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Julia is a relatively new software language which is comparable to programming in C in terms of speed and functionality, explains Spectrum Instrumentation. It additionall allows scientific and numerical computing. Spectrum has developed a software development kit (SDK) for programming its range of digitisers, generators and digital I/O products, using Julia.

The software language has been specifically designed for high-performance applications that require fast processing of data, like machine learning and scientific computing. In addition to libraries including optimised source C and Fortran code for linear algebra, random number generation, signal processing and string processing, it also offers parallelism. It allows users to call the desired script with a given number of cores and parallelise directly from a command line. It is possible to send tasks to different threads, or run loops in parallel, directly from code. While speed is simiilar to C, coding that is more comparable to Python or MatLab, says Spectrum.

Spectrum explains the synergy of Julia with its products which acquire or generate the fast electronic signals found in artificial intelligence (AI) applications or robotics. The company offers digitisers for the acquisition of analogue or digital signals, in the DC to GHz frequency range, with high precision and dynamic range. They are complemented by the company’s high-resolution arbitrary waveform generators (AWGs) for control signal generation, waveform replay and simulation. The products are available in a variety of form factors including tiny PCIe cards, which can plug directly into a PC, or LXI boxes that connect to a network or PC via Ethernet, plus PXIe modules for off-the-shelf installation into automated test systems.

Combining Julia with Spectrum Instrumentation products also helps to speed up processing and reduce latency, explains the company. The Spectrum products offer fast data transfers with a variety of different acquisition and generation modes (such as single, multiple, gated and FIFO) which helps to optimise testing throughputs. For applications which require fast decision making, such as autonomous vehicles, robotics, drones, imaging devices, medical appliances and control systems, the general purpose design of the instruments means they can also be used with almost any sensor (accelerometers, transducers, photo diodes, detectors, MEMS) commonly found in AI machines.

A recent example is Dr Josef Höffner’s work at the Leibniz Institute for Atmospheric Physics in Germany developing lidar (light detection and ranging) systems to measure temperature and wind speed in the atmosphere. The complex data acquisition and control, with 30 high speed signals uses three Spectrum Instrumentation cards and Julia which he described as a “combination of speed and dynamic programming, simplifying the software development”.

The new SDK supporting Julia is available free of charge to all Spectrum customers. Spectrum products include a five-year warranty with free software and firmware updates for each unit’s lifetime. Additionally, customers get support directly from Spectrum’s hardware and software engineers.

http://www.spectrum-instrumentation.com

Translation of sensor outputs into ready-to-use data for IoT applications and data-driven businesses is the work of EnOcean’s IoT Connector. The software is described as a link between maintenance-free IoT data from energy harvesting sensors and IoT applications to ease the integration of EnOcean products.

Explaining the need to add intelligence to data gathering for an effective network, building or enterprise, EnOcean’s Marian Hönsch said: “Data is critical to understanding, measuring and improving facility management processes and procedures. Increasingly, data needs to be analysed to ensure compliance with health and safety requirements, security and Covid-19 regulations. Facility and corporate real estate managers are gradually becoming more dependent on data analysis to identify utilisation patterns, trim costs and make buildings into better places for work.”

The software enables the collection of data from billions of sensors in buildings, using energy harvesting wireless sensors to collect data in a secure and maintenance-free way. “The essential requirements of quick on-boarding, broad applicability and water-tight security are embodied in a new IoT Connector that translates sensor outputs into ready-to-use data for IoT applications and data driven businesses,” he added.

The IoT Connector software decodes the IoT data from the EnOcean Protocol and translates it into ready-to-use data in JSON (the open standard file format and data interchange) to be used in the actual application. Algorithms are needed to unravel the data and to make it available for customer applications in the correct form. Customer applications consume IoT data in a key-value pairs format, consisting of a constant that defines the dataset (e.g. temperature, humidity or ventilation), and a value, which is a variable that belongs to the dataset (e.g. 20 degrees C, 80 per cent, level 1/2/3). To facilitate adoption by IoT users, JSON data can be exchanged with an MQTT broker or Microsoft Azure IoT Hub.

In a typical model, the IoT Connector is deployed in an existing connectivity infrastructure – either in edge devices or third-party clouds – for example in centralised controls such as lighting and building automation. The new model processes data directly at the final destination (e.g. a cloud or server from the customer, such as Microsoft Azure cloud). A typical use case would be data analytics and IoT applications allowing the client total control over the data flow. With this model there is no third party or third storage location involved when transferring the data from sensor to cloud. Customers decide what happens with their data, whether visualised, analysed or archived. Here, the IoT Connector is a containerised application and available for deployment at the Docker Hub among others, including all updates. The use of cloud computing relieves the load on the local infrastructure and allows new data-heavy applications to be created, stimulating ideas from customers for new applications.

The EnOcean IoT Connector can be integrated into IoT applications by leveraging existing building infrastructures. The infrastructure can be upgraded to send EnOcean IoT data, for example with the combination of an EnOcean USB stick and Wi-Fi access points from Aruba (a Hewlett Packard Enterprise company). Data from existing EnOcean buildings can directly be forwarded to the IoT Connector in raw format (ESP3 – EnOcean Serial Protocol 3). The Aruba access points support the EnOcean USB stick without needing additional software. The access points talk directly to the IoT Connector running as part of the infrastructure or the final data destination, so no additional gateways are needed. In this scenario, customers have exclusive control over the data flow.

http://www.enocean.com