. Special needs of mobile applications In contrast to a workstation com- puter, where dimensions and energy consumption are not particularly critical factors, ap- plications like these require for small, lightweight, energy-ef- ficient image processing com- ponents that are nevertheless real-time capable. Hardware platforms based on conventional computer architectures and pro- cessors can’t properly meet these requirements. This is why embed- ded systems using field-program- mable gate arrays (FPGAs) are of- ten used. Field-programmable gate arrays are logic components whose circuit structure can be freely configured using a special type of programming, usually involv- ing the low-level language VHDL. There’s a problem, however: The majority of image processing ap- plications are written in higher- level programming languages such as C/C++, and their migra- tion to the embedded systems is highly complicated. Not only does VHDL differ greatly from other programming languages, but the code must also be adapt- ed to the specific hardware. This means even existing VHDL pro- grams can’t be transferred to other hardware. Software devel- opers have to start virtually from scratch with every new system. A consortium of eight partners from six countries, including the Fraunhofer Institute for Op- tronics, System Technologies and Image Exploitation (IOSB) in Karlsruhe, has now considerably simplified this procedure in the Tulipp project. “The result is a de- velopment platform consisting of design guidelines, a configurable hardware platform and a real- time-capable operating system that supports multicore proces- sors, as well as a programming tool chain,” says Dr.-Ing. Igor Tchouchenkov, group manager at Fraunhofer IOSB. “A starter kit put on the market by one of our partners in Tulipp provides additional support. The starter kit makes developing such appli- cations much faster and easier. Porting C++ programs to FPGA, which frequently means several months of work for the devel- oper, can be handled within on- ly a few weeks using the Tulipp starter kit.” This means the devel- oper first has to consider, based on the software programmed in C++, which code elements should be distributed to which hardware components and which program steps could be optimised or par- allelised. The formulated design guidelines provide help with this task. Then the starter kit comes into play. It contains the config- urable hardware to which the necessary sensors and output de- vices can be connected, the mul- tiprocessor-capable real-time op- erating system, and what is called the STHEM toolchain. The appli- cations in the toolchain make it possible to optimise the C++ pro- gram in such a way that it can be ported to the FPGA as easily and quickly as possible. “One special focus of the toolchain is on en- ergy optimisation: after all, the aim is to design image processing systems that can be powered by a small battery whenever pos- sible,” says Tchouchenkov. “The toolchain makes it possible to in- dividually display and optimise en- ergy consumption for each code function.” Results are impressive: The processing, which originally took several seconds to analyse a single image on a high-end PC, can now run on the drone in real time, so now approx. 30 images are analysed per second. The Drone with the small white box holds the embedded system. Photo: Fraunhofer IOSB