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Industry of Things World is an international knowledge exchange platform bringing together the largest European community of high-level cross-industry executives who play an active role in the Industrial Internet of Things scene...

Using video cameras as sensors in industrial IoT applications is becoming more and more popular. The reason is of course that it opens up for many interesting applications.

- Learn about the key use-cases for Edge Analytics in Industrial IoT
- Explore the unique capabilities of the Crosser Edge Analytics Platform
- Find out how Crosser self-service software can dramatically speed up your innovation and reduce your total cost of ownership

Stockholm, Sweden, April 30, 2019 — Crosser Technologies AB today announced it has closed a EUR 3 million A-round financing to support an international expansion. German early stage tech VC 42CAP and Swedish VC firm Industrifonden led the round with existing investors Spintop Ventures, Almi Invest and Norrlandsfonden also participating.

Crosser, a leading provider of Intelligent Edge Analytics software for Industrial IoT, today announced that the firm has initiated a close cooperation with Advantech, a global leader in the fields of intelligent IoT systems and embedded platforms.

Stockholm and Varberg, Sweden, March 31, 2019 The two leading Swedish IoT companies, Crosser and Ekkono Solutions, today announced a joint solution that enables Crosser’s customers to embed Ekkono’s edge machine learning in their Edge Analytics & Orchestration solution.

Stockholm/Sundsvall, 22 March, 2019 - Crosser, a leading provider of Intelligent Edge Analytics software for Industrial IoT, today announced that the firm has initiated a close cooperation with Basler.

Stockholm/Sundsvall, 15 March, 2019 - Crosser, today announced that the firm has joined the Intel ® Internet of Things Solution Alliance.

The announcement of an open machine learning (ML) strategy gives customers full freedom to deploy their favourite machine learning framework in the edge.

Extracting valuable insights out of data collected from machine sensors can be hard, often requiring analyzing data from many sensors in parallel.
Due to the complexity, machine learning (ML) methods are becoming more and more popular to analyze these datasets.

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